我是 Latex 的初学者，发现处理表格非常困难（与 MS Word 相比）。所以我需要这里的专家的指导。我正在尝试使用 springer 模板撰写论文（点击此处下载模板)\documentclass[twocolumn]{svjour3}。我喜欢在新页面上以横向模式添加比较表。但是，当我编译它时，表格会移动到下一页，试图占据下一页的第一列，导致所有内容乱码
。我尝试使用 \pagebreak[4]，但没有任何效果。我给出了我能用我的初学者知识制作的 MWC。任何改动、巧妙的设计都将不胜感激，并附上主要问题的解决方案。
\documentclass[twocolumn]{svjour3}
\usepackage[utf8]{inputenc}
\usepackage[english]{babel}
\usepackage{comment}
\usepackage[center]{caption}
\usepackage{subcaption}
\usepackage{float}
\usepackage{csquotes}
\usepackage[section]{placeins}
\usepackage{graphicx}
\usepackage{rotating}
\begin{document}
\begin{sidewaystable}
\caption{Comparison among state of the art designs}
\label{tab:1} % Give a unique label
\begin{tabular}{p{1in}|p{0.5in}ccp{0.5in}p{0.5in}p{0.8in}p{1.7in}}
\hline\noalign{\smallskip}
\textbf{Reference}&\textbf{Sensors}&\textbf{Dedicated}&\textbf{Number of sensors}&\textbf{Method}&\textbf{Accuracy}&\textbf{Features}&\textbf{Limitations}\\
\hline\noalign{\smallskip}\\
Er and Tan (2018)&Sound sensor, accelerometer&Yes&More than one&Non-ambulatory&~92\%&Fuzzy logic based dual detection capability&Indoor only, no location information, costly and non-portable solution.\\
He et al.(2020)&RFID and Radar&Yes&More than one&Non-ambulatory&~94\%&Increased detection area up to 230\% compared to traditional systems.&Indoor only, no location information, costly and non-portable solution. Implementation requires specialized training.\\
Van Thanh et al.(2018)&Proprietary accelerometer&Yes&One&Wearable sensor&~92\%&Fall as well as post fall posture recognition.&Costly and bulky, no local processing on device, no text based warning SMS.\\
Zhang, Hongtao et al.(2020)&Accelerometer, Gyroscope and Magnetometer&Yes&More than one&Wearable
sensor&~96\%&Fall and post fall posture recognition. Warning SMS with location.&Costly and bulky, no local processing on device, no text based warning SMS.\\
Zurbuchen, N. et al.(2021)&Accelerometer Gyroscope&Yes&More than one&Wearable sensor&~97\%&Multiclass fall and ADL detection.& High initial cost, separate device, no local processing on device.\\
Yu, Gong, and Kollias (2017)&Camera&Yes&More than one&Image and computer vision&~96\%&Posture based detection ( Laying is treated as fall).&High initial cost, no local processing on device, not suitable for outdoor, privacy issues.\\
Juang et al.(2015)&Camera&Yes&More than one&Image and computer vision&100\%&Human joint identification along with fall.&High initial cost, no local processing on device,low portability, not suitable for outdoor, privacy issues. No local dataset used.\\
Zhang et al.(2020)& Camera& Yes&More than one&Image and computer vision& ~98\%&Fall detection based on body posture, local dataset used.&High initial cost, no local processing on device, not suitable for outdoor.\\
Shu, Francy. et al.(2021)&Camera&Yes&More than one& Image and computer vision&94\%&Multi genre fall detection using eight cameras.&High initial cost,low portability, no local processing on device, not suitable for outdoor, privacy issues.\\
\hline
\end{tabular}
\end{sidewaystable}
\end{document}
请帮忙。
答案1
我建议结合sidewaystable*使用 sidewaystable 和svjour3 从这里。
此外，我还使用了包中的水平线booktabs，tabularx以确保表格充分利用了文本块的宽度，\thead作为makecell列标题。我还引入了一些缩写，并调整了一些列长度，以减少浪费的空白空间。
\documentclass[twocolumn]{svjour3}
\usepackage[english]{babel}
\usepackage{tabularx}
\usepackage{booktabs}
\usepackage{rotating}
\setlength{\rotFPtop}{0pt plus 1fil}
\usepackage{makecell}
\renewcommand{\theadfont}{\bfseries}
\begin{document}
\begin{sidewaystable*}
\caption{Comparison among state of the art designs}
\label{tab:1} % Give a unique label
\begin{tabularx}{\linewidth}{>{\raggedright\arraybackslash}p{1in}
>{\raggedright\arraybackslash}p{0.75in}
c
c
>{\raggedright\arraybackslash}p{0.75in}
c
>{\raggedright\arraybackslash}p{1.5in}
X}
\toprule
\thead{Reference}&\thead{Sensors}&\thead{Dedic.}&\thead{No. of\\ sensors}&\thead{Method}&\thead{Acc. \\ in \%}&\thead{Features}&\thead{Limitations}\\
\midrule\\
Er and Tan (2018)
& Sound sensor, accelerometer
& Yes & $>1$ & Non-ambulatory & 92
& Fuzzy logic based dual detection capability
& Indoor only, no location information, costly and non-portable solution.\\
\addlinespace
He et al.(2020)
& RFID and Radar
& Yes & $>1$ & Non-ambulatory & 94
&Increased detection area up to 230\% compared to traditional systems.
&Indoor only, no location information, costly and non-portable solution. Implementation requires specialized training.\\
\addlinespace
Van Thanh et al.(2018)
& Proprietary accelerometer
& Yes & 1 & Wearable sensor & 92
&Fall as well as post fall posture recognition.
&Costly and bulky, no local processing on device, no text based warning SMS.\\
\addlinespace
Zhang, Hongtao et al.(2020)
&Accelerometer, Gyroscope and Magnetometer
& Yes & $>1$ & Wearable sensor & 96
& Fall and post fall posture recognition. Warning SMS with location.
&Costly and bulky, no local processing on device, no text based warning SMS.\\
\addlinespace
Zurbuchen, N. et al.(2021)
& Accelerometer Gyroscope
& Yes & $>1$ & Wearable sensor & 97
& Multiclass fall and ADL detection.
& High initial cost, separate device, no local processing on device.\\
\addlinespace
Yu, Gong, and Kollias (2017)
& Camera
& Yes & $>1$ & Image and computer vision & 96
& Posture based detection (Laying is treated as fall).
& High initial cost, no local processing on device, not suitable for outdoor, privacy issues.\\
\addlinespace
Juang et al.(2015)
& Camera
& Yes & $>1$ & Image and computer vision & 100
& Human joint identification along with fall.
& High initial cost, no local processing on device,low portability, not suitable for outdoor, privacy issues. No local dataset used.\\
\addlinespace
Zhang et al.(2020)
& Camera
& Yes & $>1$ & Image and computer vision & 98 &
Fall detection based on body posture, local dataset used.
& High initial cost, no local processing on device, not suitable for outdoor.\\
\addlinespace
Shu, Francy. et al.(2021)
& Camera
& Yes & $>1$ & Image and computer vision & 94
& Multi genre fall detection using eight cameras.
& High initial cost,low portability, no local processing on device, not suitable for outdoor, privacy issues.\\
\bottomrule
\end{tabularx}
\end{sidewaystable*}
\end{document}
答案2
这可能会让您感到困惑，因为您可能会看到一些标题的改写，例如“准确度会有所帮助”，以及问题下的一些评论。基本上我只是使用\small并调整了一些列宽。
\documentclass[twocolumn]{article}
\usepackage[utf8]{inputenc}
\usepackage[english]{babel}
\usepackage{comment}
\usepackage[center]{caption}
\usepackage{subcaption}
\usepackage{float}
\usepackage{csquotes}
\usepackage[section]{placeins}
\usepackage{graphicx,array}
\usepackage{rotating}
\newcommand\hd[1]{\bfseries\begin{tabular}[t]{@{}c@{}}#1\end{tabular}}
\begin{document}
\begin{sidewaystable*}
\caption{Comparison among state of the art designs}
\label{tab:1=zzz} % Give a unique label (dont use numbers)
\small
\setlength\tabcolsep{2pt}
\begin{tabular}{
@{}
>{\raggedright}p{1in}
>{\raggedright}p{1in}
cc
>{\raggedright}p{1in}
>{\raggedright}p{.8in}
>{\raggedright}p{1.5in}
>{\raggedright\arraybackslash}p{1.7in}
@{}}
\hline\noalign{\smallskip}
\textbf{Reference}&\textbf{Sensors}&\textbf{Dedicated}&\hd{Number of\\sensors}&\textbf{Method}&\textbf{Accuracy}&\textbf{Features}&\textbf{Limitations}\\
\hline\noalign{\smallskip}\\
Er and Tan (2018)&Sound sensor, accelerometer&Yes&More than one&Non-ambulatory&~92\%&Fuzzy logic based dual detection capability&Indoor only, no location information, costly and non-portable solution.\\
He et al.(2020)&RFID and Radar&Yes&More than one&Non-ambulatory&~94\%&Increased detection area up to 230\% compared to traditional systems.&Indoor only, no location information, costly and non-portable solution. Implementation requires specialized training.\\
Van Thanh et al.(2018)&Proprietary accelerometer&Yes&One&Wearable sensor&~92\%&Fall as well as post fall posture recognition.&Costly and bulky, no local processing on device, no text based warning SMS.\\
Zhang, Hongtao et al.(2020)&Accelerometer, Gyroscope and Magnetometer&Yes&More than one&Wearable
sensor&~96\%&Fall and post fall posture recognition. Warning SMS with location.&Costly and bulky, no local processing on device, no text based warning SMS.\\
Zurbuchen, N. et al.(2021)&Accelerometer Gyroscope&Yes&More than one&Wearable sensor&~97\%&Multiclass fall and ADL detection.& High initial cost, separate device, no local processing on device.\\
Yu, Gong, and Kollias (2017)&Camera&Yes&More than one&Image and computer vision&~96\%&Posture based detection ( Laying is treated as fall).&High initial cost, no local processing on device, not suitable for outdoor, privacy issues.\\
Juang et al.(2015)&Camera&Yes&More than one&Image and computer vision&100\%&Human joint identification along with fall.&High initial cost, no local processing on device,low portability, not suitable for outdoor, privacy issues. No local dataset used.\\
Zhang et al.(2020)& Camera& Yes&More than one&Image and computer vision& ~98\%&Fall detection based on body posture, local dataset used.&High initial cost, no local processing on device, not suitable for outdoor.\\
Shu, Francy. et al.(2021)&Camera&Yes&More than one& Image and computer vision&94\%&Multi genre fall detection using eight cameras.&High initial cost,low portability, no local processing on device, not suitable for outdoor, privacy issues.\\
\hline
\end{tabular}
\end{sidewaystable*}
\end{document}
我在这里使用文章，因为 svjour 类不在标准分布中，但同样的方法可以适用于任何类。
答案3
使用tabularx表格、makcell列标题、表格规则和附加垂直空间。对于单元格中的文本对齐，使用ragged2e包：
\documentclass[twocolumn]{svjour3}
\usepackage[skip=1ex]{caption}
\usepackage{rotating}
\usepackage{ragged2e} % <--- new
\usepackage{makecell, tabularx} % <--- new
\renewcommand\theadfont{\small\bfseries}
\renewcommand\theadgape{}
\newcolumntype{L}{>{\RaggedRight}X}
\begin{document}
\begin{sidewaystable}
\setcellgapes{3pt}
\makegapedcells
\caption{Comparison among state of the art designs}
\label{tab:1} % Give a unique label
\begin{tabularx}{\linewidth}{L|L c >{\hsize=0.6\hsize}L
>{\hsize=0.6\hsize}L c L
>{\hsize=1.8\hsize}L }
\Xhline{1.2pt}
\thead{Reference}
& \thead{Sensors}
& \thead{Dedicated}
& \thead{Number of\\ sensors}
& \thead{Method}
& \thead{Accuracy}
& \thead{Features}
& \thead{Limitations} \\
\Xhline{0.8pt}
Er and Tan (2018)&Sound sensor, accelerometer&Yes&More than one&Non-ambulatory&~92\%&Fuzzy logic based dual detection capability&Indoor only, no location information, costly and non-portable solution.\\
He et al.(2020)&RFID and Radar&Yes&More than one&Non-ambulatory&~94\%&Increased detection area up to 230\% compared to traditional systems.&Indoor only, no location information, costly and non-portable solution. Implementation requires specialized training.\\
Van Thanh et al.(2018)&Proprietary accelerometer&Yes&One&Wearable sensor&~92\%&Fall as well as post fall posture recognition.&Costly and bulky, no local processing on device, no text based warning SMS.\\
Zhang, Hongtao et al.(2020)&Accelerometer, Gyroscope and Magnetometer&Yes&More than one&Wearable
sensor&~96\%&Fall and post fall posture recognition. Warning SMS with location.&Costly and bulky, no local processing on device, no text based warning SMS.\\
Zurbuchen, N. et al.(2021)&Accelerometer Gyroscope&Yes&More than one&Wearable sensor&~97\%&Multiclass fall and ADL detection.& High initial cost, separate device, no local processing on device.\\
Yu, Gong, and Kollias (2017)&Camera&Yes&More than one&Image and computer vision&~96\%&Posture based detection ( Laying is treated as fall).&High initial cost, no local processing on device, not suitable for outdoor, privacy issues.\\
Juang et al.(2015)&Camera&Yes&More than one&Image and computer vision&100\%&Human joint identification along with fall.&High initial cost, no local processing on device,low portability, not suitable for outdoor, privacy issues. No local dataset used.\\
Zhang et al.(2020)& Camera& Yes&More than one&Image and computer vision& ~98\%&Fall detection based on body posture, local dataset used.&High initial cost, no local processing on device, not suitable for outdoor.\\
Shu, Francy. et al.(2021)&Camera&Yes&More than one& Image and computer vision&94\%&Multi genre fall detection using eight cameras.&High initial cost,low portability, no local processing on device, not suitable for outdoor, privacy issues.\\
\Xhline{1.2pt}
\end{tabularx}
\end{sidewaystable}
\end{document}
