esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background
### Projection Distance  
Let \(\mathbf{U}_{1}\in\mathbb{R}^{n\times c}\) and \(\mathbf{U}_{2}\in\mathbb{R}^{n\times c}\) be two orthonormal basis sets that correspond to two subspaces, \(span(\mathbf{U}_{1})\) and \(span(\mathbf{U}_{2})\), respectively. The distance between the two subspaces can be quantified by the principal angles between \(span(\mathbf{U}_{1})\) and \(span(\mathbf{U}_{2})\). Then, the squared projection distance between \(span(\mathbf{U}_{1})\) and \(span(\mathbf{U}_{2})\) can be formulated as [25]:  
\[\begin{split} d_{proj}^{2}(span(\mathbf{U}_{1}),\,span( \mathbf{U}_{2}))&=\sum_{i=1}^{c}\sin^{2}(\theta_{i})\\ &=c-\sum_{i=1}^{c}\cos^{2}(\theta_{i})\\ &=c-tr(\mathbf{U}_{1}\mathbf{U}_{1}^{\top}\mathbf{U}_{2}\mathbf{ U}_{2}^{\top})\\ &=\frac{1}{2}\bigg{[}2c-2 tr(\mathbf{U}_{1}\mathbf{U}_{1}^{\top} \mathbf{U}_{2}\mathbf{U}_{2}^{\top})\bigg{]}\\ &=\frac{1}{2}\bigg{[}tr(\mathbf{U}_{1}^{\top}\mathbf{U}_{1})+tr( \mathbf{U}_{2}^{\top}\mathbf{U}_{2})-2 tr(\mathbf{U}_{1}\mathbf{U}_{1}^{\top} \mathbf{U}_{2}\mathbf{U}_{2}^{\top})\bigg{]}\\ &=\frac{1}{2}\|\mathbf{U}_{1}\mathbf{U}_{1}^{\top}-\mathbf{U}_{2} \mathbf{U}_{2}^{\top}\|_{F}^{2},\end{split} \tag{11}\]  
where \(\theta_{i}\) is the \(i^{th}\) principal angle between \(span(\mathbf{U}_{1})\) and \(span(\mathbf{U}_{2})\).  
Discovering Community Structure in Multiplex Networks: Co-regularized Robust Tensor-Based Spectral Approach (Mx-CRTSA)  
In this paper, we approach the problem of discovering community structure in noisy multiplex networks and propose grouping the nodes by efficiently incorporating the information furnished by the different layers. More precisely, we propose reducing the effect of noise and outliers in the network by extracting a set of clean graphs from the original multiplex network. These clean graphs conserve the significant characteristics of the multiplex network and can be used to compute the lower dimensional subspaces by exploiting concepts from subspace analysis. The contribution of these individual subspaces is then optimized to find a representative common subspace of the network.
## 5 Enhancement of Brillouin Stokes Signal (II): Influence of Core Diameter and Fiber Length
### Experimental Setup  
Irrespective of the length and the core diameter, PFGI-POFs used in the experiment had NA of 0.185, a core refractive index of \(\sim\)1.35, and propagation loss of \(\sim\)250 dB/km at 1.55 \(\upmu\)m. The experimental setup was basically the same as that previously reported in [27], where the BGS can be observed with high resolution by heterodyne detection. A distributed-feedback laser diode (DFB-LD) at 1,547 nm was used as a light source. One end of the PFGI-POF to be measured was optically butt-coupled to the silica SMF, and the other end was kept open.
## 3 Results and Discussion
### GC-MS Assay of Specimens before and after Fermentation
#### 3.4.3 Volatile Flavor Substances of the Four Groups of Fermented Specimens  
As shown in Tables 4 and 5 and Figure 7, there were certain differences in the composition and content of odorant compounds in the four groups of fermented specimens. A total of 54 odorant compounds were detected in the four groups of fermented specimens, including 9 alcohols, 7 aldehydes, 5 acids, 6 esters, 7 ketones, 15 hydrocarbons, 2 phenols, and 3 other compounds. A total of 24, 26, 25, and 37 types of volatile aroma components were detected in CP-12 h, CP-12 h, CPY-12 h, and CPBY-12 h, respectively. Hexanol, 1-octene-3-ol, trans-2-ocrenal, nonyl aldehyde, capraldehyde, benzaldehyde, acetic acid, caproic acid, 2,3-butanedione, 2,3-pentanedione, formate heptyl, 2-pentanfuran, and dimethyl sulfide were the common odorant compounds in the four group of fermented specimens. After 12 h fermentation of lactic acid bacteria, the types of odorant substances in the four groups of fermented specimens were remarkably increased. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Serial**} & \multirow{2}{*}{**Retention**} & \multirow{2}{*}{**Odor Substances**} & \multicolumn{3}{c}{**Relative Content (\%)**} \\  & & & **CP-12 h** & **CPB-12 h** & **CPY-12 h** & **CPBY-12 h** \\ \hline  & & Hydrocarbon & 6.18\% & 3.51\% & 6.47\% & 19.82\% \\
1 & 24.98 & (E)-2-tetradecene & ND & ND & ND & 1.10\% \\
2 & 29.35 & (Z)-4-tetradecene & ND & ND & ND & 1.34\% \\
3 & 33.12 & (E)-3-eicosene & ND & ND & ND & 1.51\% \\
4 & 19.56 & caryophyllene & 1.83\% & ND & ND & ND \\
5 & 16.08 & undecane & ND & ND & 0.37\% \\
6 & 15.89 & dodecane & 4.35\% & ND & 3.26\% & 3.85\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Volatile odor substances in four groups of fermented specimens.
# Underwater Accompanying Robot Based on SSDLite Gesture Recognition
## 3 Framework and Methodology
### Gesture Recognition  
There are several problems with the currently available gesture datasets, which lead it not being possible to directly use them in this project. On the one hand, most of these datasets were obtained in the open-air environment, which is quite different from the underwater environment. On the other hand, the comprehensibility of the gestures used in these datasets is not good, and there is no special consideration for robot control. For these reasons, we designed eight simple and practical gestures for Uthe AR. Each gesture corresponds to an action of the UAR, such as open five fingers for light and the closed fist for stop, as detailed in Table 1.  
Figure 5: Underwater human body tracking test results. (**a**) The initial frame, (**b–d**) the subsequent tracking results of the tracker.  
We used the SSDLite network as the gesture recognition network and adopted the MobileNet-V3 mentioned above as its backbone network. We used self-labeled datasets for SSDLite gesture recognition network training and testing. The network is implemented on the PyTorch framework, and all of the above models were trained and tested on NVIDIA GEFORCE GTX 3080 Ti. Our training batch size was 132 with a learning rate of \(1\times 10^{-3}\), which lasted 72,000 iterations, and the learning rate decreased 10 times after the 48,000 th and 60,000 th iterations.
# Urban Free-Space Optical Network Optimization
## 3 A Genetic Algorithm for FSO Communication Networks
### Crossover Operator  
The _crossover_ operator produces new solutions for the next generation. It takes two solutions (graphs) and produces a new solution that is a combination of them. The _crossover_ operator receives two valid red-blue visibility graphs \(G1,G2\), and outputs a new graph \(G^{\prime}\) that is also valid (i.e., \"blue-connected\"). The GA activates the _crossover_ operator repeatedly; thus, it needs to be computed quickly. The _crossover_ operator is formally described in Algorithm 5.  
```
Input: Population (set of solutions) of the current generation \(Gen_{i}\) and integers \(m,k\). Result: Population of size \(k\) of the next generation \(Gen_{i+1}\). for\(m\) times do  randomly choose \(G_{1},G_{2}\in Gen_{i}\) \(G^{\prime}=\textit{crossover}\left(G_{1},G_{2}\right)\) \(G^{\prime}=mutation\left(G^{\prime}\right)\) \(G^{\prime}.grade=fitness\left(G^{\prime}\right)\)  add \(G^{\prime}\) to \(Gen_{i}\) end  sort the solutions in \(Gen_{i}\) according to their \(grade\) field \(Gen_{i+1}=\) top \(k\) solutions of \(Gen_{i}\) return\(Gen_{i+1}\)
```  
**Algorithm 5**Pseudocode of _nextGeneration_, which creates the population of the next generation by applying the _crossover_ and _mutation_ operators on solutions from the previous generation.  
In line 1, the red vertices of the two input subgraphs are intersected to obtain the set of mutual red vertices \(R^{\prime}\). Such an operation of combining the two inputs is the essence of the crossover because it preserves the \"genetic\" attributes of the parents' \"genes\". Next, the blue vertices are added to obtain \(V^{\prime}\) (line 2) before devising \(G^{\prime}\), which is the induced subgraph of \(G\) (For ease of presentation, the original graph \(G\) and the sink vertex \(s\) are omitted as inputs in Algorithms 5 and 6.) with the vertices \(V^{\prime}\) (line 3).  
```
Input: Two connected red-blue visibility graphs \(G_{1}=(R_{1},B,E_{1}),G_{2}=(R_{2},B,E_{2})\). Result: A connected red-blue visibility graph \(G^{\prime}\). \(R^{\prime}=R_{1}\cap R_{2}\) \(V^{\prime}=B\cup R^{\prime}\) \(G^{\prime}=G[V^{\prime}]\) if\(G^{\prime}\) is not blue-connectedthen \(G^{\prime}=randomConnect(G,G^{\prime},s)\)  end if return\(G^{\prime}\)
```  
**Algorithm 5**Pseudocode of the _crossover_ operator.  
Note that most red vertices in \(R_{1}\) and \(R_{2}\) are essential for the respective connectivity of \(G_{1}\) and \(G_{2}\). Therefore, the resulting graph \(G^{\prime}\) after the crossover is most probably not \"blue-connected\". Such a phenomenon is exemplified in Figure 11. If this is the case, then \(randomConnect\) is applied to ensure that \(G^{\prime}\) is a valid solution (line 5) before it is returned in line 7.
## 2 Related Work
### A Single Object Tracker in MOT  
Some of the previous MOT methods have attempted to adopt SOT methods into the MOT problem. However, SOT methods are often used to tackle sub-problems. For instance, in [7], SOT is only used to generate initial tracklets. [PERSON] et al. [8] partitioned the state space of the object into four subspaces and only utilized single object trackers to track objects in a tracked state. [PERSON] et al. [1] implement the single object tracker as a state estimation procedure and further train a CNN based classifier to handle re-tracking and occlusion problems.  
Few works integrated SOT methods throughout the whole tracking process. [PERSON] et al. [9] trained an object-specific classifier to compute the similarity for data association in a particle filtering framework. [PERSON] et al. [4] kept both the tracking results of the single object trackers and the object detections as association candidates and selected the optimal candidate using an ensemble framework. However, tracking drift caused by occlusion still remains unsolved.  
[PERSON] et al. [10] used a dynamic CNN-based framework with a learned spatial-temporal attention map to handle occlusion, where the CNN trained on ImageNet is used for pedestrian feature extraction. Although it showed a good tracking performance, memory and time consumption may explode, since it assigns a network for each object and conducts online learning. Our approach differs from these methods by using a SOT method with a two-step verification and delay processing strategies to refine the tracker results and tackle the drift problem, in order to deeply excavate the potential of applying SOT under MOT scenarios.
# The Influence of Feature Representation of Text on the Performance of Document Classification
## Appendix B Rankings According to Precision and Recall  
### Precision Rankings  
Table 11 and Figure 11 summarize the rankings of the document representation models according to the precision performance measure.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Brown2** & **Brown4** & **Brown10** & **Brown15** & **Reuters8** & **WebKB** & **20 News** & **Avg. Rank** \\ \hline bow-TF & **1.5** & **2** & **3.5** & 10 & 4.5 & **4.5** & **1.5** & **3.93** \\ bow-TFIDF & 6.5 & 12.5 & **3.5** & 10 & **1** & **4.5** & **1.5** & 5.64 \\ \hline word2 vec-25 & 10.5 & 12.5 & 11.5 & 10 & 4.5 & **4.5** & 8 & 8.79 \\ word2 vec-50 & **1.5** & 6.5 & 11.5 & **2** & 4.5 & **4.5** & 8 & 5.50 \\ word2 vec-75 & 6.5 & 12.5 & **3.5** & **2** & 4.5 & **4.5** & 8 & 5.93 \\ word2 vec-100 & 6.5 & 6.5 & 11.5 & 10 & 4.5 & **4.5** & 8 & 7.36 \\ \hline doc2 vec-25 & 3.5 & 4 & **3.5** & 10 & 4.5 & **4.5** & 8 & 5.43 \\ doc2 vec-50 & 6.5 & **2** & 11.5 & **2** & 8.5 & **4.5** & 8 & 6.14 \\ doc2 vec-75 & 3.5 & **2** & **3.5** & 10 & 8.5 & 12.5 & 8 & 6.86 \\ doc2 vec-100 & 14.5 & 6.5 & 11.5 & 10 & 10.5 & 12.5 & 8 & 10.50 \\ doc2 vec-200 & 10.5 & 6.5 & 11.5 & 10 & 14 & 12.5 & 8 & 10.43 \\ doc2 vec-500 & 14.5 & 12.5 & **3.5** & 10 & 14 & 12.5 & 8 & 10.71 \\ doc2 vec-1000 & 10.5 & 12.5 & 11.5 & 10 & 10.5 & 12.5 & 8 & 10.79 \\ \hline gow-avg & 14.5 & 12.5 & 11.5 & 10 & 14 & 12.5 & 15 & 12.86 \\ gow-qrt & 14.5 & 12.5 & 11.5 & 10 & 14 & 12.5 & 15 & 12.86 \\ gow-hist & 10.5 & 12.5 & 11.5 & 10 & 14 & 12.5 & 15 & 12.29 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Multi-objective ranking of the 16 variants of the three document representation models by document classification task with respect to precision. The last column reports the average rankings over the seven tasks. The top-ranked models in each column are shown in bold.
## 4 Joint Message-Passing and Convex Optimization Framework for UAV Scheduling
### Algorithm Details  
As summarized in Section 4.1, our proposed algorithm consists of two sub-algorithms: (1) UAV scheduling with message-passing (refer to Sections 4.2.1 and 4.2.2) joint optimization for charging matching of UAVs to charging towers and the wireless energy transfer between UAVs and charging towers (refer to Section 4.2.2).
# Dispersive FSO Performance Estimation with Gaussian Pulses and Laplace Modeled Time Jitter
## 3 FSO Link Performance  
### Link's Availability Performance Estimation  
A very significant quantity for the availability performance of the link is the outage probability (OP), which expresses the probability that the signal-to-noise ratio (SNR) is below of a given threshold, \(\gamma_{th}\), within a specified time interval [31]. Because the incoming signal is continually changing over time, the instantaneous electrical SNR is given as \(\gamma=\left({\eta I}\right)^{2}/{N_{0}}\), while the average SNR is estimated as \(\overline{\gamma}=\left({\eta E[I]}\right)^{2}/{N_{0}}\). Additionally, the detector has a constrained period of time to detect the incoming signal (known as time slot), which lasts for \(T_{sl}\). Thus, the minimum expected irradiance at the edges of the time slot is: \(I_{\min}=LB\exp\left\{{-\left[{T_{sl}}\right/\left({2T_{pw}}\right)}\right]^{ 2}}\). Lastly, the time slot duration is a function of the bit rate, \(R\), which depends on the modulation scheme used. Assuming an OOK modulation scheme, these quantities are inversely proportional to each other \(\left({T_{sl}=R^{-1}}\right)\). Thus, the probability of outage is estimated as [31]:  
\[P_{out}=F_{\gamma}(\gamma_{th})=\int\limits_{\gamma_{\min}}^{\gamma_{th}}{{f_{ \gamma}(\gamma)d\gamma}}, \tag{11}\]  
where \({f_{\gamma}(\gamma)=f_{I}(\gamma)dI/d\gamma}\), with \(I=\sqrt{\gamma/\overline{\gamma}}E[I_{tJ}]\), \(\gamma_{\min}=\overline{\gamma}\left({I_{\min}/E[I]}\right)^{2}\), and \(\gamma_{th}=\overline{\gamma}\left({I_{th}/E[I]}\right)^{2}\).  
Next, the OP versus the normalized average SNR, \(\overline{\gamma}/\gamma_{th}\), is given from Equations (8) and (11) as:  
\[P_{out}(\overline{\gamma}/\gamma_{th})=\frac{g_{T}}{4}\int\limits_{\gamma_{ \min}}^{\gamma_{th}}{\frac{\exp\left[{-g_{T}\left|{\sqrt{\ln\left({LB\sqrt{ \overline{\gamma}/\gamma}/E[I]}}\right)}\right|}\right]}{\gamma\sqrt{\ln\left({ LB\sqrt{\overline{\gamma}/\gamma}/E[I]}\right)}}d\gamma}. \tag{12}\]  
After mathematical manipulations and the use of a random variable (RV) transformation \(y_{\gamma}=\sqrt{\ln\left({LB\sqrt{\overline{\gamma}/\gamma}/E[I]}\right)}\), Equation (12) is solved in the following form:  
\[P_{out}(\overline{\gamma}/\gamma_{th})=\exp\left[{-g_{T}\sqrt{\ln\left({\frac{ LB}{E[I]}\sqrt{\frac{\overline{\gamma}}{\gamma_{th}}}}\right)}}\right]-\exp(-g_{T}h_{T}), \tag{13}\]  
where \(h_{T}=T_{sl}/\left({2T_{pw}}\right)\).
# Numerical Simulation Study on the Volumetric Efficiency Loss of CO2 Swash Plate Axial Piston Pumps
## 8 Conclusions  
Through transient numerical simulations of the fluid domain of a piston pump with fluid films on the mating pair, piston pair, and slide shoe pair, the distribution of the volumetric efficiency loss for a CO\({}_{2}\) swashplate axial piston pump was obtained, and the following conclusions were drawn:  
1. The compression characteristics of CO\({}_{2}\) under high-pressure conditions could be accurately described using the real physical property model. Compression stagnation volume loss accounted for 28.6% of the volumetric efficiency and was the main factor leading to low volumetric efficiency of the piston pump.
2. The liquid film leakage volume losses of the sliding shoe, the mating vice, and the plunger vice accounted for 3.4%, 1.5%, and 0.5% of the volumetric efficiency, respectively. During friction, leakage volume loss from the sliding shoe side of the pump accounted for the greatest loss of volumetric efficiency.
3. In the design and application of CO\({}_{2}\) plunger pumps, the bottom clearance volume of the plunger cavity could be reduced by redesigning the plunger structure, and other methods could be applied to reduce the compression retention volume loss. For the design of the friction pairs, groove structures could be set into their surfaces and hydrodynamic effects could be introduced to reduce liquid film leakage volume loss and improve the plunger pump's volumetric efficiency.  
Methodology, X.W.; software, X.W.; formal analysis, X.W.; investigation, X.W. resources, X.W.; data curation, X.W.; writing--original draft, X.W.; writing--review and editing, X.W. and S.Z. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The raw data for this study is included in the article. For further inquiries, please contact the corresponding author.  
The authors declare no conflict with financial interests.
# Two-Dimensional Beam Steering Based on Compact Programmable Coding Metasurface
## 4 Conclusions  
In this paper, a 1-bit programmable metasurface is designed by loading PIN diodes. By switching the \"on\" and \"off\" states of the PIN diode, the meta-atoms can obtain a phase difference of 180\({}^{\circ}\) between the two states, and the return loss of the metasurface is better than \(-1.5\) dB. The designed metasurface works in the Ka band, and each meta  
Figure 11: The measured results of beam steering at 28 GHz in the yoz plane. The measurements are under x\(-\)polarized incident wave.  
Figure 9: The measured results of beam steering at 29 GHz in the xoz plane. The measurements are under x\(-\)polarized incident wave.  
atom can be individually modulated by the digital circuit. With the phase compensation of the incident wave from the horn antenna, the metasurface realizes the beam steering by changing the phase distribution of the metasurface. Simulation results show that the metasurface can achieve flexible two-dimensional beam scanning in the upper half-plane with a maximum scanning angle of 60\({}^{\circ}\). Experimental results show that the designed metasurface can achieve a maximum scan of 40\({}^{\circ}\) in the upper half-plane. The design offers a promising solution of beam steering at millimeter wave freucucny. The designed programmable coding metasurfaces are expected to expand the potential applications for sensing and 5G wireless communications.  
Conceptualization, design, validation, F.Y.; measurement, F.Y., F.X. and X.Y.; formal analysis, F.X., X.F. and J.W.; investigation, Z.W., X.Y. and C.L.; original draft preparation, F.X. and C.L.; review and editing, F.Y. and C.L.; supervision, F.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the National Natural Science Foundation of China (Grant No. 12173008), the National Key R&D Program of China (Grant No.2018 YFB100302, Grant No. 2018 YFB1801505), the Aeronautical Science Foundation of China (Grant No. 201920069002), and in part by the Foundation of Science and Technology on Monolithic Integrated Circuits and Modules Laboratory (Grant No. 614280302032105).  
Not applicable.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Total Phenolic Content (TPC)  
Total phenolic content was determined using the Folin-Ciocalteu method [39], with some modifications. For this, a 50 \(\upmu\)L aliquot of the filtered sample by 0.45 \(\upmu\)m was reacted with 1.25 mL of a 1:5 diluted Folin-Ciocalteu reagent. After vigorous mixing and 1 min incubation, 1 mL of 10% sodium carbonate solution was added. The mixture was then allowed to react in the dark for 30 min. The absorbance at 760 nm was measured in a Beckman DU 640 spectrophotometer. A calibration curve was performed using gallic acid standards ranging from 0.01 to 1 g/L.
## 4 Experimental Results
### Ablation Studies  
In this section, ablation studies are performed on each component of the suggested method. As shown in Table 2, as the proposed modules are sequentially added on the UNet 3+ baseline, the model performance is gradually improved. It is clear from Table 2 that when Transformer is integrated into the UNet 3+ baseline, the DM reaches 0.907, with a 1.1% improvement compared to that of the baseline (0.896). This is because the incorporation of Transformer compensates for the inability of UNet 3+ to model the global context. Simultaneously, the combination also solves the problem of Transformer ignoring low-resolution detail features compared to using itself directly.  
To further study the impact of the loss function in the model, an ablation study is conducted for the segmentation loss (\(\mathcal{L}_{seg}\)) and SDM loss (\(\mathcal{L}_{sdm}\)). By adding an additional regression head to the segmentation network's end and using the SDM loss, the dice metric value of the model is further enhanced to 0.908, with a slight increase of 0.1% compared to that of the model only with \(\mathcal{L}_{seg}\). This result suggests that joint SDM training can implicitly force the model to learn shape information compared to traditional training using only segmented masks.
# A Review of Machine Learning and IoT in Smart Transportation
## 2 Machine Learning and IoT in Smart Transportation
### Machine Learning Algorithms
#### 2.1.4 Decision Trees  
Decision trees are structures that deal with classification tasks and they represent a decision process with several possible outcomes. A decision tree is built top down and consists of several nodes, where every node can be either a class, or a condition that will drive a testing item to a class. It is a simplistic approach to classification problems. A new classification sub-process takes place at every level of a decision tree, breaking the main task to smaller sub-tasks [45].  
As mentioned above, in [23] a decision tree is tested among other four ML algorithms (RF, MLP, SVM, and Logistic Regression), to predict traffic congestion. The decision tree performed equally well with the other algorithms in terms of precision, recall and accuracy, but Logistic Regression was slightly better.  
Among the ML algorithms implemented in [27], the authors tested a Regression Tree to forecast short-term and long-term traffic flow in work zones. Regression trees are similar to decision trees but the response variable of the tree is a numeric value instead of a binary class. In a regression tree, the independent variables are used to fit a regression to the response variable. Then, a binary recursive partitioning takes place, where the squared regression error is calculated. The branch with the variables holding the smallest sum of squared errors, is chosen. In the aforementioned research, the regression tree was less effective at the traffic predictions compared to the FF-NN or the RF.