esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Monomer and Contour Extraction
#### 2.5.4 Monomer Judgment and Contour Selection  
Based on the assumptions listed in Section 2.5.3, building monomers were determined as follows: Contour polygons of differently inclined models were compared; when all geometric similarity indicators met the predetermined requirements, the two contour polygons were considered to correspond to the same building monomer. The maximum value of the corresponding elevation plane slice and its generated vector polygon (meeting the similarity conditions) represented the contour line of the building. During calculations, small or large contours were removed based on the given area range, decreasing the interference of noisy data. Such area ranges can be customized by users according to the types of buildings present. The stepwise monomer determination and contour selection process is as follows:  
* Step 1: Beginning with the highest elevation slice, assign one of the vertical surfaces of the contour polygon as F\({}_{\rm S}\), the source of the polygon outline for the current elevation (elevation = H). At subsequent (lower) vertical \"slices\" (elevation = H \(-\)\(\Delta\)H), find the outline polygon for a target contour (Fo) that renders Sim (F\({}_{\rm S}\), Fo) = True; in this case, F\({}_{\rm S}\) and Fo belong to the same building monomer. F\({}_{\rm S}\) is accepted as the outline of the current building, incorporated into the contour set F, and the polygon Fo is determined to be extracted;
* Step 2. If no Lo can be found in the elevation plane H \(-\)\(\Delta\)H and Sim (F\({}_{\rm S}\), Fo) = True, F\({}_{\rm S}\) does not represent the contour line of a single building. The next polygon is selected from the elevation plane H as the source polygon, and Step 1 is repeated;
* Step 3. After all contour polygons in elevation plane H have been used as source contours, the H \(-\)\(\Delta\)H elevation plane is taken as the object and the contour polygons in this elevation are selected as source contour polygons. The source polygons are then searched for the next elevation at plane H \(-\)\(2\Delta\)H, and contours are added to set F. The processes outlined in Step 1 and Step 2 are repeated; (In this step, extracted contour polygons are no longer included in the calculation.)
* Step 4. Traverse all \"sliced\" surfaces of different elevations in descending order from high to low, repeating Step 1 to Step 3 until all elevation cut surfaces are traversed, and the selected contour set is F.  
In addition to buildings, there may be objects such as trees and lampposts. The outline area of these features is small, and the distribution is scattered. To minimize the influence of these ground objects on the accuracy of building monomer extraction, we established a filtering area threshold value, S\({}_{\rm t}\), and eliminated contour values whose area is smaller than from the final contour set F.
# Comparison of Measurement Possibilities by Non-Invasive Reflectometric Sensors and Invasive Probes
## 2 Materials and Methods
### Materials  
#### 2.1.1 TDR Meter Description  
The measurements were conducted with a TDR multimeter emitting a needle peak signal with rise time equal to 300 ps, produced by ETest manufacturer, Lublin, Poland. The emitted signal propagated along the coaxial cable to the sensor where reflections occurred on the characteristic points of the propagation line. Those reflections derived from both the beginning of the sensor and its termination and served as measurement markers. The time differences between those reflections were read by the TDR meter and could be automatically or manually recalculated into the apparent permittivity value that depends on a material's moisture.
# Grouped Contrastive Learning of Self-Supervised Sentence Representation
## 5 Further Investigation of GCLSR
### Effect of the LR of the Predictor  
As mentioned by [16], the predictor with a constant LR (without decay) can obtain good image representation. Therefore, we design an experiment to verify whether the same settings can obtain good sentence representation. The results are shown in Table 6.  
Experimental results show that a predictor with a constant LR can obtain better sentence representation compared with a decay LR. Specifically, as shown in Table 6 and Figure 4, the model will stop training (at the 9 th epoch) when the LR of the predictor is small or reduced by a linear scaling rule. Additionally, the model needs a bigger learning rate (LR = 1) compared with vision tasks (LR = 0.1) to obtain better results. A possible explanation is that the predictor can adapt the latest representation. Therefore, it is not necessary to force the predictor to converge before the model is trained sufficiently [16].  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**LR/STS** & **STS12** & **STS13** & **STS14** & **STS15** & **Avg.** \\ \hline Decay & 54.64 & 70.58 & 65.33 & 72.92 & 65.87 \\
0.08 & 55.22 & 70.17 & 65.17 & 73.10 & 65.92 \\
0.2 & **56.24** & 72.22 & 66.53 & 75.25 & 67.56 \\
0.5 & 56.14 & 72.59 & 66.73 & 75.36 & **67.71** \\
1 (ours) & 55.79 & **72.72** & **66.73** & **75.40** & 67.66 \\ \hline \end{tabular}
\end{table}
Table 6: The effect of the LR of the predictor. Decay: the LR of the predictor reduces with a cosine decay.  
Figure 4: Comparison experiment results. “All” means that all methods we proposed are used. “lr0.08” means that the learning rate of the predictor is 0.08. “non-att”: the self-attention we proposed is not used in whole training. “non-aug”: no data augmentations are applied in training, i.e., the two channels of network receive the same input. “non-group”: feature grouping is not adopted to make use of local information of features.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Weight Decay/STS** & **STS12** & **STS13** & **STS14** & **STS15** & **Avg.** \\ \hline
0.0001 & 55.04 & 70.79 & 65.55 & 73.36 & 66.19 \\
0.001 (ours) & **55.79** & **72.72** & **66.73** & **75.40** & **67.66** \\
0.01 & 55.22 & 70.48 & 65.43 & 74.12 & 66.31 \\
0.1 & 54.94 & 70.26 & 64.75 & 72.58 & 65.63 \\ \hline \end{tabular}
\end{table}
Table 5: The effect of weight decay.
## 3 Results
### Logistic Regression Analysis on Self-Reported Overall Fatigue and Musculoskeletal Pains
#### 3.4.2 Logistic Regression Analysis on Backache  
Results of logistic regression on backache are presented in Table 12. Four hundred and forty six subjects were classified into two groups depending on the complaint of backache. The power of explanation of the model was acceptable (Nagelkerke value = 0.084) and the model seems to fit quite well (\(\chi^{2}\) = 4.354, significance value = 0.500). The overall rate of correct classification (classification accuracy) was estimated as 92.4%.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Variables** & **N** & **\%** & **B** & **\(p\)-Value** & **Exp(B)** & \multicolumn{2}{c}{**95\% C.I. for EXP(B)**} \\ \cline{5-8} \cline{7-8} Gender & & & & & & **Lower** & **Upper** \\ Male(ref) & 363 & 81.4\% & & & & & & \\ Female & 83 & 18.6\% & 0.607 & 0.043 * & 1.835 & 1.020 & 3.300 \\ Fumes and dust & & & & \textless{}0.001 * & & & & \\ \textless{}2 (ref) & 273 & 61.2\% & & & & & \\ \textgreater{}2\textasci{}4 & 71 & 15.9\% & 0.585 & 0.102 & 1.795 & 0.890 & 3.621 \\ \textgreater{}4 & 102 & 22.9\% & 1.423 & \textless{}0.001 * & 4.150 & 2.359 & 7.300 \\ Manual heavy loads handling & & & & & 0.015 * & & & \\ \textless{}2 (ref) & 200 & 44.8\% & & & & & \\ \textasci{}2\textasci{}4 & 136 & 30.5\% & \textless{}0.170 & 0.600 & 0.844 & 0.448 & 1.591 \\ \textgreater{}4 & 110 & 24.7\% & 0.685 & 0.025 * & 1.984 & 1.092 & 3.605 \\ Repetitive motion & & & & & 0.023 * & & & \\ \textless{}2 (ref) & 47 & 10.5\% & & & & & \\ \textasci{}2\textasci{}4 & 50 & 11.2\% & \textless{}0.669 & 0.414 & 0.512 & 0.103 & 2.550 \\ \textgreater{}4 & 349 & 78.3\% & 0.826 & 0.141 & 2.284 & 0.761 & 6.853 \\ constant & & & \textasci{}2.784 & \textless{}0.001 * & 0.062 & & & \\ \hline \hline \end{tabular}  
* Significant at 0.05, Note: ref = reference, C.I. = confidence interval.  
\end{table}
Table 11: Results of logistic regression on overall fatigue.  
Exposure to awkward posture (\(p\) = 0.045) was the risk factor for backache. Complaints of backache among those who have more than four ADHE of noise exposure was 2.546 times more likely than those who have less than two ADHE of noise exposure. Complaints of backache among those who have more than four ADHE of awkward posture exposure was 2.749 times more likely than for those who have less than two ADHE of awkward posture exposure.
# Adversarial Machine Learning for NextG Covert Communications Using Multiple Antennas
## 4 Adversarial Perturbations Using Multiple Antennas at the CJ
### Proportional to Channel Gain (PCG) Attack  
To exploit the channel with the better channel gain, the [PERSON] allocates more power to better channels. Specifically, the power allocation for the \(i\)th antenna is proportional to the channel gain \(\|\mathbf{h}_{c;i}\|_{2}\), where \(\mathbf{h}_{c;i}=[h_{c;i},1,\cdots,h_{c;i},p]^{T}\), using weight \(w_{i}=\frac{\|\mathbf{h}_{c;i}\|_{2}}{\sum_{j=1}^{d}\|\mathbf{h}_{c;i}\|_{2}}, i=1,\cdots,q\). The adversarial perturbation that is transmitted by each antenna is generated using the MRPP attack as before and transmitted with the power allocated to each antenna. The detailed algorithm is presented in Algorithm 2.  
```
Inputs: input \(\boldsymbol{r}_{te}\), desired accuracy \(\epsilon_{acc}\), power constraint \(P_{max}\), and model of the classifier \(L(\theta,\cdot,\cdot)\). Initialize: \(w_{i}=\frac{\|\mathbf{h}_{c;i}\|_{2}}{\sum_{j=1}^{d}\|\mathbf{h}_{c;i}\|_{2}}, i=1,\cdots,q\). \(\epsilon_{max}\leftarrow\sqrt{P_{max}}\,\epsilon_{min}\gets 0\). for\(i=1\)to\(q\)do \(\delta_{i}=\frac{\mathbf{H}_{c;i}^{\epsilon_{\text{err}}}\
abla x_{L}( \theta,\boldsymbol{r}_{te},y^{min})}{(\|\mathbf{H}_{c;i}^{\epsilon_{\text{err}} }\
abla x_{L}(\theta,\boldsymbol{r}_{te},y^{min})\|_{2})}\) end while\(\epsilon_{max}-\epsilon_{min}>\epsilon_{acc}\)do \(\epsilon_{avg}\leftarrow(\epsilon_{max}+\epsilon_{min})/2\) \(x_{adv}\leftarrow\boldsymbol{x}-\epsilon_{avg}\sum_{i=1}^{d}w_{i}\mathbf{H}_{ c;i}\delta_{i}\) if\(\hat{l}(\mathbf{x}_{adv})==noise\)then\(\epsilon_{min}\leftarrow\epsilon_{avg}\) else\(\epsilon_{max}\leftarrow\epsilon_{avg}\) end for \(\epsilon^{*}=\epsilon_{max}\) \(\delta_{i}=w_{i}\epsilon^{*}\delta_{i}\) for \(\forall i\)
```  
**Algorithm 2**PCG attack
## 7 Discussion  
In contrast to the Landmarke system where network nodes could be freely deployed by the firefighters, the novel system must ensure that nodes maintain connectivity. However, the requirements as well as HCI studies [15] clearly state that a dedicated deployment-related activity is out of the question. This indicates that any novel technology should be integrated into processes and equipment that are already implemented and in place. Since additionally every modified piece of equipment must be re-certified and space for sensor locations is very limited, all research will focus on a single device. From our experience, when working with the firefighters, this device could be attached to the SCBA cylinder or the SCBA backplate (see Figure 4). Another option would be to replace the PASS device. As most firefighters nowadays must carry a PASS and the activity/annomaly recognition also provides PASS functionality, this existing and relied on tool would not be lost. At the same time adding another device to the equipment would be avoided. The PASS is typically attached to a chest strap of the SCBA, thus the text interface could be integrated there as well.  
Nevertheless, this single point of attachment also presents a challenge for the recognition of activities and movement abnormalities indicating an emergency. Distinguishing multiple activities from one sensing modality and location is not straightforward. Furthermore, researching a system that accurately recognizes activities of several firefighters is difficult, since the same activity can be performed quite differently by individuals trained in different schools and brigades. Independent of possible improvements of the recognition system, the system will make mistakes. Thus, it is required to evaluate if despite these inaccuracies the system could still be helpful to the IC, a question that is also linked to the presentation of activiti  
Figure 4: Captured from recordings of firefighter activities during a search and rescue mission. Sensor nodes are placed on the Landmarke transport unit on the air cylinder of the SCBA (encircled in red).  
The extracted activity information must also be integrated into the existing information management processes of the IC. To this end, we are researching new interfacing methods that are based on the tactical worksheet (German: \"taktisches Arbeitsblatt des IdF\"), a special paper worksheet which German incident commanders already use. Research is towards displaying the extracted activity information of the team in an automatic and intuitive way in a digital representation of the worksheet. Furthermore, localizing these activities on a map provides a great increase in utility, but the state-of-the-art is not yet deployable for critical applications [27].
# Device-Free Indoor Location Estimation System Using Commodity Wireless LANs
## 2 Channel Sounding Using IEEE 802.11n Wireless LANs
### CSI Acquisition  
In this study, we used an open-source software package called the CSI tool [21] to build our system. In IEEE 802.11n, the CSI was estimated in the receiver and then fed back to the transmitter, so the transmitter can use the CSI to complete calibration and beamforming. In other words, by setting the CSI estimate flag in the transmit packet, we can manage the receiver to report the CSI estimation to the transmitter. The CSI tool is a device driver for the Atheros network interface cards (NICs), athBK (kernel module), to extract the CSI using IEEE 802.11n. In the CSI tool, the transmitter repetitively sends the CSI estimate flag packet for CSI acquisition, and the receiver reports the CSI acquisition result to the user space program whenever the CSI is acquired. Here, the CSIs acquired for every combination of transmitter and receiver antennas are estimated as complex numbers for 56 subcarriers, including transmitter/receiver characteristics, propagation channel characteristics, and antenna characteristics.  
The CSI report in one packet transmitted with a bandwidth of 20 MHz is obtained in the form of a complex matrix of size \(N_{R}\times N_{T}\times N_{C}\); if the number of antennas at the receiver and transmitter is \(N_{R}\) and \(N_{T}\), respectively. Furthermore, information such as the timestamp, channel number, transmission rate, number of antennas (\(N_{R},N_{T}\)), number of sub-carriers \(N_{C}\), noise floor, PHY error, the received signal strength indicator (RSSI) of every antenna at the receiver, and the length of the payload is added. Because the real and imaginary parts of the CSI are each represented by 10 bits, their amplitude is automatically scaled according to the magnitude of the received power. Further, the phase of CSI fluctuates independently for each packet, but the amount of offset is common to all subcarriers (common phase offset).
# Unsupervised Color-Based Flood Segmentation in UAV Imagery
## 1 Introduction
* _Dataset Categorization:_ We have introduced a dataset categorization according to the depicted scenery and camera rotation angle, into rural and urban/pei-urban, and no sky and with sky, respectively. * _Efficiency and Real-Time Processing:_ The framework is efficient and suitable for on-board execution on UAVs, enabling real-time processing and decision-making during flight. The processing time per image is approximately 0.5 s, without the need for pre-processing, substantial computational resources, or specialized GPU capabilities. These contributions highlight the novelty and effectiveness of our method, particularly its suitability for rapid and efficient deployment in disaster response scenarios. The proposed system has been tested and compared with various variants of our own method, as well as with supervised approaches, using the Flood Area dataset introduced in [35], which consists of 290 color images. Our research did not identify other relevant unsupervised methodologies, and the proposed system yielded high-performance results. Additionally, experimental results of the proposed method are reported on the Flood Semantic Segmentation dataset [36], which comprises 663 color images. The rest of this paper is organized as follows: Section 2 introduces the datasets used for this article. Section 3 presents our proposed unsupervised methodology. The experimental results and a comprehensive discussion are given in Section 4. Finally, conclusions and consideration of future work are provided in Section 5.
## 2 Data Source and Experimental Methodology
### Experimental Comparison Models
#### 2.4.2 K-Nearest Neighbor Regression Model  
The K-nearest neighbor (KNN) model is a simple supervised learning algorithm. The input of the K-nearest neighbor method is the feature vector of the instance, it corresponds to the point in the feature space, and the output is the predicted value of the instance [17]. When the K-nearest neighbor model is used as regression model, it is assumed that a training data set is given, and the label value of each data has been calibrated; The KNN model calculates the average value of label values of the K nearest neighbor training instances of the new instance as the output of the model. In this experiment, the K value of the KNN model was set to 10, and the method of calculating the distance between different instances was the Euclidean distance.
## 3 Experimental Results and Analysis
### Robustness Test  
The VisDrone2019 dataset [41] was collected by the AISKYEYE Machine Learning and Data Mining Lab team at Tianjin University. It consists of 288 video clips, totaling 261,908 frames, as well as 10,209 still images. These data samples were captured using a variety of UAV cameras in diverse scenarios. The dataset includes footage from 14 different cities in China, covering both urban and rural environments. The target objects in the dataset encompass pedestrians and vehicles, and the scenarios vary in density, ranging from sparse to congested [42,43].  
By conducting experiments on this comprehensive dataset, the study aims to evaluate and compare the performance of the CCS-YOLOv8 model against the baseline algorithm. This assessment will provide insights into the model's robustness in recognizing and localizing objects in various challenging situations, contributing to the advancement of object detection techniques in aerial surveillance and monitoring applications.  
Table 4 showcases the experimental results. On the VisDrone2019 dataset, the CCS-YOLOv8 model recorded 42.6% accuracy, 33.5% recall, 31.1% [EMAIL_ADDRESS], 16.8% [EMAIL_ADDRESS], 17.1% [EMAIL_ADDRESS]:0.95, and 37.5% F1-score. Regarding accuracy, the CCS-YOLOv8 model shows an improvement of 2.2% over YOLOv8n, indicating its higher accuracy in detecting targets. Regarding recall, the CCS-YOLOv8 model demonstrates a 3.8% improvement over YOLOv8n, indicating its stronger recall ability with fewer missed detections. For [EMAIL_ADDRESS], [EMAIL_ADDRESS], and [EMAIL_ADDRESS]:0.95, the CCS-YOLOv8 model registers improvements of 3.8%, 1.9%, and 2.1%, respectively, over YOLOv8n, indicating enhanced performance across different IoU thresholds. As a metric combining precision and recall, the F1-score of the CCS-YOLOv8 model sees a 3.3% improvement over YOLOv8n, highlighting its comprehensive performance superiority. To sum up, the CCS-YOLOv8 model's remarkable robustness has been validated by the experimental findings collected from the VisDrone2019 dataset.
# TwSense: Highly Robust Through-the-Wall Human Detection Method Based on COTS Wi-Fi Device
## 3 Preliminaries
### OR-PCA and Doppler Shift  
Robust Principal Component Analysis (OR-PCA) can effectively reduce the degree of interference to CSI signals from other environmental factors, such as walls. This method requires the use of the principle of low-rank matrix decomposition, that is \(P=A+B(P\in R^{n_{1}\times n_{2}})\), it is assumed that the matrix \(A\) is a low-rank matrix. \(B\) is the sparse error, known \(P\), and finally needs to get the value of \(A\) and \(B\). Therefore, it can be expressed as the following combinatorial problem [18]:  
\[\min\
orm{A}_{\oplus}+\gamma\
orm{B}_{1} \tag{6}\]  
\[s.t.\ P=A+B \tag{7}\]  
where \(\
orm{A}_{\oplus}\) is the kernel norm of matrix \(A\) and is the sum of singular values of \(A\). That is \(\
orm{A}_{\oplus}=\sum_{i=1}^{\min\{n_{1},n_{2}\}}\tau_{i}\), \(\tau_{i}\) is the \(i\)th singular value of \(A\), where \(n_{1}\) and \(n_{2}\) are the row and column lengths of \(P\), respectively, \(\gamma\) is the weighting factor.  
Therefore, OR-PCA eliminates the influence of noise \(B\) generated by other physical environmental factors, such as walls, on the data \(P\), thereby obtaining significant CSI amplitude changes and making the Doppler frequency shift image characteristics caused by human motion more obvious.  
According to the literature, the Doppler frequency shift is the change in the oscillation frequency of the reflected signal [19], as shown in Figure 3b. Usually, the Doppler frequency shift of the reflector signal can be expressed as:  
\[F_{D}=-\frac{1}{v}\frac{d}{dt}d(t) \tag{8}\]  
where \(v\) is the wavelength of the signal and \(d(t)\) is the length of the reflection path. Using the above formula, the Doppler frequency shift of the CSI signal can be extracted. In order to extract the Doppler shift correctly, converting the noisy CSI into a spectrogram of the Doppler shift requires selecting the correct antenna pair, as shown in Figure 3a. According to Equations (2) and (6), the channel response can be calculated with the Doppler shift on each path is expressed as:  
\[D(f,t)\approx H_{\mathrm{s}}(f)+\sum_{i\in H_{d}}\alpha_{i}(t)W(F_{D_{i}}(t)) \tag{9}\]  
where \(H_{\mathrm{s}}(f)\) is the sum of the static path responses that through-the-wall when no action is triggered in the absence of people, and \(H_{d}\) is the set of dynamic paths after passing through-the-wall caused by the signal changes caused by the action in the presence of people, which \(W(F_{D_{i}}(t))\) is the cutoff of the target signal area window function. The Doppler shift of the CSI signal can be extracted using Equation (9), thus accurately extracting the characteristics of the personnel when they are present.  
Figure 2: Wi-Fi through-the-wall principle.
## 2 Materials and Methods
### Communication Technologies  
ITS applications depend on advanced wireless communication schemes, as vehicle onboard units (OBUs) are allowed to interact with each other (v2V), with remote stations and entities (V2I) in the same communication range, and with road users (V2P) using available radio interfaces. More specifically, communication of Internet of Vehicles with infrastructure is carried out through multiple access technologies, like IEEE 802.11 (Wi-Fi) customized for vehicular connectivity in the 5.9 GHz band (the DSRC/WAVE) and mobile broadband technologies (e.g., 4G/LTE, 5G). 5G is considered a promising technology, as it aims to be a revolution in terms of data rates, ultra-low latency, massive connectivity, ultra-high network reliability, and energy efficiency to support V2X (Vehicle-to-Everything) safety and non-safety applications with their different requirements. Mobile Edge Computing (MEC), Network slicing and SDN are technologies in 5G that add important enhancements to both the radio access networks (RAN) and the core networks of mobile communications [9]. Network slicing is described by [12] as an end-to-end logical network that is equipped with a collection of separated virtual resources on a common physical infrastructure. These logical networks are provided as various services to meet the various communication needs of users. SDN is regarded as an approach for 5G network slicing implementation.