esa-sceva/satcom-chunk-collection huggingface dataset:
# Ground Target Detection and Damage Assessment by Patrol Missiles Based on YOLO-VGGNet
## 6 Conclusions  
This paper proposes a damage assessment method based on YOLO-VGGNet for mobile ground targets. Unlike traditional image change detection methods, which require images before and after the strike for comparison, the proposed method learns the target model through extensive training and can assess the target's damage status based on only online perception. Specifically, with YOLOv3 as the pre-processor, the proposed method can detect, classify, and localize ground targets accurately and quickly. Then, the image blocks of detected targets are fed into the lightweight VGG networks, which can evaluate their damage level. Meanwhile, based on CAM and deconvolution, we further analyze the activation intensity of clustered convolution kernels. On the one hand, it is helpful to determine whether the targets' key components are destroyed. On the other hand, it provides a more interpretable perspective for learning-based methods. Finally, Unity simulation experiments prove the proposed method's effectiveness, which improves the accuracy of damage level assessment by 16.0% and 8.8% compared with traditional image-change-detection-based methods and the two-CNN learning-based method. The convolutional feature clustering method evaluates the status of the targets' key components with an accuracy of 72%.  
This work also has some limitations. Firstly, the recognition and localization errors of the targets may increase with the decrease in the angle of view. Further research on multiangle fusion of convolutional feature clustering may be a significant research interest. Secondly, although the proposed method can be utilized to assess the damage to any target's component, this work focuses mainly on the key components of targets, and the assessment accuracy of components with low weights is lower than those with high weights. In subsequent work, we will attempt to extract more detailed information on the damaged target components and explore information fusion from multiple perspectives to make our method more robust for ground interference.  
Conceptualization, Y.X.; data curation, Y.X.; formal analysis, Y.X. and Q.Y.; investigation, Y.X.; methodology, Y.X., Q.Y. and H.L.; project administration, H.L. and J.X.; resources, H.L.; software, Y.X.; supervision, Z.Z., J.X. and H.L.; validation, Z.Z., J.X., Y.W. and H.L.; writing--original draft, Y.X.; writing--review and editing, Q.Y., Y.W., J.X. and H.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
All data generated or analyzed during this study are included in this paper or are available from the corresponding authors on reasonable request.  
The authors declare no conflict of interest.
## 2 Methods
### Classification Model Based on Multi-Scale ViT  
The overview of the classification model based on multi-scale ViT is shown in Figure 3. The input to the model is images at 224 \(\times\) 224, 160 \(\times\) 160, and 128 \(\times\) 128. Images at these scales are individually subjected to patching and position embedding, subsequently being fed into the ViT model. Then, the classification token (CLS) of the three scales will be obtained and the corresponding classification result will be achieved by the classifier, which can be represented by the following equation.  
\[\mathbf{c}=\text{softmax}\Big{(}\mathbf{W}_{f}\cdot\mathbf{z}+\mathbf{b}_{f}\Big{)}, \tag{1}\]  
where \(\mathbf{z}\) is the [CLS] data at the respective scale, \(\mathbf{W}_{f}\) and \(\mathbf{b}_{f}\) are the weights and biases of the fully connected layer, and \(\mathbf{c}\) is the classification result vector.  
Meanwhile, in the model, the [CLS] at the three scales will be concatenated to generate the overall [CLS], which will be fed equally into the classifier to obtain the final classification result. Knowledge distillation technique [30] is used in this model in order to drive the final  
Figure 2: The multiple factors (environmental factor, growth factor, and filming factor) of the data augmentation model.  
classification results. The ViT models at each scale are used as independent teacher models in this framework, and the soft targets are output. Their predicted category probability distributions are passed as training signals to the integrated student model. Furthermore, the model combines two types of losses to establish the link between the teacher model and the student model. The hard target loss uses a cross-entropy loss that directly targets real category labels, ensuring that the student model can perform well on basic classification tasks. Smooth L1 loss is used for soft target loss, which reduces sensitivity to outliers and provides more stable gradients during training. The final loss function is shown in the following equation.  
\[L=\alpha\cdot L_{hard}+(1-\alpha)\cdot L_{soft}, \tag{2}\]  
\[L_{hard}=-\sum_{c=1}^{M}y_{o,c}\text{log}(p_{o,c}), \tag{3}\]  
\[L_{\text{soft}}=\frac{1}{N}{\sum_{i}}\Bigg{\{}\begin{matrix}0.5\cdot\left(x_{ i}-x_{t_{i}}\right)^{2},&\text{if}\ |x_{i}-x_{t_{i}}|<1\\ |x_{i}-x_{t_{i}}|-0.5,&\text{otherwise}\end{matrix}. \tag{4}\]  
where \(\alpha\) is a hyperparameter used to balance the importance of the two losses, \(M\) is the total number of categories, \(y_{o,c}\) is a binary indicator (0 or 1) that is 1 if category \(c\) is the correct categorization of observation \(o\), \(p_{o,c}\) is the probability that the model predicts observation \(o\) to be of category \(c\), \(x_{i}\) denotes the prediction of the student model, \(x_{t_{i}}\) denotes the prediction of the teacher model, and \(N\) is the batch size.
# A Survey on Monitoring Quality Assessment for Wireless Visual Sensor Networks
## 4 Reviewing Content Quality
### Perspective  
Content quality assessment with respect to perspective is related to the angle of view that a camera faces a region or a target, as illustrated in Figure 7. The angle of view \(\varphi\) represents the difference between the direction of view of the visual sensor node and the frontal face of a region or a target. A small angle of view implies in a frontal perception of the region or targets of interest, which results in a high quality of monitoring.  
Commonly, variations at the angle of view are weighted in order to quantify the application quality, through a proportional relation or using a non-linear function [31; 74; 75; 76]. In the special cases of 3D scenario, the quality function considers the azimuth angle of the target facing direction, the elevation angle of the camera and the distance to the target [75; 76].  
The angle of view can be indirectly used to determine other metrics. A correlation metric among visual nodes can be used to define the overall network quality in terms of the amount of common information from multiple views. The correlation is defined by the facing angle and nodes with disparate angles of view (low correlation) are selected to aggregate (fusion) data, in order to acquire more diverse information [74]. As the angle of view increase and depending on variations on focal length, a perspective distortion can be notice [48; 49], i.e., distortion in the image altering scale and curvature, which can considerably deteriorate the visual quality. The angle of view can also affect a bounding box approach, since the number of pixels on the detected face depends on the facing angle of the person from the camera [70; 71].  
Figure 6: Content quality assuming sharpness continuous variation.  
Figure 7: Monitoring model considering the angle of view.
# A Secure Link-Layer Connectivity Platform for Multi-Site NFV Services
## 4 Implementation and Validation
### Performance Evaluation
#### 4.2.3 Operational and Scalability Considerations  
Our proof-of-concept implementation of the L2S platform supports the provision of functional VLANs to multi-site NFV services. According to the aforementioned experimental results, with the allocation of 1 virtual CPU at each L2S VNF, these VLANs can operate at 100 Mb/s data rates. L2S operations can exploit multiple CPUs, increasing the achievable throughput on an L2S VLAN. This is most noticeable if fragmentation and reassembly of IP packets are not performed by L2S VNFs, which can be guaranteed with the configuration of an appropriate MTU value on the interfaces of the communication endpoints connected to the L2S VLANs. In that case, our prototype implementation can provide data rates up to 1 Gb/s. In a real scenario, the configuration of specific MTU values on endpoint interfaces can be automated using centralized management mechanisms, such as specific DHCP servers provided by the NFV infrastructures, management clients based on protocols such as NETCONF [29], virtualization initialization scripts, etc. These centralized mechanisms are under the control of a VIM, which can be provisioned with the MTU values to be used on the pre-created virtual links that communication endpoints can use to connect to L2S VNFs (see Figure 3). This way, appropriate MTU values can be assigned to communication endpoints in a coordinated way.  
We want to note that the previous performance evaluation provides reference indicators for the achievable throughput on a single L2S VLAN that interconnects two sites. A decrease on the maximum average throughput per L2S VLAN could be observed as the number of active VLANs increases (i.e., the VLANs actually being used). A similar situation can be expected as the average number of sites per VLAN augments. To adapt to varying traffic demands and support the scalable operation of the L2S platform, the underlying virtual nature of the platform can be exploited by increasing the allocation of resources at each L2S VNF (i.e., vertical scaling), particularly virtual CPUs; and creating multiple instances of the L2S VNF at any given NFVI (i.e., horizontal scaling), segregating the space of L2S VLANs into the different instances.
## 4 Simulation Results
IS, IS-GWO, GWO, DE, and IS-DE can not match the performance of GWOEIS, even with \(R=1000\), but at the expense of significant increase in computational cost. In Figure 6, we assess the impact of \(T_{H}\) on RMSE performance of the iterative techniques. Once again, GWOEIS outperforms all other iterative techniques in terms of TD, AoA, and channel estimation. By increasing the number of iterations, we reach the best performance for the all algorithms, as shown in Figure 6(a,i,b,ii,c,ii). Moreover,in Figure 6(a,iii,b,iii,c,iii), we notice that a small order of 10 iterations is enough to put GWOEIS on the top of the rest, with estimation accuracy gains constantly increasing with \(T_{H}\), making its potential gains in computational complexity remarkably large when compared to other techniques that require a very high number of iterations. In Figure 7, we investigate the effect of the number of paths on the estimation performance. We observe that the new approach, GWOEIS, outperforms most of the benchmarks in the high and low SNR scenarios for TD, AoA, and channel estimation when \(Q\leq 4\). We consider also the same configuration to evaluate the performance of all of the approaches in terms of temporal and angular separations. In Figure 8, we fix the first delay at \(\tau_{1}=2\ T\), and we vary the second delay at \(\tau_{2}=\tau_{1}+\Delta\tau\). It is clearly seen that our approach is still capable of achieving the CRLB, even in a challenging scenario with a very small temporal separations of \(\Delta\tau=0.5\ T\). In Figure 9, we fix the first AoA at \(\theta_{1}=-20^{\circ}\), and we vary the second AoA \(\theta_{2}=\theta_{1}+\Delta\theta\). Here, again, we can highlight the robustness and the super-resolution capacity of our approach, and to appreciate its superiority in challenging scenarios where the paths are closely spaced in both temporal and spatial domains. We have to mention that Figure 8(a,iii,b,iii,c,iii) show the superiority of UMP reaching the best techniques achieving minimum RMSE only when \(\Delta_{\tau}\geq 10/B\). Figure 8: RMSE vs. the SNR in dB and the temporal separation \(\Delta_{\tau}\) in ns with \(Q=2\), \(R=100\), and \(T_{H}=100\). Figure 7: MSE vs. the SNR in dB and the number of paths \(Q\) for \(T_{H}=100\) and \(R=100\).
## 4 Experimental Verification and Discussion  
To experimentally determine the PTE, a pair of semi-rigid coaxial cables was used to deliver power from a signal generator to the transmitting antenna, and the received power was measured with a power meter located outside the closet. The position of the transmitting antenna was kept fixed during the measurements of the PTE. Two PLA fastening hooks were used to attach the antenna to the shelf in the upper part of the closet. The hooks were created with the aid of a 3D printer. To find the optimal configuration of the antenna system resulting in the highest PTE while providing acceptably wide high-electric field intensity regions, an Ansys HFSS model of the closet containing both the antennas and their fixtures was built. The optimal antenna design parameters were found from the simulation data obtained for different combinations of model parameters. Then, the PTE for the designed WPT system was calculated at different positions along the largest dimension of the closet. Figure 13 presents photos of the experimental setup involving a carbon steel closet used as a resonant cavity containing two Yagi-like antennas.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline Feed line length & 85 mm & Dipole width & 5 mm \\ Feed line width & 5 mm & Substrate width & 180 mm \\ Reflector width & 20 mm & Substrate length & 120 mm \\ Reflector length & 160 mm & Dipole length & 65 mm \\ \hline \hline \end{tabular}
\end{table}
Table 6: RX antenna parameter values.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline Feed line length & 75 mm & Dipole width & 5 mm \\ Feed line width & 5 mm & Substrate width & 180 mm \\ Reflector width & 20 mm & Substrate length & 280 mm \\ Reflector length & 170 mm & Dipole length & 64.4 mm \\ Director width & 5 mm & Director length & 160 mm \\ Director-to-dipole separation & 190 mm & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: TX antenna parameter values.
## 4 Results and Discussion
### Height Estimation of Single Objects  
Within this investigation, we wanted to estimate the Pleiades and WorldView-3 DSMs heights at single objects as a function of object type and size. For this purpose, three object types were analyzed: non-moving vehicles, trees, and buildings. The main parameters taken into account were vehicle length/width, tree crown diameter, and building length/width.  
For each single object, the estimated heights were compared with the monoscopic reference measurements based on a ratio measure. The percentage value derived by the following equation describes how much of the reference height was reconstructed:  
\[p\left(\%\right)=h/H\times 100, \tag{4}\]  
with \(H\) the reference height, \(h\) the estimated height, and \(p\) the reconstruction percentage.
# TACRP: Traffic-Aware Clustering-Based Routing Protocol for Vehicular Ad-Hoc Networks
## 2 Related Works
Next, the ECHS method, in which clustering is performed in both a centralized and distributed manner and perfect distribution of clusters is guaranteed. An effective gateway model is presented with cluster formation based on speed limits. Through this method, the system achieves low loss, overhead, and delay. However, the efficiency is moderate and when the system is applied to high speed the efficiency is simultaneously reduced. So, it is essential to design a prototype which concentrates on reducing both the overhead and consumption of power.
# Soil Texture Estimation Using Radar and Optical Data from Sentinel-1 and Sentinel-2
## 3 Results
### Classification Scheme  
Four indicators were used to estimate the soil clay content: S-2 images in two different SWIR bands (B11 and B12), the temporal average of soil moisture, and a combination of SWIR data and soil moisture. From the sensitivity analysis, the reflectance indicators derived from optical band data (B11 and B12) are found to be more sensitive to soil clay content than the optical indices (SRCI and BI) computed from the S-2 images. As shown in Section 3.1.1, B11 and B12 provide the best sensitivity, with an R\({}^{2}\) equal to approximately 0.4. For this reason, only the optical SWIR bands were selected for the classification process. The classification workflow used to determine the clay content distribution over the study site is shown in Figure 9.  
Figure 8: Temporal mean of soil moisture over the reference fields, computed for the period between September and the beginning of December as a function of soil texture computed from ground measurements: (**a**) for clay content; (**b**) for sand content.  
Figure 9: Classification workflow for clay content mapping.
## 2 Results and Discussion  
### Determination of Sample Weight  
Sample weight is one of the most important parameters which greatly influence extraction efficiency. It is well known that the amount of analytes extracted from the sample increases with the sample size. This parameter is more prominent for the compounds having high distribution coefficient between coating and the sample. In a case of the compounds with the small value of the distribution coefficient between coating and the sample, the amount of analyte extracted is almost independent upon sample weight [19]. For SPME fibre technique it is recommended to use splitless mode of injection [18]. SPME-Arrow has 6 to 20 times larger volume of the sorption phase in comparison with the conventional SPME, thus, to overcome potential overload of GC column sometimes split mode is used [17; 22; 25]. For the above-mentioned reason, before starting the optimization process, the appropriate sample weight and injection mode on GC/MS instruments had to be determined. For sample weight 100, 300, and 500 mg were chosen, while injection mode was tested in split and splitless (1:5) mode. The results obtained in these one-factor-at-a-time experiments are presented in Figure 1.  
First all sample weights were tested in split mode, where higher sample weight resulted in higher peak areas. The statistically significant difference in peak areas is observed for all analyzed groups of compounds except acids. Based on this observation it can be assumed that all analyzed compounds except acids have high distribution coefficient between coating and the sample. As SPME-Arrow has greater adsorbing capacity in comparison with the SPME fibre, it was assumed that the great majority of adsorbed analytes are not directed to the GC column in split mode, thus, to test this hypothesis the splitless mode was chosen. Since the sample weight of 100 mg gave satisfactory results, meaning that the number of detected compounds did not change, it was tested in splitless mode. As can be seen on Figure 1, the splitless mode gave the highest peak areas for all analyzed classes of volatiles. Thus, the sample weight of 100 mg and splitless mode of injection on GC/MS instruments were used in further optimization processes.
## 5 Numerical Results for Specific Error Models
### Spatially Correlated Errors  
We also consider errors that are not generated through a simple i.i.d. noise model, as above, but instead have spatial correlations. Such error models are arguably more realistic, and have recently attracted attention [11; 12; 13].  
We consider a model for which an error occurs on each spin with probability \(p^{\prime}\). For each of these errors, an additional one will occur on a nearest neighbour with probability \(q\). This error model is therefore equivalent to that above for \(p^{\prime}=p\) and \(q=0\), but has nearest neighbour correlations between errors when \(q>0\).  
Using the same method of numerical benchmarking described above we study the threshold, \(p^{\prime}_{c}\), and the minimum system size required for error correction, \(L^{*}(p)\), for the case of \(q=0.5\). The results are shown in Figure 3.  
To compare the results with those for the uncorrelated case, let us consider the expected fraction of spins to have suffered an error in both cases. For the uncorrelated case this is simply \(p\), whereas in this case it will be approximately \((1+q)p^{\prime}\). We may therefore expect that the behaviour of the decoder for given values of \(p^{\prime}\) and \(q\) in this case will correspond to the behaviour for \(p=(1+q)p^{\prime}\) in the uncorrelated case. This gives us an estimate of \(p^{\prime}_{c}\approx p_{c}/1.5=4.8\%\) for \(q=0.5\).  
Figure 3: Results for correlated errors. (**a**) Graph of logical error rate, \(P\), against bit-flip error rates, \(p^{\prime}\), around threshold. (**b**) Graph of minimum linear system size required for \(P<p^{\prime}\), \(L^{*}\), against \(p^{\prime}\).  
The threshold is found numerically to be around \(4.75\%\)-\(5\%\), and so agrees well with the above expectations. However the logical errors rates found in this case with a given \(p^{\prime}\) fall well below those for \(p=(1+q)p^{\prime}\) in the uncorrelated case. For example, uncorrelated \(p=6\%\) corresponds to correlated \(p^{\prime}=4\%\), however for \(L=600\) we find \(P=1.6\times 10^{-4}\) in the former case and \(P=2.4\times 10^{-3}\) in the latter. The correlated case therefore gives worse logical error rates by an order of magnitude. Also the minimum system size for error correction to be evident is \(L^{*}=38\) in the former case but \(L^{*}=130\) in the latter. This decrease in performance is likely due to the fact that the correlations increase the typical length of error strings. This effectively decreases the distance of the code.  
For the minimum required system sizes, \(L^{*}\), required for \(P<p^{\prime}\), it is found that at least \(L=7\) is required for error rates as low as \(p^{\prime}=5\times 10^{-4}\). However, this small size remains sufficient until \(p^{\prime}=1\%\), after which the required system size rises steeply as \(p^{\prime}\to p_{c}\).