esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Knowledge-Distillation-Based Method for Detecting Conveyor Belt Defects
## 3 Result and Discussion
### Feature Map Comparison Analysis  
We choose to visualize the feature maps output by the network detection head, and the results are shown in Figure 11.  
Figure 11: Comparison of output feature maps of different network detection heads. (**a**) Initial input images. (**b**) Feature map of YOLOv5m. (**c**) Feature map of YOLOv5n. (**d**) Feature map of YOLOv5n(KD). (**e**) Feature map of YOLOv5n-slim(KD).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **P/\%** & **R/\%** & **[EMAIL_ADDRESS]/\%** & **Params/M** & **Inference Time/ms** \\ \hline YOLOv5n & 86.02 & 93.84 & 95.57 & 1.76 & 1.7 \\ YOLOv5s & 91.88 & 95.84 & 97.53 & 7.23 & 4.7 \\ YOLOv5m & 93.89 & 96.98 & 98.44 & 20.86 & 10.1 \\ YOLOv5n-slim & 82.22 & 88.70 & 90.74 & 0.49 & 1.2 \\ YOLOv5n(KD)(ours) & 90.75 & 93.47 & 97.33 & 1.76 & 1.7 \\ YOLOv5n-slim(KD)(ours) & 84.60 & 88.60 & 94.83 & 0.49 & 1.2 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of detection performance of each model.  
From the comparison of the feature maps, it can be seen that the distilled network has higher activation values in the target region with clearer boundaries and is less influenced by background information. Therefore, the fine-grained feature-simulation distillation method used in this paper is a good guide for improving the feature-learning performance of the network.
# PDTR: Probabilistic and Deterministic Tree-based Routing for Wireless Sensor Networks
## 3 Preliminaries
### Radio Model Used for PDTR  
The proposed PDTR uses First Order Radio Model with asymmetric channel to run the radio as depicted in Figure 2, such that it uses the same energy, to transmit the data from x to y, and to transmit the same data from y to x, this energy is denoted by \(E_{elc}\), and has the value of \(E_{elc}=50\frac{nI}{bit}\). \(E_{elc}\) depends on filtering, modulation, digital coding and signal spreading [26]. In this model, two channel models are used. The Free space channel model of transmitter amplifier is denoted by \(\varepsilon_{\beta}\), and has the value \(\varepsilon_{\beta}=10\)\(p\)/\(bit/m^{2}\). The Multi-path channel model of transmitter amplifier is denoted by \(\varepsilon_{mp}\) and has the value \(\varepsilon_{mp}=0.0013\)\(p\)/\(bit/m^{4}\)[26]. Let \(d_{(n_{i},n_{j})}\) be the distance between the transmitter \(n_{i}\) and the receiver \(n_{j}\). Let \(t_{0}\) be the distance threshold, and it has the value of \(t_{0}=\sqrt{\frac{\varepsilon_{\beta}}{\varepsilon_{mp}}}\) (meters).  
\begin{table}
\begin{tabular}{c c} \hline
**Notation** & **Description** \\ \hline \(\mathbb{N}\) & The set of sensor nodes in the network; \(\mathbb{N}=\{n_{0},n_{1},n_{2},\ldots\}\); \\ \(\mathbb{N}_{i}\) & The neighboring nodes of the node \(n_{i}\); \(m_{i}=|\mathbb{N}_{i}|\). \\ \(n_{h}\) & Node needing help, is defined by the node that loses \(\ell_{e}\) of its initial energy. \\ \(n_{mp}\) & Node needing new-parent, is defined as the removed child node from \(n_{h}\) children, when \\ \(d_{ij}\) & The Euclidian distance between: \(n_{i},n_{j}\). \\ \(h(n_{i})\) & The hop-count of the node \(n_{i}\), is the minimum number of hops to reach the sink. \\ \(E_{i}\) & The residual energy of the node \(n_{i}\). \\ \(\delta\) & The communication radius. \\ \(\alpha\) & The control parameter of the impact of hop-count distribution on the routing protocol. \\ \(\beta\) & The control parameter of the impact of transmission distance distribution on  
When \(d_{(n_{i},n_{j})}\) is greater than \(t_{0}=\sqrt{\frac{\varepsilon_{fs}}{\varepsilon_{mp}}}\) (meters), a multi-path (mp) channel model is used to run the radio. Otherwise, the free space (fs) channel model is used to run it, as given in Equations (1) and (2). Each sensor needs an energy of \(E_{Tx}\Big{(}\big{(}n_{i},n_{j}\big{)},k\Big{)}\) to transmit k bits size message over distance \(d_{(n_{i},n_{j})}\), and needs an energy of \(\mathrm{E_{Rx}}(\mathrm{k})\) to receive the transmitted message of k bits.  
\[E_{Tx}\Big{(}n_{i},n_{j},k\Big{)}=\left\{\begin{array}{l}\left(k\cdot E_{ elec}\right)+\left(k\cdot\varepsilon_{fs}\cdot\left[d_{(n_{i},n_{j})}\right]^{2} \right)d_{(n_{i},n_{j})}<t_{0}\\ \left(k\cdot E_{elec}\right)+\left(k\cdot\varepsilon_{mp}\cdot\left[d_{(n_{i}, n_{j})}\right]^{4}\right)d_{(n_{i},n_{j})}\geq t_{0}\end{array}\right. \tag{1}\]  
\[E_{Rx}\Big{(}n_{j},k\Big{)}=k\cdot E_{elec} \tag{2}\]
## 4 Evaluation
### Results of Complex Stationary and Mobile Scenarios
Both R&R/opt and SSF/opt exhibit similar power consumption trends due to their shared reliance on simplified optimization techniques. In contrast, RND/opt performs the worst, primarily due to its reliance on random decision-making, which leads to inefficient resource utilization. In the case of Proposed, the slope of the power consumption increases as the simulation progresses toward the final timesteps. This is because VUs gradually converge toward the center of the intersection over time, leading to an increased density around the central RSU. While VUs associated with the central RSU benefit from high channel gains, those linked to edge RSUs must rely on higher transmit power due to weaker channel conditions. This disparity explains the upward trend in the accumulated power consumption toward the end of the simulation. For stationary configurations, the complexity of the scenario has little impact on power consumption performance, further validating the robustness of the proposed approaches. This consistency demonstrates that the proposed and comparable methods effectively handle resource allocation regardless of scenario intricacy. However, in mobile scenarios, where VUs operate over a longer duration, the differences in accumulated power consumption become more pronounced. This extended runtime highlights a noticeable performance gap, with R&R/opt outperforming both Util&LB/opt and SSF/opt by a wider margin than in stationary scenarios. Furthermore, both Proposed and R&R/opt exhibit a relatively stable trend in power consumption throughout the simulations, underscoring the robustness of their optimization strategies. This stability indicates their adaptability to dynamic conditions and ensures efficient performance over time, even in complex mobile vehicular networks. Figure 9: Mean proportion of the NOMA channels for a complex, stationary configuration with twenty VUs and three channels.
# Networked Roadside Perception Units for Autonomous Driving
## 5 Evaluation
### Simulation Analysis
#### 5.3.2 Delay Evaluation  
In the Tokyo simulation, delays can be seen when the vehicle densities are at 25, 50, and 75 vehicles/km. The delay was nearly 10 ms under the two RSPUs scenarios till the peak distance of 1000 m. However, a delay of 290 ms was seen in the no-priority scenario calculated as per Section 5.2. The delay was reduced by the algorithm to 40 ms in a range of 740 m, wherein the PDR was observed at 100%. Subsequently, the delay rose to 990 m from 740 m as packet drop took place in this range. The linked RSPUs scenario witnessed a delay of 290 ms at densities of 75 vehicles/km. The delay was reduced by the algorithm to 100 ms within 450 m, wherein the PDR was observed at 100%. Therefore, the delay rose to 800 m from 450 m.  
Figure 12b illustrates the delays in simulations of the Paris map. A delay of nearly 10 ms was seen in both situations at 25 vehicles/km. However, this delay rose to nearly under both situations at densities of 50 vehicles/km. The delay was approximately 290 ms at 75 vehicles/km in the absence of the planned algorithm, with the distance priority-based algorithm decreasing the deferment to 65 ms.  
According to the above findings, the linked RSPUs system is capable of extensively delivering the messages. In addition, it can also be seen that the distance-based priority algorithm helps decrease the delay for information obtained from closely situated RSPUs.
## 2 Establishment of Prediction Model
### TLM-PML Method
On the other hand, a wave entering the PML at an angle \(\theta\) at \(x_{0}\) will return to the surface at a distance from the incident point \(r=x-x_{0}=2H\mathrm{tan}\theta\), i.e.,:  
\[\frac{r}{\lambda}=2\eta\tan\theta \tag{29}\]  
It can be seen from Equations (28) and (29) that the larger the horizontal region of concern, the larger \(\eta\) and \(\Omega\). Figure 5: The propagation of a wave in the PML region. The PML is constructed through a series of thin-layer elements in the TLM, and the linear size of the element is directly extended according to the horizontal and vertical positions of the elements in the PML domain [27]. Therefore, assuming that the PML domain is divided into \(N\) thin layers of the same thickness, the thin-layer extension thickness of the \(l\)-layer is as follows:  
\[\overline{h}_{l}=\overline{z}_{l}-\overline{z}_{l-1}=H\Bigg{\{}\frac{1}{N}-i \Omega\bigg{[}\bigg{(}\frac{l}{N}\bigg{)}^{m+1}+\ \bigg{(}\frac{l-1}{N}\bigg{)}^{m+1}\bigg{]}\Bigg{\}} \tag{30}\]  
where \(1\leq l\leq N;l\) increases downward. It can be seen from Equation (30) that in the TLM, for each thin-layer element, its fundamental equation is composed of element matrices \(\mathbf{A}_{a}^{e}\), \(\mathbf{C}_{a}^{e}\), and \(\mathbf{B}_{rz}^{e}\). In order to obtain the layer element matrix of the PML, \(h_{l}\) in the original element matrix of the TLM can be replaced by\(\overline{h_{l}}\). It can be seen from the above that the selection of the PML parameters \(\Omega\), \(\eta\), and \(m\) has a great influence on the concerned horizontal region. A large number of numerical experiments and regression analyses show [20] that the optimal parameters of the PML can be selected as follows:  
\[\overline{z}=z-\mathrm{i}\Psi(z) \tag{23}\]0  
where \(N\) is the number of divisions of the PML thin layer, and \(n\) is the number of shape functions used by TLM discretization. The \(x_{\max}\) is the maximum horizontal distance concerned.
## 4 Performance Evaluation
### Simulation Setup  
In the simulation, the multi-AP network is deployed in a 200 \(\times\) 200 m\({}^{2}\) area, where five APs with 10 client nodes are randomly generated (the link distance between a client and its AP is randomly selected from the range of 1-35 m); this represents a densely-populated configuration fully covering the entire area. The transmission range and carrier sensing range of legitimate nodes are set to 75 m, and 150 m, respectively. A selfish tethering pair is placed at the center of the area, whose link distance and carrier sensing are set to 1 m and 10 m, respectively. Table 2 lists the parameter values that were used in the simulation study.  
The performance is evaluated in terms of detection accuracy and time for TCP and UDP protocols. We consider a downlink scenario, where each AP transmits frames to its client nodes.
# Design and Analysis of Adaptive Hierarchical Low-Power Long-Range Networks
## 5 Real-World Evaluation
### Assessing Channel Quality and Its Effect on the Performance of the Sub-Network Discovery Module  
The evaluation starts with a collection of preliminary experiments that aim to fine-tune the sub-network discovery operation of the algorithm. This essentially includes the appropriate adjustment of the two LQI threshold pairs (see Section 2.2) and the periodicity of the broadcast beacon. It is expected that when strict LQI thresholds are set, the resulting sub-networks will be smaller and more stable. While when relaxing the LQI thresholds, the sub-networks will grow in size however making them more prone to channel quality fluctuations. The experiments conducted examined two different pairs of LQI thresholds: \((35,75)\) and \((55,95)\). The resulting sub-network sizes are depicted in Figure 5. Based on the outcome of these experiments, the LQI threshold pair \((55,95)\) is more suitable for the particular indoor deployment. For this range, the hierarchical scheme generates stable sub-networks within a short period of time and the resulting topology is dense enough to provide stable communication.  
The next step is to examine the impact of beacon interval period and the timeout period in the detection of nearby nodes. Figure 6 presents the results of four different sets of beacon intervals. For each one, the number of changes in the detected sub-networks (i.e., the events) are measured during the evolution of the experiment. It is evident that, as the beacon interval increases, the sub-network discovery experiences fewer fluctuations. Based on these results, it seems that a beacon interval of 2000 ms and above is a good trade-off between adaptivity and responsiveness to topology changes and induced overhead on the wireless medium.  
Figure 5: Average sub-network size with different LQI (Link Quality Indicators) thresholds.
# TMVDPatch: A Trusted Multi-View Decision System for Security Patch Identification
## 5 Experiments and Results
### Evaluation Metrics  
In this paper, the accuracy, precision, recall and \(F1\) score are used as evaluation metrics to evaluate the security patch identification effect of TMVDPatch. In the evaluation, we consider four statistical types, namely true positive (_TP_ ), true negative (_TN_), false positive (_FP_) and false negative (_FN_). If the sample is actually a security patch and is identified as a security patch, we count this test case as _TP_. If the sample is actually a non-security patch and is identified as a non-security patch, we count this test case as _TN_. If the sample is actually a security patch and is identified as a non-security patch, we count this test case as _FN_. If the sample is actually a non-security patch and is identified as a security patch, we count this test case as _FP_.  
**Accuracy** measures the number of patches identified accurately as a proportion of all samples. This metric can measure the overall identification effect of the model on security patches and non-security patches. The formula is as follows:  
\[Accuracy=\frac{TP+TN}{TP+FP+TN+FN} \tag{15}\]  
**Precision** measures the proportion of actually security patches among samples identified by the model as security patches. This metric can measure the false positive rate of the model for the identification of security patches. The specific formula is as follows:  
\[Precision=\frac{TP}{TP+FP} \tag{16}\]  
**Recall** measures the proportion of security patch samples identified by the model among all actual security patch samples. The specific formula is as follows:  
\[Recall=\frac{TP}{TP+FN} \tag{17}\]  
**F1 Score** comprehensively measures the precision and recall of the model. This metric is calculated by weighting the two. The calculation formula is as follows:  
\[F1=\frac{2\times Precision\times Recall}{Precision+Recall} \tag{18}\]
## 5 Deployment and Evaluation  
In this part, the performance of ViPER+ was evaluated and compared to previous solutions. Since our work is focused on construction site environments, the solution was implemented in a road construction site as an example of a real-world environment.
# Machine Learning Techniques in Structural Wind Engineering: A State-of-the-Art Review
## 3 Prior Studies on Applying ML Techniques in Structural Wind Engineering
### Aeroelastic Response Prediction Using ML
Furthermore, the drag force of a circular cylinder can be reduced by optimizing the control parameter such as feedback gain and the phase lag using neural networks by minimizing the velocity fluctuations in the cylinder wake [164].
# Energy Efficient Data Transmission for Sensors with Wireless Charging
## 4 Transmission in an Unlimited Number of Periods
### A Simple Problem with \(C=e\)  
When \(C=E\), the problem is exactly the well known Binpacking problem [20]. The Binpacking problem is defined as follows. Given \(n\) items with sizes \(s_{1}\), \(s_{2}\),..., \(s_{n}\), and \(n\) bins with capacity \(C\), the problem is to pack the \(n\) items into the bins such that the number of used bins is minimum.  
The Binpacking problem is NP-Complete, and some approximation algorithms have been proposed. We show some existing theorems to better understand this problem.  
Theorem 4: _There is no \(\alpha\)-approximation algorithm with \(\alpha<\frac{3}{2}\) for the Binpacking problem unless \(P=NP\)._  
This theorem can be derived immediately from the Partition problem. The Partition problem is to partition a set of \(n\) integers \(\{a_{1},a_{2},...,a_{n}\}\) into two subsets \(A_{1}\) and \(A_{2}\) such that the sums of both subsets are equal, i.e., \(\sum_{a\in A_{1}}a=\sum_{a\in A_{2}}a\). The Partition problem is also NP-Complete, and it is easy to find out that the Partition problem is a simple instance of the Binpacking problem. If the Binpacking problem has an approximation algorithm with ratio bound less than \(\frac{3}{2}\), then we can use this algorithm to solve the Partition problem, and it will derive an optimal result with less than three subsets (bins), which is exactly two subsets (bins). The approximation algorithm is a polynomial time algorithm, which contradicts the fact that the Partition problem is NP-Complete.  
Theorem 5: _The FirstFitDecreasing algorithm is \(\frac{3}{2}\)-approximation for the Binpacking problem._  
The FirstFitDecreasing algorithm is described as follows. Sort the items such that \(s_{1}\geq s_{2}\geq...\geq s_{n}\). Initially, all the bins are empty and we start with bin \(k=0\) and item \(i=1\). Consider all bins \(j=1,...,k\) and place item \(i\) in the first bin that has sufficient residual capacity. If there is no such bin, increase \(k\) and repeat until item \(n\) is assigned. The FirstFitDecreasing algorithm is the best approximation algorithm currently for the Binpacking problem.  
Regarding bins as the periods of energy, our problem is the same as the Binpacking problem, and the FirstFitDecreasing algorithm can generate \(\frac{3}{2}\)-approximation.
## 4 Experiments
### Experimental Results  
WikiSum is a collection of Wikipedia titles and documents under the corresponding titles. We used the ROUGE evaluation method to compare our model with other baseline models on the WikiSum dataset. Table 2 shows our experimental results.  
Lead, LexRank, and GraphSum, in the first part, are extractive baseline models. Compared with Lead and LexRank, GraphSum performed well on the WikiSum dataset because the articles usually contained rich entities, relationships, and knowledge graph information. Since GraphSum is based on a graph structure for modeling and summary generation, it can use this information to extract key content from multiple documents accurately and generate summaries.  
In the second part, T-DMCA, FT, and HT are transformer-based variants. Except for T-DMCA, which only uses a decoder, the other variants follow the encoder-decoder architecture. According to the experimental results, the HT model achieved the best results. This shows that hierarchical coding can help obtain relationships between paragraphs.  
TG-MultiSum, S-sLDA, and HierSum, in the third part, are topic-based models. Since MDS based on topic models can mine relationships between documents by analyzing latent topics, it solves the topic consistency problem of MDS. Our model did not use a method based on the LDA model but instead used a topic attention mechanism to incorporate continuous and fine-tunable topic attention weights in the decoding process, allowing the model to grasp topic content more flexibly when generating summaries.  
The fourth part was an ablation experiment based on our basic model, namely, the discourse parser and the topic attention mechanism, to compare the effects of using them alone and in combination. According to the ROUGE score results, we compared the candidate summaries generated by baseline models with standard summaries. The candidate summaries generated by our model were closer to the standard gold summaries. Adding the discourse parser significantly improved the experiment, and the ROUGE scores increased by 7.02%, 35%, and 17.9%. Since the WikiSum dataset is relatively complete regarding topic-related aspects, adding the topic attention module generated limited improvement in the overall experimental results. We combined a topic attention mechanism with a discourse parser, which outperformed the current baseline models on WikiSum. Compared with GraphSum, which had the best extraction performance, it was improved by 14%, 2%, and 2.4%. Compared with the HT model, which had the best performance of the transformers, it was improved by 5.9%, 8.7%, and 7.9%. Compared with the best-performing model based on LDA, the improvement of the transformer model with an added topic attention mechanism and a discourse parser was more prominent. Compared with the standard summary, it increased by 2.1%, 5%, and 5.6% respectively. The experimental results show that the model achieved the best results by adding a discourse parser and a topic attention module.
# A Service-Constrained Positioning Strategy for an Autonomous Fleet of Airborne Base Stations
## 6 Evaluation and Results
### Comparison to Previous Works
#### 6.3.1 Spiral Algorithm  
The approach proposed in ([PERSON] et al., 2012) aims to serve all the users in a specific region with the minimum number of UAVs flying at a fixed altitude. This is formulated as a geometric problem where a given area has to be served by the minimum number of coverage regions with the same radius and with possible overlapping. Moreover, it is developed in a scenario where a number of users are deployed at discrete positions instead of using a continuous spatial distribution of users, which is the case of our evaluated scenario. Thus, the algorithm has been adapted in order to cover the continuous spatial distribution of users in the entire scenario.  
Figure 5 top shows one possible placement of the UAVs using the spiral algorithm in the scenario described in the previous subsection. However, this algorithm places the first UAV randomly among a set of positions that depend on the border users, which has an important effect on the final placement of all the UAVs. It is important to take it into account as it also implies a variation in the final results of the algorithm. Therefore, although this variability is not important, the results have been averaged over 12 different realizations of the algorithm, which are analyzed in Section 6.3.3.
## 2 System Model
### Analog Combining Design  
The analog combining \(W_{m}\) for each AP is based on \(N_{Q}\) CPSs and \(\mathcal{Z}_{k,m}\) switches. Therefore, \(W_{m}\) is given as  
\[W_{m}=[\Delta_{1,m}\omega_{m},\Delta_{2,m}\omega_{m},\ldots,\Delta_{K,m} \omega_{m}],\forall m, \tag{5}\]  
where \(\omega_{m}=[1,e^{j\frac{2\pi}{N_{Q}}},\ldots,e^{j\frac{2\pi(N_{Q}-1)}{N_{Q}}} ]^{T}\) denotes an array of \(N_{Q}\) CPSs for \(m_{th}\) AP, while \(\Delta_{k,m}\in\mathcal{B}^{N_{r}\times N_{Q}}\) represents the switching network between the \(k_{th}\) RF chain and \(N_{r}\) at \(m_{th}\) AP. Thus, the switching matrix for all \(K\) RF chains can be expressed as \(\Delta_{m}=[\Delta_{1,m},\Delta_{2,m}\ldots,\Delta_{K,m}]\in\mathcal{B}^{N_{r }\times KN_{Q}}\). The first constraint of the switching network that should be satisfied for \(k_{th}\) RF chain inside \(m_{th}\) AP [22] is expressed as \([\Delta_{k,m}]_{n,j}\in\{0,1\}\), where \(n=\{1,2,\ldots,N_{r}\}\) symbolizes the receive antenna index at each AP, while \(i=\{1,\ldots,N_{Q}\}\) CPSs index. The second constraint restricts each antenna on each RF chain to be connected at most to one CPS. Therefore, this restriction can be presented as \(\sum_{i=1}^{N_{Q}}[\Delta_{k,m}]_{n,i}\in\{0,1\}\). Thus, the main objective of using the previous constraints in the design of \(W_{m}\) is to easily exclude the antennas that contribute more to interference than to desired signal power, and their corresponding entries in \(W_{m}\) become zeros. Furthermore, the \(\mathcal{Z}_{k,m}\) for each RF chain in \(m_{th}\) AP is shown as  
\[\mathcal{Z}_{k,m}=\sum_{i=1}^{N_{Q}}\sum_{n=1}^{N_{r}}[\Delta_{k,m}]_{n,i},1 \leq\mathcal{Z}_{k,m}\leq N_{r}. \tag{6}\]