esa-sceva/satcom-chunk-collection huggingface dataset:
# Source Anonymity against Global Adversary in WSNs Using Dummy Packet Injections: A Survey
## 3 Dummy Packets Techniques
### Trusted Computing Enabled Heterogeneous (TCH-WSN)  
The network in _TCH-WSN_[47] is also divided into clusters. Each cluster contains a high-performance node with two modules: Trusted Platform Module (_TMP_) and Mobile Trusted Module (_MTM_). These two modules are used to provide data security and integrity. The sink and cluster heads are assumed to have a continuous power supply. The sink communicates with all nodes directly. However, a sensor node communicates with the sink only through its cluster head. The cluster head assigns one of the nodes in its cluster to act as a fake source for a specific duration of time. Then, the fake source transmits dummy traffic to the sink.
## 3 Results and Discussion
### Development of Map for Soil Amplification Factor
#### 3.2.1 Determination of Soil Amplification Factor  
Soil amplification is calculated by using Equation (2) and it represents the increment of vibration as it travels upward through layers of soil at any location. In this study, the amount of vibration at the surface is represented by Psa while the amount of vibration on the bedrock is called Pga. Any specified location experienced amplification if the amplification factor is more than 1.0. Results of soil amplification factors for soil types D and E for far-field seismic events can be seen in Figure 10, as a sample of the analysis conducted. The soil amplification factors for each site were presented according to the return periods. The red line in the figures represents the boundary to show that amplification occurs if the value passes that red line. Soil type D is stiffer compared to soil type E and each site has its combination of soil layers in terms of thickness for each layer and type of soil, either clay or sand. Overall, it can be concluded that the values of soil amplification factors for soil type D are lower compared to soil type E for both far-field and near-field. When comparisons were made between far-field and near-field, the site underwent near-field seismic events that produced higher soil amplification. Thus, soil amplification is affected by the intensity of the seismic event, distance of the site to the epicenter, and the soil layers beneath the seabed. Hence, soil amplification predicted in this study was limited to the study area only.
# Development of a Generic Numerical Transtibial Model for Limb-Prosthesis System Evaluation
## 2 Materials & Methods
### Material Properties  
Determining the mechanical properties of biological tissue in vivo is challenging due to its nonlinear behaviour under load. The main characteristic of soft tissue is that it is almost incompressible and soft at low strains but becomes exponentially stiffer with increasing strains [26]. Since the main objects of the investigation were the contact pressure at the limb-liner interface and the relative deformation of the socket, the goal was to approximate a global response of the residual limb without focusing on a specific biological part within the limb, which was achieved with a hyperelastic material model. Specifically, the Ogden first-order nonlinear material model was used to represent the mechanical properties of the bulk soft tissue, including the skin [27]. Furthermore, the Yeoh third-order hyperelastic material model was used to define a silicone liner [28]. Both material models were derived from the literature, where they were also validated.  
In order to reduce the number of elements and, thus, the computational time, the bones were modelled as rigid cavities in the residual limb, as they have a significantly higher stiffness compared to the soft tissue; hence, the rigid approximation is justified. In addition, auxiliary components, such as the prosthetic shaft and the foot, needed to build the desired load case were modelled as rigid connections between remote points. Finally, the composite socket was defined using the data from the experimental study conducted by URI Soca on the samples of the material used commonly in practice for the production of the definitive socket [29]. All material models used in the simulation are listed in Table 2.  
Figure 4: Rectified definitive socket, positioned with respect to the leg skeleton (**a**); final assembly of the residual limb model, silicone liner, and socket (**b**).
# Future Is Unlicensed: Private 5G Unlicensed Network for Connecting Industries of Future
## 3 Opportunities of NR-U for Industry 4.0
### Ease of Deployment  
For the speedy adoption of new technology, the technology must possess devices that are cost-effective, fast to deploy, and simple to operate. WiFi is one of the best examples of this operation. With NR-U, 5G is now close to replicating this. 5G NR-U no longer requires a costly and scarce license band to transmit; instead, it uses an unlicensed band, which is a free band. Additionally, for enterprise deployment, it does not necessitate any licensing process, for which it can take months to acquire permission. Thus, NR-U service deployment time can be significantly shortened to a matter of days or hours. 5G also introduces the core-network-in-a-box virtualized functions, which offer remote configuration and management. These features not only help in the implementation speed of new services and new business models, but also in the dynamic scalability of network capacity. The use of common equipment such as access points, Ethernet cables, etc., for NR-U deployment has made NR-U deployment as simple as WiFi.
## 2 Design Issues of Drone Routing for Drone-Based Delivery Systems
### Drone Energy Management  
Due to the dynamic environment conditions and increasing delivery constraints, the energy in drone batteries would be insufficient to ensure successful parcel delivery. Thus, it is critical to decide additional ways from which energy can be provided to the drone during the mission. There are several ways in which drone battery can be recharged, such as battery swapping, laser-beam recharging, and charging via wireless power charging pad [21].
# A New Multi-Physics Coupled Method for the Temperature Field of Dry Clutch Assembly
## 2 Temperature Test of Dry Clutch Assembly  
The LZDZX-430A heavy-duty vehicle clutch assembly multifunctional performance test bench was used for the \(\Phi\)430 dry clutch assembly. The bench consists of an electric control cabinet, oil supply station, lifting arm, operator console, test chamber, active components, and driven components, as shown in Figure 1. Thermocouples are embedded at a downward depth of 0.2-0.3 mm from the surface of the pressure plate and located in the outer, middle, and inner diameters of the pressure plate, respectively. Figure 2 shows the installation of the clutch assembly with K-type nickel-chromium/nickel-silicon thermocouples on the rig test bench.  
Figure 1: The test bench equipment.  
Multiple sets of clutch thermal load tests are conducted under continuous engagement and disengagement operation conditions. The main structural parameters of the dry clutch assembly for the fifth test batch are shown in Table 1. The weight of the pressure plate is 28.5 kg, the outer diameter of the pressure plate is 430 mm and the inner diameter is 240 mm, the clamping force is 36,000 N, and the driven plate outer diameter is 420 mm. The B8090 type of friction material is applied to the driven plate assembly.  
For general purposes, the fifth test batch labeled with batch A is selected for the thermal modeling. In this A group test, three temperature channels labeled 1, 4, and 6 are monitored, where channel 1 is located at the outer diameter of the pressure plate, channel 4 is at the middle diameter, and channel 6 is at the inner diameter of the pressure plate. The test undergoes 45 cycles of continuous engagement and disengagement tests, with each cycle lasting 30 s. The total accumulated time is 1400 s. The complete temperature profile of the A group is shown in Figure 3. From Figure 3, it is shown that as the engagement increases, the temperature of the clutch increases. The maximum temperature of the assembly is up to 350 \({}^{\circ}\)C at the last cycle. In the initial stage, the maximum temperature is in channel 1 at the outer diameter; however, in the later stage, the maximum temperature is in channel 4 at the middle diameter.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{3}{*}{\begin{tabular}{c} **Pressure Plate** \\ **Weight (kg)** \\ \end{tabular} } & \begin{tabular}{c} **Clutch Cover Assembly** \\ **Pressure Plate** \\ **Outer Diameter** \\ **(mm)** \\ \end{tabular} & \begin{tabular}{c} **Pressure Plate** \\ **Inner Diameter** \\ **(mm)** \\ \end{tabular} & \
\begin{tabular}{c} **Clamping Force** \\ **(N)** \\ \end{tabular} &
\begin{tabular}{c} **Driven Plate** \\ **Material** \\ \end{tabular} \\ \hline
28.5 & 430 & \(\Phi\)240 & 36,000 & B8090 & 240 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Test sample details for the 5 th thermal load test.  
Figure 3: Temperature profile of three channels of group A during the 1400 s engagement and disengagement test.  
Figure 2: Installation diagram of the clutch assembly with the thermal couples.
## 3 Methods
### Efficient Channel Attention Temporal Convolutional Network
#### 3.1.2 Sliding Window Module  
The SW block uses a sliding window of length \(\mathrm{T_{w}}\) with a stride of one, where \(\mathrm{w}=1,...,\mathrm{n}\) represents the window index and \(\mathrm{n}\) is the total number of windows. The time series \(\mathcal{Z}_{\mathrm{i}}\) is divided into local sequences \(\mathcal{Z}_{\mathrm{i}}^{\mathrm{w}}\in\mathbb{R}^{\mathrm{T_{w}}\times \mathrm{F_{2}}}\) using the sliding window [54]. Then, the local sequences \(\mathcal{Z}_{\mathrm{i}}^{\mathrm{w}}\) are passed into the ATC module, which helps to extract local features separately and improves decoding accuracy. Using the sliding window as input for parallel ATC modules reduces training and inference time. The window length \(\mathrm{T_{w}}\) is determined by the following factors:  
Figure 3: Schematic diagram of the Efficient Channel Attention (ECA) module. The input \(\chi\in\mathbb{R}^{\mathrm{H}\times\mathrm{W}\times\mathrm{C}}\) is globally average pooled (GAP) to obtain aggregated features, and ECA generates channel weights by performing a fast 1D convolution of size \(\mathrm{k}\), where \(\mathrm{k}\) is adaptively determined by the mapping of the channel dimension C, and the final output has the same dimensions as \(\chi\), denoted as \(\hat{\chi}\).  
\[\mathrm{T_{w}}=\mathrm{T_{C}}-\mathrm{n}+1,\mathrm{T_{C}}>\mathrm{n}\geq 1 \tag{2}\]  
If the two average pooling layers in the ECA-Conv block are \(\mathrm{P_{1}}=8\) and \(\mathrm{P_{2}}=7\), the time series \(\mathcal{Z}_{\mathrm{i}}\) produced by ECA-Conv consists of \(\mathrm{T_{C}}=20\) vectors. We used an SW of size \(\mathrm{T_{w}}=\mathrm{T_{c}}-4\), which means the time series \(\mathcal{Z}_{\mathrm{i}}\) is divided into five local sequences.
# BSD-YOLOv8: Enhancing YOLOv8 for Book Signature Marks Detection
## 3 Experimental Results
### Detection Model Comparison  
To demonstrate the advantages of the proposed BSMD-YOLOv8 algorithm model, we conducted comparative experiments with YOLOv8n and other popular one-stage models using the same dataset and experimental environment.  
Table 3 showcases the distinctions between the proposed architecture and the standard YOLOv8n architecture, along with a performance comparison. Despite an increase in giga floating-point operations compared to YOLOv8n, BSMD-YOLOv8 demonstrates significant improvements in other metrics.  
According to the results presented in Table 4, BSMD-YOLOv8 outperforms other models by achieving the highest accuracy (P), recall (R), and mAP50. Specifically, compared to Improved-YOLOv5s, BSMD-YOLOv8 exhibits an increase in accuracy and mAP by 0.4% and 0.1%, respectively, a reduction in parameters by 6.01M, and an improvement in FPS by 23 frames per second. Figure 10 provides a comparison of actual test results, revealing that the YOLOv8n model suffers from a higher number of missed detections and false detections, while Improved-YOLOv5s also exhibits some false detections. In contrast, the BSMD-YOLOv8 model demonstrates the best actual detection performance.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Models** & **Backbone** & **P/\%** & **R/\%** & **mAP50/\%** & **Params/M** & **GFLOPs/G** & **FPS** \\ \hline SSD [13] & MobileNetV2 & 61.7 & 38.5 & 64.7 & 91.60 & 116.3 & 52 \\ YOLOv3-tiny [15] & Darknet-53 & 76.0 & 54.7 & 70.2 & 12.12 & 18.9 & 88 \\ YOLOv5n & CSP-Marknet-53 & 95.4 & 82.6 & 93.1 & 2.50 & 7.2 & 105 \\ YOLOv5s & CSP-Marknet-53 & 96.8 & 82.8 & 93.7 & 9.12 & 24.0 & 92 \\ YOLOv6n [28] & CSPStackRep & 94.1 & 81.8 & 91.9 & 4.23 & 11.9 & 103 \\ YOLOv7n [29] & ELAN & 95.8 & 83.3 & 94.5 & 6.0 & 13.2 & 97 \\ Gold-YOLO(21) & CSP-Marknet-53 & 96.1 & 80.3 & 92.5 & 6.02 & 12.0 & 98 \\ YOLOv8s [30] & CSP-Marknet-53 & 96.8 & 89.6 & 95.9 & 11.13 & 28.6 & 90 \\ YOLOv9t & - & 96.4 & 83.9 & 93.3 & 2.00 & 7.8 & 125 \\ YOLOv10n & - & 92.7 & 79.9 & 91.5 & 2.70 & 8.4 & 115 \\ Improved-YOLOv5s [8] & CSP-Marknet-53 & 97.8 & 96.8 & 99.0 & 7.06 & 25.1 & 95 \\ BSMD-YOLOv8 & LMRNet & 98.2 & 96.8 & 99.1 & 1.05 & 11.2 & 118 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of different one-stage detection models.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Model** & **Backbone** & **Neck** & **Loss Function** & **P/\%** & **R/\%** & **mAP50/\%** & **Params/M** & **GFLOPs/G** & **FPS** \\ \hline YOLOv8n & CSP-Marknet-53 & PANet & CloU & 96.0 & 88.2 & 95.2 & 3.01 & 8.2 & 111 \\ BSMD-YOLOv8 & LMRNet & Improved\_PANet & WIoU & 97.2 & 94.5 & 97.8 & 0.93 & 10.4 & 118 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison with YOLOv8n.
### Organization of the Article  
In Section 2, we provide a brief overview of applications of natural language processing to texts of medical guidelines, word embedding, and some relevant work on disagreement. Afterwards, we follow the left-to-right order of Figure 2 using the breast cancer screening guidelines as the motivating example (other experiments are described in the Appendix A). Thus, Sections 3 and 4 explain our example data sources: a CDC summary table of breast cancer screening guidelines and the corresponding full text documents.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **AAFP** & **ACOG** & **ACP** & **ACR** & **ACS** & **IARC** & **USPSTF** \\ \hline
**AAFP** & 0 & 2 & 3 & 3 & 2 & 2 & 1 \\ \hline
**ACOG** & 2 & 0 & 4 & 1 & 2 & 2 & 1 \\ \hline
**ACP** & 3 & 4 & 0 & 3 & 2 & 3 & 3 \\ \hline
**ACR** & 3 & 1 & 3 & 0 & 1 & 2 & 2 \\ \hline
**ACS** & 2 & 2 & 2 & 1 & 0 & 1 & 1 \\ \hline
**IARC** & 2 & 2 & 3 & 2 & 1 & 0 & 1 \\ \hline
**USPSTF** & 1 & 1 & 3 & 2 & 1 & 1 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: This table shows the number of differing feature values for pair of guidelines, based on Table 1. The Jaccard distances between the documents are obtained by dividing the value in the table by five (the number of features).  
In these two sections, we also discuss the steps in the conceptual analysis of the table. First, the creation of a graph of conceptual distances between the columns of the table, and then the encoding of full documents as vectors, using two standard vectorization procedures. Our method of comparing summarized recommendations and full guideline documents is presented in three algorithms and discussed in Section 5.  
After observing a roughly 70% similarity between the distances in the summaries and the distances in the full documents, we prove in Section 6 that this similarity is not accidental. We conclude in Sections 6 and 8 that this case study shows that NLP methods are capable of approximate conceptual analysis in this space (using the Appendix A for additional support). This opens the possibility of deepening this exploration using more sophisticated tools such as relationship extraction, other graph models, and automated formal analysis (as discussed in Sections 7 and 8).  
In the Appendix A, we provide information about additional experiments we performed to validate the proposed method. (We decided to put this information in an appendix in order to simplify the main thread of the presentation). There, we first discuss a few variants of the main experiment, where we filtered out some sentences from the full guidelines' texts. Then, we apply our method to two other collections of guidelines: namely, to hypertension and low back pain management guidelines. All of these experiments confirm the robustness of the proposed method and the system's ability to computationally relate background knowledge to actual recommendations.
However, as UAV nodes are battery-operated, limited resources are possible at swarm networks [19]. In such cases, task offloading by UAVs to the nearby edge servers for running artificial intelligence (AI) models is performed. The nodes are distributed, thus federated learning (FL) becomes a good option, where UAV-collected data can be trained by local models, which update the global model gradients [20]. The AI models run mundane monitoring tasks, coupled with route selection and topology and mobility formation, which is communicated to ground nodes via the 6G-ERLLC channels. The data-intrinsic computation and classification become an important part of the study for effective 3 FFs realization. Moreover, for massive sanitization purposes, specially designed hexacopter and octocopter drones are suitable, owing to their property of carrying heavy payloads. The details are presented in the next sections.
## 2 Materials and Methods
### YOLOv11-OAM Architecture Overview  
Tree species recognition in UAV imagery presents several challenges, including densely distributed objects, subtle morphological variations, and significant differences in object scale. In orchard environments, these difficulties are compounded by canopy overlap, high inter-species visual similarity, and background clutter, all of which contribute to reduced detection accuracy. These challenges call for models capable of precise localization and fine-grained semantic discrimination.  
YOLO-based detectors are widely used in agricultural remote sensing due to their balance between speed and accuracy. However, mainstream versions (e.g., YOLOv5, YOLOv8, YOLOv11) are primarily optimized for general object detection and often underperform in specialized scenarios such as detecting small, overlapping tree crowns.  
To overcome these limitations, we introduce YOLOv11-OAM, an enhanced one-stage detector that incorporates three key modules: (1) Omni-Dimensional Dynamic Convolution (ODConv) for adaptive, multi-axis feature extraction; (2) Adaptive Spatial Feature Fusion (Detect_ASFF) for dynamic, multi-scale feature integration; and (3) Multi-Point Distance IoU (MPDIoU) for geometry-aware bounding box regression under occlusion.  
Together, these components improve detection robustness and fine-grained classification for densely distributed and visually similar tree species. Figure 4 illustrates the integration of ODConv, Detect_ASFF, and MPDIoU into the YOLOv11 architecture.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Common Name**} & \
\begin{tabular}{c} **Number of Samples** \\ **(Before Augmentation)** \\ \end{tabular} &
\begin{tabular}{c} **Number of Samples** \\ **(After Augmentation)** \\ \end{tabular} & \multirow{2}{*}{**Proportion**} \\ \hline Pomegranate Tree & 1496 & 1496 & 0\% \\ Prune Tree & 1041 & 1041 & 0\% \\ Walnut Tree & 320 & 1600 & +400\% \\ Saxaul Tree & 871 & 4355 & +400\% \\ Cherry Tree & 414 & 2070 & +400\% \\ Apricot Tree & 437 & 2185 & +400\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Class-wise sample counts before and after data augmentation.  
Figure 4: YOLOv11-OAM Architecture Diagram.
## 4 Core Insights and Critical Reflections
### Critical Commentary  
6.1 Double-Edged Sword of Application-Driven Development: The Risk of Technological Stagnation Behind the Boom  
This study quantitatively reveals the overwhelming dominance of an \"application-driven\" paradigm in the development of UAV-based remote sensing (UAV-RS), where progress is primarily propelled by real-world applications rather than technological innovation. The prominence of application-centered clusters (e.g., #0 vegetation indices) and the leading roles of institutions such as the Ministry of Agriculture and Rural Affairs of China and the USDA underscore a research agenda shaped by the immediate, practical needs of sectors like agriculture and environmental management. This pattern presents a double-edged sword. On one hand, application-driven research ensures close alignment with real-world demands, generating substantial and visible economic and societal benefits. This alignment is a key reason the field continues to receive funding and policy support. On the other hand, the overwhelming focus on applied outcomes risks the strategic neglect of foundational research, which is manifested in two major ways:  
1. Persistent platform bottlenecks: Issues such as \"endurance\" and \"battery life\"--core limitations affecting UAV performance--have never emerged as prominent in burst keyword analysis, indicating a disconnect between academic priorities and industrial pain points. Yet, numerous technical reports and review papers have repeatedly emphasized endurance as the key constraint on large-scale, long-term UAV-RS applications [133].
2. Surface-level AI adoption: Although deep learning remains a trending topic, most high-frequency terms refer to the direct adoption of established computer vision models like YOLO and U-Net. There is a notable absence of original AI architectures tailored to the unique characteristics of remote sensing data, such as spatial context or multimodal inputs. This signals a risk of \"imported convenience\" taking precedence over \"remote sensing-native\" innovation, which may lead to homogenized applications and diminished technological differentiation [224; 225].  
If uncorrected, this trend may trap the UAV-RS field in a state of \"high-level repetition,\" where technologies are widely adopted but the core capabilities remain stagnant--ultimately limiting the potential to tackle more complex scientific challenges.