esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Emerging Applications of 6G
### Digital Health  
In recent years, healthcare applications have transitioned into e-Health platforms due to advancements in the Internet of Things (IoT) and Tactile Internet (TI) [10, 11]. In addition to simple services such as teleconsultations, more advanced services such as remote surgery and remote rehabilitation are also made possible thanks to advancements in TI and IoT. These e-Health applications have the ability to reduce the geographical barriers in receiving health care across regional communities. The use of IoT in healthcare has not only progressed real-time patient care, but has also improved offline administrative aspects such as hospital management systems, data gathering and analysing mechanisms, patient and drug monitoring systems, and handling data environments, which are hazardous and unreachable for humans, including high-radiation environments and underwater systems [12].  
Although some of the e-Health applications such as teleconsultations may have comparatively fewer QoS requirements, real-time e-Health applications such as telesurgery have stringent latency requirements, such as 1 ms [11]. Applications of this nature also require high bandwidth, as they usually require the transmission of data generated by high-resolution video cameras. Similarly, the use of exoskeletons for rehabilitation requires latency in the same range as for remote surgery.
## 4 Numerical Simulation and Discussions
### Axial Vibration Velocity Simulation
The friction term will not affect the resonance frequency of the pipeline under the fluid-structure interaction, while the response amplitude of each resonant frequency is impacted slightly, the greater the influence will be in the high frequency area. (2) The amplitude of axial vibration velocity response will be raised with the increase of flow rates when the pressure is constant inside the pipeline, while the increase in the flow rate will not have an impact on the general trend of the frequency domain characteristics curve. (3) The response amplitude of each resonant mode of measuring point 2 is higher than that of measuring point 1. Combined with Figure 2, we found that the measuring point 1 is located in the middle of the pipeline and the measuring point 2 is in the end of the pipeline, hence, it is reasonable to infer that the closer to the fixed end of the pipeline, the less affected by the fluid-structure interaction.
# Bounded Attitude Control with Active Disturbance Rejection Capabilities for Multirotor UAVs
## 4 ADRC Design for Attitude Tracking
### Active Disturbance Rejection Bounded Attitude Control
These systems can be viewed as a cascade system as shown in Figure 2. Since each system is ISS as was proven in Propositions 1 and 2, then the overall system is ISS. That is, the ADRC is ISS when the unknown total disturbance \(\xi\) is the input and the tracking error \(z=(\omega_{e}^{T}\ \tilde{\mathbf{q}}_{v}^{T})^{T}\) the output.
## 2 Related Work
### Research Gaps  
There are several security frameworks available in the existing literature; however, it is essential to evaluate some critical concerns carefully. That includes the obstacles in FL-IoT that arise from the convergence issues in integrating learning and communication. These issues are created by the IoT network's sensitivity to variations in sensing environments. The limitations of IoT in terms of storage and computational capability lead to longer delays and fluctuating synchronization. Current data transport techniques frequently increase energy usage, particularly when utilizing GPUs, diminishing overall energy efficiency. The trade-offs include the careful consideration of several factors, including safety, energy  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref.** & **Approach** & **Description** & **Results** \\ \hline
[47] & TensorFlow DNN & Identify pirated software through source code copying & 97.46\% classification accuracy \\ \hline
[48] & Deep learning framework for secure smart city & Utilize blockchain for decentralized communication in CPS & Precision: 0.7244, Recall: 0.7078, F1 score: 0.7118 \\ \hline
[49] & FFDNN Wireless IDS with WFEU & Intrusion detection system equipped with Wireless Feature Extraction Unit & Binary classification: 87.10\% accuracy, multiclass classification: 77.16\% accuracy \\ \hline
[50] & Sequential methodology with Text-CNN and GRU & Collect network layer and application layer attributes for intrusion detection & F1 score: 0.98 \\ \hline
[51] & Deep learning-based IDS for IoT networks & Categorize data flow for multiclass and binary classification & NSL-KSS dataset: 99.5\% accuracy, CIDDS-001 dataset: 99.3\% accuracy, UNSWNB15 dataset: 99.1\% accuracy \\ \hline
[52] & IoT-IDCS-CNN & Harness convolutional neural networks for intrusion detection & Binary classification: 99.30\% accuracy, multiclass classification: 98.20\% accuracy \\ \hline \hline \end{tabular}
\end{table}
Table 3: Deep learning models for IoT applications.  
efficiency, cost-effectiveness, and accessibility, while also prioritizing security. It can be challenging, since existing models may prioritize security above specific metrics. Moreover, the integration of privacy-conserving techniques in FL is still in its initial stages, with inadequate study into corresponding data privacy and model efficiency. An additional area necessitating further study is the energy efficacy of DL models, as the high computational costs of the models can lead to substantial energy consumption, leading to challenges for battery-functioned IoT devices. Additionally, the compliance of security models to emerging threat/vulnerability landscapes and background responsiveness in IDSs is vital for developing robust IoT security frameworks. Ensuring equilibrium among these parameters is crucial to enhance security without sacrificing other essential features.
## Appendix A  
We quantify the Dynamic Degree of the devices in the example presented in Figure 1 for clarity. The conclusions are drawn from Figure A1, where edge weights represent contact times, and \(\alpha=0.5\) is employed to give equal relative weight to the evolutionary trend of connections and the frequency of contact times. \[DTE_{T}(A)=0.5\cdot 0.0364+(1-0.5)\frac{(5+1+7+3)}{10}=0.0182+0.8=0.8182\]\[DTE_{T}(B)=0.5\cdot(-0.0788)+(1-0.5)\frac{(4+1+2+4)}{10}=(-0.0394)+0.55=0.5106\]  
\[DTE_{T}(C)=0.5\cdot 0.1151+(1-0.5)\frac{(5+3+3+2+4)}{10}=0.05755+0.85=0.90755\]  
\[DTE_{T}(D)=0.5\cdot(-0.0788)+(1-0.5)\frac{(7+3+2)}{10}=(-0.0394)+0.6=0.5606\]  
\[DTE_{T}(E)=0.5\cdot(-0.0182)+(1-0.5)\frac{(3+3+5)}{10}=(-0.0091)+0.55=0.5409\]  
\[DTE_{T}(F)=0.5\cdot(-0.0545)+(1-0.5)\frac{(5+2+4)}{10}=(-0.02725)+0.55=0.52275\]  
For instance, based on the following details, a preliminary ranking of nodes can be established: Device \(A\) interacts with its neighbors more frequently than device \(B\), despite both having the same three neighbors. As a result, device \(A\) displays a higher Dynamic Degree than device \(B\). Similar to the previous example, device \(E\) receives a better score due to its greater trend of contact development, even if device \(D\) contacts its neighbors more frequently. Device \(C\) achieves a higher Dynamic Degree than Device \(A\) due to having more neighbors and a faster rate of contact development. Consequently, the nodes are ranked as follows according to their Dynamic Degree index: Ranking\({}_{\text{DTE}}=\{C,A,D,E,F,B\}\). Let us examine now an example calculation in Figure A2, showcasing an unconnected network during \(G_{2}\). \[C_{CLO}(A)=\lim_{x\to\infty}\biggl{(}\frac{1}{6-1}\biggl{(}\frac{1}{x}+\frac{ 1}{x}+\frac{1}{2\lambda}+\frac{1}{x}+\frac{1}{x}\biggr{)}\biggr{)}=\frac{1}{ 10\lambda}\]  
\[C_{CLO}(B)=\lim_{x\to\infty}\biggl{(}\frac{1}{6-1}\biggl{(}\frac{1}{x}+\frac{ 1}{2\lambda}+\frac{1}{x}+\frac{1}{2\lambda}\biggr{)}\biggr{)}=\frac{1}{5\lambda}\]  
\[C_{CLO}(C)=\lim_{x\to\infty}\biggl{(}\frac{1}{6-1}\biggl{(}\frac{1}{x}+\frac{ 1}{2\lambda}+\frac{1}{x}+\frac{1}{x}+\frac{1}{3\lambda}\biggr{)}\biggr{)}= \frac{1}{6\lambda}\]  
\[C_{CLO}(D)=\lim_{x\to\infty}\biggl{(}\frac{1}{6-1}\biggl{(}\frac{1}{2\lambda}+ \frac{1}{x}+\frac{1}{x}+\frac{1}{x}+\frac{1}{x}\biggr{)}\biggr{)}=\frac{1}{10 \lambda}\]  
\[DTE_{T}(B)=0.5\cdot(-0.0788)+(1-0.5)\frac{(4+1+2+4)}{10}=(-0.0394)+0.55=0.5106\]0  
Figure A2: Network connections during instant \(G_{2}\), based on the example of \[DTE_{T}(B)=0.5\cdot(-0.0788)+(1-0.5)\frac{(4+1+2+4)}{10}=(-0.0394)+0.55=0.5106\]1  
Upon analyzing this small example, it becomes apparent that when computed on an unconnected network, the Closeness metric tends to result in lower values, indicating the difficulty of communication between devices belonging to different components. Additionally, the devices within the same component experience an increase in their centrality, as all values are non-zero in the calculation. Consequently, the metric places greater emphasis on nodes that are well-connected.
# Embedded Based Real-Time Monitoring in the High-Pressure Resin Transfer Molding Process for CFRP
## 2 High-Pressure RTM
### Sensor Layout  
To detect the CFRP molding conditions during the HP-RTM process, pressure and temperature sensors were installed at the inlet of the mixing head and in the mold. Additionally, LVDTs were installed in the mold. Figure 4 shows the position and number of installed sensors. The pressure and temperature sensors installed in the mixing head detected the pressure and temperature immediately before the resin was sprayed. When the resin injected into the mold reached the other end from the injection port of mold, a difference in viscosity occurs due to the temperature of the mold and the exothermic reaction of the resin, which affects the impregnation. The difference in the amount of impregnation causes the reaction temperature to change in the resin. Therefore, four pairs of cavity pressures (_CP_) and temperature sensors (_T_) were installed in the mold (see Figure 4b): (1) CP and T 1 were installed in the lower part opposite the upper mold; (2) CP and T 2 were installed in the center of the mold (at 190 mm from the injection port), and (3) and (4) CP and T 3 were installed symmetrically at the end of the mold (at 380 mm from the injection port) to monitor the resin flow and curing. The LVDTs were installed at each corner of the mold to measure the end-gap and uniformity of the CFRP thickness and monitor the controllability of the press.
## 4 Vibration and Acoustic Sensors in Non-Destructive Testing of Port Cranes
### Vibration Sensors in Port Crane Monitoring
#### 4.1.3 Performance Characteristics of Vibration Sensors  
Sensitivity is a key parameter of vibration sensors. High-sensitivity sensors can detect weak vibrational signals. For example, piezoelectric sensors have high sensitivity for dynamic vibration measurements [94]. However, high sensitivity can lead to a lower measurement range. A balance between the sensitivity and measurement range is required.  
Frequency response is the sensor's ability to respond to different vibration frequencies. The proper selection of vibration sensors with suitable frequency-response characteristics is crucial [95; 96]. For crane booms, the sensors should cover the frequency range of the vibrations caused by lifting and lowering operations [97]. Vibration sensors used in RNDT for port cranes must accommodate a wide range of amplitudes, especially those generated during heavy lifting operations. A broad measurement range ensures accurate detection under varying load conditions.  
Marine environments are harsh, and vibration sensors must have good stability and reliability to provide accurate and stable measurement results. Sensors made with high-quality materials and advanced manufacturing processes are more stable and reliable [98; 99]. As [PERSON] et al. [53] pointed out, the performance characteristics of vibration sensors directly affect their effectiveness in SHM. Therefore, the selection of vibration sensors with appropriate performance characteristics is critical for port crane maintenance.  
Table 2 lists the numerical data on the sensitivity, frequency response, environmental adaptability, and cost for piezoelectric, capacitive, and strain gauge-based sensors. Piezoelectric sensors have high sensitivity and excellent frequency response, but moderate environmental adaptability. Capacitive sensors are accurate, costly, and humidity sensitive. Strain gauge-based sensors are low-cost but have weak environmental adaptability.
## 4 Results and Discussion  
### Experiments for Winter Wheat Yield Estimation at the Field Scale  
The initial parameters and conditions regarding the soil, weather, and crop management varied and were difficult to collect on a regional scale. There were often numerous uncertainties regarding the simulation in the CERES-Wheat model when a general model was configured for the study area, especially for aspects of crop management such as planting, irrigation, and fertilization. These factors led to a lower accuracy in regional crop yield estimation and limited the potential application of the crop model. In this experiment, a series of LAIs measured at 53 field plots was assimilated into the CERES-Wheat model. Compared with the LAI simulation process with no data assimilation (Figure 2a), the POD4 DVar-based data assimilation performed the incorporation between the observed and modeled LAIs within the dynamic framework of the winter wheat growth process such that the LAI simulation over the entire growing season was optimized and the simulated LAI was more consistent with the actual LAI, with an \(R^{2}\) of 0.92, an RMSE of 0.31, and an RE of 10.25% (Figure 2b).  
Consequently, the yield estimates were dramatically improved because of the optimization of the LAI simulation process. Compared with the forecasted yields with no data assimilation (Figure 3a) and the EnKF-based data assimilation (Figure 3b), the estimated yields with the POD4 DVar-based data assimilation better predicted the measured yields, with an \(R^{2}\) of 0.73, an RMSE of 319 kg/ha, and an RE of 3.49% (Figure 3c). Furthermore, less computing time was required for the POD4 DVar-based strategy (1.60 s over an assimilation window) than for the EnKF-based strategy (11.74 s). The  
Figure 2: **(a)** Simulated LAIs of winter wheat with no data assimilation and **(b)** with the POD4 DVar data assimilation.  
experimental results support the technical and practical feasibility of POD4 DVar-based crop model data assimilation, especially in terms of accuracy and efficiency.
# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
## 2 Background and Preliminaries
### Beyond IoT Technologies
#### 2.6.5 Digital Twins  
Digital twins enhance smart grid security by providing a continuously updated virtual replica of physical infrastructure, enabling predictive analysis, vulnerability simulation, and proactive maintenance [64]. This technology enables utilities to simulate security breaches and fine-tune their actions without putting actual assets at risk. Digital twins provide a synchronized view of grid conditions when provided with real-time data from IoT devices. This enhances operational planning and anomaly detection accuracy and facilitates quick decision-making [65].
# CNN Attention Enhanced ViT Network for Occluded Person Re-Identification
## 4 Results and Discussion
### Datasets and Evaluation Protocols
#### 4.1.2 Evaluation Protocols  
The model's performance is assessed using standard evaluation metrics, such as mean average precision (mAP) [48] and cumulative matching characteristics (CMC) [50] at Rank-1, which are commonly used in general ReID methods.  
The performance of the ReID model can be comprehensively evaluated by the mean average precision (mAP), which measures how well the model places the correct predicted results of a query image at the top of the retrieved list. To calculate mAP, the average precision (AP) of the model for each identity prediction result must first be computed. The AP calculation process can be described as follows:  
\[AP_{i}=\frac{\sum_{i=1}^{N}p_{i}}{I_{i}}, \tag{12}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Datasets** & **ID/Image** & **Train (ID/Image)** & **Query** & **Gallery** \\ \hline Market-1501 & 1501/32,668 & 751/12,936 & 750/3368 & 750/19,732 \\ DukeMTMC-ReID & 1404/36,441 & 702/16,522 & 702/2228 & 1110/17,661 \\ Occluded-Duke & 1404/35,489 & 702/15,618 & 519/2210 & 1110/17,661 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset information. ID denotes the total number of identities in the dataset. The images denote the total number of images in the dataset.  
where \(\sum P_{i}\) represents the count of images that contain the \(i\)-th identity target, \(I_{i}\) denotes the number of images containing the \(i\)-th identity target, and \(N\) represents the total number of identities.  
The \(mAP\) is the average of \(AP\) scores weighted by the number of images containing each identity, and this process can be mathematically represented as:  
\[mAP=\frac{\sum_{i=1}^{N}AP_{i}}{N}, \tag{13}\]  
where \(\sum_{i=1}^{N}AP_{i}\) denotes the average precision of each identity.  
In general, the \(AP\) metric evaluates the performance of a learned model on a single category, while the \(mAP\) metric evaluates the model's overall performance across all identity targets.  
Rank-n is a frequently used evaluation metric in ReID, which is derived from the cumulative distribution curve (CMC). The value of \(n\) denotes a particular situation, which involves ranking the metric distance between the query set features and the gallery set features and measuring the accuracy of the query's correct result ranked before the \(n\)-th position. This means that the identity of the query set is found in the gallery set with the most similar image of the human person. In a ReID task, the value of \(n\) is usually set to 1, 5, 10, or 20. In this particular paper, the value of \(n\) is set to 1.
## 6 The Coordinated Internet  
Should we achieve the vision of dynamic processes, there is an even more radical future Internet that is possible and even necessary: the \"Coordinated Internet\". This is a vision of a _pro-active_ Internet that not only facilitates sharing and collaboration but also actively coordinates humans as well as various programs, most notably services, which are being consumed and provided in distributed dynamic processes. This is in contrast to mere collaboration, in which a system only supports human-initiated sharing of information.  
One definition of coordination is dependency management [22], which means that changes within some system are propagated according to some model. More specifically, we mean by \"coordination\" the notification, typically asynchronously, to the right people of changes within a design, especially a process. Process components depend upon one another's features and when these features change, sometimes the process should change but the right users should always be notified of the effects of the change propagation.  
One example of a collaboration mechanism that is not coordination is Google docs [23]: this manages the internal dependencies to keep the documents consistent but does little to coordinate the users. This is a system that invites people to share but does not otherwise help them in distributed work: authors must invite one another to share the documents.  
An example of a limited coordination mechanism would be a collaborative version of the logical spreadsheets [24] such that when when one user modifies a field on his display, appropriate updates take place in related fields in other spreadsheets on the Internet. When such a system detects a conflict (e.g., a constraint violation) for which there is no defined automatic solution, it must somehow notify all of the users involved and prototypes do this by changing the colors of affected fields. This is a minimal example of a system notifying someone that they have been affected by change and that they need to take action.  
A Coordinated Internet would go far beyond this: it would actively watch what people do (analogous to search completion on desktops today), correlate these activities, and actively notify people when and how their current tasks affect and are affected by the activities of other people. Example notifications are that a conflict has occurred and who is involved, safe and consistent solution options upon request, thrashing warnings, opportunities for synergy, and that some tasks are no longer necessary.  
The trick is to do this in a useful way, so that coordination is increased, but people are not annoyed by a \"big brother paper clip\". We can see what the minimal requirements are by looking at a distributed supply chain of the future.
# Fine-Tuned Visual Transformer Masked Autoencoder Applied for Anomaly Detection in Satellite Images
## 3 Results and Discussion
### Results Obtained by the Grad-CAM Method  
Terms such as explainability and eXplainable AI (XAI) are current emerging approaches to improving model interpretability and the rules that lie behind neural network models. To apply the Grad-CAM method, an adaptation step was required to transfer knowledge between the autoencoder model and the standard multiclass classifier architecture for which the Grad-CAM method was proposed. For this task, after training the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline
0.49 & 0.49 & 0.67 & 0.57 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance indicators for the evaluated system.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Predicted Anomaly** & **Predicted Normal** \\ \hline True anomaly & 7 & 63 \\ \hline True normal & 29 & 60 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Confusion matrix for the evaluated system.  
ViTMAE model on our custom dataset and updating its weights, we divided the autoencoder into two halves : the encoder and decoder. The encoder part was then used as the hidden layers of the standard classifier with an additional sequential layer for multiclass classification, in the same manner as the classifier proposed in [36] was created. The results of running the Grad-CAM algorithm for samples from a custom dataset are shown in Figure 6.  
After analysing the results obtained, it is clear that the model cannot properly identify the features of each image and locate the most crucial objects that could potentially lead to anomaly detection. Taking into account the bottom right example in Figure 6, the model misleadingly chose the object and natural habitat around the potentially suspicious building as the center of interest. Furthermore, looking at the bottom left example, we see that the model did not spot the entire airstrip with aircraft visible, taking into account the homogenous area around it as a base for classification. In contrast, the analysis of the Grad-CAM results showed that the model in some cases was capable of making correct decisions, which is demonstrated by the top right example in Figure 6. In general, the model proposed in this paper was capable of spotting only a subset of the anomalies present in the dataset, which implies the need to improve the model's capabilities and performance.