esa-sceva/satcom-chunk-collection huggingface dataset:
# Synchronous Remote Calibration for Electricity Meters: Application and Optimization
## 4 Results and Discussions
### Experimental Scenario Settings
#### 4.3.1 Impact of Task Quantity on Total Execution Time  
To assess the impact of the number of tasks on the maximum task completion time, we kept all parameters constant except for the number of tasks. These tasks were randomly generated, with specific quantities defined as follows: for small-scale task scenarios, the quantities were set at [10; 20; 30; 40; 50]; for medium-scale scenarios, the quantities were [20; 40; 60; 80; 100]; and for large-scale scenarios, the quantities were [60; 120; 180; 240; 300]. After conducting multiple simulation experiments, the results for the small-scale experiments are presented in Figure 9a, for the medium-scale experiments in Figure 9b, and for the large-scale experiments in Figure 9c.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Scale** & **Central Lab** & **Total Servers** & **Detection Points** & **Total Clients** & **Task Types** & **Task Number** \\ \hline Small Scale & 2 & 4 & 1 & 5 & 4 & 100 \\ Medium Scale & 2 & 8 & 2 & 10 & 6 & 200 \\ Large Scale & 3 & 12 & 3 & 18 & 10 & 300 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter settings for different scale scenariosAnalysis of these figures reveals that the performance differences between the Longest Task First (LTF), Shortest Task First (STF), and the Dynamic Synchronous Scheduling (DSS) algorithm are minimal. Moreover, the Hybrid Genetic Algorithm (HGA) consistently outperforms these three algorithms across different task quantities. Specifically, in small-scale scenarios, the HGA achieves an improvement of approximately 30%. In medium-scale scenarios, the enhancement is up to 9%, and in large-scale scenarios, the improvement reaches up to 10%. These results clearly demonstrate that the HGA exhibits optimal performance in smaller-scale applications.
# Design and Calibration of a Sensory System of an Adaptive Gripper
## 3 Results
### Calibrating the Force Sensor
These fluctuations can be attributed to multiple sources: electronic noise--electrical interference from the data acquisition system; mechanical vibrations--vibrations generated by the gripper, especially during the contact phase; surface irregularities--if the contacted object does not have a perfectly flat surface, fluctuations in the contact force can occur. The graph shows a period of time of approximately 60 s during which the sensor is in contact with the object. The fluctuations are not excessive, suggesting relatively stable contact between the sensor and the object. Similarly to Phase 2, in the graph in Figure 10, with the Phase 3 results, the force values are negative (\(-100\) mN to \(-200\) mN), with a significant negative average value. Friction variations between the jaws and the object can influence the readings. The graph shows a period of approximately 60 s during which the sensor is subjected to a clamping force. The fluctuations are relatively constant throughout the graph, but their magnitude is significant, suggesting potential variations in the stability of the clamping mechanism or in the accuracy of the sensor. Figure 8: Noise (mechanical stress) of Module 1 over a one-minute period, Phase 1. Figure 9: Results of Gripping Module 1, Phase 2. For Gripping Module 4, the following graphically represented results were obtained, corresponding to the three phases: Figure 17, Phase 1; Figure 18, Phase 2; Figure 19, Phase 3:  
Figure 16: Results of Gripping Module 3, Phase 3. Figure 17: Results of Gripping Module 4, Phase 1. Figure 14: Results of Gripping Module 3, Phase 1. Figure 15: Results of Gripping Module 3, Phase 2. The differences observed between the five data series (modules) are likely due to a combination of factors:  
* Electronic noise: Electrical interference in the measurement circuits of each module can cause signal variations. These variations can be different for each module due to differences in wiring, cable lengths, or the quality of the electronic components. * Mechanical noise: Mechanical vibrations in the system can influence the sensor readings. Differences in sensor mounting, structural rigidity, or the presence of mechanical play can contribute to variations in the vibration level and, implicitly, the measured noise. * Manufacturing tolerances: Subtle differences in the manufacturing of the mechanical components of each module can influence the rigidity of the structure and the sensitivity of the sensors. Even small variations in size or material can produce differences in the vibration response. * Calibration: Differences can also come from errors in the process of calibrating the individual force sensors of each module. The initial sensor offset, calibration procedure, or scaling factor may vary slightly between modules. These graphs show the need to calibrate the force sensor on each gripping module, each module having specific operating characteristics.
## 1 Introduction
The foundational contributions to averaging methods were made by [PERSON] [35] and [PERSON] [36], leading to the Krylov-Bogoliubov-Mitropolsky (KBM) method. This method approximates the response of nonlinear oscillators, assuming that amplitude and phase vary slowly with time, allowing them to be treated as constants over the forcing period. Recent studies on HSLDS and QZS mechanisms have explored various practical applications, ranging from automotive to biomedical systems [37]. Despite these efforts, inaccurate modelling remains a significant issue. Neglecting dissipation sources beyond linear viscous damping, or approximating the restoring force with continuous low-degree polynomials, can negatively impact the model's dynamic behavior [38]. The motivation of this paper is to improve the accuracy of nonlinear isolator modelling and provide theoretical frameworks applicable to practical systems where compressive springs are adopted and dry friction is non-negligible. An HSLDS isolator model, considering piecewise nonlinear-linear stiffness, viscous damping, and Coulomb friction, was developed in Ref. [14] to fit experimental results. However, the model was solved only through numerical integration, without analyzing the role of different parameters. Toaddress this gap, an extended analysis is presented herein, employing the KBM method to investigate the combined effect of piecewise stiffness and dry friction nonlinearities. This paper is organized as follows. In Section 2, the theoretical model is presented, and the KBM method is applied to derive an approximate solution for analyzing the steady-state response near resonance conditions. In Section 3, a comparison with direct integration and closed-form solutions is analyzed to validate the KBM method, highlighting the limitations of the approximate solution in high-friction scenarios. A parametric analysis is carried out, exploring amplitude-frequency curves, forcing conditions, and saddle-node bifurcations resulting in potentially critical oscillations. Finally, conclusions are drawn in Section 4.
## 4 Experiment
### Experiment Results
\begin{table}
\begin{tabular}{c c} \hline \hline
**Environment** & **Settings and Versions** \\ \hline Operating System & Ubuntu 20.04 \\ Deep Learning Framework & Pytorch1.11.0 \\ GPU & Tesla V100-SXM2 \\ Programming Language & Python3.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental environment. \begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **MAE** & **MAPE (\%)** & **RMSE** \\ \hline STGCN & 18.88/19.40/21.99 & 12.39/12.66/14.33 & 28.14/29.05/32.72 \\ ASTGCN & 14.98/16.67/18.65 & 9.87/10.65/11.25 & 23.22/25.99/28.70 \\ GraphWaveNet & 14.88/15.98/17.83 & 9.72/10.43/11.61 & 22.80/24.95/27.41 \\ DCRNN & 17.60/18.62/21.28 & 15.83/16.36/18.11 & 26.88/28.6/32.23 \\ FC-LSTM & 31.10/37.04/50.93 & 26.27/32.24/44.29 & 42.00/49.38/67.33 \\ STTNs & 14.59/15.96/17.56 & 9.29/10.00/11.02 & 22.56/24.47/27.06 \\ ASTGNN & **14.14**/15.53/17.78 & **8.99**/**96.76**/11.13 & **22.33**/24.69/28.03 \\ FOGS & 14.34/15.82/18.53 & 9.01/9.91/11.41 & 22.65/25.02/28.79 \\ MSSTTN & 14.98/**15.45**/**16.69** & 9.56/_9.81_/**10.60** & 23.53/**24.43**/**26.38** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of PEMS08. dependencies in a multi-scale manner. The results of the comparison experiments verify this. Figure 7 shows the 60 min prediction results on three datasets as examples, and it is obvious that the prediction data made by MSSTTN is closest to the actual real-world data.
# Quality of Experience in 6G Networks: Outlook and Challenges
## 3 Enabling Technologies of Sixth Generation Networks
### Network Management Consideration  
From an operational perspective, one key advantage of combining network-wide softwarization and intelligence is the possibility of realizing intent-based networking (IBN). As the complexity of network management increases, scalable reconfiguration also increases in difficulty. Meanwhile, the relationship between configurations and outcomes becomes further obscured given the intricacy or interrelationship of various \"degrees-of-freedom\" or \"operational choices\" in the network. Operational points that are advantageous to the operator might then be overshadowed, which may be critical. IBN aims to overcome the complexity of managing these intricacies [60].  
Specifically, an end-to-end intelligence framework with access to sufficient computing power can help explore and/or understand the configuration-to-outcome relationship and the intent-to-outcome pathway of actions. Based on these findings, an operator can specify the desired outcome from the network operation (e.g., in terms of KPIs), whether it is network-wide or specific to a campus deployment. In turn, the network would autonomously take care of verifying the intent safety and configuration viability.  
Here, \"configuration\" refers to more than updating OpenFlow tables. The expectation is that IBN extends its impact to various network aspects, ranging from radio types to topology and topology elements, and reaching service offerings and content. This overhauling view is also called \"mandate-driven networking\" [61] or \"goal-driven communications\" [62].
## 4 Methodology
### Temperature-Sensitive Variable Selection
#### 4.1.1 K-Means Clustering Algorithm  
Suppose, the sample \(X=\{x_{1},x_{2},\ldots,x_{n}\}\) of \(N\) objects, and the dimension of each object is \(M\). K-means clustering aims to classify \(N\) objects into \(K\) clusters based on the similarity of samples and ensure that one object belongs to only one cluster. The steps of K-mean clustering are as follows:  
Step 1: Initializing the clustering center, and selecting \(K\) objects randomly as the clustering center \(\{C_{1},C_{2},\ldots,C_{n}\}\), where \(1<k\leq n\).  
Step 2: Calculating the distance between \(K\) objects and the centers of each cluster based on Euclidean distance.  
\[\text{dis}(x_{i},C_{j})=\sqrt{\sum\limits_{l=1}^{m}{(x_{il}-C_{jl})^{2}}} \tag{1}\]  
where, \(x_{i}\) is the \(i\)th object (\(1\leq i\leq n\)); \(C_{j}\) is the \(j\)th clustering center (\(1\leq j\leq k\)); \(x_{il}\) is the attribute of the \(l\)th (\(1\leq l\leq m\)) dimension of the \(i\)th object; and \(C_{jl}\) is the attribute of the \(l\)th dimension of the \(j\)th cluster center.  
Step 3: According to the Euclidean distance of each object to the center of each cluster, it is classified into clusters with minimal distance, based on which \(K\) clusters \(\{S_{1},S_{2},\ldots,S_{k}\}\) are obtained.  
Step 4: Calculating the mean of all objects in the clusters and using this value as the center of the new cluster.  
\[C_{p}=\frac{\sum\limits_{x_{i}\in S_{p}}{x_{i}}}{\left|{S_{p}}\right|} \tag{2}\]  
where, \(C_{p}\) is the \(p\)th (\(1\leq p\leq k\)) new cluster center; \(\left|{S_{p}}\right|\) is the object number of the \(l\)th cluster; and \(x_{i}\) is the \(i\)th object in the \(p\)th cluster.  
Step 5: Judging whether the gap between the new and old cluster centers is small or iteration is completed. If yes, the algorithm ends. Otherwise, repeat steps 2-5.
## 3 Experiments
### Experimental Results and Analysis
#### 3.3.1 Global Positioning Error (GPE)  
The experiment varied the scene complexity (obstacle count) and electromagnetic interference strength, recording the positioning error of the BBCN model and comparing it with that of other models. The results are shown in Table 1.  
The GPE of the BBCN model remains under 5 cm under no-interference conditions. Even in high-electromagnetic-interference scenarios, thanks to the compensation from the binocular vision module and IMU, the positioning error only increases to 6.2 cm, demonstrating excellent resistance to interference. Table 2 shows a comparison of the GPE under high interference for the BBCN model and other models.  
Compared to the GNSS model, whose GPE significantly increases with interference, reaching 40.2 cm, the BBCN model reduces the error by 84.5%. The BBCN model also reduces positioning errors compared to the Vision-SLAM and GNSS-Vision models, further demonstrating the effectiveness of multi-sensor fusion in complex environments.
## 4 Problem Description and Model Formulation
### Model Formulation
#### 4.2.3 Pharmaceutical Inventory Management  
This section describes a pharmaceutical inventory management approach that uses an infectious disease model to forecast demand. The proposed method uses a demand forecasting model and an inventory control model. The demand forecasting model uses the infectious disease model described in Section 4.2.2. A study by [PERSON] et al. [26] on the inventory management of pharmaceuticals using an infectious disease model is presented. We develop the inventory management method of based on the study by [PERSON] et al. [26]. In this study, an inventory management model that forecasts demand using the infectious disease model is been proposed.  
The parameters and equations in Section 4.2.2 are used to forecast the demand for pharmaceuticals. The predicted demand for pharmaceuticals (\(D_{d}\)) in period \(d\) can be expressed in Equation (20) using the number of people in the incubation period who develop  
Figure 7: SEIR model for consideration of hospital care.  
symptons and become infected (\(\epsilon E_{N}+\epsilon E_{Q}\)) and the unit of the number of pharmaceuticals required per patient (\(n\)).  
\[D_{d}=n\epsilon(E_{N}+E_{Q}) \tag{20}\]  
The order quantity (\(OR\)) is determined based on the forecast demand (\(D_{d}\)) as in Equation (21). The adjustment parameters \(\alpha\) and \(\beta\) are used to adjust for drug order discrepancies and inventory discrepancies.  
\[OR=\max\{0,D_{d}+\alpha(DO_{d}-MO_{d})+\beta(DI_{d}-MI_{d})\} \tag{21}\]  
The desired order quantity (\(DO_{d}\)) is calculated as the product of the production lead time (\(L\)) and the expected demand (\(D_{d}\)). The desired inventory (\(DI_{d}\)) is calculated based on the desired inventory rate (\(DC\)), the forecast demand (\(D_{d}\)), and the safety stock (\(SS\)). In this study, the safety stock is assumed to be a given value in order to simplify the inventory control method.  
\[DO_{d}=L\times D_{d} \tag{22}\]  
\[DI_{d}=DC\times D_{d}+SS \tag{23}\]  
The quantity of medicines on order (\(MO_{d}\)) and the quantity in stock (\(MI_{d}\)) are expressed in Equations (24) and (25).  
\[MO_{d+1}=MO_{d}+OR_{d}-\frac{MO_{d}}{L} \tag{24}\]  
\[MI_{d+1}=MI_{d}+\frac{MO_{d}}{L}-C_{d}-P_{d} \tag{25}\]  
where \(C_{d}\) represents the number of pharmaceuticals used, expressed as in Equation (26) using the number of people who recovered in the SEIR model.  
\[C_{d}=n\gamma\left(I_{N}+I_{Q}\right) \tag{26}\]  
Since medicines have an expiration date, \(p_{d}\) represents the quantity of medicines to be disposed of due to expiration. The shelf life of a drug is \(T_{s}\), which is expressed by Equation (27). The proposed model is shown in Figure 8.  
\[p_{d}=\frac{MI_{d}}{T_{s}} \tag{27}\]  
Figure 8: Inventory control model.
## 5 Dataset Analysis  
The anomaly traffic is measured from InRow 13 with IP source 192.168.0.2 during one week, as shown in Table 2.  
All the anomalies presented are performed on the temperature message packets. During the interception anomaly, the data text line packets from the InRow 13 sensors are not sent to the client. For the modification anomaly, all the temperature measurements are adjusted to the same value, placing them at 11.56 ''C regardless of which sensor they belong to. When a duplication anomaly occurs, additional packets are sent to the client.  
As aforementioned, the sensor sends data to the client every 5 min. The CoAP data text line packets correspond to the messages from the sensors. Therefore, under normal conditions, there should be 288 daily CoAP data text line packets. Anomalous duplication and modification packets must be reflected in the number of labelled daily packets. In the case of interception, the packets are not shipped from the host; thus, these do not exist in the dataset and cannot be labelled as an anomaly. However, the request messages sent by the client, in this case, are labelled as anomalies.
## 6 Implications and Future Directions  
The theoretical and simulated evaluations conducted in this study provide substantive insights into the practical viability of the proposed Integrated Adaptive Security Framework for IoT (IASF-IoT). The framework's integration of AI-driven security orchestration, quantum-resistant cryptography, blockchain-based identity management, federated learning, and Digital Twin technologies has been demonstrated to effectively address the multifaceted security challenges inherent in contemporary IoT ecosystems.  
Theoretical analysis indicated minimal resource impact, with processing power utilization projected at 0.0208%, memory usage at 0.0152%, and daily energy consumption below 0.1% of typical battery capacity. These projections were subsequently validated through comprehensive simulations involving 1000 heterogeneous IoT devices, where processing overhead was consistently observed below 0.05%, memory usage remained under 0.1%, and energy consumption ranged between 0.3 and 1.5 mAh per day. Additionally, the framework demonstrated security effectiveness, achieving threat detection accuracy between 85% and 99%, with average response times of approximately 2 s (as detailed in Section 5).
## 6 Discussions on Future Research Directions and Challenges
### The Impacts of Adversarial Attack Models for Beamforming Systems  
Adversarial attacks and adversary models are important issues that also need to be considered in secure physical layer transmission. In the existing research, most secure design strategies assume that adversarial attacks are passive. In this case, there is no cooperation or active exchange of information with other nodes within the system. However, in practical security design, there can be active cooperation and communication in confidential information exchanges [205]. Some strong adversaries may aim to estimate the environment in order to build up their advantages and threaten secure transmission [206; 207]. Considering these observations, the design of secure strategies for PLS techniques face great challenges. One of the major challenges of this direction, specifically in beamforming systems, is information leakage into the subspace of eavesdroppers. The basic idea of beamforming is to transmit signals in the direction of legitimate channels. When transmission power is allocated between intended and unintended directions, most of the power is allocated to the main side lobes and a small portion of the power is allocated to the minor lobe beam. Transmitting signals are then leaked through the eavesdropper channel [113]. According to [125], beamforming techniques attempt to enhance the quality of signals at the main channel. In this case, the design of beamforming does not consider the possibility of eavesdroppers when its channel gain is higher than that of the main channel. Therefore, it is challenging to design a beamformer that is optimal for the legitimate user under the favorable channel settings of an eavesdropper. It is computationally expensive to achieve a perfect balance between optimal signal power and signal leakages. The challenges of the existing channel training and narrow beam scanning in multi-antenna techniques can be addressed through the use of ML and AI algorithms to achieve optimal solutions with less complexity [149]. Unfortunately, there are also potential security concerns in the integration of ML algorithms into future 5G technologies. A recent study on beamforming prediction tackled the security problems of using ML algorithms [51]. It was shown that in order to investigate beamforming prediction, adversarial attack strategies should be studied according to the loss maximization-based attacks that are relative to AI models.
## 3 Simulation Experiments and Analysis
### Comparative Experiment with Other Visualization Methods  
In this section, we compare the TSSG representation method, with traditional approaches, such as STFT, Continuous Wavelet Transform (CWT), and RP. We transformed randomly selected signals into recognizable images using each of the four methods. These transformed images were then fed into a relation network with the same set of parameters for feature extraction and classification. The simulation results are illustrated in Figure 9.  
The proposed TSSG method exhibits certain advantages over other methods. Compared to the alternatives, TSSG can achieve convergence at lower SNRs, making it more efficient in practical applications. However, in the experiments conducted in this section, STFT and CWT demonstrated relatively poor performance. This may be attributed to the limitations of these methods in handling non-stationary signals, as they struggle to capture the time-varying characteristics of the signals and provide sufficient information for effective feature extraction and classification. On the other hand, the RP method achieved relatively high accuracy under high SNR ratio conditions. However, the recognition accuracy showed slower improvement with increasing SNR ratio.  
Figure 8: Comparison of recognition accuracy with traditional methods under small sample conditions.
## 4 Results and Discussions
### Computations of Engagement State  
We input each clip from the recorded lectures and applied the trained model to it. We took one frame from every two subsequent seconds to avoid the repetition of the frames since repetitive frames output the same value of the state. We applied the bounding box on the faces in each frame of students through the face recognition library. Thus, as an output, we obtained 60 computed affective states against each student. We analyzed each frame and then collected the data of engaged and non-engaged students with unique student IDs. We applied the same process to all the recorded videos and in the end, we collected 283 observations of 45 students including males and females. The total analysis was performed on 13,510 individual frames for both engaged and non-engaged students. We compare the accuracy of our proposed model with the existing methods in Table 4.