esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Layered Satellite Communications Systems for Ultra-High Availability and Resilience
## 4 System Model: MLSS Concept
### Service Operation  
The MLSS helps to ensure that a user terminal can register to the system using any available access networks. During the registration process, the user terminal communicates its capability and specifies the list of usable access networks and their expected channel qualities. The user terminal monitors the available access networks and informs the network management entities about any changes. When a connection is initialized, the network management functions determine which of the available access networks would fulfill the quality of service (QoS) requirements most efficiently and orchestrate the end-to-end network accordingly. Any change in the system's state leads to the reevaluation of the selected radio access method and to its reconfiguration if needed.  
From an operational perspective, the main challenge of the MLSS is to configure the end-to-end network in such a way as to fulfill the heterogeneous QoS requirements of the users regardless of their geographical location. At the same time, the MLSS must maximize the efficiency of utilization of the available assets, and the system's overall capacity. Software-defined networking (SDN) enables dynamic and efficient network management, improving the network's performance and monitoring. SDN allows the centralization of the network management intelligence for the overall network.  
Because the access network of the MLSS can be composed of multiple heterogeneous radio access networks with potentially different air interfaces and/or protocol stacks, the virtualization and disaggregation of the component radio access networks via an open radio access network (O-RAN) architecture simplifies the management and operation of the MLSS and can increase flexibility quite significantly. One of the primary benefits of virtualizing the component RANs is that the individual RAN functions no longer require special proprietary hardware to run and can instead be run on standard servers (RAN processors).  
The virtualization should not be limited to the RAN but should start primarily with network functions (NFVs), such as routers, firewalls, etc. The softwarization of these functions means that the specialized hardware is replaced by software running on commodity hardware, preferably on a private or public cloud. To increase the capacity of the global network, a cooperative hierarchical radio resource management system should be used.
## 5 Results
The abbreviations are as follows: L/A: left ankle, L/K: left knee, L/H: left hip, R/A: right ankle, R/K: right knee, R/H: right hip, L/W: left wrist, L/E: left elbow, L/S: left shoulder, R/W: right wrist, R/E: right elbow, R/S: right shoulder. On an individual joint level, the MPJPE values for the proposed dataset were comparatively lower than the COCO dataset, except for the left and right hip, with differences of 0.111 mm and 0.343 mm, respectively. Both models recorded comparatively higher MPJPE values for upper body joints, with the highest error recorded for the left wrist with a value of 5.364 mm for the model trained with the COCO dataset and 4.868 mm for the model trained with the proposed dataset. For lower body joints such as the hip, knee, and ankle, the proposed dataset was able to achieve an MPJPE value of 3.268 mm (\(\pm\)1.735), which was lower by 0.265 mm compared to the COCO-trained model. The overall MPJPE value for the lower and upper body is demonstrated in Table 2.  
Furthermore, the MPJPE values for images captured in different views were also computed. The overall MPJPE values for the COCO-trained model were observed as being higher by approximate values of 0.793 mm, 0.378 mm, and 0.624 mm than the model trained with the proposed dataset for all 0, 45, and 135 degree views, respectively. However, at the individual joint level, slightly higher MPJPE values were recorded for the left and right hip at 45 and 135 degrees, respectively, and for the left knee at 45 degrees. The detailed MPJPE value recorded is represented in Table 3 and Figure 7a-c. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**MPJPE (mm)**} \\ \hline
**Dataset** & **Upper Body** & **Lower Body** & **Overall** \\ \hline Proposed Dataset & 3.154 \(\pm\) 1.724 & 3.268 \(\pm\) 1.735 & 3.211 \(\pm\) 1.730 \\ COCO Dataset & 3.985 \(\pm\) 1.823 & 3.533 \(\pm\) 1.766 & 3.759 \(\pm\) 1.795 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overall mean per joint position error (MPJPE). Two different models trained with the proposed dataset (23,741 images) vs. COCO (118,287 images) were tested with approximately 2510 images for calculating MPJPE. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{3}{c}{**MPJPE (mm)**} \\ \hline  & **Views** & **Upper Body** & **Lower Body** & **Overall** \\ \hline \multirow{3}{*}{Proposed} & 0 & 3.210 \(\pm\) 1.910 & 3.111 \(\pm\) 1.638 & 3.160 \(\pm\) 1.774 \\  & 45 & 3.272 \(\pm\) 1.578 & 3.457 \(\pm\) 1.772 & 3.272 \(\pm\) 1.578 \\  & 135 & 3.211  
Figure 7: _Cont._
# Attacking Robot Vision Models Efficiently Based on Improved Fast Gradient Sign Method
## 3 Our Technique
### RMS-FGSM Algorithm  
The process of generating adversarial samples using the RMS-FGSM algorithm is as follows: 1. Given an original image \(x\); 2. Input \(x\) to classifier \(f(x)\) and calculate its gradient \(g_{t}\) of loss function w.r.t \(x\); 3. According to \(g_{t}\), compute an exponential average \(r_{t}\) of the squared gradients based on the EWMA; 4. Calculate the adaptive learning rate \(\tau\) according to \(r_{t}\); 5. Calculate the perturbation pixel by pixel, add it to \(x\), and use the \(clip\{\cdot\}\) function to prevent pixels from going out of range; 6. Repeat steps 2 to 5 until the specified number of iterations is reached.  
The outline of our the RMS-FGSM algorithm framework is shown in Algorithm 1.  
```
Input: A classifier \(f\) with loss function \(L\); original image \(x\); ground-truth label \(y\); decay rate \(\rho\); number of iterations \(T\); initial learning rate \(\varphi\); Output: Adversarial example \(x^{*}\);
1: Initialize the variable \(r_{0}=0\); \(x^{*}_{0}=x\);
2: For t = 0 to \(T\)-1 do:
3: Input \(x^{*}_{t}\) to classifier \(f\);
4: Calculate the current gradient: \(g_{t}=\
abla_{x}L(f(x^{*}_{t}),y)\);
5: Cumulative squared gradient: \(r_{t}=\rho\cdot r_{t-1}+(1-\rho)g_{t}^{2}\);
6: Compute the adaptive learning rate: \(\tau=\frac{\phi}{\sqrt{r_{t}+\sigma}}\);
7: Add perturbation to the sample: \(x^{*}_{t+1}=clip\{x^{*}_{t}+\tau\cdot sign(g_{t})\}\);
8: End for
9: return \(x^{*}=x^{*}_{T}\).
```  
**Algorithm 1**RMS-FGSM.
# Energy-Efficient Respiratory Anomaly Detection in Premature Newborn Infants
## 2 Related Work  
Recently, machine learning-based respiratory classification techniques have shown significant promise as enablers for continuous respiratory monitoring of newborn infants. To this end, a Support Vector Machine (SVM)-based classifier is proposed in [18], achieving 82% classification accuracy. A Logistic Regression-based classifier is proposed in [19], achieving classification accuracy of 87.4%. An Ensemble Learning with Kalman filtering is proposed in [19] achieving 91.8% classification accuracy. All these techniques are proposed for respiratory classification using pulse-oximeter data collected from infants, making these approaches the relevant state-of-the-art for our work. In Section 6, we compare our approach to these state-of-the-art approaches and show that the proposed approach is considerably better in terms of both classification accuracy and energy. Thermal imaging has also been proposed recently for the respiratory classification of infants [20]. The authors reported a precision and recall score of 0.92. We achieved a score of 0.98. Respiratory classification using acoustic sensors is proposed in [21]. An accuracy of 95.7% is reported. We achieved an accuracy of 97.15%.  
Beyond respiratory classification, deep-learning-enabled techniques have been used extensively for health informatics [22]. For instance, sleep apnea classification is proposed using deep convolutional neural networks (CNNs) and long short-term memory (LSTM) in [23], achieving an accuracy of 77.2%. A deep learning approach using InceptionV3 CNN model is proposed in [24] to detect Alzheimer's disease using brain images. The authors reported an area-under-curve (AUC) score of 0.98. CNN models are used in [25] to detect metastatic breast cancer in women. The authors reported an AUC score of 0.994. A CNN-based Arrhythmia classification is also proposed in [26],where the authors reported significant improvement in classification accuracy over state-of-the-art.  
Finally, many recent SNN-based techniques have shown comparable and in some cases higher accuracy than their deep learning counterparts with significantly lower energy. An unsupervised SNN-based heartrate estimation is proposed in [27]. The authors reported a 1.2% mean average percent error (MAPE) with 35\(\times\) reduction in energy. A spiking CNN architecture is proposed in [28] to classify heart-beats in human. The authors reported 90% reduction in energy with only 1% lower accuracy than a conventional CNN. SNN-based epileptic seizure detection is proposed in [29], where the authors reported an accuracy of 97.6% with a considerable reduction in energy.  
To the best of our knowledge, this is the first work that uses SNN for respiratory classification in infants and shows that SNNs can achieve high accuracy (93.33% in our evaluation) with a considerable reduction in energy (18\(\times\) lower energy compared to a baseline 1 DCNN model).
# Reliability and Maintenance Analysis of Unmanned Aerial Vehicles +
## 1 Introduction
### Definitions
#### 1.1.2 Availability  
This parameter is extremely important for'ready to operate' system. It measures the number of times for which the system under study is available or ready with respect to the number of times in which the system is required. Typically, this parameter is presented in form of a percentage, where 100% is the theoretical goal [8; 9; 10; 11].
# Precise Calculation of Inverse Kinematics of the Center of Gravity for Bipedal Walking Robots
## 2 Materials and Methods  
### Robot Structure  
The robot reported in this paper is the numerical model of the ITU Biped bipedal walking robot [33]. The rotational axes of 3 harmonic geared (Harmonic Drive, Frankfurt, Germany) direct-current (DC) motors (Maxon, Sachseln, Switzerland) are placed at the hip joint so that they intersect at a single point, while the rotational axes of 2 DC motors (Maxon, Sachseln, Switzerland) connected to screw nuts (Rexroth, Istanbul, Turkiye) are placed at the ankle joint so that they intersect at a single point. The joint types and positions of the robot are shown in Figure 1.  
In Figure 1a, \(O_{W}\) is the inertial coordinate system, \(O_{B}\) is the base coordinate system, \(x_{0}\) is the starting point for movement, \(y_{0}\) is the pelvis length, and \(z_{0}\) is the pelvis height of the robot. The \(x\) axis is represented in red, and the \(y\) and \(z\) axes are represented in green and blue, respectively. \(O_{DL}\) is the hip point of the left leg, \(O_{6R}\) is the foot coordinate system, and \(\theta_{nR}\) are the joint angles of the right leg. To increase readability, the hip point of the right leg and the joint angles of the left leg are not shown in Figure 1. \(l_{1}\), \(l_{2}\), and \(l_{3}\) are the link lengths of the robot. Values of the length parameters are given in Table 1.  
The approximate CoGs of the legs are given in Figure 1b. Index \(R\) represents the right leg, \(L\) is the left leg, and \(T\) is the torso. \(c_{B}\) is the CoG of the robot. \(m_{T}\) in Figure 1b is the weight of the torso, and \(m_{R}\) and \(m_{L}\) are the total weights of the right and left legs, respectively.  
The CoGs with respect to the link coordinate system and the masses of the body parts used in the simulation are given in Table 2.
# Preparation and Physicochemical Properties of Tannin-Imobilized Membrane Adsorbent
## 2 Materials and Methods
### Preparation of Microporous Membrane Immobilized Tamin  
The preparation procedures of PA-BWT mainly contained two steps: adsorption and covalent cross-linking (Schematic S1). The polyamide microporous membrane was soaked in 0.5 wt% black wattle tannin aqueous solution for 15 h to obtain the adsorption equilibrium, and then the surface was washed with deionized water to remove extra tannin. The membrane was placed in a 1 wt% glutaraldehyde solution, was covalently cross-linked at 303 K for 3 h, and was then thoroughly washed with deionized water and ethanol to remove the residual cross-linking agent and uncross-linked tannin on the surface. Thus, the PA-BWT was obtained.  
The PA membrane was immersed in 100 mL of cerium ammonium nitrate solution, initiated at 40 \({}^{\circ}\)C for 0.5 h. Then acrylic acid (AA) was added to react with the PA membrane at 40 \({}^{\circ}\)C for 3 h, and the membrane was washed with deionized water. The grafted PA-AA was obtained, and then it was placed in a 1 wt% tannin solution and reacted at 30 \({}^{\circ}\)C for 5 h so that the hydroxyl groups in the tannins could be esterified with the carboxyl groups. Finally, after washing with deionized water, the PA-AA-BWT was obtained (Schematic S2).  
The PVDF membrane was soaked in a 5 wt% benzophenone (BP) solution for 20 min to allow for full adsorption of the BP onto the membrane. After which, the BP was activated under a 160 W UV lamp for 30 min. The activated PVDF membrane was immersed in a 5 wt% glycidyl methacrylate (GMA) solution and irradiated under a 160 W UV lamp for 1 h to initiate a photo-initiated grafting. Next, the PVDF membrane grafted with GMA was added to a 1 wt% black wattle bark tannin solution to react for a certain time, so that the tannins were chelated onto the PVDF membrane. Finally, deionized water and ethanol were used to remove the residual crosslinking agent and extra tannins on the surface to obtain PVDF-BWT (Schematic S3).  
Utilizing the reactive characteristics of tannin which are similar to those of dopamine, the tannin could be immobilized on the CELL membrane by dip coating. A 1 wt% alkaline tannin solution was prepared with Tris-buffer (100 mL, pH = 7.8, 50 mM), and the pretreated CELL membrane was immersed in the alkaline tannin solution for a certain time. During this process, tannin oxidation initiated an aggregation and deposited on the surface of the film to tune its wettability. Finally, after washing with deionized water, the CELL-BWT was obtained (Schematic S4).
# Enhanced YOLOv8 with BiFPN-SimAM for Precise Defect Detection in Miniature Capacitors
## 4 Experiments and Analysis of Results  
### Experimental Setup and Dataset  
#### 4.1.1 Dataset  
In the field of machine vision, the micro-capacitor surface defect (MCSD) dataset was meticulously compiled for the inspection and analysis of minute defects in various types of capacitors. This dataset is unique in its focus on the intricacies of tiny capacitors, encompassing a wide range of defect types and capacitor sizes, suitable for rigorous AVI applications.  
Our MCSD dataset contained 1358 high-resolution images of four unique types of miniature capacitors, each varying in size and defect characteristics. This dataset was rich, with 2450 meticulously annotated ground truth boxes, showcasing a wide variety of defects. For each capacitor type, the size, type, and specific defects are detailed in the table below.  
Every image in the MCSD dataset was carefully marked with precise bounding boxes, identifying the exact location and extent of each defect. This dataset presents an in-depth view of defect distribution, considering both size and position, as detailed in Table 1. Sourced from real industrial settings, the MCSD dataset is highly relevant and applicable for practical use.  
In terms of experimentation, we split the dataset into training and validation sets at a 9:1 ratio. This split was designed to optimize model training and evaluate performance effectively. Such a division provided a thorough understanding of the model's ability to detect a broad spectrum of defects across various sizes of capacitors.
# Fail-Safe Topology Optimization Using Damage Scenario Filtering
## 1 Introduction
In addressing some of the challenges inherent in the aforementioned research, scholars have rigorously established a framework for the fail-safe topology optimization of general three-dimensional structures and have developed computationally feasible solutions for industrial applications. Subsequently, the effectiveness of this approach has been validated through corresponding numerical examples. By employing the independent continuous mapping method and incorporating discrete conditions involving topological variables and fundamental frequency constraints, a fail-safe topology optimization model is formulated and solved [27; 28; 29]. The numerical results indicate that, compared to traditional frequency-based topology optimization designs, the optimized fail-safe structures exhibit more intricate configurations and comprehensive material distribution, resulting in increased redundancy and mitigating the sensitivity of structural frequencies to localized damage. By leveraging the material characteristics of the [PERSON] stress interpolation damage model, an optimization model for obtaining fail-safe structural designs is established. Within the framework of optimization criteria methods, an extended variable update scheme is proposed to suppress highly nonlinear stress behaviors and optimize oscillations. Relevant benchmark tests demonstrate the effectiveness of the proposed strategy in the process of achieving corresponding fail-safe designs [30; 31; 32; 33]. To reduce the computational demands associated with fail-safe optimization designs, by building upon the aforementioned research, this paper introduces a density-based filtering algorithm. This algorithm filters out redundant local damage scenarios while traversing through them, thereby diminishing the computational workload imposed by an excessive number of local damage scenarios. Consequently, it enhances the efficiency of fail-safe structural optimization methods. In comparison to traditional structural topology optimization algorithms [34; 35; 36; 37; 38], the improved fail-safe optimization algorithm yields designs with superior robustness. Furthermore, in contrast to conventional fail-safe topology optimization algorithms, the computational workload of the entire optimization process is significantly reduced while obtaining effective fail-safe structural designs. The remaining organization of this paper is as follows: In Section 2, we provide a detailed exposition of the theory of fail-safe topology optimization and the density-filter-based fail-safe optimization method. In Section 3, the algorithm based on density filtering is compared with standard fail-safe optimization algorithms to validate the effectiveness of the density-filter-based fail-safe optimization method. Furthermore, the impact of density thresholds on the fail-safe optimization method is investigated. Finally, in Section 4, an analysis and summary of the work presented in this paper are conducted, addressing potential issues and outlining avenues for future research.
# Resource Assignment Algorithms for Autonomous Mobile Robots with Task Offloading
## 5 Simulation Results and Discussion
### Scalability  
The capability to withstand the increase in problem dimensions is directly related to the algorithms computational cost, which is summarized in Table 5.  
Due to its exponential complexity, VEX is the least scalable approach, as it may become intractable even for moderately sized systems.  
GA and RSAM have a complexity that is linear in the system size, but their cost greatly depends on the number of calls \(N_{\text{call}}\). In our simulations, we considered \(N_{\text{call}}=10^{6}\) for GA and \(N_{\text{call}}=50,000\) for RSAM, since GA is used as a reference to find the optimal value, while RSAM could be adopted in a practical case. Vertical scalability can be guaranteed by keeping \(N_{\text{call}}\) limited and fixed as the problem dimensions increase so as to quickly find a solution; at the same time, this also causes a loss of the solution quality, since a progressively lower percentage of configurations are tested.  
Instead, a nearly quadratic complexity results for SM, while practically less than linear complexity is assigned to MLAT, which is the least-costing one. Thus, both SM and MLAT are the most scalable.  
Table 5 lists the obtained or estimated running times for the scenario _40/60/20_variable_; VEX is estimated to employ \(2\times 10^{100}\) s, while all the other algorithms have reasonable running times. Specifically, GA takes 109.4 s, RSAM needs 4.5 s, and SM only 0.0356 s. MLAT is the fastest, with 0.0028 s.  
In summary, the proposed algorithm SM has running times of a few ms for all the considered problem dimensions, achieves solutions with only slight loss from the optimal ones, and the KPIs are better than that of RSAM and MLAT.
## 4 Results
### Vulnerability Evaluation in Hybrid Space  
In this subsection, we consider the fact that clustering takes place in spatial coordinate space while the \(k\)-NN query is in Hough space. In this case, the dataset concerns the compressed version of mobile users' trajectories as derived from SMaRT. Figure 7a,b depicts information about the initial trajectory's length and the selected points, as well as the compression ratio per trajectory ID. Note that the compression ratio in the system is computed as \((1-\frac{SelectedPoints}{InitialPoints})\times 100\%\), where the selected points are 100. From the dataset, we exclude 13 trajectories whose length is much less than 100, such as the average length of compressed trajectories.  
Another observation is introduced in Figure 7c,d, where the vulnerability in hybrid space has similar behavior and performance with the one in spatial coordinates space. This relates to the fact that Hough-X constitutes a linear transformation on spatial coordinates. Again, employment of the suppressing method, as shown in Table 6, makes the vulnerability of the \(k\)-NN query with the clustering method even more secure.  
Figure 6: Clustering with \((U_{x},a_{x})\) and \(k\)-NN with \((U_{x},a_{x})\): (**a**) Mobile User 10 and (**b**) Mobile User 100 for \(N=995\) trajectories, \(L=50\) time-stamps, (**c**) Vulnerability measure in dual Hough-X and native dimensional space of \((x,y)\).