esa-sceva/satcom-chunk-collection huggingface dataset:
# Quality Index of Supervised Data for Convolutional Neural Network-Based Localization
###### Abstract  
Automated guided vehicles (AGVs) are important in modern factories. The main functions of an AGV are its own localization and object detection, for which both sensor and localization methods are crucial. For localization, we used a small imaging sensor named a single-photon avalanche diode (SPAD) light detection and ranging (LiDAR), which uses the time-of-flight principle and arrays of SPADs. The SPAD LiDAR works both indoors and outdoors and is suitable for AGV applications. We utilized a deep convolutional neural network (CNN) as a localization method. For accurate CNN-based localization, the quality of the supervised data is important. The localization results can be poor or good if the supervised training data are noisy or clean, respectively. To address this issue, we propose a quality index for supervised data based on correlations between consecutive frames visualizing the important pixels for CNN-based localization. First, the important pixels for CNN-based localization are determined, and the quality index of supervised data is defined based on differences in these pixels. We evaluated the quality index in indoor-environment localization using the SPAD LiDAR and compared the localization performance. Our results demonstrate that the index correlates well to the quality of supervised training data for CNN-based localization.  
deep learning; autonomous intelligent robots; LiDAR; localization +
Footnote †: journal: Applied Sciences
# A Computation Offloading Strategy in LEO Constellation Edge Cloud Network
## 3 System Model  
In this section, the system model is shown in Figure 2, which is composed of LEO satellites, satellite terminals, gateway stations and the edge computing orchestrator. Each LEO satellite is equipped with router units and onboard MEC servers. The router unit is used for inter-satellite data exchange, and the onboard MEC server provides edge computing services. The router unit of each satellite node is connected with the intra-plane and inter-plane links to form an inter-satellite routing and forwarding network and realize the network layer reachable between MEC servers. As the control node of the edge cloud  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**References** & **Offloading Scenario** & **Optimization Goal** & **Full Constellation Offload** \\ \hline
[3] & - & - & mentioned \\ \hline
[4] & multi-user & user response time \& & no \\  & single-satellite & user energy & & \\ \hline
[5] & - & - & mentioned \\ \hline
[6] & multi-user & system energy \& user & no \\  & single-satellite & delay & & \\ \hline
[7] & multi-user & system energy \& user & no \\  & multi-satellite & delay & & \\ \hline
[8] & multi-user & user energy & no \\  & multi-satellite & & & \\ \hline
[9] & multi-user one satellite & & & mentioned \\  & orbit & - & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of literature.  
network, the edge computing orchestrator is deployed behind the ground gateway station, which receives the computing request of the satellite terminal and centrally schedules user tasks to be offloaded by the satellite terminal among LEO satellites.  
In this model, there are M LEO satellites. The single satellite is denoted as \(m\). The set of LEO satellites can be denoted as \(\mathcal{M}=\{m\in\mathbb{N}^{+}|m\in[1,M]\}\). There are N satellite terminals, the terminal is denoted as \(n\), the set of satellite terminals can be denoted as \(\mathcal{N}=\{n\in\mathbb{N}^{+}|n\in[1,N]\}\). The notations used in this paper are summarized in Table 2. Next, we introduce the communication, network, computation, load models in detail.
# Multi-Patch Time Series Transformer for Robust Bearing Fault Detection with Varying Noise
## 2 Related Work  
### Bearing Fault Detection Research in Noisy Environments  
[PERSON] et al. conducted extensive research on bearing fault detection and diagnosis systems in industrial electrical machinery, emphasizing the challenges of achieving accurate fault diagnosis in noisy environments [2]. Their study compared the performance of various machine learning and deep learning models, including Support Vector Machines (SVM), LeNet-5, 1D-CNN, and 2D-CNN, under noisy conditions. Signal preprocessing techniques such as time-series analysis, wavelet transform (CWT), waveform decomposition (WPD), and signal-image conversion (SIC) were employed to evaluate the efficiency of these models. The findings indicated that CWT-based features were particularly effective for fault detection, with the 2D-CNN model outperforming others in noise-affected scenarios. By introducing Gaussian noise, the study examined its impact on fault detection performance and observed that higher Signal-to-Noise Ratios (SNR) resulted in better model accuracy. This work underscores the importance of designing diagnostic systems capable of maintaining robustness in noisy conditions and highlights the utility of Gaussian noise simulation for developing reliable fault detection systems.  
[PERSON] et al. proposed an adaptive multi-scale fully convolutional network (AMFCN) to enhance bearing fault diagnosis in varying noise environments [3]. The AMFCN model introduced two key strategies to improve noise adaptability. First, random sampling was utilized to reduce noise while intentionally introducing data incompleteness, which improved the model's robustness during training. Second, large-kernel convolutional layers were employed to extract wide temporal features, effectively suppressing high-frequency noise. The AMFCN architecture dynamically adjusted feature weights across multiple scales, emphasizing critical features while mitigating noise interference. Performance evaluation under different SNR conditions, ranging from \(-8\) dB to \(8\) dB, demonstrated that AMFCN consistently achieved high fault diagnosis accuracy, even under severe noise conditions. Notably, the model achieved an accuracy exceeding \(97\%\) at an SNR of \(-8\) dB. These results highlight the AMFCN's robustness against noise and its potential as a reliable solution for fault diagnosis in real-world industrial applications.
## 3 Challenges
### Mixed Traffic  
Diverse applications may need to share the same WSAN, inducing both periodic and aperiodic data. This feature will become increasingly evident as the scale of WSANs grows. Some sensors may be used to create the measurements of certain physical variables in a periodic manner for the purpose of monitoring and/or control. Meanwhile, some others may be deployed to detect critical events. For instance, in a smart home, some sensors are used to sense the temperature and lighting, while some others are responsible for reporting events like the entering or leaving of a person. Furthermore, disparate sensors for different kinds of physical variables, e.g., temperature, humidity, location, and speed, generate traffic flows with different characteristics (e.g. message size and sampling rate). This feature of WSANs necessitates the support of service differentiation in QoS management.
## 2 The Rise of the Federated Learning Approach
### Taxonomy of FL Models
#### 2.2.1 Data Partitioning  
In this case, we have three categories: _horizontal_, _vertical_ and _transfer federated learning_[18]. Horizontal federated learning refers to the process of distributing the same resources to many node devices (parallelism). On the perspective of data, in horizontal FL (or sample-based FL), the feature space (a vector space that includes all the possible feature vectors from a population of a specific domain) of the training data is the same for all used samples but may differ in the sample space. In contrast, in vertical federated learning, the resources of a powerful device, e.g., a server, are bisected in order for more nodes to leverage its capability (concurrency). In vertical or feature-based FL, the datasets share the same sample ID space but are different in respect to the feature space. Furthermore, we also have the terminology _homogeneous or heterogeneous_ federated learning, which stipulates whether there is harmonization or not, respectively, regarding device specifications and model architecture. For example, if all node devices have the same architecture and learn the same task, we have homogeneity. If either the task or the model differs, it is called heterogeneity. Horizontal FL tends to coincide with homogeneous FL, while vertical FL is commonly associated with heterogeneous FL. Nevertheless, in [19], [PERSON] et al. addressed the challenge of heterogeneous data in a horizontal federated learning scenario. In FL, the workload accomplishment can realize, either from multiple cores of a single device (vertical setting) or from multiple devices, communication with each other (horizontal setting) [20]. The third category, namely _transfer_ federated learning, combines data either with common feature, or a common sample space, with the aims of alleviating the problem of limited resources among the nodes and, hence, building an efficient neural model.
## 3 Pharmacological Properties of GP
### Hypoglycemic Effect
Inflammatory factors such as TNF-\(\alpha\) and IL-6 produced by nonspecific inflammation could interfere with the conduction of insulin signaling pathway and thereby lead to decreased insulin sensitivity. Studies showed that insulin sensitivity in diabetic rats treated with GPS was improved by blocking the NF-\(\kappa\)B signaling pathway and reducing the secretion of inflammatory factors. However, GPS had no influence on the streptozotocin-induced rat islet cells damage [128]. A large number of studies proved that GPP had hypoglycemic effect. [PERSON] et al. [119] established a model of type 2 diabetic rats by a high-fat and high-sugar diet combined with a small dose of streptozotocin to study the effects of GPP on blood glucose and its possible mechanism. The results showed that the fasting blood glucose and glucose tolerance were reduced by the treatment with GPP. Moreover, the fasting serum insulin level was increased in a dose-dependent manner, suggesting that the hypoglycemic effect of GPP was related to the secretion of insulin. In vitro studies showed that GPP had a strong inhibitory effect on \(\alpha\)-amylase in a dose-dependent manner, which was equivalent to about 50% of the inhibitory effect of acarbose at the same concentration [118]. The ethanol extract of GP grown in Hainan could significantly reduce the blood glucose level of alloxan-induced hyperglycemia mice. There was no statistically difference between the medium-dose group (2 g/kg) and the metformin group; the high-dose group (4 g/kg) showed a stronger hypoglycemic effect than the metformin group. More importantly, it could significantly inhibit the increase in blood glucose induced by glucose, but it exhibited no obvious effect on the blood glucose of normal mice [120]. [PERSON] et al. [129] estimated the effects of different components of GP on blood biochemical indexes of hyperglycemic mice and found that both GPE and 90% ethanol elution parts via macroporous resin chromatography could reduce the blood glucose of diabetic mice and alleviate the symptoms of \"three more and one less\". [PERSON] et al. [121] explored the effect of GPE (1600 mg/kg/d) on hepatic glucose output (HGO) in spontaneously type 2 diabetic Gotok-Kakizaki rats, and the results showed that the hepatic insulin sensitivity was improved, while HGO and plasma glucose levels were reduced. This was in agreement with results from a clinic trial conducted to evaluate the anti-diabetic effect of GP tea in drug-naive type 2 diabetic patients [130; 131]. Moreover, the biosecurity of tea was proved clinically, as no hepatotoxicity and nephrotoxicity or other adverse effects such as hypoglycemia were observed in the trial. In addition, the antidiabetic effect of GP was associated with the stim ulation of insulin release from the islets. An oral treatment of GP water extract (0.3 g/kg of body weight daily) for two weeks in GK rats improved glucose tolerance and stimulated insulin release. GP-induced insulin release was partly mediated via K-ATP and L-type Ca\({}^{2+}\) channels [132].
## 2 Materials and Methods
### Measurement
#### 3.1.3 Temperature Sweep Test  
Temperature sweep tests were conducted on pre-cut SBS-modified asphalt samples mixed for different amounts of time. The experimental results are presented in Figure 5.  
When mixed for 15 min, the complex modulus of the pre-cut SBS-modified asphalt was lower than that of the conventional-process asphalt and the phase angle was greater, indicating that the modification effect at this point was not significant and the proportion of viscosity in the modified asphalt was higher. This can be attributed to insufficient blending of the SBS modifier agent and the asphalt due to the larger particle size of the SBS in this state and the short mixing time.  
After 30 min of mixing, the complex modulus of the pre-cut SBS-modified asphalt significantly increased and surpassed that of the conventional-process asphalt. The phase angle was also notably smaller, indicating a substantial enhancement in the modification effect. A higher complex modulus and a smaller phase angle led to a larger rutting factor and better resistance to rutting deformation. This can be attributed to the filamentous and granular structure of the SBS in this state, which increased the interlocking effect of the SBS and enhanced the strength of the spatial network structure.  
After 60 min of mixing, the complex modulus of the pre-cut SBS-modified asphalt slightly decreased compared to that assessed at the 30 min mark and the phase angle increased, suggesting a minor reduction in the resistance to rutting and an increase in the proportion of viscosity. This was due to the narrower filamentous structure and smaller particle diameter, which weakened the strength of the network structure.  
After 120 min of mixing, the complex modulus of the pre-cut SBS-modified asphalt decreased further and the phase angle continued to increase, indicating a further weakening of the high-temperature performance, though it remained better than that of the conventional-process asphalt. This was due to the atomized particle form of SBS at this stage, which weakened the strength of the network structure. This implies that an excessively long mixing time is not advisable.
# Simulation of a Low Concentrator Photovoltaic System Using COMSOL
###### Abstract  
The use of photovoltaic (PV) systems presents a great solution to high energy demand. Many factors limit the output of PV systems. One method of increasing the output of PV systems is to employ concentrators. The function of these concentrators is to increase the amount of solar radiation falling on a PV panel using optical devices. In this work, a simulation of a low concentrated photovoltaic system (LCPV) (V-trough model) will be conducted using COMSOL Multiphysics software package. The ray-tracing technique, based on the finite-element method, was used to study the performance of a V-trough without the incorporation of a tracking system. By investigating the effect of the mirrors' inclination angles on the performance of the system, the optimum inclination angles were determined. The simulation was done for a non-tilted concentrator photovoltaic (CPV) system if placed in different geographical locations in Saudi Arabia with the inclination of the mirrors being changed every hour of the daylight. It was found that the concentration ratio of the suggested model increased for the city of Jeddah, for example, by 171% and 131% for double and partial coverage cases, respectively. In order to reduce the operation cost, the simulation was repeated with the restriction of the mirrors' inclination to only three positions during the day. The concentration ratio decreased in this case by not more than 14%. When mirrors were fixed throughout the day, the concentration ratio dropped to about 50%. Such simulations will assist in investigating different designs of PV systems prior to their manufacturing. In addition, it could assist in determining the best geographic location for such CPV systems.  
COMSOL; solar energy; low concentrator photovoltaic; simulation; V-trough 2022123450. [[https://doi.org/10.3390/app12073450](https://doi.org/10.3390/app12073450)]([https://doi.org/10.3390/app12073450](https://doi.org/10.3390/app12073450))
# Prediction of Health Problems Using Deep Learning Images and Bio-Signals
## 3 Materials and Methods
### Prediction System of Health Status
#### 3.2.2 Health Status and HRV  
In order to examine health conditions, recently, not only physical indicators, but mental and stress-related indicators are also considered. The HRV is useful in analyzing such physical and mental health conditions in an integrated manner. Methods to analyze the HRV include ways to examine the frequency range and time domain in examination of autonomous nerve states. In reference to parameters collected from such methods as variables, stress indicators are derived and used in such methods.  
Heart beat changes reflect complex biological activities related to autonomous nerves and various biological elements such as respiration, blood pressure, temperature, blood vessel, exercise, etc. Biological mechanisms have different frequency elements, respectively, and heart beat changes reflect the sum of such frequency elements. When the HRV is examined, a certain frequency range is analyzed to measure the influence of each mechanism. For frequency range analysis, parameters of very low frequency (VLF; 0.0033-0.04 Hz), low frequency (LF; 0.04-0.15 Hz), high frequency (HF; 0.15-0.4 Hz), and LF/HF are used. LF is a parameter reflecting activities of both the sympathetic system and parasympathetic system. When there is a disease causing an excessive reaction of the sympathetic system, LF increases. HF elements are closely related to the electric stability of a hart. When there are reactions such as stress, panic, and anxiety, HF decreases [2].  
The time-domain analysis method is to calculate and analyze the time interval of normal heart beats. Parameters related to time domain analysis include SDNN (standard deviation of normal to normal interval) and RMSSD (root mean square of successive differences). As shown in Equation (1), SDNN is a parameter reflecting the general state of heart beat variance in reference to the standard deviation of heart beats. It indicates the ability to resist stress. As shown in Equation (2), RMSSD reflects short-term variance of  
Figure 4: Pulse wave measurement method.  
heart beats. It is highly related to the parasympathetic system and decreases when the object is stressed [39].  
\[SDNN=\sqrt{\frac{1}{N}\sum_{i=1}^{N}RR_{i}-\overline{RR}^{2}} \tag{1}\]  
\[RMSSD=\sqrt{\frac{1}{N-1}\sum_{i=1}^{N-1}(RR_{i+1}-RR_{i})^{2}} \tag{2}\]
## 4 Dynamic Characteristics Analysis  
As in the simulation results shown in Section 3, the dynamic characteristics of the IRW are highly affected by the gravitational restoring forces, the gyroscopic moment, and their interactions. Three types of dynamic motions can be observed through the comparisons under different conditions. They are derailment, hunting, and offset running. In addition, the dynamic motions change with the variations in velocity, yaw stiffness, and yaw damping. In this section, how these three factors' variations affect the dynamic motions of the IRW will be analyzed. The simulation results are totally from the G2-IRW model after comparisons with the one built in SIMPACK 2018 for precise and accurate descriptions. Consistent with Section 3, the initial step-input of all the simulations in Section 4 is also set as \(y_{0}=0.005\) m.
## 1 Introduction
Iron oxide, such as hematite Fe\({}_{2}\)O\({}_{3}\), exhibits a high theoretical capacity of 1007 mAh g\({}^{-1}\)[4; 33; 34; 35; 36] and high energy density and has been widely used as an anode material for LIBs, due to its low cost and non-toxicity [32; 37; 38]. In contrast to TiO\({}_{2}\), this metal oxide suffers a large volume expansion, which can lead to a rapid capacity decay upon cycling, making it an unstable material [3; 39; 37; 33]. Therefore, combining the high capacity of Fe\({}_{2}\)O\({}_{3}\) with the structural stability of TiO\({}_{2}\) in a binary system could result in a promising Fe\({}_{2}\)O\({}_{3}\)/TiO\({}_{2}\) composite anode material for LIBs. Several research groups have recently reported results on Fe\({}_{2}\)O\({}_{3}\)/TiO\({}_{2}\) composite anodes for LIBs [4; 13; 33; 34; 36; 40]. For example, [PERSON] et al. [36] synthesized a binary system of Fe\({}_{2}\)O\({}_{3}\)/TiO\({}_{2}\) for use in LIBs. The results reported by [PERSON] et al., showed that the Fe\({}_{2}\)O\({}_{3}\)/TiO\({}_{2}\) anode delivered a specific capacity of 642 mAh g\({}^{-1}\) at a current rate of 100 mAh g\({}^{-1}\). Another way to improve LIB performance has been to develop nanostructured and binder-free anodes such as electrospun nanofibers, graphene composites, carbon nanotubes, or nanowires [6; 41; 42]. As opposed to anodes produced by coating conductive powder materials in the form of a slurry on a copper current collector, binder-free anodes eliminate the need for a non-conductive binder, which in return increases the electronic conductivity [22; 43; 44]. In fact, powder materials (e.g., graphite) have a long diffusion path for lithium-ions and slow electrode reaction kinetics, and as a result, the performance of LIBs has not reached its full potential. Therefore, the use of flexible and binder-free electrodes, such as composite nanofibers, could overcome the problems associated with current commercial graphite anodes due to the fact that they have a shorter diffusion path relative to the commonly employed powder materials, and faster intercalation kinetics is expected, due to their high specific area [15]. In this work, we seek to fabricate ternary \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\)/TiO\({}_{2}\)/C binder-free anodes via the centrifugal fiber-spinning (CF) [22; 45; 46; 47; 48; 49; 50; 51] method followed by subsequent thermal treatment. The prepared composite fibers were characterized using SEM, TEM, XRD, EDS, and XPS. The electrochemical measurements demonstrate that the \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\)/TiO\({}_{2}\)/C composite fibers exhibit good cycling performance and rate capability.