esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Analysis
### Evaluation Metrics  
To quantitatively assess the performance of the proposed Cauchy-TB algorithm, we utilized statistical metrics such as sensitivity, specificity, precision, and accuracy to study the relationship between the segmentation outcomes and the ground truth. The Jaccard coefficient (JC) and the Dice similarity coefficient (DSC) are utilized to measure the overlap of the segmented region with ground truth. The formulas for calculating these metrics are as follows:  
\[Sensitivity=T_{P}/(T_{P}+F_{N}) \tag{23}\]  
\[Specificity=T_{N}/(T_{N}+F_{P}) \tag{24}\]  
\[Precision=T_{P}/(T_{P}+F_{P}) \tag{25}\]  
\[Accuracy=(T_{P}+T_{N})/(T_{P}+T_{N}+F_{P}+F_{N}) \tag{26}\]  
\[J\mathrm{C}=|I_{g}\cap I_{l}|/|I_{g}\cup I_{t}| \tag{27}\]  
\[DSC=2|I_{g}\cap I_{t}|/|I_{g}|\cup|I_{t}| \tag{28}\]  
In the formulas, \(I_{g}\) represents the ground truth. \(T_{N}\), \(T_{P}\), \(F_{N}\), and \(F_{P}\) denote true negative, true positive, false negative, and false positive, respectively. Higher values of \(T_{N}\), \(T_{P}\), \(F_{N}\), and \(F_{P}\) indicate better performance.  
We also introduced three new metrics for algorithm performance evaluation. The Peak Signal-to-Noise Ratio (PSNR) is employed to evaluate the quality of reconstructed or compressed images, with higher values indicating better quality. The Structural Similarity Index (SSIM) compares the similarity in structure between two images, ranging from \(-1\) to 1, where 1 indicates perfect similarity. The Feature Similarity Index (FSIM) assesses image similarity, ranging from 0 to 1, where 1 indicates perfect similarity. The original image and the segmented image are used as inputs. The formulas are defined as follows:  
\[PSNR=10\log_{10}\left(\frac{Max_{I}{}^{2}}{MSE}\right) \tag{29}\]  
where MSE is the Mean Squared Error and \(Max_{I}\) represents the maximum pixel intensity of image I.  
\[SSIM(I_{a},I_{b})=\frac{(2\mu_{I_{a}}\mu_{I_{b}}+c_{1})*(2\sigma_{I_{a}I_{b}}) }{(\mu_{I_{a}}^{2}+\mu_{I_{b}}^{2}+c_{1})*(\sigma_{I_{a}}^{2}+\sigma_{I_{b}}^{ 2}+c_{2})} \tag{30}\]  
Let \(I_{a}\) and \(I_{b}\) represent the two input images, where \(\mu\) and \(\sigma\) represent the average and standard deviation of pixel intensities in the images, respectively. \(\sigma_{I_{a}I_{b}}\) denotes the covariance between \(I_{a}\) and \(I_{b}\), with \(c_{1}\) and \(c_{2}\) being constants used to stabilize the denominator.  
\[FSIM=\frac{\sum_{x\in\Omega}S_{L}(X)*PC_{m}(X)}{\sum_{x\in\Omega}PC_{m}(X)} \tag{31}\]  
\(S_{L}(x)\) represents the overall similarity between two images, \(I_{a}\) and \(I_{b}\). \(PC\) denotes the phase consistency between images, and \(\Omega\) represents the full spatial domain of the images.
## 1 Introduction  
MEMS (Mechanical Electric Micro-System), miniature mechanical systems, and mini-and micro-tools have a significant risk of wear and corrosion without suitable surface treatment to each application for each component [1]. In particular, mechanical tools and parts in the order of mm and sub-mm ranges, must have sufficient wear resistance and corrosion-toughness for operations even in severe conditions [2]. Dry coatings by PVD (Physical Vapor Deposition) and CVD (Chemical Vapor Deposition) are the first policy to protect them from wearing and corrosion [3]. Their deposition layer is often limited by several to 10 \(\upmu\)m, and inner surfaces as well as holes are difficult to coat [4]. Among some candidate alternatives, low-temperature plasma nitriding has been highlighted to provide the thick nitrided layer up to 0.1 mm with higher hardness than 1200 HV and less nitride precipitates [5; 6; 7; 8]. This plasma nitriding at a lower temperature than 700 K was characterized by the nitrogen supersaturation; after [5], this processing was expected to be applied to various surface treatments such as carburizing and nitrocarburizing as the S-phase engineering. In addition, this nitrogen supersaturation process accompanied by two-phase nano-structuring to harden and strengthen the stainless steel parts and members as pointed in [8; 9]. As demonstrated in [9; 10; 11], the corrosion toughness was also improved in these nitrogen supersaturated stainless steels. Furthermore, inner surfaces and small holes in the dies and punches are efficiently nitrided and hardened with more ease than coatings [12; 13]. These intrinsic features of low-temperature plasma nitriding were accommodated to miniature tools, and even parts, by scaling down the chamber size [14; 15]; e.g., the hollow cathode device assisted the high-density plasma nitriding process in the smaller chamber systems.  
In the present paper, a table-top plasma nitriding system is developed for low-temperature plasma processing of mini- and micro-tools. Micro-nozzles as well as micro-springs are plasma nitrided at 673 K for 7.2 ks to describe their nitriding and hardening behavior. In particular, a AISI316 micro-nozzle specimen is utilized to analyze the nitrogen super-saturation on the outer and inner surfaces as well as its outlet. In addition, its surface contact angle is measured to prove that the nitrided surfaces become hydrophobic. Furthermore, a stack of micro-nozzles is also homogeneously nitrided to demonstrate the capacity of the present system in application to industries. Two micro-springs are also nitrided to demonstrate that they have higher stiffness by 10 % than before nitriding. The present table-top system works to locally increase the stiffness and hardness of various tools and parts by selective plasma nitriding.
## 2 Related Work
### Other QAR-Based Flight Safety Studies  
In addition to studying runway overrun, some other studies have been performed regarding flight safety issues based on QAR data, among which the main category is exceedance events. [PERSON] et al. [22] proposed a new method for partitioning the risk subspaces of exceedance events based on rough set theory and a particle swarm multiobjective optimization algorithm. [PERSON] et al. [23] developed a risk assessment model for exceedance events, defining exceedance risk as the product of the probability of exceedance events and the severity of the events. They developed a quality assessment system for pilot operations based on their model. [PERSON] et al. [24] investigated the relationship between the risk cognition of pilots and exceedance events based on QAR. [PERSON] et al. [25] investigated the tail strike risk and proposed an unsupervised learning method to discover patterns of unsafe pilot stick operations during the landing stage.  
Hard landing is another typical flight safety incident that researchers are concerned about. [PERSON] et al. [26] designed a prediction model based on a support vector machine (SVM) for hard landing. [PERSON] et al. [27] employed RBF neural networks and the K-means clustering algorithm to predict hard landing. [PERSON] et al. [28] addressed the problem of hard landing using a deep learning framework. Considering the temporal characteristics of QAR data, they proposed a hard landing prediction framework based on long short-term memory (LSTM) networks. Additionally, they applied LSTM networks to predict aircraft landing speeds [29]. [PERSON] et al. [30] used scalar measurements and aggregated QAR data to detect the influential features of hard landing. Recently, [PERSON] et al. [31; 32] performed automatic classification and identification for the causes of hard landing using the K-means clustering algorithm. [PERSON] et al. [33] proposed a deep learning neural network model with time-aware attention for interpretable hard landing prediction. [PERSON] et al. [34]developed transfer learning methods for high-dimensional quantile regression and applied the methods to solve the problem of determining the hard-landing risk for flight safety.  
In terms of abnormal flight analysis, [PERSON] et al. (2007); [PERSON] et al. (2008); [PERSON] et al. (2009) applied clustering and outlier detection methods to identify abnormal flights from massive QAR data. Later, [PERSON] et al. (2010) improved their model to detect the specific flight phase of abnormal flights in which the QAR parameters deviate from their normal behavior.
# AFGN: Adaptive Filtering Graph Neural Network for Few-Shot Learning
## 2 Materials and Methods
### The Proposed Method
#### 2.2.3 The Construction of Graph
Furthermore, within the BDM framework, the distance \(A_{bc}\) between vectors \(\mathbf{b}\) and \(\mathbf{c}\) is calculated by:  
\[\mathbf{z}=\frac{1}{2}(\mathbf{b}+\mathbf{c});\] \[D_{KL}(\mathbf{b}\|\mathbf{z})=\sum_{i}b_{i}log\frac{b_{i}}{z_{ i}}\qquad D_{KL}(\mathbf{c}\|\mathbf{z})=\sum_{i}c_{i}log\frac{c_{i}}{z_{i}}; \tag{6}\] \[A_{bc}=D_{JS}(\mathbf{b}\|\mathbf{c})=\frac{1}{2}D_{KL}( \mathbf{b}\|\mathbf{z})+\frac{1}{2}D_{KL}(\mathbf{c}\|\mathbf{z}),\]  
where \(D_{KL}\) and \(D_{JS}\) represent the Kullback-Leibler (\(KL\)) divergence and Jensen-Shannon (\(JS\)) divergence respectively. In the meta-training stage, we build the one-to-one correspondence between tasks and graphs. The task \(\tau_{i}=\{S,Q\}\) is fed into the backbone, and get the extracted feature tensor matrix \(\tilde{F}\in\mathbb{R}^{n\times d\times hw}\) for this task, where \(n\) represents the number of samples in task \(\tau_{i}\). By utilizing Formulas (1)-(3), we can calculate the Brownian covariance matrix \(F_{BD}\in\mathbb{R}^{n\times d\times d}\) for the features of each sample. By extracting the upper triangular matrices of these matrices and performing vectorization, we can obtain the node feature matrix \(F\in\mathbb{R}^{n\times t}\) where \(t=\frac{d\times(d+1)}{2}\). According to Formula (6), we can derive the edge matrix \(A\in\mathbb{R}^{n\times n}\), where each element \(A_{ij}\) in \(A\) represents the distance calculated using BDM between node \(i\) and node \(j\). Thus, based on the node features \(F\) and edge matrix \(A\), we can construct the graph \(G\) that corresponds to task \(\tau_{i}\) and integrate it into the model training process. In general, unlike most GBFS models such as GNN-FL, TPN, and HDGN, AFGN constructs the graph based on the non-linear dependency relationships within the features. This way of constructing graphs can capture the potential correlations between features, and the graph structure can better reflect the interaction relationships between nodes. Moreover, the graph constructed by BDM exhibits a certain robustness to changes in images, because it does not solely rely on image features but on the interactions between features. This can effectively alleviate the impact of noise on training and improve the model's performance.
# A Review of SATis5: Perspectives on Commercial and Defense 5G SATCOM Integration
## 3 Survey of Existing 5G Satellite Integration (SATis5) and Associated Challenges
### Overview of SATis5 Architectures
The handover events are technically different from the 5G terrestrial access networks. This requires a new study to investigate NGC protocols with various modes of satellite 5G-UE in the SATis5 SATCOM systems ensuring that the NGC can manage the access information efficiently. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Payload Type** & **User Access—Architecture** & **Satellite Hop** & **Possible SATis5 Scenario** \\ \hline \multirow{4}{*}{Transparent} & SATis5 A1—Direct Access: & \multirow{2}{*}{Single} & eMBB with GEO Sats \\  & SatGW-2-Sat-2-UE & & \\ \cline{2-4}  & SATis5 A2—Non-Direct Access: & \multirow{2}{*}{Single} & mMTC-NB-IoT with Large \\  & SatGW-2-Sat-2-RN-2-UE & & Constellation (LC) LEO Sats \\ \hline \multirow{4}{*}{Regenerative} & SATis5 A3—Direct Access: & \multirow{2}{*}{Single} & \multirow{2}{*}{eMBB with GEO Sats} \\  & A3(a): SatGW-2-Sat-2-UE & & \\ \cline{1-1} \cline{2-4}  & A3(b): SatGW-2-Sat-2-Sat-2-UE & & \\ \cline{1-1} \cline{2-4}  & SATis5 A4—Non-Direct Access: & \multirow{2}{*}{Single} & mMTC-NB-IoT and uRLLC \\ \cline{1-1} \cline{2-4}  & A4(a): SatGW-2-Sat-2-RN-2-UE & & \\ \cline{1-1} \cline{2-4}  & A4(b): SatGW-2-Sat-2-Sat-2-REN-2-UE & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of Four Baseline SATis5 Architectures. Figure 4: SATis5 Single-Hop Transparent Satellite Payload: (**a**) Direct Access SATis5 A1, and (**b**) Non-Direct Access SATis5 A2. Figure 5: SATis5 Single-Hop and Multiple-Hop Generative Satellite Payload: (**a**) Direct Access SATis5 A3, and (**b**) Non-Direct Access SATis5 A4. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**SATis5 Architecture** & **UE Air-Interface Design Feature** & **Satellite Design Feature** & **SATis5 Gateway Design Feature** \\ \hline A1: SATis5 with Direct User for Transparent Satellite & UEs access satellites directly & The satellite connects to 5G UEs directly & Satellite Gateway process gNB & Satellite Gateway process gNB & and NGC protocols \\ A2: SATis5 with Non-Direct User for Transparent Satellite & UEs access satellite via terrestrial RN & Satellite connects to terrestrial RN & Satellite Gateway processes \\ \hline A3: SATis5 with Direct User for Generative Satellite & UEs access satellites directly & Satellites process full & Satellite Gateway processes gNB protocols & NGC protocols \\ A4: SATis5 with Direct User for Generative Satellite & UEs access satellites via terrestrial RNs & The satellites process part of gNB protocols and the other part can be performed at RN & NGC protocols \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Design Features Associated with Four Baseline SATis5 Architectures.
# Crack Location and Degree Detection Method Based on YOLOX Model
## 2 Methodology
### The Detection Principle of the Proposed Detector  
The crack location and degree detector proposed in this study is based on the latest YOLOX model, which cleverly combines some excellent target detection methods. YOLOX is a classical one-stage detection network. Unlike two-stage approaches (such as Faster R-CNN), YOLO algorithm turns the detection problem into the regression problem, where the bounding boxes and confidence of objects can be obtained immediately. By changing the width and depth of the backbone network, four versions of the YOLOX model are obtained, which are YOLOXs, YOLOXm, YOLOXl, and YOLOXx. Figure 2 describes the architecture of YOLOX. It is composed of four general parts, including the input part, backbone network, neck network, and prediction part, corresponding to the red dotted rectangular box in Figure 2. Among them, the CBL is the basic module composed of a convolution layer (Conv), a batch normalization layer (BN), and a Leaky ReLU activation function. Resunit is a residual structure to be utilized to deepen neural network, which contains 2 CBL modules and an Add layer; the Add layer realizes tensors stacked directly, whereas the Concat layer stitches tensors with expanding dimensions of tensors; the spatial pyramid pooling (SPP) [44] is mainly for multi-scale fusion; other network modules are introduced later.
## 3 Optimization-Based Strategies
### Global Information-Driven Optimization  
Global information-driven optimization control strategies look for the optimal control sequence based on global information to obtain the minimum fuel consumption, and include the [PERSON]'s minimum principle (PMP) strategy, the deterministic dynamic programming (DDP) strategy, and the stochastic dynamic programming (SDP) strategy. These strategies can obtain the global optimal energy management over the whole driving cycle, and the results of the DDP-based strategy can be regarded as a benchmark for assessing the optimality of other energy management strategies.
## 2 Smart Homes
### Home Application Areas
#### 2.2.2 Renewable Energy Management Driven Smart Home  
The universal concept of an energy management driven smart home could include the use of solar, wind and/or other renewable energy sources with an intelligent power consumption mechanism for the electric appliances placed inside the house and a collaborative smart grid to ensure the interconnections between them [22].  
With the use of advanced metering and display technologies in an established smart home, the user or the system itself is capable of lowering the energy consumption or postponing the energy demanding operations concerning the present electricity price by managing the household electrical features and under the condition of ensuring a positive comfort level [23].  
[PERSON] _et al._, present a paper where they propose an Energy Management System that includes renewable energy sources for the efficient use of the power created by a Smart Home and the power consumed by the electric appliances of the house with an overall system description, from software protocol to employed hardware is presented [22]. [PERSON] _et al._ investigate an experimental smart home with several renewable energy sources and storage systems in terms of various aspects such as appliances control, in-home energy management and power flow. Additionally, the study embodies one of the very first challenges to assess the contribution of power forecasting of renewable energy sources on the performance of smart home concepts [23]. [PERSON] _et al._ present the design, implementation and testing of an embedded system that integrates solar and storage energy resources to a smart home. It was made by utilizing a controlled load bank to simulate scaled random real house consumption behaviour [24].
# A New Knowledge-Distillation-Based Method for Detecting Conveyor Belt Defects
## 3 Result and Discussion
### Dataset and Evaluation Indicators  
Since the conveyor belt defect samples were different from the scratch samples in terms of morphology and location, and were more difficult to obtain than the scratches, they could not be enhanced using GANs. Therefore, in order to solve the problem of insufficient conveyor belt defect samples and the imbalance between the number of samples and scratch samples, this paper adopted an oversampling method to enhance the conveyor belt defect samples. The samples were oversampled by repeatedly adding an image to the training several times during the network training process. We divided the original defective samples into two parts: training set and test set, and then performed conventional data enhancements, such as rotation and cropping. Finally, the data-enhanced training set samples were three-times oversampled to generate the final conveyor belt's defective samples dataset.  
In this paper, we used a total of 1533 conveyor belt images taken at the production site and manually annotated them; then, the dataset was enhanced by rotating, cropping and adding noise to generate 3066 images. A total of 1500 images were generated by the joint GAN and copy-pasting strategy; 1177 samples were generated by oversampling the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Batch\_size & 60 \\ Img\_size & 640 \(\times\) 640 \\ Momentum & 0.937 \\ IOU\_thresh & 0.20 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Relevant training parameters.  
Figure 9: Comparison of teacher model and student model.  
defective conveyor belt samples, then, we obtained a dataset containing 7276 conveyor belt samples. The training set and test set were divided with a ratio of 8:2 and, finally, 5821 samples were obtained from the training set and 1455 samples were obtained from the test set.  
To have a more scientific evaluation standard for the model's performance, the evaluation indexes used in this paper included classification accuracy (Precision, P), recall rate (Recall, R), mean average accuracy (mean Average Precision, [EMAIL_ADDRESS]), number of model parameters (Params) and inference time (Inference/ms), which are formulated as follows:  
\[Precision=\frac{TP}{TP+FP} \tag{13}\]  
\[Recall=\frac{TP}{TP+FN} \tag{14}\]  
\[AP=\sum_{i=1}^{n-1}(r_{i+1}-r_{i})P_{inter}(r_{i}+1) \tag{15}\]  
\[mAP=\frac{\sum_{i=1}^{k}AP_{i}}{k} \tag{16}\]  
The TP (true positive) indicates the number of correctly predicted positive samples, FP (false positive) indicates the number of incorrectly predicted positive samples, and FN (false negative) indicates the number of incorrectly predicted negative samples.
## 3 Results
### Break-Even Analysis  
In response to the research question of when the availability of secondary Co, Ni, Li or Mn from LIB recycling is sufficient to satisfy the global cobalt demand for EV applications, the results show that secondary Co supply will be sufficient to start satisfying the demand between 2029 and 2035. This is under the assumption that a closed material loop for batteries is implemented by that time (see Figure 3a).  
For the remaining materials, the secondary-material supply is not sufficient to cover the material demand for new battery production. Instead, the gap between secondary-material availability and material demand is increasing for Li and Ni and in all scenarios. Here, Ni shows the highest total demand (see Figure 3b,c). This can be linked to the increasing use of LIB cell chemistries with high contents of Ni and Li as part of the battery-technology rollout. This means that the amount of recycled material for Li and Ni cannot match the demand for new batteries if the EV development continues as forecasted. A similar trend can be observed for the less critical material Mn (see Figure 3d).  
Furthermore, the results obtained for the other technology-rollout scenarios indicate that the supply of secondary Co can satisfy Co demand at the earliest in 2030 (Scenario A) and the latest in 2035 (Scenario C) (see Figure 4). Meanwhile in Scenario D, no break-even point is observed for the secondary-material availability and the projected material demand. This can be explained by the rising Co demand, which results from the LIB cell chemistries used in the technology rollout in Scenario D.  
Figure 3: Raw-material demand vs. availability of secondary raw material worldwide and total annual battery recycling in green all in t; (**a**) Cobalt with intersection marked in red; (**b**) Lithium; (**c**) Nickel; (**d**) Manganese.  
At the same time, none of the other materials in the scope of the study show an intersection of secondary-material supply and demand before 2035 in any of the scenarios investigated. This again is under the following assumptions: worldwide scope, 10 years battery lifetime, 5% 2 nd life rate, 1% failure rate before warranty lifetime, 2% production waste and mechanical-hydrometallurgical recycling.
## 3 Proposed DC-GCDRAM Architecture  
### Overview  
Figure 3 represents the overall structure of the reconfigurable DC-GCDRAM. The bit storage element of this CIM macro is the 3T gain cell described in Section 2. Basically, the memory array comprises a pair of 16 kbit storage blocks. Each block consists of 128 columns and 128 rows. The memory I/O is 8 bits wide. This CIM macro contains two WWL boosters, dedicated to 16 kbit block each, supplying the V\({}_{\text{PP}}\) voltage. The entire CIM macro is operating at single power supply.  
The proposed DC-GCDRAM allows functional mode switching among memory, logic-arithmetic, and convolution operations. The operational modes are selected with the external signals named FM\({}_{0}\) and FM\({}_{1}\). If both FM\({}_{1}\) and FM\({}_{0}\) are low or high, the CIM macro operates in the memory mode where it performs read/write/refresh as regular DRAM. If  
Figure 3: Block diagram of the reconfigurable DC-GCDRAM macro.  
Figure 2: Signal voltages for bit cell operation.  
FM\({}_{1}\) is low and FM\({}_{0}\) is high, the macro operates in the logic-arithmetic mode. In this mode, the macro turns on two rows at once and uses binary logic SAs within the computing logic to generate bit-wise Boolean logic primitives and addition results. If FM\({}_{0}\) is low and FM\({}_{1}\) is high, the macro enters the convolution mode. In this functional mode, the macro allows the XNOR and population count operation for binarized neural networks (BNNs). For each cycle, the CIM macro utilizes XNOR sense amplifiers (XNOR-SAs) within each memory block to compute bit-wise XNORs between activation inputs and row-wise stored weights, and then generates the accumulation results at each population counter output. The details of these three modes of operation are described in the following subsections.