esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Analytical Approach for Spacecraft Fly-Around Formation Design with a Low-Thrust Maneuver
## 3 Typical Fly-Around Configuration
### Spatial Circle  
According to the above analysis, when the conditions \(\underline{\mathbf{r}}\bot\mathbf{v}\) and \(\mu_{s}=\underline{\mathbf{r}}v^{2}\) are met, the fly-around trajectory is a spatial circle. The position of the vector from the target to the center of the circle is \(\delta\mathbf{r}\). If the target is within the circle, the constraint \(\|\delta\mathbf{r}\|<\underline{\mathbf{r}}\) needs to be met. Note that when the initial relative states are determined, the design parameters of the fly-around trajectory have only one degree of freedom, that is \(\underline{\mathbf{r}}\) and a series of fly-around trajectories can also be obtained. The simulation results are shown in Figure 9. Figure 10 shows the control inputs as a function of time.  
Figure 8: Control inputs with typical fly-around speed parameters k are 1.5 (a), 2 (b), and 3 (c), respectively.  
Figure 7: Fly-around ellipses centered on target spacecraft.  
Specifically, if the expression \(\delta r=0\) holds, that is \(r=\underline{r}\), the fly-around trajectory obtained is then the spatial circle centered on the target. The following focuses on the analysis and discussion of the spatial circle design where the reference orbit is a circular orbit. Referring to Equation (19), the coordinate component expression in the LVLH coordinate system of Equation (5) is described as follows:  
\[\begin{pmatrix}u_{x}\\ u_{y}\\ u_{z}\end{pmatrix}=\omega^{2}\begin{pmatrix}-3x\\ 0\\ z\end{pmatrix}+\omega\begin{pmatrix}-2\dot{y}\\ 2\dot{x}\\ 0\end{pmatrix}-(k\omega)^{2}\begin{pmatrix}x\\ y\\ z\end{pmatrix} \tag{24}\]  
According to Equation (4), the dynamics of the desired fly-around trajectory are as follows:  
\[\begin{pmatrix}\ddot{x}\\ \ddot{y}\\ \ddot{z}\end{pmatrix}=-(k\omega)^{2}\begin{pmatrix}x\\ y\\ z\end{pmatrix} \tag{25}\]  
The solution of Equation (25) is as follows:  
\[\begin{array}{l}x(t)=\frac{\dot{x}_{0}}{k\omega}\sin(k\omega t)+x_{0}\cos( k\omega t)\\ y(t)=\frac{\dot{y}_{0}}{k\omega}\sin(k\omega t)+y_{0}\cos(k\omega t)\\ z(t)=\frac{\dot{z}_{0}}{k\omega}\sin(k\omega t)+z_{0}\cos(k\omega t)\end{array} \tag{26}\]  
According to Equations (24) and (26), the control input can be calculated as follows:  
\[\begin{array}{l}u_{x}=[-2\omega\dot{y}_{0}-\omega^{2}(3+k^{2})x_{0}]\cos(k \omega t)+[2k\omega^{2}y_{0}-\frac{\omega(3+k^{2})}{k}\dot{x}_{0}]\sin(k\omega t )\\ u_{y}=(2\omega\dot{x}_{0}-k^{2}\omega^{2}y_{0})\cos(k\omega t)-(2k\omega^{2} x_{0}+k\omega\dot{y}_{0})\sin(k\omega t)\\ u_{z}=\frac{(1-k^{2})\omega}{k}\dot{z}_{0}\sin(k\omega t)+(1-k^{2})\omega^{2} z_{0}\cos(k\omega t)\end{array} \tag{27}\]  
Figure 9: Fly-around circles.  
According to Equation (27), if the fly-around period and initial relative states meet the following conditions:  
\[k=\pm 1,\ \ \dot{y}_{0}=-2\omega x_{0},\ \ \dot{x}_{0}=\frac{\omega}{2}y_{0} \tag{28}\]  
Then the fuel consumption required for fly-around is zero, and Equation (28) is the necessary condition for circular formation in circular reference orbit under natural conditions.
# Design of a Single-Stage Transformerless Buck-Boost Inverter for Electric Vehicle Chargers
## 7 Conclusions  
In this paper, a single-stage transformerless buck-boost inverter has been proposed to directly achieve dc to ac conversion regardless of the relationships between its dc-link voltage and peak ac output voltage. The proposed inverter is an equivalent replacement of a conventional boost converter cascaded with an inverter, while the former features simpler circuit structure, lower cost, smaller volume, and straight-forward implementation. The analysis based on its state space average model indicates that the stability of the proposed inverter is dependent on the value of its duty cycle. Fortunately, this issue can be addressed by a well-designed outer voltage-loop plus inner current-loop controller. Simulation results are presented to validate its effectiveness.  
Conceptualization, J.F.; methodology, J.F.; software, J.F.; validation, L.W.; formal analysis, L.W.; data curation, L.W. and C.L.; writing--original draft preparation, L.W., C.L. and J.F.; writing--review and editing, L.W., C.L. and J.F.; visualization, L.W. and C.L.; project administration, J.F.; funding acquisition, J.F. All authors have read and agreed to the published version of the manuscript.  
Figure 8: Simulation results of the proposed buck–boost inverter working as boost-type inverter.  
Figure 7: Simulation results of the proposed buck–boost inverter working as buck-type inverter.  
**Funding:** This research was funded by Shandong University Distinguished Young Scholars Project, grant number: 31400082260522.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Evaluating Machine Learning and Remote Sensing in Monitoring NO2 Emission of Power Plants
## 3 Materials and Methods
### Tools and Hardware  
The code for data collection, formatting, processing, and training were all written in Python 3.6 based on pandas, scikit-learn, Keras, TensorFlow, shapefile, and H\({}_{2}\)O to create ML models, prepare data, and train and test the models.  
We used two different computational environments over the project. Initial data collection and processing were conducted on Microsoft Azure. The main experiment was carried out on three Linux servers with 48 cores Intel(r) Xeon(r) Silver 4116 CPU, 128 GB memory, and 4 NVIDIA K80 GPUs.
## 4 Simulation Results
### Summary and Application Space  
The Cryo-SIMPLY scheme demonstrates significant reliability enhancements at cryogenic temperatures (77 K) compared to room temperature. Conventional STT-MRAM-based SIMPLY schemes operating at room temperature suffer from high design complexity due to low sensing margins. By operating at 77 K, read sensing margins are improved, enhancing the robustness of both SMTJ- and DMTJ-based implementations, with the DMTJ-based scheme emerging as the more energy-efficient and reliable choice. The relatively high sensing margins at 77 K make reliability levels achievable at cryogenic conditions that are unattainable with conventional MTJ-based designs at room temperature. Therefore, Cryo-SIMPLY presents a compelling STT-MRAM choice for LIM applications and beyond.  
Driven by the recent interest in cryogenic in-memory computing [28; 42], Cryo-SIMPLY is presented as a logic-in-memory scheme tailored to low-temperature operation. Its ability to perform basic logic operations, such as NAND, XOR, and XNOR, directly within memory cells makes it ideal for data-intensive applications [52], enabling faster and more energy-efficient computation while minimizing data transfer overheads. Its efficient reconfigurable computing scheme can also be used for AI applications, in particular for binarized neural networks [53]. Lastly, Cryo-SIMPLY can be applied to energy-constrained scenarios that require highly reliable operations, such as deep space systems [27].
# CERRT: A Mobile Robot Path Planning Algorithm Based on RRT in Complex Environments
## 2 Background
### Rrv  
RRV is an algorithm developed to overcome the narrow passage problem found in the RRT algorithm. It uses principal component analysis to identify the local environment type, as it can effectively map high-dimensional data to a lower-dimensional space while maximizing information retention, which facilitates the extraction of data features. Specifically, this process involves mapping sampled points within obstacles into a single feature vector that maximally retains information from the obstacle points. Subsequently, the \(x_{rnd}\) point is projected onto the feature vector passing through \(x_{nearest}\). The resulting projected point, \(x_{projected}\), serves as a novel direction for the expansion of RRV. This strategic extension enables the random tree to circumvent obstacles, similar to the growth of a vine, along the obstacle boundaries.  
As illustrated in Figure 1, the RRV algorithm generates local random sampling points (shown in red and green) and performs principal component analysis on the red obstacle points. The confidence ellipse is then used to determine the type of environment. If the ellipse does not contain a green point, it is classified as a convex obstacle environment (Figure 0(a)). If it contains a green point but not \(x_{nearest}\), it is identified as a passage entrance environment (Figure 0(b)). If it contains both a green point and \(x_{nearest}\), it is a passage interior environment (Figure 0(c)).  
Then, the \(x_{rnd}\) point is projected onto a principal component analysis feature vector passing through \(x_{nearest}\) to obtain \(x_{projected}\), and the tree is expanded toward this point to avoid growing toward obstacles. If the environment is identified as a convex obstacle or a passage interior, the tree is expanded along the obstacle (as shown in Figure 0(d),f). If it is identified as a narrow passage entrance, as shown in Figure 0(e), the tree is further expanded along the obstacle to \(x_{new1}\) and toward the interior of the passage to \(x_{new2}\). This enables RRV to discover narrow passages more effectively than the classic RRT algorithm, and once a narrow passage is discovered, the expansion tree can grow quickly.  
However, when applied to environments without narrow passages, the performance of RRV falls short compared to the original RRT algorithm. This indicates a high degree of environmental dependence in its performance.
## 3 Results
### Meteorological Forecasting and Assimilation Performances
#### 3.1.1 Irrigation-Focused Demonstrators
Across all cases, the general trend confirms that the data assimilation (DA) step consistently improves the model state compared to both the background forecast (BF), which includes observations from previous cycles but not the most recent ones, and the open-loop (OL) simulation without any assimilation. The DA run systematically shows better agreement with observations, especially for thermodynamic variables, indicating the effective integration of drone data into the model. However, the benefit of assimilation is not uniform across all cases and variables. In several instances, the BF does not significantly outperform the OL simulation, suggesting that the improvement introduced by previous assimilation cycles may dissipate depending on atmospheric dynamics and model characteristics. Additionally, while DA consistently improves temperature and humidity profiles, its impact on wind-related variables, particularly wind speed, exhibits greater variability, with performance depending on specific vertical levels and prevailing meteorological conditions. Figure 6 provides a detailed example of this behavior, focusing on two consecutive assimilation cycles: 30 June 2024 at 21:00 UTC and 1 July 2024 at 00:00 UTC. The top panels show the detailed vertical profiles of temperature, dew point temperature, wind speed, and wind vector cosine similarity (WCOS), comparing the observations, analysis (DA), and background forecast (BF). In both cycles, the temperature and dew point profiles reveal that DA successfully corrects the biases present in BF, particularly near the surface and around 850-700 hPa. Wind speed, which is generally underestimated by BF, is also better represented in the DA run, though some residual discrepancies persist in certain layers, especially near 850 hPa. The WCOS indicator shows that DA improves the directional agreement of wind vectors with observations, especially in the lower layers, though the improvement is less pronounced in the first cycle. The lower panels of the figure present the same variables at predetermined pressure levels, adding the OL simulation (blue lines) for a comprehensive comparison. This enables the evaluation of the cumulative effect of assimilation cycles and assesses whether BF shows any improvement over the OL run. For temperature, all three simulations (OL, BF, DA) are relatively close to observations, with DA offering slight refinements, particularly near 925-850 hPa.
## 3 Finite Element (FE) Investigation
### Fracture Criterion  
As explained in the preceding section, two types of ductile fracture criteria exist: coupled fracture criteria and uncoupled fracture criteria. The coupled ductile fracture criteria would provide more realistic material softening than the uncoupled one as the physical mechanism including void nucleation is considered. However, considering the  
Figure 5: **(a)** True stress–strain curves; (b) Calibration outcomes of Specimen Tension-1.  
difficulties in calibrating a large number of model parameters, it has not been favorable for industrial application.  
One of the uncoupled fracture models, the Bao-Wierzbick (BW) model [11] was, therefore, adopted for simulating fracture in the present paper. Such a model gives the strain at the onset of fracture as a function of the stress triaxiality \(\eta\), and requires two parameters to be calibrated from experimental tests involving tension and shear fracture. It is noteworthy that the Lode angle parameter is known to play a significant role in ductile fracture, especially at low stress triaxialities, but this parameter was neglected here for the purpose of simplification. Indeed, the calibration of the Lode angle parameter is very difficult and costly as it needs a large number of specimens generally involving in combined tension and shear fractures.  
The BW model can be expressed mathematically as follows:  
\[\bar{\varepsilon}_{f}(\eta)=\begin{cases}\infty&\eta\leq-1/3\\ C_{1}/(1+3\eta)&-1/3\leq\eta\leq 0\\ C_{1}+(C_{2}-C_{1})/(\eta/\eta_{0})^{2}&0\leq\eta\leq\eta_{0}\\ C_{2}\eta_{0}/\eta&\eta_{0}\leq\eta\end{cases} \tag{5}\]  
where \(C_{1}\) and \(C_{2}\) are the strains at fracture initiation in pure shear (\(\eta=0\)) and uniaxial tension (\(\eta=\eta_{0}=1/3\)), respectively.  
The model indicates that shear fracture dominates for negative stress triaxialities, but fracture does not take place for stress triaxialities less than \(-1/3\). On the other hand, ductile fracture by void growth dominates for stress triaxialities higher than \(1/3\). For stress triaxialities between two regions, fracture occurs as a combination of shear and ductile fracture.
# Fault-Resilient Manufacturing Scheduling with Deep Learning and Constraint Solvers
## 3 System Design and Scheduling Problem Definition
### System Overview  
The following key components of the production line collectively form the foundation of the proposed scheduling system:  
* Production Line: A set of machines \(M=\{M_{1},M_{2},\ldots,M_{n}\}\) capable of executing various jobs \(J=\{J_{1},J_{2},\ldots,J_{m}\}\).
* Job and Task Definition: Jobs consist of multiple tasks with associated durations, deadlines, and priorities.
* Machine States: Machines operate under varying conditions, including temperature, vibration, operating hours, and tool wear, which influence performance and fault probability.
* Fault-Aware Scheduling: Scheduling decisions aim to reduce fault risks while ensuring timely job completion and efficient resource utilization.
# Communication Optimization Schemes for Accelerating Distributed Deep Learning Systems
## 4 Communication Optimization Schemes
### Operation of the Worker for Communication Optimization  
The right side of Figure 4 shows how the worker operates when performing distributed deep learning by applying layer dropping. When distributed deep learning is started, the worker receives initialized model parameters from the parameter server. The worker checks whether each layer of the received model parameter is a list [0]. The list [0] is a compressed hidden layer and a list with  
Figure 3: Changes of communication data size through layer dropping.  
a size of one and a value of zero. In the first iteration, the worker receives the model parameters initialized by the parameter server, so all hidden layers are not the list [0]. However, after the first iteration, the worker receives compressed data from the parameter server. Therefore, some hidden layers may be the list [0]. Among the hidden layers of the received model parameter, the layers with [0] are replaced with the old parameters that the workers already had. In the case of a layer other than the list [0], the old parameters that the worker had are updated with the new parameters received from the parameter server. After that, the worker computes gradients using the updated parameters. After computing the gradients, the worker performs the following tasks to compress the data to be transmitted to the parameter server. The worker compares the predetermined threshold with the mean of the computed gradients on each layer. We will present how to pick the threshold value in the next section. If the mean of the gradients is less than the threshold, the layer is changed to a list [0]. If the average of gradients is larger than the threshold, the computed gradients are maintained. The worker compresses gradients based on the threshold and sends the compressed gradients to the parameter server.  
Figure 4: Flowchart of the operation of the parameter server and workers.
# Combining Keyframes and Image Classification for Violent Behavior Recognition
## 2 Related Works  
### Datasets  
Existing violent behavior datasets include Real Life Violence Situations Dataset (RLVS) [18], Hockey Fights Dataset [19], RWF-2000 [20], CCTV Fights Dataset [21], and Violent-Flow [22]. The RLVS contains 2000 short videos divided into 1000 violence videos and 1000 non-violence videos. The RWF-2000 contains 1000 violent videos and 1000 non-violent videos captured by surveillance cameras in real-world scenes. The CCTV dataset contains 1000 videos of real fights, with more than 8 h of annotated CCTV footage. The Hockey Fights dataset introduces a video database containing 1000 sequences divided in two groups: fights and non-fights. Existing semantic segmentation datasets include PASCAL Context [23], Cityscapes [24], Microsoft Common Objects in Context (MS COCO) [25], and VSPW [26], all of which contain human annotations. Human poses are normal in the semantic segmentation, while human poses are abnormal in the violence datasets. In addition, semantic segmentation datasets mostly consist of high-quality images (for example, the pictures in the COCO dataset are high resolution and taken with professional equipment; the latest VSPW dataset is the first large-scale dataset for video scene parsing in the wild, but more than 96% of its video frames are high-quality and lack abnormal behavior), while images in violent behavior datasets usually are collected by security cameras and mobile phones, meaning that they are usually low resolution and unevenly illuminated. Therefore, models trained with semantic segmentation datasets will experience widespread feature loss on violent behavior datasets. To overcome these problems, we will need to annotate video frames containing violence to supplement the semantic segmentation dataset, enabling the model to better adapt to violent scenes.
## 3 Construction of the Model
### Feature Fusion Optimization Based on CBAM  
Based on strengthening the ability to detect small targets, extract effective features further, reduce the computational pressure, and improve the recognition accuracy, the CBAM is added after the head layer upsampled the results and the MP-2 structure. The CBAM structure is shown in Figure 5. Entering the feature map \(F\), \(F\in R^{C\times H\times W}\), and using the channel attention module to generate a one-dimensional channel attention map \(M_{C}\),  
\[\begin{split} M_{C}(F)&=\sigma(MLP(AvgPool(F))+MLP( MaxPool(F)))\\ &=\sigma(W_{1}(W_{0}(F^{\text{C}}_{avg}))+W_{1}(W_{0}(F^{\text{C} }_{\text{max}})))\end{split} \tag{1}\]  
where \(\sigma\) is the Sigmoid function, \(W_{0}\in R^{C/r\times C}\), \(W_{1}\in R^{C\times C/r}\), \(F^{\text{C}}_{avg}\), and \(F^{\text{C}}_{\text{max}}\) represent the average pooling feature and the maximum pooling feature, respectively. The attention output of the channel is \(F^{\prime}\), and the formula is:  
\[F^{\prime}=M_{C}(F)\otimes F \tag{2}\]  
The spatial attention module performs average pooling and maximum pooling operations along the channel axis [28] and then uses a standard convolutional layer to obtain a two-dimensional spatial attention map \(M_{S}\),  
\[\begin{split} M_{S}(F)&=\sigma(f^{\gamma\times 7}([AvgPool(F); MaxPool(F)]))\\ &=\sigma(f^{\gamma\times 7}([F^{\text{S}}_{avg};F^{\text{S}}_{ \text{max}}]))\end{split} \tag{3}\]  
where \(\sigma\) is the Sigmoid function, and \(f^{\gamma\times 7}\) is the convolution kernel of \(7\times 7\). The spatial attention output is \(F^{\prime\prime}\), and the formula is:  
\[F^{\prime\prime}=M_{5}(F^{\prime})\otimes F^{\prime} \tag{4}\]  
As a lightweight module, CBAM can improve the network representation classification and detection performance by focusing on essential features [29].
## 1 Introduction
### Motivation  
In this subsection, we discuss a problematic scenario and our motivation behind this topic. As discussed earlier, different IoT applications require different QoS in terms of availability, low latency, high throughput, etc. [PERSON] et al. [10] addressed a scenario example regarding QoS in IoT. Consider an industrial application of IoT, where QoS plays a vital role. An intelligent nuclear reactor monitoring system is designed to continuously monitor reactor temperature through thermal imaging. When any anomaly is detected, the system should inform in a timely manner. In this IoT example, tracking in real time is required for safety. In such scenarios, the system should provide data to the station in real time accurately and without any delay. These types of services and applications are time critical, and their traffic is categorized as delay-centric. Issues such as heterogeneity affect the QoS and could have catastrophic results in some applications.  
The SDN controller response time has a huge impact on QoS and the security of IoT networks. Heterogeneity also involves increasing the response time of the controller, and in this case, the endpoint security solution will also take time to identify any violation [11]. In an interconnected nodes network, the heterogeneity of the nodes affects the flow, specifically QoS, and also impacts security adoption [12]. Heterogeneous networks are less secure as compared to homogeneous networks because heterogeneity increases the complexity of the system, which makes it harder to differentiate between normal behaviors from attacks by the system. The controller response time is a key metric to meet the specification of QoS. Bandwidth, delay, packet loss, throughput, and others are also important metrics of QoS. Different applications need different QoS key metrics (e.g., multimedia-type applications need to maintain bandwidth, real-time monitoring of critical data needs less delay, etc.). It means that these parameters affect the QoS, and response time plays a crucial part in it.
## 2 TH-2 Spaceborne GNSS Data Assessment  
### Spaceborne Receiver Feature  
For orbit determination, the TH-2A and TH-2B are mounted with identical GNSS receivers that can track the GPS L1 and L2 and the BDS2 B1 and B3 signals simultaneously. All of the GPS satellites, and part of the BDS2 satellites from C01 to C14, can be observed. Besides the GNSS receiver, a rubidium atomic clock is installed on each satellite to provide timing and frequency for the receiver. GPS signals are used to generate the results of in-orbit positioning, clock offsets, and clock drifts, while the GC combined and the BDS2 positioning solutions are chosen as backups. For the time calibration, the time tags and pseudorange offsets of the raw observations are corrected by the combination of the clock offsets and clock drifts. It is noteworthy that the corrections of the receiver to GPS are generated from the last GPS or GC combined positioning results. However, the corrections to BDS2 are obtained from the last GC combined or BDS2 positioning results. Moreover, when the radar is in operation, the receiver suspends the time calibration to guarantee the time synchronization of the InSAR system.