esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Automated Segmentation and Classification of Calcaneal Fractures in CT Images
## 2 Materials and Methods
### Step 1: Detection of Calcaneus Location
A training sample is set as \((x_{m},y_{m})\), \(m~{}=~{}1,2,\ldots,M\), where \(y_{m}~{}=~{}0\), or \(1\) for negative, or positive labels, respectively, is the class label for the sample \(x_{m}\). Firstly, the initial weight vector is set as \(\omega_{1}(m)~{}=~{}1/M\). The classifier is defined as \(\lambda_{n}(x_{m})~{}=~{}0,~{}\text{or}~{}1\) where \(n~{}=~{}1,2,\ldots,N\) is the iteration number. The error associated with the classifier is evaluated as:  
\[\varepsilon_{n}~{}=~{}\sum_{m~{}=~{}1}^{M}\omega_{n}(m),~{}\text{if}~{}\big{|} \lambda_{n}(x_{m})\
eq y_{m}\big{|}. \tag{4}\]  
The selected classifier is used to update the weight vector as:  
\[ \begin{split}\omega_{n+1}(m)~{}=~{}\omega_{n}(s)\beta_{n}^{1- \tau_{m}}\\ \text{where}~{}\tau_{m}~{}=~{}\left\{\begin{array}{c}0,~{} \text{if}~{}x_{m}~{}\text{classified correctly}\\ 1,~{}\text{otherwise}\end{array}\right.\end{split}  \tag{5}\]  
and \(\beta_{n}\) is the weighting parameter computed from:  
\[\beta_{n}~{}=~{}\frac{\varepsilon_{n}}{1-\varepsilon_{n}} \tag{6}\]  
The result of the training stage is the labeled result of each region which is represented as:  
\[W(x)~{}=~{}\left\{ \begin{array}{c}1,~{}\text{if}~{}\sum\limits_{n~{}=~{}1}^{ N}\Big{[}\lambda_{n}(x)\times\log\Big{(}\frac{1}{\beta_{n}}\Big{)}\Big{]}\geq \frac{1}{2}\sum\limits_{n~{}=~{}1}^{N}\log\Big{(}\frac{1}{\beta_{n}}\Big{)}~{} ~{}.\end{array} \right. \tag{7}\]  
In the testing stages, the shape of the tarsal bones is detected by the sliding window method [24], in which each sub-window contains labels for each anatomical plane orientation from the training stage. Each area, which passes by each sub-window, is labeled at each classification stage either as, positive (1) or negative (0). If the label detected is positive, the region is recognized as the object and the classifier passes to the next stage. Otherwise, the region is rejected immediately. Then, the last stage will show the result of the detector in the current window, as shown in Figure 6. If there is more than one anatomical plane orientation detected in the input image, then the largest area of the current window is selected. The location of the calcaneus, based on the selected anatomical plane orientation, is determined according to Figure 7. Then, the calcaneus ROI is used as the location of the input image for the next step. Figure 5: Example images of the tarsal bone: (**a**) Coronal; (**b**) Transverse; and (**c**) Sagittal.
# Recent Advancement of Data-Driven Models in Wireless Sensor Networks: A Survey
## 4 Types of Wireless Sensor Network
### Terrestrial WSNs  
Terrestrial WSNs are land-dwelling spaces containing hundreds of thousands of sensor nodes deployed in interesting places. These nodes are very low cost or inexpensive. Two types of terrestrial WSNs are deployed such as unplanned and pre-planned (sensor nodes are arranged in 2D or 3D placement) [35]. These nodes are driven by battery power. Due to the harsh or hostile environmental condition of nodes deployment, nodes are not rechargeable and not replaceable [33]. Energy preservation is one of the hardest challenges [10] in terrestrial WSNs. In addition, energy consumption within in-network communication is one of the most critical challenges due to data redundancy. The data aggregation techniques are used for energy conservation at each sensor node during data collection. Therefore, energy can be preserved through multi-hop optimal routing, short transmission range, data aggregation, eliminating data redundancy, and delays [36]. Low-energy adaptive clustering hierarchical protocol (LEACH), hybrid energy-efficient distributed routing (HEED), geographic adaptive fidelity (GAF), power efficient gathering in sensor information systems (PEGASIS), sensor protocol for information via negotiation (SPIN), and energy-aware routing (EAR) are routing protocols that are mostly used. The main terrestrial application is weather forecasting, glacier, forest, military surveillance, natural disaster, earthquakes, and volcanos. The major challenges and issues are the efficiency of energy, transmission of data, operation environment, data aggregation, battery lifetime, difficult deployment of the grid, and optimal and 3D models due to the harsh environment for terrestrial WSNs. The major challenge of the continuous data stream in WSNs is to get valuable data by deployed sensor nodes due to with limited battery power. The skyline query is the one of popular query-driven models; [37] study snapshot skyline query on a data stream by sensor nodes in WSNs. The query-driven model is used for environmental monitoring and event surveillance application, which are considered in the terrestrial WSNs application type.  
There are various complex monitoring applications of terrestrial WSNs; for example, fire detection requires smoking density and temperature observation data; security requires vibration and infrared radioactivity data, and many sensitive areas where people are not allowed and people are tracked by privacy emergence monitoring need an event-driven data model [38].  
Time-driven data models are used for time-series data collection in periodic wireless sensor networks. The time-driven data model is divided into periods, and the further periods are divided into slots with respect to fixed time slots on every sensor node. Time-driven data applications generate large volumes of data by the sensor nodes due to this high energy consumption and reduced network lifetime [39].
# TrojanDetector: A Multi-Layer Hybrid Approach for Trojan Detection in Android Applications
## 4 Experimental Validation
### Experimental Settings  
The proposed work performed extensive tests to verify the effectiveness of the proposed approach for detecting Trojans coming from a comprehensive Android malware dataset. TrojanDetector uses the train/test split method: 80% of the dataset is used for training and 20% for testing.
## 2 State of the Art
### Methods for Optimal Layout Problems
#### 2.1.6 Machine Learning Techniques  
Recently, machine learning methods have been applied to layout optimization problems even if they are less investigated. [PERSON] et al. conducted a study on the use of machine learning as resolution techniques for facility layout problems in [58]. Among machine learning techniques, Neural Networks [60], Reinforcement Learning [61; 96] and Bayesian Optimization [59; 97; 98] have been explored for solving optimal layout problems.
## 3 Methods  
Overall procedure of soil moisture retrieval in the vegetated regions is based on remotely sensed SAR and optical data are illustrated in the flowchart of Figure 3. From the flowchart, the whole retrieval scheme is given as follows:  
Figure 2: Violin diagram of soil moisture contents measured in the field campaign (The unit of average soil moisture content is cm\({}^{3}\)/cm\({}^{3}\)).  
Step 1: The two SAR data were preprocessed, and the observed backscatter coefficient was obtained from the processed images for soil moisture estimation at X- and C-bands. The Sentinel-2 optical data were also preprocessed to calculate the vegetation indices. The values of backscatter coefficient and vegetation indices were extracted for each sampling point.  
Step 2: A semi-empirical model was developed according to the vegetation scattering model proposed by [PERSON] et al. [37]. The original forest scattering model was modified by substituting the expressions of parameters using empirical equations [55]. The parameters include vegetation water content (VWC) and backscatter coefficient of bare soil. A new vegetation index was used to describe the VWC.  
Step 3: Based on the extracted remotely sensed data and ground measurements, the semi-empirical model was constructed and the optimal coefficients were derived using the least squares regression. Then, soil moisture retrieval at X- and C-bands was conducted and mapped in the study area.
## 2 Sensor Calibration
### Hand-Eye Calibration  
Hand-eye calibration is used to determine the position and orientation of a camera mounted on a robot relative to its end-effector. Generally, this involves moving the robot arm to multiple positions and orientations to capture images of a fixed target ball of known dimensions. When the camera moves by \(A=\left(R_{a},t_{a}\right)\) and the end-effector moves by \(B=\left(R_{b},t_{b}\right)\), the hand-eye transformation matrix between them is \(X=\left(R_{x},t_{x}\right)\). The hand-eye calibration model can be expressed as follows:  
\[AX=XB \tag{12}\]  
where (\(X\)) is the unknown variable, (\(B\)) is known, and (\(A\)) is the estimated value. Applying Equation (12) to each movement results in (\(i\)) being divided into groups as described in [5]:  
\[R_{ai}R_{x}=R_{x}R_{bi} \tag{13}\]  
\[R_{ai}t_{x}+t_{ai}=R_{x}t_{bi}+t_{x} \tag{14}\]  
Utilizing a combinatorial optimization method [30], a laser vision sensor is mounted on a robot to scan a standard sphere from various postures. All combinations of measure ment data are used to calculate multiple transformation matrices, thereby deriving the corresponding contour data sets in the base coordinate system. Since all contour data are obtained from the same stationary standard sphere, sphere fitting is applied to each data set to determine the optimal hand-eye transformation matrix with the minimum fitting error.
# MoCoformer: Quantifying Temporal Irregularities in Solar Wind for Long-Term Sequence Prediction
## 4 Results
### Multivariate Results  
Table 1 compiles the evaluation outcomes for multivariate prediction on the two solar wind time series datasets. The five main methods included in the evaluation are the empirical solar wind method WSA [10]; the common deep learning algorithms LSTM [37]; GRU [38]; and the Transformer-based long-term series prediction methods Transformer [40], Autoformer [50], and FEDformer [51], along with the method MoCoformer proposed in this study. The experimental configuration maintains a consistent input length of 96, with fixed prediction lengths of 96, 192, 336, and 720 for both training and evaluation, enhancing comparability across the methods. In the context of varying prediction durations, it is crucial to acknowledge that the training and test datasets will differ. For instance, the randomized time window of Case96 (eight days) and of Case192 (twelve days) could serve as reasonable baselines for training various quarterly cases. It is crucial to address the issue of continuity, as time series are inherently distinctive. The sliding window method is employed, whereby the final 30% of a continuous data segment is designated as the test set while the initial 70% is utilized as the training set. This method ensures temporal continuity between the training and test sets. The investigation presents an analysis of MAE, RMSE, and MAPE metrics in different models across varying prediction intervals. Across all metrics, MoCoformer demonstrated
# Additive Manufacturing of Variable Density Lenses for Radio Frequency Communications in X-Band
## 5 Results  
The electromagnetic response of the assembled prototypes was evaluated in an anechoic chamber, with the experimental setup depicted in Figure 11. The simulated directivity of the horn antenna and the set with the three different lenses, using the CST Dassault Systems Suite, is presented in Figure 12a. Therefore, the horn directivity at the maximum of the radiation (\(\phi=0\)) is 13.4 dB. By adding lenses to the radiation set, a substantial increase in directivity is achievable (up to total directivity of 21 dB for lens A and 20 dB for lenses B and C). The directivities of the different lens antennas were measured and their results are given in Figure 12b. Regarding lens A, the achieved directivity is 19.5 dB, less than the 21 dB predicted by the simulation. This is most probably due to the fact that the lens was manufactured in four separate pieces. Therefore, airy gaps may appear due to an imperfect mechanical assembly, see Figure 9a, so the degradation in the farfield measured directivity response is observed. Regarding lenses B and C, the simulation results match very well, even with better results, showing a slightly more directive radiation pattern in the maximum (22 dB for lens B and 22.1 dB for lens C at \(\phi=0\)). Additionally, the lenses, as well as the horn antenna and the support, were weighed, with the results presented in the Table 5. As shown, both the horn antenna and the support are very light, with the lens providing the main contribution to the overall weight. The total weight of the sets ranges from approximately 500 g for lens A to 280 g for lens C.  
Table 6 displays the results of our three lenses, which are compared to the recent literature. The main conclusion is that the lenses present similar measured directivity. Lenses B and C present similar or better aperture efficiency compared to the lenses with  
Figure 11: X-Band Antenna Assembly Measurements in laboratory anechoic chamber. Figure 12: Directivity of the lenses at 10 GHz, \(\phi=0\): (**a**) simulated; (**b**) measured. similar sizes but with an important reduction in thickness, which substantially decreases the volume of the whole set. This is achieved by using variable densities of the printing material in different sectors with consistent thickness, instead of varying the lens thickness--a traditional approach for these lenses. This approach also enhances lens aperture efficiency, as evidenced by our measured results. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Device:** & **Conical Horn** & **Support** & **Lens A** & **Lens B** & **Lens C** \\ \hline Weight (g): & 22 & 96 & 376 & 207 & 163 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Weight of the X-Bend Antenna Assembly. \begin{table}
###### Abstract  
With the increasing implementation of renewable energies, the impact that this type of installation can have on the electricity supply grid is of great importance. In this context, the aim of this work is to analyse how the production and injection of electricity generated by a series of small photovoltaic (PV) installations influence some parameters of the electricity grid in the low-voltage (LV) distribution networks to which they are connected, by analysing the basic data provided by the inverters of these installations. The presence of a slight rise in the grid voltage values in the Point of Common Coupling (PCC) as the production of the PV plants increases has been verified, with maximum slopes between 0.95 and 0.00027 V/kW, but which do not result in voltage values close to the limits set by the regulations. In addition to assessing this impact, the results obtained made it possible to determine the hosting capacity that the networks into which these installations inject their energy would have for this type of installation so as not to have a detrimental effect on the voltage values of the grid. The possible influence of the production of PV installations on the voltage imbalance between phases or on the frequency of the grid has also been analysed in this work. Although the values recorded by the inverters have limitations in their measurements that do not make them valid for assessing events such as the presence of harmonics or flickers, they do allow a first analysis to be made of the influence of PV plant production on some grid parameters, without the need to incorporate additional measurement systems in these renewable installations. However, it is important to be aware of the limitations of the measurements of this equipment and, as far as possible, to choose inverter models that carry out this type of measurement as completely as possible.  
photovoltaic plants; power quality; inverter measurements; grid voltage; grid frequency; voltage imbalance +
Footnote †: journal: Applied Sciences
## 6 Conclusions  
A double-column self-centering pier with shear links was proposed to improve the seismic resilience of bridges. A corresponding FE model was established, and a theoretical model was developed to calculate the pier lateral force-displacement skeleton curve. Moreover, a series of parametric analyses were conducted to determine the effects of key parameters. The following conclusions can be drawn:  
1. The prestressed steel strands provide the proposed pier with a stable self-centering ability, which efficiently reduces the residual displacement. Additionally, the rocking behavior effectively prevents the development of plastic hinges in the columns of conventional reinforced concrete piers. Furthermore, the shear links between columns guarantee the pier has a preferable energy  
Figure 19: Influence of the initial prestress of steel strands on the hysteretic curve. (**a**) Double-column self-centering pier with shear links; (**b**) double-column self-centering pier without shear links.  
dissipation ability. Therefore, the proposed pier can be used to improve seismic resilience with the remarkable features mentioned above.
2. The results of the FE model indicate that, for the short piers, it is reasonable to neglect the deformation of each column when the pier rocks. The integrated spring model and the uniformly distributed spring model show almost the same hysteretic performance. These two observations are the basis of the theoretical model.
3. The results derived from the theoretical models show good agreement with the results from the FE models. In this case, the theoretical model based on the matrix displacement method and the virtual work principle can be used for future analysis of the innovated pier.
4. According to the parametric analyses, the influence of the initial stiffness of the shear links can be neglected. By enhancing the yield strength of shear links, the energy dissipation capacity of the pier can be improved but the residual displacement will increase. Further, the bigger the initial prestress is, the larger the rocking stiffness of the pier is and the smaller the residual displacement is.  
[PERSON] conceived the concept and guided the research, [PERSON] conducted all the analytical work and wrote the manuscript, and [PERSON], [PERSON], and [PERSON] reviewed the manuscript.  
The authors are grateful for the financial support received from National Key R&D Program of China (2016 YFC0701202), the National Natural Science Foundation of China (No. 51808074), and the Fundamental Research Funds for the Central Universities (No.106112017 CD)X200001).  
The authors declare no conflict of interest.
## 2 Spaceflight Environment and Ocular Risks  
### Microgravity and Its Impact on Ocular Health  
Microgravity profoundly affects human physiology, with significant implications for the visual and neurological systems. Cephalad fluid shifts induced by prolonged exposure disrupt normal pressure gradients, increasing intracranial pressure (ICP) and intraocular pressure (IOP), which directly impair ocular functions [17]. Elevated IOP has been associated with corneal edema and, in severe cases, bulla formation, further underscoring the vulnerability of the cornea in microgravity environments [18; 19]. In this unique environment, astronauts face increased risks of corneal injuries such as abrasions and perforations, often caused by suspended particulates or high-velocity debris [8]. While these injuries are typically minor and treatable on the International Space Station, longer-duration missions may require advanced diagnostic and treatment strategies to ensure visual performance is maintained.  
The cornea, a metabolically active tissue essential for maintaining transparency and refractive accuracy, is particularly vulnerable. Prolonged microgravity exposure has been linked to corneal edema, thickening, and tear film instability, which heighten the risk of abrasions and environmental damage [9; 20; 21]. These changes can degrade visual acuity and pose long-term risks to ocular health.  
Figure 1 illustrates the interplay between environmental stressors in spaceflight, such as microgravity-induced fluid shifts, radiation exposure, and celestial dust, and their downstream biological responses, including oxidative stress, immune suppression, and microbial biofilm formation. These factors collectively could potentially lead to adverse ocular outcomes, including corneal abrasions, infections, and potential vision loss [8; 20; 22].  
Although SANS is primarily associated with optic nerve and retinal changes, fluid redistribution and increased intracranial pressure have been suggested as potential contributors to anterior segment alterations, including possible corneal edema and tear film instability [11].The proposed association between increased ICP and choroidal alterations highlights the multifactorial complexity of SANS [23].  
Efforts to mitigate this microgravity-associated risks are gaining momentum. Gelatin methacryloyl (GelMA) hydrogels provide a biomimetic platform to study corneal biomechanics under pressure variations, offering insights into therapeutic approaches [24]. Similarly, computational fluid dynamics (CFD) models have deepened understanding of microgravity-induced hemodynamic changes, revealing increased shear stress in retinal and cerebrovascular networks [1].  
As space exploration ventures further, addressing the ocular risks posed by microgravity remains vital. Developing innovative diagnostic and therapeutic strategies is essential to preserving astronaut vision and ensuring mission success in future space missions to other planets.
## 6 Worst-Case Latency Analysis of the FWOS Model  
To analyze the worst-case latency performance for a TT frame with a given priority class, the arrival and service curves must be defined. The arrival curve depends on the limitations described in the previous section, and the service curve has to be determined based on the predefined GCL schedule. For the worst-case analysis, the service curve requires one to find the duration of the guaranteed service (contention-free) intervals for the corresponding queue and the maximum waiting time needed to serve that frame using these intervals. This section presents these required calculations considering the proposed scheduling algorithm, which includes all overlapping scenarios between TT open windows. First, we compute the length of the contention-free interval based on all pessimistic cases. Then, the maximum waiting time is determined for every selected node. After that, these calculations are used to define the service curve and, then, analytically implement the worst-case end-to-end latency.
## 2 Fundamental Topologies of SST  
Various architectures are available for each of the three stages of SST. The topology is determined depending on the application of SST. The different types of topologies are Type-A (A-1 and A-2), Type-B, Type-C, Type-D, and Type-E [32]. This study adopts the Type-D-based SST topology [33] because it has numerous advantages, such as DC connectivity, fault isolation, proper voltage regulation, easy integration with RESs and storage, and volt-ampere reactive (VAR) compensation. In the design, the architecture for each stage should be chosen appropriately, and the problems should be addressed by integrating them into a single system to deliver regulated voltage at each stage.  
The fundamental three-stage architecture of SST is presented in Figure 1. The detailed input, output, and other critical parameters can be seen in Appendix A. The input stage, which is also called the AC-DC stage, contains a single-phase AC-DC IGBT-based converter that operates at a high switching frequency. The isolation stage, which is also called the DC-DC stage, comprises two H-bridges with a high-frequency transformer. A DAB converter is also applied in this stage [34]. The output of the input stage is fed to the DAB converter, and the output of the converter is fed to the inverter or is considered the output stage. In the output stage, a filter circuit is added to mitigate the voltage and power quality issues. The output stage is usually supplied to low-voltage user applications.