esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimal View Estimation Algorithm and Evaluation with Deviation Angle Analysis
## 4 Experiment
### Viewpoint Estimation Results  
From the generated dataset, we randomly select \(\left\lfloor 75\%\times N\right\rfloor\) images for training, where \(\left\lfloor x\right\rfloor\) rounds number \(x\) to the lower integer. Since each class consists of 29 images, there are 21 images in each class in the training set, and the remaining eight images are used for testing. The maximum number of iterations is set to 20,000, with other parameters set to their default values. The training process takes 10,719.46 s, and the training accuracy is 100% due to the small training set. The confusion matrix of the test set is shown as a heat map in Figure 11. By summing the diagonal elements and taking the total number of images, the classification accuracy can be calculated to be 0.6855, corresponding to \(\delta_{0}=0\).  
Figure 11: Using the traditional method to calculate the confusion matrix of the classification results of the test set. The colors are only used for differentiation and have no other meaning.  
Figure 10: The nearest neighbor relationships of 31 viewpoints when \(\delta_{0}=0,\pi/6,\pi/3\).  
If the viewpoint evaluation method with the angle deviation metric proposed in this article is adopted, with a tolerance angle of 30 degrees (\(\delta_{0}=\pi/6\)), as shown in Figure 8b, the accuracy of the viewpoint estimation is 0.8105. The number of images with deviation angles within \(\pi/6\) is marked as a red number in the confusion matrix in Figure 12.  
Similarly, if we increase the deviation angle to 60 degrees (\(\delta_{0}=\pi/3\)), as shown in Figure 8c, the accuracy of the viewpoint estimation increases to 0.8911, improved by approximately 9.94% compared to \(\delta_{0}=\pi/6\). The number of images with deviation angles within \(\pi/3\) is also marked as a red number in the confusion matrix in Figure 13.  
Figure 12: The confusion matrix of the classification results of the test set when \(\delta_{0}=\pi/6\).  
Figure 13: The confusion matrix of the classification results of the test set when \(\delta_{0}=\pi/3\).
# Body Area Networks in Healthcare: A Brief State of the Art
## 3 Physical Layer Requirements  
### Properties According to IEEE 802.15.16 Standard  
Standards are developed so that industries and companies generate customer trust and enhance safety in their products. Standards help in the production of items which interact with each other regardless of their manufacturers. Standards define rules, protocols and other important parameters to ensure that products are in compliance with certain technical specifications [17]. The IEEE TG6 published the 802.15.6 standard on February 2012. This standard provides guidelines for BANs. IEEE 802.15.6 defines the physical (PHY) and data link layers. The purpose of 802.15.6 is to provide a standard for a short range, highly reliable and low-power wireless communication close to or inside the human body.  
The standard IEEE 802.15.6 defines three PHY layers. These layers are narrow band PHY (NB-PHY), ultra-wide band PHY (UWB-PHY) and human body communication (HBC-PHY) [9].  
NB-PHY was designed for wireless medical applications, divided into two groups: wearable and implantable. In the wearable group, the cables in a medical solution are replaced with highly reliable wireless communication methods, and one or both ends of the links are on the human body. The implantable group has a frequency band but not a standard yet. In order to support transmission and reception, a device must use one of the following bands: (a) for implantable devices, 402-405 MHz, and (b) for wearable devices, 420-450 MHz, 863-870 MHz, 902-928 MHz, 950-958 MHz, 2360-2400 MHz, 2400-2483.5 MHz.  
The ultra-wide band (UWB) layer offers high quality, low complexity, and ultra-low power operations. This layer is divided into two technologies: impulse radio (IR-UWB) and wide-band frequency modulation (FM-UWB). The band in this layer is classified into two groups, low (3.25-4.75 GHz) and high (6.6-10.25 GHz), each group being divided into 500 MHz bandwidths. UWB-PHY applications have two operation modes. There is a default mode where IR-UWB is mandatory, while FM-UWB is optional. The default mode can be used in medical and non-medical applications. The second is a high quality of service mode, which is used in high priority medical applications. Therefore, FM-UWB is required to achieve a high quality of service.  
Lastly, HBC-PHY uses the human body for exchanging information. It uses an electrode for transmitting or receiving an electrical signal from body to device. The operation band of this layer is 21 MHz with a bandwidth of 5.25 MHz.
# A New Plasma Surface Alloying to Improve the Wear Resistance of the Metallic Card Clothing
## 2 Experimental Procedure
### Strength Testing Methodologies  
The surface microhardness of the metallic card clothing before and after plasma surface alloying was tested using HXS-100A microhardness tester (Ningbo yonghui instrument technology co. LTD, Ningbo, China) equipped with pyramid diamond indenter. There was a significant difference in the shape between the tip and the tooth body of metallic card clothing. Therefore, three different parts of each sample were selected as the test point, as shown in the Figure 2. The applied load was 50 g and loading time is 10 s. Each specimen is tested 5 points to get the arithmetic mean.  
Nanoindentation tests were conducted with the Shimadzu dynamic microhardness tester (DUH-W201). The indenter was pressed onto the sample surface at the speed of 1.3239 mN s\({}^{-1}\); the maximum load was 150 mN [12]. In order to avoid the substrate effect on the nanoindentation test, the maximum indentation depth should not exceed 10% of the coating thickness [15]. Three parallel experiments were conducted to improve the testing accuracy and the results were averaged.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Influence Factors of Double Glow Plasma Treatment** & **Settings** \\ \hline distance between the source and substrate (mm) & 20 \\ voltage of the substrate (V) & 450–480 \\ electric current of the substrate (A) & 1.1 \\ voltage of the source (V) & 900–960 \\ electric current of the source (A) & 1.7 \\ soaking time (h) & 3 \\ working pressure (Pa) & 35 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Process parameters of double glow plasma treatment.  
Figure 2: The schematic diagram of the test point. (**a**) The tooth point position of the metallic card clothing; (**b**) The position of the tooth base of the metal needle cloth; (**c**) The bottom of the metallic card clothing.
## 3 Simulation Results
### Receiver Constellation and Error Vector Magnitude  
A qualitative prerequisite for achieving SIC is a large minimal distance for the composite constellation. One of the performance metrics for quantifying this feature is error vector magnitude (EVM), which is the magnitude of the difference between the measured symbol (vector) and its true (transmitted) value. In Figures 10-14 the symbol for which the EVM was evaluated in indicated by a red arrow. As NOMA contains irregular constellations--resulting from weighting coefficients, the information about the shape of the transmitted constellation is critical for implementing the receiver. Common policy for weight associations is the \"weakest signal\" in terms that the greatest value is assigned to the user with the worst channel conditions. The goal of the simulation is then to minimize the EVM at the receiver.  
When properly synchronized, constellations generated as in Figure 10 are received with minimal EVM, typically less than 2%, and an EVM spectrum below 0 dB; see Figure 11. In practice, the transmitter gathers signal quality indicators (e.g., RSSI, CSI) measured and reported by the receiver and then calculates optimal power weights. The power weights \(p_{1},\ p_{2},\ldots,p_{n}\) can be manipulated in this model (by means of the **Manipulate[]** function, for example) and thus facilitate observing dynamic constellation behavior, which is extremely useful for EVM evaluation, and for observing the variation of phase and amplitude of each symbol in the superimposed constellation due to power coefficients variations.  
Figure 10: The effect of pulse-shaping observed in TP2 for: (**a**) two 4-QAM users, (**b**) three 4-QAM users, and (**c**) four 4-QAM users.  
Figure 9: Constellation diagrams with decision regions of the superimposed signal measured at TP1 for: (**a**) two 4-QAM users, (**b**) three 4-QAM users, and (**c**) four 4-QAM users.  
Figure 11: Diagrams with decision regions of NOMA constellations at receiver for: (**a**) 16 symbols, (**b**) 64 symbols, (**c**) 256 symbols, (**d**) EVM, and (**e**) EVM spectrum.  
Figure 12: NOMA constellations and EVM for different LO frequency offset: (**a**) 10 ppm, (**b**) 5 ppm, (**c**) 2 ppm, (**d**) 1 ppm, and (**e**) corresponding EVM.
# Semantic Distributed Data for Vehicular Networks Using the Inter-Planetary File System
## 1 Introduction
### Related Work  
The term Semantic Web of Things that combines the Semantic Web and IoT devices was coined in 2009 by [PERSON] and [PERSON] at [6]. This work has been extended by multiple authors since then. For instance, the Semantic Smart Gateway Framework by [PERSON] and [PERSON] is remarkable [7], as is the interoperability analysis by [PERSON] et al. [8]. The recent article by [PERSON] et al. provides an ample review of this area of research [9]. The work with semantic distributed sensors by [PERSON] and [PERSON] [10] is especially interesting--research that extends the work by [PERSON] et al. [11]. Besides semantic data, the natural human-vehicle interaction requires vehicles' proficiency of spatial language [12]. Moreover, the work by [PERSON] and [PERSON] reflects the complexity of spatial and temporal reasoning systems [13].  
Prior to any mention of the Semantic Web, [PERSON] et al. proposed the usage of P2P networks for distributing sensors in [14], and [PERSON] et al. investigated how utilize P2P sensor networks for cooperative localization in [15]. Due to the convenience of cloud computing, the fog computing [16] paradigm emerged as an alternative that provides some of the benefits of P2P networks without disregarding the usage of central servers. The recent research by [PERSON] and [PERSON] [17] delves into the advantages of using a pure P2P approach for fog computing. One of the major impediments to a more widespread use of P2P networks has been the lack of standard protocols and applications. However, in recent years, there has been growing research interest in blockchain technology, a highly popular and promising P2P solution. Blockchains have been studied and applied to IoT and vehicular networks by multiple authors [18; 19; 20]. Although related to blockchain, IPFS is a distinctive P2P protocol. In fact, IPFS and blockchain are considered complementary technologies [21]. [PERSON] and [PERSON] researched the feasibility of using IPFS in IoT devices in [22].  
This paper offers experimental results of a real implementation of a semantic network created with IPFS and compares them with the simulation results published by [PERSON] and [PERSON] [23]. These simulation results were obtained using an optimized ICN protocol that provides content-addressed data delivery. The usage of ICN has been considered by several authors as the optimal solution for reducing the usage and latency of IoT [24] and vehicular networks [25]. The aforementioned experiment used IPFS over QUIC, a new Internet protocol whose performance has been already analyzed by [PERSON] et al. [26]. There are no prior studies, to the best of the authors' knowledge, evaluating the performance of IPFS or QUIC as content-delivery protocols for vehicular networks.
## 3 DAB LCC Resonant Converter TPS Control Validation
### Performance Evaluation for Inverter-to-Rectiver Phase Shift Angle \(\delta\) Setting Change  
In the below experimental test, TPS close loop control performance is measured for different phase shift angle \(\delta\) (deg) settings to evaluate its effect on power efficiency. This test aims to validate that the DAB should work at \(\delta\) (deg) = 90\({}^{\circ}\) to achieve unity power factor operation and maximum efficiency, as what is proved mathematically in analytical derivation and is validated in PLECS circuit simulation.  
Figure 26 shows the measurement data for input DC power vs. phase shift angle \(\delta\) (deg) change. The DAB converter operates at input DC voltage Vdc = 30 V and output DC voltage V0 = 10 V, 20 V, 30 V, 40 V with different DC load resistor Rdc connected. For the same Rdc and Vo, DC output power is the same; increasing input power means reduced power efficiency.  
The measurement data show that the input power increases when \(\delta\) (deg) > 90\({}^{\circ}\) for Vo = 10 V and Vo = 20 V for all Rdc values. Therefore, the efficiency will be reduced for a larger phase shift angle \(\delta\) setting. At higher power levels for Vo = 30 V and Vo = 40 V, the input power is not sensitive to \(\delta\) (deg) setting change in the range of 80\({}^{\circ}\) < \(\delta\) (deg) < 100\({}^{\circ}\). However, inverter voltage feedback control could saturate at \(\beta\)p = 0.5 and \(\beta\)p (deg) = 180\({}^{\circ}\) when \(\delta\) (deg) > 100\({}^{\circ}\) and \(\delta\) (deg) < 80\({}^{\circ}\).  
Based on the above analysis, \(\delta\) (deg) = 90\({}^{\circ}\) is the preferred setting for DAB LCC resonant converter to operate reliability and achieve high efficiency. This conclusion tallies with the conclusion drawn from TPS open loop control simulation results for unity power factor operation of the DAB converter with \(\delta\) (deg) = 90\({}^{\circ}\).  
Challenges and Possible Methods to Implement TPS Control in Decoupled Transmitter and Receiver Control Hardware  
For the control scheme presented in the previous content, the TPS modulator is implemented in one PLECS RT box control hardware. Therefore, all three phase shift angles can be synchronously controlled by shifting the positive zero-crossing timing instants of the triangle PWM carrier signals of the inverter Legs and the rectifier Legs in the same PWM modulator hardware implemented in the high-timing resolution FPGA.  
Figure 26: Input power vs. inverter-to-rectifier phase shift angle \(\delta\) (deg) when DAB converter operating at Vdc = 30 V and Vo = 10 V, 20 V, 30 V. 40 V with different Rdc connected.
# Repurposing Historic Drugs for Neutrophil-Mediated Inflammation in Skin Disorders
### Granulomatous Diseases  
Cutaneous granulomatosis is marked by granulomatous inflammation and macrophage nodules [15]. Non-infectious examples include granuloma annulare, sarcoidosis, and necrobiosis lipoidica.  
Granuloma annulare (GA) is a common non-infectious granulomatous disease, with its pathogenesis likely rooted in immunologic mechanisms. Evidence suggests that GA represents a cell-mediated immune response to an unidentified antigen, where granuloma formation by macrophage and histiocytes is mediated through interactions with T-helper lymphocytes [16]. Localized GA often resolves spontaneously within a few months to a few years, whereas GGA can persist for decades. High-potency topical corticosteroids are considered the first-line therapy for GA.  
Sarcoidosis, known for its heterogeneous morphological manifestations, is considered one of the \"great imitators\" in medicine. Physicians treating sarcoidosis must recognize its diverse organ involvement, particularly in the skin, where lesions can be disfiguring, carry prognostic significance, and may be challenging to diagnose. Therapeutic challenges arise from the chronic nature of the disease and the lack of spontaneous regression of lesions, which necessitates balancing the benefits of treatment with long-term tolerability. Oral corticosteroids, which are beneficial in cases of severe visceral involvement, are not warranted for less severe cases due to their associated adverse effects and the frequent relapses observed during dosage tapering. Given the toxicity of long-term steroid use, there has been increasing interest in corticosteroid-sparing agents for sarcoidosis.  
Necrobiosis lipoidica (NL) is a rare, chronic granulomatous skin disease, and its treatment remains challenging. Initial therapy often involves topical or intralesional corticosteroids, though multiple therapies have been tried with inconsistent results. Most data on treatment efficacy come from case reports, case series, or small, uncontrolled studies, with a lack of high-quality trials to confirm the effectiveness of individual therapies.
###### Abstract  
High-resolution Earth observation data is routinely used to monitor tropical forests. However, the seasonality and openness of the canopy of dry tropical forests remains a challenge for optical sensors. In this study, we demonstrate the potential of combining Sentinel-1 (S1) SAR and Sentinel-2 (S2) optical sensors in order to map the tree cover in East Africa. The overall methodology consists of: (i) the generation of S1 and S2 layers, (ii) the collection of an expert-based training/validation dataset and (iii) the classification of the satellite data. Three different classification workflows, together with different approaches to incorporating the spatial information to train the classifiers, are explored. Two types of maps were derived from these mapping approaches over Tanzania: (i) binary tree cover-no tree cover (TC/NTC) maps, and (ii) maps of the canopy cover classes. The overall accuracy of the maps is >95% for the TC/NTC maps and >85% for the forest types maps. Considering the neighboring pixels for training the classification improved the mapping of the areas that are covered by 1-10% tree cover. The study relied on open data and publicly available tools and can be integrated into national monitoring systems.  
East Africa; dry forests; Copernicus; Sentinel-1; Sentinel-2; machine learning; Collect Earth; cloud computing Article  
Mapping Canopy Cover in African  
1 Joint Research Centre (JRC), Directorate D--Sustainable Resources, European Commission, Via E. Fermi, 2749, I-21027 Isprra, Italy; [EMAIL_ADDRESS] (V.S.); [EMAIL_ADDRESS] (H.E.); [EMAIL_ADDRESS] (F.A.)  
2 ARHS Developments S.A., 4370 Esch-sur-Alzette, Luxembourg; [EMAIL_ADDRESS] 2
# Containment Control-Guided Boundary Information for Semantic Segmentation
## 2 Related Works
### Semantic Segmentation of Geometric Relations  
Several network architectures propose leveraging boundary information, achieving high accuracy while also considering inference speed. These can be summarized as follows. The authors of BNFNet [1] propose a model that integrates a fully convolutional network with boundary cues, aiming to enhance the object localization capability by leveraging boundary information. Graph-Segmenter [18] is a transformer-based semantic segmentation approach that enhances relation modeling between image regions and pixels using a graph transformer and boundary-aware attention module. AMKBANet [19] proposes an attention-based multi-kernelized and boundary-aware network that performs well in semantic segmentation tasks. CBLNet [20] provides a plug-and-play conditional boundary loss approach, which effectively improves the performance of boundary segmentation. BANet [21] adopts an encoder-decoder architecture and additionally incorporates boundary information to accurately delineate the shape and boundary of objects. AGLNet [22] introduces a global attention upsampling module to capture object shapes and boundaries. The authors of EdgeNet [23] propose a category-aware edge loss module that improves inference accuracy without compromising model inference speed. PIDNet [24] innovatively incorporates PID control methods into semantic segmentation research. The authors map proportional, integral, and derivative controllers to detail feature branches, context information branches, and boundary attention branches, achieving exceptional inference accuracy while maintaining high inference speed.
# Multi-Path Routing Algorithm Based on Deep Reinforcement Learning for SDN
## 5 Results and Validation  
In this section, we thoroughly examine the details of the experiment and the results to assess the reliability of our proposed system. We compare the performance of the TBPPO algorithm with three other algorithms: DRSIR, PPO, and Dijkstra. Additionally, we explore  
Figure 2: Flowchart of parameter tuning and model establishing.  
the performance of TBDRSIR, which integrates the DRSIR algorithm with our trust value mechanism within the network topology.
## 2 Theories and Methods
### 3D FWI Algorithm for GPR
#### 2.2.1 Misfit and Gradient
By taking the first-order partial derivative of the objective function with respect to the model parameter \(\mathbf{m}\), the gradient of FWI can be expressed as [14]  
\[\mathbf{g}_{d}=\sum_{i=1}^{N_{\omega}}\sum_{i=1}^{N_{\omega}}\sum_{k=1}^{N_{ \omega}}\Re\left\{\eta_{k}\frac{\partial\Big{[}\mathbf{d}_{i,j,k}^{cal}( \mathbf{m})\Big{]}^{\dagger}}{\partial\mathbf{m}}\Big{[}\mathbf{d}_{i,j,k}^{ cal}(\mathbf{m})-\mathbf{d}_{i,j,k}^{obs}\Big{]}\right\}=\Re\left\{ \mathbf{f}^{\dagger}\Delta\mathbf{d}\right\}, \tag{9}\]  
where \(\Re\) indicates that we take the real part of the data in the complex number field. The matrix \(\mathbf{J}\) representing the first-order partial derivative is also known as the Frechet partial derivative matrix or the Jacobian matrix, and \(\Delta\mathbf{d}=\mathbf{d}^{cal}-\mathbf{d}^{obs}\) is the error vector. The most crucial aspect in solving the above-mentioned gradient is to obtain the Jacobian matrix \(\mathbf{J}\)--that is, the derivative of the simulated data \(\mathbf{d}^{cal}\) with respect to the model \(\mathbf{m}\). For both sides of the partial differential Equation (3), take the first-order partial derivatives with respect to m and substitute them into the gradient calculation Formula (9) to obtain [14]  
\[\mathbf{g}_{d}=\Re\left\{\mathbf{u}^{\dagger}\left(-\frac{\partial\mathbf{A}}{ \partial\mathbf{m}}\right)^{\dagger}\left(\mathbf{A}^{-1}\right)^{\dagger} \mathbf{P}^{T}\Delta\mathbf{d}\right\}=\Re\left[\mathbf{u}^{\dagger}\left(- \mathbf{G}\right)^{\dagger}\mathbf{v}\right], \tag{10}\]  
where \(\mathbf{G}\) is the diffraction matrix (also known as the sensitivity kernel) that characterizes the parameter sensitivity, while \(\mathbf{v}\) is the solution of the linear system \(\mathbf{A}^{\dagger}\mathbf{v}=\mathbf{P}^{T}\Delta\mathbf{d}\), defined as the adjoint wavefields. At this point, the gradient can be determined as the product of the incident wave field \(\mathbf{u}\) and the backpropagating wavefields (or adjoint wavefields) \(\mathbf{v}\), and, generally, an additional forward calculation is required. We adopt the L-BFGS method to iteratively solve the inversion procedure. When the initial model is given, the iterative sequence of the model is defined as  
\[\mathbf{m}_{k+1}=\mathbf{m}_{k}-\alpha_{k}\mathbf{G}_{k}\mathbf{g}_{k}, \tag{11}\]  
where \(\alpha_{k}\) represents the step size, which is calculated by using the inaccurate line search method based on the Wolfe condition. \(\mathbf{G}_{k}\) is the inverse Hessian approximation matrix and \(\mathbf{g}_{k}\) is the gradient. If the initial step size \(\alpha_{k}\) is set to 1, the L-BFGS algorithm will solve two forward modeling problems in each iteration, each source, and each frequency. Otherwise, greater computational costs will be incurred during the step size search process. For more details regarding L-BFGS, please refer to [11; 26]; we do not elaborate further.