esa-sceva/satcom-chunk-collection huggingface dataset:
Herein, we report a facile one-step solvothermal synthesis method for growing In\({}_{2}\)S\({}_{3}\) and In\({}_{2}\)S\({}_{3}\)/SWNT heterostructures on Ni foam. The resulting heterostructure exhibits a high specific capacitance of 1267 F.g\({}^{-1}\) at a scan rate of 5 mVs\({}^{-1}\), due to the improved electronic environment and a well-arranged nanostructure framework [14]. This approach, In\({}_{2}\)S\({}_{3}\) as the electrode material and its role of SWNT on the In\({}_{2}\)S\({}_{3}\) nanostructure, realized during the in situ synthesis for enhancing electrode performance through heterostructure and microstructure design, is promising, and has not been explored comprehensively before. Furthermore, the In\({}_{2}\)S\({}_{3}\)-SWNT heterostructure provides an exceptional charge transfer pathway and a well-arranged robust structure. As a result, In\({}_{2}\)S\({}_{3}\) and its SWNT-based heterostructure-based electrodes are one of the ideal configurations for the improved electrode performance through hetero and microstructure design [15]. Batteries are commonly used in wearable and implantable electronic devices but pose safety concerns due to overheating, shorting, and combustion [16; 17; 18; 19]. Alternatively, supercapacitors (SCs) powered by wireless power transmission (WPT) offer advantages such as low maintenance, a high cycle operation, and the absence of hazardous chemicals. However, wireless communication, such as Bluetooth, can require a transient high current consumption despite the device's low power consumption. Thus, SCs are used as a short-term energy storage solution to support the WPT's inconsistent power supply [20; 21; 22; 23; 24]. This paper showcases, for the first-time, the effective integration of SSC in the wireless powered BLE/PPG (Bluetooth/Photoplethysmogram) module for a health monitoring system with SCs and an android application. This power transmission in an integratedhealth monitoring module is a Bluetooth-enabled system, in which the SCs' assembly charges when there is surplus power in the system, while it supplies power in burst mode during the subsequent data reading process. To summarize, this work reports on: (1) the first time heterostructure of a SWNTs-integrated 2D In\({}_{2}\)S\({}_{3}\) network via facile one-pot direct synthesis, (2) tailoring of the SWNTs-In\({}_{2}\)S\({}_{3}\) heterostructure, tuned towards superior electrodes, (3) the excellent cyclic retention furnished due to the reinforced robust structure of the SWNTs-In\({}_{2}\)S\({}_{3}\) network framework, (4) the extensive study of SD and leakage current via a VH test, and (5) the first time demonstration of an integrated module of SSC in a wireless health monitoring module. Overall, TMS-based supercapacitors have the potential to play a significant role in the growing energy market and energy security by improving the performance, reliability, and sustainability of energy systems.
# A Review on Applications of Fuzzy Logic Control for Refrigeration Systems
## 3 Fuzzy Driver Applications on RACs
### Energy Saving  
According to the previous sections, the implementation of this type of controller has gradually shown that it is possible to attain significant reductions in energy consumption through the implementation of different strategies. In this sense and trying to outline the importance of these strategies in the RACs, Table 3 shows those studies that were able to obtain some energy savings. The table includes the results from the simulations as well as from experimentation. Observe that most of the experimental studies present a higher percentage of energy savings than the simulation studies. It is important to mention that it is not always possible to attain energy savings, for instance, [PERSON] et al. (2006) showed that when only one variable was controlled, the system can consume more energy than when several variables are used. Additionally, most of the studies were able to get a reduction in the energy consumption when the controller was integrated with other control systems, such as PID and artificial neural networks, or when new variables were integrated into the controller.  
As discussed in the previous sections, the use of fuzzy controllers in RACs is extensive and is shown to be a viable option for control, demonstrating that adequate thermal conditions can be maintained, and energy consumption reduced. In addition to being simple and intuitive controllers in their implementation, they have been suitable for obtaining satisfactory results in nonlinear systems, such as RACs. Integration with other controllers helps overcome the limitations of the fuzzy controller. Control strategies are important in the foreseeable future when systems are required to be more efficient and consume less energy, helping to take advantage of increasingly scarce energy resources.
# Semantic Matching Based on Semantic Segmentation and Neighborhood Consensus
## 2 Related Work  
### Representation for Semantic Matching  
Early works on semantic matching employ hand-crafted descriptors such as SIFT [18], DAISY [19], and HOG [20] to extract semantic representations. SIFT representation describes the neighborhood feature of the scale-invariant landmark, which is robust to perspective and lighting changes. The DAISY descriptor retains the robustness of SIFT and can be computed quickly at every single image pixel. HOG representation is also similar to SIFT, as it computes the locally normalized histogram of gradient orientation features, but it considers the histograms in dense overlapping grids, providing a larger receptive field. Although these representations have a certain intra-class invariance and are robust to the differences in object appearance, they provide limited matching performance due to low semantic-discriminative power.  
Recent methods use convolutional neural networks to extract semantic representations, and some specific feature extraction networks have appeared [6; 8; 21]. [PERSON] et al. [6] first introduced CNN features into the semantic matching task. Their work retains the architecture of SIFT flow [12] and replaces the hand-crafted feature with the CNN feature. The feature extraction networks of other methods [8; 21] are similar with [6]. However, such methods have essential problems: on the one hand, their network depth is shallow, which restricts their extraction of deep semantics; on the other hand, the semantic matching datasets have less data, and directly training on them limits the performance of the network.  
To solve these problems, other methods use pre-trained deep neural networks such as ResNet [26] and VGGNet [25] to extract semantic features [22; 32; 33]. Because these deep networks are trained on the huge database [24], and their output deep features contain rich semantics, [32] combines features from different layers of ResNet and then encodes them to generate category-agnostic representation; [22] transforms the feature of VGGNet with full-convolutional local self-similarity operation, which makes the representation robust to intra-class variation; [33] uses self-similarity operator to modify ResNet's features to be able to perceive local context. Our approach differs from these methods, as we embed semantic segmentation information into the representation and enhance its semantic diversity and distinguishability.
# EOR: An Enhanced Object Registration Method for Visual Images and High-Definition Maps
## 3 Materials and Methods
### Enhanced Pose Estimation
#### 3.5.3 EKF Pose Fusion  
In the proposed EOR method, the EKF integrates pose estimations across frames, enhancing system robustness and accuracy. For the first frame, a 3D ICP algorithm determines the initial pose. This pose undergoes refinement and is used to initialize the EKF for state prediction and error estimation. For subsequent frames, the EKF predicts an initial pose, which is refined to adapt to the current scene. The EKF then updates the pose by integrating the refined result with previous estimates, reducing uncertainty and ensuring a stable trajectory. This EKF-based pose fusion maintains smooth localization, especially in challenging environments requiring rapid adjustments. The process, including initialization, prediction, refinement, and updates, is outlined in the flowchart in Figure 8.
## 3 Materials and Methods
### Post-Processing of SAR and Temperature Variation Signals with the FFNN
The proposed technique provides accurate predictions, as the network learns directly from the historical data, is flexible, can be adapted for both regression and classification, and, most importantly, can be used for real-time analysis. The training phase was carried out using the Mean Squared Error (MSE) as a Loss Function, Adam Optimizer, 32 batch size, and 50 epochs. Dataset split between the training and testing phase was, respectively, an 80-20 ratio. Framework was implemented with the Tensorflow and Keras library ([[https://keras.io](https://keras.io)]([https://keras.io](https://keras.io)) accessed on 19 September 2024) on Python 3.11.9.
# Modeling of Downlink Interference in Massive MIMO 5G Macro-Cell +
## 1 Introduction
Joint modeling of beamforming and angular spread is required to obtain an accurate estimation of realistic interference levels. Used spatial filtering of multipath components by the antenna pattern is sensitive to time-variant radio channel conditions. Such an approach to the modeling of 5G systems performance is gaining more attention. For example, in [31], the results of link budget calculations in the real propagation environment of the mmWave system can be found, whereas the corresponding impact on the efficiency of antenna array tapering is described in [32]. The study presented in this article follows the same modeling principles. Therefore, it can be considered as valuable input to the current state of the art. The rest of the paper is organized as follows. Section 2 describes practical ways of using multi-beam antenna systems. Section 3 presents the basis for assessing the interference level in the DL based on the use of the MPM and 3 GPP channel model. Assumptions, obtained results, and conclusions from the performed simulations are presented in Sections 4 and 5, respectively.
## 3 Results
### Inter-Satellite LEO-MEO Link  
In a communication link between an LEO and an MEO satellite, the use of LDPC codes can bring significant advantages due to the variable channel conditions [25]. This approach is useful when robust link performance is required to maintain a stable connection between satellites at different altitudes and velocities. In the results obtained below, we considered a 1/2 rate LDPC code. In Table 6, we highlighted the coordinates of the two satellites used for the communication link, and in Table 7 we highlighted the parameters of the LEO transmitter, MEO receiver, and the link.  
In Figure 7 we see that the BER analysis using the LDPC code reflects the gain coding due to performance optimization, compared with the theoretical analysis from Figure 4. For example, 16 DPSK modulations have an Eb/No value of 21 dB for a BER of \(1\times 10^{-6}\). After we encode the data, for the same BER value and the same type of modulation, we get an Eb/No of 14.5 dB, so a coding gain of 6.5 dB.  
In Figure 8 we see the performances obtained in the context of the LEO-MEO communications link. Considering that the coding used is the same as the one presented in the previous case, the results are similar. OFDM modulation seems to provide superior results from the perspective of the margin value regarding the modulation order. The highest link margin is still obtained using BDPSK modulation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **LEO Satellite** & **MEO Satellite** \\ \hline Latitude [deg] & 35 & 36 \\ Longitude [deg] & \(-\)40 & \(-\)42 \\ Altitude [km] & 700 & 20,000 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The LEO and MEO satellite proprieties.  
Figure 6: Link margin vs. modulation scheme.
# Set-Based Group Search Optimizer for Stochastic Many-Objective Optimal Power Flow
## 2 Modelling of Stochastic Many-Objective Optimal Power Flow Problem
### Objectives  
To optimize the power flow problem, several key factors are taken into account, including the overall fuel cost of distributed generators, power loss, voltage deviation, and voltage stability index. These considerations are essential for meeting the economic, power quality, and safety requirements.
# Slotted ALOHA on LoRaWAN-Design, Analysis, and Deployment
## 7 S-ALOHA Evaluation in a Real Deployment  
### The end Device  
The end node used in this paper is based on a custom board developed for multiple purposes. It embeds a STM32L476 (STMicroelectronics, Agrate Brianza, Italy) the LoRa RFM95W transceiver, an energy harvester sub-circuit (BQ25570) that manages the power supply, a temperature and humidity sensor (SHT21). Lastly, an expansion connector enables the board to be connected with several analogue and digital external sensors. The current in sleep mode is 4 \(\mu\)A at 3 V with the RTC enabled; the STM32L476 uses 8.25 mA in RUN mode @48 MHz. The RFM95W power consumption in TX and RX is respectively 76 (@10 dBm) and 11.5 mA. The LoRaWAN firmware comes from I-CUBE-LRWAN libraries package from STMicroelectronics, which is configured to work as our S-LoRaWAN. If the MCU is programmed to send a packet every 30 s, the battery (1000 mAh) lifespan is seven months. Figure 10 presents a picture of the board and the related block diagram.
# Evaluation of Structural Stresses of Mountain-Embedded Railway Systems
## 4 Analytical Investigation of the Behavioral Characteristics of the Mountain-ERS Track System
### Effects of the Gradient Variation on the Stress of the Mountain-ERS Track  
By varying the gradient conditions of the mountain-ERS, safety assessments and behavioral analyses were conducted, including gradients of 0%, 50%, 100%, 150%, 180% (reference model), and 200%. As described in Section 2.4, the gradient variations resulted in loads applied in the form of divided loads at these angles. Consequently, the self-weight and train loads were adjusted to consider the variations in the gradients of the structural system.  
The stress inside this section is shown in Figure 22. In the case of S22, slight differences in the values were observed at the position where the precast concrete panel contacted the 50-kg rails as the gradient of the mountain-ERS changed; the stresses, on the other hand, in the bedding and subgrade layers exhibited similar trends. For S11, a trend similar to that of S22 was observed; the tensile stresses increased slightly as the gradient of the mountain-ERS decreased, particularly when the precast concrete panel contacted the rail.  
For S33, significant changes were observed in the precast concrete panels. This is because an increase in the gradient triggered a significant increase in the longitudinal forces acting on the mountain-ERS. Specifically, at a gradient of 0%, the largest difference from the reference model (180%) was observed as the divided force in the transverse direction became zero.  
In each case, the magnitude of the maximum principal tensile stress in the concrete components did not exceed the tensile strength of the concrete, as listed in Table 7. The contours of the principal stress of concrete and the location of the maximum principal tensile stress with variations in the gradient of the mountain-ERS are shown in Figure 23. The von Mises stress contours and location of the maximum value of the reinforcing bars are shown in Figure 24. The occurrence of stress and the location of the maximum principal tensile stress were similar, and their values were also similar to the variation in the gradients.  
Figure 21: Von Mises stress contours and location of the maximum stress value of reinforcing bars inside the precast concrete panel (example: precast concrete panel strength, 60 MPa).
# Joint Interference and Phase Alignment among Data Streams in Multicell MIMO Broadcasting
## 4 Bayesian Estimation of Delay Error CSI
can be modelled as:  
\[\mathbf{H}_{k_{g}j}=\frac{\rho}{1+\delta_{e}^{2}}\hat{\mathbf{H}}_{k_{g}j\tau}^{H}+\bm {E}_{k_{g}j} \tag{13}\]  
Among them, \(\mathbf{E}_{k_{g}j}\) is the error CSI factor which obeys a complex Gaussian distribution with a mean of 0 and a variance of \(1-\frac{|\rho|^{2}}{1+\delta_{e}^{2}}\).
# Key Bit-Dependent Side-Channel Attacks on Protected Binary Scalar Multiplication +
## 2 Conventional SCAs on Scalar Multiplication
### Differential Power Analysis (DPA)  
DPA is a statistical analysis method that analyzes multiple power-consumption traces to find the secret scalar [9]. Typically, DPA is based on the fact that power consumption depends on data values being manipulated. To perform DPA, input or output values of cryptographic algorithms have to be known. Similarly, there is an address-bit DPA based on the fact that power consumption depends on the address value of the register that loads or stores data during the operation. Thus, even if an SPA countermeasure [19; 21; 22], which has a regular power-consumption sequence, as shown in Figure 1b, is applied, it is vulnerable to DPA. To cope with this, randomization techniques that eliminate association between all possible intermediate values and power consumption are generally used [17; 18; 20].
# A Novel Optimization Algorithm Combing
## 4 Experiments
### Performance Comparison between ABC, GABC, and GABCG
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Function**} & \multirow{2}{*}{\(\mathbf{D}\)} & \multicolumn{2}{c}{**ABC**} & \multicolumn{2}{c}{**GABC**} & \multicolumn{2}{c}{**GABCG**} \\ \cline{3-8}  & & **Mean** & **SD** & **Mean** & **SD** & **Mean** & **SD** \\ \hline \multirow{2}{*}{**Rosenbrock**} & 10 & \(4.25\times 10^{-1}\) & \(2.19\times 10^{-1}\) & \(3.40\times 10^{-2}\) & \(1.73\times 10^{-2}\) & \(\mathbf{1.47\times 10^{-2}}\) & \(1.46\times 10^{-2}\) \\  & 30 & \(6.15\times 10^{-1}\) & \(3.28\times 10^{-1}\) & \(5.45\times 10^{-2}\) & \(3.65\times 10^{-2}\) & \(\mathbf{3.16\times 10^{-2}}\) & \(3.71\times 10^{-2}\) \\ \multirow{2}{*}{**Sphere**} & 30 & \(7.62\times 10^{-16}\) & \(1.47\times 10^{-16}\) & \(4.33\times 10^{-16}\) & \(6.79\times 10^{-16}\) & \(\mathbf{2.39\times 10^{-18}}\) & \(3.36\times 10^{-17}\) \\  & 60 & \(7.14\times 10^{-15}\) & \(3.84\times 10^{-15}\) & \(1.62\times 10^{-15}\) & \(2.10\times 10^{-16}\) & \(7.76\times 10^{-15}\) & \(2.88\times 10^{-14}\) \\ \multirow{2}{*}{**Elliptic**} & 30 & \(4.23\times 10^{-7}\) & \(1.70\times 10^{-6}\) & \(4.80\times 10^{-16}\) & \(7.03\times 10^{-17}\) & \(\mathbf{2.75\times 10^{-16}}\) & \(6.66\times 10^{-17}\) \\  & 60 & \(7.22\times 10^{-3}\) & \(7.63\times 10^{-3}\) & \(9.89\times 10^{-15}\) & \(1.57\times 10^{-14}\) & \(\mathbf{2.11\times 10^{-15}}\) & \(7.63\times 10^{-16}\) \\ \multirow{2}{*}{**SumSquares**} & 30 & \(7.43\times 10^{-15}\) & \(1.06\times 10^{-16}\) & \(4.37\times 10^{-16}\) & \(4.87\times 10^{-17}\) & \(\mathbf{2.17\times 10^{-16}}\) & \(3.62\times 10^{-17}\) \\  & 60 & \(7.10\times 10^{-15}\) & \(5.03\times 10^{-15}\) & \(1.95\times 10^{-15}\) & \(3.51\times 10^{-16}\) & \(7.72\times 10^{-16}\) & \(1.03\times 10^{-16}\) \\ \multirow{2}{*}{**Quartic**} & 30 & \(2.78\times 10^{-16}\) & \(6.52\times 10^{-17}\) & \(1.07\times 10^{-16}\) & \(3.37\times 10^{-17}\) & \(\mathbf{5.55\times 10^{-17}}\) & \(1.71\times 10^{-17}\) \\  & 60 & \(2.72\times 10^{-15}\) & \(2.12\times 10^{-15}\) & \(7.21\times 10^{-16}\) & \(1.22\times 10^{-16}\) & \(\mathbf{2.37\times 10^{-16}}\) & \(3.27\times 10^{-17}\) \\ \multirow{2}{*}{**Himmelblau**} & 100 & \(-78.3323\) & \(1.28\times 10^{-11}\) & \(-78.3323\) & \(1.04\times 10^{-13}\) & \(\mathbf{-78.3323}\) & \(6.01\times 10^{-14}\) \\  & 200 & \(-78.3273\) & \(2.06\times 10^{-2}\) & \(-78.3323\) & \(1.06\times 10^{-11}\) & \(-7
# Convergence Gain in Compressive Deconvolution: Application to Medical Ultrasound Imaging
## Appendix A Proof of the Equivalence of Equations (25a) and (6a)-(6b)  
Proof.: From Equation (25a) and the definitions around Equation (7), it is not difficult to verify  
\[w^{k+1}= \arg\min_{w\in\mathbb{R}^{n}}\|w\|_{1}-\lambda_{2}^{k}(v^{k}-\Psi w )+\frac{\beta}{2}\|\Psi w-v^{k}\|_{2}^{2}+\frac{\beta}{2}\|w-w^{k}\|_{\frac{1 }{\tau}-\Psi^{\mathsf{T}}\Psi}^{2}\] \[= \arg\min_{w\in\mathbb{R}^{n}}\|w\|_{1}+\frac{\beta}{2}\|\Psi w+ \frac{\lambda_{1}^{k}}{\beta}-v^{k}\|_{2}^{2}+\frac{\beta}{2}\|w-w^{k}\|_{ \frac{1}{\tau}-\Psi^{\mathsf{T}}\Psi}^{2}\] \[= \arg\min_{w\in\mathbb{R}^{n}}\|w\|_{1}+\frac{\beta}{2\tau}\|w-w^{ k}\|_{2}^{2}-\frac{\beta}{2}\|\Psi w-\Psi w^{k}\|_{2}^{2}+\frac{\beta}{2}\| \Psi w+\frac{\lambda_{1}^{k}}{\beta}-v^{k}\|_{2}^{2}\] \[= \arg\min_{w\in\mathbb{R}^{n}}\|w\|_{1}+\frac{\beta}{2\tau}\|w-w^ {k}\|_{2}^{2}+\beta\Psi^{\mathsf{T}}(\Psi w+\frac{\lambda_{1}^{k}}{\beta}-v^ {k})x\] \[= \arg\min_{w\in\mathbb{R}^{n}}\|w\|_{1}+\frac{\beta}{2\tau}\|w-w^ {k}+\tau\Psi^{\mathsf{T}}(\Psi w+\frac{\lambda_{1}^{k}}{\beta}-v^{k})\|_{2}^{2}\] \[= \prox_{\tau\|\cdot\|_{1}/\beta}\Big{(}v^{k}-\tau\Psi^{\mathsf{T} }(\Psi w+\frac{\lambda_{1}^{k}}{\beta}-v^{k})\Big{)}.\]  
The above equation is exactly Equation (6a). Similarly,  
\[x^{k+1}= \arg\min_{x\in\mathbb{R}^{n}}\alpha\|x\|_{p}^{p}-\lambda_{2}^{k} (v^{k}-Hx)+\frac{\beta}{2}\|Hx-v^{k}\|_{2}^{2}+\frac{\beta}{2}\|x-x^{k}\|_{ \frac{1}{\tau}-H^{\mathsf{T}}H}^{2}\] \[= \arg\min_{x\in\mathbb{R}^{n}}\alpha\|x\|_{p}^{p}+\frac{\beta}{2} \|Hx+\frac{\lambda_{2}^{k}}{\beta}-v^{k}\|_{2}^{2}+\frac{\beta}{2}\|x-x^{k}\| _{\frac{1}{\tau}-H^{\mathsf{T}}H}^{2}\] \[= \arg\min_{x\in\mathbb{R}^{n}}\alpha\|x\|_{p}^{p}+\frac{\beta}{2 \gamma}\|x-x^{k}\|_{2}^{2}-\frac{\beta}{2}\|Hx-Hx^{k}\|_{2}^{2}+\frac{\beta}{ 2}\|Hx+\frac{\lambda_{2}^{k}}{\beta}-v^{k}\|_{2}^{2}\] \[= \arg\min_{x\in\mathbb{R}^{n}}\alpha\|x\|_{p}^{p}+\frac{\beta}{2 \gamma}\|x-x^{k}\|_{2}^{2}+\beta H^{\mathsf{T}}(Hx+\frac{\lambda_{2}^{k}}{ \beta}-v^{k})x\] \[= \arg\min_{x\in\mathbb{R}^{n}}\alpha\|x\|_{p}^{p}+\frac{\beta}{2 \gamma}\|x-x^{k}+\gamma H^{\mathsf{T}}(Hx+\frac{\lambda_{2}^{k}}{\beta}-v^{k} )\|_{2}^{2}\] \[= \prox_{\tau\|\cdot\|_{p}^{p}/\beta}\Big{(}x^{k}-\gamma H^{ \mathsf{T}}(Hx^{k}+\frac{\lambda_{2}^{k}}{\beta}-v^{k})\Big{)}.\]The above equation is exactly Equation (6b), so the proof is complete.
# Estimation of Terrestrial Water Storage Changes at Small Basin Scales Based on Multi-Source Data
###### Abstract  
Terrestrial water storage changes (TWSCs) retrieved from the Gravity Recovery and Climate Experiment (GRACE) satellite mission have been extensively evaluated in previous studies over large basin scales. However, monitoring the TWSC at small basin scales is still poorly understood. This study presented a new method for calculating TWSCs at the small basin scales based on the water balance equation, using hydrometeorological and multi-source data. First, the basin was divided into several sub-basins through the slope runoff simulation algorithm. Secondly, we simulated the evapotranspiration (ET) and outbound runoff of each sub-basin using the PML_V2 and SWAT. Lastly, through the water balance equation, the TWSC of each sub-basin was obtained. Based on the estimated results, we analyzed the temporal and spatial variations in precipitation, ET, outbound runoff, and TWSC in the Ganjiang River Basin (GRB) from 2002 to 2018. The results showed that by comparing with GRACE products, in situ groundwater levels data, and soil moisture storage, the TWSC calculated by this study is in good agreement with these three data. During the study period, the spatial and temporal variations in precipitation and runoff in the GRB were similar, with a minimum in 2011 and maximum in 2016. The annual ET changed gently, while the TWSC fluctuated greatly. The findings of this study could provide some new information for improving the estimate of the TWSC at small basin scales.  
TWSC; small basin scales; the water balance equation; GRACE; ET; runoff +
Footnote †: journal: remote sensing  
Article  
Estimation of Terrestrial Water Storage Changes at Small Basin Scales Based on Multi-Source Data