esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Workability and Compressive Strength of RRAC
#### 3.1.2 Compressive Strength  
The compressive strength of all concrete groups and the strength loss rate of RRAC relative to NAC are shown in Figure 4. The loss rate of compressive strength for RRAC increased, and its compressive strength gradually decreased with the growth of RRCA replacement rate and recycling number. The loss rates of RAC-1 were only 1.43%, 2.86%, and 3.68%, indicating that RAC-1 owned well strength close to that of NAC, especially at a low replacement rate (25%). But the strength loss rate of RAC-2 and RAC-3 increased significantly with the increase of the RRCA replacement rate. At a 100% replacement rate, RAC-3 had the largest loss rate, reaching 17.38%. Even though the compressive strength of RRAC decreased severely at a high replacement rate and great recycling number, all three generations of RRAC reached the target strength of 40 MPa. Therefore, it was feasible to prepare RRAC by using multiple recycling RCA for structural use in terms of compressive strength.  
The falloff of the mechanical property of RRAC was attributed to the degraded quality of RRCA, such as high porosity, low density, high water absorption, etc., as well as the complex interfacial transition zone (ITZ) in RRAC (Figure 5). NAC owned only new ITZ between NCA and new mortar. However, as shown in Figure 5a, for the first generation RAC, three types of ITZs were introduced: (1) The old ITZ (O-ITZ) between the old attached mortar and original aggregate in RCA (black line); (2) the new ITZ (N-ITZ) between the original aggregate and new mortar (blue line); and (3) the mortar ITZ (M-ITZ) between the old attached mortar and new mortar (orange line). O-ITZ and M-ITZ produced by the attached mortar were the weakest part in recycled concrete [40]. Thus, new cracks in RAC were more likely to appear under the action of external force. When the waste concrete was multiple recycled, the ITZ became more complex, as displayed in Figure 5b, because another old mortar ITZ (marked as OM-ITZ) between the old attached mortar and next generation attached mortar appeared (green line). In addition, a great number of micro-cracks occurred in the attached mortar even inside the aggregate due to the iterative crushing processes in the preparation of RRCA [41]. These factors aggravated the decline in the quality of RRCA and compressive strength of RRAC.  
Figure 4: Compressive strength (**a**) and its loss rate (**b**) of RRAC at 28 d.
## 4 Results and Discussion
### Model Fitting
#### 4.3.4 Carbonates Purity Grade (CPG)  
As the dosage of QT and PT was increased, the amount of carbonate in the froth phase decreases. This supports work carried out by previous studies [14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37] regarding carbonate depression. Otherwise even with high doses of OA it does not present itself as a carbonate collector.  
Since WD is low energetic depressant of carbonates, it does not show any considerable effect on CPG. This is due to the termination of the dispersing effect of PT.  
SS in Figure 7 has an effect similar to that presented by QT in Figure 6. High doses of SS promoted the flotation of carbonates. This fact leads to the conclusion that for this type of deposit it is not convenient to depress carbonates and silica at the same time.  
As can be seen in Table 6 QT and SS are the only factors that have significance effect on CPG. Those corroborate what is shown in Figure 7 and discussed in the paragraphs before. Table 6 also shows some interaction between factors that have a statistical effect on CPG. They are PT-QT, PT-WD, PT-SS, QT-WD, QT-OA, WD-OA, WD-SS, and OA-SS.  
Figure 7: 3D surface graphs of CPG showing the effect of PS, QT, SS, OA, and WD.
## 4 Data Aggregation
### Spatiotemporal
There are two differences between the structure of sensor networks and video streaming that affect the performance of data encoding using MPEG encoder in a negative way or positively:  
* The most significant difference that degrades the efficiency of algorithm concerns the uneven dispersion of sensor nodes within the network field opposite to the uniform pattern of pixels in a video image. This evenness is a requisite for implementation of block-matching algorithms. To address the issue, PREMON interpolates the readings at regular grid points. In other words, the mechanism assigns readings of sensor nodes to the closest grid point. Grid points with no sensor readings are labeled as \"_transparent_\" pixels, which match other pixels in encoding process. * Another major difference is the real-time requirements of sensor networks compared to video encoding. Since real-time expectancy in sensor networks commonly is not as constraint as in video encoding, the prediction model can be generated based on a bigger number of previous images and can be applied for large time-scale patterns. This leads to significant improvements in the accuracy of prediction and thus energy-efficiency. Some other relaxed constraints such as less image resolution and lower frame rate also reduce computation and complexity of implementation of the paradigm in sensor networks. The results of the test bed implementation of the algorithm, presented in the paper, show the efficiency of the algorithm in cutting down the number of data transmissions and hence achieving great energy savings.
# SITAEI HC1 Low-Current Hollow Cathode
## HC1 Description  
The HC1 technical specifications are summarized in Table 1. The following discussion outlines the main activities performed to obtain the final cathode assembly.  
The first cathode design was based on a LaB\({}_{6}\) emitter, due to the heritage gained at SITAEL in the development of higher current cathodes with this type of low work function material [4]. Given the relatively high operating temperature, developing a heater to reach the emission temperatures of LaB\({}_{6}\) was a significant challenge, especially regarding the material selection. For this reason, a considerable effort was dedicated to a systematic experimental campaign to test different material combinations, with the aim of improving the heater performance and operational capabilities. The resulting heater configuration was adopted in the cathode assembly, proving the effectiveness in starting and operating the cathode in the expected range of currents and mass flow rates.  
In a further endeavor to improve the cathode efficiency, the LaB\({}_{6}\) emitter was replaced by a barium-based dispenser (BaO-W) emitter, due to the lower operating temperature, which is particularly  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Discharge current & 0.3–1 A \\ Mass flow rate & 0.08–0.2 mg/s \\ Heater power & \(\leq\)50 W \\ Keeper ignition voltage & \(\leq\)100 V \\ Lifetime & \(\geq\)10,000 h \\ On–off cycles & \(\geq\)10,000 \\ Cathode mass & 40 g \\ Geometrical envelope (including interface flange) & \(\Phi\)40 mm diameter, 50 mm length \\ \hline \hline \end{tabular}
\end{table}
Table 1: HC1 technical specifications.  
Figure 1: General schematic of an orificed hollow cathode.  
favorable in such a small device. The heater that had already been developed for the LaB\({}_{6}\) cathode version proved to be even more efficient in increasing the dispenser emitter temperature prior to ignition. At the same time, in the framework of the uHETSat program, the cathode's mechanical structure underwent a redesign phase to achieve the robustness needed to survive environmental tests [9].  
The history of HC1, starting from different development models (DMs), passing through an engineering model (EM), and progressing to the latest version tested under the uHETSat program, is summarized in Table 2. Two engineering qualification model (EQMs) and two proto flight model (PFMs) HC1 cathodes mounted on the uHETSat thruster unit successfully passed the performance, environmental, and thermal vacuum tests, approving them for the in-orbit demonstration.
# Decreasing the Rail Potential of High-Speed Railways Using Ground Wires
## 4 Electric Parameter Calculation
### Calculation of Conductor Internal Impedance  
For standard copper wire, copper alloy stranded wire, aluminum stranded wire and aluminum alloy stranded wire, the internal impedance calculation is equivalent to that of a cylindrical conductor. The internal impedance of a cylindrical conductor can be expressed by the Kelvin function:  
\[Z=\frac{jm\rho}{2\pi r}\frac{\mathrm{ber}(mr)+\mathrm{j}\mathrm{bei}(mr)}{ \mathrm{ber}^{\prime}(mr)+\mathrm{j}\mathrm{bei}^{\prime}(mr)}\ (\Omega\ /\mathrm{m}) \tag{6}\]  
where \(m=\sqrt{\frac{\omega\mu}{\rho}}\); \(\mu=\mu_{r}\mu_{0}\) is the permeability of the conductor; \(\mu_{0}=4\pi\times 10^{-7}\) T\(\cdot\)m/A is the permeability of the vacuum; \(\mu_{r}\) is the relative permeability (for a non-ferromagnetic conductor, generally, \(\mu=\mu_{0}\)); \(\rho\) and \(r\) are the resistivity and radius of the conductor, respectively; and \(\omega\) is the angle frequency in rad/s.  
For steel-core aluminum stranded wire, the calculation of the internal impedance is equivalent to that of a tubular circular conductor. For tubular circular conductors, when the current returns from a distance outside the conductor (the proximity effect can be ignored), the internal impedance calculation formula can be represented by the Kelvin function as  
\[Z=\frac{jm\rho}{2\pi r}\frac{(\mathrm{ber}\left(mr\right)+\mathrm{j}\mathrm{bei }(mr))-\frac{\mathrm{bei}^{\prime}(mq)}{\mathrm{ker}^{\prime}(mq)+\mathrm{j} \mathrm{bei}^{\prime}(mq)}(\mathrm{ker}(mr)+\mathrm{j}\mathrm{kei}(mr))}{( \mathrm{bei}^{\prime}(mr)+\mathrm{j}\mathrm{bei}^{\prime}(mr))-\frac{\mathrm{bei }^{\prime}(mq)+\mathrm{bei}^{\prime}(mq)}{\mathrm{ker}^{\prime}(mq)+\mathrm{ j}\mathrm{kei}^{\prime}(mq)}(\mathrm{ker}^{\prime}(mr)+\mathrm{j}\mathrm{kei}(mr))}\ (\Omega\ /\mathrm{m}) \tag{7}\]  
where \(r\) and \(q\) are the outer and inner radii of the tubular circular conductor. A solid circular conductor can be regarded as a special case of tubular circular conductor when \(q=0\). In this case, Equation (7) degenerates into Equation (6). The calculation of the Kelvin functions of ber, bei, ker and kei involved in the formula above can be found in [23].  
For contact wires with irregular cross-sections, the influence of the cross-section shape on the effective resistance should be considered when calculating their internal impedance,especially when the concerned harmonic frequency is high. The equivalent radius \(r\) of the contact wire can be calculated as  
\[r=r_{p}-(r_{p}-r_{a})e^{-(\frac{r_{p}}{155})} \tag{8}\]  
where \(r_{p}\) and \(r_{a}\) are the radii obtained based on an equal perimeter and equal area of the conductor cross-section, respectively, and \(\delta\) is the penetration depth.
# Stochastic Memristor Modeling Framework Based on Physics-Informed Neural Networks
## 4 Result and Discussion
Unlike previous memristor models based on neural networks that did not account for stochastic behavior [16; 28], the PINN model effectively captures these stochastic phenomena. This capability significantly enhances the analysis of device characteristics, particularly when dealing with inherent variabilities. The memristor can be applied in various forms such as oscillators, logic circuits, chaotic circuits, and neuromorphic systems [38]. In particular, the nonlinearity and variability of the memristor may induce chaotic behavior in oscillators and logic circuits, potentially leading to malfunction in circuits [38; 39]. Additionally, noise can cause instability and signal distortion issues [40; 41]. Therefore, it is crucial to analyze the memristor characteristics with its intrinsic nonlinearity and variability to address these issues. PINN can be utilized extensively to analyze variability, including noise, in these applications. It is observed that memristors exhibit multiple sources of variability, including temperature, voltage dependencies, random telegraph noise (RTN) and cycle-to-cycle variations [18; 42; 43; 44; 45]. These variabilities can be described using various forms of equations. The versatility of the PINN methodology allows it to model these diverse forms of variability by converting them into differential equations. The memristor exhibits RTN characteristics depending on a double-well potential in the high resistance state (HRS) [46]. Furthermore, memristors can exhibit a double-well potential even under white noise and \(1/f\) noise [47]. The weak signals can be amplified by overcoming energy barriers due to the presence of noise. This phenomenon is called stochastic resonance at certain noise level [22; 23; 24; 48; 49]. The stochastic resonance phenomenon is also suitable for applications such as signal processing and random number generation [47; 48; 49; 50]. Such a phenomenon can be analyzed by adding a noise term to existing physical models of memristors [22; 23]. In PINN, the stochastic model including the noise term is rewritten as a FPE and then solved. The FPE is a method of transforming the noise term into the form of a differential equation. The solution is represented as a probability density function with respect to time and state. The time, state, and probability density values obtained by a PINN can be used to analyze stochastic resonance phenomena. This makes the PINN a powerful tool for analyzing complex, stochastic systems where traditional neural network models fall short. This approach enables handling the stochastic influences and more accurately predict the memristor characteristics, thus contributing to the overall understanding of its behavior in neuromorphic and other advanced electronic systems. Figure 10: I-V curves obtained from Langevin Equation (2) and PINNs implemented in Verilog-A [22].
## 6 Denoising Processing of Positioning Data Based on Particle Filter
### Principle of Particle-Filter Algorithm  
The design idea of the standard particle-filter algorithm is to approximate the probability density function of the system with some discrete random sampling points and replace the integration operation with the sample mean to obtain the minimum variance estimate of the state. First, a set of random particle samples was generated according to the prior condition of the system state vector, and the weight of each sampled particle was calculated. Then, according to the system observation information, the particle weights and positions were continuously corrected, and the corrected particles were used to approximate the posterior probability density function of the target state, so that the approximate posterior probability density function can be used to estimate the target state. Finally, the particle filter state was output, namely  
\[\hat{x}_{i}(k)=\sum_{i=1}^{n}\omega_{i}(k)x_{i}(k) \tag{40}\]  
\[P_{i}(k)=\sum\omega_{i}(k)[x_{i}(k)-\hat{x}_{i}(k)][x_{i}(k)-\hat{x}_{i}(k)]^{T} \tag{41}\]  
where \(x_{i}(k)\) is the state of \(i\)th particle iterated at time \(k\), \(\hat{x}_{i}(k)\) is the weighted estimation state of the particle, \(P_{i}(k)\) is the estimated variance, and \(\omega_{i}(k)\) is the particle weight, \(k=1\), \(2\), \(\ldots\), \(T\), \(i=1\), \(2\), \(\ldots\), \(n\).
# On the Use of Biased-Randomized Algorithms for Solving Non-Smooth Optimization Problems
## 1 Introduction
According to our previous experience with using BRAs to solve OPs in different application fields, these algorithms can be especially useful in cases where the solution space is highly irregular (non-convex and/or non-smooth) and requires an extensive exploration stage, thus reducing the  
Figure 1: Exploring the solution space using biased-randomization algorithms. effectiveness of traditional optimization methods. Actually, BRAs have been already proposed to solve non-smooth OPs in different application areas. For instance, they have been used to solve different rich and realistic variants of the well-known vehicle routing problem (VRP), including the two-dimensional VRP [13], VRP variants with horizontal cooperation [14], multi-agent versions of the VRP [15], the location routing problem [16], the fleet mixed VRP with backhauls [17; 18], the multi-period VRP [19], and even other versions of the multi-depot VRP [20]. BRAs have also been employed in solving other OPs, such as the single-round divisible load scheduling [21], the stochastic flow-shop scheduling [22], scheduling heterogeneous multi-round systems [23], the minimization of open stacks problem [24], the dynamic home service routing [25], waste collection management [26], or the maximum quasi-clique problem [27]. Accordingly, the main contributions of this paper are as follows: (i) a discussion on the importance of considering non-smooth objective functions in realistic combinatorial OPs, mainly due to the existence of soft constraints which might be violated to some extent by incurring non-smooth penalty costs, and (ii) a discussion on how BRAs can be employed in different applications to solve these non-smooth OPs in short computing times. The remainder of the paper is structured as follows: Section 2 reviews some basic concepts related to non-smooth OPs. Section 3 presents a review of recent works on BRAs. Sections 4-6 review applications of BRAs to non-smooth OPs in logistics, transportation, and scheduling, respectively. Section 7 provides an overview on current trends and open research lines. Finally, Section 8 concludes by highlighting the main contributions of this work.
## 2 Materials and Methods
### Sample Preparation  
A 1 L bottle of full synthetic SAE 5W-30 grade engine oil (Top Tec 4600, LiquiMoly GmbH, Ulm, Germany; API SN/CF service category) marketed for both gasoline and diesel engines was purchased from a local market in Mannheim, Germany. Four samples of engine oil were prepared by pouring 50 mL of the fresh oil into separate 60 mL amber Boston round glass bottles (Qorpak GLC-01909, Fisher Scientific, Waltham, MA, USA). A 1.5 L container of antifreeze (Kuhler Frostschutz Konzentrat, Gut & Gunstig, Germany) containing ethandiol (glycol) was also purchased from a local retailer in Mannheim, Germany. For each oil sample, an appropriate oil volume was removed by pipette and replaced with the same volume of glycol via pipette to create contaminated samples with 0 ppm, 150 ppm, 300 ppm, and 500 ppm of glycol. The removed/replaced volumes were 7.5 \(\upmu\)L, 15 \(\upmu\)L, and 25 \(\upmu\)L for 150 ppm, 300 ppm, and 500 ppm, respectively. The sample bottles were shipped to Technische Universitat Darmstadt (TUD), Germany for THz-TDS analysis. Prior to THz-TDS analysis, all samples were shaken by hand for 60 s and let stand for 24 h to allow air bubbles to dissipate, after which all the samples appeared homogeneous to the human eye.
## 1 Introduction
In summary, the main contributions of this work are as follows:  
* We propose a lightweight enhancement network (NRGS-Net) for nighttime road glare suppression, which effectively mitigates glare interference in nighttime road images. * We design a lightweight and efficient Uformer backbone by reengineering the upsampling and downsampling structures, reducing model parameters from 20.47 M to 17.88 M while preserving essential spatial information. In addition, we enhance the feed-forward network with adaptive channel weighting to improve detail restoration and structural fidelity under nighttime conditions. * We introduce a gated positional attention (GPA) module, which integrates positional encoding and local contextual information to effectively guide the network in accurately locating and suppressing glare regions of various shapes, thereby enhancing local contrast and visibility in affected areas. * We construct and release the Night Road Glare Dataset (NRGD), a real-world dataset featuring diverse nighttime glare scenes. This dataset supports future research and enables fair performance comparison. * We conduct extensive experiments on NRGD and Flare7k++ dataset using five commonly used evaluation metrics to compare our method with six state-of-the-art approaches. The results demonstrate the effectiveness and generalization ability of the proposed method in glare suppression, visibility enhancement, and detail restoration under nighttime road scenarios.
# Electrochemical Characteristics of Amorphous Ni-P Electroplated Thin Film
## 2 Experimental Procedure
### Evaluation of Electrochemical Properties as Negative Electrodes for Secondary Lithium Batteries  
A two-electrode half cell was built to evaluate the electrochemical characteristics of the Ni-P electrodes synthesized under four different plating conditions. The half cell was fabricated in a glove box (Glove box; Mbraun-Unilab, Garching, Germany) in a high-purity Ar atmosphere. The oxygen and moisture concentrations were maintained below 0.1 ppm during cell fabrication and evaluation. A Ni-P electrode was used as a working electrode, and 16\(\upmu\)b lithium foil was used as a counter electrode. A polypropylene separator (Celgard 2400) with a thickness of 25 \(\upmu\)m was used by cutting it at 23\(\upmu\)b, and 1 M LiPF\({}_{6}\) in ethylene carbonate: ethyl methyl carbonate at a 3\(\cdot\)7 (vol%) ratio was used as the electrolyte. After fabricating the two-electrode half cell, a 3 h rest was provided for electrode soaking and interfacial stabilization. Then, the electrochemical properties were evaluated.  
To verify the specific capacity, coulombic efficiency, and cycle characteristics of the Ni-P electrode as the negative electrode for a lithium-ion battery, it was charged and discharged at a 0.2 C rate at the potential range of 0.02-3 V vs. Li\({}^{+}\)/Li (here, \(n\) C rate means that the charge/discharge current fully charges/discharges the electrode in  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Sample Number** & **pH Adjuster** & **Denomination** & **Concentration of P Source (M)** & **pH** & **Current Density (mA cm\({}^{-2}\))** & **Electrodeposition Time (s)** \\ \hline
1 & & P16 & 0.1 & 2.0 & \(-\)100 & 5 \\ \cline{2-6}
2 & & hydroxide & P21 & 0.2 & 2.0 & \(-\)80 & 10 \\ \cline{2-6}
3 & & P28 & 0.6 & 2.0 & \(-\)40 & 30 \\ \hline
4 & 3M NaOH & P28\_5 & 1.8 & 1.5 & \(-\)40 & 50 \\ \hline \end{tabular}
\end{table}
Table 1: Preparation conditions of nickel phosphide thin films.  
\(1/n\) h). To calculate the specific capacity of the electrode, the mass of the plated Ni-P thin film was obtained by subtracting the mass of the substrate before plating from the mass of the electrode after plating. When evaluating the high-rate characteristics, first, the effect of the irreversible capacity was excluded by charging and discharging twice at a 0.2 C rate; then, the rate performance tests were performed three times each while increasing the charge and discharge rates in the order of 0.2, 0.5, and 1 C rates. Finally, the test was performed three times at the initial 0.2 C rate. The capacity retention was calculated using the specific discharge capacity. The experiment was conducted using a VMP3 system (Biologic Co., Seyssinet-Pariset, France).
# Gateway-Assisted Retransmission for Lightweight and Reliable IoT Communications
###### Abstract  
Message Queuing Telemetry Transport for Sensor Networks (MQTT-SN) and Constrained Application Protocol (CoAP) are two protocols supporting publish/subscribe models for IoT devices to publish messages to interested subscribers. Retransmission mechanisms are introduced to compensate for the lack of data reliability. If the device does not receive the acknowledgement (ACK) before retransmission timeout (RTO) expires, the device will retransmit data. Setting an appropriate RTO is important because the delay may be large or retransmission may be too frequent when the RTO is inappropriate. We propose a Gateway-assisted CoAP (GaCoAP) to dynamically compute RTO for devices. Simulation models are proposed to investigate the performance of GaCoAP compared with four other methods. The experiment results show that GaCoAP is more suitable for IoT devices.  
Constrained Application Protocol (CoAP); Internet of Things (IoT); Message Queuing Telemetry Transport for Sensor Networks (MQTT-SN); retransmission mechanism; retransmission timeout (RTO); round-trip time (RTT) +
Footnote †: journal: Sensors