esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The focus of this work is on the relationship between the quantitative structural characterization of bimetallic Au-Pd nanoparticles dispersed in an amorphous polymer matrix and their catalytic activity in the direct synthesis of hydrogen peroxide (DS reaction). Resonant X-ray powder diffraction with synchrotron radiation was employed to probe selectively and to reveal fine details of the structure of bimetallic nanoparticles embedded in the support. The semi-quantitative analysis of the resonant X-ray powdered diffraction data, made on a large number of metal nanoparticles, shows that in one of the polymer-supported Au-Pd catalyst for the DS reaction (P75) featured by an overall molar Pd/Au of about 5.54, the smallest metal nanoparticles (MNPs), which account for more than 99.9% of the total MNPs number and for more than 95% of the metal surface, are formed by practically pure palladium. The relative number of bimetallic alloyed nanoparticles is very small (less than \(4\times 10^{2}\) ppm) and they contribute to only about 2% of the total metal surface. In a second gold-enriched catalyst (P50) with an overall molar Pd/Au of 1.84, the proportion of the bimetallic alloyed nanoparticles increased to about 97% and they account for about 99% of the metal surface. As a result of the metal intermixing, the catalytic productivity for the DS reaction increased from 97 to 109 mmol\({}_{\text{H}_{2}\text{O}_{2}}\)/mol\({}_{\text{H}2}\), owing to the gold-promotion of palladium.  
Keywords:hydrogen peroxide; anomalous diffraction; palladium; gold +
Footnote †: journal: Applied Physics Letters B
## 2 Experimental Setup
### Data Acquisition Device  
To measure actual current change in this experiment, a precise current measurement was performed using a high-battery battery ammeter clamp (9287 Universal Clamp on CT, Hioki Co., Nagano, Japan) as shown in Figure 2a. An ammeter clamp was installed on high-voltage wire connected to the high-voltage battery and drive motor. Measured current data was stored through the PicoScope shown in Figure 2b and charge and discharge states were analyzed by analyzing measurement data. Figure 2c shows the real-time estimation of SOC, Motor RPM, Engine RPM, HSG RPM, Injection Duration, and Voltage. Specifications of the ammeter clamp are shown in Table 2. Figure 3 shows an overall schematic diagram of the experimental setup used in this study.
# On Construction of a Campus Outdoor Air and Water Quality Monitoring System Using LoRaWAN
## 4 Experimental Results
### Discussion  
From the results, we can summarize that the largest information rate (in units of information per unit time) that can be obtained with arbitrarily tiny error probability is defined as the channel capacity. That is the tight upper bound on the rate at which information may be successfully sent across a communication channel. The greatest mutual information between the channel's input and output determines the channel's capacity, where the maximization is with respect to the input distribution.
## 3 Optical Properties
### Microdisk Lasers Based on QWDs
#### 3.5.1 CW Performance
The threshold was determined from the light-current characteristic showing a pronounced knee accompanied by linewidth narrowing down to 18 \(\pm\) 2 pm (our spectral resolution limit). A representative dependence of the dominant mode intensity and linewidth on injection current for a 31 \(\upmu\)m in diameter QWD MD laser is shown in Figure 18b. Figure 17: Total power in free space and voltage of a 31 \(\upmu\)m in diameter MD laser as a function of current. Dotted line: 0.35 mW/mA. Figure 18: Threshold current of QWD (circles) and QD (squares) MD lasers as a function of diameter (**a**). Current dependence of integrated lasing line intensity (squares) and FWHM (circles) for the 1088 nm mode of a 31 \(\upmu\)m MD laser (**b**). When QWD MD diameter decreases from 31 \(\upmu\)m to 15 \(\upmu\)m threshold current density grows from 710 A cm\({}^{-2}\) up to 2300 A cm\({}^{-2}\) (Figure 18a). Such a sufficiently steep threshold current increase can be explained by the fact that the relative contribution of non-radiative recombination at MD sidewalls into total threshold current increases with a decrease in MD diameter. In MD lasers based on 1.3 \(\upmu\)m InAs/InGaAs/GaAs QDs threshold current density is nearly independent of MD diameters in the range of 14-31 \(\upmu\)m. These facts are in agreement with the results of mesa arrays studies (Section 3.2), showing that carrier lateral diffusion lengths in QDs are shorter than in QWDs. In 31 \(\upmu\)m in diameter, QWD MD lasing was observed up to 110 \({}^{\circ}\)C, and the maximal temperature was restricted by the available equipment. For 19 \(\upmu\)m in diameter MD laser, the maximal lasing temperature was 90 \({}^{\circ}\)C, whereas for 10.5 \(\upmu\)m in diameter device it was only 40 \({}^{\circ}\)C. The smaller is the MD size; the steeper is the growth of threshold current density with temperature increase. The population of the continuum states in QWDs increases with temperature, and a greater fraction of carriers can diffuse towards etched sidewalls and experience non-radiative recombination. Thus, the component of the total threshold current, which corresponds to surface non-radiative recombination, rises with a decrease in MD size. We expect that passivation of MD sidewalls [80] will allow achieving low threshold high temperature lasing in QWD MD with diameters down to 10 \(\upmu\)m
## 3 Main Methodologies
### GMM-Based Fixed-Time Controller Design  
In this section, a novel nonlinear feedback controller is proposed for safe proximity operation to spacecraft with complex shape, by combining the GMM with fixed-time control method.  
By defining \(\mathbf{y}_{1}=\mathbf{e}\in\Re^{3}\) and \(\mathbf{y}_{2}=\dot{\mathbf{e}}\in\Re^{3}\), and ignoring the external disturbance \(\mathbf{f}_{d}\), Equation (  
where,  
\[\
abla_{r}\phi=\mathbf{P}+\mathbf{M}\sum_{j=1}^{K}\eta_{j}-\sum_{j=1}^{K}\eta_{j}\partial_ {j}\mathbf{y}_{1}^{T}\mathbf{M}-\mathbf{M}\mathbf{y}_{1}\sum_{j=1}^{K}\eta_{j}\partial_{j}^{T}+ \frac{1}{2}\mathbf{y}_{1}^{T}\mathbf{M}\mathbf{y}_{1}\sum_{j=1}^{K}\eta_{j}\Big{[}\theta_{j }\partial_{j}^{T}-\Sigma_{j}^{-1}\Big{]}, \tag{27}\]  
where, \(\eta_{j}\) and \(\theta_{j}\) are written as  
\[\eta_{j}=\frac{\pi_{j}}{2\pi\sqrt{\big{|}\Sigma_{j}\big{|}}}e^{-\frac{1}{2}( \mathbf{r}-\mathbf{\mu}_{j})^{T}\Sigma_{j}^{-1}(\mathbf{r}-\mathbf{\mu}_{j})}\,\ \theta_{j}=\Sigma_{j}^{-1}\Big{(}\mathbf{r}-\mathbf{\mu}_{j} \Big{)}. \tag{28}\]  
Substituting Equation (20) into Equation (22) yields  
\[\begin{split}\dot{\mathbf{s}}_{1}&=-\alpha_{1}\mathbf{s}_{1 }-\beta_{1}\mathbf{s}_{1}^{3}+\mathbf{s}_{2}\\ \dot{\mathbf{s}}_{2}&=\Lambda\Big{(}\mathbf{y}_{1}\Big{)}\bm {y}_{2}+k_{s}\Gamma\Big{(}\mathbf{y}_{1},\mathbf{y}_{2}\Big{)}+\Big{[}k_{s}\Gamma \Big{(}\mathbf{y}_{1}\Big{)}+\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\bmbm{\bmbm{\bmbm{\bmbmbm }}}}}}}}}}}}}}}}}} \Substituting Equation (33) into Equation (34) yields  
\[\dot{V}\leq-\sum_{i=1}^{2}\lambda_{i-\min}(2V)^{\frac{\zeta_{i}+1}{2}}\leq-\sum_ {i=1}^{2}\sqrt{2}\lambda_{i-\min}V^{\frac{\zeta_{i}+1}{2}}. \tag{35}\]  
Therefore, from Corollary 1, it can be concluded that the system Equation (22) with GMM-FTC is stable in fixed-time.  
The convergence time \(T_{\max}\) is given as  
\[T_{\max}=\frac{\sqrt{2}}{\lambda_{1-\min}(1-\zeta_{1})}+\frac{\sqrt{2}}{ \lambda_{2-\min}(\zeta_{2}-1)}. \tag{36}\]
## 3 Measurement Setup
### The Experimental Procedures  
In order to characterize the mmWave channel, two different antenna polarizations were used, i.e., vertical-horizontal (V-H) and vertical-vertical (V-V) for both the LOS and NLOS environments. In order to verify the precision and to confirm the experimental outputs, the measurement results were verified five times for a specific environment. As for the LOS measurements, the transmitter and receiver were realigned at the clear-cut point, and using the laser pointer both were aligned at the same azimuth and elevation angles to ensure that the boresight of both the transmitter and the receiver were pointing to each other. In order to minimize the random errors for each scenario, the measurements were fixed at 100-200 samples/reading period. The recorded data were stored in an internal storage of the spectrum analyzer, which were later transferred to the PC for successive processing. Both Tx and Rx were rotated in the x-y plane at azimuth angles of -20\({}^{\circ}\), 0\({}^{\circ}\), and +20\({}^{\circ}\). As for the LOS characterization, the Rx was placed at 1 m from Tx and then the measurement points were collected in increments with a 5-m gap between each subsequent reading point, up to 50 m (see Figure 1a). In the NLOS, the measurements were scaled up to 50 m, while the Rx was placed at a minimum distance of 20 m, as a starting point, to achieve a perfect NLOS scenario, and then recorded the measurements starting from 50 m with a 1-m gap (see Figure 1b).
# A Robust Timing Synchronization Algorithm Based on PSSS for LTE-V2X
## 3 Traditional PSSS-Based Time Synchronization Algorithm  
### PSSS-Based Time Domain Cross-Correlation Algorithm  
The traditional cross-correlation algorithm is a commonly used symbol synchronization method in the LTE system, and it can also be applied to the LTE-V2X system. This algorithm involves cross-correlating two distinct local synchronization signals with the received signal, which is intercepted by a sliding window, as illustrated in Figure 4. Subsequently, the algorithm performs a maximum likelihood decision to identify the correlation peak, allowing the determination of the local signal type and the Fast Fourier Transform (FFT) window position.  
The cross-correlation value and maximum likelihood decision are defined as  
\[C_{i}(\theta)=\left|\sum_{n=0}^{L-1}r^{*}(\theta+n)p_{i}(n)\right|^{2} \tag{3}\]  
\[(\hat{i},\hat{\theta})=\underset{i,\theta}{\operatorname{argmax}}\{C_{i}( \theta)\} \tag{4}\]  
where \(r(n)\) is the received signal, \(p_{i}(n)\) is the local PSSS of two types, \(\theta\) is the start point of the sliding window and \(L\) is the length of the sliding window.  
The cross-correlation algorithm is straightforward to implement; however, its complexity can become unexpectedly high when dealing with long received signals or large sliding window lengths. In situations involving a significant frequency offset or low SNR, a'secondary peak' may emerge near the correct peak position, leading to a failure in peak judgment. Consequently, the algorithm may struggle to adapt to the synchronization detection requirements of V2X.
## 2 Materials and Methods
### Task Scheduling Algorithm
#### 2.2.1 Classical Scheduling Algorithms  
The random scheduling (RS) method iterates through all the viable task and machine combinations and creates a tuple for each combination. The algorithm then determines, for each possible combination, which combination to select, based on a randomly generated value and a threshold. The combination is selected if the randomly generated value is larger than the threshold; otherwise, the loop continues, and another combination is sought. If there are no possible task and node combinations, the algorithm yields none. The objective  
Figure 4: Service delivery paradigm of the CEHIS.  
Figure 3: A computational job and tasks, including the task instances that compose it.  
of this technique is to schedule tasks efficiently by satisfying node capacities and task requirements while selecting tasks and nodes randomly.  
First Fit (FF) is an algorithm designed to assign tasks sequentially to the first available node that fits their requirements. Specifically, at the start of the scheduling procedure, the FF algorithm iterates through all task and node combinations in search of a node that fits the requirements and is available. After locating a suitable node, the algorithm assigns the task to it and removes it from the task list. If there are no available nodes, the task remains in the task list until an appropriate node becomes available.  
The Tetris packing algorithm is named after the popular video game Tetris because it places tasks into nodes by stacking blocks in accordance with certain principles. The Tetris algorithm computes the fitness between all the viable nodes and tasks, then selects the node-task combination with the highest fitness. The evaluation criteria for the fitness computation is the product of the resource utilization rates of the node and task; the greater the fitness, the more balanced the resource utilization between the node and task. This method of selection enables the Tetris algorithm to prevent fragmentation of residual capacity to a certain degree, resulting in a higher utilization rate.  
Classical scheduling methods, such as the aforementioned RS, FF, and Tetris algorithms, can partially serve the scheduling needs of computational tasks. However, they have two constraints, as listed below:  
* Lack of adaptability. Classical scheduling algorithms are often inflexible, meaning that once a task has been scheduled on a machine, it cannot be rescheduled. In real-world applications, however, task characteristics may change with time and environmental conditions, demanding ongoing modifications and optimizations of the task scheduling systems.
* Lack of optimization. Traditional scheduling algorithms are frequently dependent on heuristic principles or greedy strategies, making it difficult to identify the globally best solution. In addition, due to the interactions between activities, simple task scheduling is frequently insufficient to address the optimization needs of the system.
# Indoor Building Reconstruction from Occluded Point Clouds Using Graph-Cut and Ray-Tracing
## 2 State-Of-The-Art
[PERSON] and [PERSON] [20] developed a grammar-based methodology to reconstruct MW indoor spaces by iteratively placing, connecting, and merging cuboids. This methodology was further extended to extract the topological relations between indoor spaces for indoor navigation [21]. These approaches are productive in large-building reconstruction but they have been tested in the absence of clutter. Apart from building bounding modelling, doors and windows are also permanent structures in indoor scenes and several methods have been proposed in recent years. A voxel-based labelling approach based on a visibility analysis [22] was proposed by [23; 24]. The Generalized Hough Transform is used on wall orthoimages generated from colored point clouds to detect closed doors [25]. The methodology was further extended to classify door candidates into open and closed doors by studying the point-to-plane distribution of points close to the door candidate [26]. Another approach based on orthoimages is the one presented by [27], in which doors are detected by finding horizontal and vertical lines. Trajectories are also used to detect doors crossed during data acquisition with an IMMS [7]. Assuming doors as typically lower in height than the walls they are contained, doors are extracted from searching peaks in a vertical profile of the point cloud extracted along the trajectory. Another approach based on a ray-tracing algorithm to detect openings from occluded areas in a voxelized point cloud was implemented by [28].
# Soil Erosion Estimates in Arid Region: A Case Study of the Koutine Catchment, Southeastern Tunisia
## 4 Results and Discussion  
### Rainfall Data and Erosivity (R)  
The analysis and evaluation of the rainfall data trends were conducted based on the spatial and inter-annual variation coefficients. The selection of different stations was determined based on the quality and availability of rainfall data. \(R\) factor calculated for the eight rain-gauge and weather stations around Koutine catchment shows, after interpolation, a gradient of values evolving between 24.97 and 33.15 MJ\(\cdot\)mm/ha/h/yr (Figure 7a).  
Figure 6: Cistern system components. In situ measured soil loss was taken from the deposited eroded sediment in the settlement basin.  
During the monitoring period, the spatial distribution of the rainfall erosivity in the year 2017 shows that the Koutine catchment is subjected to moderate rainfall intensity upstream. The \(MFI\) was equal to 109.0, 106.8 and 113.0, respectively, in Beni Khedache, Zammour and Allamet stations. The \(R\) factor values used in our study were adopted from [PERSON] et al. [42], considering the different soil properties and rainfall characteristics of both studies. This results in different \(R\) factor ranges (\(R\) is in [24; 25; 26; 27; 28; 29; 30; 31; 32; 33] in our study \(<  
19.4% are Sandy (S) and 1.6% are Sandy Clay Loam (SCL). The study area had a high sand percentage that varied from 54.7% up to 94.4%. In contrast, the clay content ranged from 3.7% up to 32.2% and silt content from 0.0% up to 29.7%. Detailed information on soil properties of the Koutine catchment and impluvium of different cisterns are given as Supplementary Materials.  
Erodibility is low for soils with a high percentage of clay because clay particles aggregate together and resist detachment and transport processes [58]. According to Equation (6), it was found that erodibility is negatively correlated with clay content (Figure 8a) but is positively correlated with sand content. This result corroborates with the finding of [PERSON] et al. [59].  
The results of this analysis showed that organic matter content varied from the minimum of 0.18% up to a maximum of 2.02%, with 60% of samples ranging from 1.01% up to 2.02%. Organic matter content is considered a moderate level that can prevent soil from detachment by the rainfall kinetic energy and provide a shallow threshold of soil erodibility.  
According to USDA classification, 96.0% of Wadi Koutine catchment have soils with moderate to rapid permeability. It was observed in numerous studies [60] that the presence of clay decreases the level of soil permeability.
## 2 Method for SOC Estimation  
### Gated Recurrent Unit Neural Network  
Like long short-term memory neural network (LSTM) [24], GRU is also a variant of recurrent neural network (RNN) [25; 26], which is mainly proposed to solve problems such as long-term memory and gradients in backpropagation. However, compared to LSTM, GRU has a simpler structure due to one less gate. As shown in Figure 1, it is obvious that GRU has only two gates: a reset gate and an update gate, which makes the model less parameterized, less computationally expensive, and faster to train. The role of the reset gate is to control the retention of historical information. The larger the value of the reset gate, the less historical information is ignored. The update gate is used to control the effect of the previous cell state and the current new input on the current new cell state. The mathematical calculation process of GRU is as follows:  
\[\tanh(x)=\frac{e^{2x}-1}{e^{2x}+1} \tag{6}\]  
The GRU architecture performed well in processing long time series data, and can share the same parameters at different time steps. The battery data, such as battery voltage, current, and temperature, are all one-dimensional time series data. It is reasonable to use GRU to estimate SOC [27]. In this paper, the dataset \(\{(x_{1},y_{1}),\ \{(x_{2},y_{2}),\ \cdots,\{(x_{t},y_{t})\}\}\) was fed into the GRU network for training, wherein the input vector of the GRU was \(x_{t}=[V_{t},I_{t},T_{t}]\) and the output value of the GRU was \(y_{t}=SOC_{t}\).
## 1 Introduction
The rheological properties have been the subject of several investigations for various powders and binder systems used in MIM [31; 32; 33]; such feedstock properties are affected by the binder system and powder characteristics. An optimum powder loading is also crucial for achieving an appropriate rheological behavior. While some researchers have carried out several rheological investigations to study the impacts of powder loading on the MIM process [30; 31; 32], finding the optimal powder content should be carried out for each powder and binder system since each feedstock behaves differently. Therefore, the rheological properties of the Ce-containing powders produced in this study and the corresponding feedstocks with variable powder contents should be assessed, which was one of the aims of this study. After thermal debinding, low residual carbon content is one of the most critical desirable characteristics of the binder system [34; 35; 36; 37]. Carbon contamination derived from the binder system and oxygen pickup during the various steps of the MIM process can drastically reduce the magnetic characteristics of a permanent magnet [20; 38; 39; 40; 41]. Several studies have been carried out to use different backbones, such as high-density polyethylene (HDPE) [42; 43; 44], low-density polyethylene (LDPE) [45], ethylene-vinyl acetate copolymer (EVA) [46; 47; 48], polypropylene (PP) [43; 49; 50; 51], and polymethyl methacrylate (PMMA) [52; 53; 54; 55] in the binder system. [PERSON] et al. [56] suggested that a polyoxymethylene (POM) based binder system could be a good choice due to decomposition at lower temperatures, which reduces the risk of reaction between the binder system and the powder particles during debinding and sintering. However, it was later proved that the POM binder system starts degrading during the injection molding step at the processing temperature of 170 \({}^{\circ}\)C in the presence of magnetic alloys, which makes it challenging to use this binder system for manufacturing magnets [57]. In this investigation, linear low-density polyethlene (LLDPE) was selected as the backbone since it has a low price, and it decomposes by linearly reducing its molecular weight without crosslinking, as is the case of LDPE. Although other studies have investigated the impact of carbon residue from the binder system on the magnetic properties [43; 58], different binder systems and powder characteristics have unique influences. Therefore, this study aims to provide additional information on minimizing carbon and oxygen contamination of permanent magnets produced using MIM by varying the binder formulation while maintaining adequate rheological properties and selecting LLDPE as a backbone in the binder. In summary, the current investigation studied the effects of unique binder system formulations and powder loading on the rheological behavior, contamination, and magnetic properties of Ce-containing Nd-Fe-B magnets, which are crucial aspects for the development of economical, reliable permanent magnet production via MIM.