esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Electro-Fenton-like Tests
#### 3.3.1 Electro-Fenton-like vs. Anodic Oxidation  
The first step was the evaluation of the removal of Rhodamine B by an electro-Fenton-like process. For this purpose, a test was performed using 50 mg of the ZnO/g-C\({}_{3}\)N\({}_{4}\) composites as a catalyst, and the obtained results were compared with the simplest AOP's anodic oxidation under the same conditions (25 mA). The degradation profiles, shown in Figure 7a, highlight the catalytic effect of the composites being clearly observed and reaching 82.39% degradation in 45 min. Comparing the total degradation achieved after 90 min, the electro-Fenton-like treatment demonstrated a 35% improvement over the anodic oxidation. Figure 7a also illustrates the exponential decrease in Rhodamine B concentration, following a pseudo-first-order kinetic model (Equation (2)). The kinetic constants calculated for each of the treatments are presented in Figure 7b, and their values were 0.0115 and 0.0415 min\({}^{-1}\), respectively. These results demonstrate the improved efficiency of the electro-Fenton-like process, with a kinetic coefficient four times higher than that of anodic oxidation.  
In addition, the energy consumption (EC) was evaluated by Equation (5). The values obtained (Figure 7b) show a significant reduction in EC for the electro-Fenton-like process compared to the anodic oxidation. These facts confirmed the good catalytic activity of the synthesized composites to decompose H\({}_{2}\)O\({}_{2}\) into reactive radicals. In this configuration, the selection of PLA conductive electrodes allowed for the generation of H\({}_{2}\)O\({}_{2}\), reaching an excess concentration of 1.5 mg/L after 90 min in the degradation treatment. Given this maintained production, O\({}_{2}\) was not added during the course of the treatment, which resulted in cost savings.
of multiple organizations, is combined with several scientific payloads to achieve wideband spectral detection [5]. The Hubble Space Telescope (HST) is extensively used for detecting the atmospheric species of numerous exoplanets, of which the observation using spatial scanning mode is performed for exoplanet transit-spectroscopy studies [6]. Despite the great significance to astronomical studies, these telescopes are general multipurpose facilities having a series of scientific goals as opposed to solely the observation of exoplanets, without simple optical designs and simultaneous wide-band observations. A dedicated survey mission with carefully designed payloads and a stable satellite platform for transit spectroscopy is necessary to study exoplanet-atmosphere characteristics.  
According to the characteristics of a transit-spectrum signal, the optional parameter ranges of the corresponding optical system were derived in this paper. The influences of the signal-to-noise ratio (SNR) and number of transits were particularly considered when setting the aperture size and throughput. Furthermore, to overcome the shortcomings mentioned above, an optical system for the transit spectral observation of the atmosphere of exoplanets is presented. The system consists of a space-based telescope with a large collecting area and high optical efficiency, and an infrared spectrometer capable of dual-channel wide-band spectrophotometric detection. The imaging performance of the optical system was simulated in ZEMAX software.  
The contributions of this paper are summarized as follows: (1) in order to study the atmosphere characteristics of exoplanets, related optical instrument indices were systematically derived; (2) transit SNR is introduced to the determination of telescope aperture and throughput; and (3) to verify the feasibility of the optical indices, a novel optical system was designed.
## 1 Introduction
In view of the development of the second generation of Eurocode EN 1991 (Part 1-3--Snow Loads) [12] by Mandate M/515 of the European Commission [13] to the European Committee for Standardization (CEN) [14], an in-depth assessment of roof snow load value design is illustrated in the paper, based on the statistical analyses of an extensive database of measurements directly collected by in-situ measurements from many European buildings. The proposed methodology aims to evaluate the reference value of roof snow loads, accounting for uncertainties affecting both ground snow loads and conversion factors. First, appropriate probability density functions (_pdf_s) of ground and roof snow loads are defined starting from the in-situ measurements taken in Europe [10; 15; 16]. Subsequently, the joint _pdf_s and the design conversion factors (shape coefficients) to be used for structural design are determined in such a way that the reference values of roof snow load and ground snow load are associated with the same probability of exceedance (e.g., \(p=0.02\) in one year). Since the design values of the conversion factors are mainly dependent on the scattering of ground snow load, curves for the derivation of design conversion factors are provided as a function of the coefficient of variation of ground snow loads (\(COV_{g}\)), accounting for the roof's geometry and its exposure to wind. In the study, in accordance with [12], three relevant wind exposure conditions are considered: sheltered or non-exposed, semi-sheltered or normal, and windswept or exposed. Finally, the design conversion factors for flat and pitched roofs are compared with the values provided in EN1991-1-3:2003 [12] and in the new version being developed [17], showing the estimated probability of exceedance of snow loads on roofs in association with the Eurocode models. It must be stressed that available roof snow load data generally cover limited periods of observation, relying on the assumption of the stationarity of the atmospheric phenomena and of the exposure conditions. Consequently, the potential influence of climate change cannot be directly assessed by looking at the existing measurements. Methodologies for the elaboration of climate projections can provide tools to tackle that issue, as has already been done for ground snow load [1; 2], but it must be underlined that refined information about the dependance of the shape coefficients on the local climate evolution at the building scale is still lacking. Moreover, specific studies show that future trends of wind velocity due to climate change are not so significant as to modify exposure conditions [18; 19].
## 2 Background
### Wireless Sensors Network (WSN)  
As the name implies, a Wireless Sensor Network (WSN) comprises a collection of sensors located in different areas of a building or campus that are linked wirelessly. As can be seen in Figure 1, a sensor node, also known as a mote, is an electronic device that incorporates a CPU, memory, a transceiver module, a single sensor or many sensors, an analog-to-digital converter (ADC) [36, 37, 38], and a power supply, typically a battery. A positioning unit and a mobilizing unit are available additions. A sensor node's sensor(s) monitors the surrounding environment for changes in the present conditions. To handle these readings, the CPU at the node first converts them from analog to digital form using the ADC unit [39, 40, 41, 42]. The data processed by the node can be sent wirelessly to other nodes and a designated sink point, called the Base Station, using the node's transceiver [43].  
The Base Station can exercise supervisory control over the WSN to which it belongs and relay relevant data to either human users or/and other networks by using the data transmitted to itself [44]. By working together, many sensor nodes in a WSN can simultaneously collect data from multiple sites of interest dispersed across large regions. Constant technical progress has made possible the cheap mass production of such sensor nodes, which despite their diminutive stature [45], have incredibly sophisticated sensing, processing, and communication capacities. This is why, even though WSNs were initially employed for military functions, they currently support an ever-expanding variety of uses [32].
## 3 Efficient Network AOS Comprehensive Multiplexing Algorithm
### Asynchronous Non-Real-Time Traffffic
#### 3.3.2 Virtual Channel Scheduling Algorithm  
In the virtual channel multiplexing layer, a uniform queue length virtual channel scheduling algorithm is employed. For asynchronous non-real-time traffic, it is essential to ensure the uniformity of scheduling across multiple virtual channels. Therefore, the cache occupancy of the current VC is utilized as the scheduling decision parameter. Let \(o_{e\_p}\) represent the cache occupancy of the \(p\)-th VC at the \(\left(r,k\right)_{sche}\) scheduling moment. Assuming that the \(n_{e}\)-th virtual channel exhibits the maximum cache occupancy, then the \(n_{e}\)-th channel is selected as the channel to be scheduled during the current time slot. The scheduling algorithm can thus be formulated as follows:  
\[Z_{e}(r,k)=n_{e}(r,k) \tag{15}\]  
where the channel \(n_{e}\) satisfies the following:  
\[o_{e\_n_{e}}(r,k)=\max(o_{e\_0}(r,k),o_{e\_1}(r,k),o_{e\_2}(r,k)\ldots\ldots \ldots o_{e\_N_{p}-1}(r,k)) \tag{16}\]
## 6 Results and Discussion
### CFD Model Validation  
A comparison of the air quantity survey with the CFD simulation results is presented in Tables 6 and 7. The values in Tables 6 and 7 show the percentage difference between survey and CFD results, which is calculated by dividing the difference of the survey and CFD results by the survey result. The first stage comparison for the total quantity of air passing through each cross-section indicates a maximum variation of 9.9% at the first cross-section for the 0% fan speed case (Table 6). The second stage comparison for the velocities at six points at each cross-section shows a maximum variation of 18.2% at the first cross-section for the 100% fan speed case, except for one outlier of 23.9% at the fourth cross-section for the 100% fan speed case (Table 7). This large difference could be due to the occurrence of recirculation in that area and the inability of a hotwire anemometer to read air velocities in three dimensions. The simulation results are considered to be reasonably  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Cross-Section No.**} & \multicolumn{4}{c}{**Quantity (m\({}^{3}\)/s)**} \\ \cline{2-5}  & **0\% Fan Max RPM** & **50\% Fan Max RPM** & **100\% Fan Max RPM** \\ \hline
1 & 23.23 & 23.23 & 23.23 & 23.23 \\
2 & 23.64 & 23.62 & 23.62 \\
3 & 24.22 & 24.38 & 25.16 \\
4 & 23.33 & 20.33 & 18.56 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of computational fluid dynamics (CFD) simulation showing quantity through different cross-sections for different scrubber fan capacities.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Cross-Section No.**} & \multicolumn{4}{c}{**Velocity (m/s)**} \\ \cline{2-9}  & \multicolumn{2}{c}{**0\%**} & \multicolumn{2}{c}{**50\%**} & \multicolumn{2}{c}{**100\%**} \\  & \multicolumn{2}{c}{**Scrubber Capacity**} & \multicolumn{2}{c}{**Scrubber Capacity**} & \multicolumn{2}{c}{**Scrubber Capacity**} \\ \hline
1 & 3.31 & 3.28 & 3.25 & 3.31 & 3.28 & 3.25 & 3.32 & 3.28 & 3.25 \\
1 & 3.81 & 3.18 & 3.26 & 3.82 & 3.18 & 3.26 & 3.84 & 3.18 & 3.26 \\
2 & 3.92 & 3.75 & 3.61 & 3.95  
accurate considering the complexity of the geometry, simplifications made in the CFD models to address computational resources, and the experimental errors in measurement.
## 4 Mapless Localization
### 2-D Position Estimator Based on Particle Filter and Gaussian Process  
A particle filter involves obtaining a recursive estimate of the posterior distribution \(x\in\mathcal{R}^{2}\) at current time \(k\), given all the observations \(z_{1:k}\in\mathcal{R}^{r}\). When we define \(\{x_{k}^{i},w_{k}^{i}\}_{i=1}^{N_{p}}\) as the set of \(N_{p}\) particles and corresponding weights, the posterior density function is  
\[p\left(x_{k}|z_{1:k}\right)\approx\sum_{i=1}^{N_{p}}w_{k}^{i}\delta(x_{k}-x_{k }^{i}). \tag{37}\]  
In Equation (37), \(\delta(\cdot)\) is the Dirac delta function. The weights are normalized so that \(\sum_{i=1}^{N_{p}}w_{k}^{i}=1\). The estimate of the state \(x_{k}\) is given by  
\[\hat{x}_{k}=E[x_{k}|z_{1:k}]\approx\sum_{i=1}^{N_{p}}w_{k}^{i}x_{k}^{i}, \tag{38}\]  
and the weights are updated using the likelihood \(p(z_{k}|x_{k}^{i})\):  
\[w_{k}^{i}=w_{k-1}^{i}p\left(z_{k}|x_{k}^{i}\right).\]  
In this study, the likelihood is defined as a Gaussian process to achieve a nonlinear relationship between positions and RSSI observations, and is given by  
\[p\left(z_{k}|x_{k}\right) = \prod_{j=1}^{r}p(z_{k}^{j}|x_{k})=\prod_{j=1}^{r}N(\mu_{x_{k}}^{ j},\sigma_{x_{k}}^{j}), \tag{39}\]  
where \(N(\mu_{x_{k}}^{j}(\cdot),\sigma_{x_{k}}^{j}(\cdot))\) is a Gaussian distribution whose mean and variance are as follows,  
\[\mu_{x_{k}}^{j} = k_{*}^{\text{T}}(K(\bar{X},\bar{X})+\sigma_{G}^{2}I)^{-1}z_{*}^{j}\] \[\sigma_{x_{k}}^{j} = k(x_{k},x_{k})-k_{*}^{\text{T}}(K(\bar{X},\bar{X})+\sigma_{G}^{ 2}I)^{-1}k_{*}. \tag{40}\]  
In Equation (40), training input \(\bar{X}\in R^{n\times 2}\) is defined as the pseudolabels of the x-y positions obtained in Section 2.2, and training input \(z_{*}^{j}\in R^{n}\) is the Wi-Fi observation set corresponding to the \(j\)-th pseudolabels in \(\bar{X}\). Further, the kernel function and matrix, \(k(\cdot,\cdot)\) and \(K\), respectively, are defined by Gaussian kernel, and \(k_{*}\) is the \(n\times 1\) vector of covariances between \(x_{k}\) and \(\bar{X}\). More details about the derivation of the Gaussian process and parameter selection can be found in the work by the authors of [47].  
Here, \(z^{j}\), for \(j=1,\cdots,r\) (\(r=10\) in this paper) are the PCA-driven observations from Section 3.2.2, that is, \(z=T^{\text{pca}^{\dagger}}y\), where \(y\) is the raw Wi-Fi RSSIs and \(T^{\text{pca}}\) is the transformation matrix in Equation (23). As \(r=10\), ten different Gaussian process models as in Equation (39) are used.
## 4 Routing Layer
### Mobility
An evaluation has concluded that DUCS has an increased throughput and lower overhead than low-energy adaptive clustering hierarchy (LEACH). Finally, the so-called mobicast routing protocol [101] is intended to improve throughput and efficiency. This algorithm has two steps. Autonomous underwater vehicles gather messages from nodes within a 3D zone of reference in the first step. After that, they wake up the sensor nodes in the next 3D zone, avoiding topology holes. In mobicast, disadvantages are possible, considering that the delivery rate and energy efficiency decrease with high water flow velocity.
# Fatigue Performance of Different Rib-To-Deck Connections Using Traction Structural Stress Method
## 3 Fatigue Performance of Rib-To-Deck Connection Types in Orthotropic Steel Bridges
### The Law of Traction Structural Stress of Geometric Parameters of the Rib-To-Deck
The trends of various stresses with the U-rib thickness are shown in Figure 24. The comparative results demonstrate that the Mises stress, stress intensity and equivalent structural stress of Weldline1 decreased slightly with an increase in the U-rib thickness. The trends of Mises stress and stress intensity were similar. The Mises stress, equivalent structural stress and stress intensity of Weldline2 decreased by 44% with an increase in the U-rib thickness. The principal stresses of Weldline2 decreased slightly and the 3 rd principal stress decreased by 34.5% with an increase in the U-rib thickness from 6 mm to 26 mm. The comparative results indicated that the equivalent structural stress decreased by 13.4%, with the groove angle increasing from 20\({}^{\circ}\) to 60\({}^{\circ}\). The connection of the rib-to-deck was strengthened with the increase in the angle. The equivalent structural stress reduced by 21.4%, with an increase in the radius from 5 mm to 25 mm. The equivalent structural stress at the middle of the bridge deck decreased by 24.6% with an increase in the U-rib from 8 mm to 16 mm. The equivalent structural stress at the middle of the bridge deck decreased significantly by 64.1% (from 156 MPa to 56 MPa), with an increase in the deck plate thickness from 12 mm to 24 mm. The bridge deck thickness influenced the equivalent structural stress and fatigue performance of the connection of the OSBD significantly and this observation substantiates why the equation of equivalent structural stress method includes the parameter of bridge deck thickness, t.  
The comparative results from the analysis of geometric parameters of the rib-to-deck connection in OSBD will provide guidance for engineering fatigue design using the traction structural stress method. It is recommended that the variation in equivalent structural stress due to various geometric parameters should be added to the OSBD specification of Eurocode3.
## 3 Optimization-Based Strategies
### Data-Driven Strategies
#### 3.3.1 Neural Network-Dynamic Programming (NN-DP)
Based on the multi-mode trip information prediction module (MTCPM), the optimal strategy calculation module (OSCM) calculates the optimal control sequence by DP to obtain the optimal fuel consumption in real-time. To solve the problem that DP cannot be applied in real time, it is possible to develop a machine learning strategy to learn the optimal power split by generalizing the knowledge through neural learning from multiple standard drive cycles. This includes driving environment prediction and optimal energy management by machine learning. A multilayered and multiclass neural network can be developed to predict the road environment including roadway types and traffic congestion levels over a driving trip. Meanwhile, another neural network could be developed to predict the driver's instantaneous reactions to the driving environment at any given moment. Based on the above predicted road information and driving trends, the optimal energy management strategy dictated by DP for the current conditions can be emulated. On the intelligent optimization of energy management for HEVs, a machine learning framework that combines dynamic programming with machine learning to learn about roadway type and traffic-congestion level was developed in [96]. For each of the 11 drive cycles, two neural networks were trained to emulate the optimal engine speed generated by DP and the optimal battery power. The simulation results indicated that the proposed framework not only minimized the fuel consumption, but also maintained vehicle performance. Compared with the CD-CS strategy, the engine operating points of the NN-DP strategy are mostly concentrated on the region with the lower fuel consumption, which can greatly decrease the total cost and can be regarded as an approximated global optimal energy management strategy. Due to the fact that the optimal control sequences are based on the DP results obtained from a set of standard driving cycles, which is an approximate nonlinear relationship based on statistical data to some extent, it cannot guarantee the optimality of energy management when the real driving conditions differ from the training samples.
# Optimum System for Diagnosing Power Quality in Electrical Microgrids
## 3 Developed Electrical Measurement Network  
In this section, the proposed electrical measurement network, DiagElect, is introduced. DiagElect consists of two main components: the measurement device developed by the authors, SDMD, and the communication network, also created by the authors, which includes hardware and software elements for connecting all SDMDs, requesting and collecting measurements, and processing the collected data. Therefore, the description of DiagElect is divided into two distinct parts. The first part describes the SDMD, detailing its architecture and characteristics, and also presents the prototype. The second part focuses on the communication network, covering both hardware and software elements, as well as the employed communication protocols and synchronization mechanisms, which are crucial for reducing electromagnetic interference.
## 2 Experimental
### Mass Spectrometry  
Tryptic digests were analyzed with an LC-MS system consisting of a nano-LC (Easy-nLC II, Thermo Fisher Scientific, Bremen, Germany) coupled to a hybrid-Fourier Transform (FT)-mass spectrometer [Linear Trap Quadrupole (LTQ) Orbitrap Velos, Thermo Fisher Scientific]. Peptide separations were performed on a C18 column (EASY column; 10 cm, ID 75 \(\upmu\)m, particle diameter: 3 \(\upmu\)m) at a flow rate of 300 nL/min and a linear gradient of 5% to 40% B in 150 min (A: 0.1% formic acid in water, B: 0.1% formic acid in ACN). A voltage of +1.9 kV was applied to electrospray peptide ions. A capillary temperature of 275 \({}^{\circ}\)C for peptide transfer and a lock mass of 445.120024 \(m\)/\(z\) were used. Precursor mass scanning was performed from 400 to 1,850 \(m\)/\(z\) in the Orbitrap with a resolution of 30,000, and the 20 most intense precursor ions were selected for subsequent collision-induced dissociation (CID) fragmentation in the linear quadrupole mass analyzer (LTQ). Singly-charged ions were rejected from fragmentation. Dynamic exclusion was enabled (repeat count: 1; repeat duration: 20 s; exclusion list size: 500; exclusion duration: 30 s).
# Manifolds-Based Low-Rank Dictionary Pair Learning for Efficient Set-Based Video Recognition
## 6 Conclusions  
In this paper, a novel manifold-based image set classification method called MbLRDPL was proposed. By combining the manifold learning and dictionary pair learning algorithms, MbLRDPL combines the advantages of both static and dynamic modeling methods: its classification accuracy is high and its computing time is fast. Extensive experimental results on both set-based video face recognition task and set-based object classification task demonstrated the superiority of the proposed method. Given its excellent performance, the proposed MbLRDPL can be applied to many video processing applications, such as video-based fugitive tracking, video retrieval, video-based security monitoring, micro expression recognition, etc. MbLRDPL can also be used in the edge computing field, because it has very fast inference speed.  
Figure 5: Convergence curves of MbLRDPL-SPD and MbLRDPL-GM on (**a**) Honda and (**b**) ETH-80 datasets. We find the proposed MbLRDPL algorithm can converge very quickly.  
The main limitations of the proposed algorithm include: (a) ignoring the importance of feature learning; (b) only one representation is used to model image set or video, while different representations can provide complementarity information; (c) when the number of frames in a video is small, its covariance matrix may be influenced by noise disturbance. Considering the limitations and applications of the research, our future studies include: (a) designing a joint learning network for combining the deep features and MbLRDPL; (b) constructing a new multi-model dictionary learning-based image set classification method; (c) improving the robustness of the covariance matrix by introducing the fractional-order embedding strategy.  
Conceptualization, X.G. and K.W.; methodology, X.G.; software, K.W.; formal analysis, S.N.; investigation, Z.S.; data curation, X.G.; writing--original draft preparation, X.G.; writing--review and editing, S.N.; visualization, H.Z.; supervision, J.L.; funding acquisition, X.G. All authors have read and agreed to the published version of the manuscript.  
This study was funded in part by the National Natural Science Foundation of China (grant numbers 62101213 and 62103165), the Shandong Provincial Natural Science Foundation (grant numbers ZR2020 QF107 and ZR2021 MF039), the Development Program Project of Youth Innovation Team of Institutions of Higher Learning in Shandong Province, and the Big Data Project of University of Jinan (grant number XKY1926).  
Not applicable.  
The data presented in this study are available upon request from the corresponding author.  
The authors declare no conflict of interest.