esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Plasma Precursors
### Hydrogen
#### 5.1.11 Comparative Analysis  
For hydrogen and each of the plasma precursors, the parameters were evaluated based on the literature regarding its plasma chemistry and interaction with typical lead frame materials and contaminants.  
To summarize the characteristics outlined in this section, Table 11 presents a grading of the parameters relevant to hydrogen plasmas, outlining their suitability for use in cleaning applications.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Summary** & **Parametric Score** \\ \hline Removal of Organic & Hydrogen plasma contains active species, such as atomic & \\ Contaminants Through & hydrogen (H), ions (such as H\({}^{+}\), H\({}_{2}^{+}\), and H\({}_{3}^{+}\)), and electrons. & \\ Chemical Reduction & These species react with organic contaminants such as oils and & \\ and Oxidation & deposits, and break them down into volatile by-products, like & \\  & H\({}_{2}\)O, CH\({}_{4}\), which are then evacuated from the chamber [1]. & 8/10 \\ \hline Removal of Oxides & Hydrogen plasmas clean and activate lead frame surfaces by & \\  & reducing copper oxides, which is crucial for improving & \\  & subsequent processes like wire-bonding or moulding [76]. Studies & \\  & have shown that, for substrates with a temperature below 150 \({}^{\circ}\)C, & \\ Removal of Oxides & oxide layers may not be completely reduced. The reduction & \\ Through Chemical & process appears limited, potentially by the rate of hydrogen & \\ Reduction & diffusion through the oxide or the kinetics of the reaction between & \\  & hydrogen and the oxide. In contrast, substrates with temperatures & \\  & higher than 150 \({}^{\circ}\)C showed effective oxide reduction by the & \\  & hydrogen. At low temperatures, increasing processing time & \\  & removes a larger amount of the oxide layer [77]. & \\ \hline \hline \end{tabular}
\end{table}
Table 11: Grading of parameters for hydrogen plasmas.
## 4 Discussion
As shown in Figures 4 and 5, there was an upward spike on September 3 and 27 (Figures 4 and 5), with reported daily precipitation of 33.4 mm and 2.9 mm, respectively (Figure 2). Thus, change detection through time-series SAR data analysis should take into consideration climate conditions at the time of SAR image acquisition. In this study, a simple algorithm has been developed for the detection the timing of crop seeding and harvest through analysis of time-series InSAR coherence and SAR backscattering coefficients. It is noted that a sigma-nought calibration has been applied on SAR backscattering to ensure signal consistency across space and over time. This is suitable for our study area because of the following reasons: (1) the agricultural land is relatively flat so that variability of SAR signal due to within-field variability of micro-topography or soil variability is small compared with that due to surface structural changes induced by agricultural management practices; (2) with the InSAR acquisition configuration of Sentinel-1, the incidence angle for a given ground area is relatively constant over time, so that angular effects is minimized; (3) the method proposed in this study mainly relies on exploitation of field-level temporal signatures of the time-series SAR backscattering and InSAR coherence. Nevertheless, converting SAR signals to backscattering coefficients as a normalization measure is preferred in most applications. In this study, only 77 fields were surveyed for harvest and 66 of them were recorded with seeding dates. Although the sampling sites were limited for a robust statistical modeling, it is sufficient for analysis based on physical principles, as shown by interpretations of the time-series signatures. An automated procedure could be developed based on the simple rules presented in Section 3.2, however more systematic field observations are needed in order to capture not only seeding and harvest events, but also other events such as pre-planting seedbed preparation, post-harvest residue removal, and fall tillage. The transition between seeding/harvest operations and different field conditions due to other treatments will be different. A larger number of samples will be needed to characterize the signatures of different combinations of field treatments, crop types, and seeding/harvest in order to develop a more robust procedure for automated information extraction. SAR backscattering coefficients have been exploited intensively for soil and crop parameter retrieval in the literature. Time-series SAR data and InSAR coherence will bring in new dimensions of information for agricultural studies, particularly in detecting but not limited to changes associated with field operations. With two satellites, the Sentinel-1 constellation can acquire data with interferometric capacity at a six-day revisiting cycle. It is anticipated that the Canadian tri-satellite RADARSAT Constellation Mission at four-day repeat time can provide improved capability for agricultural studies.
# An Evaluation Framework and Algorithms for Train Rescheduling
## 4 Framework Part I: Classification of Algorithm Capabilities and Characteristics
\begin{table}
\begin{tabular}{p{42.7 pt} p{85.4 pt} p{85.4 pt} p{85.4 pt} p{85.4 pt} p{85.4 pt}} \hline \hline
**Algorithm Characteristic** & **Algorithm 1** & **Algorithm 2** & **Algorithm 3** & **Algorithm 4** & **(**[PERSON] and [PERSON] [29]**)** \\ \hline Infrastructure granularity & Mesoscopic granularity & Mesoscopic granularity levels. & Configurable to any of the three granularity levels. & Mesoscopic stations (microscopic), lines (macroscopic) \\ \hline Time representation & Continuous & Continuous & Continuous & Continuous \\ \hline Special considerations & Platform and track allocation of trains without considering train and track properties. & Platform and track allocation based on train length and track length. & Connecting trains, (de)coupling of trains, track length, maximum train speed attainable on the track. & Train properties are considered in some cases. \\ \hline Applicable infrastructures & Network & Network & Network & \\ \hline Applicable sections & multi-tracked & multi-tracked & multi-tracked & multi-tracked \\ \hline Applicable railway tracks & bi-directional & bi-directional & uni-directional & uni-directional, can be made applicable to bi-directional tracks by adding multi-commodity flow variables and constraints. \\ \hline Rescheduling tactics & Retining, and local rerouting & Retining, reordering, and local rerouting & Retining, reordering, and rerouting (local, global) & Retining, reordering, and local rerouting \\ \hline Optimization objective(s) & Minimize total train delay, total passenger delay, number of delayed passengers, etc. & () Minimize train delays at their final destination or (ii) Minimize final destination or (ii) Minimize final destination or (iii) Minimize final destination or (iv) the use of detours. & Minimize the penalties due to delays, (ii) logical dependency breaking, (iii) soil capacity violations, and (iv) the use of detours. & Minimize the sum of delay costs for all trains at every station in their route. \\ \hline Objective(s) & Objectives are easily observable functions to an objective function. & final delays. The two alternative objective functions can easily be extended and replaced. & The objective function can be easily modified to consider other indicators. & The objective function can be made to secondplace various goals by adding specific variables. \\ \hline Solving strategy & Centralized & Centralized & Centralized & Decomposition \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Algorithm characteristics of four train rescheduling algorithms.
## 4 Empirical Study
### Results
In general, our procedure to find a state space model also forecasts more accurately than the seasonal seasonal naive method: for Boots time series, the RMSE and the MAE (MASE) are 11% and 4% smaller, respectively; for Boots time series, the RMSE is 15% larger, but the MAE is 1% smaller and the MASE is equal; for Flats time series, the RMSE and the MAE (MASE) are 47% and 44% smaller, respectively; for Sandals time series, the RMSE and the MAE (MASE) are 19% and 47% larger, respectively; for Shoes time series, the RMSE and the MAE (MASE) are 49% and 56% smaller, respectively. The seasonal naive model did not pass the Ljung-Box test in three of the five time series considering the usual significance level of 5%. Another observation that can be made from these results is that both transformation and differencing are important for improving ARIMA's ability to model and forecast time series that contain strong trend and seasonal components. The log transformation was applied to four of the five time series. With the exception of flats, all other time series were differenced: second-order differences were made in Sandals series and first differences were made in Boots, Booties and Shoes series. The Flats, Sandals and Shoes time series were seasonally differenced. Transformation seems not to be significant for state space models. Log transformation was made only on Shoes series. To see the individual point forecasting behavior, the actual data versus the forecasts from all approaches were plotted (see Figure 3). The ARIMA Cross-Validation and ETS Cross-Validation models selected by our procedure are identified in the legend by ARIMA_CV and ETS_CV, respectively. The forecasts from the Hyndman and Athanasopoulos [16] statistical framework are identified in the legend by ETS_H-A. The forecasts from the Hyndman and Khandakar [23] algorithm are identified in the legend by ARIMA_H-K. In general, it can be seen that both ETS and ARIMA models selected
# Stripe-Assisted Global Transformer and Spatial-Temporal Enhancement for Vehicle Re-Identification
## 4 Experiment
### Comparisons with State-of-the-Art Methods  
We compared our method with some state-of-the-art (SOTA) approaches from the last three years, categorized into three groups: (1) Global feature-based (GF) methods, such as SN [13], VARID [12], VAT [18], and MsKAT [32], mainly concentrate on extracting whole representation for vehicle images. (2) Local feature-based (LF) methods, including DPGM [14], LG-CoT [36], HPGN [37], DFR [38], DSN [39], SFMNet [40], GIT [31], SOFCT [22], MART [41] integrate local features with the global feature to learn reliable vehicle representations. (3) Spatial-temporal (ST) methods, such as DPGM-ST [14] and DFR-ST [38], exploit extra timestamp and camera location information to enhance vehicle re-identification using visual features. The Baseline refers to our model using only a branchfor global feature learning without the SFM. Since our method only takes the global feature for inference, we classified it as a GF-based method.
## 2 Mathematical Modeling
### Objective Function  
The objective function comprises the sum of two functions: one of them represents the operating costs due to energy losses (\(f_{1}\)), and the other one represents the investment costs of the D-STATCOMs. Next, the way in which Equations (1) and (2) define each objective function is shown:  
\[f_{1}=C_{WH}T\sum_{k\in\mathcal{H}}\sum_{k\in\mathcal{N}}\sum_{ m\in\mathcal{N}}Y_{km}V_{kh}V_{mh}\cos\big{(}\delta_{kh}-\delta_{mh}-\theta_{km} \big{)}\Delta_{h}, \tag{1}\] \[f_{2}=T\frac{k_{1}}{k_{2}}\sum_{k\in\mathcal{N}}\bigg{(}\alpha \Big{(}Q_{k}^{DS}\Big{)}^{2}+\beta Q_{k}^{DS}+\gamma\bigg{)}Q_{k}^{DS}, \tag{2}\]  
Equation (1) defines the annual operating costs due to power losses in the distribution network, where, starting by the left \(C_{WH}\) is the average cost of energy in kilowatts per hour; \(T\) is a constant associated with the period under study (365 days in this case); \(Y_{km}\) is the magnitude of the admittance matrix that relates \(k\) nodes to \(m\) nodes with an angle \(\theta_{km}\); \(V_{kh}\) and \(V_{mh}\) are the voltages that relate nodes \(k\) and \(m\) in period \(h\) with voltage angles denoted as \(\delta_{kh}\) and \(\delta_{mh}\), respectively; \(\Delta_{h}\) represents the time associated with the power flow (1 h); and \(\mathcal{H}\) and \(\mathcal{N}\) refer to the set of time periods and nodes in the network, respectively. On the other hand, Equation (2) represents the objective function corresponding to the investment costs of the D-STATCOMs, where \(k_{1}\) and \(k_{2}\) greater than zero are constant. The first represents the annual investment entailed by the D-STATCOM and the second refers to the device's useful life. The parameters \(\alpha\), \(\beta\), and \(\gamma\) are scalars greater than zero that correspond to the installation costs of the D-STATCOMs with nominal power and generation capacity \(Q_{k}^{DS}\). Thus, the general objective function is proposed as defined in Equation (3).  
\[\min A_{cost}=f_{1}+f_{2}. \tag{3}\]  
Note that \(A_{cost}\) represents the expected annual cost of the distribution network, which corresponds to the sum of the power losses and installation costs of the D-STATCOMs.  
The main feature of the objective function defined in (3) and its components \(f_{1}\) and \(f_{2}\) in (1) and (2) is that both components (i.e., \(f_{1}\) and \(f_{2}\)) are nonlinear, non-convex functions of the main decision variables. The former is nonlinear and non-convex due to the presence of products among voltages and trigonometric functions [19], and The latter is nonlinear and non-convex with a mixed-integer structure; its non-convexity is governed by the presence of a cubic function in its formulation [20].  
Note that, due to the complexities of the objective function in Equations (1)-(3) and the large dimensions of the solution space regarding the discrete variables, most of authors deal with their solution by applying metaheuristic optimization methods [7], which is the case of this research (SSA), as will be detailed later in this document.
# Wireless Channel Models for Over-the-Sea Communication: A Comparative Study
## 4 Channel Models for Over-the-Sea Wave Propagation
### Ray-Tracing-Based Wireless Channel Modeling for Over-the-Sea Communication Near Diaoyu Islands  
The Diaoyu archipelago is one of the main islands of its kind located in East China Sea. It is quite challenging and critical to design and implement a channel model considering this area due to the extremely uncertain sea waves and islands that work as propagation barriers. [PERSON] et al. collected and analyzed the three-dimensional contour data of Diaoyu islands to generate a sea surface scenario, using MATLAB, in order to design a channel model in that area [18]. The ray tracing technique was used to model the wireless channel.  
Authors of [18] obtained calculations for both the reflected and diffracted rays. Propagation path loss was calculated as the ratio of the effective transmitting power and the received power. It was formulated as:  
\[PL(dB)=20 log\frac{E_{r}}{E_{t}} \tag{35}\]  
where \(E_{t}\) and \(E_{r}\) are electric field intensities of transmitting and receiving rays, subsequently.  
The proposed channel model is incomplete and incompetent for real time deployment because it did not consider the atmospheric duct effects. Furthermore, there was no consideration for the refractive rays while designing the channel model. Refraction caused due to evaporation ducts plays a prime role in maritime wave propagation. Nevertheless, it has a very small range.
# Deep Learning Realizes Photoacoustic Imaging Artifact Removal
## 2 Materials and Methods
### Deep Learning Algorithm for Photoacoustic Image Segmentation  
The YOLOv8 algorithm [32; 33] is a fast single-stage target detection method with excellent performance and efficient real-time performance. It is able to accurately capture target features in photoacoustic images and provide accurate segmentation results while maintaining a fast processing speed. Its network structure draws on the design principles of the YOLACT network to achieve real-time object-instance segmentation and maintain a high accuracy rate.  
The YOLOv8 network [34] is mainly composed of a backbone network and a head network, as depicted in Figure 2. The backbone network utilizes 3 \(\times\) 3 convolution, the C2f module, and the SPPF module, which are lighter than YOLOv5. The C2f module replaces the traditional C3 module and enhances the gradient flow through jump connections and  
Figure 1: Schematic diagram of deep learning structure for photoacoustic images.  
segmentation operations. Additionally, the CSP version of the network employs residual connections and direct connections to optimize the information flow. The neck structure is enhanced with the FPN+PAN configuration to facilitate feature fusion, which helps in detecting objects at multiple scales. During training, YOLOv8 improves the training data with image enhancement techniques such as mosaic enhancement, enhancing the model's adaptability to various scenes. The technique forces the model to learn how to detect objects that are partially obstructed and in different locations. Over the past 10 training cycles, the YOLOv8 network has deactivated mosaic enhancement, a method that has proven effective in improving network accuracy.  
Applications of YOLOv8 to photoacoustic image processing of brain tumors include artifact removal and segmentation of brain tumors. The model was trained on labeled brain and brain tumor pictures, and it can successfully remove artifacts from non-signal sources and segment brain tumors, allowing brain tumor segmentation and artifact removal from photoacoustic images.
## 3 Introduction of Input Data and Software
### Homer Pro Software  
Comprehensive simulation tools could implement techno-economic evaluation of the power system, as an alternative to the complicated and lengthy algorithms and costly laboratory experiments. There exist different softwares for design, simulate, and optimization renewable energy systems, primarily for assess techno-economic aspects. HOMER, iHOGA, PVSyst, Hybrid2, RETScreen, and TRNSYS are among the most frequently used as pointed out in literature.  
Homer is superior on others due to it has capability to handle various power system configurations, develop design, simulate, and perform optimization. Furthermore, it can undertake sensitivity analysis, demonstrate detail explanation of economic aspects, and system operation [14]. In addition, it is friendly to the user and presents powerful graphics. Homer is an abbreviation for hybrid optimization of multiple electric renewable which is developed by national renewable energy laboratory in United States for designing, simulating and optimizing integrated power systems. It includes a mix of conventional generators, combined renewable energy sources.  
Homer is used currently by thousands of researchers around the world for simulation of power systems either off-grid or grid-connected. Homer helps to determine the economic feasibility of the integrated power system through optimizes the design and seeks for the optimal configuration [78]. To simulate any power system, users need to define meteorological climate, load data, elements technology in Homer which are required to design systems; for example, searching space size or numbers of each element, suitable dispatch energy that is efficient schedule power among components, technical and economic data, system constraints, real discount rate, and inflation rate regarding the economic policy in the region of the project. Therefore, we can use Homer to undertake the simulation, calculation, and seek for optimal system configuration belongs to low investment cost.
# Hydrological Drought Frequency Analysis in Water Management Using Univariate Distributions
###### Abstract  
The study of extreme phenomena in hydrology generally involves frequency analysis and a time series analysis. In this article we provide enough mathematics to enable hydrology researchers to apply a wide range of probability distributions in frequency analyses of hydrological drought. The article presents a hydrological drought frequency analysis methodology for the determination of minimum annual flows, annual drought durations and annual deficit volumes for exceedance probabilities common in water management. Eight statistical distributions from different families and with different numbers of parameters are used for the frequency analysis. The method of ordinary moments and the method of linear moments are used to estimate the parameters of the distributions. All the mathematical characteristics necessary for the application of the eight analyzed distributions, for the method of ordinary moments and the method of linear moments, are presented. The performance of the analyzed distributions is evaluated using relative mean error and relative absolute error. For the frequency analysis of the annual minimum flows, only distributions that have a lower bound close to the annual minimum value should be used, a defining characteristic having the asymptotic distributions at this value. A case study of hydrological drought frequency analysis is presented for the Prigor River. We believe that the use of software without the knowledge of the mathematics behind it is not beneficial for researchers in the field of technical hydrology; thus, the dissemination of mathematical methods and models is necessary. All the research was conducted within the Faculty of Hydrotechnics.  
low flow; volume; duration; Pearson III; Pearson V; Wilson-Hilferty; fatigue lifetime; log-normal; Wakeby; lambda; Weibull; approximate form; method of ordinary moments; method of linear moments
# Agricultural Residues of Lignocellulosic Materials in Cement Composites
## 2 Materials and Methods  
### Lignocellulosic Materials  
For production of the lignocellulosic residue cement composites, the following lignocellulosic materials were used: eucalyptus (_Eucalyptus grandis_), sugarcane bagasse (_Saccharum officinarum_), coconut shell (_Cocos nucifera_), coffee husk (_Coffea arabica_), and banana pseudostem (_Musa acuminata_).  
Eucalyptus tree wood and banana pseudostem were obtained from local experimental cultivation at the Federal University of Lavras--UFLA. Sugarcane was obtained from a commercial cachaca distillery producing sugarcane liquor (cachaca) in Lavras, Minas Gerais state (MG), Brazil. The coconut shell was collected from local floriculture in Lavras, MG, Brazil. Coffee husk was obtained from a farm in the municipality of Santo Antonio do Amparo, MG, Brazil.  
Eucalyptus wood was cut into short logs, which were passed through a laminator for delaminating. Coconut shell, sugarcane bagasse, coffee husk, banana pseudostem, and eucalyptus wood veneers were processed in a hammermill. The material particles were selected through sieving, and the fraction retained between 20 (0.841 mm) and 40 (0.420 mm) mesh sieves was used to produce the composites.  
The particles of the lignocellulosic residues were treated with an alkaline solution to remove compounds that might hinder cement solidification. The alkaline treatment followed the procedures described in [PERSON] [16] and [PERSON] [17], in which the particles were immersed in NaOH solution (5%) for 48 h.