esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Water Transport Systems of Mining Industry: Evaluating Advanced Inspection Technologies
### Characterizing Water Transport Systems
Low resistivity (<1000 \(\Omega\)-cm), coupled with high chloride and sulfate content, enhances the electrochemical environment that accelerates corrosion. Pitting corrosion (Figure 2c), characterized by localized, deep pits or holes, is a significant problem in water with high chloride concentrations or low pH [43]. This form of corrosion results from the breakdown of the protective passive oxide layer by oxidizing agents [44]. For example, the Tocontai aqueduct has high RSI values (average 9.3), confirming the corrosiveness of the water, with pH values between 7.4 and 8.6 exacerbating localized corrosion risks. The Inacatac aqueduct, although neutral on the LSI scale (average 0.2), experiences pitting due to high velocities (0.76-2.12 m/s) and variable pH (7.4-8.7). These conditions increase the risk of pitting and material degradation, especially in critical areas. Erosion corrosion is primarily caused by the relative movement of the transported fluid against the inner surface of the pipe. Fluid turbulence can cause a rapid increase in the erosion rate, and if the fluid also contains suspended solid particles, the erosive effect tends to be further accelerated due to the deterioration of the metal. This type of corrosion is detected in the Tocontai aqueduct, as shown in Figure 2d. To effectively address these challenges, inspection technologies are essential in identifying early signs of corrosion and structural weaknesses. Additionally, preventive measures such as using protective coatings, implementing cathodic protection, and selecting materials with high corrosion resistance in design stage can help mitigate these risks. For operating system investigated, integrating advanced inspection technologies with preventive strategies, the mining industry can improve the durability, reliability, and safety of its water transport infrastructure.
# A Multichannel, High-Bandwidth Wirelane Receiver for D2D Interconnects
## 2 Receiver Design
### Delay Matching \(\delta\)  
The deviation of the channel length and the difference of the PVT of the chip lead to the inconsistent time of each signal reaching the receiving end, so each channel needs to be matched with a delay. As shown in Figure 5, the MINI-PLL samples the data to the best position after locking through the L0 channel, and L1 to L15 use this scheme to delay the clock. Taking L1 as an example, when the transmitter sends PRBS31 for training, the PI in the \(\delta\) is traversed under the control of the control table (from 00000 to 111111), and the output data receive the bathtub curve. At the best position (Time = 0 ps), the control code in the query control table is \(X_{1}X_{2}X_{3}X_{4}X_{5}X_{6}\), and input it into the PI. L1 to L15 use the same method to get 15 control bits input to \(\delta\) in L1 to L15.  
Figure 3: Integral structure of receiver.
## 3 Results and Discussion
### Characterization of Nanocellulose Gels and Films  
#### 3.2.1 Solids Content of CNF Gels  
The results obtained for both gels on solids content are quite similar and slightly higher than 1% in CNF, more precisely 1.039 to CNF\({}_{1}\) and 1.078 to CNF\({}_{2}\), with the main constituent being water. The low CNF content of these gels is associated with their stability and has been reported in the literature by other authors [18].  
At 1% concentration, CNF suspensions are more stable due to fibril network formation and its high viscosity. The gel-like properties exhibited by these suspensions prevent the mobility of fibrils and reduce the probability of aggregation and settling. Stability is enhanced because Brownian motion is constrained, and the nanofibers are confined in place.
## 5 Experimental Results and Discussion
Conversely, the LSTM model exhibited more pronounced overestimations, as shown in Figure 6c. Regression Plots in Figure 6 illustrates that the AHW and HMs models displayed higher error rates, whereas the LSTM model tended to overshoot the target values. The AHW method is a simple and efficient approach that has been shown to functions effectively as a PVPF method. However, it has been observed that this method may sometimes underestimate data. In contrast, the LSTM approach is a more complex method that can capture intricate patterns in the PV power data. However, this method is prone to overestimating data. To address the limitations of both the methods, a hybrid approach was employed. This can help produce more accurate forecasts by mitigating the limitations of each approach. The AHW method provides a baseline forecast, whereas the LSTM model enhances and refines the forecast by considering the intricate data patterns. Our study demonstrates that this refinement can be achieved by training the LSTM model on the residuals between AHW predictions and observed data. By integrating different approaches and using a hybrid model, better results can be achieved, and forecast accuracy can be enhanced. Figure 5: Examples of PV power forecasting in watts (W vertical \(y\)-axis) in a row of five days, with 120 hourly observations ranging from hour no. 1047 to hour no. 1167 (horizontal \(x\)-axis). Real observed values labeled as “Targets” (blue line). Predicted data labeled as “Outputs” (orange line). Three techniques: (**a**) AHW, (**b**) LSTM, and (**c**) HM. Figure 6: Scatter graph of the actual and predicted PV power in watts (W) along with the regression lines. Target values on the vertical \(y\)-axis. Prediction values on the horizontal \(x\)-axis. The test data consisted of 73 days and 1752 hourly predictions. Three techniques: (**a**) AHW, (**b**) LSTM, and (**c**) HM.
# eSCorpius-m: A Massive Multilingual Crawling Corpus with a Focus on Spanish
## 5 Technical Environment Used for Corpus Generation  
The selected massively parallel processing infrastructure relies on open source solutions for data analytics, Apache Hadoop and Apache Spark framework, integrated in the Amazon Elastic Map Reduce (EMR) service of Amazon Web Services (AWS). In particular, the following application stack has been used:  
* Hadoop File System (HDFS), a distributed and scalable file system, was used as an auxiliary repository in the tasks executed in EMR, which is built with part of the cluster nodes (Core Nodes).  
* Amazon Simple Storage Service (S3), in particular EMR FS, which is a Hadoop reimplementation of AWS object storage.
* Apache Spark, this open source engine was used for the parallelisation of data cleaning tasks. The parallel WARCs processor is based on PySpark.
* Apache YARN was used as resource manager used for scheduling and monitoring the execution of tasks in the EMR cluster.
* Ganglia has been used for monitoring the cluster status. Cluster monitoring is important in the early stages, for node load visualization and selecting the number and type of nodes used in the cluster.  
For the EMR cluster, we used three types of nodes: one Master Node, which is the orchestrator of the cluster and on which YARN resource manager runs; three Core Nodes, which support the local HDFS file system; and a large collection of Task Nodes, which are nodes dedicated exclusively to the execution of tasks. All of these nodes are supported by virtual compute instances (similar to the concept of a virtual machine in a AWS data center environment) thanks to the Amazon Elastic Compute Cloud (EC2) service.  
Amazon EMR supports different types of EC2 instances depending on the characteristics of the task to be executed. For the corpus generation process, general purpose instances have been considered, although instances with GPU and advanced networking capabilities are also available. In particular, we used the following EC2 instance types:  
* MS instances ([[https://aws.amazon.com/ec2/instance-types/m5/](https://aws.amazon.com/ec2/instance-types/m5/)]([https://aws.amazon.com/ec2/instance-types/m5/](https://aws.amazon.com/ec2/instance-types/m5/)), accessed on 15 September 2023): general-purpose instances powered by Intel Xeon Platinum 8000 series processors up to 3.1 GHz. They have a network bandwidth ranging from 10 to 25 GBPS depending on the selected size.
* RS instances ([[https://aws.amazon.com/ec2/instance-types/r5/](https://aws.amazon.com/ec2/instance-types/r5/)]([https://aws.amazon.com/ec2/instance-types/r5/](https://aws.amazon.com/ec2/instance-types/r5/)), accessed on 15 September 2023): memory-optimized instances, with the same type of processors as the M5 instances but with a vCPU:RAM ratio of 1:8, allowing more memory-demanding tasks to be executed.
## 3 D-MSR Protocol Stack Architecture  
In WirelessHART and ISA100.11a, a central network manager schedules all the network communications, constructs all the routes, and establishes end-to-end connections in the network. The protocol stack of WirelessHART, the connection between tables in different layers, and the managing procedures are shown in Figure 5(a). The network manager configures the communication tables in the data link layer and the routing table in the network layer through the _system manager module_ implemented in each device. WirelessHART uses graph routing as well as source routing [4] in the network layer and use the Route Table and Source Route Table.  
In D-MSR the network setup is performed in a distributed manner. This requires the implementation of various mechanisms in different layers. The D-MSR protocol stack is shown in Figure 5(b), in which the new sub-layers, modules and tables are displayed in a different color. The data link layer consists of two sub-layers: the lower and the upper data link sub-layer. In the lower data link sub-layer, we use the IEEE 802.15.4e (TSCH mode) standard, after having modified it to fit our requirements. A two-hop neighborhood schedule-matrix is added in this layer in order to schedule interference-free communications in the network. The modification details are discussed in Section 3.1. The upper data link sub-layer (the resource reservation layer) supports several features and functionalities which are normally data link layer functions, but are not currently included in the lower data link sub-layer. In this sub-layer, we implement D-SAR and _neighbor connection manager_ modules that configure locally the communication tables in the lower data link sub-layer. These two modules use the information provided by the schedule-matrix to construct interference-free schedules in different network operation phases. These two modules are discussed in more detail in Section 3.2.  
Figure 5: WirelessHART (**a**) and D-MSR (**b**) protocol stack.  
The _end-to-end connection manager_ module is implemented in the transport layer. This module establishes the end-to-end connection through the D-SAR protocol. The modifications carried out in the lower data link sub-layer, the upper data link sub-layer, routing layer, and transport layer, as well as the ways in which they can work together, are discussed in Sections 3.
## Appendix A Solution of Quine-McCluskey Algorithm to Minimize the MetS Types  
The Quine-McCluskey algorithm aims to minimize the logical sum of products, which are the MetS types that we will call implicants. If the MetS type is a combination of 5 variables, it is represented in the algorithm as an implicant of order 0. If it is a combination of 4 variables, it would be of order 1, and if it is a combination of 3 variables, it would be of order 2.  
1. We select the implicants of order 0 as show Table A1 that were obtained from the positive logic truth table as shown Table 3.
2. All those implicants of order 0, where only one variable has changed its state are grouped together. The group is obtained by eliminating the changed variable of those implicants of order 1. An example is the implicant of order 0, number 7 (W'P'GHT) and number 15 (W'PGHT), which are grouped together, resulting in W'GHT, which is of order 1.
3. Then the implicants of order 1, where only one variable has changed its state are grouped together, obtained by eliminating changed variable. For example, the implicants 7, 15 (W'GHT) and 23, 31 (WGHT) (both implicants of order 1) are grouped together, resulting in GHT, which is of order 2.
4. This process is carried out on all the implicants of order 0, until all implicants are minimized as shown Equation (6) \[MetS_{HMS}=WPT+WPH+WPG+WGT+WGH+WTH+PGT+PGH+PHT+GHT\]
## 1 Introduction
This method is of great significance to the evaluation of distributed energy acceptance capacity, power planning, energy storage planning, operation planning and scheduling. The existing scenario analysis methods can be divided into three categories: (1) scenario subtraction for day-ahead scheduling, which generates a large number of prediction scenarios based on scenario generation technology, and then reduces the large-scale prediction scenarios by scenario subtraction technology, and merges a large number of scenarios into a few typical scenarios [17]; (2) mid-and long-term power planning for the purpose. In the typical Japanese method, the method often chooses a day or a specific day which is close to the average value of the cycle as a typical day based on user experience and the research purposes [18]. (3) Time series simulation method for medium and long term power planning [19]. This method can characterize the time series variation characteristics of load/wind power output in the cycle, and provide realistic simulation scenarios and rich results for the whole network optimization of unit start-up and shutdown. Although time series simulation method has engineering application value, it generally needs to simulate the whole year time series, which has the problem of low computational efficiency. Scenario analysis has been widely used in the optimal configuration of DG at this stage. In the process of scenario analysis, all historical data are used for multi-scenario analysis. Although the result of analysis is comprehensive, it will lead to a sharp increase in computational complexity and difficulty in solving problems. Therefore, it is necessary to reduce large-scale scenarios. In this paper, a scene reduction method based on improved clustering algorithm is proposed to reduce large-scale scenes in order to take into account both the efficiency and accuracy of computation and to form a scene reduction set that can reflect the data characteristics of the original scene set. In view of the above analysis, this paper mainly studies the following work aspects:  
1. The uncertainties of wind power and photovoltaic distributed energy are modeled. The characteristics of annual scenarios, seasonal scenarios, continuous multi-day scenarios and typical day scenarios are analyzed. The uncertainties and time series characteristics of the two types of distributed energy are analyzed. 2. In order to fully reflect the timing characteristics of two types of intermittent DGs and to avoid the difficulties caused by large-scale data in multi-scenario analysis. In this paper, a typical scenario set generation method based on improved clustering algorithm is proposed to reduce the wind power and photovoltaic data in the computing cycle and form a typical scenario set that can reflect the historical data characteristics in the computing cycle. 3. The proposed scenario reduction method based on improved clustering algorithm is validated by using the wind power output scenarios obtained from uncertain modeling.
## 2 Materials and Methods
### Methods
#### 2.4.4 Ridge Regression Analysis  
Multiple linear regression can be used to analyze the effects of meteorological factors on WUE. The model can be expressed as follows:  
\[\mathrm{WUE}=\sum_{j=1}^{P}\beta_{j}x_{j}+b \tag{7}\]  
where \(x_{j}\) represents an independent variable (j = 1, 2, \(\ldots\), p), p denotes the number of independent variables, b is the constant term, and \(\beta_{j}\) is the regression coefficients, which are calculated using the least squares method.  
However, due to the interactions and multicollinearity among meteorological variables, where \(X^{T}X\approx 0\) in Equation (8), the results are unstable [58; 59]. Therefore, ridge regression analysis is proposed, which is used for biased estimation of collinear data. [PERSON] et al. [60] and [PERSON] et al. [61] have already used this method to analyze and quantify the sensitivity of interannual ET to meteorological factors and vegetation changes. The issue of multicollinearity is addressed by introducing a perturbation term \(\lambda I\), allowing the coefficients b to be better estimated, as shown in Equation (9) [62].  
\[\beta =\left(X^{T}X\right)^{-1}X^{T}y \tag{8}\] \[\hat{\beta}_{\mathrm{ridge}} =\left(X^{T}X+\lambda I\right)^{-1}X^{T}y \tag{9}\]  
This study will utilize ridge regression analysis to quantify the sensitivity of WUE changes to Prec, Srad, Temp, and VPD. The calculation method is as follows:  
First, we normalize the four meteorological datasets.  
\[X_{m}=\frac{x-\min(x)}{\max(x)-\min(x)} \tag{10}\]  
where \(X_{m}\) represents the normalized meteorological factor data. This enables variables with different units to be calculated together.  
Next, the WUE and meteorological variable data are input into the ridge regression model.  
\[Y_{m}=\sum_{j=1}^{n}a_{i}x_{im}+b \tag{11}\]where \(Y_{m}\) is the normalized WUE data; \(x_{im}\) are the normalized meteorological factors; and \(a_{i}\) are the ridge regression coefficients.  
Finally, the relative and absolute contributions of the four meteorological factors to WUE are calculated based on the ridge regression coefficients and the normalized trends of the meteorological factors.  
\[n_{c,1}=a_{1}\cdot X_{1s,\text{trend}} \tag{12}\]  
\[n_{rc,m}=\frac{\left|n_{c,m}\right|}{\left|n_{c,1}\right|+\left|n_{c,2}\right|+ \cdots+\left|n_{c,m}\right|+\cdots+\left|n_{c,n}\right|} \tag{13}\]  
\[n_{ac,1}=\frac{n_{c,1}}{Y_{n_{\text{-trend}}}}\cdot Y_{\text{trend}} \tag{14}\]  
where \(X_{1s,\text{trend}}\) is the normalized trend of the meteorological factor, \(Y_{\text{trend}}\) is the actual trend of WUE, \(Y_{n_{\text{-trend}}}\) is the standardized trend of WUE, \(n_{rc}\) is the relative contribution of the meteorological factor, and \(n_{ac}\) is the absolute contribution.
## 3 Results and Discussion
### Ground Track Analysis  
Ground track analysis (GTA) is used to forecast and evaluate the coverage that a satellite can offer to various points on the surface of the Earth. It is a crucial tool for satellite operators since it enables them to deploy and operate the satellites optimally to increase coverage.  
The coverage time probability for various inclined satellites is plotted in Figure 8 as a function of both latitude and inclination angle for various inclination degrees from 0\({}^{\circ}\) to 70\({}^{\circ}\). The chart is symmetrical for negative values of latitude. As the latitude increases, the coverage time probability also increases until it reaches a certain latitude value, where it drops sharply to zero. Although the inclination increases, greater global coverage is achieved despite a lower coverage time probability per region. This is desired in global IoT applications that do not require the frequent revisit time offered by lower-inclination orbits.  
Additionally, when the inclination angle is smaller, the possibility of coverage at higher latitudes is impossible. This is because LEO satellites with lower inclination angles do not reach areas at greater latitudes since their ground tracks are more closely aligned with the equator. On the other hand, satellites with greater inclination angles have ground tracks that are inclined further toward the poles and are therefore more likely to cover areas at higher latitudes.  
Looking at Figure 8, for the near-equatorial regions residing in the 0-\(\pm\)27\({}^{\circ}\), the optimum inclination angle is chosen at 24\({}^{\circ}\). This is because it provides a coverage time of -12% for the near-equatorial regions.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variables** & \\ Satellite elevation angle & 0 to 90\({}^{\circ}\) \\ Spreading Factor & 7 to 12 \\ n in Code Rate & 1 to 4 \\
**Limitation and constrains** & \\ \hline Ground transceiver power (IoT Terminal) & 25 dBm \\ Nonlicenses central frequency for LoRa & 865.07 MHz \\ Band width & 125 kHz \\ Satellite antenna gain & 6.5 dBi \\ Altitude & 550 km \\
**Constants** & \\ \hline Boltzmann & \(-\)228.6 dBW/Hz/K \\ Receiver noise figure & 458 K \\
**Worst case** & \\ \hline Ground antenna gain & 0 dBi \\ Losses & (dB) \\ Pointing loss & 0.5 \\ Polarization loss & 0.5 \\ Rain loss & 0.2 \\ Implementation loss & 0.5 \\ Req. Eb/N0 & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Design Parameters.
## 3 Results and Discussion
### Dissolved and Particulate Hg and MeHg: The Effect of the River Discharge
#### 3.3.1 Occurrence and Distribution of Dissolved and Particulate Hg and MeHg
A strong correlation (N = 50, r = 0.934, \(p\) < 0.01) occurred between DMeHg and DOC, especially in summer as also evident from the 3W-PCA output (Figure 3), as well as between PMeHg and POC (N = 17, r = 0.651, 0.01 \(<\)_p_<0.1; N = 16, r = 0.768, \(p\) < 0.01; N = 17, r = 0.784, \(p\) < 0.01 for spring, summer and autumn) (Figure 6) thus suggesting that organic carbon strongly interacted with MeHg playing a crucial role in MeHg binding, distribution and fate in the water column. Indeed, it has been demonstrated that complexation with DOC can limit the solubility of MeHg and subsequently its bioaccumulation [75; 76; 77]. Particulate Fe and Mn were found to be notably present at the Nalon River estuary and a clear increase of the amount of these elements in the SPM as a function of the three different levels of river discharge (summer \(<\) spring \(<\) autumn) was evident (Figure S2). Indeed, the highest concentrations were observed in autumn (358-2022 and 192-792 \(\mu\)g L\({}^{-1}\) of PFe and PMn, respectively) followed by spring (152-516 and 90-319 \(\mu\)g L\({}^{-1}\) of PFe and PMn, respectively) and summer (86-389 and 66-221 \(\mu\)g L\({}^{-1}\) of PFe and PMn, respectively) conditions of river discharge (Table S2). Generally, PFe and PMn were found to be slightly higher in freshwater and brackish water with respect to saltwater as the result of dilution effects involving riverine and marine particles [78,79]. Moreover, the significant correlation between PFe and PMn (N = 50, r = 0.963, \(p\) \(<\) 0.01) testified to a common source and behaviour of these elements in the SPM which is also confirmed by the fact that both PFe and PMn show strong correlations with SPM (N = 50, r = 0.985, \(p\) \(<\) 0.01 and N = 50, r = 0.954, \(p\) \(<\) 0.01 for PFe and PMn, respectively) (Figures 3 and 6).
## 3 System Scheme and Implementation
### MAFS Traffic Scheduling System
The CTN-MADDPG algorithm is based on the MADDPG algorithm model, which combines the one-dimensional convolutional neural network (1D-CNN) and long short-term memory network (LSTM) to form a compact network model and introduces temporal information to design a multi-step experience replay strategy. It can extract the structural and temporal features of network state information. The network state information that occurred in the past time is transferred to the calculation of the present time. Therefore, the action information of the last time can be used to make an effective selection of the next routing action. The CTN-MADDPG algorithm can match the traffic to the optimal path based on real-time network state information, thereby achieving balanced distribution and effective transmission of traffic in the DCN.