esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
### Descriptive Statistics  
For comparison, studies conducted in Spain by Valero in 2012 [57] indicate that the largest changes in the values of parameters within the tested hydropower plant concerned EC and DO (7.50% and 7.12%); the Fantin-Cruz and other studies from 2015 in Brazil [58] concerned turbidity, TP, and NO\({}_{3}\) (respectively: 38.00%, 28.00%, and 14.00%); the studies conducted in Lithuania by [PERSON] and others from 2015 [59] concerned TP and TN (26.97% and 15.49%); the studies conducted by [PERSON] and others in China concerned TP and DO (366.7% and \(-\)35.09%) [60]; the studies conducted by [PERSON] and others in China concerned EC and DO (\(-\)7.50% and 7.12%) [61]; and the studies conducted by [PERSON] and [PERSON] in Poland concerned NH\({}_{4}\)-N (from \(-\)38.89% to 22.86%) and turbidity (from \(-\)7.14% to 5.95%) [62]. Detailed results are summarized in Table 14. Analyzing the results in more detail, especially for TP, for which there is an almost full cross section of case studies, it is noted that the size of changes within hydropower plants is influenced by the damming height and the type of hydropower. The higher the damming height, the greater the impact usually, and storage hydropower plants have a stronger impact than run-of-river hydropower plants on the parameters of water quality. No convergent relationships were found in the changes in  
Among the phenomena that concern the impact of hydropower plants on physico-chemical parameters, it is possible to note, e.g., supersaturation (occurring in cascade reservoir hydropower plants during higher water flows and manifested by a reduction in DO content--this is reflected in the DO results from Luo and other studies from 2019) [63; 64; 65], resuspension of sediments rich in nutrients (release of nitrogen and phosphorus compounds contained in the sediments into the water in periods of higher flows downstream of hydropower plants; during periods of low and medium flows, accumulation of sediments rich in nutrients upstream of hydropower plants occurs) [66; 67; 68], changes in thermal and oxygen conditions (mainly in storage hydropower plants due to the effect of the swirling movement of water below the damming) [69; 70; 71]. Future research should consider the operating cycles of hydropower plants as well as hydrological conditions that greatly impact the physicochemical properties of water.
# Two-Way Linear Probing Revisited
## 2 Background and History
### Probing and Replacement  
Open addressing schemes are determined by the type of the probe sequence, and the replacement strategy for resolving the collisions. Some of the commonly used probe sequences are as follows:  
1. **Random Probing [16]:** For every key \(x\), the infinite sequence \(f_{i}(x)\) is assumed to be independent and uniformly distributed over \(\mathcal{T}\). That is, we require to have an infinite sequence \(f_{i}\) of truly uniform and independent hash functions. If for each key \(x\), the first \(n\) probes of the sequence \(f_{i}(x)\) are distinct, i.e., it is a random permutation, then it is called **uniform probing [1]**.
2. **Linear Probing [1]:** For every key \(x\), the first probe \(f_{1}(x)\) is assumed to be uniform on \(\mathcal{T}\), and the next probes are defined by \(f_{i+1}(x)=f_{i}(x)+1\mod n\), for \(i\in\llbracket n\rrbracket\). So we only require \(f_{1}\) to be a truly uniform hash function.
3. **Double Probing [17]:** For every key \(x\), the first probe is \(f_{1}(x)\), and the next probes are defined by \(f_{i+1}(x)=f_{i}(x)+g(x)\mod n\), for \(i\in\mathbb{N}\), where \(f_{1}\) and \(g\) are truly uniform and independent hash functions.  
Random and uniform probings are, in some sense, the idealized models [18; 19], and their plausible performances are among the easiest to analyze, but obviously they are unrealistic. Linear probing is perhaps the simplest to implement, but it behaves badly when the table is almost full. Double probing can be seen as a compromise.  
During the insertion process of a key \(x\), suppose that we arrive at the cell \(f_{i}(x)\), which is already occupied by another previously inserted key \(y\), that is, \(f_{i}(x)=f_{j}(y)\), for some \(j\in\mathbb{N}\). Then a replacement strategy for resolving the collision is needed. Three strategies have been suggested in the literature (see [20] for other methods):  
1. first come first served (fcfs) [1]: The key \(y\) is kept in its cell, and the key \(x\) is referred to the next cell \(f_{i+1}(x)\).
2. last come first served (lcfs) [21]: The key \(x\) is inserted into the cell \(f_{i}(x)\), and the key \(y\) is pushed along to the next cell in its probe sequence, \(f_{j+1}(y)\).
3. robin hood [22; 23]: The key which traveled the farthest is inserted into the cell. That is, if \(i>j\), then the key \(x\) is inserted into the cell \(f_{i}(x)\), and the key \(y\) is pushed along to the next cell \(f_{j+1}(y)\); otherwise, \(y\) is kept in its cell, and the key \(x\) tries its next cell \(f_{i+1}(x)\).
# Three-Dimensional Finite Element Analysis for Pressure on Flexible Wall Silos
## 1 Introduction
### Limitations of Actual Analysis Method  
In circular silos, pressure distributions around the perimeter are uniform at any level due to the condition of the axisymmetric load and geometry. While filling or discharge operations stem from the eccentric point, the distribution of wall pressure will be non-uniform. Recent changes to silo design standards recommend using local loads to account for bending moments caused by non-uniform pressure distribution. Local unsymmetrical low pressures are typically far more destructive than extremely high symmetrical pressures [20].  
However, in rectangular planform silos, it is critical to consider the non-uniform pressures that occur around the perimeter as a consequence of the structure's geometry and wall flexibility (Figure 2). The flexibility of the wall and the boundary conditions have a major effect on the stress state of the stored material. The observed wall pressures were significantly different from those expected by conventional theories and design standards. This is because standard theories were established for circular silos with inflexible walls and neglected the impacts of bulk solids/structure interaction [21]. Additionally, numerical models have demonstrated that wall flexibility affects the values of wall pressure distribution in circular silos, and even more substantially in rectangular planform silos [19; 22], when it affects both values and the distribution of wall pressure.  
[PERSON] et al. [11] mentioned that the mean lateral filling pressure may be close to [PERSON]'s prediction at any height. However, the distribution of such filling pressures is far from uniform over any horizontal section. He also demonstrated that the distribution of lateral wall pressures depends on the relative elastic stiffness of the bulk solids and structural elements, where slight changes in the characteristics of granular materials can have significant effects on the pressure magnitudes and patterns, thus strongly affecting the design of silo structures [16; 23].  
Since most design codes use the Janssen equation as a reference for the prediction of filling wall pressure, all methods would underestimate the maximum existing pressure at any level, which leads to unsatisfactory design if used without amplification or safety factors. Hence, it is essential to develop an accurate tool to predict the straining actions on the walls of flexible silos and to identify the limitations of the codes.
## 3 Network Model  
Similar to [PERSON] et al. (2017), we assume that there are \(N\) nodes in a dynamic network environment in which each node periodically performs a scheduling mechanism. Each node in the network can adjust its state between the listening or sleeping state. The scheduling mechanism can be expressed as a tuple, \(T_{i}=<b_{i},T,T_{slot}>\). \(T\) is the length of a period, which can be divided into multiple slots. \(b_{i}\) is the bitmap of node \(i\) and each bit in \(b_{i}\) represents that node \(i\) is in the listening or sleeping state at the time of the corresponding slot, 1 and 0 represent the listening and sleeping states respectively. \(T_{slot}\) represents the duration of each slot. In this paper, it is supposed that all nodes in the network have the same \(D\) for the duty cycle operations, which can be defined as \(D=T_{w}/T_{p}\), where \(T_{w}\) is the number of listening slots in \(T\), and \(T_{p}\) denotes the total number of slots in \(T\). Then, the scheduling mechanism of node and the state of node in each slot can be shown in Figure 1.  
In Figure 1a, nodes \(i\) and \(j\) execute the scheduling mechanism of \(T_{i}=<\)1101001001, 300, 30\(>\) and \(T_{j}=<\)1010011001, 300, 30\(>\) respectively at time \(T_{0}\) to \(T+T_{0}\), where 1 and 0 represent the listening and sleeping states respectively. We set \(T=300\) s, \(T_{slot}=30\) s, and \(D=50\%\). Hence, in Figure 1a node \(i\) is in listening state when the slot number is 1, 2, 4, 7, 10 and in sleeping state in other slots. Nodes in listening state can send a message to discover the contacts and exchange data with the neighbors. In contrast, nodes in the sleeping state will shut down their wireless radio to conserve energy and cannot communicate with other nodes.  
Figure 1: Effective (**a**) and Missed (**b**) Contacts between nodes \(i\) and \(j\), where blue slots represent the listening state and the white slots represent the sleeping state.
## 1 Introduction  
Digital technologies are changing ways of working, studying, communicating and entertaining [1; 2], among other things. During the past few years, the continuous growth of the use of mobile devices and media technologies has transformed traditional games [3; 4; 5]. In this context, mobile device gaming has opened up new dimensions for entertainment applications [6].  
This article is focused on the use of Pokemon Go game, one of the most popular smartphone games of all time [7; 8; 9; 10]. It was launched by the company Niantic Lab in July 2016 and immediately became a global phenomenon [11]. According to [12], this game has reached 1 billion downloads and it is available in more than 90 countries [13]. In addition, as stated in [14], based on December 2018 statistics, Pokemon Go dominates the location-based game category in terms of revenue.  
Pokemon Go players must capture virtual characters (Figure 1) mapped to real-world locations [15; 16; 17]. At a certain point in the game, players have to join one of three teams and battle for the prestige and ownership of gyms [18], which can be found at real locations in the world, with their Pokemon. The objective of Niantic, a software development company specialized in augmented reality mobile games, was to develop a product that would encourage exercise, but not in a heavy-handed way [19]. Currently, Pokemon Go is included in the augmented reality mobile games category [4; 8].  
Considering that Pokemon Go pioneered more sophisticated software because of the need for location information [20; 21], developers and game companies will be building this into future games. Thus, it is necessary for the online game industry to develop an understanding of the players' intentions [22; 23; 24; 25].  
Two objectives are proposed: (1) to research the motivations of those using augmented reality mobile games; (2) to define a structural model based on the Uses and Gravification Theory [26; 27] for the adoption of augmented reality mobile games. Indeed, this theory has been applied widely in Information and Communication Technologies (ICT) research due to its potential for analysing online activities [28; 29].  
The remainder of this article is as follows: the theoretical background and hypotheses are shown in Section 2. Section 3 then provides the details of the research methodology and preliminary analysis. Thereafter, Section 4 describes the model testing and Section 5 incorporates the discussion. Finally, Section 6 describes conclusions, implications, limitations and future research directions.
## 4 Discussion
Caffeic acid, with \(\Delta\)E= 0.36 V, achieves an ideal match for charge distribution in the donor-acceptor transition state, resulting in a maximal rate constant of \(k_{2}\) = 1.3 \(\times\) 10\({}^{8}\) L-mol\({}^{-1}\).s\({}^{-1}\). Plant phenols with both lower and higher reducing powers exhibit slower electron transfer rates, in agreement with [PERSON] theory. This observation further supports the notion that plant phenols with excessively high reducing power fall within the inverted region predicted by [PERSON] theory [26]. Syr, with \(\Delta\)E = 0.32 V, has a smaller driving force than caffeic acid and reduces Trp\({}^{\bullet+}\) slightly less efficiently with \(k_{2}\) = 8.7 \(\times\) 10\({}^{7}\) L-mol\({}^{-1}\).s\({}^{-1}\). Similarly, catechin with \(\Delta\)E = 0.62 V, has a larger driving force than caffeic acid but reduces Trp\({}^{\bullet+}\) less efficiently with \(k_{2}\) = 7.5 \(\times\) 10\({}^{7}\) L-mol\({}^{-1}\).s\({}^{-1}\), due to entering the 'inverted region' predicted by [PERSON] theory. The formation of a precursor complex between the Trp radical cation and the phenolate anion appears to be critical for establishing the donor-acceptor transition state, where a match in reorganization energy ensures the maximal electron transfer rate, according to the [PERSON] theory for electron transfer [27]. Beyond this kinetic factor, the resonance stabilization of the phenoxyl radical following electron transfer serves as an important thermodynamic factor. As illustrated in Figure 5B, the relationship between electron transfer rate constants and \(\Delta\)E demonstrates the dependence of rate constants on driving force. The rate constants obtained for Trp regeneration support the free energy relationship predicted by the [PERSON] theory for electron transfer involving plant phenols [24]. In future applications, plant phenols could serve as additives to mitigate oxidative damage during the processing and preservation of protein-based foods. The incorporation of plant phenols may extend the shelf life of protein-rich foods, enhance their antioxidant capacity, and ensure that the protein content remains bioavailable and nutritionally effective for consumers. This approach holds the potential for better preserving the structural integrity and nutritional value of proteins, thereby improving the overall stability and health benefits of protein-based food products.
###### Abstract  
Land cover products obtained from remote sensing image classification inevitably contain a large number of false classification or uncertain pixels because of spectral confusion, image resolution limitation, and ground object complexity. The confusion matrix used to evaluate the classification accuracy cannot reflect the spatial variation. The information provided to users of land cover products is incomplete and uncertain. In this study, a method is presented to evaluate and improve the accuracy of land cover classification products by coupling Geo-Eco zoning and Markov chain geoscience statistical simulation. Validation points collected from various sources are used in the model calculation and accuracy verification of results. The pre-classified image that needs to be improved and Geo-Eco zoning attribute data are used as auxiliary data for co-simulation. Results show that the accuracy of Globeland30 data can be improved by more than 10% by coupling Geo-Eco zoning and Markov chain geostatistical simulation.  
Geo-Eco zoning; geostatistical simulation; Co-MCSS; accuracy improvement Article  
Improving the Accuracy of Remote Sensing Land Cover Classification by GEO-ECO Zoning Coupled with Geostatistical Simulation. _Appl. Sci._**2021**, _11_, 553. [[https://doi.org/10.3390/app11020553](https://doi.org/10.3390/app11020553)]([https://doi.org/10.3390/app11020553](https://doi.org/10.3390/app11020553))
## 6 Grid Integration and Interface of Mobility Technologies
### Matrix Converters in Direct Current (DC) Fast-Charging Stations  
Charging stations are expected to have near unity power factor, high power density and low THD. CSR are one of the suitable topologies to tackle these challenges. They are characterized by its high-power density while providing the ability to improve the quality of the grid currents and guarantee unity power factor in the connection to the grid [127]. The scheme of a CSR can be obtained with a \(3\times 2\) DMC as shown in Figure 18.  
Many DC fast charging topologies often use a bulky capacitor on the DC link, while matrix converters can link the AC source to the battery with minimum filtering components and without the requirement of an additional DC-DC converter [128], still allowing bidirectional power flow. The absence of an energy storage link makes this topology an attractive alternative to the VSI as the size of the converter can be reduced and a higher reliability of the system can be achieved [129, 130].  
For safety reasons, DC fast-charging stations may require galvanic isolation between the AC grid and the DC link [131]. In [132] the author proposed a resonant matrix converter topology for a 500 V DC fast-charging station that uses a high-frequency transformer to guarantee galvanic isolation with increased power density.  
Several modulation strategies have been proposed to control the CSR [133, 134]. In [135] an ultrasparse matrix rectifier topology is applied to a battery charger where only 3 switching components are used. It provides low harmonic distortion in absorbed currents and a near unity power factor is achieved over a wide operating output power range. However, this topology does not allow bidirectional power flow.
# Human Skeleton Data Augmentation for Person Identification over Deep Neural Network
## 6 Experimental Evaluation and Results  
### Dataset and Evaluation Protocol  
We evaluated the proposed method on the existing publicly available person identification dataset, called [PERSON]'s dataset [23; 24; 25]. To the best of our knowledge, this dataset contains the largest number of subjects, compared to other publicly available datasets. For this reason, this dataset has been widely used to test person identification methods. It contains 164 subjects in total, with five sequences for each subject. However, there are only three or four sequences for the following four subjects: \"Person002,\" \"Person015,\" \"Person158,\" and \"Person164.\" We eliminated these four subjects from the dataset. In addition, there are six sequences for the following seven subjects: \"Person003,\" \"Person034,\" \"Person036,\" \"Person052,\" \"Person053,\" \"Person074,\" and \"Person096.\" Observing the six sequences for each subject, we decided to eliminate the noisiest sequence. Consequently, the 4 th sequence of \"Person003,\" 1 st sequence of \"Person034,\" 3 rd sequence of \"Person036,\" 5 th sequence of \"Person052,\" 5 th sequence of \"Person053,\" 3 rd sequence of \"Person074,\" and 6 th sequence of \"Person096\" were eliminated from the dataset. As a result, the dataset contains a total of 800 \((=160\) subjects \(\times\) 5 sequences per subject) sequences.  
We used the 5-fold cross-validation to evaluate the performance of each method. In each cross-validation, for each subject, four sequences were selected from a total of five. The selected 640 sequences \((=160\) subjects \(\times\) 4 sequences per subject) were used for the training dataset. In addition, the remaining 160 \((=800-640)\) sequences were used for the testing dataset.
# An Interactive System Based on the IASP91 Earth Model for Earthquake Data Processing
## 2 Data Pre-Processing  
1. Filtering. The filtering settings are Gaussian filtering and band-pass filtering [26]. The filtering function can be achieved by setting the low cut-off frequency Flow and the high cut-off frequency Flugh for the band-pass filter in the Filtering section of Setting and selecting Filtering, and then running Display\(\rightarrow\)Display Seismograms. If a Gaussian filter is selected, the Gaussian filter is additionally selected with a center frequency of fc = (Flow + Flugh)/2. A is the amplitude, \(\alpha\) is the smoothing factor and fc is the center frequency of the Gaussian filter.
2. Time correction. If a seismic trace has a known time delay or advance, the trace can be corrected in the Settings display by setting dTcorrect = dT and running Processing\(\rightarrow\)Correct trace time. dT can be positive or negative, with dT > 0 corresponding to a time delay and dT < 0 corresponding to a time advance.
3. Removing bad traces. Due to a variety of interferences, such as random noise from the environment, industrial interference, low-frequency linear interference, etc., there are some recording traces with a too low signal-to-noise ratio in the seismic records, commonly known as bad traces. They cannot be used and therefore need to be removed. To remove some bad traces, make sure that Picking is selected in the Settings display, then select the bad trace by moving and clicking the mouse, and finally run Processing\(\rightarrow\)Remove Bad Traces to remove the bad trace. The removal process actually marks the traces and is not called up again during the process; the data remains in the computer memory. If deleted by mistake, you can run Processing\(\rightarrow\)Recover traces removed to recover all the removed bad traces and remove them again. The bad trace removal function can remove multiple bad traces at the same time.
4. Resampling. In the seismic data processing process, it is sometimes necessary to resample the data according to practical needs, encrypting or pumping thin seismic data. This system uses the cubic spline algorithm for interpolation [27; 28; 29] to realize the resampling of seismic data.
# A Data-Driven Approach to Improve Customer Churn Prediction Based on Telecom Customer Segmentation
## 5 Factor Analysis to Characterize Expense, Call, and SMS Attributes
### Factor Analysis of Telecom Customer Calls
#### 5.2.5 Component Matrix  
The component score coefficient matrix is shown in Table 12. F4 had more significant loads for the total long-distance MOU and total roaming MOU. Therefore, long-distance and roaming calls were resumed as the second factor F4. Additionally, the total monthly called MOU, total local MOU, and total local called MOU numbers showed significant loads for the first factor F3. Therefore, the first factor F3 can explain the called MOU factor.  
Therefore, we confidently concluded that F3 (common factors of the called MOU) and F4 (common factors of long-distance and roaming call) characterize the call attributes. The formulas for calculation were as below, which were adapted from Zhang [6]:  
F3 = 0.179 \(\times\) Total monthly traffic MOU + 0.073 \(\times\) Total monthly caller MOU + 0.257 \(\times\) Total monthly called MOU + 0.283 \(\times\) Total local MOU + 0.294 \(\times\) Total local called MOU - 0.119 \(\times\) Total long-distance MOU - 0.160 \(\times\) Total Roaming MOU.  
F4 = 0.120 \(\times\) Total monthly traffic MOU + 0.317 \(\times\) Total monthly caller MOU - 0.100 \(\times\) Total monthly called MOU - 0.160 \(\times\) Total local MOU - 0.199 \(\times\) Total local called MOU - 0.553 \(\times\) Total long-distance MOU - 0.540 \(\times\) Total Roaming MOU.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Component**} & \multicolumn{4}{c}{**Eigenvalue Starting Value**} & \multicolumn{4}{c}{**Squared Sum Extraction Loading**} & \multicolumn{4}{c}{**Square Sum Rotation Loading**} \\ \cline{2-11}  & **Sum** & **Variance \%** & **Accumulative \%** & **Sum** & **Variance \%** & **Accumulative \%** & **Sum** & **Variance \%** & **Accumulative \%** \\ \hline
1 & 4.713 & 67.330 & 67.330 & 4.713 & 67.330 & 67.330 & 4.191 & 59.869 & 59.869 \\
2 & 1.129 & 16.133 & 83.463 & 1.129 & 16.133 & 83.463 & 1.652 & 23.594 & 83.463 \\
3 & 0.910 & 13.000 & 96.464 & & & & & & \\
4 & 0.245 & 3.502 & 99.966 & & & & & & \\
5 & 0.002 & 0.026 & 99.992 & & & & & & \\
6 & 0.001 & 0.008 & 100.000 & & & & & & \\
7 & 1.148 & 0.000 & 100.000 & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 11: Total interpretation variance.  
Figure 3: Scree plot.