esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Application of Artificial Neural Network  
The machine learning model of ANN with MultiLayer Perceptron (MLP) algorithm was used for learning in this study. The MLP algorithm adds hidden layers to compensate for the limitation of the linear classification of the conventional perceptron [45]. The MLP algorithm consists of three groups of layers: input layer, hidden layer, and output layer. The MLP algorithm adjusts the connection strength between layers and processes it repeatedly to perform prediction and estimation by using the error back propagation learning technique for learning. In the error back algorithm, an input signal is transmitted to a hidden layer through a feedforward network and generates the final output. The weight is corrected by propagating the error in the direction of reducing the error by comparing the  
Figure 5: (a) Digital Terrain model (10 m) from national geographic information institute (NGII). (b) WorldDEM (12 m).  
output with the true value. An activation function is used to adjust the strength of the connection. In this study, logistic function, the commonly used unipolar sigmoid function, is used as follows.  
\[\sigma(z)=\frac{1}{1+\textit{exp}(-z)} \tag{8}\]  
This function adjusts the result to a value between 0 and 1, which represents the result with a probability of 0 to 1 [46].  
The input layers of NDVI, two types of NDWI, two types of NDre, PCA Texture, average canopy height, and standard deviation canopy height map produced from the above process are assumed to be correlated with forest vertical structure. To estimate the forest vertical structure, Waikato Environment for Knowledge Analysis (WEKA), a widely used data mining tool, was used in this study [47]. Training (70%) and test (30%) data were randomly set for each layer. Training (including validation) was processed with 70% (1,608,912 pixels including 297,365, 186,573, 1,124,975 for single-, double- and triple-layers, respectively) and test was performed with rest of data. The training and validation process of ANN algorithms were performed by using 10-fold cross validation technique; it is a method of repeating the training and validation by dividing the training and validation set into 10 and finding the optimal condition by the average of the errors recorded in each fold attempt to tune the optimal hyperparameters. For this purpose, the training/validation dataset was divided into 10 groups with nine groups of training data and one group of validation data; each group was used once for validation and nine times for training. Finally, forest vertical structure classification was conducted using the preprocessed data above as an input layer to the ANN and test accuracy was calculated by using test dataset.
## 3 Method
### FP-Growth Algorithm  
The classified variables obtained from K-means clustering are further analyzed using the FP-Growth algorithm to mine association rules from the monitoring data. The FP-Growth algorithm, proposed by [PERSON] and [PERSON] [37], aims to address the inherent limitations of the Apriori algorithm, particularly its prolonged computation time and low efficiency. Compared to traditional statistical correlation methods, FP-Growth allows for the discovery of complex, non-linear relationships and dependencies between variables without requiring prior assumptions about their distribution.
# A Type of Low-Latency Data Gathering Method with Multi-Sink for Sensor Networks
## 5 Simulation Results
### Efficiency of Data Gathering  
In LDGM, the leader carries a big load its energy consumption is much higher than any other node. However, there are no differences in size of cache as well as the ability to transmit data between these two types of nodes. So how to speedily upload data to the mobile Sink and how to avoid a buffer overflow on leaders are also important problems requiring resolution.  
Firstly, we analyze the time interval of the two consecutive data uploading process of each leader with the size of the networks set to 100 m \(\times\) 100 m and 200 m \(\times\) 200 m, as shown in Figures 26 and 27. In the single Sink based network, each TP could only be visited once during one round of data gathering time. So, the distribution of these time intervals is nearly uniform. It should be pointed out that the different moving paths of Sinks could also influence the length of time intervals but after many experiments, it was found that the impact is limited. While with the increase of \(N_{s}\), the time interval between two consecutive times of data uploading is obviously shorter for example, in the 100 m \(\times\) 100 m network with four mobile Sinks for more than 80% of the leaders, the time intervals are shorter than 120 s. Thus, LDGM could greatly increase the frequency of data uploading and therefore reduce the probability of buffer overflow.  
Moreover, Percentage of Valid Data collected by Sinks (PVD) is another important indicator to show the efficiency of data gathering. PVD in LDGM is defined as the ratio between the amount of data received by all the mobile Sinks and the total amount of data collected by all active nodes in a round of data gathering time. Experimental result is shown in Figure 28. With the increase of \(u_{ik}\), the value of PVD decreases. In LDGM, the cache size of each node is set to 4 KB so it will overflow when the sensing rate is too high. On the other hand, PVD will be higher when \(N_{s}\) is larger, e.g., when \(N_{s}=4\), even if \(u_{ik}\) is up to 0.5 bit/(m\({}^{2}\cdot\) s), the value of PVD is still nearly 90%.  
Figure 25: Average time delay with different moving speeds of Sinks.  
Figure 28: Percentage of valid data collected by Sinks (100 m \(\times\) 100 m, 200 nodes).  
Figure 26: Time interval of two consecutive times of data upload (100 m \(\times\) 100 m, 200 nodes).  
Figure 27: Time interval of two consecutive times of data upload (200 m \(\times\) 200 m, 500 nodes).
## 3 Results
### General Characteristics of Studies  
All included studies' general characteristics and major outcomes are summarized in Supplementary Table S1.  
Nine (9) studies were published during the present decade. Eight (8) originated from South Korea and 3 from Europe (2 from Finland and 1 from Spain). Among the 8 articles from S. Korea, 7 reported findings from the same sample of patients. In a similar vein, both articles from Finland reported outcomes derived from the same group of participants. In the Korean studies, participants were exclusively of Asian descent; in the Finnish studies all participants were Caucasian; and in the Spanish study, the authors do not state the racial origin of their sample. In 8 articles, the sample comprised exclusively schizophrenic patients; in 2, the sample included patients with schizophrenia, schizoaffective or delusional disorder; and in 1 study the participants were either schizophrenic patients or healthy controls. As far as types of sleep disorder are concerned, in 7 articles, the authors focused on patients with antipsychotic-induced RLS; in 3 articles the participants suffered from antipsychotic-induced daytime somnolence/sedation; and in 1 study the authors focused on the presence of either insomnia or hypersomnia. In total, in this review we analyzed data derived from 777 patients (of which 39% were females).  
The genetic variations of 36 different genes were examined in eligible studies. These genes encode several protein products which include histamine, dopaminergic, serotoninergic, glutaminergic and adrenergic receptors (16 genes), enzymes implicated in histamine, dopaminergic, noradrenergic and serotoninergic pathways (7 genes), melatonin receptors (2 genes), a protein implicated in limb formation and hematopoiesis (1 gene), proteins implicated in the circadian system (2 genes), enzymes of the cytochrome P450 (3 genes), a protein implicated in ion channels regulation (1 gene), a serotonin transporter (1 gene), a drug transporter (1 gene), a neurotrophic factor (1 gene) and a G-protein (1 gene).  
Figure 1: Process of articles selection.  
Supplementary Table S2 provides data regarding the genes and variations which were found to be significantly associated with the presence of sleep disorders in patients with schizophrenia, including their protein products and the exact pathophysiological pathways in which they are implicated.
###### Abstract  
We conducted an in-depth exploration of the use of different machine learning (ML) for regression algorithms, including Linear, Ridge, LASSO, Bayesian Ridge, Decision Tree, and a variety of Deep Neural Network (DNN) architectures, to estimate the slope of the high-frequency feature (HFF), a prominent emergent feature found in the gravitational wave (GW) signals of core collapse supernovae (CCSN). We created a data set of CCSN GW signals generated by an analytical model that mimics the characteristics of the signals obtained from numerical simulations, particularly the HFF. This enabled us to simulate a wide range of HFF slope values and analyze their properties. We opted to employ ML for regression techniques, particularly a supervised learning approach, to analyze the data set due to the parameter chosen for estimating the slope of the HFF. This type of architecture is ideal for this purpose as it can detect the connections between input and output data. In addition, it is suitable for handling high-dimensional input data and produces efficient results with low computational cost. We evaluated the efficiency and performance of the ML algorithms using a set of metrics to measure their ability to accurately predict the HFF slope within the data set. The results showed that a DNN algorithm for regression exhibits the highest accuracy in estimating the slope of the HFF.  
gravitational waves; machine learning; high-frequency feature; core-collapse supernovae; parameter estimation Article
# Linking Singular Spectrum Analysis and Machine Learning for Monthly Rainfall Forecasting
## 5 Conclusions  
The current study examined the reliability of combining a data preprocessing method (SSA) with the machine learning techniques, LS-SVR and RF, for monthly rainfall forecasting in two reservoir watersheds of Taiwan. One of the major findings is that the hybrid models (SSA-LSSVR and SSA-RF) have better performance than the standard models (LS-SVR and RF) for both watersheds. It can be concluded that the hybrid models are a prospective modeling approach that can be applied to forecast the monthly rainfalls in the present study region. However, the two hybrid models perform differently in the two watersheds (i.e., SSA-LSSVR performs better than SSA-RF in one watershed but performs worse in the other). Linking the rainfall characteristics of watersheds to the forecasting model performances is suggested to be further investigated in future work. In addition, only one data preprocessing method (SSA) has been adopted; thus, future work may consider various preprocessing techniques (wavelets) and compare their efficiencies to achieve more accurate predictive outcomes. Moreover, only the areal monthly rainfall data from two reservoir watersheds was used in the current study. More study areas should be included for validating the findings.  
Everyone who authored this work was equally helpful. [PERSON] and [PERSON] conceived and designed the study; [PERSON] and [PERSON] contributed and supervised data and analytical methods; [PERSON] and [PERSON] developed the Software for rainfall forecasting; [PERSON] performed the modeling; [PERSON], [PERSON] and [PERSON] did the analysis; [PERSON] wrote the draft manuscript, [PERSON] and [PERSON] revised and approved the final version of the manuscript. All authors have read and agreed to the published version of the manuscript.  
No external funding was granted to this research.  
The authors sincerely thank the anonymous reviewers for their valuable comments to improve this paper.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Experimental Results
#### 3.2.4 Channel Dimension  
At last, OFI sensor behavior dependence on the fluidic channel dimension was considered as well. In Figure 8, we performed the simulation and measurement of an OFI signal spectra in 1 mm \(\times\) 1 mm macroscale PDMS at 100 \(\upmu\)L/min flow rate and 96 \(\upmu\)m \(\times\) 96 \(\upmu\)m microscale DF channel at 1 \(\upmu\)L/min, respectively. The 10 times flow rate difference was applied to maintain the similar velocity and Doppler frequency in the cases of different channel cross-section dimension.  
In the simulation of the macro PDMS channel (red in Figure 8a), the relatively small sensing volume with respect to the entire channel size (shown in Figure 3b) led in a sharp Doppler frequency peak at _fd_2 = 2.7 kHz in the frequency spectrum. Considering the entire laser spot is located inside the channel, almost all the laser emission power can be scattered by the flowing particles, the simulated spectra exhibited a high signal level. However, in the micro DF channel, the comparable sensing volume with the entire channel dimension in Figure 4b shows that all the particles can contribute the OFI signal of different Doppler frequency shift segments, giving rise to a frequency plateau (blue in Figure 8a). Considering only part of the emission is coupled in the microchannel, the resulting spectra was 15 dB lower. Again, a secondary frequency peak _fd_1 appears in DF channel due to the reflection enhancement. In Figure 8b, the measured spectra in both channels exhibit the semblable appearances at _fd_2 = 2.7 kHz, sharp peak in the macro channel and plateau in the microchannel, denoting the maximal velocity at the center where the laser focal point was positioned, and the secondary peak _fd_1 = 0.4 kHz was only in DF channel. The signal difference was 20 dB between the two channels, indicating a consistent agreement with the simulation.
## 3 Results and Discussion
### Mo-Re-Cr-Ni (\(\sigma\) Phase)  
Finally, as an outlook, our analysis could be extended to quaternary systems. For example, let us examine the two quaternary systems Mo-Re-Cr-Co and Mo-Re-Cr-Ni. These two systems are remarkable because the \(\sigma\) phase exists in many of its constituent binaries (Mo-Re, Re-Cr, Cr-Co, Mo-Co, metastable in Cr-Ni) and both Re and Cr may switch from high CN (Re in Re-Cr, Cr in Cr-Co and Cr-Ni) to low CN (Re in Mo-Re, Cr in Re-Cr) positions. One can therefore expect complicated site reversal in this system.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Nominal Composition (at. \%)** & **Measured Composition (EPMA, at. \%)** & **Phase** & **Lattice Parameters (Å)** \\ \hline Re\({}_{39}\)Cr\({}_{42}\)Co\({}_{19}\) & Re\({}_{39(3)}\)Cr\({}_{39(1)}\)Co\({}_{21(2)}\) & \(\sigma\) phase & \(a=9.093\) \\ \hline \multirow{2}{*}{Re\({}_{18}\)Cr\({}_{48}\)Co\({}_{34}\)} & \multirow{2}{*}{Re\({}_{19.1(18)}\)Cr\({}_{47.4(7)}\)Co\({}_{33.5(12)}\)} & \multirow{2}{*}{\(\sigma\) phase} & \(c=4.702\) \\ \cline{1-1} \cline{3-3} \cline{5-  
For the first time, in a recent work, we have calculated by DFT all the \(4^{5}=1024\) configurations of the \(\sigma\) phase in a quaternary system [6]. We may use this calculation to compute the site occupancies from Mo2 Re to Cr2 Ni in the tetrahedron of compositions (Figure 16). The figure shows the two site reversals of Re and Cr. Re reversal is evident around 0.1 mole fraction Ni, Cr reversal can be seen by the fact that low CN sites are first occupied and then decrease and are overtaken by the high CN occupancies.  
Figure 14: Isothermal section at 1150 \({}^{\circ}\)C of the Re–Cr–Co system (redrawn from [27], Copyright 1986, Elsevier Science). The red triangle shows the three ordered stable or metastable compositions of the \(\sigma\) phase, Re\({}_{2}\)Cr, Re\({}_{2}\)Co, and Cr\({}_{2}\)Co, as in Figure 2 (solid lines: normal substitution, dashed line: site reversal). The site occupancies of Figure 15 are calculated along the blue line joining experimental data points.  
Figure 15: Calculated and experimental site occupancies for the \(\sigma\) phase in the Re–Cr–Co system at 1150 \({}^{\circ}\)C along the blue line in Figure 14 (new results). The experimental data for the binary systems are taken from [20,26].
###### Abstract  
We report the synthesis and properties of a new thiazolothioazole (TzTz)-based semiconducting polymer incorporating the dithienothienothiophenebisimide (TBI) unit, named PTzTBI. PTzTBI showed relatively deep HOMO and LUMO energy levels of \(-5.48\) and \(-3.20\) eV, respectively. Although PTzTBI mainly formed face-on backbone orientation unfavorable for transistors, PTzTBI functioned as an ambipolar semiconductor for the first time with TzTz-based polymers, with reasonably high and well-balanced hole (0.02 cm\({}^{2}\) V\({}^{-1}\) s\({}^{-1}\)) and electron (0.01 cm\({}^{2}\) V\({}^{-1}\) s\({}^{-1}\)) mobilities.  
**Keywords: semiconducting polymer; thiazolothiazole; organic field-effect transistors; n-type; ambipolar**
# Novel Real-Time Compensation Method for Machine Tool's Ball Screw Thermal Error
## 3 Screw Thermal Error Due to Feed Movement  
The thermal error of the screw due to the feed movement is calculated according to the process illustrated in Figure 4. The algorithm is implemented by matlab in the parameter identification process and implemented in the HNC-848 CNC system by C++ in the error compensation process. An analysis of the thermal boundary of the screw is conducted under the following assumptions:  
1. Heat generated by the bearing in the fixed end enters the screw from the face in contact with the bearing.
2. Heat generated by the nut enters the screw from the surface in contact with the nut.
3. Heat convection between the screw and the air occurs on all free surfaces of the screw.  
Figure 4: Flowchart of screw thermal error prediction.  
Under those assumptions, the screw is simplified to a one-dimensional thin rod. According to [PERSON]'s law of thermal conductivity:  
\[Q(x,t) =-\lambda\frac{\partial T(x,t)}{\partial x}, \tag{3}\]  
where \(Q(x,t)\) is the heat flow through the cross-section \(x\) at time \(t\), \(T(x,t)\) is the temperature of the cross-section \(x\) at time \(t\), and \(\lambda\) is the thermal conductivity of the screw material.
## 3 Drone Routing Algorithms
### Charging
#### 3.2.1 Optimized Deployment of Charging Station (ODCS)  
In [44], the author proposed ODCS to address the issue of finding the optimal number and location of CSs in a given area to serve maximum customers. The placement of CSs prioritizes the most demandable locations, which can be determined based on the delivery frequency or historical data. ODCS considers several constraints in the optimization process, such as limited parcel weight, distance between two CSs according to the flight time of a drone, connection of the CSs till depot according to the drone's flight range, and threshold coverage ratio of the CSs. In ODCS, initially, it deploys the CSs in a triangular pattern to cover the entire demand area. Next, it recursively removes the extra CSs based on the least demand locations and re-adjusts the locations of other CSs to achieve higher coverage ratio. This re-adjustment process continues until no further removal of CSs is possible, thus satisfying the abovementioned constraints.
# Investigation of a Gravel-Bed River's Pattern Changes: Insights from Satellite Images
## 1 Introduction
The application of these technologies presents still today many difficulties and uncertainty because of the reflection in the air-water interface, especially when the water is too shallow (i.e., less than about 20 cm deep) so that a combination of hardware and software is generally applied to detect more accurate information. Thus, this approach requires not only long time and expensive monitoring activities but also hard and long computing time [40; 41]. The present study aims to give a contribution in investigating stream pattern changes and explores the possibility to analyze them by identifying the evolving morphological features by detecting information from satellite images easily available from Google Earth. This methodology could be used for a first-step analysis driving a successive, more accurate and specific field monitoring. Here attention is focused on a reach of the Tagliamento River (Italy) which has been also object of previous literature studies [32; 33; 34; 35; 43]. The general objective is achieved by two tasks: the first task consists in identifying the variation in time both of the stream pattern and of the vegetated patches and of the frequency of occurrence of flood events; the second task consists in analyzing their interconnections identifying the evolving river's morphological features. In the following Section 2, the data and the methods used are explained; the results are analyzed in Section 3, discussion is presented in Section 4 and finally conclusions are reported in Section 5.
## 4 Results and Discussion
### Measurement Scenarios  
The field trial measurement tests aimed to validate the functionality of the rescue VANET system through integrated testing. The tests provided quantitative performance data and valuable insights for future improvements. Two cars, namely Car A and Car B, were used in the field study; Car A was used as the transmitter and Car B as the receiver. Figure 8 illustrates a typical V2V scenario that was tested, and each test and scenario involved different VANET schematic demonstrations.  
All scenarios were conducted with non-line of sight (NLOS) conditions in which the wireless router had a fixed height of 1m, and the transmitter was moved along the parking lot. Table 5 lists the five practical scenarios that we considered in this study in a controlled environment.  
Figure 6: Field measurement planning and setup.  
Figure 7: Flowchart of field measurement process.