esa-sceva/satcom-chunk-collection huggingface dataset:
# W-Band Modular Antenna/Detector Array for the Electron Cyclotron Emission Imaging System in KSTAR
## 4 Fabrication and Measurement
### Measurements
In Figure 11, the measured gain increase of the proposed antenna/detector array as compared with the conventional antenna/detector array is presented. In Figure 11a, with an RF frequency range of 75-105 GHz, the gain increases are displayed with two IF frequencies (2 GHz and 9 GHz). The proposed antenna/detector array provides the maximum gain increase of 20.8 dB with the IF frequency of 2 GHz, and the minimum gain increase of 9.4 dB with the IF frequency of 9 GHz. It can also be observed that the gain is increased by over 15 dB for all RF frequencies from 75 GHz to 100 GHz. This implies that the obtained gain increase is an aggregate gain of the overall components of the antenna/detector array (e.g., optimized antenna with balun, elliptical mini-lens) in addition to the LNA with 15 dB gain. However, as RF frequency increases, the gain increase is deteriorated at frequencies over 100 GHz due to the higher conversion loss of the Schottky diode. Nevertheless, gain increase of over 12 dB has been achieved at all RF frequencies. In Figure 11b, with an IF frequency range of 2-9 GHz, the gain increases are displayed at two RF frequencies (75 GHz and 105 GHz). When the RF frequency is set at 75 GHz, the gain increase is over 15 dB for most frequencies with the maximum value of 19.6 dB and the minimum value of 14 dB. Almost flat gain increases are shown except near the IF frequency of 8 GHz. At the RF frequency of 105 GHz, the gain increase is under 15 dB with  
Figure 11: Measured gain increase of the proposed antenna/detector array as compared with the conventional antenna/detector array: (**a**) gain increase with IF frequencies of 2 GHz and 9 GHz, (**b**) gain increase with RF frequencies of 75 GHz and 105 GHz. Figure 10: Measured radiation patterns of the proposed unit antenna/detector module in the H-plane: (**a**) IF frequency = 2 GHz, (**b**) IF frequency = 9 GHz. the maximum of 12.7 dB and the minimum of 9.4 dB. It can be observed that a gain increase of over \(\sim\)10 dB as compared with the conventional antenna/detector has been achieved.
# Application of Artificial Neural Networks in Assessing Mining Subsidence Risk
## 4 Analysis and Discussion
### Analysis B
#### 4.2.3 Mine Subsidence Risk Index  
The mine subsidence risk (MSR) index is defined as in Equation 5 by combining the two subsidence risks by the direct influence group and the indirect influence group.  
\[\rm MSR\;index=W_{D}\cdot F_{D}+W_{I}\cdot F_{I} \tag{5}\]  
Here, \(\rm W_{D}\) and \(\rm W_{I}\) are the weights of subsidence risk for the direct influence factor group and the indirect group, respectively. The two weights can be determined by a series of sensitivity analyses on subsidence for the direct group and the indirect group.  
Finally, the MSR index is defined as in Equations (6) and (7), and the final MSR index for all of the subsidence locations is summarized as Figure 15. A higher MSR index implies a higher possibility of subsidence.  
\[\rm MSR\;index\;for\;coal\;mines=0.5\cdot F_{D}+0.5\cdot F_{I} \tag{6}\]  
\[\rm MSR\;index\;for\;other\;types\;of\;mines=0.65\cdot F_{D}+0.35\cdot F_{I} \tag{7}\]  
Figure 14: Influence weights for (**a**) coal mines and (**b**) other types of mines.
# Joint Task Offloading and Power Allocation for Satellite Edge Computing Networks
## 2 Related Work  
With the development of satellite communication, the on-board computing power of satellites has increased rapidly and the launch cost of satellites has decreased gradually, contributing to the feasibility of the SECN. Inspired by the advantages of the SECN over traditional satellite networks, many efforts have been put into both the architecture and real-world industrial applications of the SECN in the past few years. In this study, we briefly review the relevant literature and divide the literature into two categories, namely, SECN task offloading strategy design, which aims to find the appropriate offloading strategy under different optimization objective functions and constraints, and SECN architecture design, which optimizes the SECN architecture and working mode to improve network performance.
# Small-Target Detection Based on an Attention Mechanism for Apron-Monitoring Systems
## 4 Experimental Results
### Experimental Details  
We used the Mask Scoring R-CNN model as the baseline in our comparison experiments. All the pre-trained models used in this study are publicly available, and the accuracy of the different pixel ranges was taken as the evaluation index. All the models were trained on a GPU with two images per batch, and the network weights were updated using the Adam optimiser. Unless otherwise specified, all the models were trained for 96 epochs with an initial learning rate of 0.001, and the policy was updated with a cosine cooling learning rate. The minimum learning rate was set to 0.001 times the initial learning rate, and the models were initialised with ImageNet pre-trained weights. A multiscale training strategy was added to the final model, and the short side of the input image was randomly sampled between 640 and 768 with a step size of 32.
# mDARAL: A Multi-Radio Version for the DARAL Routing Algorithm
## 3 Multi-Radio Dynamic and Adaptive Routing Algorithm (mDARAL)
### Cluster Creation Using the DRSP Algorithm in a Multi-Radio Environment  
Despite the fact that the role selection is still based on the link quality indicator between a VC and an EN, \(mDARAL\) leads to several minor changes in this process. When an EN starts to send an Association Request message waiting for an answer of a VC, it uses the control channel (CC) as it is set as the standard pre-defined communication channel. VCs include, in the _Association Reply_ message, information about the secondary channel or local channel (LC) use within its sub-network, being the  
Figure 1: \(mDARAL\) basic concepts.  
information stored for the selection process. Consequently, once a node has selected a VC as a parent, it turns off its primary radio and sends the Association Reply Acknowledgement through the secondary radio using the local channel information received during the association polling process. Figure 2 shows a graphical UML (Unified Modeling Language) description of the algorithm, including the radio status changes.  
Figure 3 shows a particular example, where node _EN-A_ connects to node \(VC_{2}\), answering through channel \(LC_{2}\). It is important to mention that every Association Reply contains the information about the LC for that VC, thus the node running _DRSP_ stores this information for all the gathered answers while it is trying to get connected to the network.  
This strategy tries to reduce the power consumption of ENs, keeping only one radio active during most of the communication time.  
Figure 2: Dynamical Role Selection Process (DRSP) with multi-channel use.
## 2 Design Issues
### Design Principles  
After investigating existing research, we could pick out several design principles to create a desirable protocol bridge as follows:  
**Reusability of Sensing Data**  
The reported sensing data from sensing fields should be reused when multiple external sensor applications use the same WSN. This data reuse can decrease the total number of transmission on WSNs and it will lead to the improvement of energy efficiency. It will also enable more rapid response to external sensor applications.  
**Providing Diverse Communication Methods**  
The designed system should provide both the data-centric and the node-centric communication methods. Usually, the data-centric method is well suited for the characteristics of WSNs [10], but the node-centric method is still required to integrate WSNs with the conventional TCP/IP network. This feature will permit an external sensor application to select its preferred communication method.  
**Dynamic Adoption of Various Sensing Data**  
Depending on the type of application domains, WSNs may maintain various kinds of sensing data such as sensor's physical location, velocity, or residual energy. However, in most cases, it is very difficult to extend the schema of sensing data after the system deployment. The proposed system should provide a method to easily adopt various sensing data without system redesign and modification.  
**Dynamic Service Extension at Runtime**  
A desirable system supporting the data-centric communication should provide an easy way to add new queries or functions dynamically when external sensor applications require new services. Undoubtedly, this service extension should be performed with on-the-fly manner not to disturb other running sensor applications and even not to cause system stop.  
**Providing Sophisticated Functional Components**  
The basic reuse of sensing data can improve the energy efficiency and the response time, but the cached sensing data could be utilized in various ways. In addition to simple functions such as returning the maximum value of the specific area's temperature, the target system should provide highly sophisticated functional components which can compose sensing data and deduce the meaningful information from the data.
# Ambient Environmental Parameter Estimation for Reliable Diffusive Molecular Communications
## 3 Proposed Demodulation Method with Estimation of Ambient Environmental Parameters  
### Conventional Threshold-Based Demodulation Method  
The demodulation by threshold is used to determine the reception sequence via comparison with threshold \(\xi\), as expressed by  
\[b_{l}^{RX}=\left\{\begin{array}{ll}1&(\text{if }N_{\text{C}}^{\text{obs}}(t^{Threshold}_{l}|r_{0})\geq\xi)\\ 0&(\text{otherwise})\end{array}\right. \tag{12}\]  
where \(b_{l}^{RX}\) denotes the first bit of the received bit sequence \(b_{RX1}\), \(b_{RX2},\ldots,b_{RX1},\ldots,b_{RXL}\). Furthermore, \(t^{Threshold}\) denotes the time required to acquire \(N_{\text{C}}^{obs}\) when demodulating with a threshold, which can be calculated as follows:  
\[t^{\text{Threshold}}_{l}=(l-1)T+t^{\text{Threshold}} \tag{13}\]  
The sampling is based on the timing when the number of received molecules is maximum, which can be calculated using (8). Furthermore, the threshold value \(\xi\) is set to a value that is \(1/2\) times the number of received molecules \(N_{\text{C}}(t^{Threshold}|r_{0})\).  
\[\xi=\frac{1}{2}N_{C}(t^{\text{Threshold}}|r_{0}) \tag{14}\]
# A Review of Deep Learning Based Speech Synthesis
## 4 Deep Learning Based Speech Synthesis
### Speech Synthesis Using Deep Mixture Density Networks
#### 4.3.2 Deep MDN-Based Speech Synthesis  
When predicting speech parameters with deep MDN, the text prompt is first converted into a linguistic feature sequence \(\{x_{1},x_{2},...,x_{T}\}\), and then the duration of each speech unit is predicted using a duration prediction model. The acoustic features including the F0, spectral parameters and their corresponding dynamic features are estimated with the forward algorithm and the trained deep MDN. Finally, the acoustic feature parameters are generated by the parameter generation algorithm and speech is synthesized with a vocoder.
# Forecasting Landslides via Three-Dimensional Discrete Element Modeling: Helong Landslide Case Study
## 2 Background  
### Study Area  
The Helong landslide is located in the southeast of Yanji, Jilin province, China (Figure 1a). The study is a hilly area with an elevation between 59 and 1098 m (Figure 1b). The mountainous ridge is mostly east-west, with slopes between 25\({}^{\circ}\) and 30\({}^{\circ}\). Several faults are located in the study area, and the strata around the landslide is mainly composed of granite, sandstone, and mud rock (Figure 1c). Loose material, including eluvium, talus accumulation, and alluvium, is widely distributed on the ground surface.  
The study area has a continental monsoon climate with distinct rainy and dry seasons. The average annual rainfall is 521.8 mm, and the wet season occurs from June to August, which receives 60% of the annual precipitation. Concentrated rainfall adversely affects the stability of the slopes, promoting the occurrence of landslides.
## 3 Results
### Access to Educational Centers  
The current section is dedicated to analyzing access to public educational centers. The section is divided into three parts, starting with public elementary schools, followed by public middle schools, and ending with public high schools.  
Figure 10: Distrito Tec average travel time to the closest supermarket by bicycle.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Accessibility Level** &
\begin{tabular}{c} **Stanford District** \\ **(Block Groups)** \\ \end{tabular} & **Distrito Tec (AGEBs)** \\ \hline Low (>30) & 0/79–0\% & 0/11–0\% \\ Medium (16–30) & 9/79–11.39\% & 0/11–0\% \\ High (0–15) & 70/79–88.61\% & 11/11–100\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Accessibility level to supermarkets traveling by bicycle per aggregation area.  
It is important to mention that, in contrast to other destinations, schools are meant for a given age group. Therefore, the demand for schools depends, to a high extent, on the existence of a respective population in a given area. To analyze whether such a population exists or not in the study area goes beyond the scope of work of the current research project; hence, we assumed that such a population exists in both Stanford District and Distrito Tec.  
As students are not always capable of going to the closest school due to capacity limits, social preference, or other constraints, the accessibility measurement was performed considering travel time to the thee closest destinations (schools).
# Production and Processing of a Spherical Polybutylene Terephthalate Powder for Laser Sintering
## 3 Results and Discussion
### Laser Sintering  
Multiple laser sintering trials were carried out to test the performance of the powder in a commercial laser sintering machine and to find the optimal process parameters. The powder flowability was excellent. The powder could be deposited at process temperature without any issues. The powder bed was smooth and fault-free. The scanned areas of parts formed a clear melt pool, as can be seen in Figure 10a. The narrow sintering window of the material that was already observed in the DSC thermograms caused process issues. Due to a high crystallization rate and associated shrinkage, the parts showed increasing degrees of curling as they were built. Eventually, this caused the parts to be shifted on the powder bed by the recoater, as can be seen in Figure 10b.  
Figure 9: Apparent viscosity of PBT granulates and powders at 250 \({}^{\circ}\)C.  
The occurrence of part curling is a clear indication that the powder bed and feed temperatures are too low. Increasing these temperatures however was not possible. As depicted in Figure 1, the powder bed temperature was at the upper limit, ascertain regions started to melt spontaneously, without being scanned by the laser. There were still regions where parts could be built without curling and preliminary melting, but the temperature differences in the machine are too large to accommodate for a material with a sintering window this narrow. In parallel, the feed temperature can be increased to reduce the thermal shock when a new layer with colder powder is deposited on freshly molten surfaces. In this case however, the maximum feed temperature was limited by the fact that small-scale inserts were used. By careful positioning of the parts in locations where the temperature was exactly right so no curling or excess melting occurred, small parts could be built and tested. The parts, shown in Figure 11, are yellowish but otherwise stable and able to withstand sandblasting at mild pressures.  
The process window of the PBT can be increased if the material is annealed. By annealing the material at 210 \({}^{\circ}\)C for 12 hours in a DSC, the onset melting temperature could be increased by almost 8 \({}^{\circ}\)C to 227.1 \({}^{\circ}\)C. A major disadvantage of annealing however is the limited thermal stability of the material, and the associated thermal degradation in the absence of stabilizers. For the material in its current state, annealing is not a viable option.
# Modeling a Dual-Mode Controller Design
## 2 QRF Converter with Dual-Mode Control  
In order to illustrate the principle of a QRF converter with Dual-Mode Control, a fly-back converter circuit is shown in Figure 1. The fly-back transformer consists of a magnetizing inductor \(L_{m}\), a leakage inductor \(L_{lk}\), and a primary side inductor \(L_{p}\). The turn ratio is n. The other elements are the power switch \(Q_{1}\), diode \(D_{1}\), and drain-source capacitor \(C_{\text{oss}}\) in the QRF converter. In this section, the operation mode and control strategy are described. The design consideration of passive components and a small-signal model are derived.
# Two-Step Classification with SVD Preprocessing of Distributed Massive Datasets in Apache Spark
## 5 Evaluation
### PAMAP Dataset
#### 5.2.1 Discussion over PAMAP Dataset  
To sum the results up, we can see that in the case of multinomial classification, the proposed two-step classifier performs almost identically to the one-step classification method in terms of the \(F1\) metric. However, in all cases, two-step classifier was faster and the produced models, as in the random forest and decision tree case, were simpler. This in an indication that the preprocessing step has successfully identified a smaller attribute set which captures the essence of the larger one, as it was also the case with the Higgs dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Dataset Size** & **Precision** & **Recall** & **F1 Metric** & **Time** \\ \hline \multicolumn{5}{c}{one-step classifier} \\ \hline
5.000 & 0.755 & 0.755 & 0.749 & 0:02:03 \\ \hline
10.000 & 0.789 & 0.791 & 0.789 & 0:02:15 \\ \hline
25.000 & 0.788 & 0.  
Overall, it can be argued that the best choice in PAMAP dataset is the decision tree classifier, since it performed better in the metric scores from all other classifiers and its training time scaled well regarding the dataset size. As we have discussed in the Higgs dataset as well, logistic regression again was the fastest one to train, although the comparison is not fair since there was no parameter optimization.
# Detection of Cracks in Arch Dams Using Monitoring Data and Numerical Models
## 2 Methods
### Numerical Approach
The methodology applied for numerical simulations takes into account that the dam structure is changed before the development of hydrostatic pressures, but after self-weight has already taken effect, by sealing the transverse joints and, therefore, binding the individual blocks together into a monolithic structure. It was also considered that, after joint sealing, the dam changes again to a third configuration at the moment a crack emerges. Consequently, at the first stage, when self-weight is the only load, the sides of the joints can propagate compressive or frictional forces while moving independently, as long as they do not intersect each other, following their actual behavior. On the contrary, in the second and final stages, the dam joints react in a monolithic way. Therefore, the sides of the same joint are deformed as one, while always keeping the same relative position they showed at the end of the self-weighing phase. This study builds on a theoretical model, based on the actual geometry of a dam, to which several simplifications have been made to limit the number of possible variables, thereby making direct evaluation with the real dam measurements no longer applicable; however, the model was previously validated in [15], where the geometry and properties of the dam belong to the same type. The boundary conditions applied in the numerical models correspond to those common for this type of simulation: constrain the horizontal movements on the sides of the soil body and constrain any movement at the lower base of the soil. The extent of ground included in the 3D model on each side of the structure was the conventional dimensions for structural analyses [16], so that the applied boundary conditions do not alter the obtained results. Specifically, it was completed with terrain horizontally up to 50% of the length of the dam on the left and right sides, while the depth, upstream, and downstream directions of the terrain extend 150% of the height of the dam.
# A Novel OFDM Format and a Machine Learning Based Dimming Control for LiFi
## 3 System Description of DPO
### DPO Transmitter  
The transmitter block is described in Figure 1, where binary bits of DCO and PAM-DMT are given as input individually. Initially, the binary data for DCO are mapped to a QAM modulator, and the binary data for PAM-DMT are mapped to a PAM-modulator; both are then converted from serial to parallel. The input vector maintains Hermitian symmetry. For this property, and for the unused zeroth (DC) subcarrier, independent data are carried by only \(N/2\)-\(1\) out of a total of \(N\) subcarriers. Bits of DCO are mapped to odd subcarriers, and it carries \(N/4\) independent data without Hermitian symmetry. Now the input vector of the IFFT block for DCO is as follows.  
\[X_{DCO}=\left[0,X_{1},0,X_{2},0,\ldots,X_{N/2-1},0,X_{N/2-1}^{*},0,\ldots,X_{0} ^{*}\right] \tag{1}\]  
PAM-DMT modulates the imaginary part of even subcarriers. The input of IFFT for PAM-DMT can be expressed as  
\[Y_{PAM}=\left[0,0,Y_{0},0,Y_{1},0,\ldots,Y_{N/2-2},0,0,0,Y_{N/2-2}^{*},0,\ldots,Y_{0}^{*},0\right] \tag{2}\]  
The outputs of both IFFT blocks are time domain signals. Next, a DC bias, BDC, is added to the DCO time domain signal. The biased signal is then clipped at amplitude zero. The time domain PAM-DMT parallel signal is converted to serial. Next, the serial signal is clipped to zero and specified by \(x_{DCO,C}\) and \(y_{PAM,C}\) for DCO and PAM-DMT, respectively. After clipping, these signals are added together, and then a cyclic prefix is added. The resultant signal is denoted by \(z\), where \(z_{w}\) is the wanted signal and \(n_{DCO}\) and \(n_{PAM}\) are the clipping noise of DCO and PAM-DMT, respectively.  
\[z=(x_{DCO,C}+B_{DC})+y_{PAM,C} \tag{3}\]  
The expression in (3) can be rearranged as follows.  
\[z=x_{DCO,C}+B_{DC}+y_{PAM,C} \tag{4}\]  
Figure 1: DC-biased pulse amplitude modulated orthogonal frequency division multiplexing (DPO-OFDM) transmission side where dc-biased optical OFDM (DCO-OFDM) and pulse amplitude modulated discrete multitone (PAM-DMT) electrical signals are formed and then combined and converted to the optical domain. Note that QAM stands for quadrature amplitude modulation; while IFFT, S/P, P/S, CP and D/A stand for inverse fast Fourier transform, serial-to-parallel, parallel-to-serial, cyclic prefix, and digital-to-analog, respectively.  
The signal \(z\) in (4) can be expressed in terms of the wanted signal and clipping noise in the following format.  
\[z=z_{w}+B_{DC}+n_{DCO}+n_{PAM} \tag{5}\]  
The combined DPO signal is then transformed to the analog domain. This is performed with the use of a digital-to-analog (D/A) converter. Next, optical modulators, for example, LEDs, are used to transform the analog signal from the electrical to the optical domain.