esa-sceva/satcom-chunk-collection huggingface dataset:
# Wake-Up Receiver with Equal-Gain Antenna Diversity +
## 5 Conclusions  
The ultra low-power wireless sensor node presented in this article successfully demonstrated the use of equal-gain-combining in combination with a low-frequency wake-up receiver. Resulting from the wake-up frequencies in the kilohertz range, energy-demanding phase control circuits are not required. The implemented and investigated equal-gain wake-up diversity node requires only two additional antenna switches to combine the multiple input signals and that increase the power consumption only marginally. The polarization diversity technique was implemented, although this is no limitation and other diversity techniques like space diversity could also be utilized. It could be verified that the antenna diversity achieved 3 dB gain under ideal laboratory conditions where the signals were free from noise, but not phase aligned. A laboratory multipath environment experiment confirmed the performance gain between 0.2 and 2.5 dB of the equal-gain wake-up diversity system.  
**Acknowledgments:** We gratefully acknowledge support from the German Federal Ministry of Education and Research (BMBF) (13N11746) and the German Federal Highway Research Institute (BASt) (FE 88.0126/2012) who funded this research. The article processing charge was funded by the German Research Foundation (DFG) and the University of Freiburg in the funding programme Open Access Publishing.  
**Author Contributions:** All authors contributed to the main idea of this paper. [PERSON] and [PERSON] designed the wireless sensor nodes and the experiments. [PERSON] wrote the article, and [PERSON] supervised the overall research work, critically evaluated and revised the paper.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 4 Conclusions  
This paper presents a WPTM-based PQD detection algorithm that is applicable to reliable smart grid construction and demonstrates its simulation under real stochastic electricity channel conditions, including random phase offset, impulsive noise, and low SNR. We define several WPTM-based metrics, such as MV and RMS, that exhibit more degrees of freedom than conventional WTM-based metrics. In addition, in the proposed PQD feature detection scheme, we define a modified threshold (\(TH_{M}\)) rather than a conventional universal threshold (\(TH_{U}\)), which makes it more robust against such severe electricity channel conditions. The simulation results indicate that the proposed algorithm,which has an SM filter-based adaptive denoising function, is robust to such severe channel environments, including impulsive noise. The results of the numerical analysis indicate that the proposed scheme is more reliable and has a better detection capability than the existing wavelet-based or wavelet packet-based and non-wavelet-based detection schemes. Furthermore, the presented algorithm is found to work well for various PQD events. Using the experiment for an implemented distribution network with MATLAB(r) Simulink(r), we prove that the proposed MV or RMS scheme guarantees better performance over both AWGN and IMPN channels.  
Conceptualization, J.Y. and S.C.; methodology, S.C.; software, J.Y.; validation, J.Y.; formal analysis, J.Y. and S.C.; investigation, J.Y. and S.C.; data curation, J.Y.; writing--original draft preparation, J.Y.; writing--review and editing, S.C.; visualization, J.Y.; supervision, S.C.; project administration, S.C.; funding acquisition, S.C. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Korean government (MSIT) (2017R1A2B4005840) and the Catholic University of Korea, Research Fund, 2021.  
Not applicable.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflict of interest.
# Analysis of Information Availability for Seismic and Volcanic Monitoring Systems: A Review
## 2 Methodology Validation
### Data Analysis
The database was focused on values of peak ground acceleration, velocity, and displacement (PGA, PGV, and PGD), instead of common parameters such as hypocenter distance. [PERSON] et al. [66] used hybrid convolutional neural network (ConvLSTM) techniques to indirectly predict seismic response of mid-rise and high-rise buildings in earthquake-prone areas, and to assist earthquake early warning systems. They used accelerometer mesh with a sampling frequency of 10-20-100 Hz for torsional vibration and waveform detection. For it, some parameters and filtering techniques were applied (including the fast Fourier transform (FFT) Butterworth filter and discrete wavelet transform (DWT) decomposition). [PERSON] et al. [71] proposed a seismic simulation workflow to deliver accurate short-time reports of earthquake effects. The objective was to reduce high computational resources for simulations in detailed geological models used in the impact evaluation of large earthquakes. It contains four subsystems deployed as services to produce ground-shaking maps and useful information for civil protection agencies. The simulation procedure contains an automatic alert service, smart access, a control center, and a post-process service. [PERSON] et al. [63] proposed an automated information system (AIS) for data processing in a specific geographic area for observation science data integration. Their main characteristic was the homogeneity of the instrumental network through Reftek Sw/Hw, the RTPD protocol, and the Zabbix monitoring system. Within the methodology used by several authors, it was possible to identify the mechanisms or platforms, protocols, formats, and topologies. Furthermore, these evaluation parameters applied to the selected articles were contrasted with four application areas, and, as a result of this analysis, the next section contains the most relevant contributions from reviewed studies.
# CNN-Based Model for Landslide Susceptibility Assessment from Multispectral Data
## 2 Data Preparation
### Labeling Using a Heuristic Methodology  
The label of each pixel can be determined by applying a heuristic geomorphological method, such as the one specified by the Colombian National Geological Service [31]. The application of this methodology is based on information related to terrain variation,morphogenetic environments and the degree of moisture and infiltration in the area that can affect soil strength. In consequence, the lithological composition of the rock (geology), the denudation environment in which it is formed (geomorphology) and the type of land use given to that area (land cover) are used.  
First, for the determination of susceptibility by geological factors, three weighted components are used (weathering resistance of rocks (\(R\)), differences in rocks in terms of strength and directionality of mechanical properties (\(T\)), and fracture density (\(F_{D}\))), which measures regional structural discontinuities in rock masses. Accordingly, the susceptibility by geologic factor is given by Equation (1),  
\[G=0.25R+0.25T+0.5F_{D}. \tag{1}\]  
Regarding the susceptibility component due to geomorphology, the geomorphological units generated by the Colombian Geological Service are considered, which are delimited in more detail using a DEM (12.5 m) and satellite images. This element is then qualified according to its morphogenesis (i.e., the causes and processes that shaped the landscape [31]). With \(M_{g}\) being morphogenesis and \(M_{m}\) being morphometry, the susceptibility factor for geomorphology (\(G_{m}\)) is given by Equation (2),  
\[G_{m}=0.4M_{g}+0.6M_{m}. \tag{2}\]  
Thirdly, it is necessary to take into account that, in general, the areas where more movements occur are directly related to the type of land use (e.g., bare or uncovered soils), as well as to areas of greater slope.  
Finally, the total landslide susceptibility is calculated from the weighting of the three previous factors (i.e., the susceptibility obtained by geology (\(G\)), by geomorphology (\(G_{m}\)) and by landcover (C)). The weighting of these three components is shown in Equation (3).  
\[S=0.2G+0.6G_{m}+0.2C. \tag{3}\]
## 3 Problem Statement  
In this paper, let \(N\) be the number of rectangle departments to be placed, without any overlap, on \(F\) floor space. Each department \(i\) is defined by its dimensions \((w_{i},l_{i})\) with area requirements \(a_{i}~{}=~{}w_{i}*l_{i}\), \(i=1,\ldots,N\). Besides the departments to be arranged, there are also a series of fixed facilities, the positions and dimensions of which are unchangeable due to the requirements of the hospital. No department can overlap any fixed facility. All the departments are arranged into two rows on each floor. Different from the general double-row model, the width and length of the two rows are not the same. The dimensions of the two rows, the lower row and the upper row, are \((lr_{1},\,wr_{1})\) and \((lr_{2},\,wr_{2})\), respectively. Note that the centers of the two rows are aligned vertically. A corridor, with the dimensions \((lr_{1},\,w_{c})\), is horizontally lined between the two rows. Halls with dimensions \((l_{h},wr_{1}~{}+~{}w_{c})\) are located in the middle of each floor, overlapping the lower row and the corridor. Elevators are located inside the hall and are assumed to be a series of points, without area requirement, on coordinates \((xe_{i},\,ye_{i})\) (\(i=1,\,\ldots,\,e\)), respectively. An example of empty floor space is depicted in Figure 1, where the green areas are available space where departments can be placed; the elevators are represented by two red points. A layout is defined by the x-coordinates \(x_{i}\) of the centroid, the horizontal \(w_{i}\) and vertical \(l_{i}\) dimensions, the located row \(y_{ir}\), and the located floor \(lf_{i}\) of department \(i\). We use the total movement distance of patients as the first objective, based on quantitative factors, and the closeness rating score of departments based on qualitative factors as the second objective. The notations used in the formulation are presented in Table 1.  
Figure 1: An empty floor space. The assumptions in this paper are described as follows:  
1. Patients move between two departments which are not on the same floor by elevators located inside the hall. The elevators are regarded as a series of points that do not occupy any actual floor space. 2. The vertical distance between two adjacent floors is equal to a fixed floor height. 3. The doors of departments are located in the midpoint of each department's edge that is adjacent to the corridor. Patients move between doors when they want to move to another department. 4. Patients always follow the shortest path when moving between two departments. Patients cannot pass through any departments and can only move along the X or Y axes. That is to say, all patients' movements are along corridors. 5. No department can be located in the hall or overlap the hall
# [PERSON]: An HPC Job Scheduler Using Deep Reinforcement Learning and Remaining Time Prediction
## 4 Experimental Results
### Analysis of RLSchert's Performance Gain  
#### 4.6.1 Resource Utilization  
Both RLSchert and DeepRM are scheduling algorithms based on deep reinforcement learning. Compared with DeepRM, RLSchert dynamically adjusts the resources based on the remaining time prediction, thereby reducing the resource fragmentation (as shown in Figure 12).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **VASP** & **Others** & **Overall** \\ \hline UserBehavior [28] & 101.5\% & 121.2\% & 110.8\% \\ XGBoost & 39.8\% & - & 39.8\% \\ RLSchert & 10.3\% & - & 10.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 10: The MAPE of each prediction model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline Method & Tetris & Random & SJF & Tetris* & SAF & DeepRM & **RLSchert** \\ \hline AveSlowdown & 14.42 & 12.81 & 11.9 & 11.08 & 11.7 & 9.89 & **8.41** \\ \hline \hline \end{tabular}
\end{table}
Table 11: The average slowdown of each scheduler.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scheduler** & **0\%** & **50\%** & **80\%** & **100\%** \\ \hline SJF & 10.29 & 10.29 & 10.29 & 10.29 \\ RLSchert & 9.63 & 8.55 & 8.36 & 8.32 \\ \hline \hline \end{tabular}
\end{table}
Table 9: The average slowdown with different predictable proportions.  
RLSchert can better utilize the cluster resources. To verify this, we counted the number of resource holes along with the time step. Resource holes are the resources wasted at the current time step (idle resources). Figure 11(a) shows the accumulative resource hole with the time step. RLSchert consistently outperforms DeepRM, which reduces the occurrence of resource holes. Figure 11(b) shows the wait time distribution of jobs when using different schedulers. Although the wait time for long jobs using RL is longer, the wait time for short jobs is shorter, indicating that short jobs are more likely to be backfilled into the resource hole through runtime prediction. We can conclude that RLSchert precisely adjusts the resources, giving more opportunity to better utilize the cluster resources and improve the performance.
# Fuzzy Logic in Selection of Maritime Search and Rescue Units
###### Abstract  
The article discusses methods of ships assessment when determining their suitability for search and rescue action (SAR) at sea. Selection of the most preferable ships is one of the action planning elements. Due to various construction and equipment the civilian ships can only perform rescue task to a certain degree. According to the Multi-Criteria Decision Analysis (MCDA), many parameters and data have to be compared in order to create a ranking of vessels ordered according to the coordinator's preferences. When data are missing, incomplete or uncertain, a similar effect can be obtained using fuzzy logic. The author discussed the nature of the criteria, evaluation methods and presented a simulation of a ship study using fuzzy logic. The author developed fuzzy rules and presented the principle of operation of the controller. The article deals with the main principles of a decision support system (DSS) for the selection of ships in SAR operations.  
search and rescue; fuzzy logic; selection criteria; decision support systems; SRU Article  
Faculty of Navigation, Maritime University of Szczecin, Waly Chrobrego 1-2, 70-500 Szczecin, Poland; [EMAIL_ADDRESS]
## 6 Application Scenarios of the Proposed Approach
### Applications in Automatic Speech Recognition for Speakers in Motion  
Automatic speech recognition is a crucial step in the overall speech processing pipeline, one which requires the application of appropriate speech enhancement technique to improve speech intelligibility. This is most critical in scenarios involving moving speakers who change their positions across different time frames while speaking intermittently. The movement of speakers introduces additional challenges, such as changing acoustics, noise, as well as motion-induced acoustic impairments which hamper accurate recognition of the received speech signal. Automatic speech recognition is an important procedure in speech transcription systems [60]. The proposed technique can be leveraged to such scenarios where the source tracking assisted neural beamformer can effectively isolate the desired speech component from the interference signals, thereby leading to better recognition of the target speech as well as better transcription.
## 3 Sensor Data Security Estimator
### Security Level Estimation  
The main goal of SDSE is to provide the SL of sensor data to WSN users. In order to achieve this goal, SDSE estimates the security of the route by which the data have traveled until reaching the base station. The security of a given route is measured on the basis of the security of the sensors that comprise it. SDSE uses the metrics defined in Section 3.2 to compute the security of the nodes of the route. As stated previously, the security level measured by SDSE is a value in the interval \([0,1]\), calculated for each sensor datum in order to indicate how secure that datum is.  
Typically, sensor data refer to readings performed by sensor nodes sent to the BS in response to a query made by a user. Whenever a query response arrives at the base station, SDSE obtains from the routing protocol information about the route traveled by the response. Furthermore, SDSE obtains from the security mechanisms the parameters necessary to compute the security metrics for each sensor of the discovered route. After computing the security metrics, SDSE combines them in order to obtain the SL.  
Formally, for a route \(R=\{n_{i}\mid i=1,2,\ldots,q\}\), SDSE initially calculates for each node \(n_{i}\) belonging to \(R\) its security degree (\(g_{i}\)). The value of \(g_{i}\) is calculated as the product of the security metrics computed for node \(n_{i}\), defined in Equations (6), (7) and (19) (if the node has been accused of being malicious by the IDS or Equations (6), (7) and (20) otherwise). The value of \(g_{i}\) is given by the multiplication of the metrics, because each metric corresponds to an independent event. This is because each security mechanism has different vulnerabilities and deals with different types of attack. The value of \(g_{i}\) is given by the following equation:  
\[g_{i}=P_{F}^{i}\times P_{R}^{i}\times P_{L}^{i} \tag{21}\]  
Hence, we obtain the set \(G=\{g_{i}\mid i=1,2,\ldots,q\}\), which contains the security degrees of all nodes of route \(R\). As the node having the lowest security degree of the route is the weakest link [35] and is, therefore, more likely to compromise the data, SL is given by the minimum value of \(G\), as can be seen in the following equation:  
\[SL=\min(g_{1},g_{2},\ldots,g_{q}) \tag{22}\]  
Lastly, SDSE assigns the computed SL to its respective query response and then deliver both of them to the requesting user.
## 3 Design and Implementation
### Implementation of the Model  
#### 3.3.1 Defining the Base Model  
Before implementing the model, we first compared and analysed the performance of three models, simple RNN, LSTM, and GRU, all suitable for simple natural language processing.  
In this paper, the cyclical learning rate provided by the TensorFlow plugin was used as an optimizer. This approach, introduced in [PERSON]'s 2015 paper [12], adjusts the learning rate by increasing and decreasing the repetition learning rate. In this case, accuracy may be temporarily reduced, but the overall results will be better [13]. Due to the imbalance between the label distributions, a micro-mean F1-score was used as the scoring function. Out of a total of 11,856 data items, 10,495 messages were finally used after eliminating duplicates, and 3000 of the most frequently used words were marked as used.  
The embedding and dense layers were added to simple RNN, LSTM, and GRU. The hidden layer was set to 100, the vocabulary size of the number of keywords to be used was set to 3000, and there were 6 categories of classification, so Dense was set to 6. Since this is a multi-category classification problem, the categorical cross entropy was used as the loss function.  
The results of comparing the three models, Simple RNN, LSTM, and GRU, are shown in Figure 5. The learning results using the three models are shown in Table 1. The validation F1-score is 92% and the validation loss is 0.3, the results indicate that the LSTM with the largest number of parameters performs relatively well. Therefore, the LSTM was identified as the base model.
## 3 Results
### 3.3D Mapping and Flight Path Analysis  
The 3D mapping system recorded obstacles and the drone's flight path based on sensor data collected during flight. The accuracy of the mapping in Table 3 was evaluated by comparing the generated 3D representations with the physical obstacle and path locations.  
The drone's movements during this test, including minor wobbles and deviations, were consistently captured on the 3D map, resulting in zero deviation between the recorded flight path and the 3D map. For obstacle placement accuracy, the dimensions and layout of the physical maze were measured and compared with the mapped results. The first obstacle showed the most deviation, likely due to instability during the drone's initial takeoff. The obstacle placement is based on the safety threshold distance of \(\rho_{th}=0.35\) m,  
Figure 8: (**a**) Correct gesture representing “left”; (**b**) “Left” gesture misclassified as “down”; (**c**) Gesture failing to register as “left.” The red points in the figures indicate the land marks of hands.  
meaning that any adjustments to the code could easily result in misplaced obstacles: a flaw within the customizable system.  
The mapping system only placed encountered obstacles on the map--meaning obstacles that directly affected the drone's path. If the drone flew near a wall or in front of an obstacle without attempting to move into it, that obstacle was not recorded in the 3D map. Instead, when the drone needed to determine a viable flight direction and an obstacle blocked that path, its location was noted and placed on the map. This explains why the 3D map (Figure 9b) contained fewer obstacles than the physical course (Figure 9a), and this discrepancy should not be interpreted as inaccuracy.  
The flight path mapping was highly accurate, consistently starting at (0,0,0) and tracking movement relative to the takeoff position. Even when the drone wobbled or slightly deviated from a straight path, these deviations were accurately reflected in the 3D map. However, obstacle placement accuracy depended on sensor-based distance estimation. When an obstacle was detected, it was mapped at 0.35 m from the detection point. Any slight sensor inconsistencies in distance measurement could lead to minor errors in obstacle placement, but the overall flight path remained precise and reliable. This 3D mapping example along with gesture control, obstacle avoidance, and drone FPV streaming can be seen in the Supplementary Materials (Video S2).