esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion  
Geological hazards are a dynamic process from conception through development to stabilization (cessation). With the passage of time, the situation of geological hazards may change, possibly greatly, or produce new geological hazards. Specifically, the old geological hazards have been effectively managed to stabilize, or neglected to manage, protect, and gradually deteriorate. Thus, long-term deformation observation should be performed, monitoring its movement situation, to carry out governance or prevention and effectively reduce the losses caused by this type of disaster.  
Since the landslide process is dynamic, we combined the geological background of a pregnant disaster, high-resolution optical remote sensing images, SAR satellite images, and other technical means to identify landslides in the study area for constructing the landslide susceptibility evaluation model. We also adopted on-site surveys and unmanned aerial vehicle (UAV) technology to investigate the situation of landslides and geologic hazards in the study area, which laid the foundation for the evaluation of the susceptibility of landslides. The INSAR deformation rate and other conventional factors served as evaluation factors at the same time, and the Pearson correlation coefficient was adopted to exclude the covariance factor to construct the CNN landslide susceptibility evaluation model with powerful feature extraction, probability prediction, and linear regression fitting functions. Next, we used the BO algorithm to seek the optimization of the hyper-parameters of the CNN to establish the BO-CNN landslide susceptibility model, which increased the stability of the model and thus improved the model accuracy.  
Despite the improvement in the model's accuracy and robustness, this study also has limitations. Firstly, we only adopted the raster unit for evaluation instead of other units for evaluation and comparative analysis, and the research based on other evaluation units should be further strengthened. Secondly, when we built the landslide susceptibility model, due to the many internal and external factors for the evolution of landslides, the selected evaluation index factors had certain limitations; therefore, in subsequent research, the experiment should be optimized in the selection of evaluation factors to build a more comprehensive and accurate prediction model.
# Discovering Moving Flock Patterns in Movement Data: A Reeb Graph-Based Approach
## 5 Case Study  
### Dataset  
The movement data used in this study originated from a real and complete football match between Club Brugge KV and Standard de Liege that took place on 2 March 2014. For simplicity, we refer to them as Club Brugge and Standard Liege throughout this paper. The dataset includes both spatiotemporal and semantic information. The spatiotemporal information is recorded in the format (_id_, \(x\), \(y\), _t_), where _id_ identifies a particular player, \(x\) and \(y\) denote the \(x\) and \(y\) coordinates of the player's position, and \(t\) represents the corresponding timestamp. The semantic information mainly includes the basic information of both teams (e.g., players' names, players' IDs, and played positions) and the events that occurred during the match (e.g., event name, time of occurrence, and IDs of the actors). The spatiotemporal information can be used to discover moving flock patterns based on the approach proposed in this paper, and the semantic information can be used to validate the discovered moving flock patterns if necessary.  
As football is considered a highly interactive sport in which players need to interact frequently with their teammates, various types of interaction patterns may be involved. Among them, moving flock patterns are of particularly interest in this study, as discovering and exploring these patterns can provide valuable insights into a team's playing style, which could inform tactical assessments. This paper emphasizes methodology, so we focused only on the movement data of Club Brugge's outfield players (excluding the goalkeeper) during the first five minutes. This early match period is often crucial, as coaches typically provide tactical instructions before the match and aim to evaluate them in these initial moments. In the original dataset, the locations of all players were tracked with a temporal resolution of 0.1 s. To reduce computational complexity, we down-sampled this to 1 s. After processing, 3010 discrete points and 10 trajectories were generated, and they are visualized in Figure 4. For privacy reasons, the ten players are labeled as player 1, player 2, player 3, \(\ldots\), and player 10.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Types of Moving Flock Patterns** & **Moving Flock Patterns Being Discovered** \\ \hline A & \(\{1,3\}\,|\,[t_{0},t_{2}]\) \\ A & \(\{2,4\}\,|\,[t_{2},t_{4}]\) \\  & \(\{2,5\}\,|\,[t_{2},t_{4}]\) \\ B & \(\{4,5\}\,|\,[t_{1},t_{4}]\) \\ C & \(\{2,4,5\}\,|\,[t_{2},t_{4}]\) \\ D & none \\ \hline \hline \end{tabular}
\end{table}
Table 3: The four distinct types of moving flock patterns being discovered in the sample dataset.
## 3 Simulated and Measured Results Discussion

Table 1: Table of performance comparison between the proposed antennas and previous works. Figure 16: Radiation patterns of the proposed antenna in H-plane (YOZ plane). (**a**) Simulated results, (**b**) measured results. Figure 17: Simulated radiation patterns of the proposed antenna in YOZ plane.
## 1 Introduction
The main contributions of this paper are as follows:  
* The OLSR protocol's configuration settings have been optimized to make it appropriate for UANETs;
* Analyzes routing protocol performance in the OPNET simulator by creating realistic UANET scenarios where optimized OLSR configurations outperform the default one;
* Finally, a comparison of the existing well-known topology-based routing protocols (AODV, OLSR, DSR, and GRP) with optimized E-OLSR based on the performance metrics such as throughput, delay, and data drop rate is conducted. The rest of the paper is arranged in the following manner: In Section 2, the basic concept of a UAV-based routing protocols and the description of the E-OLSR are presented. Research methodology is illustrated in Section 3. Section 4 describes simulation modeling of the E-OLSR routing algorithm. The performance evaluation of the E-OLSR network in terms of delay, throughput, and data drop rate, as well as comparisons to existing routing protocols, is described in Section 3.1. Finally, Section 6 brings the article to a conclusion by pointing the direction forward for future opportunities.
## 6 Conclusions  
With an increased implementation of solar and wind power facilities alongside the introduction of distributed generation such as energy storage devices, ungrounded systems, which serve as a connection, have consequently increased. Currently, IMD is utilized to monitor the internal insulation of solar arrays and energy storage devices while maintaining a continuous operation of the ungrounded system. Conversely, IMD carries a major drawback, in which a minimum duration of several [s] is required to detect insulation deteriorations or ground faults. Hence, the occurrence of a secondary fault prior to processing the first fault may provoke fatal situations of power shutdowns. To prevent such damage, the cooperative use of IMD and GPT, which is commonly installed as a ground fault protection device in an ungrounded system, has been considered. However, international standards have prohibited the concurrent use of IMD and GPT.  
In this paper, series grounding of the inductor and capacitor on the primary side of GPT is proposed for the cooperative use of IMD and GPT within a distribution system connected with a transformerless inverter. First, the capacitor functions in securing the operability of the IMD. When measuring the insulation resistance of the existing IMD, a current loop is formed through the primary side of the GPT, which results in the measurement of a low insulation resistance, regardless of the intactness of the system insulation. To resolve the issue, a capacitor operating with an infinite impedance to DC voltage at a normal state was additionally grounded, and accurate measurements of insulation resistance using IMD was possible. Moreover, the inductor was grounded to ensure a small impedance with respect to the common frequency characteristics of the ground fault current and at the same time to reduce the zero-phase voltage constantly induced to the GPT at steady state. This has the advantage of reducing burnouts of the CLR and GPT and diminish occurrences of associated protection device malfunctions. Through verification using PSCAD/EMTDC, the cooperative use of IMD and GPT within the applied distribution system was confirmed. In addition, the operating characteristics of the IMD and GPT were considered according to the capacities of inductors and capacitors to be grounded. In conclusion, the proposed method is expected to contribute to improving the insulation monitoring and stable system operations of the DC mixed systems connected with transformerless distributed power sources.  
Conceptualization, W.-H.K. and J.-E.K.; methodology, H.-M.L.; software, H.-M.L.; validation, H.-M.L. and W.-K.C.; visualization, W.-H.K.; project administration, J.-E.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Korea Electric Power Research Institute (Grant Number: R20 DA24).  
Data sharing not applicable.  
The authors declare no conflict of interest.
# YOLOv4-MN3 for PCB Surface Defect Detection
## 2 Methodology
### Proposed YOLOv4-MN3
#### 2.2.3 Loss Function  
The loss function of YOLOv4 includes three parts, confidence, classification, and bounding box regression loss. YOLOv4 employs a novel complete-intersection over union (_IoU_) loss (_CIoU_), replacing the mean-squared-error loss adopted in YOLOv3 with bounding box regression loss [20]. _CIoU_ takes the overlap area, center point distance and aspect ratio into consideration simultaneously, improving detection speed and accuracy. _CIoU_ introduces a penalty item \(\alpha v\) on the basis of distance-_IoU_ loss to impose a consistency of aspect ratio for the ground truth bounding box (\(bb^{st}\)) and the relation bounding box (\(bb\)). _CIoU_ loss can be defined as in Equation (1)  
\[\begin{split}& Loss_{CIoU}=1-(IoU-\frac{\rho^{2}(b,bb^{st})}{c^{2}}- \alpha v)\\ &\alpha=\frac{v}{1-IoU+v},v=\frac{4}{\pi^{2}}(\arctan\frac{\overline {u}\overline{u}^{st}}{\overline{h}^{st}}-\arctan\frac{\overline{w}}{\overline {h}})^{2}\end{split} \tag{1}\]  
where \(b,bb^{st}\) are the centers of \(bb,bb^{st}\) respectively, \(\rho(\cdot)\) denotes Euclidean distance, \(c\) represents the diagonal length of the smallest enclosing rectangle covering \(bb,bb^{st}\) and \(\alpha\) is a positive trade-off value, \(v\) means the consistency of aspect ratio. \(w\),\(w\)\(w\)\(bb^{st}\) are the widths of \(bb,bb^{st}\) respectively. \(h,hs^{st}\) are the heights of \(bb,bb^{st}\), respectively.
## 2 Materials and Methods
### Statistical Analysis  
Measurements and analyses on soils were based on oven-dried soil samples. Each treatment (control, temperature, moisture, or combined stresses) was replicated three times and data reported as mean \(\pm\) standard deviation (SD). In order to assess the effect of the imposed stress, each soil property was analyzed using one-way analysis of variance (ANOVA) within each soil type and management (calcareous conventional, calcareous organic, acid conventional, and acid organic), followed by Tukey HSD post hoc test. In order to assess the effect of soil type and soil management, a two-way ANOVA was conducted. Differences between treatments were considered significant at \(p\) < 0.05 and identified in the figures and tables with different letters. Pearson coefficient was calculated to measure the correlations between factors.  
In order to compare single or combined stress effects of temperature and soil moisture in all soils, the index Hedges' g was applied as a measure of the standardized effect size [43]. Hedges' g is an unbiased estimate of Cohen's d not affected by sample size, scale of measure, or magnitude. Null values indicate no difference between the original (19 \({}^{\circ}\)C 40% WHC) and the final condition derived from temperature stress (29 \({}^{\circ}\)C 40% WHC), moisture stress (19 \({}^{\circ}\)C 10% WHC), or the combination of the two stresses (29 \({}^{\circ}\)C 10% WHC), whereas positive or negative values imply an average increase or decrease, respectively, following the stress treatment. Effect sizes lower than 0.2 were considered negligible.  
Principal component analysis (PCA) was performed in R software version 4.0.3 using the package \"FactoMineR\" [44]. Biplot of PC1 and PC2 was obtained using the \"factoextra\" package.
## 2 Materials and Methods
### Antimicrobial Activity  
Antimicrobial activity was tested with the disc diffusion method. Altogether, eight strains of microorganisms were used, including three yeasts (_Candidia glabrata_ CCM 8270, _Candida albicans_ CCM 8186, and _Candida tropicalis_ CCM 8223), one Gram-negative bacteria (_Haemophilus influenzae_ CCM 4454) and three Gram-positive bacteria (_Staphylococcus aureus_ CCM 2461, _Clostidium perfringens_ CCM 4991, and _Enterococcus faecalis_ CCM 4224). All the tested strains were from the Czech Collection of Microorganisms. The bacterial and yeast suspensions were cultured in the nutrient broth (Imuna, Bratislava, Slovakia) at 37 \({}^{\circ}\)C for 24 h before testing. A 0.1 mL aliquot of the suspension of the tested microorganism (10\({}^{5}\) cfu/mL) was spread on Mueller Hinton Agar (MHA, Oxoid, Basingstoke, UK). Filter paper discs of 6 mm in diameter were impregnated with 15 \(\mu\)L of the extract and placed on the inoculated agar plates. The plates were left at 4 \({}^{\circ}\)C for 2 h and then incubated at 37 \({}^{\circ}\)C for 24 h. The diameters of the inhibition zones were measured after incubation. The analysis was carried out in triplicate.
# Privacy-Aware Cloud Architecture for Collaborative Use of Patients' Health Information
## 3 Proposed Architecture
### Architecture Fundamental Aspects
#### 3.1.2 Searchable Symmetric Encryption (SSE)

Table 3: Encrypted index and Trapdoor for a document. **Search Algorithm:** Uses the decrypted index and the trapdoor for one document to find it. It takes the decrypted index and the trapdoor as inputs and identifies the encrypted document as an output. \[\text{Search}\ (\text{C}_{1},\text{T}_{1})\rightarrow\ \text{Encrypted ciphertexts}\]  
Similarly, in the proposed system, the process of preparing patients' information for storage involves five steps:  
1. Generating a secret root key (\(\text{S}_{\text{KR}}\)) for the patient. 2. Generating a secret key (\(\text{S}_{\text{K}}\)) for every document of patient information and choosing a keyword for each document. 3. Keywords are encrypted using their corresponding secret keys, and the resulting ciphertexts are listed to form an encrypted index. 4. Trapdoors are then created, which involve combining the secret keys with the ciphertexts. Trapdoors will be used to identify and decrypt documents. 5. Documents are encrypted using their corresponding secret keys. By following the above five steps, it becomes feasible to search for patients' documents while they are encrypted without having to perform decryption operations on them. Further explanation of how information is obtained from the cloud and decrypted is provided in the following section.
## 2 Phase Synchronization Scheme
### Synchronization Scheme of LT-1
Each antenna is designed to be identical and with a wide beam coverage, so the transceiver antenna pair can be selected according to the principle of maximum signal-to-noise ratio (SNR) to realize 360\({}^{\circ}\) omnidirectional communication during an orbital period. 3. Signal selection: linear frequency modulation (LFM) signal with the same carrier frequency as radar signal is transmitted in synchronization link, which reduces the complexity of system design and processing algorithm. After demodulation and pulse compression, the synchronization phase can be extracted from the peak position of the synchronization signal with high SNR. Therefore, when the LT-1 system operates in BiSAR mode, phase synchronization will not result in data loss by periodically interrupting the normal radar signals. The synchronization signal is also prevented from being jammed by the ground echo through time-division transmission. Moreover, the maximum synchronization frequency can be set to PRF/2, which contributes to reducing the interpolation and aliasing errors and improving the accuracy of phase synchronization.
## 2 Modeling of Distributed Energy Resources (DERs)  
### Modeling of Distributed Generators  
The model of a wind turbine (WT) is derived from its power output curve, as illustrated in Figure 1. Though the curve of each type of wind turbine is slightly different from others', they can be generally represented as a piecewise function [36].  
\[P_{\text{WT}}=\begin{cases}0&0\leq v<v_{\text{i}}\text{ or }v>v_{\text{o}}\\ a_{1}v^{3}+a_{2}v^{2}+a_{3}v+a_{4}&v_{1}\leq v<v_{\text{r}}\\ P_{\text{r}}&v_{\text{r}}\leq v\leq v_{\text{o}}\end{cases} \tag{1}\]  
where \(P_{\text{WT}}\) is the output power of wind turbine (WT), \(P_{\text{r}}\) is the rated power of WT, \(v_{\text{r}}\) is the rated wind speed of WT, \(v_{\text{i}}\) is the cut-in wind speed of WT, \(v_{\text{o}}\) is the cut-out wind speed of WT, \(a_{1}\)-\(a_{4}\) are coefficients.  
The output power of a photovoltaic (PV) panel array is determined by variables including \(P_{\text{r}}\), \(R_{\text{c}}\), \(R_{\text{r}}\), \(T_{\text{c}}\), \(T_{\text{r}}\) and \(k\)[37].  
\[P_{\text{PV}}=n_{\text{PV}}P_{\text{r}}(\frac{R_{\text{c}}}{R_{\text{r}}})[1+k (T_{\text{c}}-T_{\text{r}})] \tag{2}\]  
where \(P_{\text{PV}}\) is the output power of PV panel array, \(P_{\text{r}}\) is the rated power of a single PV unit under standard conditions, \(R_{\text{c}}\) is the current sunlight intensity, \(R_{\text{r}}\) is the rated sunlight intensity under standard conditions, \(T_{\text{c}}\) is the current temperature, \(T_{\text{r}}\) is the rated temperature under standard conditions, \(k\) is the power temperature coefficient, \(n_{\text{PV}}\) is the number of units in the PV panel array.
## 6 Two-Tier Data Source Authentication Scheme
### Implementation of Tier-2 Authentication  
Our second-tier authentication mechanism requires statistical or machine learning techniques to recognize medical data as being from a particular patient. We have investigated a neural network approach for implementing this mechanism. Each patient is associated with a neural network, which is first trained on data from that patient and some reference patients. The neural network then monitors incoming data for consistency with the training data. Both the initial training and the detection mechanism are run on the base station, in order to minimize computation on the more. Once trained, the neural network can quickly process incoming data.  
We use a feed-forward neural network (Figure 3) that consists of a number of inputs, hidden cells, and outputs, along with a set of _connection weights_ that determine how the output is computed from the input. Our current implementation uses patient electrocardiogram (ECG) signals to train the neural network, due to the high amount of identifying features that this data offers. The input to the neural network is one heartbeat of ECG data; the output is 0 or 1 (NO or YES) depending on whether the neural network believes this data is consistent with the training data.  
One issue with this approach is that raw ECG data for a single heartbeat can contain hundreds of individual samples. Using this data in its entirety is impractically complex. To address this problem we opted to extract a set of representative features from the raw ECG data to use as neural network inputs. A typical ECG signal for a single heartbeat is shown in Figure 4; it consists of a small _P wave_, followed by a large _QRS complex_, followed by a small _T wave_. We extract eight features per heartbeat: the lengths of the (1) PR interval, (2) PR segment, (3) QRS complex, (4) ST segment, and (5) QT interval; and the peak amplitudes of the (6) P wave, (7) QRS complex, and (8) T wave.  
Figure 3: A generic three-layer feed-forward neural network.
# Systematic Review of Discrete Event Simulation in Healthcare and Statistics Distributions
## 4 Discussion
### Software
#### 4.1.5 Future Trends  
Recent developments analyzed by [PERSON] et al. [32] indicate a shift toward more integrated and accessible simulation platforms. The adoption of cloud-based collaboration tools, enhanced mobile accessibility, and improved interoperability with hospital systemsis becoming increasingly important. Furthermore, advancements in visualization technologies are expected to facilitate more intuitive and interactive simulation environments, enabling stakeholders to derive greater insights from complex healthcare models.
Future Internet_**2012**, \(4\), 575-591; doi:10.3390/fi4020575  
_future internet_  
**ISSN 1999-5903**  
www.mdpi.com/journal/futureinternet  
Using Crowdsourced Indoor Geodata for the Creation of a Three-Dimensional Indoor Routing Web Application  
**[PERSON]**  
GIScience Research Group, Institute of Geography, University of Heidelberg, Berliner Strasse 48, 69120 Heidelberg, Germany; E-Mail: [EMAIL_ADDRESS]; Tel.: +49-6221-54-5501;  
Fax: +49-6221-54-4529  
_Received: 5 April 2012; in revised form: 4 May 2012 / Accepted: 29 May 2012 / Published: 6 June 2012_  
**Abstract:** Routing services for outdoor areas are omnipresent and also three-dimensional (3D) visualization is quite common within this area. Recent research efforts are now trying to adapt well known outdoor routing services to complex indoor environments. However, most of the current indoor routing systems only focus on two-dimensional visualization, thus only one level can be depicted. Especially multi-level routes therefore lack visualization. Also, most of the (few) existing 3D indoor routing services utilize proprietary software or plugins, thus a widespread accessibility for those services by using common computers or mobile devices is not feasible. Therefore this paper describes the development of a web-based 3D routing system based on a new HTML extension. The visualization of rooms as well as the computed routes is realized with XML3D. Since this emerging technology is based on WebGL and will likely be integrated into the HTML5 standard, the developed system is already compatible with most common browsers such as Google Chrome or Firefox. Another key difference of the approach presented in this paper is that all utilized data is actually crowdsourced geodata from OpenStreetMap (OSM). Such data is collaboratively collected by both amateurs and professionals and can be used at no charge under the Open Data Commons Open Database License (ODbL). Our research combines user-generated geo content of the Web 2.0 with future Internet technology for the provision of a ubiquitously accessible 3D indoor routing application.  
**Keywords:** 3D-GIS; 3D routing; crowdsourced geodata; future internet; indoor LBS; indoor routing; OpenStreetMap (OSM); XML3D