esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Limitation of the Study  
The availability of crash data recorded with precise incident locations in coordinates (using Global Coordinate System, GPS) or mileposts that could be linked to roadway geometric characteristics drives a well-organized RTC analysis as well as results for specific roadway segments and intersections. The Ethiopian ministry of transport, regional transport authorities and federal police commission-traffic police departments are in charge of collecting and reporting the essential crash data to safety researchers in need of it. However, crash locations that occur along a road alignment (milepost) are not recorded in Ethiopia. This study was unable to investigate the spatial dependence of crashes with roadway parameters (such as road length, degree of curvature, AADT, gradient, stopping sight distance and others) as a contributing factor to crash occurrences due to a lack of crash locations in the given data. Moreover, the crash severity index of each severity level is derived from the respective crash costs in which these costs vary from region to region. However, due to the lack of an agreed-upon or defined crash costing platform for specific crash severity levels in Ethiopia, the severity index developed by the Roads and Traffic Authority of New South Wales [52] was used in this study.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**OBJ\_ID** & **Crash Severity** & **Shape L (m)** & **Shape\_A. (m\({}^{2}\))** & **GiZScore** & **GiPVValue** & **NNNeighbors** & **Zone\_Names** \\ \hline
7 & 1520 & 1,269,948 & 9,892,678,054 & 2.90573 & 0.00366 & 8 & E.Shewa \\
20 & 429 & 60,226 & 85,906,978 & 2.80042 & 0.00510 & 7 & Burayu T. \\
6 & 1501 & 913,429 & 11,530,834,348 & 2.13166 & 0.03303 & 7 & N.Shewa \\
5 & 1488 & 1,577,743 & 14,806,415,071 & 2.01316 & 0.04410 & 7 & W.Shewa \\
15 & 712 & 25,993 & 29,858,260 & 2.01316 & 0.04410 & 7 & [PERSON]. \\
13 & 1154 & 681,347 & 6,508,288,032 & 2.00548 & 0.04491 & 8 & S-W.Shewa \\
19 & 149 & 756,608 & 8,097,272,756 & 1.59692 & 0.11028 & 5 & [PERSON] \\
17 & 885 & 1,080,199 & 11,776,723,820 & 1.31245 & 0.18937 & 4 & [PERSON] \\
10 & 1155 & 1,390,038 & 18,239,926,953 & 0.80006 & 0.42367 & 2 & [PERSON] \\
9 & 724 & 768,631 & 16,523,003,204 & 0.73969 & 0.45949 & 4 & [PERSON] \\
8 & 798 & 909,816 & 20,696,957,154 & 0.41390 & 0.67895 & 7 & Arsi \\
4 & 634 & 1,009,026 & 18,075,624,315 & \(-\)0.19253 & 0.84733 & 5 & Jimma,Zone \\
2 & 597 & 980,901 & 13,830,420,375 & \(-\)0.19309 & 0.84689 & 6 & E.Wellega \\
14 & 649 & 985,714 & 18,577,054,735 & \(-\)0.43489 & 0.66364 & 3 & Guij \\
16 & 303 & 38,118 & 50,520,944 & \(-\)0.72780 & 0.46674 & 3 & Jimma\_City \\
12 & 246 & 3,121,707 & 45,463,584,611 & \(-\)0.82827 & 0.40752 & 2 & Borena \\
18 & 352 & 642,890 & 9,851,170,119 & \(-\)0.83608 & 0.40311 & 3 & Kelem\_Wellega \\
1 & 507 & 892,523 & 12,744,967,754 & \(-\)0.86970 & 0.38446 & 4 & W.Wellega \\
3 & 632 & 999,481 & 16,516,931,736 & \(-\)1.24901 & 0.21166 & 6 & I.A.Bora \\
11 & 218 & 1,434,621 & 44,912,392,310 & \(-\)1.49546 & 0.13479 & 3 & Bale \\ \hline \hline \end{tabular}
\end{table}
Table 3: The Attribute of Crash Hot Spot Result of Getis Ord \(Gi^{*}\).
# Adaptive Frame Sampling and Feature Alignment for Multi-Frame Infrared Small Target Detection
## 5 Discussion  
In this paper, we proposed an adaptive frame sampling method based on mutual information, to expand the input motion information of a multi-frame infrared small target detection model without increasing the computational complexity. At the same time, a multi-frame based infrared small target detection network was proposed that is aligned at the feature map level, and the feature maps are aligned by estimating the homography flow matrices. Experimental results demonstrated the effectiveness of our proposed method. Our proposed adaptive frame sampling method could sample more reasonably and make the sampled frame motion distribution more uniform. The proposed detection network containing a feature alignment module can be trained end-to-end, which enhances the robustness of the model. Our detection model could achieve an inference speed of 37 fps with a detection rate of 98% and a false alarm rate of 1% and a \(F_{1}\) score of 98.5% on a NVIDIA 1080 Ti GPU with 4 frames of input.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**FAM** & **Feature Minus** & **Triplet Loss** & **Precision (\%)** & **Recall (\%)** & \(\mathbf{F_{1}}\) (\%)** & \(\mathbf{P_{d}}\) (\%)** & \(\mathbf{F_{a}}\) **(\%)** \\ \hline \(\bigvee\) & \(\bigvee\) & \(\bigvee\) & 93.9 & 94.7 & 94.3 & 94.7 & 6.06 \\ \(\bigvee\) & \(\bigvee\) & \(\bigvee\) & 97.0 & 96.3 & 96.6 & 96.3 & 2.98 \\ \(\bigvee\) & \(\bigvee\) & \(\bigvee\) & 97.1 & 97.9 & 97.5 & 97.9 & 2.87 \\ \(\bigvee\) & \(\bigvee\) & \(\bigvee\) & 97.9 & 96.4 & 97.1 & 96.4 & 2.09 \\ \(\bigvee\) & \(\bigvee\) & \(\bigvee\) & **98.5** & **98.7** & **98.6** & **98.7** & **1.52** \\ \hline \hline \end{tabular}
\end{table}
Table 9: Quantitative results of ablation experiments with components of FAM.  
Figure 9: Sample eight frames from 40 continuous frames in data14.  
**Author Contributions:** Conceptualization, C.Y. and H.Z.; methodology, C.Y. and H.Z.; software, C.Y.; validation, C.Y.; formal analysis, C.Y. and H.Z.; investigation, C.Y. and H.Z.; resources, C.Y. and H.Z.; data curation, C.Y.; writing--original draft preparation, C.Y.; writing--review and editing, C.Y. and H.Z.; visualization, C.Y.; supervision, H.Z.; project administration, H.Z.; funding acquisition, H.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China (NSFC) under Grant 62173143 and Grant 61973122.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** A publicly available dataset was analyzed in this study. These data can be found here: DSAT (accessed on 17 august 2020 ).  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 Results
### Water Transport in Membrane  
The transportation of gas species on the MEA domain exists on both sides of the membrane. Crossover gases are ignored due to low-pressure conditions in the open-cathode fuel cell. The transport of oxygen and hydrogen occurs only on the cathode side and the anode side, respectively. Water vapor also exists in mixtures on both sides. The only interdiffusion of the gas species is considered with the assumption of uneven distribution pressure in the steady state [34; 35].  
The water and charge transport in the ionomer are determined by ionomer conductivity, water activity, and diffusivity. The unsaturated flow theory based on soil physics is employed to depict the liquid water transport of the fuel cell model, which is also commonly utilized in two-phase MEA modeling. \(\lambda\) represents the humidification degree by estimating the water molecule number in each acidic group. Water transport through the membrane is mainly caused by electro-osmotic drag and osmotic diffusion. The calculation of the molar flux of dissolved water also refers to the superposition of electro-osmotic drag and osmotic diffusion phenomena. The phenomenon of electro-osmotic drag refers to hydrogen ions dragging water molecules through a membrane under the influence ofan electric field. The related parameters of water activity are provided in Figure 3. The phenomenon can be described by the following equation:  
\[\dot{N}_{electro}=n_{d}\frac{i}{F} \tag{8}\]  
Diffusion through the membrane also occurs due to the different gradient of water concentration \(\
abla s\) between the anode and cathode channels. The osmotic diffusion process is determined by the following Equation [36]:  
\[\dot{N}_{diffusion}=D_{w}\frac{c_{v,ca}-c_{v,an}}{t_{m}} \tag{9}\]  
where \(c_{v,ca}\) and \(c_{v,an}\) are the water concentration on the surface area of the membrane at cathode side and anode side, respectively defined by the following equations.  
\[C_{v,ca}=\frac{\rho_{m,dry}}{M_{m,dry}}\lambda_{ca} \tag{10}\]  
\[C_{v,an}=\frac{\rho_{m,dry}}{M_{m,dry}}\lambda_{an} \tag{11}\]  
\(D_{w}\) is the water diffusion coefficient related to the water activity, which is determined by the water content of the membrane as the following Equation [17].  
\[D_{w}=D_{\lambda}\exp\biggl{(}2461\biggl{(}\frac{1}{303}-\frac{1}{T_{st}} \biggr{)}\biggr{)} \tag{12}\]  
where \(D_{\lambda}\) is a function of the water content of the membrane.
# Survey on Unmanned Aerial Vehicle for Mars Exploration: Deployment Use Case
## 3 Preliminary Studies
### Different Types of UAVs in Space  
It is necessary to justify how UAVs fulfil the primary mission specifications for specified missions to target solar bodies. For instance, it will be a challenge for traditional UAV geometry to travel on other solar bodies. In addition, the size and weight of the UAVs are usually constrained because of the packaging restrictions imposed on the intended solar bodies by the launch vehicle [70]. Therefore, different aerodynamic, performance, regulation, and structural analyses are carried out in the design process to improve the performance of the UAVs. For planetary exploration, there are many configurations, such as balloons, airships, fixed wings, helicopters, rotary wings cyclocopters, gliders, VTOLs, flapping wings, and tilt-rotors. Figure 1 shows the type of UAVs for planetary investigation.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reference** & **Mission** & **Lander** & **Orbiter** & **Rover** & **Human Crew** & **Cause of Failure** \\ \hline [62] & MCO & \(\times\) & \(\blacktimes\) & \(\times\) & \(\times\) & Cost constraint. \\ \hline \multirow{3}{*}{[63]} & \multirow{3}{*}{Chandrayaan-2} & \(\blacktriangledown\) & \(\blacktriangledown\) & \(\times\) & \(\times\) & 500 m short of the lunar surface, \\  & & & & & & [PERSON] lost control and \\ \hline \multirow{3}{*}{[64,65]} & \multirow{3}{*}{Columbia Space Shuttle} & \(\times\) & \(\blacktriangledown\) & \(\times\) & \(\blacktriangledown\) & Damage in the left-wing. \\  & & & & & & \\ \hline \multirow{2}{*}{[66]} & \multirow{2}{*}{Viking project} & \(\blacktriangledown\) & \(\times\) & \(\times\) & Software updates error. \\ \hline \multirow{2}{*}{[67,68]} & \multirow{2}{*}{DART project} & \(\times\) & \(\blacktriangledown\) & \(\times\) & \(\times\) & Wrong estimation of distance \\  & & & & & & through the computer. \\ \hline \multirow{2}{*}{[69]} & \multirow{2}{*}{MPL} & \(\blacktriangledown\) & \(\times\) & \(\times\) & \(\times\) & Faulty transmitter. \\ \hline \multirow{2}{*}{[69]} & \multirow{2}{*}{NOAH 19} & \(\times\) & \(\blacktriangledown\) & \(\times\) & \(\times\) & Mechanical malfunctioning. \\ \hline \multirow{3}{*}{\(\times\)–No; \(\blacktriangledown\)–Yes.} & & & & & \\ \end{tabular}
\end{table}
Table 3: Discussion table on previously failed missions.
# Microseismic Location Error Due to Eikonal Traveltime Calculation
## 2 Methods
### Eikonal Solution Using Physics-Informed Neural Networks (PINNs)
Once the DNN is trained, it is evaluated on a regular grid (\(\mathbf{x}\)) to yield an estimate of the unknown traveltime \(\hat{\tau}(\mathbf{x})\) which is then multiplied by \(T_{0}(\mathbf{x})\) providing the estimated traveltime solution \(\hat{T}(\mathbf{x})\).
###### Abstract  
Due to their low orbit, low-Earth-orbit (LEO) satellites possess advantages such as minimal transmission delay, low link loss, flexible deployment, diverse application scenarios, and low manufacturing costs. Moreover, by increasing the number of satellites, the system capacity can be enhanced, making them the core of future communication systems. However, there have been instances where malicious actors used LEO satellite communication equipment to illegally broadcast events in large sports stadiums or engage in unauthorized leakage of military secrets in sensitive military areas. This has become an urgent issue in the field of communication security. To combat and prevent abnormal and illegal communication activities using LEO satellites, this study proposes a LEO satellite downlink distributed jamming optimization method using a non-dominated sorting genetic algorithm. Firstly, a distributed jamming system model for the LEO satellite downlink is established. Then, using a non-dominated sorting genetic algorithm, the jamming parameters are optimized in the power, time, and frequency domains. Field jamming experiments were conducted in the southwest outskirts of Xi'an, China, targeting the LEO constellation of the China Satellite Network. The results indicate that under the condition that the jamming coverage rate is no less than 90%, the proposed method maximizes jamming power, minimizes time delay, and minimizes frequency compensation compared to existing jamming optimization methods, effectively improving the real-time jamming performance and success rate.  
LEO satellite downlink; distributed jamming; non-dominated sorting genetic algorithm 2024 IEEEexample:BSTcontrol
## 2 Mathematical Modeling of HESS
### System Disturbance  
The system disturbances in this paper are mainly divided into two parts. One part is the load mutation, and the other part is the system uncertainty disturbances caused by changes in parameters such as the charge/discharge efficiency and internal resistance of the  
Figure 5: Schematic diagram of the operation of a bidirectional _DC/DC_ converter.  
Figure 6: Schematic diagram of the operation of a bidirectional DC/DC converter.  
lithium battery and supercapacitor, as well as fluctuations in renewable energy or external factors like the power grid. The mathematical model of the load mutation is as follows:  
\[\begin{cases}R=R_{1},0<t\leq t_{1}\\ R=R_{2},t_{1}<t\leq t_{2}\\ R=R_{3},t_{2}<t\leq t_{3}\end{cases} \tag{6}\]  
where \(R\) represents the load impedance of the hybrid energy storage system, \(R_{1}\), \(R_{2}\), \(R_{3}\) represents different impedance values, \(t\) represents the operating time of the hybrid energy storage microgrid and \(t_{1}\), \(t_{2}\), \(t_{3}\) represents the specific time at which the hybrid energy storage microgrid operates.  
Based on the literature [30], which mentions that uncertainties in the hybrid energy storage system microgrid are mostly uniformly varying (such as battery current, system power), the uncertainty disturbance model in this paper is designed as follows:  
\[\varphi=\cos t+1 \tag{7}\]
# Complex Wavefront Shaping through a Multi-Core Fiber
## 6 Conclusions  
We proposed a novel phase retrieval algorithm for the MCFs called Core-GS, facilitating robust wavefront shaping for complex light fields through MCFs, which leads to a paradigm shift in light control through MCFs. The spatial sampling problem induced by the discrete core distribution is thus overcome, enabling complex wavefront shaping through a 50 cm long bent MCF with fidelity up to 96.2%. High-quality phase distortion compensation with DOPC optimizes the stability of the wavefront shaping through the MCF for a long time. Dynamic on-the-fly closed-loop control of the light field leads to a near-perfect homogeneous rotating dual-spot profile with a mean internal intensity difference of 1.4%, which paves the way for precise optical manipulation of cells employing MCFs for lab-on-a-chip applications.  
The following are available at [[https://www.mdpi.com/article/10.3390/app11093949/s1](https://www.mdpi.com/article/10.3390/app11093949/s1)]([https://www.mdpi.com/article/10.3390/app11093949/s1](https://www.mdpi.com/article/10.3390/app11093949/s1)), Video S1: Rotation of the on-the-fly controlled dual-spot beam profile. A supporting video article is available at doi: link.  
[PERSON] and [PERSON] contribute the concept of using multi-core fiber as a phased-array for complex wavefront shaping. [PERSON] designed, built, programmed, and characterized the simulation and experiments and it was supervised by [PERSON] [PERSON] supervised the whole research work. [PERSON] wrote the manuscript. [PERSON] and [PERSON] revised the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Deutsche Forschungsgemeinschaft (DFG) grant number (CZ 55/40-1). We accept Open Access Funding by the Publication Fund of the TU Dresden  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon request.  
Special thanks to [PERSON] for valuable assistance on the software. The authors appreciate the helpful discussions and suggestions of [PERSON], [PERSON], and [PERSON].  
The authors declare no conflict of interest.
## 2 State-of-the-Art  
This section provides a structured overview of the key technologies and methodologies that inform and inspire the design and development of the SPARK-IT platform. We examine state-of-the-art solutions in four core dimensions: business model innovation, expert profile validation, AI-driven matchmaking, and decentralized data management with incentive mechanisms. This clearly illustrates how SPARK-IT addresses existing gaps and introduces novel enhancements.  
Firstly, we discuss the business model canvas (BMC) and its contemporary variations. We highlight their strengths in organizing and visualizing business ideas. We also address limitations, such as their complexity and lack of standardization. By analyzing these models, SPARK-IT can strategically adopt or adapt specific canvas structures. The goal is to make them user-friendly and tailored specifically to innovators and mentors on our platform.  
Secondly, we examine techniques for creating accurate and verified expert profiles from external sources, emphasizing methods such as data scraping and AI-driven information extraction from platforms like LinkedIn. These methods allow SPARK-IT to ensure expert credibility and reliability, directly addressing common concerns innovators have about finding trusted mentors.  
Next, the analysis focuses on AI-driven matchmaking solutions, comparing various methods including ontology-based approaches, NLP algorithms, and advanced deep learning models. Understanding the advantages and challenges associated with these techniquesguides SPARK-IT towards a balanced, hybrid matchmaking strategy, combining semantic understanding with keyword analysis to achieve precise and meaningful connections between innovators and experts.  
Lastly, we review current blockchain-enabled decentralized storage, reward, and reputation management systems, illustrating their importance in fostering trust, transparency, and effective collaboration. We highlight how incentive mechanisms, such as reputation scores and reward tokens, motivate high-quality interactions. This underscores SPARK-IT's commitment to transparency, accountability, and user-centric incentives within its ecosystem.  
By clearly outlining these technological foundations, this section sets the stage for SPARK-IT's unique contributions, showcasing how our platform builds upon and advances current state-of-the-art approaches to better support secure, transparent, and effective innovation-driven collaborations.
# Regressive Machine Learning for Real-Time Monitoring of Bed-Based Patients
### Related Works  
Bed alarms are another prominent modality used to reduce fall risk [11]. Functioning by alerting staff of when a patient is off the bed yet requiring fewer costs and less personnel allocation, the bed alarm is an intuitive tool in fall risk management [15]. By directly alerting staff of an adverse event, it expedites the spread of information and the time to action. However, bed alarms are only effective after an event has already occurred, making alarms another passive modality [16]. Although effective in making patients feel more secure and hastening a call to action, bed alarms are ineffective in actively reducing falls.  
Using machine learning (ML) to monitor bed-bound patients can mitigate the current issues associated with monitoring bed-bound patients. The current literature on ML for fall detection and prevention indicates that many studies use wearable inertial measurement units (IMU), which generate data that will be analyzed by an ML algorithm. Some of the ML algorithms include Support Vector Machine (SVM), Artificial Neural Network (ANN), Random Forest (RF), k-Nearest Neighbors (kNN), k-means, and Linear Discriminant Analysis (LDA) [10]. Each algorithm has its own benefits and shortcomings depending on the specific data that are fed. Overall, the accuracy, sensitivity, and specificity of fall detection and prevention were comparable among the different ML algorithms, with SVM having an accuracy of 98%, kNN having an accuracy of 99%, and ANN having an accuracy of 95.25% when using an IMU sensor at the waist [10]. Sensor location is another important aspect as different studies place the sensor in different locations in the body to maximize the data that can be captured. Most studies seem to place the sensors at the waist.  
In the field of ML for patient care [17], a significant focus has been on fall detection and prevention, as evidenced by a systematic review by [PERSON] et al. [16]. This review underscores the increasing adoption of wearable sensors, such as IMUs, to monitor patient movements. It also highlights the effectiveness of ML models like SVM, RF, and ANN, which have achieved high accuracy rates (up to 99%) in fall detection. However, our approach extends beyond fall detection. We aim to detect six different movements typically performed by bed-based patients, thereby providing a more comprehensive understanding of patient mobility. By integrating Electronic Medical Record (EMR) data, including mobility restrictions, history of falls, and cognitive impairments, with sensor data, we aim to further optimize the predictive accuracy of our regression models [18]. This approach allows for the generation of personalized alerts tailored to the patient's specific condition and treatment regimen, thereby creating a more dynamic and proactive system for patient care [19].
# Research on Seismic Signal Denoising Model Based on DnCNN Network
## 4 Experimental Results and Analysis
### Denoising Effect on Test Set  
To assess the efficacy of the noise reduction model, the DnCNN-based seismic signal denoising model was subjected to evaluations using the established test dataset. The outcomes of these tests are depicted in Figures 6 and 7. In these illustrations, segment (a) denotes the pristine seismic event signal, segment (b) is the signal post-noise addition, and segment (c) illustrates the signal following the denoising process. Figure 6 presents the outcomes for high-frequency noise, demonstrating the DnCNN model's capability to efficiently eliminate noise and restore the original seismic signal when comparing (a) and (c). Figure 7, on the other hand, exhibits the results for low-frequency noise scenarios, showing that the DnCNN model is adept at mitigating the significant oscillations induced by such noise when comparing (a) and (c). These visual comparisons validate the model's proficiency in noise reduction across different frequency bands.  
Figure 6: Test result example of noise with higher frequency: (**a**) shows the original seismic event signal, (**b**) shows the noise-added seismic signal, and (**c**) shows the denoised seismic signal.