esa-sceva/satcom-chunk-collection huggingface dataset:
# Mathematical Foundations and Implementation of CONIKS Key Transparency
## 3 Results
### Experimental Setup  
To accurately evaluate the performance of the CONIKS system, a rigorous experimental setup was designed to test various aspects of its functionality. The setup involved creating Merkle trees of varying sizes and performing digital signature operations under controlled conditions. The primary goal was to measure critical performance metrics such as hash computation time, tree construction efficiency, and signing and verification times. By systematically varying parameters and configurations, the experimental setup ensures that the data collected reflect the system's behavior under a range of operational scenarios, providing a comprehensive view of its performance characteristics. The methods employed in this study are designed to ensure a rigorous and reproducible evaluation of the CONIKS system. Each step of the experimental process is clearly defined to provide transparency and enable other researchers to replicate the results. The Merkle tree construction process, for instance, was systematically varied by changing the number of leaves in the tree, which ranged from 1000 to 100,000, allowing for a detailed analysis of how the system scales with different loads. Additionally, the digital signature operations were tested across multiple key sizes (1024, 2048, 3072, and 4096 bits) to examine how the performance changes with increasing cryptographic complexity. Time measurements for each operation were captured using high-precision timers to ensure accuracy, and any anomalies in the data were noted and handled by repeating the experiments under identical conditions to confirm reliability. Moreover, edge cases--such as scenarios with minimal or excessive system load--were intentionally included to evaluate the system's robustness in extreme situations. To further ensure validity, multiple runs of each experiment were conducted and the results were averaged to minimize the impact of random variability. By thoroughly documenting these procedures, this study offers a comprehensive methodology that ensures both the reliability and replicability of the experimental findings. The experiments were conducted using adedicated development environment equipped with state-of-the-art hardware and software tools. This environment was configured to simulate realistic usage conditions, including typical workloads and data sizes. Key considerations included the accuracy of time measurements and the handling of edge cases and errors. The following sections detail the methodology and procedures used in the experimental setup, including how data were collected and analyzed to ensure robust and reliable results.
## 2 Design and Experimental Setup
### Physionet SCA Data  
PhysioNet's SCA database supports research and provides an opportunity to progress in the prediction of SCA. The MIT-BIH Malignant Ventricular Arrhythmia Database (collected initially by [PERSON] at MIT) was used to generate the SCA dataset. SCA includes half-hour excerpts from 23 Holter recordings (13 males, 8 females, and two unknown genders; ages 17 to 82). The database consists of subjects with underlying conditions: 18 patients with sinus rhythm (4 with intermittent pacing), 1 with a pacemaker, and 4 with atrial fibrillation. Each patient had a sustained ventricular tachyarrhythmia, and most suffered from a prior cardiac arrest. Twelve subjects' ECG signals were sampled at 250 Hz and annotated by expert physicians. For each of the twelve annotated subjects, a VF onset elapsed time parameter (hh:mm:s) is listed by the Physicians suggesting the actual occurrence of a cardiac arrest [18]. There is a significant difference in the ECG signal after the VF onset compared to before the VF onset. Based on the sampling rate of 250 Hz, the heartbeats are separated, and each heartbeat is annotated. The annotations are divided into two categories: beat annotations and non-beat annotations. The annotations are labeled with an alphabet. Symbols specify the non-beat annotations, e.g., a vertical line \(|\) indicates an Isolated QRS-like artifact. The heartbeat and non-beat annotations and their description are listed in Table 2. More details about the patient's age, prior heart condition, and heartbeat annotation have been presented in [PERSON] et al. For the  
Figure 4: Experimental setup for ECG wireless transmission.  
Figure 3: The core PCB circuit diagram.  
Figure 2: (a) Zeolite-based dry electrodes (b) Read-out-circuit (c) ADC (d) nRF24L01 wireless transreceiver.  
identification of normal and abnormal heartbeats, an expert Physician is needed. The process involving a senior Physician to separate the heartbeats into various categories is tedious, time-consuming, and could result in manual errors. An ML model is developed for accuracy, time efficiency with extensive data, and high learning.
# On Two Approaches to Slope Stability Reliability Assessments Using the Random Finite Element Method
## 5 Slope Reliability Assessment--The Two Approaches  
### Definitions of Factor of Safety  
There are two definitions for the factor of safety: The first uses as a ratio of absolute strength (structural capacity) to actual applied load; this is a measure of a particular design (i.e., for a particular realisation from a Monte Carlo simulation). The other uses of factor of safety, which is a constant value imposed by law, standard/code, specification, contract or custom to which a structure must conform or exceed. As the definition is sometimes confusing, it is important to be aware of which definition(s) are being used.  
* Realized factor of safety, \(F_{R}\): This is a calculated value from a specific realisation of a Monte Carlo simulation. For different realisations from the same set of statistics \((\mu,\sigma,\theta_{\sigma},\theta_{h})\), this value may be different as per realisation. Typically, there is a range of values following some distributions for this type of FOS.
* Traditional factor of safety, \(F_{d}\), based on the mean property value: This is the factor of safety (a required value) as a design factor. This FOS is traditionally defined in a deterministic point of view, i.e., in traditional mechanics, as the ratio of mean strength to mean applied load.  
In a RFEM analysis, each traditional \(F_{d}\) is associated with some reliability level (\(F_{R}\leq 1\) indicates failure, and stable otherwise). In other words, there may be a range of realised \(F_{R}\) for each target traditional \(F_{d}\) for which those values of \(F_{R}\) may be larger or smaller than \(F_{d}\), due to different realisations (which is unknown because of limited site investigation) of the spatial variability of soil strength properties.
# Fault Injection Emulation for Systems in FPGAs: Tools, Techniques and Methodology, a Tutorial
## 3 Background Concepts
### Emulation-Based Fault Injection  
Emulation-based fault injection consists in a mechanism that implements the design under test into an FPGA. Unlike the simulation option that uses a circuit high-level description running into a commercial simulator, emulation requires a synthesizable implementation on an FPGA. For these platforms, the development board is connected to a host computer used to: (i) define the fault injection campaign, (ii) control the injection experiments and (iii) display the results.  
Some examples of emulation-based fault injection systems for SRAM-based FPGAs are: FT-UNSHADES [37], FLIPPER [38], SPFFI [39], and XRTC [40].  
For some of the above solutions, the ASIC failure model (errors into the user memory elements like sequential logic of the target circuits) is not always supported. This is because the vulnerability of SRAM-based FPGAs designs to soft errors is higher than ASIC implementations because of the resources dedicated to memory for programming the board, not for the circuit (device configuration memory). As a rough comparison, the number of the user memory bits can be 10 times higher than RAM bits and 300 times higherthan flip-flop bits for an FPGA. SEUs in these configuration bits could cause permanent errors on the FPGA implementation of the design. This fault model is carried out by modifying one bit of the information stored in the configuration memory, also known as bitstream. Each campaign consists in doing a bit flip to each of the bits that belong to the configuration memory, followed by one run-time reconfiguration to restore the original state of the design, avoiding accumulative effects (in the case of SEUs).
# Study on the Steepness of Landslide-Induced Wave and Nonlinear Motion of Container Ships
## 6 Conclusions  
Slope instability in the Three Gorges Reservoir area induces landslide surges in the reservoir water area. In this paper, the generation stage and propagation characteristics of landslide-induced surges are analyzed. By means of an orthogonal test with physical models and regression analysis, height and steepness characteristics of landslide-induced surges as well as their influence on a ship's motion are studied. The main conclusions are as follows:  
Figure 13: Sway motion of the ship by different positions in the landslide-induced wave region.  
1. A landslide-induced surge is clearly non-linear. The first wave peak reaches the maximum value in a very short time. The larger the landslide square, the earlier the maximum crest of the generated water appears.
2. The greater the amount of water entering the landslide, the earlier the maximum crest of the generated water appears. When a ship is coupled with a landslide surge, the amplitude of the ship's roll and heave motion increases.
3. A landslide-induced surge has a great influence on the rolling and swinging motion of ships in the landslide section, and has an effect on ships in straight rivers. In the process of ship design, the center of gravity of the ship should be lowered, the inherent period of the ship should be improved, and the roll performance of the ship should be improved.
4. When a ship is sailing into the curved section of a river, if it encounters the external load of impulse, it should pay attention to any initial damage of the ship's rolling motion and take reasonable measures to ensure the ship's safe navigation.  
Conceptualization, Y.Z.; Data curation, P.Y. and Y.Z.; Formal analysis, P.W. and P.Y.; Methodology, P.W.; Investigation, P.Y.; Writing-original draft, P.Y. All authors have read and agreed to the published version of the manuscript.  
This study was financially supported by the National Natural Science Foundation (51479015), Social Science and Technology Innovation Program for People's Livedihood in Chongqing (cstc2017 jcyjBX0070, cstc2019 jscx-msxmX0302, cstc2018 jscx-msybx0233, and cstc2019 jscx-lyjsAX0008), the Science and Technology Program of Chongqing Education Commission (KQN201900735, KJ1600517).  
The authors declare that there is no conflict of interest.
## Materials and Methods
### Data Availability  
#### 2.2.1 Rainfall and Discharge Observations  
To assess the multi-source remote-sensing-based precipitation products, the China Gauge-based Daily Precipitation Analysis (CGDPA) product developed by the China Meteorological Administration was used in this research [53]. Using climatological optimal interpolation (OI) based on ~2700 national rain gauges over China's mainland, this product is updated in real time at a spatial resolution of 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) ([[http://cdc.nmic.cn](http://cdc.nmic.cn)]([http://cdc.nmic.cn](http://cdc.nmic.cn)), accessed on 1 January 1998). Because of CCDPA's superior quality, it is often used as a reference for satellite precipitation information both in data analysis and hydrologic evaluations [54]. As shown in Figure 1, there are 18 and 10 grid cells with at least one rain gauge per grid cell at 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) over the entire Beijing and Dongjiang River Basins, respectively. This research uses only 0.25\({}^{\circ}\) grid cells to assess various products spanning the period 2006-2018; these are resampled by the nearest neighbor allocation method using ArcGIS 10.6 software.  
Daily streamflow records of two catchments were obtained from hydrologic year books published by the Hydrologic Bureau of the Ministry of Water Resources of China. The hydrological modeling was restricted to the time period from 2006 to 2018 due to the availability of both streamflow observations and satellite precipitation.
## 1 Introduction
### Relevant Contribution to This Field  
There are different concepts already available in the literature (for example, [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12]), which are addressing these partial load issues. The authors try to solve it using either sophisticated control strategies, modulation strategies, costly and high volume paralleled converter approaches or partial load converter topologies. Especially approaches from [1; 2; 3; 4; 5; 6; 7; 8; 9; 10] show some lack in different areas, e.g., simplicity, cost-effectiveness, power density, flexibility, etc. Another example, is discussed in [13]. There, the authors describe a novel FFCS (flywheel fast-charging system) aiming to improve converter efficiency with a sophisticated control strategy.  
This study proposes and discusses an optional light-load efficient circuit extension, which can be enabled during this inefficient light-, ultra-light or no-load conditions and still guarantees high performant power electronics operation for a more comprehensive load range while re-utilizing already existing parts of the high-power inverter such as input filter, commutation inductance, capacitive DC-link, etc. (Figure 1b). The proposed solutions would be of special interest for renewable energy systems such as PV, wind power BESS which are operating a significant amount of time under light-load conditions or for high power applications with space restrictions (such as transportation, aviation etc.). In this study, initial design guidelines are discussed, and the potential of this solution is highlight by two examples (high-power converter with and without light-load upgrade).  
The rest of the study is organized as follows. Section 2 introduces different general examples on how such an upgrade box could be integrated into different power electronics topologies. Design parameters for a comparison between a 125 kW six converter with and without optional light-load converter are specified in Section 3. In Section 4 different switching and conduction losses of a high-power Si-IGBT converter and a low power SiC-MOSFET converter are compared to each other. Moreover, the potential of utilizing GaN transistors is discussed. Some conclusions and a short outlook is given in Section 5.
# LoRaWAN Physical Layer-Based Attacks and Countermeasures, A Review
## 5 Discussion
### Research Potential
#### 5.2.3 Further Physical Layer Security Concepts  
Wireless-physical-layer security as a research field encompasses several interesting topics, which, to the authors best knowledge, have not been covered before but might be of interest for LoRaWAN security. A good example towards increased resilience and security includes multiple-input-multiple-output (MIMO) antenna techniques, which have already been successfully applied for range extension and angle-of-arrival localization with LoRa waveforms [85]. By using multiple synchronized receivers, as in a MIMO setup, even strong jamming signals can be suppressed without knowledge of the channel characteristics, as demonstrated in [86]. To this end, further development is necessary to apply such methods to LoRaWAN, as most of the state-of-the-art jamming-suppression techniques are based on OFDM signaling instead of the CSS signaling used in LoRa modulation. An additional advantage connected to MIMO techniques concerns randomness generation in wireless secret-key-agreement methods. In particular, opportunistic beamforming, as suggested in [87], can improve key-agreement rate in static wireless-channel conditions. Finally, the MIMO and the beamforming antenna concepts might become helpful to reduce packet collisions in dense LoRaWAN networks.
# A Survey of Visible-Light-Communication-Based Indoor Positioning Systems
## 4 Techniques of VLC-Based Indoor Positioning Systems  
This section introduces different techniques of VLC-based IPSs, including RSS, TDOA, AOA, etc. For various indoor scenarios, these methods offer accurate and reliable location estimation with minimal infrastructure modification while providing benefits of increased energy efficiency and security.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Technology** & **Accuracy (m)** & **Cost** \\ \hline GPS & 5–40 & High \\ Cellular & 5–50 & High \\ Wi-Fi & 1–5 & Low \\ RFID & 1–2 & Low \\ Infrared & 1–2 & Medium \\ Bluetooth & 1–5 & Low \\ Ultrasonic & 0.03–1 & High \\ UWB & 0.1–1 & High \\ VLC & 0.05–0.1 & Low \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of different positioning systems.
# Efficient Melody Extraction Based on Extreme Learning Machine
## 3 Extreme Learning Machine-Based Melody Extraction
### Pitch Fine-Tuning  
In real-world recordings, the pitches are continuous. However, the pitches are quantized to generate different classes in this paper. The estimated pitch contour might have some pitch jumps between adjacent frames. Hence, pitch fine-tuning is employed to derive a smoother melody contour herein.  
As sinusoidal components exhibit a spectral peak in the CQT amplitude spectrum, pitch fine-tuning is conducted by searching around the estimated rough pitches. Suppose the rough pitch at frame \(t\) is \(f_{t,r}\), the interval of peak search is set to be \([f_{t,r}-\delta,f_{t,r}+\delta]\), where \(\delta\) is the search radius. If some peaks are found, the frequency with the highest amplitude is chosen as the final pitch at this frame. If no peak is found at this interval, the rough pitch is chosen as the final one.
# Integrated DVB-X2 Receiver Architecture with Common Acceleration Engine
## 4 DVB-T2/C2 Demodulator Architecture
### Residual STO and CFO Synchronization Unit  
A residual STO continuously changes the phase of frequency domain signals over time, and it can be restored by estimating the phase variation of pilots over time. A residual CFO changes the phase of a fixed size for frequency domain signals over time, and it can be restored by estimating the phase variation of pilots.  
Figure 13 shows the block diagram of the synchronization block to estimate and compensate the residual STO and CFO. The residual STO and CFO synchronization block operation is as follows. During the first cycle, it estimates STO included in pilots, and then removes STO from pilots. After STO correction, it estimates CFO by using STO-recovered pilots. By using the estimated CFO and STO, it performs fine STO and CFO correction. The proposed STO and CFO synchronization block can estimate and remove all STO and CFO by using an average unit, a phase estimator, a NCO, and a complex multiplier. The proposed architecture achieves low H/W complexity by sharing the average unit, the phase estimator, the NCO, and the complex multiplier.
# Lithological Discrimination of Khyber Range Using Remote Sensing and Machine Learning Algorithms
## 3 Materials and Methods
### Machine Learning and Deep Learning-Based Techniques
#### 3.7.4 Support Vector Machine (SVM)  
The mineral exploration industry commonly uses support vector machines (SVMs) to handle data from remote sensing [3; 68; 79; 80]. SVM can offer nonlinear decision limits in high-dimensional variable space [81; 82], which can be utilized to resolve a quadratic optimization problem [72]. SVM uses a hyperplane to categorize the provided dataset in an n-dimensional space as its classification method. The hyperplane that indicates the greatest gap between two classes is a logical option for the best hyperplane. It is possible to represent target detection using the SVM approach as a dichotomy.  
Figure 5: Sequence flow diagram of the procedure used for lithological classification in the study.  
Basic SVM theory states that several hyperplanes can separate different classes in a nonlinearly separable dataset with points from two classes. Only a portion of the \"k\" training samples--referred to as support vectors--are utilized to create the decision boundary, or the hyperplane that separates classes in the most efficient manner. The greatest decision boundary between the support vectors yields the best outcome across classes.  
The lithological units were mapped using the ENVI SVM classifier on the basis of the training dataset. SVM received the chosen training data from the ML algorithm right away. The kernel type, cost parameter (C), and gamma coefficient are the three most important variables that have a major impact on SVM performance [1]. While the gamma parameter regulates the SVM model's nonlinearity, the penalty parameter sets a limit on the amount of error that may be permitted in the training set. Trial and error were used to randomly modify the algorithm's parameters in order to assess its performance. These cost (penalty) parameters were examined together with the gamma coefficient and other kernels.  
According to a previous study [83], the SVM technique can use a variety of standard kernel functions, such as sigmoid, linear, polynomial, and radial basis functions. The accuracy of SVM training and classification depends on the choice of kernel functions. Since degree 1 polynomials are the simplest, they identify patterns more quickly than other kernels. For lithological mapping in this work, the SVM classifier built in ENVI was employed. The kernel type chosen was the radial basis function, the gamma in the kernel function was 0.091, and the penalty parameter was set to 100 [68].
###### Abstract  
Printed circuit board (PCB) defect detection faces challenges like small target feature loss and severe background interference. To address these issues, this paper proposes PCES-YOLO, an enhanced YOLOv11-based model. First, a developed Pre-convolution Receptive Field Enhancement (PRFE) module replaces C3k in the C3k2 module. The ConvNeXtBlock with inverted bottleneck is introduced in the P4 layer, greatly improving small-target feature capture and semantic understanding. The second key innovation lies in the creation of the Efficient Feature Fusion and Aggregation Network (EFAN), which integrates a lightweight Spatial-Channel Decoupled Downsampling (SCDown) module and three innovative fusion pathways. This achieves substantial parameter reduction while effectively integrating shallow detail features with deep semantic features, preserving critical defect information across different feature levels. Finally, the Shape-IoU loss function is incorporated, focusing on bounding box shape and scale for more accurate regression and enhanced defect localization precision. Experiments on the enhanced Peking University PCB defect dataset show that PCES-YOLO achieves a mAP50 of 97.3% and a mAP50-95 of 77.2%. Compared to YOLOv11n, it shows improvements of 3.6% in mAP50 and 15.2% in mAP50-95. When compared to YOLOv11s, it increases mAP50 by 1.0% and mAP50-95 by 5.6% while also significantly reducing the model parameters. The performance of PCES-YOLO is also evaluated against mainstream object detection algorithms, including Faster R-CNN, SSD, YOLOv8n, etc. These results indicate that PCES-YOLO outperforms these algorithms in terms of detection accuracy and efficiency, making it a promising high-precision and efficient solution for PCB defect detection in industrial settings.  
PCB defect detection; PCES-YOLO; Pre-convolution Receptive Field Enhancement; feature fusion; loss function +
Footnote †: journal: Applied Sciences  
Article