esa-sceva/satcom-chunk-collection huggingface dataset:
# mmHSE: A Two-Stage Framework for Human Skeleton Estimation Using mmWave FMCW Radar Signals
## 5 Experimental Results and Analysis  
### Experimental Setup  
All experiments were conducted on a high-performance computing platform equipped with an AMD EPYC 7742 CPU and eight NVIDIA V100 GPUs. The implementation was based on the PyTorch 1.9.0 deep learning framework. To accelerate training, data parallelism was employed across the available GPUs. The dataset used in this study is a self-constructed radar-based human skeletal heatmap dataset, where each sample comprises an azimuthal heatmap, an elevational heatmap, and the corresponding 3D keypoint annotations.  
A stratified sampling strategy was adopted to divide the dataset into training and testing subsets in an 8:2 ratio, with 80% of the data allocated for training and the remaining 20% reserved for evaluation. To assess the generalization performance of the proposed method, two types of domain adaptation scenarios were explored: (1) cross-domain, where the model was trained on one scene and tested on two distinct scenes; and (2) cross-user, where training was conducted on data from two individuals and testing on data from nine different individuals. During training, the data augmentation techniques detailed in Section 4.1, including temporal sequence shuffling and 3D keypoint rotation, were applied. No augmentation was applied during inference to maintain the authenticity and objectivity of the evaluation.  
The model was trained using a batch size of 32 and an initial learning rate of 0.001. A step-based learning rate scheduler was applied, decaying the learning rate by a factor of 0.5 every 20 epochs. The optimization was carried out using the Adam optimizer for up to 100 epochs. Early stopping was triggered if the validation loss failed to improve for 20 consecutive epochs. In the residual fusion module (Equation (18)), the fusion weight\(w\) was set to 0.6 to balance spatial accuracy and anatomical regularization. The model dimensionality \(d_{\text{model}}\) was set to 64, while the hidden dimension of the GCN \(d_{h}\) was set to 128. Both the self-attention and cross-view attention modules were configured with four attention heads.
## 4 Materials and Methods
### Activities of O. syriacum Essential Oil against A. simplex
#### 4.3.2 Larvicidal Activity on _A. simplex_  
Ten larvae were introduced in each well of polystyrene six-well plates with a final volume of 2 mL containing different concentrations of the test solution as well as control wells without treatments [27]. _O. syriacum_ essential oil was tested on _A. simplex_ in the range of 0-1 mg mL\({}^{-1}\). The parasites were incubated at 37 degC in 5% CO\({}_{2}\) for 24-48 h. _O. syriacum_ essential oil-based treatments and the control were tested in triplicates on three different days. Larvae were examined at 24 and 48 h under a microscope and immobile L3 were considered dead. Levamisole was used as the positive control of dead _A. simplex_ larvae.
# Evaluation of BLE Star Network for Wireless Wearable Prosthesis/Orthosis Controller +
## 3 Materials and Methods
### Statistical Analysis  
Initially, the Kolmogorov-Smirnoff test of normality was applied. This test showed that the data for all measurements did not follow a normal distribution. Thus, the Friedman test was chosen as a non-parametric alternative to the one-way ANOVA. If the Friedman test showed statistically significant differences, the pair-wise Nemenyi test was applied post hoc [33]. The Nemenyi test was chosen as it inherently adjusts for multiple comparisons. Differences were statistically significant for \(p\)-values less than 0.05.
# A Survey of Ledger Technology-Based Databases
## 3 Centralised Ledger Databases  
### LedgerDB  
According to the creators of LedgerDB [21], many applications that are built on permissioned blockchains stumble into performance issues, resulting in low throughput, high latency, and significant storage overhead. Thus, if a decentralised structure is not necessary, it is possible to shift towards a centralised system, as current trends in the industry lean towards a single service provider supplying the total nodes of an application. As a result, the CLD solves the mentioned issues through its inherent architectural characteristics. LedgerDB, in an effort to meet customer needs, concentrates on the achievement of high write performance, whilst maintaining acceptable read and write verification performance. A new possibility presents itself for LedgerDB on Alibaba Cloud, which is a centralised database with tamper-evidence and non-repudiation features similar to blockchain and provides strong auditability. The majority of ledger databases tend to assume Ledger Service Provider (LSP) trust; however, LedgerDB takes up multi-granularity verification and non-repudiation protocols, thus foregoing the trust assumption. Therefore, it opposes user and LSP malicious behaviour.  
There are three key node types in LedgerDB: ledger master, ledger proxy, and ledger server. As a ledger master manages the corresponding cluster-level events and all the corresponding cluster data, it is at the heart of LedgerDB. The ledger servers and ledger proxies issue resolution is seamless as a result of their statelessness. Clients' requests are preprocessed and forwarded to the corresponding ledger server by a ledger proxy. The preprocessed client requests are completely processed by a ledger server, which then communicates with the underlying storage layer. The storage layer, which stores data, can take a variety of forms, for example, key-value stores, file systems, and so on. The storage layer, following Raft protocol, is implemented in the shared abstraction, which makes the replication of data possible within various zones and regions. There is a possibility to delete from the database through API without breaking the verification or audit process. Therefore, the requirements for storage can be reduced.  
LedgerDB requires a third-party timestamp authority (TSA) to be trusted. This ensures the maintenance of the above-mentioned threat model. The TSA attaches a true and valid timestamp to a given piece of sent data. A new, special journal type which contains a ledger snapshot and a timestamp, called a TSA journal, is approved by the TSA. The use of a TSA is of assistance in proving the existence of a piece of data before a given point in time. This is a two-way pack protocol, wherein a ledger digest is first signed by the TSA, then it will appear as a TSA journal on the ledger. As a result, the vulnerability time window is reduced.
# Enhancing Security of Online Interfaces: Adversarial Handwritten Arabic CAPTCHAs Generation
## 3 Methodology  
The aim of this research is to develop Arabic handwriting CAPTCHAs with enhanced security against machine learning by integrating adversarial perturbation techniques. This section describes the methodology of our study, as shown in Figure 1.  
The methodology has three main phases:  
1. Generation of Arabic handwritten words: * We have used the Alsuhibany and Alquraishi [33] model in order to generate meaningless Arabic handwritten words. * We have used the same model [33] to generate meaningful Arabic handwritten words.
2. Applying adversarial perturbation: * We have applied five adversarial models on the generated samples. These techniques are: EOT, SGTCS, JSMA, IAN, and CTC. The generated CAPTCHAs use single perturbations (e.g., JSMA-only, EOT-only). The selection of these approaches was based on their empirical robustness against perturbation removal attacks demonstrated by [PERSON] [49]. Using five approaches provided sufficient data while keeping the scope manageable for the study.
3. Evaluation * We have evaluated the security level of the generated samples via the Google vision API [50]. * We have evaluated the usability level of the generated samples through an experimental study.  
In the following sections, we will explain the main phases of the methodology in detail.
# Design and Implementation of a Multi-Function Hydrophone for Underwater Acoustic Application
## 3 Results and Analysis
### Experimental Results in Mode 1  
In mode 1, raw underwater acoustic data are stored on an SD card. The file names consist of the serial number, date, time, and file number. By using such a file name, the users can read and analyze the corresponding signals. Figure 10(a) shows a signal waveform of the underwater acoustic data saved on the SD card, the signal was linear frequency modulation (LFM). From Figure 10(a), one may see that the SNR is very high, which demonstrates the effectiveness of the data reception and preservation. Figure 10(b) shows the corresponding spectrum, the bandwidth of the LFM signal was 13 kHz to 18 kHz. After the bandpass filter, the average SNR is 19.9 dB.  
In a word, the proposed multi-function hydrophone acquires underwater acoustic signals with high resolution and accuracy.
# An Overview of Key Technologies in Physical Layer Security
## 2 Background
### Concept and Evolution of PLS
With [PERSON]'s model, the assumption is that the signal arriving at the receiver of the eavesdropper is degraded by some degree in comparison to that of the legitimate receiver [36]. However, it was proved in [39] that secret communication is possible regardless of the statistical channel state of the eavesdropper. It was further shown in [30] that weak secrecy was also insu  
Figure 4:0 [PERSON]’s transmission rate vs equivocation rate. Figure 4:1 [PERSON]’s wiretap channel. prove secrecy in communication. Another metric, referred to as strong secrecy, was defined by [40]. The metric states that a scheme is said to achieve strong secrecy if  
Figure 4:2  
Subsequently, in [41], strong secrecy was proved to be inefficient for some applications. This is based on the assumption of random and uniformly distributed message symbols over the message alphabet at the input of the secrecy encoders. In practice, the limitation is caused by the unavailability of universal compression algorithms capable of providing messages that have the distribution mentioned above.
## 2 Materials and Methods
### Study Design  
The tests that were carried out during the experimental session are illustrated in Figure 1. Specifically, all participants performed a hearing test at the beginning of the testing session, including air conduction thresholds at 500-4000 Hz in both ears, to assure hearing thresholds less than 20 dB HL. Following the hearing test, only the children performed an SRTn assessment. All participants then continued to perform 12 VD measures: 6 in quiet conditions, 2 with each cue (F0, formants, combined), and 6 in background noise conditions, 2 with each of the cues. The order of presentation of voice cues and sentences for the VD test was controlled across participants, with half of the participants in each group first tested in noisy conditions and the other half in quiet conditions. Prior to the formal testing phase, each participant engaged in a brief familiarization exercise involving sentences that exclusively varied in terms of F0 within a quiet context. This entailed 5-10 trial runs, featuring the most substantial distinction between the reference and comparison stimuli. This preliminary step was implemented to ascertain the participants' comprehension of the task before commencing testing. Overall, the VD assessment, conducted both in quiet and noisy environments, lasted approximately 75 to 90 min, including brief intervals of rest lasting 5 to 8 min each. It is noteworthy that the adult participants did not receive any form of compensation for their participation in the study, while the children were rewarded with stickers, which served as positive reinforcement.
## 4 Discussion  
This study has systematically evaluated the efficacy of various predictive models in the context of real estate valuation in Kazakhstan, with a particular focus on ANFIS. Our findings reveal that while traditional models such as linear regression and decision trees provide baseline accuracy, ANFIS offers superior precision due to its capability to model non-linear relationships and handle data imprecision effectively. The performance of the ANFIS model stands out when compared to traditional econometric and machine learning models. Unlike linear models that assume a direct correlation between variables, ANFIS integrates the advantages of fuzzy logic and neural networks, accommodating the inherent uncertainties of the real estate market. This integration enables ANFIS to capture complex patterns that are typically missed by other models, as demonstrated by its lower RMSE and higher R-values. The application of ANFIS can significantly enhance the accuracy of property valuations, which is critical for investors, policymakers, and financial analysts involved in real estate development and market analysis. By providing a more reliable tool for valuation, ANFIS helps in better assessing property values, thus supporting more informed decision-making processes. While our results are promising, they are not without limitations. The models were tested within the specific context of Kazakhstan's real estate market, and their applicability in other markets might differ due to varying economic and regulatory conditions. Future research could expand the application of ANFIS to other regions and compare its effectiveness across different economic contexts.  
Additionally, incorporating more dynamic data, such as time series analysis of property prices, could further refine the predictive capabilities of these models. Integrating ANFIS into real estate valuation practices could pave the way for standardizing property appraisal methods, reducing the subjectivity involved in manual valuations. Policymakers might consider these findings when updating regulatory frameworks to adopt more scientific and data-driven approaches for property valuation.
## 1 Introduction  
Earth fissure, a discontinuous and broken rupture on the ground surface, could be formed as a result of tectonic activity [1; 2; 3; 4]. This lesser-known geohazard feature has the potential to impact the environment by causing significant damage to civil foundations such as roads, dams, buildings, and underground pipes due to stress. The fissures pose significant risks to farmlands, properties, and infrastructures. Ground surface discontinuities, on the other hand, can be interpreted as indicators of deformation history and stress patterns. In structural geology, analysis, evidence, and their corresponding interpretations can provide a proper consideration in controlling physical processes and supporting any related hazard assessment [5]. Analyzing the structural discontinuities such as faults, joints, bedding planes, and fractures in hard-rock slopes allows us to define unstable areas [6; 7; 8; 9; 10]. In contrast, the analysis of surface fissures in soft-rock slopes may imply an explanation for future failures, which is frequently accounted for as a geo-indicator of a fault zone's activity phases [11]. In some areas, the characteristics of the surface fissure affect water infiltration and drainage, which in turn affects the kinematic response of slopes to hydrogeological events and the groundwater system. Large fissures along a fault may be detectable in very high-resolution (VHR) space-borne images [12], but their widths are typically in the decimeter range, and the real-time kinematic (RTK) technique and airborne photographs can provide sufficient details to detect them.  
In this paper, we investigate the mechanism of ground fissure formation in the Birjand area (as a case study) using tectonic analyses, taking into account the scientific importance of ground fissures and the potential for environmental damage.  
The tectonics of the study area is characterized by active faulting, instrumental earthquakes, and young volcanism around a rigid block (Lut block) in eastern Iran. Tectonic activity along the western and eastern boundaries of the Lut Block contributes significantly to geomorphic landscapes and natural hazards [13; 14; 15; 16; 17; 18]. The Sistan suture zone (SSZ) is overprinted by the Nehbandan fault system at Lut's eastern boundary. Some splays of this fault with EW strikes, such as the North Birjand fault, are located in the center of the Sistan suture zone. These faults with reverse and left-lateral strike-slip movements formed some fault-propagation folds in the Quaternary sediments. Due to its proximity to cities such as Birjand, the study of structural features associated with the North Birjand fault (e.g., fissure structures) is critical.
# Fast Thumbnail Extraction for H.264/AVC, HEVC and VP9
## 2 Proposed Partial Decoding of H.264/AVC, HEVC and VP9 for Thumbnail Extraction
### Partial Intra Prediction  
The intra prediction process was designed to improve the compression efficiency by eliminating redundancy among adjacent pixels in an image. In the H.264, HEVC and VP9 decoders, reference pixels of neighbouring blocks are filtered and the filtered pixel values are then used for predicted signals. The predicted and residual signals from the inverse transformation are added.  
For thumbnail extraction, partial prediction can be performed from the filtered reference samples. In a manner similar to the partial inverse transformation, the bottom and rightmost pixel lines as well as one pixel per inner 8 \(\times\) 8 sub-block are predicted. As a result, it is possible to omit the memory copy operations as much as the number of unnecessary pixels for thumbnail. Figure 6 shows the necessary pixels to be predicted and reconstructed for the thumbnail extraction process for a 16 \(\times\) 16 block.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Transform** & \multicolumn{2}{c}{**Full 2D-IDCT**} & \multicolumn{2}{c}{**Partial 2D-IDCT**} & \multicolumn{2}{c}{**Reduction Ratio**} \\
**Size** & **Add** & **Mul** & **Add** & **Mul** & **Add** & **Mul** \\ \hline
4 \(\times\) 4 & 64 & 64 & 52 & 49 & 19\% & 23\% \\
4 \(\times\) 8 & 224 & 224 & 105 & 126 & 53\% & 44\% \\
8 \(\times\) 4 & 224 & 224 & 212 & 209 & 5\% & 7\% \\
8 \(\times\) 8 & 384 & 384 & 265 & 286 & 31\% & 26\% \\
8 \(\times\) 16 & 1216 & 1344 & 530 & 672 & 56\% & 50\% \\
16 \(\times\) 8 & 1216 & 1344 & 1097 & 1246 & 10\% & 7\% \\
16 \(\times\) 16 & 2048 & 2304 & 1362 & 1632 & 33\% & 29\% \\
16 \(\times\) 32 & 5184 & 7552 & 2412 & 3464 & 53\% & 54\% \\
32 \(\times\) 16 & 5184 & 7552 & 4498 & 6880 & 13\% & 9\% \\
32 \(\times\) 32 & 8320 & 12,800 & 5548 & 8712 & 33\% & 32\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: The numbers of additions (Add) and multiplications (Mul) of the full and partial inverse transformations.  
Figure 5: Partial reconstruction flow of the butterfly structure for a 16-point 1D inverse discrete cosine transform (ID-IDCT).
# A Crypto Accelerator of Binary Edward Curves for Securing Low-Resource Embedded Devices
## 2 Mathematical Background
### Unified Mathematical Formulation  
Table 1 lists differential addition instructions for BEC over \(GF(2^{m})\) as described in the work by [23]. The table outlines a total of seven complex instructions, each contributing to the efficient computation of BEC operations. To accommodate the storage requirements for various results (initial, intermediate and final), a memory unit with a capacity of \(11\times m\) is required. Here, the number 11 shows the required number of memory locations. Similarly, the value of m corresponds to the width of each memory location. Consequently, the architecture ensures the seamless storage and management of data throughout the computation process. The BEC curve parameters defined in Table 1 encompass the values of \(e_{1}\), \(e_{2}\), and \(w\), which are calculated as follows: \(e_{1}=\sqrt[4]{e}\), \(e_{2}=\sqrt{e}\), and \(e=d_{1}^{4}+d_{1}^{3}+d_{1}^{2}d_{2}\). Additionally, \(w\) represents a rational function for an elliptic curve \(E\) over \(GF\) and is computed as \(w(P)=\frac{x+y}{d_{1}(x+y+1)}\), where \(P=(x,y)\) belongs to \(E_{B,d_{1},d_{2}}\). Within the architecture, the initial projective points are represented by \(W_{1}\), \(Z_{1}\), \(W_{2}\), and \(Z_{2}\). Similarly, the final points are denoted as \(Z_{a}\), \(Z_{d}\), \(W_{a}\), and \(W_{d}\). The intermediate results are placed in registers A, B, and C, facilitating the efficient computation and handling of data throughout the execution process.
## 3 Model Validation and Comparison
### Numerical Setup
The first numerical result modeled the slump as an ideal Newtonian material, while the second numerical result modeled the slump as a Bingham material. Both numerical results were obtained by coupling the rheology model with NASA-VOF2D, solving the 2D Navier-Stokes equations with the VOF surface tracking algorithm. [PERSON] [53] presented a wave result from the extension of the non-hydrostatic wave (NHWAVE) model. NHWAVE solves the incompressible Navier-Stokes equations in terrain and surface following sigma coordinates. The free surface movement is controlled via depth-integrated continuity equations (Ma [54]). [PERSON] [10] simulated the landslide-induced wave by using Fluidity. Fluidity is a flexible finite-element/control-volume model, which solves incompressible Navier-Stoke equations [55]. Fluidity uses flexible unstructured meshes, combined with adaptive meshes. [PERSON] simulated the landslide-induced wave under three multi-material approaches.
## 3 Materials and Methods
### Fractal Dimension Combination Pipeline
#### 3.2.1 Signal Preprocessing and Artifact Removal  
The preprocessing approach began by eliminating powerline interference using a notch filter [58], a crucial step in removing a frequent artifact from EEG recordings. This ensured that subsequent filters and spatial transformations were not confounded by unwanted noise. Next, a high-pass filter [59] removed low-frequency drifts, stabilizing the baseline by filtering out gradual fluctuations. To further increase the signal-to-noise ratio, a common average reference (CAR) montage [60] was applied, incorporating the original reference channel to maintain the data's full rank [63]. Verification of the rank before and after CAR confirmed that no dimensionality was lost. Following these steps, the common spatial pattern (CSP) algorithm [10] was introduced to uncover spatial filters that highlighted the largest variance differences among the motor imagery conditions. A separate spatial filter [64] was then used to accentuate the relevant regions corresponding to each task. In the final stage, a band-pass filter [65] was used to isolate the principal EEG rhythms, completing the preprocessing sequence.