esa-sceva/satcom-chunk-collection huggingface dataset:
# Differential Analysis of a Cryptographic Hashing Algorithm HBC-256
## 2 Related Works  
The HBC-256 (Hash based on Block Cipher) hashing algorithm is a new algorithm for computing a hash value based on the Merkle-Damgard structure. A description of the HBC-256 algorithm and some approaches to its analysis were presented in [16]. Research [16] presents analysis of the avalanche effect of CF encryption algorithm and statistical analysis of the HBC-256. As results CF encryption algorithm (HBC-256 hashing algorithm itself) is efficient to provide a good avalanche effect and binary sequence generated by the HBC-256 algorithm is close to random (using the NIST and statistical test suite).  
We remark, however, that in the classical setting the basic approach to implementing collision attacks that can exploit vulnerabilities in a particular hashing algorithm is differential cryptanalysis [17]. A general scheme for applying the differential analysis method to a hash algorithm based on a Merkle-Damgard structure is presented in the next three papers.  
The authors of the article [18] in their work describe in more detail the design models of a hash function and discuss various types of attacks and possible ways to overcome them. According to experts, the most successful attack on the Merkle-Damgard hash function is the differential attack. It is used to create a successful collision attack in hash functions.  
The Merkle-Damgard construction proves that the security of a hash function depends on the security of the compression function. Hash functions designed without taking into account the weakness of the Merkle-Damgard construction are vulnerable to various attacks. Thus, to build a collision-resistant hash function, it suffices to develop a collision-resistant compression function. Therefore, the use of a block cipher as a compression function imposes higher requirements on its security level.  
The problem of building and studying S-boxes with given properties is well known. In [19], the authors present a new framework for efficient analysis of a nonlinear node, the S-box, and analyze the differential probability when the difference is expressed usingmodulo 2 addition (xor). This probability was obtained using graph theory and calculated using matrix multiplications.  
The aim of the research is to identify strong and weak properties for the new HBC-256 hash function to differential analysis.
# Phenotypic, Genetic, and Metabolite Variability among Genotypes of _Vicia sativa_ L.
## 1 Introduction
Additionally, SCoT markers in legume species have been found to be useful in identifying superior alleles and help in studying genetic relationships in _Cicer arietinum_[25], in _Vicia faba_[26; 27; 28], and in _Phaseolus vulgaris_[29]. Limited information is available about the genetic diversity of the common vetch's germplasm. AFLPs were used for the evaluation of more than 20 _Vicia_ species of Russian germplasm [30], SCoT molecular markers for the assessment of the genetic diversity of _Vicia faba_[6] and SSR [31] markers were used for the Chinese germplasm. Most of the research focuses on comparative studies for genetic diversity and phylogenetic relationships among _Vicia_ species [5; 21; 32]. The development of new cultivars with a higher quality suitable for animal feed, forage, and plant breeding is under growing pressure. Quality characteristics can be linked to the high nutritional value (i.e., rich protein composition) of the legume/pulse seeds and their rich secondary metabolite content. To justify the benefits of legumes for animal feed, it is therefore extremely desirable to create standardized analytical procedures for showing their metabolism, such as metabolomic analyses [33]. Until now, reports on the metabolomic profile of _V. sativa_ are mainly focused on its correlation to abiotic stress resistance [34; 35] and have been carried out on specific developmental stages [35]. A recent study refers to the differentiation of species of the genus _Vicia_ based on secondary metabolites [33]. To our knowledge, there are no studies exclusively on _V. sativa_ varieties showing their differentiation based on the seed's metabolomic profile. Breeders always have crucial duties to complete, including the efficient use of germplasm resources and efficient conservation, both of which need a thorough understanding of the genetic variety present in germplasm arrays and the genetic linkages between genotypes [36]. In this respect, in the present study, four populations and two commercial Greek varieties were studied in terms of phenotypic variability, genetic diversity, and seed's metabolomic profile. All the above data combined could offer a useful tool for the efficient use of the germplasm in breeding programs.
# Point-Graph Neural Network Based Novel Visual Positioning System for Indoor Navigation
## 4 Experimental Results  
### Voxel Labeling  
#### 4.1.1 Voxel Labeling in the Point Cloud Database  
We used deep learning datasets to select 10 types of objects for the evaluation. We considered fixed objects with a low probability of position movement in indoor space. Azure Kinect DK was used with 500 training datasets and 100 test datasets to generate point clouds for training and evaluation. Figure 5 depicts the objects selected for the self-performance evaluation.  
Azure Kinect DK was used to generate a point cloud for evaluating the improved point-GNN. The implementation was written in Python 3.6 using TensorFlow for graphics  
Figure 5: Objects selected for the evaluation, including air conditioner, chair, desk, sofa_1, sofa_2, flower table, flower pot, refrigerator, round table, and podium table.  
processing unit (GPU) computation. Evaluation details measured the inference times on a desktop using Linux Ubuntu 16.04 LTS, Ryzen 9 3900X CPU and RTX 2080 SUPER 8G GPU (two sets). To increase the speed of estimating the object pose by reducing the number of computations in the dense point cloud generated using Azure, the improved point-GNN was learned by down-sampling 20,000 points sparse for each object in indoor space.
been previously implemented using sensor networks with wires. However, the great advancements in wireless technology have given birth to wireless sensor networks (WSNs). The installation and maintenance costs of WSN platforms are much less than wired sensor networks. Therefore, WSN platforms have been frequently employed for the SHM of bridges [15; 16; 17; 18; 19; 20].  
The WSNs are deployed during the SHM process, either with global or local damage identification techniques [21]. Global techniques are used to identify the presence of damage and evaluate the health of the complete structure. On the other hand, the local techniques help in finding the damage at a specific place or focusing on a particular parameter in the structure [22]. In other words, global measurement techniques are suitable for detecting large faults, whereas local measurement techniques are generally employed for detecting all those damages that cannot be measured using global measurement techniques [23].  
To carry out any damage identification technique (local as well as global), there exist many damage detection methods. In these damage detection methods, an excitation is provided through a source. Subsequently, the response of the structure is monitored accordingly. The damage detection methods are classified into two main categories: static and dynamic. Examples of static response monitoring are strain and stress. Similarly, examples of dynamic response monitoring are frequencies, mode shapes, and modal damping. It has been observed that the measurement of static responses is more straightforward as compared to dynamic measurements [21]. Nevertheless, static responses are relatively less sensitive to changes resulting from the damage. Consequently, the monitoring of dynamic responses is generally preferred for the detection of abrupt as well as gradual changes. The limitations of dynamic monitoring are the collection of correct data due to certain environmental and operational factors.  
Finally, most of the WSN systems for the monitoring of the structural health of the bridges are battery-powered. As a result, a major constraint in this context is the limited lifetime of the battery. Therefore, periodical high-cost battery maintenance is needed to overcome this major constraint for WSN sensors. In other words, the constraint of the limited lifetime of the battery is generally addressed by applying energy harvesting technologies. In an energy harvesting (EH) process, the power is collected from ambient energy sources. There are different types of ambient energy sources including solar, vibrations, wind, thermal, and radiofrequency (RF). The selection of the most appropriate energy harvesting technique for a specific SHM system depends on different factors such as the environmental conditions, the type of bridge, and the availability of the source for radio frequency signals [24].
## 3 Interference Mitigation Schemes
### Cognitive Radio Approach
#### 3.3.2 Adaptive Cognitive Radio MAC  
In [39], an adaptive cognitive radio MAC algorithm is adopted on a hybrid cognitive validation platform (HCVP) to minimize WBASN interference. HCVP is a platform for cognitive radio evaluation and includes two parts. The lower part comprises the PHY and MAC layers based on real hardware, and the upper part comprises computer software that includes the cognitive radio algorithm, the network layer, and other layers. In HCVP, WBASN nodes are connected to a computer and the coordinator is associated with the cognitive radio software of the computer.  
In HCVP, this algorithm is implemented at the coordinator of WBASN and includes spectrum sensing, channel estimation, and channel accessing. The RSSI is chosen to indicate the working status of the spectrum. Channel estimation is performed in cognitive radio software, which results in the prediction of channel usage and the optimization of access times. The second phase can be divided into two stages: In the first stage, RSSI is used to estimate channel usage distribution; in the second stage, the packet collision rate is used to indicate the channel status that is compared to the result of the first stage. This comparison creates an optimized tradeoff between throughput and sensing time. In the channel access phase, the coordinator senses the spectrum then obtains the result of an optimized access time and compares it to the collision rate threshold. If the channel is idle, the coordinator broadcasts a beacon with boundary information about a superframe setting according to an optimized access time. Nodes in WBASNs continuously wait for a beacon; if they receive a beacon, each node senses the spectrum for a small time before accessing the channel to avoid packet collision between WBASNs.
## 6 Limitations and Future Work  
Though the Edge-IIoTset dataset provides a rather structured view of attack paths, its reflection of the real-world diversity and complexity of DDoS threats targeting IoT environments remains limited. Particularly when considering adversarial strategies and concept drift, where attack behaviors evolve in unanticipated ways, controlled testing scenarios often fail to capture the adaptive nature of modern attacks. The models archit  
Figure 4: Comparison of processing times across different models.  
tures, which were precisely tuned to the dataset, represent another limitation. While they perform well within that controlled context, their generalizability remains questionable. Real-world IoT deployments often involve different traffic patterns, attack types, and noise levels compared to those represented in curated datasets. Without appropriate architectural adaptation, these differences can significantly impair model performance. Simply put, a model designed for one dataset is unlikely to hold up across different real-world scenarios.  
Several key research directions for future exploration include the following: automated feature engineering using AutoML techniques, model compression and optimization strategies, hybrid model approaches, unified learning methodologies, and cross-dataset validation using diverse IoT security datasets. Cross-dataset evaluation, including validation on real-world IoT traffic from operational deployments, would enhance generalizability and provide insights into algorithmic performance across heterogeneous IoT environments. In particular, AutoML-driven feature engineering should be further investigated to help identify optimal feature subsets tailored to specific DDoS variants. Research should also be conducted on model compression and optimization techniques specifically tailored for DDoS detection in resource-constrained environments such as the case in IoT. It would also be beneficial to investigate ensemble approaches combining high-accuracy DL models with efficient traditional approaches. Further research on transfer learning techniques could help to achieve enhanced applicability across a range of IoT deployment scenarios by lowering the processing requirements of DL models while yet preserving detection performance.
## 3 Approach
### Anomaly Classification
#### 3.4.3 Anomaly Detection  
The anomaly-detection model is based on the multivariate time series anomaly-detection model DGHL [32]. We utilize the Encoder and Decoder components from the DGHL model architecture to reconstruct the input sequence \(I\) as described earlier. The syscall vectors \(I\) are passed through the Encoder block. The Decoder combines the compressed vector with the previously generated target sequence. This process iterates to generate the reconstructed representation O. In the training phase, we apply random masks to the system call types, thus enhancing the robustness of the model.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type of Parameter** & **Parameters Included** & **Embedding Method** \\ \hline \multirow{2}{*}{Typed Parameters} & evt.type, evt.dir, & \multirow{2}{*}{OneHot Embedding} \\  & fd.type, evt.arg.mode & \\ \hline \multirow{3}{*}{Numerical parameters} & evt.deltatime, fd.num & \multirow{2}{*}{Signed normalization} \\  & evt.rawres, evt.arg.size, & \\  & evt.buflen & \\ \hline \multirow{4}{*}{Structural parameters} & proc.sname, proc.pname, & \multirow{4}{*}{Template-based embedding} \\  & proc.name, proc.name, proc.exe, & \\ \cline{1-1}  & proc.args, evt.arg.name, & \\ \cline{1-1}  & evt.arg.addr, evt.arg.tuple, & \\ \cline{1-1}  & evt.arg.path & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter Embedding Methods.  
Once the reconstructed output \(O\) is obtained, the loss between \(O\) and \(I\) is calculated using the L2-norm, denoted as \(L=\|O-I\|\), and is used as an anomaly score. In the training phase, we minimize the L. In the testing phase, the POT threshold (Peak Over Threshold) method [33] is then used to dynamically select the threshold. This method is a statistical approach that fits the data distribution to a generalized Pareto distribution using extreme value theory and determines an appropriate risk value for dynamic thresholding. Ultimately, during testing, if there are items in the sequence with an anomaly score greater than the POT threshold, they are considered to be detected anomalies; otherwise, the sequence is considered normal behavior. The loss of each parameter is calculated separately to demonstrate the root of the anomalies.
## 4 Numerical Results and Discussion
### Critical Speeds and Mode Shapes  
The critical speeds and corresponding mode shapes are helpful for giving insight into the dynamic characteristics of the gas turbine rotor with blade off. The first step is to conduct critical speed analysis. The first three-order critical speeds are calculated, respectively, to be 10,782 r/min, 22,856 r/min, and 73,527 r/min, which shows that there are two critical speeds below the maximum speed. Figure 8 represents the mode shapes corresponding to the first three-order critical speeds, which are respectively the translational mode, the pitching mode, and the first bending mode, where the first two can be regarded as rigid modes due to most of the strain energy being concentrated on the bearings (see Figure 9a,b). According to the rotor dynamics theory, the first two rigid body modes are most likely to be excited when the rotor is disturbed by internal or external interference. Obviously, the modal analysis is helpful for the understanding of the transient responses which will be discussed later.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Blade Loss Mass (kg)** & **Radius of Blade Mass Center (mm)** \\ \hline Value & 8.46 \(\times\) 10\({}^{-3}\) & _97.77_ \\ \hline \hline \end{tabular}
\end{table}
Table 2: Blade released parameters.  
Figure 8: The first three-order mode shapes. (**a**) First order. (**b**) Second order. (**c**) Third order.
# Brain Extraction Methods in Neonatal Brain MRI and Their Effects on Intracranial Volumes
## 3 Results
### Comparison of Manual vs. Automated Brain Extraction Methods  
#### 3.3.1 Segmentation Metrics  
According to Tables 3 and 4, generally, the best segmentation metrics were obtained when comparing the manual segmentation with HD-BET followed by SWS, with median DC values of 0.968 and 0.966, as well as MSD values of 0.596 mm and 0.651 mm, respectively (Figure 10(a),k in Appendix C). Regarding VS, despite the smaller volume size difference given by SynthStrip (\(-\)0.083%), it has a wider IQR (4.718%) than those of the other methods (BET2, 2.798%; SWS, 2.902%; HD-BET, 2.687%). SynthStrip did not show statistically significant differences from SWS ( \(|\,Z\,|\) = 0.455; \(p\) = 1.000) and HD-BET ( \(|\,Z\,|\) = 1.000; \(p\) = 0.061), which had a tendency to underestimate volume (\(-\)2.033%) and overestimate volume (2.609%), respectively. All of our violin plots are available in Appendix C. Table 5 shows the DC obtained for each brain mask subset (bottom, middle, and top slices) with the different BE methods. HD-BET generally performed well in all slices (DC\({}_{\text{Bottom}}\) = 0.944, DC\({}_{\text{Middle}}\) = 0.979, and DC\({}_{\text{Top}}\) = 0.962), but in the middle, SWS was slightly better (DC  
We identified general differences between the methods in all segmentation metrics (using the Friedman test with \(p<0.001\)), with [PERSON]'s W coefficient of concordance ranging from 0.548 to 0.808 across the different metrics, representing a large effect size [71], showing the strength or the meaningfulness between the manual and automated segmentation metrics. Concerning the DC results obtained for the bottom, middle, and top brain masks, statistically significant differences (using the Friedman test with \(p<0.001\)) were also identified. Regarding the post hoc pairwise comparisons (Table 6), statistically significant differences were identified for DC and JC between the methods (using the Bonferroni correction with \(p<0.05\)), except for the comparison of HD-BET and SWS (\(\mid\!Z\!\mid\!=0.273\); \(p=1.000\)). However, if we analyse the surface based-metrics, in regards to HD95, MSD, and MDSD, there were no statistically significant differences between HD-BET and SWS, between SWS and SynthStrip, or between HD-BET and SynthStrip (except for MSD with \(\mid\!Z\!\mid\!=1.045\); \(p=0.043\)). The VS showed no differences between HD-BET and SynthStrip (\(\mid\!Z\!\mid\!=1.000\); \(p=0.061\)) or SWS and SynthStrip (\(\mid\!Z\!\mid\!=0.455\); \(p=1.000\)). The post hoc pairwise comparisons of the brain mask subset (Table 7) showed variable results between the methods and slices, with an emphasis on non-statistically significant differences between HD-BET and SWS in the middle (\(\mid\!Z\!\mid\!=1.868\); \(p=0.370\)) and top slices (\(\mid\!Z\!\mid\!=0.117\); \(p=1.000\)), as well as between HD-BET and SynthStrip in the bottom (\(\mid\!Z\!\mid\!=2.335\); \(p=0.117\)) and top (\(\mid\!Z\!\mid\!=1.985\); \(p=0.283\)) slices. Three slices (one from each subset) overlapped with the brain masks are shown in Figure 2 as examples.
## 3 Classification Based on Function
### Communication Support Sensors  
An earthquake can destroy communication channels by collapsing mobile base stations and power lines. At the same time, traffic lines between the disaster area and the outside world can be blocked, meaning the victims in the disaster area cannot communicate with the outer world [70]. This creates a great challenge in search and rescue operations. Special network service avenues can play a critical role in the first 72 h after an earthquake, in which conventional communication services are disrupted. These network service categories of earthquake relief sensors can be classified into wireless emergency communications and wired emergency communications.
# Design of Time-Synchronized Switch Based on Zynq
## 3 System Hardware Design
### Clock Circuit  
The WR technology requires an external adjustable hardware clock to support the digital dual-mixing time reference technology. Therefore, a 25 MHz Voltage Controlled Crystal Oscillator (VCXO) is selected to drive the low-jitter clock generator chip CDCM61004 RHBT, which provides the required 62.5 MHz clock and serves as the reference clock for the DDMTD. The specific scheme is shown in Figure 6.  
The AD9516 can select either one differential or two single-ended reference inputs. In this design, the two single-ended reference input configurations are chosen. By configuring the SPI module, the chip outputs multiple 62.5 MHz clocks, with one of the outputs serving as the reference clock for the PL. Two of the outputs are extended to five outputs, all serving as reference clocks for the GTH. The specific scheme is shown in Figure 7.
# RF Frequency Selective Switch by Multiple PMIM Conversions
## 1 Introduction
Multiple PMIM conversions can then form multiple channels [16]. Specifically, multiple PMIM conversions are realized by an optical frequency comb with a large frequency interval, as well as a single-notch optical filter. The notch optical filter is an optical ring resonator, and its free spectral range (FSR) is slightly different from the comb spacing of optical frequency comb. Through experiments, we show that although the optical ring resonator is usually considered as a narrow-band signal processor, it can process large-bandwidth RF signals by using its multiple resonant peaks at the same time. According to the Vernier effect, the signal bandwidth is far greater than the resonant peak bandwidth. Combined with a commercial optical wavelength selective switch (WSS), we then realize a RF frequency selective switch (FSS). We experimentally implement an arbitrary reconfigurable microwave photonic spectrum processing in a single-output channel of the FSS. When multiple output ports of the FSS are used at the same time, we can arbitrarily map one or more specific frequency channels to these ports, so that the FSS is realized. Without large dispersion and associated delay, which are common in traditional incoherent schemes, we achieved 50 MHz spectral resolution in our experiments. By increasing the number of comb teeth, the instantaneous bandwidth can reach tens of GHz.