esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods  
### HMMs  
An HMM is a stochastic finite state machine used to model a speech segment. The segments may be words, syllables, subword units, or even a complete phrase [22]. Fundamentally, an HMM is simply a set of states connected by transition arcs, as illustrated in Figure 1. When modeling time-sequential patterns, the states of the HMM are typically connected with state transitions from left to right, representing time.  
Figure 1: HMM with GMM state observations aligned to an Asian Elephant Squeak vocalization. The first and last state represent entry and exit points for the model, while internal states, called “emitting” states, are aligned to signal frames to maximize overall model likelihood.  
An HMM is defined by its transition matrix \(A\), in which each element \(a_{ij}\) represents the probability of moving from state \(i\) to \(j\), and state observation probabilities \(b_{(}\left(o_{i}\right)\), which represent the probability distributions for each state \(i\). Observations \(o_{i}\) represent the data being modeled, in this case the feature vectors of the vocalization frames. Because the transition matrix is two-dimensional, the system has the Markov property, i.e. the probability of the next state is dependent only on the current state. In human speech, Gaussian Mixture Models (GMMs), a weighted sum of Gaussians that can represent a wide variety of unimodal and multimodal distribution characteristics, are commonly used to model the state observation probability densities. The programming toolkit used here is the Hidden Markov Model Toolkit (HTK) from Cambridge University (2002) [23]. Parameter training is implemented using the Baum-Welch method (an implementation of the well known Expectation Maximization algorithm) for maximum likelihood estimation [24,25], and classification is accomplished using the Viterbi algorithm [26] to identify the most likely HMM or HMM sequence given an unknown vocalization.  
Figure 2: GMM model aligned to a non-stress condition poultry vocalization.  
The left to right topology and number of states in the model may be adjusted to accommodate a wide variety of sequential patterns. Patterns with more variation and complexity are typically assigned models with more states to represent the different regimes. In contrast, it is possible to use HMMs to classify patterns without any temporal structure at all by utilizing a single state HMM with a GMM observation model, as illustrated in Figure 2, which reduces it to a simple statistical classifier. Transitions may also be added so that some states may be skipped completely. This is particularly useful in terms of modeling beginning and ending silence regions within vocalizations. Simply by including extra beginning and ending states, arbitrary amounts of surrounding silence regions are implicitly included in the model and there is no need for precise segmentation of individual vocalization start and end points. Accommodating the presence of silence and pauses within vocalizations is another example of temporal nonlinearity that can be easily handled by the HMM framework but is extremely difficult to incorporate into template based models.
# Fingerprint Based Codebook for RIS Passive Beamforming Training
## 1 Introduction
In [12], CBs were designed in TX, RIS, and RXs, and then a multi-lobe beamforming (BF) training algorithm was proposed to reduce the needed time for the A-PBF process. However, this scheme suffers an outage if the first layer beam or following beams miss the TX-RIS-RX link. Moreover, both schemes still need high overhead, especially when considering large-sized or multi-layer CBs, respectively. Hence, a more efficient CB design that covers the area of interest is mandatory. Furthermore, the time consumption for searching shall be reduced based on intelligent principles. Inspired by the availability of positioning information (PI) beyond 5G networks [13; 14; 15], we propose a new fingerprint-based CB (FP-CB) for the PBF process, where this CB is designed considering the target network environment. Then, depending on the estimated RX location information, the system selects the codeword that can be sub optimally used for configuring the RIS. To the best of our knowledge, passive beamforming based on PI is still an open direction for research to reduce system overhead and enhance its performance. In the proposed fingerprint-based PBF (FP-PBF) scheme, first offline, we divide the area of interest into a number of points, and using the CE and AO-based schemes, an optimal RP corresponding to each point is determined. Subsequently, the FP points' locations and selected RP pairs are stored in a database to be further used. Second, in the online phase, when a new RX requests to connect with the TX, the system estimates the RX's location. Then, it calculates the Euclidean distances between the RX and the FP-CB points to select a group of RPs as candidate codewords to handle the localization error. Thereafter, by searching these elected RPs, the system can select the best RP that can provide the RX with the maximum received power to be used in the data transmission period. The proposed PBF scheme reduces the overhead and time consumption in the beam training step by searching within a few RPs instead of the entire codebook. Moreover, it can provide the network with a higher rate compared to the random-based CB, CE, and AO schemes, particularly in rapidly changing channels, even if the RX location estimation has a high error. The novelty of the proposed scheme is that its CB is built based on the environment; hence, it describes it. Moreover, the search algorithm is performed using PI to reduce the required overhead. The paper structure is as follows: Section 2 presents the system model of the RIS-aided communication system, while a brief discussion about codebook-based schemes is given in Section 3. Then, Section 4 illustrates the procedure of the proposed FP-PBF scheme. Meanwhile, the performance evaluation and the conclusion are presented in Section 5 and Section 6, respectively.
## 3 Results
### Performance Testing and Analysis of Soil Electrical Conductivity Prototype  
#### 3.2.1 Static Performance Testing and Analysis of Soil Electrical Conductivity Prototype  
The specific steps of the stability test are as follows:  
1. The soil was obtained from the teaching experimental forest farm of Beijing Forestry University in Sujiatuo Township, northwestern Haidian District, Beijing, where the soil type was clay loam [32]. Soil samples with a conductivity of 2250 \(\upmu\)S/cm (measured using a Spectrum model 2265 FS portable (Spectrum Technologies, Inc. USA) [33] soil electrical conductivity meter) were prepared by taking 2 kg of dried soil at a room temperature of 25 \({}^{\circ}\)C and sealing them in a large beaker to rest for 12 h;
2. The rested samples were then placed in a thermostat set at 25 \({}^{\circ}\)C for 1 h;
3. The prototype was inserted into the soil sample, and after allowing the readings to stabilize, data were collected every 2 min over a period of 2 h, resulting in a total of 60 measurements. The data obtained during the 2\(-\) h period were analyzed, as shown in Table 3 and Figure 9.  
The relative standard deviations of the test data are all as low as 0.0573%, indicating no significant variations within the dataset. This demonstrates that the prototype designed in this study exhibits excellent stability. Moreover, the steady\(-\)state error is minimal, making the prototype suitable for in situ measurements.
# Coregistration and Spatial Compounding of
## 2 Materials and Methods
### Image Reconstruction, Stitching and Unmixing  
Reconstruction of three-dimensional OA images was performed as follows. The acquired signals were first deconvolved with the electric impulse response of the individual piezoelectric elements and band-pass filtered between 0.1 and 7 MHz. Subsequently, a graphics processing unit (GPU)-implemented back-projection reconstruction algorithm was used to separate reconstruct the images corresponding to each excitation wavelength [22]. After reconstruction, the image sequences were synchronized based on phase analysis (Section 2.3) and further processed. Specifically, the images corresponding to different positions of the spherical array were stitched (compounded) by considering the voxels with maximum intensity in each frame [23]. Spectral un-mixing was performed to estimate the bio-distribution of two specific chromophores, namely the oxygenated (HbO\({}_{2}\)) and deoxygenated (HbR) hemoglobin, from OA images at multiple wavelengths. Specifically, least square fitting of OA images at different wavelengths on a pixel-by-pixel basis to the molar extinction coefficients of HbO\({}_{2}\) and HbR was performed for this purpose [24].
# An Improved Confounding Effect Model for Software Defect Prediction
## 3 Confounding Effect Model for Software Defect Prediction
### Improved Confounding Effect Model  
This paper proposes an improved confounding effect model, represented by a causal diagram, as shown in Figure 2.  
Figure 1: Confounding effect model in software defect prediction.  
In Figure 2, we introduce a new confounding variable signed by c, representing the confounding effect caused by unknown factors other than existing code metrics, including the development cycle and the number of developers mentioned above. Moreover, we divide the software metrics into two sets: a and b. variable a is pointed to by the confounding variable c, while variable b is not. Variable c also directly points to the software defect signed by d. Since there are paths a-d and a-c-d, the predictive power of variable a on d is confounded by c. We will analyze which software metrics belong to set a and which belong to set b in the following sections.
## 5 Analysis Techniques for WebAssembly
### Detecting Vulnerabilities in WebAssembly Binaries  
Although WebAssembly was designed with security in mind, vulnerabilities still exist (Section 2.2.2). As a result, various techniques for detecting vulnerabilities in WebAssembly binaries have been proposed. This section presents these techniques and discusses their versatility, which is determined by factors such as compatibility with different runtimes, support for the WASI, and whether they require high-level source code for analysis.  
Techniques based on static analysis (Section 5.2.1), dynamic analysis (Section 5.2.2), and hybrid analysis (Section 5.2.3) are discussed in the following sections. Additionally, a comparative analysis of the detection techniques is presented (Section 5.2.4).  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline \multirow{2}{*}{**Scheme**} & \multirow{2}{*}{**Feature(s)**} & \multirow{2}{*}{**Classifier**} & \multicolumn{3}{c}{**Dataset**} & \multicolumn{3}{c}{**Performance**} \\ \cline{3-10}  & & & **Source** & **Samples** & **Precision** & **Recall** & **F\({}_{1}\)** & **Overhead** \\ \hline \hline \multirow{9}{*}{SEISMIC [46]} & WebAssembly code, & & Asteroids, & & & & & \\  & & A-Star, & & & & & & \\  & & Tanks, & & & & & & \\  & & & Bullet (1000), & & & & & \\  & & CoinHive\_v0, & & & & & & \\  & & & CoinHive, & & & & & \\  & & obtained at runtime & & & & & & \\  & & & HushMiner, & & & & & \\  & & & CreaturePack & & & & & \\  & & & FunkyKarts, & & & & & \\  & & & NFWebMiner, & & & & & \\  & & YAZECMiner & & & & & & \\ \hline \hline \multirow{2}{*}{RAPID [48]} & JavaScript API calls, & & & & & & & & \\  & memory, processor and & & SVM & Alexa 330K & 71,450 & 97\% & 96\% & 96\% & 9–40\% \\  & network usage & & & & & & & \\ \hline \multirow{9}{*}{OutGuard [50]} & Parallel tasks, & & & & & & & \\  & WebAssembly, hashing & & & & & & & \\  & algorithms, WebSockets, & & & & Alexa 1M, & & & \\  & PostMessage event load, & & or RF & & Alexa 600K & 29,700 & 99\% & 97\% & 98\% & 2\% \\  & MessageLoop event & & & & & & & \\  & load & & & & & & & \\ \hline \hline \multirow{2}{*}{CoinSpy [52]} & JavaScript stack & & & & & & & & \\  & execution & & Alexa 1M, & & & & & \\  & time, JavaScript heap, & & & & & & & \\  & network usage & & & & & & & \\ \hline MineThrottle [51] & WebAssembly code, & & & & & & & \\  & processor usage & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Data for dynamic detection techniques for identifying malicious WebAssembly binaries.
## 4 Results and Discussion  
### Radial Porosity Distribution  
In the cylindrical area resulting from each cross-sectional area (\(A_{fluid}\)), the adequacy of bulk packing is typically assessed by evaluating the radial porosity distribution of the packed bed. The change in the circumferential average radial porosity in the bed can be mathematically expressed to determine the reasonability of the packing [18,19]:  
\[\epsilon(r)=\frac{A_{fluid}(r)}{A_{cyl}(r)}=\frac{A_{fluid}(r)}{2\pi rH}, \tag{1}\]  
where \(\epsilon(r)\) is the radial porosity, \(A_{fluid}(r)\) is each cross-sectional area (m\({}^{2}\)), and \(A_{cyl}\) is the area of the circle on each section (m\({}^{2}\)).  
Figure 4 illustrates the radial porosity distributions of the packed bed models with particle size ratios of 4.44, 5.6, and 6.25. The distributions are based on several cylindrical subsurfaces intercepted at various radial locations within the filled beds, with the distance between the subsurface and tank wall ranging from 0.5 to 2 \(d_{p}\). Based on the surface porosity, the porosities of the subsurfaces located 0, 1, and 2 \(d_{p}\) from the wall should be higher than those of other subsurfaces. The oscillation in radial porosity is related to the particle accumulation process and the spherical particle shape. The particles tend to occupy the space near the wall during the packing process before filling the center space [20]. This results in varying subsurface porosity along with a higher density of particles near the wall and a lower density farther from the wall. Specifically, the particles at the wall surface are in point contact with the wall, resulting in a porosity close to 1 and a subsurface distance of approximately 0.5 \(d_{p}\) from the wall. The subsurface occupies the majority of the particle, and the porosity is reduced to 0.2. Conversely, at a distance of 1 \(d_{p}\) from the wall surface, the particles near the wall are in point contact with internal particles, creating a gap at the point of contact and increasing the porosity to 0.5. As a result of these oscillations, the porosity of the subsurface in the radial direction is variable [21].  
Figure 4: Cont.  
Figure 4: Radial porosity distribution and subsurfaces along the radial direction: (**a**) \(D/d_{p}\) = 4.44; (**b**) \(D/d_{p}\) = 5.6; and (**c**) \(D/d_{p}\) = 6.25.
## 5 Applications of the Study  
The work in this paper presents a continuation of sampling strategies of the previous work in [52, 53] and thus augments the related domain with new experiments and results. The work in [52] analyzed the impact of processing the reduced amount of actual data on machine learning model performance. Subsequently, the work in [53] investigated the impact of two different random sampling techniques with two different machine learning classifiers. Complementary, the current study focused on the data attributes rather than the dataset itself. The current results supported the results of the previous studies in terms of reducing the processed data. The results discussed and presented in Figures 2 and 3 showed that the performance either improved or slightly decreased. Looking at different factors in the decreasing cases, advantages like reducing processing time as well as using limited or available computing resources have been achieved in return, which makes this decrease acceptable and reasonable.  
Processing large amounts of data normally require great computing resources. From this arises an issue not only on data processing but also on the availability of computing resources for most researchers. Additionally, using the actual amounts does not necessarily ensure that it is the only way to provide useful outcomes. This study provided an appropriate scenario by processing only a reduced amount of data that can lead to a reasonable performance using fewer computing resources. In this case, the processing time will be extremely reduced as well. On the other hand, the machine learning model can be achieved by processing only subsets of the actual data attributes and no need to use all the data attributes, which might be very complex if they do not properly cover data and classes.
## 5 Discussion  
In their study on the application of machine learning for detecting surface deformation in InSAR imagery, [PERSON] and [PERSON] [21] developed a coseismic InSAR dataset comprising 32 real observational images. To facilitate a rigorous comparative analysis between their dataset and the one developed in this study, we applied identical data augmentation techniques to their dataset, increasing its size to 184 coseismic InSAR images. For clarity, the dataset constructed in this study is hereafter referred to as Dataset A, and the dataset from [PERSON] et al. [21] is referred to as Dataset B. In terms of scale, Dataset A comprises 14,000 coseismic InSAR interferograms, while Dataset B, after augmentation, contains only 184 images. Dataset A encompasses a broader range of deformation patterns, including earthquake events of varying magnitudes and fault types, thereby offering a more comprehensive set of training samples for deep learning applications. In contrast, Dataset B exhibits limited coverage of diverse scenarios, which may constrain its ability to capture the full spectrum of deformation features and reduce its generalization capability.  
The mean entropy and the standard deviation of entropy are commonly used metrics for evaluating the information richness and statistical stability of different datasets [45]. Entropy serves as an effective indicator of the complexity and variability of seismic deformation patterns in the images. Specifically, a higher mean entropy indicates that the dataset encompasses a wider range of deformation features, reflecting greater complexity and diversity in coseismic deformation patterns [46]. This, in turn, provides more challenging and generalized learning scenarios, thereby improving the robustness and adaptability of deep learning models.
# Ten Years of Experience with Scientific TerraSAR-X Data Utilization
## 3 Results  
### Proposal Analysis Overview  
All 1636 proposals were classified into one of the six spheres as a result of the keyword scan. Generally, a largely homogeneous distribution over the different fields of research can be noticed, as shown in Figure 3. The share ranges from 13 percent for methodological and technical developments to 20 percent for research in the geosphere domain.  
Figure 2: Number of scientific data takes of the TerraSAR-X mission between 19 June 2007 and 30 June 2017.
# Lower-Limb Flexibility Profile in 142 Competitive Female Football Players: A Cross-Sectional Study
###### Abstract  
Background/Objectives: The ROM-SPORT is a test battery for assessing flexibility that measures the range of motion (ROM) in the sport. Restricted or optimal ROM is associated with an increased risk of injury or improved athletic performance, respectively. The aim of the present study was to determine the normative values of the lower limb's flexibility profile by ROM assessment in female football players. Methods: Lower-limb flexibility (11 ROM tests) was measured using the ROM-SPORT battery in 142 female football players (19.42 \(\pm\) 4.45 years). The tests were performed at the beginning of the training sessions in the first two weeks of the pre-season. Standardized Z-scores (on a scale of 0 to 100 [T-score]) were calculated from the mean and standard deviation to classify the ROM of each movement into three qualitative categories (poor [\(>\)-3 to \(-\)2], average or normal [\(-\)1 to 1; 68.2%], and optimal [1 to \(>\)3]) and a traffic light system to facilitate interpretation of the results. Results: The results show normal (or average) ROM values at 32-37\({}^{\circ}\) for ankle dorsiflexion with the knee extended; 37-43\({}^{\circ}\) for ankle dorsiflexion with the knee flexed; 46-54\({}^{\circ}\) for hip internal rotation; 54-61\({}^{\circ}\) for hip external rotation; 31-37\({}^{\circ}\) for hip adduction; 70-76\({}^{\circ}\) for hip abduction with the hip flexed; 41-45\({}^{\circ}\) for hip abduction with the hip neutral; 135-141\({}^{\circ}\) for hip flexion; 73-82\({}^{\circ}\) for hip flexion with the knee extended; 15-21\({}^{\circ}\) for hip extension; 121-132\({}^{\circ}\) for knee flexion. The normative data presented in this study can be utilized by researchers and practitioners in the women's football league in a variety of ways, provided similar data collection methods are used. Conclusions: Flexibility training is recommended to overcome normal or average ROM and optimize athletic performance with lower injury risk in female football players.  
s _Article_
## 2 Data and Methods
### Wave Data  
Wave data parameters (\(H_{s}\) and \(T_{p}\)) for the future period, 2026-2045, were obtained through the simulating waves nearshore (SWAN) model [54]. The model was previously implemented and validated for the northwest Iberian Peninsula coast [30]. The simulations consider the recommendations of the International Electrotechnical Commission (IEC) for a reconnaissance-stage model [55], which are required for an energy resource assessment.  
The SWAN simulations are the dynamical downscaling (\(0.11^{\circ}\times 0.11^{\circ}\)) of the MIROC5 GCM. The Commonwealth Scientific and Industrial Research Organisation (CSIRO) database for wave hindcast [56] and wind-wave climate projections [57] offers eight different GCMs simulations. These simulations were statistically analyzed through the overlap percentage between the probability distribution functions (PDFs) [58] of the waves hindcast and the GCMs projections. The MIROC5 was found to be the most accurate model [30]. The time required to perform each of the SWAN simulations limits the use of only one GCM out of the eight possible. Winds provided by MIROC5 CCLM4-8-17 RCM [36] were also used in the SWAN simulations.