esa-sceva/satcom-chunk-collection huggingface dataset:
# Distributed Joint Source-Channel Coding Using Quasi-Uniform Systematic Polar Codes
## 3 QSPC and Punctured QSPC
### Punctured QSPC  
By puncturing \(MP\) parity bits from a \((MN,MK)\) QSPC code, we can construct a punctured QSPC code with a lower rate \(\tilde{R}_{i}\) (in channel-use per source-symbol). This punctured QSPC is dominated by the parameter \((MN,MK,MP)\). As depicted in Figure 4, there are three kinds of bits (systematic bits, parity bits, and punctured bits) in the codeword, and the coordinates of punctured bits are \(\mathcal{Q}(MN,MP)\). At the stage of code construction, punctured bits are regarded as the inputs of the zero-capacity channel to calculate BERs of \(\mathbf{u}\) and bit-swap coding. When this punctured QSPC is used to encode source message \(\mathbf{v}=[v^{1},v^{2},...,v^{MK}]\) with side information \(\mathbf{w}=[w^{1},w^{2},...,w^{MK}]\), Theorem 2 is obtained similarly as follows.  
**Theorem 2**.: _For any \(0\leq\epsilon<\frac{1}{2}\),_  
\[\begin{split}&\lim_{M\to\infty}\frac{1}{MN}|\{i:H(u_{i}|\mathbf{y} \mathbf{u}_{1:i-1})\in[0,\epsilon)\}|=\lim_{M\to\infty}\frac{1}{MN}|\{i:Z(u_{i} |\mathbf{y}\mathbf{u}_{1:i-1})\in[0,\epsilon)\}|\\ &=1-\frac{KH(v|w)+(N-K-P)H(x|y)+P}{N},\\ &\lim_{M\to\infty}\frac{1}{MN}|\{i:H(u_{i}|\mathbf{y}\mathbf{u}_{1:i -1})\in(1-\epsilon,1]\}|=\lim_{M\to\infty}\frac{1}{MN}|\{i:Z(u_{i}|\mathbf{y} \mathbf{u}_{1:i-1})\in(1-\epsilon,1]\}|\\ &=\frac{KH(v|w)+(N-K-P)H(x|y)+P}{N},\end{split} \tag{12}\]  
_where \(\mathbf{y}_{\tilde{Q}(MN,MP)}=\mathcal{O}\) because of puncturing._  
Proof of Theorem 2.: The codeword structure of Figure 4 ensures that \(Z(u_{i}|\mathbf{y}\mathbf{u}_{1:i-1})\) and \(H(u_{i}|\mathbf{y}\mathbf{u}_{1:i-1})\) converge to either 0 or 1, as \(M\) goes to infinity, which is similar to QSPC. The total entropy can be calculated by  
\[\sum_{i=1}^{MN}H(u_{i}|\mathbf{y}\mathbf{u}_{1:i-1})=\sum_{i=1}^{MN}H(x_{i}|y _{i})=M(N-K-P)H(x|y)+MKH(v|w)+MP. \tag{13}\]  
Therefore, the fraction of the high entropy part (\(H\approx 1\)) is  
\[\frac{1}{MN}\left(M(N-P-K)H(x|y)+MKH(v|w)+MP\right)=\frac{1}{N}(N-P-K)H(x|y)+ \frac{1}{N}KH(v|w)+\frac{P}{N}, \tag{14}\]  
and the fraction of the low entropy part (\(H\approx 0\)) is \(1-\frac{1}{N}(N-P-K)H(x|y)-\frac{1}{N}KH(v|w)-\frac{P}{N}\).  
This theorem also shows that \(P_{\text{BLER}}\) of punctured QSPC tends to 0 as \(M\) goes to infinity, if \(H(v|w)/(1-H(x|y))=H(v|w)/C\leq(N-K-P)/K\). Since punctured bits contributes zero channel-use, punctured QSPC has no performance loss. After the establishment of supermartingale for \(Z(u_{i}|\mathbf{y}\mathbf{u}_{1:i-1})\), we also have \(P_{\text{BLER}}\leq 2^{-M^{\beta}}\to 0\), for any \(\beta\in(0,\frac{1}{2})\).
## 1 Introduction  
In spite of the development in the traditional primary and secondary recovery methods, two-thirds of the original oil is still left in the reservoir [1,2]. It has become an urgent problem for petroleum scientists and technicians to study the methods to improve the oil recovery and develop efficient substituted techniques for maximizing the exploitation and utilization of existing resources. Chemical enhanced oil recovery (CEOR) methods involve the injection of suitable chemicals such as polymers, surfactants, and alkalis, either individually or in combination, to increase oil production [3, 4, 5]. Traditionally, synthetic and non-biodeegradable surfactants and polymers were used for improving the oil recovery due to relatively lower costs [6,7]. However, due to stringent environmental restrictions,there is a need for the petroleum production and exploration industry to adopt the microbial enhanced oil recovery (MEOR), which is environmentally friendly and cost effective [8]. MEOR is an alternative method of advanced oil recovery that takes the advantage of the ability of the microorganisms to synthesize products (e.g., biosurfactants, biopolymers, biogases, or acids) that are useful for improving oil recovery [9; 10; 11].  
MEOR mainly improves the oil recovery by increasing the displacement efficiency and the sweep volume of reservoir [12]. Some scholars have screened the different biosurfactant-producing bacteria for improving the oil displacement efficiency [4; 13; 14], while others used the biopolymer-producing bacteria to improve the sweep efficiency [8; 15; 16]. [PERSON] et al. studied the compatibility between the W5 strains and the weak gel and highlighted the application potential of weak gel-assisted microbial flooding [17]. [PERSON] et al. studied the combined influence of the biosurfactant and biopolymers on the solubilization and mobilization of oil from the reservoir [18].  
Biopolymer-producing strain FY-07 is capable of producing bacterial cellulose (BC) under both aerobic and anaerobic conditions [19], while the biosurfactant-producing strain WJ-1 produces a rhamnoid, which is a biosurfactant widely used in oil extraction [20]. The present research envisages the evaluation of the potential of combined flooding of the biopolymer-producing bacteria FY-07 and biosurfactant-producing bacteria WJ-1 through systematic experimental investigation.
## 2 Theoretical Foundations
### SWSDS-Net Network Architecture
The incorporation of SWS and a DSCN also enhances depth in feature learning by facilitating a better fusion of shallow and deep features, ultimately leading to improved segmentation accuracy [24]. After preprocessing, the logging images are fed into the encoding section, which sequentially undergoes two convolutional layers and a pooling layer in a repetitive manner. This progressive process gradually increases the number of channels while reducing the size, resulting in an output feature size of \(1024\times 16\times 16\). In the decoding section, through transposed convolution and concatenation operations, the channel count is initially doubled and subsequently reduced to ultimately generate a feature map with identical dimensions as the input image. In Figure 1, both the encoder's and the decoder's conventional convolutional layers are substituted with DSCN for enhanced performance, while SWS is incorporated after upsampling in both sections.
## 5 Comparative Evaluation of Different FOVs  
We performed an additional comparative evaluation for the 3D matching task using AR smart glasses with different FOVs. This task was similar to the task performed in Section 4. The difference is that, in Section 4, FOV was defined based on visual contexts on the same see-through display. On the other hand, in Section 5, different AR smart glasses were used. HoloLens provides wider FOV based on the hardware specification.  
Note that, in this experiment, the control object is physical, and the target object is virtual. In other words, the user holds a physical control object instead of a virtual control object and tries to move and rotate it for 3D matching with a virtual target object. One of the main reasons to perform this experiment is that sometimes workers must hold physical tools and parts to complete assembly and inspection operations in manufacturing environments. Visual guidance and virtual objects are useful to conduct the required task effectively. In this comparative evaluation, the indirect augmentation with and without video background was excluded since it showed worse performance than the others in the previous assessment. As mentioned in Section 4.4, this method showed the worst quantitative and qualitative evaluations. In particular, the task performance concerning the different sizes of FOV was measured for the stereo rendering and direct augmentation.
# Automated Rain Detection by Dual-Polarization Sentinel-1 Data
## 3 Co-Analysis of Rain Rate and C-Band SAR Backscattering  
### A First Qualitative Assessment  
Different forms of rain exist in nature, such as rain cells, stratified rain, rain bands or squall lines. The nature of hydrometeors (type, size, distribution in the atmosphere column) and local effect on the ambient wind are different in these rain forms leading to different signatures on C-band SAR images. This variety is illustrated with three examples in Figure 2:  
* Case a (Top in Figure 2): This is a typical example of storm cell footprint as discussed by [PERSON] [28]. Both VV- and VH-polarized images show two rain areas with concurrent bright and dark patches within the cell, the downdraft area and the wind gust. Here the surrounding wind speed is about 3 m/s. Collocated NEXRAD measurements presented in Figure 2(a3) indicate two separate rain cells. Bright patches are located at the same location in VV and VH images and correspond to NEXRAD base reflectivity measurements higher than 40 dBZ. This suggests an increase of C-band radar backscattering at high rain rates. As also noted by [PERSON] et al. [23], the extent of base reflectivity is less than the storm cell signature in the SAR image as rain radar is not sensitive to the wind acceleration due to downdraft or its associated wind gust. In this case, the relationship between NRCS and base reflectivity confirms the NRCS increases with base reflectivity for values larger than 40 dBZ.
* Case b (Middle in Figure 2): This case acquired in the Gulf of Mexico corresponds to a stationary front with stratiform precipitation without intense vertical convection. As indicated by Figure 2(b3), wind speed is significantly different on each side of the atmospheric front with ambient background wind speed lower than 2 m/s in the western area and higher than 6 m/s in the eastern area. In this case, there is also a good match between bright patches observed on both co- and cross-polarization channels and the highest values of base reflectivity (see Figure 2(b3)). The relationship between NRCS and base reflectivity also confirms that for values larger than 25 dBZ, the NRCS increases with base reflectivity.
* Case c (Bottom in Figure 2): Here, the background ambient wind is stronger than in the previous cases, about 13 m/s from the model. As observed, the distribution, size and intensity of the bright patches are not the same in co- and cross-polarization. More bright patches are observed in VH than in VV. In particular, dark areas are observed in the VV image where the VH image exhibits bright patches associated with high base reflectivity. As a result, in this case, the VV NRCS shows decreases when the base reflectivity increases whereas the VH NRCS stays almost constant.  
As confirmed by these examples, the impact of rain on co- and cross-polarization NRCS at C-band is quite complex and depends on both rain rate, ambient sea state and polarization.
# Performance Optimization of MANET Networks through Routing Protocol Analysis
## 3 Results  
This section contains the research results based on PDR, throughput, packet loss, delay, energy consumption, and routing overhead. For each result, we compare the AODV and OLSR protocols with a default parameter. Then, we compare the impact of RREQ_RETRIES and MAX_RREQ_TIMEOUT in the AODV protocol.
# A Methodology Linking Tamping Processes and Railway Track Behaviour
## 3 Principles of Tamping Processes
### Tamping Parameters  
The success of a tamping process, i.e., the stability of the created track geometry, depends on multiple parameters. On the one hand, the condition of the track before the tamping operation, more specifically the geometrical quality of the track and the condition of the ballast, affect the outcome of the tamping work [2; 9]. On the other hand, different factors within the realm of the tamping machine are highly important for the quality of the tamping process. Depending on the source, these factors are referred to as working parameters [30], tamping parameters [31], or operating parameters [32]. In this paper they are commonly named tamping parameters:  
* Lift of the track panel [30; 31; 32];
* Shift of the track panel [30];
* Squeezing time [30; 31; 32];
* Tamping depth [30; 32];
* Tamping time oscillation frequency [31; 32];
* Tamping time oscillation amplitude [31; 32];
* Squeezing velocity [31; 32];
* Squeezing force/pressure [31];
* Number of insertions/interventions [21].  
Lift and shift determine how far the track panel is raised vertically and moved horizontally during the tamping process, depending on the deviation of the track panel from its origin position [21]. The oscillation parameters (frequency and amplitude) are usually predefined, ideal values have been established empirically [33] and tested with computer models [34; 35]. Other tamping parameters need to be selected carefully by the machine operator, considering the displacement of the track panel and the local track condition. The application of optimal machine settings is crucial to form a well-compacted sleeper bearing [31; 32]. A tamping machine may also incorporate additional compaction tools besides the tamping tines, such as ballast shoulder consolidators or dynamic track stabilisers [21]. However, the scope of this paper is confined to the compaction achieved by the tamping tools.
## 5 Discussion
### Future Work  
Future research should aim to enhance the reliability of large models in handling complex legal scenarios, especially in mitigating potential \"hallucinations\" related to external references such as laws and regulations. To address these challenges, it is essential to develop mechanisms that can automatically detect and rectify inconsistencies in information, ensuring accuracy and consistency in the legal judgments produced by the models. Additionally, these models require further optimization to more effectively integrate and interpret multimodal information from both textual and visual data, particularly in determining legal liability. This would enhance the transparency of the decision-making process and provide more credible and fair judgments within the legal framework, thereby reducing legal risks associated with inaccurate information or misinterpretations.  
Regarding deployment in real-world applications, the robustness of the software architecture is paramount. Future design efforts should focus on constructing an architecture that is highly fault-tolerant and scalable, capable of handling multi-source data inputs--including video, sensor data, and legal databases--and performing real-time processing to meet diverse scenario demands. Moreover, designing user interaction modes is essential; the system must provide an intuitive and interpretable interface that enables users to easily understand the basis and process of its judgments. Interactive feedback and customizable options should be included, enabling users to intervene or challenge decisions as needed, thereby enhancing user trust and the practical effectiveness of the system. A user-centered design will significantly enhance the system's applicability and acceptance in real-world operations.  
An important direction for future research is to explore the system's potential to prevent traffic accidents, particularly those \"intentional\" incidents staged for fraudulent purposes. Leveraging predictive analytics and pattern recognition, the system could proactively identify and alert authorities to suspicious behaviors indicative of staged accidents, thereby contributing to safer roads and reducing fraudulent activities [35; 36].  
To enhance the accuracy of vehicle detection and collision recognition across various scenarios, it is crucial to expand and diversify the training dataset. The current dataset may not adequately cover all scenarios and conditions, potentially limiting the model's performance in complex or unusual situations. Therefore, collecting and annotating a broader set of traffic video data--particularly under varying weather conditions, lighting, road types, and traffic densities--will significantly improve the model's generalization ability. Additionally, including more samples featuring abnormal behaviors, such as sudden braking, speeding, and intentional collisions, will enable the model to more accurately identify and distinguish different types of traffic incidents.
## 8 Cross-Layer Optimization
Communication under traditional network architecture is based on the address of the information provider and the conversations between places of data origin and destination. This location-oriented method was a necessity in the early days of Internet where computers and conductivities were not as ubiquitous as nowadays. Content-centric networking proposes to replace the emphasis of \"where\" with \"what,\" as it is what service users are actually interested in [134]. Under the new architecture, resource identifiers are abstracted content rather than IP address and users only receive information for which they have explicitly solicited, as implemented in [135]. This ideology is similar to, but less conservative than, WoT and it facilitates fundamentally different ways of optimization. For example, congestion control is no longer performance in a end-to-end basis since, in a content-centric network, the location/identity of the sender has little significance, and the receiver is only concerned with links between neighbors. CCN also comes with a built-in security mechanism, whereby protection is associated with individual pieces of content via digital authorization and encryption [134].
## 3 Experimental Design and Implementation
### Experimental Data
#### 3.1.3 Data Attributes and Quality  
The trajectory data contains rich attribute information, primarily including:  
1. Vehicle identifier (vid): Uniquely identifies each floating car
2. Timestamp (time): Unix timestamp format, accurate to the second
3. Spatial coordinates (xcoord, ycoord): Real-time vehicle position
4. Instantaneous speed (speed): Vehicle's instantaneous speed in meters per second
5. Passenger status: Indicates whether the vehicle is carrying passengers  
Regarding data quality, despite a 30.80% redundancy rate and some anomalous data, the overall quality is good and can meet analytical requirements after preprocessing. Particularly noteworthy is the high sampling frequency (primarily 3-s intervals), which ensures accurate trajectory analysis.
## 3 Results
Figure 5: Comparison of mean weighted frequency values (Hz) between methods for each IMF from the EEG of 47 healthy volunteers. The indices were calculated using spectral methods based on the Hilbert–Huang method (yellow) and Fast Fourier Transform method (orange). Significant differences between methods are indicated by (*) for a \(p\) value \(<\) 0.05 in ANOVA “F” statistics. For every lead depicted, the box represents interquartile range, whiskers were calculated as 1.5\(\times\) interquartile range, and circle represents data mean. “IMF” refers to intrinsic mode function. The grand average ranges of the mWf considering both method values depicted in Figure 6 revealed that IMF1 showed values from 40.3-52.4 Hz, IMF2 showed values of 23.84-31.03 Hz, IMF5 showed values from 3.56-5.04 Hz, and IMF6 showed values 1.72-2.77 Hz, but for IMF3, values were observed from 10.01-15.79 Hz, while for IMF4 the observed values ranged between 6.74-10.71 Hz and both frequency ranges (IMF3 and IMF4) were slightly overlapped. Figure 6: Comparison of normalized units (%) of power spectral density between methods for each IMF from the EEG of 47 healthy volunteers. The indices were calculated using spectral methods based on the Hilbert—Huang method (light green) and Fast Fourier Transform method (dark green). Normalized units (%) of power spectra were shown in logit transformation (\(\log\) (x/1 - x)). Significant differences between methods were indicated by (*) for a \(p\) value < 0.05 in ANOVA “F” statistics. For every lead depicted, the box represents interquartile range, whiskers were calculated as 1.5\(\times\) interquartile range, and circle represents data mean. “IMF” refers to intrinsic mode function. A more precise analysis of the overlapping for all groups between IMF3 and IMF4 considering the range for the mWf values (mean \(\pm\) 1.96\(\times\) standard deviation) for each IMF is shown in Figure 7. The spectral analysis of the IMFs with the FFT method produced more overlapping between IMFs than the HHT method. Despite the statistical differences obtained through the ANOVA test, the agreement analysis between methods shows that only differences in mWf values on IMF1, IMF2, and IMF3 are relevant for clinical purposes (see Table 1 and Figure 8). The IMF1 showed a bias of 5.83 Hz and a 95% distribution range of 9.0 to 2.6, while IMF2 depicted a 3.42 Hz bias with a 5.7-1.1 range. Taking into consideration our previously established limits for this analysis, another minor disagreement was detected also for the range of the IMF-3 [3.3/\(-\)1.0]; however, the bias (1.15) was acceptable.
## 3 Methods
### Feature Extraction Stage
#### 3.2.3 Dimensionality Reduction  
In most of our experiments, especially those employing the Neurovoz and ItalianPVS corpora, the dimensionality of the feature vectors was larger than the number of recordings. In order to remove redundant or irrelevant information, we employed two dimensionality reduction techniques, similarly to other previous studies [12; 27; 37]. The first technique we utilized to reduce dimensionality was PCA from the Python scikit-learn module [57]. This is an invertible linear transformation which uses singular value decomposition to project a vector into a lower dimensional space. We used PCA to reduce the dimensionality of the feature vectors to 10, 50, and 100 coefficients. We also used feature selection algorithms to determine which features contain the most important information pertaining to PD detection. For this, we employed the ANOVA F-Value classifier from the scikit-learn SelectKBest module [57]. Using this method, we selected between 10, 30, 50, and 70 features.  
In all the cases, the dimensionality reduction matrix or model was trained on the training folds and applied to their respective testing folds.  
\begin{table}
\begin{tabular}{l l} \hline
**Feature Family** & **Coefficients** \\ \hline Amplitude and frequency & Absolute and relative jitter and shimmer, RAP, PPQ5, APQ3, \\ perturbation and fluctuation & APQ5, HNR, NHR, CHNR, NNE, GNE, FTRI and ATRI \\ \hline Spectral-Cepstral & CPP, Ltr, MFCC (12 coefficients + delta + delta delta) and PLP (12 coefficients + delta + delta delta delta) \\ \hline Complexity & D2, LLE, ApEn, SampEn, GSAmpEn, FuzzyEn, mSampEn, PE, \\  & RPDE, LZC and DFA \\ \hline Modulation Spectra & MSH, MSW, CIL, RALA, LMR, MSP, centroids and \\  & dynamic margin. \\ \hline \end{tabular}
\end{table}
Table 8: AVCA library base features.  
\begin{table}
\begin{tabular}{l l} \hline
**Feature Family** & **Coefficients** \\ \hline Amplitude and frequency & Absolute and relative jitter and shimmer, APQ3, APQ5, APQ11, \\ perturbation and fluctuation & FPQ3, FPQ5, FPQ11, VFER, HNR, NHR, GQ andGNE \\ \hline Spectral-Cepstral & MFCC (12 coefficients + delta + delta delta delta) \\ \hline Complexity & ApEn, PPE, DFA, RPDE \\ \hline Series decomposition & EMD-ER, EMD and wavelet features \\ \hline \end{tabular}
\end{table}
Table 9: DARTH-VAT library base features.