esa-sceva/satcom-chunk-collection huggingface dataset:
# Image De-Quantization Using Plate Bending Model
## 6 Discussion
### Dither and Noise  
Another general challenge for bit depth enhancement is noise and artifacts from dithering. This affects every method, but more advanced methods tend to use more information from the image. Unfortunately, this also means higher sensitivity in handling the noise.  
In the beginning of the article it was assumed that the image was not subject to dithering. However, if it was, it could generate serious artifacts: close to an edge, a dithering algorithm would generate points from both shades. There is a function which fits nicely to this data: a flat surface with equal distance from both quantization levels. This would lead to an image where there is a flat region in the neighborhood of the edges, which is exactly the opposite of the original aim. Further away from the edges, a smooth transition is achieved.  
This artifact could be mitigated with relaxed upper and lower envelopes. The original assumption was that the rounding/flooring of the signal is perfect. Depending on the gradients in the image and the dithering method, a locally increased range for the allowed values (decreased lower envelope, increased upper envelope) would allow the algorithm to effectively mitigate dithering artifacts. However, the cost of this flexibility might be some blurring in neighboring areas. Similarly, noise which exceeds a quantization level deforms the plate.  
Figure 5: Reconstruction of night sky. Top row from left to right: original 8-bit image, mask for region of interest, 2-bit quantization. Bottom row from left to right: Laplace reconstruction with mask, biharmonic reconstruction solving inside the mask only, biharmonic reconstruction where the masked values were added back.  
From an algorithmic point of view, masking and relaxing is the same numerical problem, with two exceptions: in masked out points, the upper and lower envelopes are set to dynamic range limits, and after the iterative solution, in these points the original low bit depth pixels are used.  
Adding a data fidelity term to the original problem, when the sum of the bending energy and the error term should be minimized, it would transform the problem into a denoising problem. This seems to be appealing, and modeling the signal as with a bended plate could be beneficial in some applications, however it does not suit the de-quantization problem. The reason lies in the fact that minimizing the error term would flatten out the reconstructed signal further from the edges. Technically, if the fidelity term is too small, it has no effect, and if it has significant effect, it would force the reconstructed signal to closely follow the flat regions.  
Considering these arguments, our recommendation is to locally increase the distance between the envelopes, or remove the problematic pixels with noise filters or anomaly detection, as discussed in the next section.
# BSM-Data Reuse Model Based on In-Vehicular Computing
## 3 Connected Vehicles and Data Availability  
Connected-vehicles solution is of the many technological innovations currently jostling for attention. This new technology has revolutionized the automotive industries, and built the cornerstone of the internet of vehicles. According to [22], in 2020, the internet will be integrated in around 90% of modern cars, whereas it was integrated at less than 10% in 2013 and that certainly can help supporting next generation intelligent transportation systems. Vast research work and various industrial efforts have accelerated the achievement of connected vehicle technology. Different countries, such as the U.S. (in California, New York, Arizona, Florida, and Michigan), China, Germany, the U.K., and others, have established connected vehicles testbeds and pilot programs [23,24]. That is why the latter is commonly regarded as an area of development where applications find prosperous ground in the IoV epoch. By way of example, Figure 2, summarized the main categories of connected vehicle applications in the U.S.  
Indeed, great endeavors are being made by researchers towards innovative and cost-effective vehicular applications. Moreover, several applications that are proposed or under investigation are mainly related to safety, mobility, efficiency and infotainment, similar to emergency warning, traffic management, and weather information. The two most important aspects for connected vehicles to succeed technologically are then, first, numerous data need to be collected from diverse systems and sources. Second, these data should be treated and widely diffused through various communication technologies, such as DSRC, WiFi, 5G, and cellular.  
Against this background, the general understanding is that better availability of data source is of the utmost importance to feed the plethora of connected vehicle applications and provide an intelligent transportation system management.  
As the main thrust behind the connected vehicles is traffic safety, the BSM was initially designed to be the main message used by safety applications to shaire data among connected vehicles. Being considered as \"heartbeat\" messages, the BSM data tend to constitute the overwhelming majority of the CV data. Unfortunately, this valuable CV data source remains restricted in context, time and space.
# A Self-Recovery Fragile Image Watermarking with Variable Watermark Capacity
## The Proposed Scheme
### Watermark Generation and Embedding
#### 3.1.3 Watermark Embedding  
Before watermark embedding, a block mapping sequence is generated by:  
\[X^{\prime}=(k\times X){\rm mod}N+1, \tag{9}\]  
where \(X\) is the index of image block, \(X^{\prime}\) is the index of the corresponding mapping block, \(N\) is the total number of blocks, and \(k\in[1,N-1]\) is a prime number. To improve the image recovery ability and reduce the synchronous tampering problem, a push-aside operation proposed in [34] is applied to further disrupt the order of the block mapping sequence.  
To complete the watermark embedding process, the encrypted authentication watermark of an image block is embedded into the 2 LSBs of the first pixel in block itself. The encrypted recovery watermark is embedded into the 2 or 3 LSBs of its mapping block. That means the watermark capacity is 2-3 bpp (bit per pixel). Besides, the secret keys (\(k\), \(key1\), and \(key2\)) used in watermark embedding process will be sent to the receiver through a secure communication channel. Generally speaking, it is more convenient and secure to transmit three secret values than to transmit the images. Table 1 lists the parameters used in watermark generation and embedding processes.
## 2 Materials and Methods
### Apparatus and Procedure
#### 2.2.2 Reversed-Flow Gas Chromatography Apparatus  
The experimental configuration of R.F.G.C. technique is depicted in Figure 1. Two conventional gas chromatographs, Pye Unicam Series 104 (Cambridge, UK) and Shimadzu 8A (Kyoto, Japan) equipped with a flame ionization detector, were employed. A stainless-steel sample column, possessing an internal diameter of 4 mm and a total length of 2 m, devoid of any packing material, was connected to the diffusion column at the midpoint within the oven of the initial chromatograph. The sampling column was bifurcated into two identical sections, denoted as \(l\) and \(l^{\prime}\) (each 100 cm + 100 cm).  
The diffusion column comprised two sections, labeled as \(z\) and \(y\) (refer to Figure 1). Section \(z\) extends for 45 cm and shares the same diameter as the sampling column, whereas section \(y\) is a glass vessel measuring 4 cm in length, also with the same diameter as the other columns.  
To enable versatile control over carrier gas flow through the sampling and diffusion columns, constituting the \"sampling cell\", the carrier gas input and detector were affixed to these columns. A four-port valve, connecting the ends \(x=0\) and \(x=l+l^{\prime}\) of the sampling column to the carrier gas supply and detector, facilitated the ability to alter the direction of carrier gas flow at will. The reversal was sustained for a duration of 6 s before returning to its initial direction. This reversal was shorter than the gas hold-up time in the sampling column and led to the creation of narrow and fairly symmetrical chromatographic peaksover the continuous elution curve. These extra chromatographic peaks, called \"sample peaks\", and their heights \(H\) are measured as a function of the time \(t_{0}\) when the reversal occurs. The height is proportional to the concentration \(c\) of the ethanol at the junction point \(z=0\) and \(x=l^{\prime}\) of the sampling column (Figure 1).  
More details and analytical description of the experimental procedure, as well as the mathematical analysis, are given in the literature [46].  
The operational temperature of the sampling cell was maintained at 80 \({}^{\circ}\)C. Additionally, to discern the ethanol eluted fraction from potential volatile by-products in the fermented sample, an analytical column was introduced before the detector. This column (\(2\,\mathrm{m}\times 1/4\) in I.D. \(\times\) 2 mm thick glass) was packed with 5% Carbowax-20 M and 80/120 mesh Carbowax BAW and maintained at 110 \({}^{\circ}\)C in the oven of the second gas chromatograph, equipped with the F.I.D. as illustrated in Figure 1.  
For detector operation, high-purity hydrogen (99.999%) was sourced from Aeroscopio S.A. (Athens, Greece), and atmospheric air was supplied through a compressor apparatus. The detector's operational temperature was set at 150 \({}^{\circ}\)C.  
Helium served as the carrier gas in all experiments, maintaining a constant flow rate of 0.5 cm\({}^{3}\) s\({}^{-1}\) with a 1.6 atm pressure drop across the sampling cell. The helium used, obtained from Aeroscopio S.A. (Athens, Greece), was of 99.999% purity.
## 4 Space-Qualified RF Generator Techniques
### DDS Inside an FPGA
The results show an output power level between +0.5 dBm at 180 MHz and +7 dBm at 45 MHz, depending on the output frequency. The generated harmonics and spur levels are shown in Figure 8 for different generated frequencies. As can be seen, many different spurs and harmonics exist, depending on the selected fundamental frequency. The intensity varies as a function of frequency selection. The power consumption of the setup is 4.1 W for the complete frequency range. This is mainly due to the FPGA-DAC combination. The output power uncertainty is less than 1 mW. Although not necessary for the AOTF application, this setup has the advantage of having a high resolution and accuracy (up to the Hz-range). Also, the requirements of long-term stability and stabilization time are easily met. Figure 7: DDS RF generator setup—Virtex4/DAC5675A-SP.
# LDPC Codes on Balanced Incomplete Block Designs: Construction, Girth, and Cycle Structure Analysis
## 3 Girth and Cycle Structure Analysis of BIBD-LDPC Codes
### A Method for Counting Short Cycles of BIBD-LDPC Codes
#### 3.3.1 Number of Cycles of Length \(6\)  
In this case, we only consider \(i=3\), and parameter \(t\) takes only one value, namely \(3\). Therefore, for the cases of \(P_{2}\), the only scenario to consider is when three points chosen from \(\mathbf{X}\) lie in the same block. In other words, we calculate the number of cases where any three points of \(\mathbf{X}\) are contained within a single block. For any block in \(\mathbf{B}\) with \(\kappa\geq 3\), the number of ways to choose three points from the block is given by  
\[C_{\kappa}^{3}=\frac{\kappa(\kappa-1)(\kappa-2)}{3!}.\]  
Since the total number of blocks in the BIBD is  
\[\frac{v(v-1)}{\kappa(\kappa-1)},\]the total number of invalid cases (\(P_{2}\)) is  
\[P_{2}=\frac{v(v-1)(\kappa-2)}{3!}.\]  
Additionally, the values of \(P_{1}\) and \(P_{3}\) are calculated as follows:  
\[P_{1}=C_{v}^{3}=\frac{v(v-1)(v-2)}{3!},\]  
and  
\[P_{3}=\frac{(3-1)!}{2}=1.\]  
Combining \(P_{1}\) and \(P_{2}\), the exact number of cycles of length 6 (\(N_{6}\)) is  
\[N_{6} =(P_{1}-P_{2})\times P_{3}\] \[=(\frac{v(v-1)(v-2)}{3!}-\frac{v(v-1)(\kappa-2)}{3!})\times 1\] \[=\frac{v(v-1)(v-\kappa)}{3!}. \tag{3}\]  
After a simple verification, it can be observed that even for \(\kappa=2\), the number of cycles of length 6 remains consistent with the expression for \(N_{6}\) in (3). Specifically, substituting \(\kappa=2\) into the formula expressed as  
\[N_{6}=\frac{v(v-1)(v-\kappa)}{3!},\]  
yields  
\[N_{6}=\frac{v(v-1)(v-2)}{3!}.\]  
This result aligns with the combinatorial interpretation, as the case of \(\kappa=2\) inherently prevents the formation of cycles of length 6 due to the inability to have three points in a single block. Thus, the formula remains valid for \(\kappa=2\), confirming its generality.
# A Cloud-Based Framework for Large-Scale Log Mining through Apache Spark and Elasticsearch
## 4 Methodology  
### Proposed Framework Architecture for Log Mining  
After an extensive study of the open-source tools supporting log mining, we propose an architecture integrating Elasticsearch with Spark for log mining. Elasticsearch, a component in the ELK stack ecosystem, provides a series of log management functionalities including log ingestion, index, query, statistical analyses and visualization. Spark is an in-memory parallel computing framework for big data. The goal of this framework is to utilize existing resources and tools to establish a flexible and scalable system for the entire log-mining process. The Elasticsearch ecosystem provides the framework as a log management system, in which logs are indexed for query, visualization, and statistical analyses. Spark accelerates the mining process through parallel computing. Figure 4  
Figure 3: Sample FTP log data in FTP format.  
Figure 2: Sample HTTP logs in the combined format.  
shows the system architecture. A cloud infrastructure, e.g., Amazon Web Service (AWS) [25] and OpenStack [26], automatically and efficiently launches virtual resources like virtual machines and network configurations. Once a virtual machine starts, a series of open-source tools cooperate with each other to manage and analyze logs. The Hadoop Distributed File System (HDFS) stores massive logs distributedly; Elasticsearch provides the capabilities of indexing and searching logs in a near real-time manner; Spark accelerates log-mining subtasks through data parallelism. An image can be created in advance to facilitate the installation of log-mining clusters on demand, including an HDFS cluster, a Spark cluster, and an Elasticsearch cluster.  
The framework indexes and analyzes multi-source, heterogeneous logs through user identification, crawler detection, session reconstruction, and knowledge extraction. User identification recognizes all users from billions of logs. The IP or IP combined with other fields, e.g., agent, identifies a user. Crawler detection recognizes crawlers with well-known robots and visiting rate. Session reconstruction splits logs belonging to one user into small groups, and each group records a user's behavior in a relatively short time. Knowledge extraction summarizes a user's behavior in a session including the dataset the user clicked or viewed [4]. With the framework, big raw logs could be efficiently converted to knowledge, e.g., vocabulary linkage or metadata linkage, which can be adopted to improve search engines through better ranking, recommendation, and query understanding [2; 5; 8].
## 3 Research Methodology  
An advanced active safety system to protect vehicles from collisions is the AEBS. It is made to assist drivers in preventing or lessening crashes with other drivers on the road. The Simulink model of MATLAB was used to implement the AEBS. The implementation of an EBS can be performed in MATLAB and Simulink and the radar and vision-based systems were widely used in the previously implemented system, but the proposed system uses lidar, radar, and vision with sensor fusion. The sensors model based on the acquisition of MATLAB and Simulink was used as a basic form to track vehicles, pedestrians, or any hurdle in both good and bad weather. In the case of the emergency braking process, three advanced sensors based on the sensor fusion method were projected and incorporated by the model provided by MATLAB [30; 31].  
At the start, three sensors radar, vision, and lidars detect and track objects near the ego vehicle. When an ego vehicle approaches a leading vehicle, the sensor measures the ego vehicle's distance from the leading vehicle, calculates the speeds of both vehicles, calculates the time to collision, activates the forward collision alert, and then calculates the stopping distance. When the lead vehicle's TTC is less than the TFCW, the FCW alert is activated. Due to distractions, the driver may fail to engage the brakes; in this situation, the AEBS operates autonomously to avoid or lessen the impact. All the processes are shown below in the flow chart and block diagram in Figures 4 and 5.  
Our proposed system is divided into two parts: sensor fusion and AEB control. First, the AEB algorithm's control system is divided into two primary subsystems, the AEB control subsystem, and the speed control subsystem, both of which are illustrated in this section. The AEB control subsystem is responsible for conducting vehicle braking, while the speed control subsystem is in charge of accelerating the vehicle. This study only examines the scenario in which the leading and ego cars both move in the same lane.
# Game Theory Meets Wireless Sensor Networks Security Requirements and Threats Mitigation: A Survey
## 4 Game Theory Defense Strategies Against WSN Attacks
### Clear and Passive Attacks
#### 4.3.2 Passive Attack  
The passive attack has the ability to capture the transmitted traffic without disturbing the network. In other words, it eavesdrops the incoming traffic stream without modification. The inherent risk of this attack is that the attack targets the sensitive data (e.g., the encryption codes). Moreover, this attack can represent selfish sensor nodes which strive to save their power as maximum as possible. For example,the selfish nodes go to sleep mode without a permission from the network administration, whether these nodes are involved in cooperative or non-cooperative games. Furthermore, the malicious nodes that aim at saving their power consumption by ignoring packet forwarding to their neighbors are also considered passive attack.  
The following papers consider different types of WSN passive attacks. In [84], a bargaining game is introduced to confront the passive attack due to the selfish behavior of some of visual event-driven sensors. This represents a highly sensitive and vulnerable application in which the selfish nodes tend to conserve their power. In addition, in [42], the authors propose Nash bargaining games, which lead to the system Pareto optimality aiming at optimizing the network reliability. In [85], the authors consider a form of passive attack where a selfish sensor saves its power by going to sleep mode without a permission, by deceiving the network intrusion detection system (IDS). In [77], a game theoretic approach is proposed to address the passive DoS attack that causes malfunction of the forwarding mechanism of sensor nodes at which the nodes agree to forward packets but fail to do so. Furthermore, in [76], a win-lose scenario is used to model the relationship between the system and the attacker using a zero-sum game to secure the forward data path in which the attack strives to drop the passing packets. In addition, a sampling mechanism, inspired from the learning automata methodology, is applied in this game-theoretic context to treat the passive attack impact based on the reward and punishment technique [86].
# A Comprehensive Review and Benchmarking of Fairness-Aware Variants of Machine Learning Models
## 4 Fair Editions of Logistic Regression
### \(\eta\)-Neutral Logistic Regression  
The authors of [16] introduced the concept of \(\eta\)-neutrality, which defines a probabilistic model as \(\eta\)-neutral if the joint probability distribution of the target variable \(Y\) and the sensitive attribute \(V\) satisfies the following condition:  
\[\forall v\in V,y\in Y,\quad\frac{Pr(y,v)}{Pr(y)Pr(v)}\leq 1+\eta.\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
Here, \(\eta\) is a tunable neutrality parameter that controls the permissible level of dependency between \(Y\) and \(V\).  
The authors incorporated this concept into Maximum Likelihood Estimation (MLE)-based learning algorithms, specifically logistic and linear regression, to enforce fairness constraints while optimizing prediction accuracy. Their method was evaluated on five real-world classification datasets: Adult Income, German Credit Data, Bank Marketing, Credit Approval, and Dutch Census. Experimental results demonstrated that their approach outperformed existing methods, including 2-NB (Naive Bayes) [3] and Prejudice Remover [15], in achieving a better balance between accuracy and fairness trade-offs.  
Differentially Private and Fair Logistic Regression  
The authors of [17] addressed the dual challenge of ensuring differential privacy and fairness in Logistic Regression models without significantly compromising model utility. In this study, fairness is considered at the group level, whereas differential privacy is defined at the individual level. The authors propose two novel methods to enforce both differential privacy and fairness in Logistic Regression. The first method, Private and Fair Logistic Regression (PFLR), incorporates fairness constraints into the Logistic Regression objective function as a penalty term. Subsequently, the functional mechanism [22] is applied to ensure differential privacy by introducing Laplace noise into the polynomial coefficients of the objective function. However, this approach results in reduced model accuracy due to excessive noise injection. To address this limitation, the authors introduce an improved method, PFLR*, which integrates the fairness constraint directly into the noise injection process rather than treating it as a penalty term. While both methods are mathematically proven to satisfy the \(e\)-differential privacy criterion, PFLR* is more efficient, optimizing the trade-off between utility and privacy. The proposed methods were evaluated on two real-world datasets: the Adult Dataset and the Dutch Census. Experimental results demonstrate that PFLR* consistently outperforms PFLR, achieving higher accuracy while simultaneously preserving both privacy and fairness.
# Infrared Stripe Correction Algorithm Based on Wavelet Analysis and Gradient Equalization
## 4 Implementation Details
### Procedure  
The pseudoalgorithm for our proposed is presented as Algorithm 1.  
```
Input: The raw infrared image U.
1 Wavelet decomposition original image. Parameter: Use db1 wavelet base. Initialization: Decompose the raw image U into approximate components A\({}_{1}\), vertical components V\({}_{1}\), horizontal components H\({}_{1}\), diagonal components D\({}_{1}\).
2 Column gradient equalization Parameter: Column equalization window value is 1. Column gradient equalization window size is N. Column equalization: Generating a one-dimensional vector using Gaussian kernel function H. The variance is 5. The cumulative histogram of V\({}_{1}\) is M\({}_{1}\). for aj = 1: 2N+1  Correlate M\({}_{1}\) with H to get the Output V\({}_{1}\)' endfor
3 Spatial filtering with guided filter Parameter: Regularization parameter \(\varepsilon=0.2^{2}\). Filter window h = 0.3H. H represents the height of the image.
4 Filtration: V\({}_{1}\)' as the input image of the guided filter, Approximate component A\({}_{1}\) as guide image. Output filtered image \(v_{k}\). Output: The final corrected result I = A\({}_{1}+v_{k}+\) H\({}_{1}\) + D\({}_{1}\).
```  
**Algorithm 1**The proposed method for single infrared image stripe non-uniformity correction