esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In a tumor microenvironment, B cells can be activated by cancer antigens, neoantigens, or self-antigens through T-dependent or T-independent response [40; 41]. This activation can lead to the differentiation of B cells into short-lived plasmablasts, long-lived plasma cells, and memory cells, which all produce high-affinity antibodies that mediate opsonization, antibody-dependent cellular cytotoxicity (ADCC), and activation of the complement-dependent cytotoxicity (CDC), thus making B cells the pivotal effector cell of humoral immunity [37; 41; 42]. Moreover, B cells can act as APCs to activate T effector cells in TME [37; 39], though the role of regulatory B cells (Bregs) in cancer immunity and their intervention in tumor growth is under great investigation. These cells can arise with many different phenotypes from different subpopulations of B cells, while the CD19 + CD24 hiCD38 hi phenotype seems to be the most common human one for Bregs. Their signature characteristic is the production of cytokines such as IL-10, IL-35, and TGF-\(\beta\) and the capacity to inhibit T cell-mediated tumor cytotoxicity [37; 39; 41; 42; 43; 44]. Furthermore, activated B cells and Bregs that express in their surface the PD-L1, an immune checkpoint, can, as mentioned before, induce tolerance and limit effector T cell responses [37; 40; 41; 42; 43]. Finally, it is necessary to mention that the age-associated B cells (ABCs), a unique memory B cell subpopulation, are implicated in aging, autoimmunity, and chronic inflammation through autoantibody production, cytokineinduction, and T cell helper activation [45; 46], yet their role in cancer and immunotherapy is understudied. The role of circulating B cells as a prognostic factor in patients treated with ICIs is not established. In this study, we wanted to explore the role of distinct circulating B cell populations during treatment with immune checkpoint inhibitors in cancer patients and investigate the possibility of finding a precise biomarker for treatment response. Additionally, we wanted to explore if there are any alterations in different circulating B cell populations in patients who developed immune-related adverse events (irAE group). Our aim was to examine whether any circulating B cell population could potentially be used as a prediction marker for the development of irAEs.
## 3 Image Acquisition and Methodology
### Single-Channel Gravitational Kernel Density Clustering Algorithm
#### 3.3.3 Steps of the Single-Channel Gravity Kernel Density Clustering Algorithm  
Assuming that the number of clusters determined according to the feature histogram of the diseased leaf is k, the initial cluster center corresponding to each cluster is _lp_, then the gravity kernel density clustering algorithm is described as follows:  
1. Calculate the gravity \(F_{sp}\) of each pixel \(l_{s}\) to a different \(l_{p}\);
2. Divide pixels \(l_{s}\) into corresponding clusters according to the magnitude of gravity \(F_{sp}\) that is, select the cluster center with the greatest attraction to the pixels, and divide the pixels into this category;
3. For the divided clusters, used the centroid of this class as the new cluster center to update the original cluster center;
4. Go to (1) and repeatedly iterate until the cluster center does not change;
5. Algorithm stops.
# Hybrid Locomotion Evaluation for a Novel Amphibious Spherical Robot
## Appendix A Waypoints Tracking Algorithm  
As mentioned in Section 5, we designed a waypoint tracking algorithm including position and heading angle control. The following three algorithms are the position control algorithm, the heading angle control algorithm, and the waypoint tracking algorithm, respectively.  
```
1:System Initialization
2:Waypoints set up
3:Go to the next waypoint
4:Calculate the distance \(d=\sqrt{\left(w_{x}^{i}-x_{r}\right)^{2}+\left(w_{y}^{i}-y_{r}\right)^{2}}\)
5:IF\(d<\delta_{d}\)
6:IF this waypoint is the last one
7:Waypoints tracking ends
8:ELSE
9:Go to the next waypoint
10:ELSE
11:IF Road is smooth
12:Do ASR-III sliding motion with heading angle control
13:ELSE
14:Do ASR-III climbing motion with heading angle control
```  
**Algorithm A2** Heading Angle control algorithm  
```
1:Initial the parameters
2:Read the current heading angle
3:Calculate the differential angle \(\alpha=\alpha_{expected}-\alpha_{heading}\)
4:While
5:IF\(\alpha\geq\delta_{d}\) or \(\alpha\leq-\delta_{d}\)
6:Get Leg PID angle \(\alpha_{p}\)
7:Swing angle of legs \(\alpha_{legs}\leftarrow\alpha_{i}+\alpha_{p}\)
8:Stop and execute the sliding rotary gait
9:ELSE
10:Get leg PID angle \(\alpha_{p}\)
11:IF\(0<\alpha<\delta_{d}\) (turn left)
12:Left legs \(\alpha_{1}\leftarrow\alpha_{i}-\alpha_{p}\)
13:Right legs \(\alpha_{r}\leftarrow\alpha_{i}+\alpha_{p}\)
14:ELSE
15:Left legs \(\alpha_{i}\leftarrow\alpha_{i}+\alpha_{p}\)
16:Right legs \(\alpha_{r}\leftarrow\alpha_{i}-\alpha_{p}\)
17:Go forward with different swing angles
18:End while
```  
**Algorithm A1** Waypoints tracking algorithm with Closed-loop PID Control
## 5 Results and Discussion
### Analysis of the Enhancement Technique
#### 5.1.1 Analysis of Retinal Fundus Images  
The McCann Retinex algorithm was used to normalize the contrast of the retinal fundus images and brain MRI. The effect of each iteration was observed, and the evaluation was based on the measurements of the PSNR and contrast. Figure 4 illustrates the effect of the iterative process of the McCann Retinex. On the left side (Figure 4a), the low quality image is observed. On the right (Figure 4b) is a high quality image from the HRF retinal image database. Figure 5 shows the output of the low (Figure 5a) and high quality images (Figure 5b) from the HRF retinal image database at each iteration from 1 to 20.  
Figure 4a shows a low quality retinal image from the HRF database that suffered from nonuniform contrast that affected the visualization of blood vessel detail. The iterative McCann Retinex algorithm was used, and each iteration affected the image quality and obtained more details of the image. After each iteration, the PSNR and PSNR improvement and contrast were measured. The low quality green band image from the HRF database had a PSNR of 37.22 dB, and it improved up to 3 dB from iteration 1 to 20 (Figure 6a,b); similarly, the contrast increased from 20.9 at iteration 1 to 34.12 at iteration 20 (Figure 6c). The high quality green band image from the HRF database had a PSNR of 39.12 dB, and it improved up to 3.74 dB from iteration 1 to 20 (Figure 6d,e); similarly, the contrast increased from 40.1 at iteration 1 to 51.1 at iteration 20 (Figure 6f). This improvement on high quality HRF images allows better observation of the retinal vessels and retinal abnormalities and also impacts the performance of the segmentation process.  
Figure 4: (a) Low Quality Green Band Image from the HRF Retinal Image Database. (b) High Quality Green Band Image from the HRF Retinal Image Database.
# Spatial-Temporal Variability of Future Rainfall Erosivity and Its Impact on Soil Loss Risk in Kenya
## 3 Results
### Baseline Soil Erosion by ACZ and Climate Zones  
Humid areas with an average annual rainfall erosivity of 9100.7 MJ mm ha\({}^{-1}\) h\({}^{-1}\) yr\({}^{-1}\) had the highest mean erosion rate, followed by sub-humid and semi-humid areas, as represented in Table 6. Regions under the tropical rainforest climate around Lake Victoria recorded the highest soil erosion rate (27.84 t ha\({}^{-1}\) yr\({}^{-1}\)), while the arid desert climate zones had the lowest mean annual soil loss rate (0.38 t ha\({}^{-1}\) yr\({}^{-1}\)) (Table 7).
## 3 Criteria for and Types of Probiotics and Its Use in Foods  
In order for a microorganism to be classified as a probiotic, the microorganism must be properly characterized, safe for its intended use, shown to confer a health benefit to the host by at least one human clinical trial, and be viable at the required dose until the end of the product shelf life [27]. In general, probiotic viability is affected by temperature, water activity, pH, ingredients, oxygen level, packaging materials, and storage time [28]. Selection criteria for incorporation of a probiotic into a food includes being of a human or food origin, safe for human consumption, maintaining desirable properties in the final product, survival during passage through the gastrointestinal tract including sufficient acid and bile tolerance, adhesion to intestinal mucosal surface, and providing proven health benefits to the host [29]. For example, survival of greater than 7 log cfu/g for Limosilactobacillus_ (formerly _Lactobacillus_) _mucosa_ CNPC007 that was incorporated into goat milk Greek-style yogurt was found after exposure to simulated digestion, allowing this strain to be considered a possible probiotic [30]. Probiotics that confer health benefits or pathogens that cause diseases are often strain specific. In some cases, a certain strain of a species may be a probiotic while another strain of the same species may be a pathogen. For example, _E. coli_ Nissle 1917 is a probiotic [31], while _E. coli_ O157:H7 and O104:H4 are pathogens. _Bacillus_ sp. DU-106 from the _Bacillus cereus_ group is a potential probiotic but other strains of _B. cereus_ are pathogens [32]. Some strains of _Cistoridium butylricum_ can be used as a probiotic to produce bacteriocins, secrete butyrate, and inhibit pathogens while other strains are linked with botulism in infants and necrotizing enterocolitis in preterm neonates [33]. Sometimes, probiotics can cause illness under certain conditions, so potential safety issues must be addressed as will be discussed in more detail later. There are many types of probiotic bacteria. Bacteria and yeast with claimed probiotic or potential probiotic properties that have been incorporated into or isolated from yogurt or yogurt-related products are listed in Table 1[34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72]. A list of GRAS (generally recognized as safe) probiotics that mention incorporation into yogurt or related milk products is provided in Table 2. GRAS rules originated from the Food Additives Amendment of 1958 to the Federal Food, Drug, and Cosmetic Act of 1938 (sections 201(s) and 409) and require successful safety evaluations for their intended use by experts in the field, unless it has been shown to be safe by common use before 1958 [73]. For example, Endres et al. [74] demonstrated that _Bacillus coagulans_ GBI-30, 6086 is safe for human consumption.
# Performance Evaluation of GPS Auto-Surveying Techniques
## 3 Results
### Performance Comparison with Existing Auto-Survey Methods  
In order to benchmark the results of the proposed methods, they were compared against known auto-surveying methods. To this end, the survey-in mode of the u-blox 6T receivers was used, as well as the static EKF positioning method of RTKLIB [24] using the ProFlex 500 as a receiver. These surveys were done in the same conditions and for the same time, and the results are presented in Figure 11.  
Comparing these results with the results presented in Figures 3 and 4, it can be seen that the EKF and UKF methods proposed provide a significantly better estimate than the survey-in mode, and, although worse, within 25 cm, after roughly 4 h, of the results obtained with the ProFlex 500 receiver, which is a much higher quality receiver than the u-blox 6T used in the survey.  
Figure 11: Auto-survey results for the u-blox 6T and ProFlex receivers for a 24 h survey.  
Figure 10: Mean error for 24 h position survey of the reference station antenna using the finalized position methods.
# Application of Thin-Film Microextraction to Analyze Volatile Metabolites in A549 Cancer Cells
## 1 Introduction
In the current work, we present, for the first time, an application of TFME to the analysis of volatile metabolites in the headspace of Adenocarcinoma cell cultures. Four different sorbents were compared, including CAR, DVB, PDMS and HLB, and optimal extraction conditions were selected for the chosen material. The A549 in vitro model was used to demonstrate the usefulness of the TFME technique in the investigation of volatile metabolites originating from cancer cells.
## 5 Attacks and Countermeasures
### P2P Countermeasures
#### 5.2.2 Countermeasures for Routing Table Manipulation  
The original specifications of most DHT-based P2P systems do not prevent corrupting routing tables of benign nodes by sending forged routing table updates. To address this issue, several countermeasures for a malicious routing table manipulation were proposed recently. [PERSON] and [PERSON] [22] suggested to send a probing message to links provided by other nodes to check the correctness of the routing information. This way, incorrect entries such as not existing nodes can be detected. [PERSON] [23] proposed to generate a second, more secure routing table. The first routing table is the routing table as defined by the DHT, that is using the standard metrics to maintain the routing table. As most proposed metrics for routing table maintenance were developed to reduce traffic and, therefore, favor links to nodes that are in physical proximity, malicious nodes may misuse this metric and feign proximity to increase the probability of being added to the routing table. To prevent this, the second routing table uses a metric that ignores the physical proximity but adds nodes to the routing table that are virtually close to a specific node-ID. For example, whereas for the first routing table in Pastry the entries are chosen based on matching prefixes and a proximity metric, the second routing table as proposed by [PERSON] _et al_. prefers entries that are next to the required prefix for the routing table entry combined with a predefined ending of the node-ID. As node-IDs are assigned randomly, malicious nodes can not misuse the metric of the second routing table. During routing, the first routing table is used until a lookup process fails. Whenever a routing failure occurs, rerouting is initiated based on the second routing table.
# Agriculture 5.0 in Colombia: Opportunities Through the Emerging 6G Network
## 1 Introduction  
Agriculture 5.0 refers to the integration of advanced technologies and the promotion of human-machine interaction for more resilient and sustainable agricultural production, aiming to overcome the limitations of Agriculture 4.0 [1]. This paradigm places people at the heart of digital transformation. The aim of this initiative is to advance technology to solve societal problems, thus establishing a strong nexus between the digital and physical realms. In this context, Agriculture 5.0 encompasses not only optimizing processes but alsoprioritizing environmental sustainability and improving quality of life, using tools such as AI, big data, robotics, cloud computing, and automation [2;3].  
Figure 1 illustrates the key technological components of Agriculture 5.0, including the IoT, AI, robotics, sensors, 6G, and environmental monitoring technologies.  
One of the most pressing challenges facing society worldwide is food security, which is directly linked to economic stability and the well-being of populations [4;5]. In this context, agriculture is not only the foundation of food supply but also a key pillar of economic growth and social cohesion, especially in rural communities [1;6]. Against this backdrop, the emergence of Agriculture 5.0--driven by the convergence of cutting-edge technologies--has become essential for improving resource efficiency, sustainably boosting crop yields, and meeting the escalating global food demand while safeguarding environmental integrity [7;8].
## 2 Experimental Study
### Methods
#### 2.2.3 Measurements and Characterizations  
The surface structure and morphology of electrospun nonwovens fabrics were observed via scanning electron microscopy (Novat(tm) NanoSEM 230, FEI Company, Hillsboro, OR, USA). The diameters of the fibers were measured using imaging software (ImageJ, National Institutes of Health, Madison, WI, USA). The average fiber diameter and distribution for each experiment were estimated using micrographs of fiber morphology and 100 random measurements.  
The chemical structure of the HPO, PLA, and HPO/PLA electrospun samples was analyzed by Fourier transform infrared spectroscopy (FTIR; Nicolet 6700, Thermo Electron Corp., Madison, WI, USA) with an attenuated total reflectance (ATR) accessory. All spectra were recorded in the wavelength range from 4000 to 600 cm\({}^{-1}\). The investigation was carried out using the SpectraGryph program (Dr. [PERSON], Oberstdorf, Germany).  
The thermogravimetric analysis of all electrospun mats was carried out with a Perkin Elmer TGA 7 thermal analyser in a platinum measuring cell and using the Pyris program for data handling. The measurements were performed in artificial air with a 20 \({}^{\circ}\)C/min heating rate. Electrospun mats were heated up to 600 \({}^{\circ}\)C, starting from room temperature. Electrospun mats were acclimatized for at least one day in dry conditions (humidity below 5%) before measurement. The thermogravimetric analyzer is controlling sample weight before and during measurement.  
The thickness of the electrospun nonwoven fabrics was measured using a digital thickness gauge for nonwovens (Checkline J-40-V, Electrostatic Equipment Co., New York, NY, USA) at ten points. It was reported values were the average and standard deviations of obtained results.  
The weight of the electrospun nonwoven fabrics was measured using a digital balance (Radwag WPS 510/C, Radom, Poland) at five different samples (5 \(\times\) 5 cm\({}^{2}\)). It was reported values were the average and standard deviations of obtained results. The weight of the PLA, 3 HPO/PLA, and 5 HPO/PLA electrospun nonwoven fabrics were measured as 2.86 \(\pm\) 0.37 g/m\({}^{2}\), 3.04 \(\pm\) 0.29 g/m\({}^{2}\), and 3.13 \(\pm\) 0.88 g/m\({}^{2}\), respectively.  
The air permeability of the electrospun nonwoven fabrics was measured using a Textest FX 3300 air permeability tester (Textest AG, Schwerzenbach, Switzerland) by fixing the airflow rate at 100 Pa with a test area of 20 cm\({}^{2}\) at ten points. It was reported values were the average values and standard deviations of obtained results.
## 4 Simulations and Measurements
It can be understood in principle that this method directly improves IP1 dB, but does not truly eliminate high-order nonlinear components, but just uses it to expand IP1 dB, so the improvement of IIP3 is limited, which is also the focus of our follow-up work. Although IIP3 does not increase significantly with IP1 dB, its value is reasonable and meets the application requirements. The simulated SSB (Single Side Band)-NF is 9.7-12.1 dB in different IF states. The measured RF to LO isolation is greater than 34.5 dB, which meets the system requirements.
# Joint Luminance Adjustment and Color Correction for Low-Light Image Enhancement Network
## 4 Experiments
### Quantitative Evaluation
#### 4.3.3 Learned Perceptual Image Patch Similarity (LPIPS)  
Learned Perceptual Image Patch Similarity (LPIPS) is a learning-based perceptual image patch similarity metric used to evaluate the perceptual quality of images. The design of LPIPS is inspired by the human eye's perception of images, and it approximates the visual similarity perceived by humans by learning a neural network model. The model uses a convolutional neural network (CNN) to extract features from local patches of an image and calculate similarity scores between patches. The calculation formula of LPIPS is not a simple mathematical formula but is implemented through a deep neural network. Typically, LPIPS models use two images as input, and they output a perceptual similarity score between them. Specifically, the calculation process of LPIPS is as follows: a pre-trained CNN model (usually a deep learning-based image classification model) is used to extract feature representations of the original image and the reconstructed image. Using the extracted feature representation as input, a distance metric function calculates the similarity score between images. The similarity score represents the perceptual difference between images. The smaller the value, the smaller the perceptual difference between images, and the better the image quality. The LPIPS score usually ranges from 0 to 1. The smaller the value, the higher the perceived quality of the image. Compared with traditional image quality evaluation indicators (such as PSNR and SSIM), LPIPS focuses more on factors the human eye perceives and can better capture the perceptual differences between images. It is widely used in tasks such as image generation and editing and is especially suitable for scenarios where perceptual quality needs to be considered. It should be noted that LPIPS is a learning-based metric, and its performance is affected by the CNN model and training data used. Therefore, when using LPIPS for image quality assessment, it is necessary to use datasets and pre-trained models similar to the training model to ensure the accuracy and reliability of the assessment results.
## 2 Summary of the Paper
### Cognitive Radar and Anti-Jamming  
Three ingredients are fundamental to the cognitive radar [14]: (1) intelligent signal processing, which builds on learning through interactions of the radar with the surrounding environment; (2) feedback from the receiver to the transmitter, which is a facilitator of intelligence; and (3) preservation of the information content of radar returns, which is realized by the Bayesian approach to target detection through tracking.  
This cognitive cycle performed by a cognitive radar system--a two-way process--is similar to the time reversal communication system [42], in the spirit that the environment has a strong and continuous influence on the radar returns (i.e., multipath echoes). In doing so, the radar builds up its knowledge of the environment from one scan to another and make decisions of interest on possible targets at unknown locations in the environment. Unlike a communication system, the feedback mechanism, which is a necessary requirement of the cognitive system, is easier to implement, as the radar transmitter and receiver are usually co-located.  
To simplify the description, we will use the cognitive radio as the motivating system; most of contents are, however, valid to cognitive radar. For anti-jamming (shared by both communication and radar), we emphasize the new possibility enabled by wideband (multi-GHz) front end. Hardware is our primary concern in the concept proof stage. New algorithms will be developed when more experimental results are available.
# Windowed Hamming Distance-Based Intrusion Detection for the CAN Bus
## 4 Proposed Methods
### Overview of Our Proposed Methods  
We present an overview in Figure 3 and provide an explanation of the method based on this diagram. In a normal CAN state, we divide the data into windows of size \(N-K\) and store them for further analysis and comparison. Here, \(N\) denotes the number of windows, while \(K\) represents the size of each window. We utilize \(N\) windows, each with size \(K\), to establish the reference state for normal vehicle behavior.  
The system consists of two main modules.  
The first module, referred to as the \"calculate threshold\" module (the dotted area in the figure), is responsible for determining the range of residual similarity in the normal state of the vehicle. During preprocessing and feature extraction, we collect CAN messages and convert them to binary while ensuring consistent message length. To establish the threshold range, we compute the similarity between a sliding window of size \(K\) and the data in the normal state. We then calculate the threshold using a three-sigma approach.  
The second module, referred to as the \"intrusion detection\" module (the solid line area in the figure), is dedicated to detecting abnormal conditions using the threshold range obtained from the first module. In this module, we measure the similarity between the test data and the recorded sliding window. Subsequently, we compare the calculated similarity value with a predefined threshold to identify potential attacks.