esa-sceva/satcom-chunk-collection huggingface dataset:
# Miniaturized Near-Infrared Analyzer for Quantitative Detection of Trace Water in Ethylene Glycol
## 3 Results and Discussion
### Absorption Fingerprint Analysis  
Figure 2 presents the raw near-infrared (NIR) spectra of 36 ethylene glycol samples. The observed absorption peaks result from molecular anharmonic vibrations, which facilitate transitions from the ground state to higher energy levels [36]. The spectral profiles indicate that the absorption intensity at 1940 nm significantly increases with water content. This peak is attributed to the O-H stretching and combination band absorption of water molecules, serving as a primary indicator of water-specific spectral responses and illustrating the high sensitivity of the NIR spectroscopy to trace amounts of water [37].  
The characteristic absorption peak of ethylene glycol is observed at 1700 nm, corresponding to the C-H combination band. This sharp peak, which is distinct from water-related signals with minimal overlap, serves as a reliable marker for the identification of ethylene glycol. Furthermore, in the 2200-2500 nm range, the combination bands of C-H and O-H vibrations in ethylene glycol are clearly discernible. These peaks result from the combination modes of C-H and O-H bonds, providing essential spectral evidence for the recognition of ethylene glycol.  
By comparing the water absorption peak at 1940 nm with the C-H characteristic peak of ethylene glycol at 1700 nm, the spectral signals of water and ethylene glycol can be clearly distinguished, allowing for precise differentiation between the two components.
# Tilted Beam Fabry-Perot Antenna with Enhanced Gain and Broadband Low Backscattering
## 2 Proposed Antenna System and Its Operation  
### Fabry-Perot (F-P) Cavity Antenna  
The schematic of the proposed F-P antenna system is given in Figure 1. The excitation source of the cavity is a rectangular patch antenna resonating at 6.6 GHz, within C-band. C-band has been selected as many radar deployments can be found within this band [23]. The operating frequency can be easily shifted by varying the dimensions of the patch, keeping under consideration the resonance condition of the F-P cavity. The F-P cavity is formed between the ground plane and the partially reflecting surface (PRS). The designed PRS is a composite superstrate which means both of its sides have been etched with a metallic pattern. All dielectric laminates used in the design are Rogers RO4003C, with dielectric constant \(\varepsilon_{x}\) = 3.38 (process), and thickness of 1.52 mm. Above the PRS superstrate with some air gap, absorptive FSS composed of resistive periodic elements is present. The cavity resonance condition for the F-P antenna shown in Figure 1 can be calculated using Equation (1) given as [24]:  
\[h=\left(\left(\varphi_{PRS}+\varphi_{G}\right)/\pi\right)\lambda/4+N\,\lambda/2 \tag{1}\]  
where \(h\) is cavity height, \(\varphi_{PRS}\) is the reflection phase of PRS, \(\varphi_{G}\) is the reflection phase of the ground plane, \(\lambda\) is free space operating wavelength, and \(N\) is the resonance order taken zero here (zeroth order resonance).  
Figure 1: Schematic of designed antenna system.  
For full metallic ground plane, its reflection phase value is taken as \(\pi\) rad. A conventional cavity usually resonates around \(h=\lambda/2\) at free space wavelength. The cavity height of our design is 20 mm. Next, the working principle of the superstrate assembly is explained by its unit cell configuration.
## 3 Results
### Analysis of the Robustness of Ensemble Model  
We also did an assessment of the variation in the predicted labels of each voxel corresponding to all the individual models (Figure 4). The main aim here was to determine the number of cases where the labels predicted by the individual models were matching with the correct label. The y-axis in Figure 4 shows the number of models that provided the same label for a given voxel whereas the x-axis shows correctness count of final label assigned by the ensemble classifier after majority voting. The results show that as the number of models providing the same label for the voxels increase, the correctness of the final label, which is achieved after adding the predictions of all these labels also increases, thereby underscoring the robustness of the proposed ensemble methodology. Only 7 (out of the 11) models are shown here for simplicity.
## 3 Results and Discussion
### Volatile Sulfur Compounds Characterized by GC-MS  
Sulfur compounds with low odor thresholds are the most important factors that lead to the unique aroma of onions. However, most of them are heat-sensitive and easily volatile, meaning that it is very easy for them to be denatured in the process of pretreatment and subsequent processing, resulting in flavor changes. According to previous research, in onions, S-1-propenyl cysteine sulfoxide (CSOs) is the main precursor of sulfur-containing volatiles [28]. If onions are chopped, a typical aroma appears within seconds. Due to the disruption of the cells, the enzyme allinase comes into contact with CSOs and cleaves the C(\(\char 60\))-S bond, thus releasing ammonia, pyruvate, and a series of unstable sulfenic acids. These sulfenic acids yield, by purely chemical reactions, more stable volatiles, such as the well-known thiosulfinate allicin in garlic, and further volatiles, such as disulfides [26].  
A total of 16 sulfur compounds were identified, such as propan-1-thiol, 2,4-dimethylthiophene, and dipropyl disulfide. The relative contents of all compounds are exhibited in Table S1. According to the research by [PERSON] [29], compounds with a propyl thiol group in their molecular structure generally have an onion-like flavor. Among these 16 compounds, the structure of 10 compounds contained propyl thiol, among which propyl mercaptan, dipropyl disulfide, and methyl propyl trisulfide were the most important onion characteristic species. Propan-1-thiol has a strong irritation effect and is the main source of the antibacterial effect of onion. Dipropyl disulfide and dipropyl trisulfide are the main volatile compounds present in fresh onions and are the main reason for the strong aroma of fresh onions [30]. In addition, dipropyl disulfide, (_E_)-1-(prop-1-en-1-yl)-3-propyltrisulfane, and 1-(1-(methylthio)propyl)-2-propyldisulfane have also been identified in onion oil [31]. Of note, 2,4-dimethylthiophene, 1-allyl-2-isopropyldisulfane, 2-mercapto-3,4-dimethyl-2,3-dihydroiophene, 1-(1-(methylthio)propyl)-2-propyldisulfane, 1-methyl-2-(1-(propylthio)propyl)disulfane, 2-methoxy-5-methyl-thiophene, methyl 1-(1 propenylthio)propyl disulfide, trans-3,6-diethyl-1,2,4,5-tetrathiane, 6-ethyl-4,5,7,8-tetrathia-undecane, and (_Z_)-1-(1-propenyldithio)propylpropyl disulfide were identified in onion for the first time.
# A Neural Network-Inspired Matrix Formulation of Chemical Kinetics for Acceleration on GPUs
## 2 Methodology
### Organization of Data  
The notion of \(N_{C}\), \(N_{R}\), and \(N_{S}\) introduces three matrix types that represent the backbone of the methodology discussed above: an \(N_{C}\times N_{R}\) reaction matrix, an \(N_{C}\times N_{S}\) species matrix, and an \(N_{S}\times N_{R}\) (or the transpose) stoichiometric matrix. As per the formulations in Section 2.1, reaction matrices are used to define quantities such as rate constants and reaction rates, whereas species matrices are used to define concentrations, mass fractions and species net production rates. Stoichiometric matrices can be interpreted as tools that transform a matrix from the species representation to the reaction representation or vice versa.  
Assuming the matrices occupy contiguous blocks of memory, there are two ways to go about their storage: a row-major or a column-major representation. As an example, consider some given \(N_{C}\times N_{R}\) matrix \(\mathbf{A}\) (a reaction matrix). In the row-major format, the reaction matrix is represented as an \(N_{C}\)-sized stack of \(1\times N_{R}\) row vectors. On the other hand, in the column-major format, the matrix is interpreted as an \(N_{R}\)-sized stack of \(N_{C}\times 1\) column vectors. This difference is shown in Figure 2a. There are a few reasons the column-major representation is more beneficial here. First, in most realistic high-fidelity applications, we have the condition \(N_{C}\gg N_{R}>N_{S}\). From a GPU efficiency perspective, it is important to have memory coalescence along the highest dimension \(N_{C}\), which necessitates a column-major storage format for \(\mathbf{A}\). (Good coalescence ensures that, for a given number of floating point operations (FLOPs) in the kernel, the FLOPs executed by the GPU per byte of transferred global memory (arithmetic intensity) is as high as possible. Arithmetic intensity is used as a general metric to assess GPU performance, and it is discussed further in Section 3.1. For more information on the specifics of global memory coalescence, the reader is referred to the CUDA toolkit documentation.) Second, the practical implications of designing a matrix as a collection of \(N_{C}\times 1\) vectors is appealing--it allows for naturally extracting contiguous \(N_{C}\times N_{X}\) subsets of the \(\mathbf{A}\) matrix, where \(N_{X}\leq N_{R}\). This becomes especially useful when dealing with different reaction types as discussed in Section 2.3. Lastly, the column-major format allows for seamless integration with cuBLAS, a highly efficient GPU-based linear algebra library.
## 2 Background and Related Work
### Overview of Related Studies
#### 2.2.2 Route Selection  
The route selection problem is mainly investigated in the context of half-duplex operation (see, e.g., [9; 39; 40; 41; 42; 43; 44; 45; 46]). Specifically, the authors of [39] conducted an extensive simulation of an IAB network with a two-hop backhaul. The cell was divided into three sectors, each having two IAB-nodes. The IAB-donor is placed in the center and has three physical antennas. The proposed resource allocation algorithms uses information on the nominal position of the IAB-nodes and determines their real position to further maximize the metric of interest, which is the spectral efficiency.  
The study in [40] also considered the question of maximizing the spectral efficiency, but at the expense of forming an adaptive network topology. A related graph optimization problem was formulated and solved by combining machine learning and graph embedding methods. The works in [41; 42] studied the coverage probability in single-hop IAB networks.  
\begin{table}
\begin{tabular}{l l c c} \hline \hline
**Challenge** & **Sub-Problem** & \multicolumn{2}{c}{**Related Papers**} \\  & **Half-Duplex Assumed** & **Full-Duplex Assumed** \\ \hline Topology management & Protocols and architecture design & [20; 21; 22; 23; 24; 25] & [26; 27; 28; 29; 30; 31] \\  & Control and user plane procedures for multi-hop & [32; 33; 34; 35; 36] & [37; 38] \\  & traffic forwarding and QoS handling & & [37; 39; 40; 41; 42; 43; 44; 45; 46] & N/A \\ \hline Route selection & Management of backhaul links and dynamic route & [9; 39; 40; 41; 42; 43; 44; 45; 46] & N/A \\  & selection and benefit evaluation & & [48; 49; 40; 41; 42; 44; 45; 46] & \\ \hline Resource allocation & Multiplexing & [11; 12; 47] & [48; 49; 40; 41; 42; 43; 44; 45; 46] \\  & Cross-link interference & [10; 15; 16; 51; 52; 53; 54; 55; 56] & [52; 53; 54; 55; 57; 58; 59; 60; 61] \\ \hline Spectral efficiency & Physical Layer solutions or enhancements & N/A & [57; 58; 59; 60; 61] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of IAB-related challenges in the recent literature.  
Specifically, the authors of [41] explored the impact of environmental factors and compared the IAB architecture with the wired backhauling option.  
The authors in [44] focused on measuring the end-to-end throughput and delay at the application layer. Resources were allocated so as to maximize the first metric and to minimize the second by using a tree maximum weight matching algorithm. The studies [45; 46] were devoted to the design of fair scheduling and flow control algorithms. However, the authors did not explicitly capture packet delays caused by buffering at IAB-nodes. The system model in [9] was based on the assumption that the number of hops is limited to just two network steps and the IAB-nodes are located in the best line-of-sight positions. The work aimed at developing a scheduling policy for the backhaul network.
## 2 Materials and Methods
### Data Acquisition and Image Preprocessing  
Under the parameter settings of this paper, the sampling value of each channel was the result of averaging 4 sub-samples. One thousand background signal (with radiation but without materials) values and one thousand transmission signal values of X-rays through each calibration sample were collected and averaged for calibration. As a result, the measurement value of low-energy integral of transmission signal before attenuation \(T_{L0}\), the measurement value of high-energy integral of transmission signal before attenuation \(T_{H0}\), measurement value of low-energy integral of transmission signal after attenuation \(T_{L}\) and measurement value of high-energy integral of transmission signal after attenuation \(T_{H}\) of most line-scan-direction channels could be obtained.  
For the actual in-line data acquisition, when the acquisition of a frame (320 lines) of an image was completed, the real-time value of the Y-direction encoder was read immediately for position correction. Then the next frame of image acquisition was continued immediately. This was repeatedly conducted.  
The image data need to be preprocessed. First, background noise should be removed from all collected original data. Second, the images should be filtered, which can be realized by using the adjacent data substitution method to filter the cross-talk noise of the fixed channels and then median filtering for the random salt-and-pepper noise of the whole image. Third, the initial low-energy \(T_{L0}\) and high-energy \(T_{H0}\) should be obtained before production begins. In order to reduce the influence on R_value calculation caused by ray-source fluctuation, the \(T_{L0}\) and \(T_{H0}\) of a certain number of real-time image data outside the range of materials should be averaged, that is, updating them by moving and filtering the mean value. Fourth, images should be segmented for threshold judgment and binary processing by low-energy shading \(S_{L}\). After that, the projection of each material in the X-Y plane can be considered as a material range and should be numbered. Then \(T_{L}\), \(T_{H}\) and \(S_{L}\) values of each pixel within the material range can be obtained to calculate R_value of each pixel according to Equation (7).
# Adopting Signal Processing Technique for Osteoporosis Detection Based on CT Scan Image
## 5 Results and Discussion
### Frequency Index  
The frequency index is obtained by transforming the image into a frequency domain through a 2D Fourier transform. The selected ROI is transformed, and the value of the spectral is used to calculate the frequency index for the selected ROI. The frequency index is defined as the total intensity of spectral in the ROI divided by the number of pixels in the ROI. Implementation of this frequency attribute on the data set is illustrated in Figure 4. The image resulting from the Fourier transform indicates the frequency distribution, wherein the low-frequency component is distributed in the center, while the high-frequency component is distributed in the peripherals. The frequency index showed a significant increment from normal to osteopororotic bone at the pelvis, but decreased at the hip location on the CT scan images. Meanwhile, there are no differences between normal and osteopororotic bone at the lumbar spine location from DEXA scanning. It is still difficult to see the consistency of this attribute in differentiating osteoporosis from normal bone.
# iRoute--An Adaptive Route Planning Solution for Commercial Vehicle Fleets
## 5 Discussion and Results
The survey was about the experience of the respondents with planning solutions, feedback about the differences between the plan and execution, what the causes of these differences are, and how are they dealing with them. In the end, we asked about the benefits and disadvantages of an adaptive algorithm and whether they are considering evaluating such a solution. The results of the survey are as follows:  
* A total of 64.7% of the respondents are using a software solution for planning and monitoring the routes. That means that, on one side, there are businesses that do not have a planning solution and they can use solutions such as iRoute. On the other side, the ones that have a solution already know the shortcomings when it comes to the difference between the plan and execution and they could be convinced to try a different solution that will better suit their needs;
* More than 68% of the respondents visit most of their customers at least on a monthly rate. This type of customer could benefit from a solution like iRoute. Their historical routes will be available in a few months;
* A total of 70% of the respondents have a difference between the execution and plan for more than 10%. Only 15% have differences of less than 10%. The rest do not know what the difference is as they probably do not use a solution that shows them this indicator;
* The main reasons for these differences were the longer stops, the road conditions, and the road traffic. The first reason is not addressed by our algorithm (yet), but the latter two can be reduced by using iRoute;
* The most voted-for benefit of an adaptive algorithm was better predictability, followed by increased customer satisfaction;
* Next, we asked about the downsides of using an adaptive algorithm and the most voted-for one was that it needs historical execution data to be used. For this, we will try to find other sources of historical data to be used in the beginning, such as road traffic patterns, anonymously using data from other customers in the same area, and so on;
* A total of 73.5% of the respondents are willing to evaluate a solution such as iRoute. From this survey, we can see that the companies would need a solution that uses our adaptive algorithm, as they need better predictability in their daily schedule and need to know if their drivers are using the company resources in the most sustainable way possible.
# Can Walnut Serve as a Magic Bullet for the Management of Non-Alcoholic Fatty Liver Disease?
## 3 Results and Discussion
### Mechanisms by Which Walnut Contributes to the Management of NAFLD
#### 3.2.5 Walnut and Gut Microbiota  
Fiber and polyphenols--abundant in walnuts--exert a prebiotic effect and alter the gut microbiota profile, suggesting that walnuts may correct dysbiosis associated with NAFLD. An ability to influence gut microbiota may also be useful for the management of NAFLD because gut microbiota are known to influence the lipid metabolism, glucose metabolism, insulin resistance, immune function, and mucosal and systemic inflammation.  
In a meta-analysis of randomized controlled trials that determined the metabolic effects of microbiome-targeted therapies, the use of probiotics/synbiotics was associated with improvement in the levels of liver-specific markers of hepatic inflammation, liver stiffness, and steatosis. This study suggested that modulation of gut microbiota can be useful for the management of NAFLD [89].  
The effects of walnut on gut microbiota were also examined in a controlled-feeding, randomized crossover study in healthy men and women who received isocaloric diets containing either 0 or 42 g walnuts per day for three weeks, with a one-week washout period between the two diet periods. Walnut consumption affected the gastrointestinal microbiota profile with higher relative abundance of Faecalibacterium and Clostridium and lower relative abundances of Ruminococcus and Bifidobacterium, and reduced LDL-cholesterol. These results suggest that walnuts may provide health benefits through the modulation of gut microbiota [90].  
In a randomized, controlled, prospective, crossover study [91], a walnut-enriched diet was provided to the subjects, and 165 rRNA gene sequencing was conducted using the stool samples. Daily intake of 43 g walnuts for eight weeks significantly affected the gut microbial environment by enhancing probiotic- and butyric acid-producing species in healthy individuals.  
An animal study using Fischer 344 rats also demonstrated that alterations in the gut microbial profile occurred after consuming a diet containing walnut [92]. Dietary walnuts increased the abundance of Firmicutes and reduced the abundance of Bacteria-odetes. Walnuts increased the abundance of probiotic-type bacteria including Lactobacillus, Ruminococcaceae, and Roseburia but decreased that of Bacteroides and Anaerobic-durentcus. Altering the gut microbial environment might be a new mechanism by which walnuts confer their beneficial health effects.  
Clinical trials and animal study have suggested that walnut, which is rich in fiber, polyphenol, polysaccharides and \(\alpha\)-linolenic acid, exert positive effects on gut microbiota signatures, resulting in a positive effect on the gut health. Thus, walnuts might affect NAFLD by modulating the gut microbiota.
# Energy-Aware Next-Generation Mobile Routing Chains with Fog Computing for Emerging Applications
## 3 Proposed Load-Balanced and Energy-Efficient Mobile Routing Protocol
### Proposed Algorithm
#### 3.3.2 Reliability-Based Lightweight Authentication (RLA)  
In this section, the detail of network reliability with lightweight authentication methods is discussed. The proposed algorithm provides a simple and lightweight security mechanism for achieving reliable communications. It explores the concepts of hybrid cryptography and utilizes the methods of symmetric and asymmetric key management. In the proposed algorithm, the peer devices \(d_{i}\) and \(d_{j}\) nodes shared the session key \(SK\) for the link \((i,\ j)\), as defined in Equation (6).  
\[d_{i}->\ d_{j}:\ SK(i,\ j) \tag{6}\]  
Moreover, before sharing the \(SK\), the proposed algorithm encrypts the key with the support of private/public keys. \(d_{i}\) encrypts the \(SK\) denoted by \(SK\), using the public key of \(d_{j}\) and upon receiving, the \(SK\) is decrypted by using the private key of \(d_{j}\). In this way, both the devices have the secured session key and can use it for data forwarding of message \(M\) along with time stamp \(TS\) as defined in Equation (7).  
\[d_{i}->\ d_{j}:\ E(SK(M)+TS \tag{7}\]  
In Figure 3, key management and reliable forwarding are the two main stages. In keys management, various keys are generated based on hybrid cryptography. Moreover, keys are verified and securely distributed among the devices. Afterward, the received keys are utilized for N2N low-level security. Furthermore, later, fog nodes are communicated with sink nodes with the same practice for achieving secure key distribution and data encryption. Algorithm 2 shows the pseudocode of the proposed algorithm for securing the network connections with lightweight authentication.  
```
ProcedureRLA if devices \(d_{i}\) and \(d_{j}\) has any data to transmit then  call con_dev() endif  devices \(d_{i}\) and \(d_{j}\) nodes shared the session key \(SK\) for the link \((i,\ j)\) \(d_{i}->\ d_{j}:\ SK(i,\ j)\) \(d_{i}\) encrypts the \(SK\) denoted by \(SK\), using the public key of \(d_{j}\) \(SK\) is decrypted by using the private key of \(d_{j}\) if \(SK^{\prime}\) is verified then  send the data packets \(M\) \(d_{i}->\ d_{j}:\ E(SK(M)+TS\)  endif if the session time is expired then  call con_dev() endif endprocedure
```  
**Algorithm 2** Secured network connections with a lightweight authentication scheme  
Figure 3: Developed components of the proposed model.