esa-sceva/satcom-chunk-collection huggingface dataset:
# Sharpening of Worldview-3 Satellite Images by Generating Optimal High-Spatial-Resolution Images
## 3 Results and Discussion
### Quality Evaluation of Sharpened Image
Similar to the outcome of the quantitative and qualitative evaluation, applying the selected  
Figure 6: Results before and after applying the spectral distortion reduction technique (SWIR bands 8, 4, and 1 are shown as RGB): (**a**) original SWIR image; (**b**) results before applying the spectral distortion reduction technique; (**c**) result after applying the spectral distortion reduction technique. band scheme and GS2 technique yields results that are most similar to the spectral information of the original images.
###### Abstract
In addition, it can converge relatively fast to achieve a better normalized-mean-squared error (NMSE), which confirms its superiority to some existing methods. The remainder of this paper is organized as follows. In Section 2, the overall channel nonlinearity is analyzed and the impairment compensation is formulated as a spatial memory pattern prediction problem. The corresponding network architecture and training specification are illustrated in Section 3. Simulation results and discussions are demonstrated in Section 4, and conclusions are given in Section 5. _Notations_: Matrices and column vectors are denoted by upper and lower boldface letters, respectively. \(x(n)\) denotes the \(n\)-th element of \(\mathbf{x}\). The set of real numbers is denoted by \(\mathbb{R}\). In addition, \(\ast\), \(\otimes\), \((\cdot)^{T}\), and \(|\cdot|\) are employed to represent the convolution, the Kronecker product, the transpose, and the absolute operators, respectively. Let \(\|\cdot\|_{p}\) denote the \(\ell_{p}\)-norm and \(\bar{a}\) be an estimation of the parameter of interest \(a\). \(\mathcal{N}(\mu,\sigma^{2})\) is the Gaussian distribution with mean \(\mu\) and variance \(\sigma^{2}\).
## 3 Results and Discussion
### Heat Flux Regimes and Dominant Mechanisms
This suggests that the specific geometry of the Staggered structure is highly effective in this range at facilitating liquid replenishment and delaying the onset of dry spots, thereby enhancing heat transfer. This region's main heat transfer mechanisms are driven by increased nucleation, regulated bubble evolution, and capillary action, maintaining surface wettability and preventing drying;  
Super-high heat flux region (2000 kW/m\({}^{2}\) < \(q\))  
At these extremely high heat fluxes, vapor dynamics become increasingly complex, involving significant local heat flux variations, potential hot spots, and a high risk of reaching the critical heat flux (CHF) and transitioning to film boiling on conventional surfaces. The surface structure is critical in preventing this transition and sustaining ultrahigh heat fluxes by effectively managing vapor removal and liquid supply. The Staggered surface eventually reached CHF within this high heat flux region and transitioned to film boiling. The delayed CHF for this test piece, compared to the Sintered Plain, was due to the enhanced mechanisms discussed in the medium-to-high flux region (improved liquid replenishment due to capillary forces). However, a unique phenomenon was observed with the Large Lattice and Small Lattice structures: traditional CHF was not reached within the tested limits. Instead, the temperature gradient remained constant, and only the wall superheat began to increase gradually. This phenomenon, which indicates a suppression of the catastrophic transition to film boiling, has not been widely reported in previous studies. The authors attribute this to the lattice's unique 3D internal connectivity. It is believed that this structure effectively disrupts the formation of a stable vapor film near the heated surface by breaking up the rapidly growing vapor structures three-dimensionally. While the lattice structure successfully sustains high heat flux by avoiding film boiling, the sheer rate of vapor generation likely exceeds the capacity of complete vapor removal and local liquid replenishment through internal transport alone, leading to the observed continuous increase in wall superheat despite the sustained heat flux. In this region, the surface characteristics are paramount, with the porous structures preventing film boiling through complex bubble management and liquid transport, primarily driven by capillary action and local heat flux management.
## 5 Simulation of Coded Laser Pilots-Based HILPB WPT System
### Simulation Results
#### 5.2.2 Pairing Algorithm Based on Least Receivable Power  
In the second pairing algorithm, the PRX chooses the PTX with the least power first, as illustrated in Table 8 and Figure 15. If the first chosen PTX did not have a sufficient power supply due to lacking MTP, the PRX picks additional PTX with the smallest MPT among the remainings. In the first round, the PRX R1 chose PTX T3 and T4, transmitting the smallest power among PTXs. PRX R1 received 315.3 kW power, which is greater than the required power of 200 kW. The PTX T3 used WPC (161, 283), and T4 used WPC (173, 153). In the second round, the PRX R2 chose PTX T1 and T2, transmitting the smallest power among the remaining PTXs. PRX R2 received power 492.8 kW from PTX T1 and T2 together, greater than the MPQ of 300 kW. The PTX T1 used a WPC (153, 103), and the PTX T2 used a WPC (149, 56). In the third round, PRX R3 could not receive any power because there was no unassigned PTX. Using this algorithm, the PRX selects the PTX that can provide the smallest power first, regardless of the amount of power required by the PRX, and then select an additional PTX that can provide the second smallest power. So, the PRX received power from two PTXs at the same time, not from one PTX. As a result, PRX R1 received 158 % of the MPQ from the two PTXs, PRX R2 provided 164 % of the MPQ from both places, and PRX R3 has no provider.  
\begin{table}
\begin{tabular}{l c c c c} \hline  & & \multicolumn{3}{c}{**PRX-PTX Pairing Sequences**} \\  & & **First Round** & **Second Round** & **Third Round** \\ \hline \multirow{4}{*}{PRX} & R1 & 289.8 kW, 200 kW & 289.8 kW, 200 kW & 289.8 kW, 200 kW \\  & R2 & - & 433.65 kW, 300 kW & 433.65 kW, 300 kW \\  & R3 & - & - & 129.9 kW, 150 kW \\ \hline \multirow{4}{*}{PTX} & T1 & (162, 189), 300 kW & (162, 189), 300 kW & (162, 189), 300 kW \\  & T2 & - & (173, 333), 250 kW & (173, 333), 250 kW \\  & T3 & - & (154, 283), 200 kW & (154, 283), 200 kW \\  & T4 & - & - & (150, 236), 150 kW \\ \hline \end{tabular}
\end{table}
Table 7: Results of WPC using the highest power-based PTX selection algorithm.  
Figure 14: Results of power transmission path establishment.
# Pneumonia Disease Detection Using Chest X-Rays and Machine Learning
###### Abstract
BackgroundOther studies have been conducted using deep learning techniques and chest X-rays in pneumonia detection. Ref. [17] used deep learning models RestNet-101 and RestNet-50 to diagnose pneumonia, used deep learning models for detecting fourteen diseases, including pneumonia, and achieved 96% precision, but experienced limitations that affected precision, due to the intricate [2]. Ref. [18] used AlexNet, ResNet 18, DenseNet201, and SqueezeNet to detect bacterial and viral pneumonia in digital X-ray images. The accuracy of the study was up to 98%, 95%, and 93.3%, making the study useful for screening pneumonia. Ref. [19] built a deep learning architecture with VGG16 architecture to detect pneumonia using chest X-ray images. The model was trained on 5856 chest X-ray images. The model's accuracy was 96.6%, with a sensitivity of 98.1%, specificity of 92.4%, precision of 97.2% and an F1 score of 97.6% [20]. Ref. [21] worked on pneumonia detection in chest X-rays using a convolutional neuro network and transfer learning. There were six models, including VGG16, VGG16, ResNet 50, and Inception v3, trained to classify chest X-ray images into pneumonia and non-pneumonia categories. The models were able to achieve validation accuracy of 85.26% and 92.31%, respectively [7]. The highest accuracy reported in the above-mentioned literature in classifying pneumonia using chest X-rays is 98% [18]; as such, there is room for improvement. The primary aim of this research is to detect pneumonia using chest X-rays and machine learning. This research develops a robust machine learning model for the early detection of pneumonia using chest X-rays, leveraging advanced image processing techniques and deep learning algorithms, the system will accurately identify pneumonia patterns, enabling prompt diagnosis and treatment. This research develops a convolutional neuro network (CNN) model from scratch and also uses a pretrained ResNet50 as the base model. This research addresses the following key questions:  
1. How correctly will the models identify pneumonia using chest X-rays compared to already existing work? 2. How effective is the CNN model developed ground up compared to the pretrained model ResNet-50 for accurately detecting pneumonia? 3. To what extent do advanced image processing techniques, in particular augmentation, affect the detection of pneumonia using chest X-rays and deep learning algorithms? 4. What is the impact of dataset size on the performance of the models?
## 3 Results  
Table 2 shows the baseline clinical and demographic characteristics of the participants. There were no significant differences in the analysis between groups at baseline for any of the variables (\(p>0.05\) for all). No adverse events were recorded. Table 3 shows the baseline and post-intervention scores, as well as the mean differences between and within groups for all of the variables under study. Compared to the baseline values, both the BFR + NMES group and the NMES group showed statistically significant changes in all thigh circumference variables (\(p<0.05\)), as well as a low effect size (d \(\leq\) 0.2). However, no statistically significant differences were found between groups in any variable (all \(p>0.05\)). \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Total Sample (\(n=20\))** & **NMES Group (\(n=10\))** & **NMES + BFR (\(n=10\))** & **\(p\) Value \({}^{*}\)** \\ \hline Age (years) & \(20.4\pm 2.58\) & \(20.5\pm 3\) & \(20.3\pm 2.32\) & 0.87 \\ Weight (kg) & \(52\pm 0\) & \(52\pm 0\) & \(52\pm 0\) & 0.87 \\ Height (cm) & \(1.64\pm 0.76\) & \(1.64\pm 0.73\) & \(1.64\pm 0.82\) & \\ BMI (kg/m\({}^{2}\)) & \(7.05\pm 1.89\) & \(22.11\pm 2.43\) & \(21.37\pm 1.13\) & 0.4 \\ Exercise (h/week) & \(7.05\pm 2.93\) & \(7.2\pm 3.01\) & \(6.9\pm 3\) & 0.83 \\ KJPS 90\({}^{\circ}\) Seated (\({}^{\circ}\)) & \(88.79\pm 8.56\) & \(86.13\pm 9.08\) & \(91.45\pm 7.53\) & 0.17 \\ KJPS 90\({}^{\circ}\) Prone (\({}^{\circ}\)) & \(81.32\pm 9.74\) & \(79.19\pm 11.12\) & \(83.45\pm 8.03\) & 0.34 \\ KJPS 135\({}^{\circ}\) Seated (\({}^{\circ}\)) & \(143.48\pm 11.56\) & \(148.06\pm 5.84\) & \(138.89\pm 14.18\) & 0.06 \\ KJPS 135\({}^{\circ}\) Prone (\({}^{\circ}\)) & \(135.39\pm 6.33\) & \(134.63\pm 6.75\) & \(136.15\pm 6.14\) & 0.61 \\ High circumference 5 (cm) & \(41.3\pm 3.76\) & \(41.75\pm 4.39\) & \(40.85\pm 3.17\) & 0.61 \\ Thigh circumference 10 (cm) & \(45.95\pm 4.37\) & \(46.68\pm 5.18\) & \(45.25\pm 3.52\) & 0.49 \\ Thigh circumference 15 (cm) & \(50.56\pm 4.51\) & \(50.77\pm 5.34\) & \(50.35\pm 3.79\) & 0.84 \\ Thigh circumference 20 (cm) & \(54.81\pm 4.23\) & \(54.82\pm 5.07\) & \(54.8\pm 3.47\) & 0.99 \\ MVEC (N) & \(207.3\pm 76.8\) & \(204.7\pm 51.1\) & \(215\pm 35.4\) & 0.77 \\ MVCC (N) & \(209.9\pm 43.1\) & \(201.9\pm 63.1\) & \(212.6\pm 91.8\) & 0.61 \\ \hline \hline \end{tabular} Data are reported as mean \(\pm\) SD. BMI: body mass index; cm: centimeters; kg: kilograms; KJPS: knee joint position sense; MVCC: maximal voluntary concentric contraction; MVEC: maximal voluntary eccentric contraction; N: Newtons. \({}^{*}\) Between-group statistical significance (one-factor ANOVA).  
\end{table} 
Table 2: Baseline characteristics of participants (mean \(\pm\) standard deviation). \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Group** & **Baseline** & **Post-Intervention** & **Pre-Post-Differences** & \begin{tabular}{c} **Between-Group** \\ **Mean Changes** \\ **Post-Intervention** \\ \end{tabular}
# A Review of Small-Scale Vapor Compression Refrigeration Technologies
## 3 Small-Scale Vapor Compression Refrigeration (VCR) Systems
### Components
#### 3.4.3 Expansion Devices  
The expansion device plays a very important role in the cooling process. It receives the refrigerant at the condenser outlet as a saturated or subcooled liquid at high pressure and throttles it into a low pressure in an isenthalpic expansion process, producing a low-pressure and temperature two-phase flow.  
There are different types of expansion devices available according to system needs or conditions. A poor selection of the expansion device could cause problems such as insufficient flow of refrigerant to the evaporator, causing liquid to reach the compressor, or excessive overheating at the evaporator outlet, which is reflected in deficient system performance. This is why different expansion devices and their effect on performance of small-scale vapor compression refrigeration systems have been studied, including, for example, a study of the effect of expansion valve opening vs. COP [95].  
The most popular expansion devices used for these systems are needle valves [48; 60] and capillary tubes, although micro-orifice [96] and miniature electronic expansion valves [63] have also been used. In the case of capillary tubes, more attention has been paid to determining their length under a previously selected fixed diameter. Some works simply evaluate different lengths and choose the one that shows the best results [14], while others look for the best match between this variable and some other operating condition, such as refrigerant charge, in terms of system performance [61; 62]. Finally, others describe an approach to size this component [46; 51; 52; 97], or even apply various methods to the calculation of single-phase and two-phase region to obtain the overall capillary length, as considered by [PERSON] et al. [98]. The main advantage of capillary tubes over other expansion devices is their simplicity and low cost [68].
# Advances in Improving Energy Efficiency of Fiber-Wireless Access Networks: A Comprehensive Overview
## 2 Fiber-Wireless Broadband Access Network
### Architecture of FiWi Networks
The R&F concept requires the integration of wireless and optical segments on the physical and media access control (MAC) layers, which then requires the translation of protocols on ONU and BS interfaces. This translation introduces additional cost and complexity in the realization of such networks [17]. The application of FiWi networks is particularly interesting in sensor networks which represent the basis for the realization of IoT concepts in the fields such as telemedicine,smart agriculture, Industry 4.0, smart cities, smart buildings, autonomous driving, etc. This will result in the rapid enlargement of machine-to-machine (M2M) communications, which are characterized by a massive connectivity of communicating nodes (sensors) and transmissions of smaller-size data. Improvements to the FiWi network that could facilitate application within the IoT concept include the use of the millimeter-wave communication band (mmWave) for the transmission at high data rates over shorter distances and the concepts of network function virtualization (NFV) and SDN, which could significantly affect the reduction of capital expenditure (CAPEX) and OPEX of the network [18].
## 2 Materials and Methods
### Second Step: Duration Estimation by Sliding Windows and Markov-Based Approaches
#### 2.3.1 Discretization by Sliding Windows  
The original methodology proposed a two-step approach. First, for each time instance within the prediction horizon and for each basic operation, the probability of having good-enough weather is calculated using the forecasts lower and upper bounds (gray area) and the corresponding limits indicated by the red line in Figure 4. Therefore, the approach assumes a uniform distribution for the interval. Consequently, it interprets the fraction of this interval, which lies below the limit, as the probability for having good-enough weather. In a second step, this approach estimates the duration of aggregate operations using a sliding window. This approach estimates the duration of aggregate operations as the sum over each basic operations' duration plus the respective waiting times between those. It commences with the first basic operation at the requested starting time instance. It calculates the joint probability for a time window, corresponding to the length of the operation's base duration, and compares this probability to a user-specified threshold \(\omega\). If the joint probability is high enough, the window is accepted, and the algorithm proceeds to the next basic operation. This next operation is assumed to commence as soon as the previous one finishes. If the algorithm rejects the current window, i.e., the probability to successfully conduct the operation is too low, it shifts the window forward by one hour and reevaluates the new window. This shift represents the vessel waiting for one hour until it commences the operation. The algorithm performs this procedure for every aggregate operation and every possible starting hour within the prediction horizon.  
While this approach provides a straightforward way to discretize the stochastic nature of weather forecasts, its estimation quality strongly depends on the selection of the parameter \(\omega\). Therefore, this article proposes a more general way to estimate the duration of aggregate operations.  
Figure 4: Simulated weather forecasts for three weeks starting on the 1 st of June 2000.
# Research on Lifespan Prediction of Cross-Linked Polyethylene Material for XLPE Cables
## 1 Introduction
### Physicochemical Properties
#### 2.1.2 Differential Scanning Calorimetry (DSC) Test
molecules in the material increases, more cross-linked reactions occur in samples under oxidation reactions, leading to a rapid decrease in crystallinity.
# A Robust Mutual Authentication with a Key Agreement Scheme for Session Initiation Protocol
## The Proposed Scheme
### Offline Password Guessing Attack  
Suppose an attacker \(A\) has obtained client's memory device and extracted secret data \(\{B_{i},C_{i},m,b,K_{s}\}\) stored in it. \(A\) can then start to guess the possible pair \(ID_{i}^{*}\), \(PW_{i}^{*}\), and compute \(B_{i}^{*}=h(h(ID_{i}^{*}||h(PW_{i}^{*}||b))mod~{}m)\). However, even if \(B_{i}^{*}\) is equal to \(B_{i}\), then \(A\) still cannot be sure if the guessed \(ID_{i}^{*}\) and \(PW_{i}^{*}\) are correct. This is because there are various pairs of \((ID_{i}^{*},PW_{i}^{*})\) which can obtain the same \(B_{i}^{*}\). Therefore, the proposed scheme resists offline password guessing attacks.
# A Survey of the Applications of Text Mining for the Food Domain
## 2 Methodology  
In the process of conducting this comprehensive review, a thorough literature search was meticulously carried out to gather relevant information from reputable English databases, including the Web of Science, PubMed, IEEE Xplore, Google Scholar, and others, employing strategic keywords using the following terms: 1. \"text mining\" and \"food\", 2. \"data mining\" and \"food\", 3. \"Text Classification\" and \"food\", 4. \"Sentiment Analysis\" and \"food\", 5. \"Information Extraction\" and \"food\", 6. \"Named Entity Recognition\" and \"food\", and 7. \"Text Clustering\" and \"food\". This series of searches resulted in an initial set of papers. These papers were screened to exclude those that did not meet the following conditions: 1. duplicates from different indexing repositories, 2. topics other than agriculture and text mining, and 3. a lack of peer review. This set of papers was then further expanded by looking for papers that cited or were cited by this initial batch of papers.  
In the literature classification stage, we categorized the selected literature according to different criteria. Firstly, the literature was classified into different categories based on its subject matter or content. Figure 1 visually illustrates the specific application of text mining techniques in the food domain. Secondly, the literature was categorized according to the text mining techniques used; five common techniques were selected for this review, and Figure 2 illustrates the proportion of papers in each area. Finally, we categorized the literature by data source into government and science data, media and social data, and consumer data.  
Figure 1: Application classification in the food domain.  
The organizational structure of this review is as follows: text mining overview (Section 3), data sources in the food domain (Section 4), text mining in food quality control (Section 5), text mining in recipe recommendation and personalized nutrition (Section 6), text mining in food safety regulation (Section 7), challenges and future directions (Section 8), and conclusions (Section 9).
# Predicting Road Crash Severity Using Classifier Models and Crash Hotspots
## 3 Data Collection and Data Processing
#### Descriptive Statistics  
A total of 4093 crashes from 2015 to 2018 made up the dataset used for the analysis. A total of 9031 people were hurt in the collisions. Nineteen percent were fatal crashes, and the remaining was severe injuries. The three leading causes of collisions--sudden turning, speeding, and failure to yield--accounted for roughly 47 percent, 18 percent, and 15 percent of collisions, respectively. Within the available dataset, the pattern of crashes over time demonstrates a decline in serious injury and fatal crashes, as illustrated in Figure 4. In their study, which similarly focused on Saudi Arabia, the authors of [64] ascribed the decline in serious crashes due to the installation of roadside cameras.  
Fatality and serious injury R.T.C.s involving moving vehicles made up about 38 percent of the total number of crashes in the dataset that was made accessible. Numerous vehicle crashes (21 percent of R.T.C.s) and running over the road were two more common collisions (14 percent R.T.C.s). The authors of [62] cited the first two crash categories as being typical of those reported in the Eastern Province. This Saudi Arabian governorate includes the study region from which the data for this study was gathered.  
The available dataset's descriptive statistics for the number of people who suffered fatalities and significant injuries in R.T.C.s are shown in Table 1. Because practically all R.T.C.s involve, on average, at least one fatality victim, the severity index is relatively high. Approximately 1.15 million people reside in the Al-Ahsa region [66]. Comparing Table 1's numbers to those from other research in the literature, they are high. According to the authors of [67], there were just 3.3 fatal crashes per year in Iowa city of U.S.A., as opposed to 352 in Al-Ahsa, in the available data that were made accessible.  
Figure 4: Chronological trend of serious injury and fatal crashes in the research area (adopted from [65]).  
Another study [68] found that the study area had 311 deaths per 100,000 persons, compared to 14.93 deaths per 100,000 in Columbia. It is interesting to note that the crash rates per year for the research area were not found to be more significant when compared to earlier studies. The yearly fatal crash rate in a study conducted by the authors of [67] was roughly 350, which is close to the number for the research area. Although the authors of [69] did not mention serious injury and fatal crash rates per year, the following studies further supported their argument. For fatal and significant injury R.T.C.s, [PERSON] et al. [70] recorded an annual rate of 1463 crashes for Addis Ababa, while [69] recorded an annual rate of 3631 for Torino (Italy). On the other side, there were 1333 fatal and serious injury R.T.C.s annually in the research area.  
This illustrates how R.T.C.s are greater in severity and involve more victims in the research area, although having lower occurrence rates than in other regions of the world. Further details about the crash dataset can be found in [63; 65].