esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Simulation-Based Investigation of the Limits of Different Quasistatic Models
## 3 Different Quasistatic Models  
In low frequencies, by neglecting the radiation effects, several approximated models can be derived from [PERSON]'s equations based on the known Galilean limits [14; 15]. There are three main different approaches: one is electric, called the EQS model, one is magnetic, called the MQS model, and one is electric and magnetic at the same time, called the Darwin model.  
Let us denote \(v\) the velocity of the system with modulus \(|v|=L/T\), where \(L\) and \(T\) denote the units of space and time, respectively. We compare this with the light celerity in the continuous medium \(c=1/\sqrt{\varepsilon\mu}\). In the literature, the condition \(|v|\ll c\) is used to classify the quasistatic models, but this condition is not sufficient to distinguish between the EQS, MQS, and Darwin models.  
In vacuum, if we consider this in the one-dimensional case, Equation (1) directly implies  
\[\frac{\partial E^{\prime}}{\partial x}\sim\frac{\partial B^{\prime}}{\partial t} \tag{12}\]  
then, we can obtain  
\[\frac{E^{\prime}}{L}\sim\frac{B^{\prime}}{T}\Longrightarrow E^{\prime}\sim|v| B^{\prime} \tag{13}\]  
where \(X^{\prime}\) is the order of magnitude of \(\mathbf{X}\), (\(\mathbf{X}\) represents the electromagnetic fields \(\mathbf{E}\) or \(\mathbf{B}\)). The notation \(x\sim y\) means that the quantities \(x\) and \(y\) have the same order of magnitude.  
Similarly, from Equation (2), we can obtain  
\[\frac{\partial H^{\prime}}{\partial x}\sim\frac{\partial D^{\prime}}{\partial t }\Longrightarrow\frac{B^{\prime}}{\mu L}\sim\frac{\varepsilon E^{\prime}}{T} \Longrightarrow B^{\prime}\sim\frac{|v|}{c^{2}}E^{\prime}. \tag{14}\]
## 4 Evaluation and Discussion
### Image Quality Analysis
#### 4.4.3 Secret vs. Extracted Image  
In addition to generating a stego image that closely resembles the cover image, a proficient image steganography algorithm must ensure the extraction process yields an image similar or identical to the original secret image. Table 11 presents the Mean Squared Error (MSE) between the original secret image and the extracted secret image for each color channel of the pixels as well as the aggregate. In each case, the MSE for every channel and the aggregate is 0.0, signifying that the extracted secret image remains entirely undistorted.  
Table 12 shows the PSNR between the original secret image and the extracted secret image for each color channel of the pixels and the aggregate. In each scenario, the PSNR for each channel and the aggregate is infinite. This means that, similar to the MSE results, the extracted secret image is not distorted at all. Therefore, we consider the visual inspection of the quality of the secret image and the extracted secret image to not be necessary.
# Bridge Assessment under Earthquake and Flood-Induced Scour
## 1 Introduction
Much research revolves around analyzing the risk and reliability of bridges during specific extreme occurrences like earthquakes, storm surges, winds, floods, and collisions, typically relying on proprietary computational models. Recent findings underscore the reality that bridges are exposed to extreme events over their operational duration. Albania faces multiple sources of hazards. Hence, it is imperative to reevaluate the design criteria that have traditionally been formulated using a single-hazard approach. [PERSON] and [PERSON] (2016) [8] implemented a stress testing approach focused on assessing the combined risk posed to critical infrastructure networks by various severe hazard events, including earthquakes, landslides, and floods, and to assess how effectively the entire network can endure highly unlikely yet severe natural hazard events. This framework offers a valuable tool for transport infrastructure owners and managers to address the challenges posed by extreme natural hazards and enhance the resilience of transportation networks. [PERSON] et al. (2020) [9] and [PERSON] et al. (2022) [10] emphasized the importance of evaluating the impact of the corrosion effects caused by the carbonation phenomenon (for three different levels) on the seismic performance of existing concrete and precast bridges. The corrosion effects are applied by reducing the cross-section of steel rebars. The proposed procedure identifies the first structural component reaching failure providing valuable insights into retrofitting strategies. Compared with current research on continuous concrete bridges, the study work carried out by [PERSON] et al. (2014) [11] shows that the multi-span, simply supported bridge is more sensitive to flood hazards than a continuous concrete bridge. Also, both bridges benefit from scouring due to longer modal periods. This paper suggests enhancing the stiffness of foundations to mitigate the seismic risk of bridges with a scoured foundation, which leads to the same conclusion as the results achieved by this study. The aim of this work is to assess the bridge's performance using a probabilistic-based approach under multi-hazard scenarios involving seismic action and local scour induced by floods to the bridge by utilizing fragility functions. This study examines how the bridge responds when facing the sequential and combined effect of local scour and an earthquake, assessing its performance under these circumstances. Moreover, the study area is situated in a region with a notably elevated annual likelihood of experiencing floods. Fragility analyses are developed, and the probabilities of bridge failure considering different earthquake levels and local scour depths are calculated. These tools are valuable for determining the likelihood of bridge failure across various potential damage scenarios, correlating ground motion intensity with local scour depths [12; 13; 14]. This article emphasizes the importance of making bridges more resilient by adapting their structural capacities to the combined effect of natural hazards compounded by climate change.
## 5 Case Study: Applying CVMan to Analyze Clones in Ubuntu
### Answering PQ1
#### 5.2.2 Clone Prevalence  
The clone prevalence describes the number of clones in one fragment. We obtain the code prevalence from the SCG of Ubuntu 18.04. In Ubuntu 18.04, the clone with the maximum clone prevalence is the _at_impl_ procedure, located in the file _boost/fusion/container/vector/detail/cpp03/preprocessed/vector40.hpp_ in the boost program, with a clone prevalence of 1558. This clone's high clone prevalence is attributed to the presence of numerous procedures with similar functionality in the same file and directory. The code for these procedures is automatically generated and is essentially similar in structure and function. On the other hand, the minimum clone prevalence is 2, indicating that the procedure has only one clone, such as the _snd_pcm_plugin_build_copy_ procedure in alsa-driver and Linux.  
**Insight 1.**_Clones are prevalent, with about one-sixth of the code being cloned. Intra-program clones are often attributed to polymorphisms or functionally-similar procedures, while inter-program clones are often attributed to shared code repositories and the reuse of libraries._
## 3 Results and Discussion
### Total Flavonoid Content of Leptadenia pyrotechnica Stem  
Figure 3 shows the total flavonoid content (TFC) of control and treated _L. pyrotechnica_ samples. Like the total phenolic content, the TFC of the dried samples was higher than that in the control sample. The samples treated for 8 min in the microwave oven exhibited the highest TFC (3.32 mg CE/g DW), while the control sample showed the lowest TFC value (1.38 mg CE/g DW). Statistically, there was no difference (\(p>0.05\)) between the TFC of the sample treated under microwaves for 10 min (3.07 mg CE/g DW) and that of the sample heated conventionally for 120 min (3.04 mg CE/g DW). Although the TFs were comparable, microwave heating was better in terms of a shorter processing time of only 8 min compared to the 120 min for hot-air oven heating.  
The microwave drying (500 W, 430 s) yielded a higher total flavonoid content (20.28 mg trutin equivalent (RE)/g) as compared to (19.60 mg RE/g) the oven drying (60 \({}^{\circ}\)C, 160 min) of coriander leaves [20]. Previous research stated that the heating process liberated the bound phenolic compounds from citrus peel and pomace powder, increasing the free phenolic compound's content. In turn, the antioxidant activity was enhanced [28; 29]. Also, the drying processes involve lowering the samples' water content, which might lead to the  
Figure 3: Effect of microwave heating (MW) and hot-air heating (HA) methods on the total flavonoid content (TFC) of _Leptadenia pyrotechnica_ stem samples. The bars for the treatments bearing different letters are significantly different from each other at \(p<0.05\).  
inactivation of enzymes in the plant matrix and a high level of flavonoids in the extract [30]. The longer drying times for the microwave and hot-air drying resulted in a lower TFC in the samples. For example, the TFC of hot-air dried samples at 60, 120, and 180 min were 2.67, 3.04, and 2.78 mg CE/g DW, respectively. This shows that the phenolic compounds were destroyed under prolonged treatment, and these results follow those of the previous studies [28; 29].
# Minimum Barrier Distance-Based Object Descriptor for Visual Tracking
## 3 The Proposed Algorithm  
### Overview  
We propose a novel tracking algorithm named the MBD-based descriptor for tracking. Given a candidate bounding box of a target in a search region at the current frame, we first divide it into \(n*n\) patches and expand the bounding box to \((n+2)*(n+2)\) patches, as shown in Figure 2b. Each patch is described with a combination of RGB color histogram and HOG feature. Then, in each frame, an MBD map is constructed for each bounding box, and we calculate the MBD from each patch to the seed set, which is represented as \(S\), and generate an MBD transform map by performing the raster scan, as well as the MBD of each patch corresponding to the weight of the patch, which reflects the difference between target and background. Finally, we combine MBD with corresponding patch features to construct a robust object descriptor and evaluate the descriptor on structured SVM to carry out the tracking. The pipeline of our algorithm is shown in Figure 2.  
Figure 2: The pipeline of our proposed algorithm. (**a**) A frame with some candidate bounding boxes in a search region around the position from the previous frame. (**b**) An extended bounding box and the bounding box shown in red. (**c**) The minimum barrier distance (MBD) map of the bounding box in the current frame. The MBD of boundary patches in the extended bounding box is set to 0 (the dark blue area) because they are seeds. (**d**) The MBD map of the bounding box in the first frame. (**e**) The bounding box obtained by our tracker. Structured support vector machine (SVM) is learned online to provide adaptive tracking.
# A Validated Thermal Computational Fluid Dynamics Model of Wine Warming in a Glass
## 2 Problem Configuration and Experiments  
The study investigated the warming of 150 mL of non-sparkling wine in a medium-sized red wine glass. Figure 1b shows the glass dimensions of the CAD (computer-aided design) model that was derived from available drawings and measurements. The glass has a maximum outer diameter of 90 mm and a height of 225 mm. Its total capacity is 490 mL. With the coordinate system originating in the bottom of the glass's center, the wine surface is located at \(y_{\text{surf}}=142\) mm. The glass's dimensions are similar to universal wine glasses used for white and red wine. The glass is only slightly larger than many white wine glasses. Therefore, it could also be used to consume white wine. The authors purchased the glass from the producer Stolzle Lausitz GmbH (Wei\(\ss\)wasser/Oberlausitz, Germany).  
The filled glass was positioned on a wooden table in an empty room. Table, air, and room walls were initially at the same temperature, referred to as the room temperature \(T_{\text{R}}\). The initial wine temperature \(T_{0}\) was less than \(T_{\text{R}}\). The configuration did not experience external influences like hands touching the glass or swirling. The effect of the pouring process was neglected. Thus, the wine warmed up only through heat exchange with the warmer surroundings.
## 2 Measurement Mechanism
### Identification of Geometric Errors  
We took the \(X\)-axis geometric error measurement as an example; measurement for the \(Y\)-axis and \(Z\)-axis was similar. As Figure 4a shows, the spindle of the machine tool drives the angle sensor in the \(X\) direction with the 1-D artifact laid on the \(X\)-axis horizontally. The roll error \(\varepsilon_{x}(x)\), the pitch error \(\varepsilon_{y}(x)\), the position error \(\delta_{x}(x)\), and the horizontal straightness error \(\delta_{y}(x)\) can be identified by measurement  
Figure 3: An illustration of the method of measuring the optical path.  
Figure 2: (**a**) Sketch of the 1-D artifact; and (**b**) sketch of the measurement target.  
of the plane mirrors and revolutionary paraboloids. In the case of good repeatability of the machine tools, the geometric errors remain approximately constant along the same motion path. Therefore, when the 1-D artifact is laid on the \(X\)-axis vertically, as shown in the Figure 4b, the yaw error \(\varepsilon_{z}(x)\) and the vertical straightness error \(\delta_{z}(x)\) can be identified.  
Actually, due to the different orientation and position of each target, what the angle sensor obtains is the relative 2-D angle and 2-D position information of targets. Before identifying the geometric errors of the machine tools, the relative angle and position information should be calibrated by a laser interferometer or by machine tools with higher precision. Through comparing the calibration values with the measurement values obtained by machine tools, the geometric errors of the machine tools at the location of the targets can be identified by calculation.
# Industry 4.0 towards Forestry 4.0: Fire Detection Use Case +
###### Abstract  
Forestry 4.0 is inspired by the Industry 4.0 concept, which plays a vital role in the next industrial generation revolution. It is ushering in a new era for efficient and sustainable forest management. Environmental sustainability and climate change are related challenges to promote sustainable forest management of natural resources. Internet of Forest Things (IoFT) is an emerging technology that helps manage forest sustainability and protect forest from hazards via distributing smart devices for gathering data stream during monitoring and detecting fire. Stream processing is a well-known research area, and recently, it has gained a further significance due to the emergence of IoFT devices. Distributed stream processing platforms have emerged, e.g., Apache Flink, Storm, and Spark, etc. Querying windowing is the heart of any stream-processing platform which splits infinite data stream into chunks of finite data to execute a query. Dynamic query window-based processing can reduce the reporting time in case of missing and delayed events caused by data drift.In this paper, we present a novel dynamic mechanism to recommend the optimal window size and type based on the dynamic context of IoFT application. In particular, we designed a dynamic window selector for stream queries considering input stream data characteristics, application workload and resource constraints to recommend the optimal stream query window configuration. A research gap on the likelihood of adopting smart IoFT devices in environmental sustainability indicates a lack of empirical studies to pursue forest sustainability, i.e., sustainable forestry applications. So, we focus on forest fire management and detection as a use case of Forestry 4.0, one of the dynamic environmental management challenges, i.e., climate change, to deliver sustainable forestry goals. According to the dynamic window selector's experimental results, end-to-end latency time for the reported fire alerts has been reduced by dynamical adaptation of window size with IoFT stream rate changes.  
ToT; query; industry 4.0; stream processing; window size; forestry 4.0; internet of forestry things; forest fire detection; forest sustainability +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 1 Introduction
Considering the complex relationship between the water quality parameters and spectral characteristics, the GBDT model has the potential for faster and more accurate application of remote sensing retrieval of Chl-a concentrations in coastal waters. In light of the above considerations, the main aim of this study was to (1) develop a machine learning algorithm for Chl-a estimation in the coastal region of the Beibu Gulf in Guangxi, exploring the potential of GBDT model in the retrieval of water quality parameters in coastal waters, (2) compare the performance of the GBDT with that of conventional models, and (3) analyze the factors determining the temporal-spatial distribution of the Chl-a concentration in the Beibu Gulf in Guangxi.
# Detection of Volatile Constituents from Food Lures by Tephritid Fruit Flies
## 1 Introduction
In the current study, we used gas chromatography-coupled electroantennaographic detection (GC-EAD) together with gas chromatography-coupled mass spectrometer (GC-MS) to identify antenally active volatiles from five commercially available food-baits. We compared antennal responses from three polyphagous tephritid pests _B. dorsalis_, _B. zonata_, and _C. capitata_ and one oligophagous pest _Z. cucurbite_, to volatiles of the five lures. Furthermore, we compared the antennal responses with an existing database of responses of olfactory receptors of the non-pest fruit fly _Drosophila melanogaster_ (DoOR). Based on antennal responses of the four tephritid fruit flies, we constructed one general blend, and based on antennal responses of each fruit fly species we constructed species-based blends and tested these in an olfactometer assay and in the field.
## 4 Performance Analysis  
The security of the BTPP scheme is based on cryptographic techniques, including the discrete logarithm assumption, secure hash functions, and public-key cryptography. This section will analyze the BTPP scheme from the perspectives of security and privacy.
# Entity Relationship Extraction Based on a Multi-Neural Network Cooperation Model
## 4 Experiments and Results  
### Construction of the Small Sample Dataset  
DuIE2.0 is a commonly used public dataset for entity relationship extraction at present. Its triple composition mainly comes from Baidu Encyclopedia, Baidu feed flow text, and Baidu tieba. It contains a total of 50 entity relationships, as well as up to 172,983 training sets and 19,981 testing sets. However, there is no average sampling for different relationship categories in DuIE2.0, and there are few small samples of public datasets about entity relationship extraction [38].  
To construct the experimental dataset, we analyzed the published DuIE2.0 entity relationship dataset for optimization and simplification. On this basis, we proposed a method that was used to construct a small and balanced dataset. It mainly sets the average value of relationships for big datasets and performs average sampling on the relationships within the big dataset to obtain a simplified average sampling for small datasets. To construct an average sampling small sample dataset, during the experimental process this paper simplified the setting of an average sampling upper limit of 30 for the training set relationship and 10 for the testing set average sampling upper limit. Finally, we built a small sample data set with 462 rows of test data, 1451 rows of training data, and 50 relationship types (including an unknown relationship). Although the dataset was small, there were many types of relationships; this also increased the difficulty of relationship extraction. Some examples of sampling results are illustrated in Table 1.
###### Abstract  
In order to successfully study the condensation and separation of a steam-CO\({}_{2}\) mixture, a boundary layer model was applied to the mixture condensation of steam and CO\({}_{2}\) on horizontal and vertical plates. The modified condensation boundary layer model of steam and CO\({}_{2}\), given the CO\({}_{2}\) solubility in the condensate, was established, numerically solved, and verified with existing experimental data. Different condensation data of steam-air and steam-CO\({}_{2}\) mixtures were compared, and the effect of CO\({}_{2}\) solubility on the mixed gas condensation was analyzed under multiple pressure conditions (1 atm-10 MPa). The simulation data show that the presence of CO\({}_{2}\) will deteriorate the condensation heat transfer, just like air. Given that CO\({}_{2}\) is slightly soluble, some CO\({}_{2}\) can pass through the gas-liquid interface to enter the condensate film and reduce the accumulated CO\({}_{2}\) on the gas-liquid interface, which improves the condensation. However, the solubility of CO\({}_{2}\) is only significant under high-pressure conditions, inducing its effects on condensation. A comparison of the condensation coefficients of the steam-CO\({}_{2}\) mixture shows the lower impact of CO\({}_{2}\) condensation on the horizontal plate compared to that on the vertical plate. For most conditions, the steam-CO\({}_{2}\) mixture gas condensation heat transfer coefficient on the vertical plate surface is still larger than that on the horizontal plate surface, and the improvement in the condensation heat transfer coefficient caused by low CO\({}_{2}\) solubility (2 or 10%) at 10 MPa on the vertical plate is also larger than that of the horizontal plate.  
Keywords: steam-CO\({}_{2}\) mixture separation; condensation heat transfer; boundary layer analysis; CO\({}_{2}\) solubility; horizontal and vertical flat plate +
Footnote †: journal:
### Related Work
#### 1.3.4 Wearable Device Integration  
In [18], [PERSON] aimed to develop an automated algorithm for assessing ECG signal quality using supervised ML. In this study, two datasets were collected using wearable devices, one involving six subjects using an ECG belt under controlled conditions and the other using four subjects using an ECG patch under natural conditions. The goal was to estimate the ECG signal quality for better heart rate monitoring and cardiac diagnosis. The study compared various feature extraction methods (wavelet transforms, an AutoCorrelation Function (ACF), and Principal Component Analysis (PCA)) using a 5-fold cross-validation classification model. After feature selection, an SVM was trained on 10 s segments labeled by experts. The discrete wavelet transform outperformed the others, enabling an Ac of 92.5% and an AUC of 94.8%, followed by PCA and the ACF.  
[PERSON] et al. [10] conducted a study on ECG SQA using data from the Lenovo H3 wearable ECG device, developed by their laboratory and Lenovo (Table 1). They classified ECG signals into two categories, namely \"acceptable\" and \"unacceptable\", where unacceptable signals could be affected by environmental noise, electrode displacement, and other artifacts. They analyzed 9.000 ten-second one-lead ECG segments, labeled by cardiologists, using non-morphological quality indices such as the approximate entropy, sample entropy, and fuzzy measure entropy. The Long Short-Term Memory classifier achieved the highest Ac of 95.8% on these segments.  
[PERSON] et al. [11] validated an SQA algorithm on ECG and respiratory signals from a SensEcho wearable (Table 1) using semi-supervised ML with an Isolation Forest. The algorithm categorized 10 s ECG segments into good, acceptable, and unacceptable levels, achieving an accuracy of 94.97% and 95.58% for ECGs in the validation and test sets, respectively, surpassing the Self-Organizing Map model and performing moderately compared to traditional supervised models.