esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Saliency-Guided Binary Change Detection  
Dynamic wetlands monitoring in large-scale areas was an effective method that firstly extracted changed and unchanged areas, and then identified different change categories. Changed and unchanged areas were extracted using binary CD methods, and different change categories were identified using multiple CD methods. Many binary CD methods have been proposed, they can be divided into pixel-based, sub-pixel-based, object-based, and scene-based CD methods, according to different analysis units [35].  
Pixel-based and object-based binary CD methods are widely used, but each of them has its advantages and disadvantages. Pixel-based CD methods are sensitive to image registration errors, and their salt-and-pepper phenomena are serious. Object-based CD methods can improve salt and pepper phenomena, and they are less affected by image registration errors, but they are greatly affected by image segmentation parameters. In order to utilize their advantages and solve their disadvantages, we previously proposed a saliency-guided binary CD method combining pixel-based and object-based CD methods, and describe it as follows. Firstly, the different images are obtained and saliency detection is used to generate saliency maps of the different images using a maximum symmetric surround (MSS) algorithm. Then, the combination of fuzzy C-means (FCM) with Markov random field (MRF) is used to extract the initial CD result at a pixel-based level. Secondly, a multi-scale segmentation algorithm is utilized for object-oriented image segmentation, in which the rate of change of local variance (ROC-LV) is used to estimate the optimal segmentation scales. Finally, the uncertainty index of segmentation objects is constructed to adaptively select changed and unchanged samples, and these samples are then used for training random forest classifier, to obtain the final CD results. The reference paper [36] can be consulted for further details.
# Transformation of Trigonometric Functions into Hyperbolic Functions Based on Cable Statics
## 2 Background
### Theory of Parallels  
In the geometry of Euclid's elements, five postulates are stated from which the other prepositions are deduced. The first four postulates are simple and intuitive; [PERSON] demonstrated 27 prepositions referring to triangles (their construction, relations between sides and angles of the same triangle or of two different triangles) as well as adjacent angles and angles opposite the vertex. It is stated by the Fifth Postulate that if, in the same plane, a straight line intersects two other lines, referred to as lines \(a\) and \(b\), and the sum of the interior angles on the same side of the transversal is less than two right angles (less than 180 degrees), then these two lines, when extended indefinitely, will eventually be intersected on the side where the sum of these angles is less than 180 degrees. To visualize this, a plane with two parallel lines, \(a\) and \(b\), that do not intersect is imagined. A third line, known as the transversal, is then considered, which is cut across both parallel lines. According to the Fifth Postulate, if the sum of the interior angles formed on the same side of the transversal is less than \(\pi\), the two parallel lines will eventually be met at some point when extended. The behaviour of parallel lines in Euclidean geometry is described by this postulate, with a clear condition being established for when lines will intersect, as opposed to remaining parallel indefinitely, as is illustrated in Figure 1 [24].  
Euclidean geometry remained unchallenged for several centuries. But in the Renaissance, efforts to understand [PERSON]'s work brought up the \"parallel problem\" again. The authors [PERSON] and [PERSON] demonstrated that [PERSON]'s fifth postulate is not necessary and independently discovered hyperbolic geometry. The appearance of hyperbolic geometry brought with it the inevitable task of defining which of the two geometries was \"true\". This meant studying whether material space was Euclidean or hyperbolic. In the end, it was determined that both geometries are consistent and neither is truer than the other [18; 19; 24].  
Generalized hyperbolic distributions have been of great importance in recent years because their parameters have broad particular applications. They have been used to evaluate financial models, using optimal portfolio selection and risk measurement assessment. Non-Euclidean (hyperbolic) geometries allowed for the advancement of new theories such as relativity. Hyperbolic functions have wide application with respect to special relativity, with particular reference to Lorentz transformations and kinematics [25].
## 3 Results and Discussion  
### Enzyme Production  
In this study, _A. foetidus_ was grown in submerged liquid culture supplemented with soybean husk as the carbon source and screened for its ability to induce \(\beta\)-mannanase activity. The highest levels of enzymatic activities were observed for \(\beta\)-mannanase, pectinase and xylanase. The induction profile during growth of _A. foetidus_ on soybean husk showed that \(\beta\)-mannanase activity increased steadily without a lag phase and reached the highest level (1.987 IU/mL) on the fifteenth and sixteenth days of culture, while xylanase and pectinase showed highest levels on the seventeenth day of culture with activities of 0.858 IU/mL and 0.798 IU/mL, respectively (Figure 1). The growth profile was accompanied by several protein spikes, which peaked at the seventh and seventeenth days of cultivation (results not shown). This protein profile probably includes other mannan-degrading enzymes in addition to \(\beta\)-mannanase, which are simultaneously produced and may be involved in soybean husk breakdown. Filamentous fungi often produce multiple forms of mannan-degrading enzymes [3]. This multiplicity may be due to the requirements to bind and degrade substrates of varying complexity. Therefore, based on the growth curve of the fungus, we established fifteen days as optimal for fungal growth in liquid medium containing soybean husk.  
The bromatological analysis showed that soybean husk was a rich source of lignocellulosic. The highest representation (36.6%) was found for lignin, followed by hemicellulose (33.8%) and cellulose (24.4%).
## 2 State of the Art
### Linked Building Models  
As a working method, BIM requires close and coordinated collaboration, which increases the need for a shared space where the project team can put, hold, and access information throughout BIM projects [14]. Such a shared space is a Common Data Environment (CDE). According to the ISO 19650-1:2018 standard, a CDE is an \"agreed source of information for any given project or asset, for collecting, managing, and disseminating each information container through a managed process\" [49]. The integration of technical or spatial part models into a shared coordination model using the Industry Foundation Classes (IFC) data format is supported by most authoring tools and collaboration platforms [50]. Furthermore, the building model and the inherent model elements can be linked to further data, such as documents, plans, schedules, or Bills of Quantities (BoQs) [51]. This integration of document-based information can be realized either by referencing the documents using the IFC schema or with a more general approach using a decoupled set of links in a separate file with a predefined schema and structure [51]. However, the extensions of the IFC data schema to integrate cross-domain information into a common shared model do not increase the interoperability between applications [52]. Therefore, a schema for modeling link sets was employed in a generic multi-model approach [52] and the COINS container [53], which both proposed a comprehensive data model comprising a hierarchy of different link types and meta-data descriptions for information models. The efforts of the container developments mentioned here were bundled and published in an international standard called the Information Container for linked Document Delivery (ICDD) [54]. The ICDD supports object-oriented and document-oriented data and supplements the integrated lifecycle information management. It is modeled using Semantic Web technologies recommended by the World Wide Web Consortium (W3C) and enables an extendable structure of the container schema, unique identification of objects, and independent localization of data for the integration of distributed data [53]. The main purpose of the container-based data exchange is to provide a format for linked domain models utilized by several different application cases [52]. These can be used to, e.g., link autonomous part models to a coordination model, versioned models to an overall model, or create cross-domain models, such as a 4D scheduling model or 5D costing model.
## 3 Results and Discussion  
### Correlation Study of Signal Data  
In the CNN model developed, using digitized data of the ECG/TMT-ECG signal as input, 6630 data were considered in the single-lead case; in the case of 12-leads, 552 data were considered for a period of 1 s. Out of these, a set of 100 sample data were randomly selected for the data validation process [11; 32]. A least-square fit analysis was conducted for the randomly selected 100 leads (considering both CAD and normal data). The table showing the summary of test results, i.e., Mean\(\Delta\) and Median\(\Delta\), indicates the mean and median of the sample-by-sample difference (derived ECG minus manual (original) ECG) of every single lead. Results show a good fit (Table 3). The results showed a good degree of concurrency, as illustrated in Figure 11. Moreover, Pearson's correlation coefficient [62] was also adopted to find the similarity between normalized original and extracted data for the selected leads. These were one-dimensional datasets, such as in the earlier case. The result of the averaged correlation was found to be 96%. Thus, the developed prediction model for CAD incorporated a better approach for feature extraction in terms of PQRST (Figure 11) and data extraction from the ECG signals (Figure 12).  
Figure 11: Comparison of features of the ECG sample and digitized ECG for the sample data.
# A Review of Acoustic Emission Source Localization Techniques in Different Dimensions
## 3 Sound Source Localization Methods for Two-Dimensional (2D) Structures
### Beam Forming
The results obtained by this method are compared with the results obtained by the beam forming technique and large deviations are discarded; otherwise, they are used as the reference direction for the next ping data in an iterative loop improving the localization accuracy. [PERSON] et al. [27] proposed a method to determine the position of a sound source in an anisotropic structure by using an L-shaped transducer array, as shown in Figure 6b.
## 5 Results
### Validation and Comparison  
Figure 9a,b illustrates the ROC plot assessment results based on the training and validation subsets. The AUC value for the success rate curve using the RF model was estimated to be 0.909, which corresponds to a prediction accuracy of 90.90%, followed by the KLR (0.877) and ADTree (0.812) models.  
RF showed the lowest SE value (0.0225), followed by KLR (0.0294) and ADTree (0.0341), and also the shorter CI value (0.088) followed by KLR (0.1) and ADTree (0.118) (Table 5). Similar performance patterns were estimated when using the validation subset, with the AUC value for the predictive rate curve using the RF model estimated at 0.811, followed by the KLR (0.797) and ADTree (0.773) models. Again, the RF model showed the lowest SE (0.0526), followed by ADTree (0.0578) and KLR (0.0591). As for the CI values, RF showed the shortest interval (0.183), followed by KLR (0.188) and ADTree (0.195) (Table 6). Based on the validation analysis, all three models appear to provide good accuracy, with the RF model producing slightly better results in term of AUC values, low SE, and short CI values for both the training and validation subsets. Concerning the performance of KLR based on the training subset, it was found that it provides results relatively close to the RF model AUC, SE, and CI values.
# Optimizing Automated Trading Systems with Deep Reinforcement Learning
## 3 Research Methodology
### Artificial Intelligent Agent  
The agent interacts with the environment \(\mathcal{E}\) with the task of maximizing the expected discounted reward. They execute actions from the action space and receive observations and rewards. At each time step, which ranges over a set of discrete time intervals, the agent selects an action \(a\) at state \(s\). The behavior of the agent is governed by a stochastic policy, \(\pi:\mathcal{S}\rightarrow\mathcal{A}\), which tells the agent which actions should be selected for each possible state. As a result of each action, the agent receives a scalar reward \(r\), and observes the next state \(s^{\prime}\). The policy is used to compute the true state-action value, \(Q_{\pi}(s,a)\), as:  
\[Q_{\pi}(s,a)=E_{\pi}\Big{[}\sum_{t=0}^{\infty}\gamma^{t}r_{t}|s_{0}=s,a_{0}=a \Big{]}, \tag{5}\]  
where \(\gamma\in[0,1]\) represents the discount factor balancing between immediate and future rewards. This basically helps to avoid infinity as a reward in case the task has no terminal state.  
The aim of the agent is to learn an optimal policy which defines the probability of selecting action that maximizes the discounted cumulative reward, \(\pi^{*}(s)\in argmax_{a}Q^{*}(s,a)\), where \(Q^{*}(s,a)\) denotes the optimal action value. One of the most popular value-basedmethods for solving RL problems is Q-learning algorithm [29]. The basic version of the Q-learning algorithm makes use of the Bellman equation for the Q-value function, whose unique solution is the optimal value function \(Q^{*}(s,a)\):  
\[Q^{*}(s,a)=E_{\pi}\Big{[}r+\gamma max_{a^{\prime}}Q^{*}(s^{\prime},a^{\prime})|s _{0}=s,a_{0}=a\Big{]}. \tag{6}\]
# Design and Signal-Decoding Test Verification of Dual-Channel Round Inductsyn Decoding Circuit
## 3 Decoding-Circuit Hardware Design
### Design of Power Amplification Circuit for Excitation Signal  
The differential excitation signal generated by the decoding chip AD2S1210 is output from the EXC+ and EXC\(-\) pins, with a typical value of 7.2 Vp-p. The amplitude of the signal meets the input requirements for the excitation signal of the round inductsyn used in this paper. However, the excitation signal current directly output by the AD2S1210 is too small, resulting in poor driving effect on the round inductsyn. To address the issue of low output current, this paper builds a proportional amplification circuit based on the high-output-current operational amplifier AD8397. By increasing the output current of the excitation signal and slightly boosting the signal's amplitude, the driving capability of the excitation signal on the round inductsyn is enhanced. The schematic diagram of the excitation signal power amplification circuit is shown in Figure 4.  
In Figure 4, resistors \(R1\), \(R2\), \(R3\), and \(R4\) are used to set the amplification factor, capacitors C1 and C2 are for filtering to remove high-frequency components from the excitation signal, and potentiometer \(R5\) is used to set the in-phase input voltage or bias voltage of AD8397. By adjusting the bias voltage, the average voltage or common-mode voltage of the output at terminals OUTPUT_EXC+ and OUTPUT_EXC\(-\) of the power amplification circuit can be adjusted. The calculation relationships between the bias voltage  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Signal Type** & **Design Specifications** \\ \hline Excitation Signal Amplitude & 7.2 Vp-p \(\pm\) 1.0 V \\ Induced signal amplitude(single-ended) & 1.6 Vp-p \(\pm\) 0.3 V \\ Induced Signal Amplitude (Differential) & 3.2 Vp-p \(\pm\) 0.6 V \\ Amplitude Difference between the _SIN_ and _COS_ Terminals & \textless{0.1 V} \\ Phase Difference between the _SIN_ and _COS_ Terminals & \textless{5.0}\({}^{\circ}\) \\ Phase Difference between Induced and Excitation Signal & \textless{44.0}\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Decoding-circuit design specifications.  
Figure 4: Excitation signal power amplification circuit schematic.  
\(V_{b}\), input voltage \(V_{I}\), output voltage \(V_{o}\), and various component parameters are shown in Equation (2).  
\[\left\{\begin{array}{l}V_{b}=\frac{R_{4}}{R_{4}+R_{5}}V_{REF}\\ V_{O}=\frac{R_{1}+R_{2}}{R_{1}}V_{b}-\frac{R_{2}}{R_{1}}V_{I}\end{array}\right. \tag{2}\]  
\(V_{REF}\) is used to configure the external input voltage for bias voltage. Adjusting the appropriate common-mode voltage can help avoid signal clipping or distortion. When the common-mode voltage is approximately half of the operational amplifier's supply voltage, the signal can obtain a larger amplitude output.
## 3 Results
### Effects of DWP on Fecal Microbiota
#### 3.6.2 Taxonomic Analysis  
To figure out how DWP affects the fecal microbiota, a taxonomic analysis was carried out. In terms of the relative abundance as illustrated in Figure 5C, the dominant phyla of C0 comprised Firmicutes (67.12%), Actinobacteria (15.89%), Bacteroidota (9.53%), and Proteobacteria (7.44%). The relative ratio of Actinobacteria and Bacteroidota significantly decreased during fermentation. However, Bacteroidota in both L12 and H24 turned out to have a higher relative abundance than the amount in C0. H24 also had a higher relative abundance of Proteobacteria than other samples. However, the relative abundance of Firmicutes in groups L and H was lower than that in groups C and R.  
Meanwhile, the dominant genera of C0 contained _Roseburria_ (16.10%), _Bifidobacterium_ (15.64%), _Veillonella_ (13.14%), _Blautia_ (12.70%), _Bacteroides_ (9.48%), _Escherichia-Shigella_ (6.94%), and _Ruminococcus torques group_ (6.20%). Conspicuous diminutions were observed at the relative abundance of _Blautia_, _Ruminococcus torques group_, _Roseburria_, _Veillonella_, _Bifidobacterium_, and _Fusicatenibacter_ during fermentation, while expansions were observed on _Escherichia-Shigella_ and _Paraclostridium_. A huge augmentation, followed by a later shrinkage, was detected at the relative abundance of both _Enterococcus_ and _Costiridium sensu stricto 1_. There were always fewer percentages of _Enterococcus_ and _Costiridium sensu stricto 1_ to be observed in samples from groups L or H, instead of groups C and R. Conversely, more percentages of _Escherichia-Shigella_ were found in samples from L and H. It was also noteworthy that _Bacteroides_ became a stronger dominant genus in L24 and H24 after 24-h fermentation (from 9.48% in C0 to 14.39% in L24 and 22.85% in H24, respectively), even though the relative abundance in other samples did not exceed 1.00%. _Paraclostridium_ was converted into a new dominant genus in L12, H12, C24, L24, and H24. Results of linear discriminant analysis effect size (LEfSe) analysis are supplied as Figure S6. Specifically, members of _Lactococcus_, _Escherichia Shigella_, _Paraclostridium_, _Eggerthella_, _Pygmaobacter_, _Bacteroides_, _Anoxyobacillus_, and _Costiridioides_ were the differentially abundant genera (LDA score over 3) in groups L and H.
# Dynamic Initial Weight Assignment for MaxSAT
## 4 Experimental Results and Analyses
### Results and Analysis
In general, the problem sets were chosen randomly to show the impact of the InitWeight mechanism on the performance of DDFW when applied to handling MaxSAT problems and how it compared to YaJSAT, ProbSAT, and Sparrow. Overall, DDFW+InitMaxSAT had the best average performance on all problem sets. In more detail, DDFW+InitMaxSAT had a better average CPU time in comparison to DDFW. Furthermore, DDFW+InitMaxSAT performed better in all problem sets inclusively, as shown below. ** pigeon hole problem results:** DDFW+InitMaxSAT performed \(\approx\)5 times better than YalSAT, \(\approx\)2.5 times better than ProbSAT, and \(\approx\)2.4 times better than Sparrow. **AIM problem results:** For the aim problems, DDFW+InitMaxSAT's performance was better than YalSAT, ProbSAT, and Sparrow by \(\approx\)1.5 times. **jnh problem results:** For the jnh problem results, the performance gains by employing the InitWeight mechanism were more significant due to the ratio of variables to clauses where the number of clauses was between 800 and 900 and the number of variables was 100. DDFW+InitMaxSAT was \(\approx\)15 times better than YaJSAT, \(\approx\)20 times better than ProbSAT, and \(\approx\)two times better than Sparrow. **uuf problem results:** The uuf problem set results were similar to the results of the aim problem sets where DDFW+InitMaxSAT performed better than the other algorithms by \(\approx\)1.5 times.
## 3 Results and Discussion
### Emission Characteristics  
#### 3.3.1 CO Emissions  
Figure 8 represents the CO emissions of the CRDI diesel engine fuels various palm oil blends at different idling speeds. As shown in Figure 8, it can be observed that the CO emission first increases and then decreases as the engine speed increases from 750 rpm to 1500 rpm and finally to 2250 rpm. Other researchers also reported that the CO emissions first increased (1000 rpm to 1500 rpm) and then decreased (1500 rpm to 2000 rpm) with the increase in engine speed [33]. The main reason why CO emission produces the least at the higher speed of 2250 rpm may be attributed to the optimal conditions such as temperature and pressure in the cylinder, especially the increase of temperature provides favorable conditions for the oxidation of CO to carbon dioxide (CO\({}_{2}\)). On the other hand, the CO emission of all blended fuels is higher than that of neat diesel at each engine speed. At 750 rpm, the CO emissions of COP10, COP30, and COP50 are increased by 8.37%, 22.17%, and 46.79% compared with CPO0. At 1500 rpm, the CO emissions of COP10, COP30, and COP50 are increased by 7.04%, 31.69%, and 36.62% compared with CPO0. At 2250 rpm, the  
Figure 7: COVimep of all test fuels according to various engine speeds.  
CO emissions of COP10, COP30, and COP50 are increased by 8.59%, 12.27%, and 20.25% compared with CPO0. The formation of CO is mainly caused by the incomplete combustion of fuel due to insufficient oxygen. The high viscosity and density of palm oil mean that good atomization during fuel injection is not obtained to reduce the uniformity of fuel and air mixing and form more oil-rich areas. The main observations from CO emission results are as follows: (1) the higher the engine speed, the lower the CO emissions for all test fuels, due to a better mixing of air and fuels at the highest speed for higher turbulence; (2) the higher the CPO percentage in palm oil-diesel blends, the higher the CO emissions. This could be due to the high viscosity and density of palm oil, which worsens the atomization effect. Other researchers have also reported that the formation of CO emission in diesel engines strongly depends on the temperature and air equivalence ratio in the cylinder, and it increases with the increase of incomplete combustion [34].
# Calibration and Impact of BeiDou Satellite-Dependent Timing Group Delay Bias
## 3 BeiDou Timing Group Delay Bias from Network Solution
### Estimated Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**PRN** & \(dtgd_{0}\) & \(dtgd_{1}\) & **PRN** & \(dtgd_{0}\) & \(dtgd_{1}\) & **PRN** & \(dtgd_{0}\) & \(dtgd_{1}\) \\ \hline
1 & \(-\)1.96 & \(-\)0.37 & 12 & 3.10 & \(-\)2.66 & 26 & \(-\)0.53 & 0.13 \\
2 & 1.44 & \(-\)1.01 & 13 & 1.54 & \(-\)1.42 & 27 & 0.42 & \(-\)0.43 \\
3 & 1.78 & \(-\)1.28 & 14 & 2