esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Model Validation and Application  
Because there is no analytical inversion solution of \(\overline{\xi}_{\rm WD}(s)\), it is difficult to discuss the accuracy of the Laplace transform by comparing the analytical inversion solution with the numerical inversion solution [43]. In order to validate the proposed model and show how this model was used in practice, the drawdown test data of a vertical fractured well with four fracture wings were collected from the published literature [44]. The drawdown test data in the literature [44] were generated by the reservoir simulator which is based on the implicit finite difference method. The vertical fractured well produces at a constant production rate of 0.1639 m\({}^{3}\)/s. Basic parameters of the vertical fractured well are listed in Table 2. The proposed model is employed to simulate the bottom-hole pressure under the constant-rate-production condition. The simulated bottom-hole pseudo-pressure and bottom-hole pressure are compared with the results published in the literature [44]. It is shown from Figure 5 that the simulated bottom-hole pseudo-pressure and bottom-hole pressure by the proposed model are in good agreement with the bottom-hole pressure data published in the literature [44], which validates the proposed model.  
Furthermore, the proposed model can be used to predict the bottom-hole pressure under the constant-rate-production condition. Based on the proposed model, it was easy to obtain the bottom-hole pressure of the vertical fractured well at any given time. For example, when the vertical fractured well produces at a constant production rate of 0.1639 m\({}^{3}\)/s, the bottom-hole pressures are 41.132 MPa, 37.676 MPa, 31.484 MPa at \(t=100,\ 1000,\ 10000\) hours, respectively.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Reservoir thickness, \(h\), m & 7.62 \\ Reservoir porosity, \(\phi\) & 0.075 \\ Initial reservoir permeability, \(k_{\rm i}\), mD & 0.05 \\ Initial fracture permeability, \(k_{\rm fi}\), mD & 3927 \\ Initial reservoir pressure, \(p_{\rm i}\), MPa & 44.785 \\ Initial reservoir pseudo-pressure, \(\psi_{\rm i}\), MPa\({}^{2}/(\rm mPa\cdot s)\) & \(9.6321\times 10^{4}\) \\ Total compressibility, \(c_{\rm t}\), MPa\({}^{-1}\) & \(1.43\times 10^{-2}\) \\ Wellbore radius, \(r_{\rm w}\), m & 0.0762 \\ Gas viscosity, \(\mu\), mPa \(\cdot s\) & 0.0252 \\ Reservoir temperature, \(T\), K & 412.04 \\ Fracture width, \(w_{\rm t}\), m & 0.0061 \\ Well production rate, \(Q_{\rm sc}\), m\({}^{3}\)/s & 0.1639 \\ Polar angles of the hydraulic fractures, \(\theta_{\rm fj}\), degree & 0, \(\pi/2,\ \pi\), \(3\pi/2\) \\ Lengths of the hydraulic fractures, \(L_{\rm fj}\), m & 149.413, 2.987, 149.413, 2.987 \\ \hline \end{tabular}
\end{table}
Table 2: Parameters of a vertical fractured well with four fracture wings in the published literature [44].
# Design of Edge-IoMT Network Architecture with Weight-Based Scheduling
## 2 Related Works
### Priority-Based Clustering Scheme
#### 3.3.2 Phase 2: Scheduling Algorithm for CHs and Inter-CHs  
Considering a scenario with three priority weights, an adaptive transmission scheme is developed to ensure reliable collection of the medical information. Three levels of priority for each patient are given by  
* Level 1: Intensive Care Unit S1 (e.g., weight of S1, \(w_{S_{1}}\)= 3)
* Level 2: General ward S2 (e.g., weight of S2, \(w_{S_{2}}\)= 2)
* Level 3: Normal people S3 (e.g., weight of S3, \(w_{S_{3}}\)= 1)  
Therefore, for inter-CH \(i\), total weights \(L_{i}\) can be obtained through the weight equation to decide the transmission priority of the data.  
\[L_{i}=N_{CH(S_{1})}^{(i)}\times 3+N_{CH(S_{2})}^{(i)}\times 2+N_{CH(S_{3})}^{(i)} \times 1, \tag{1}\]  
where \(N_{CH(S_{j})}^{(i)}\) represents the number of CHs associated with level \(j\) and inter-CH \(i\). Figure 7 shows the relationship between inter-CHs and the corresponding relay after applying the weight equation.  
Figure 6: The procedures of the clustering phase.  
Figure 7: An example of weight calculation given the network topology in Figure 5 (left).  
Referring to Figure 7 with weight calculation, the transmission order of the inter-CHs base on TDMA protocol [43] is described in Figure 8 for communicating with their respective relay nodes. Note that the computation of total weight considers the number of inter-CHs and the priority levels of CHs (e.g., for \(L_{1}\) in the Intensive Care Unit, there were three level 2 clusterheads and a level 1 CH in this inter-cluster. Based on the weight equation, we obtain \(L_{1}=9\). Since there are two inter-clusters in the Intensive Care Unit, two time slots are arranged to transmit the data to the relay node. As the result of each inter-CH's weight, the transmission time duration is divided into two different time slots, as depicted in Figure 8.
## 3 The Human Role in Teleoperation
### Frame of Reference
#### 4.7.1 Egocentric or First-Person View  
Why provide different views of the remote site? Depth cues provided by human's binocular system are important for the perception of the environment. In the absence of pure stereo vision, different points of view can contribute to the visual perception. Additionally, mental models are matched against reality through egocentric information arriving from the different sensory modalities [32]. For example, an observer expects to see the scene's elements changing when he or she moves. He/she expects that parts of the scene initially occluded become visible and others become occluded. Moreover, it is expectable that the views of a nearby object or part of it present higher changes in images than those more distant. Thus, all these reality cues need to be replicated by the egocentric visualization system because the human brain uses them to estimate distances.  
In traditional teleoperation systems, the operator controls the remote camera's orientation manually. He/she has to control two or three degrees of freedom of the camera and, additionally, several degrees of freedom of the robot, which increases the operator's efforts. To address these problems, we suggest a virtual cockpit for the operator where he/she experiments with the sensation of controlling the robot inside of it, i.e., telepresence [17; 98]. Operators can browse the vehicle's surroundings as if they were aboard it and simultaneously realize that the controls and instrumentation panel at his/her disposal present coherent feedback. This is implemented using a head-mounted display (HMD) whose orientation is used to control a pan and tilt unit (PTU) that supports the remote camera. With this solution, the user's head movements are implicitly transposed into camera movements. The approach enables the operator to look around, just by naturally rotating his/her head, viewing what was supposed to be seen in the remote place with that movement. By superimposing real video feeds with virtual elements, synchronous with the operator's point of view, we contribute to the visual perception of being immersed in the remote place.
# A Finite State Machine-Based Improved Cryptographic Technique
## 3 Proposed Methodology
### Cryptographic Turbo Code Encoder  
Figure 3 depicts the architecture of the MCTC encoder that is being considered for use.  
\[G(D)=\left[1,\frac{g_{1}(D)}{g_{o}(D)}\right] \tag{7}\]  
\[g_{0}(E)=1+E^{2}+E^{3}\,g_{1}(E)=1+E+E^{3} \tag{8}\]  
Figure 1 illustrates the shift register implementation of the transfer function of each RSC encoder. The delay in the shift register is denoted by the E in this equation.  
An MCTC sits in between the two RSC encoders to provide separation. The input bit sequence A, which is delivered to the MCTC, is given to the first RSC encoder, which then encodes the sequence.  
It is sufficient to send either the A or the B systematic bits since they are both equivalently represented by scrambled copies of the other. However, to prevent the data from being stolen, the MCTC will send out systematic bits, B. In addition, to prevent the confidential data from being guessed by a user who is not permitted to access them, the code word of the first encoder is interleaved with MCTC-2 to produce the letter E. The MCTC's code word is referred to as MCTC, and it is provided as:  
\[C_{\text{CTC}}=\{\,B,\,E,\,F\,\} \tag{9}\]  
The term \"code rate\" refers to the proportion of the number of information bits to the number of encoded bits. The amount of information bits included in the MCTC is L, while the number of parity bits is 2L. This combination yields a coding rate of:  
\[R_{\text{CTC}}=\frac{M}{M+M+M}=\frac{1}{3M} \tag{10}\]  
On the other hand, this results in an increase in the system's bandwidth. An increase in the code rate and a decrease in the code's bandwidth cost may be achieved by puncturing the parity bits or removing some of them. A decrease in the performance of the code's error-correcting mechanism is brought on by puncturing; however, a competent puncturing technique may keep this decrease in performance to a minimum. In the field of cellular transmission, the rate of 1/2-pierced FSM is used. The secret word for the rate of 1/2 MCTC is as shown in Figure 3.  
When it is time to decode the information, the decoder will substitute false bits with a value of zero for the bits that have been punctured. This will allow the information to be decoded. When the RSC encoders have completed encoding all the bits in the input bit sequence, they must reset to an all-zero state before going on to the next input sequence. This ensures that the encoders are ready for the next input sequenc  
Figure 3: Cryptographic Turbo Code (CTC) encoder using MCTC FSM.  
registers to their original state, which is all zeroes, the input bit sequence is extended with what are known as termination bits, which are also called tail bits. Termination bits are introduced into a code at the end of each code word. This is performed to ensure that the error-correcting performance of the code does not deteriorate because of the introduction of the termination bits.
## 3 Results and Discussion
### Composition and Structure of CHT Films
#### 3.1.2 Raman Spectroscopy  
Figure 2 shows Raman spectra of chitosan mixed with 15% and 30% of HNa, crosslinked with either (GA) or crosslinked PEDGE. Pristine chitosan is reported to exhibit intense peaks at 2924 and 2885 for C-H stretching vibrations; medium intensity peaks at 1419, 1376, shoulder at 1340 cm\({}^{-1}\) for C-H bending vibrations, 1116 and 1094 cm\({}^{-1}\) and 925 and 898 cm\({}^{-1}\) for the pyranose ring. Finally, small intensity peaks at 1666, 1634, 1535, cm\({}^{-1}\) for amide I and II, respectively ([PERSON] et al., 2016; [PERSON] et al., 2016). We observed peaks at 2930, 1604, 1411, 1382, 1115, 1093, 925, and 898 cm\({}^{-1}\) absorptions (see supplementary information, S2) in agreement with previous studies.  
On the other hand, pristine HNa spectra (see supplementary information, S3) showed five subregions of different intensities(i = intense, m = medium and s = small: (1) C-H stretching vibrations (2940i, 2934i and 2900s cm\({}^{-1}\)), (2) stretching vibrations of carbonyl compounds (1645s cm\({}^{-1}\)), (3) deformation modes of C-H\({}_{2}\) y C-OH (1410s, 1360m, 1329m, and 1305m cm\({}^{-1}\)), (4) C-O and C-C stretching vibrations (1130i, 1086m, 1048m cm\({}^{-1}\)) and, (5) complex skeletal vibrations out of plane (941s and 893s cm\({}^{-1}\)) in agreement with previous studies ([PERSON] et al., 2016; [PERSON] et al., 2016). However, as our study was on solid hyaluronate films and not in solution, the presence of shoulder at 1630 and 1600 cm\({}^{-1}\) assigned to carboxylate were not observed. Despite this, a very low intensity peak at 1552 cm\({}^{-1}\) was observed and can be assigned to amide II in HNa.  
When these two polysaccharides are blended, the C-H vibration at 2935-2930 cm\({}^{-1}\) increased as HNa concentration increased with respect to the vibration at 2887 cm\({}^{-1}\). The small intensity absorption at 1657 cm\({}^{-1}\) located between the ones of the pristine polymers  
Figure 2: (a) Raman spectra of PEGDE crosslinked CHT films. HNa 15% **(a)** and HNa 30% **(b)**.  
suggests a shift for amide I band. The band a 1538 cm\({}^{-1}\) (amide II) also shifted, which was more evident with increasing amounts of HNa. However, vibrations at 1371 cm\({}^{-1}\) and 1115 cm\({}^{-1}\) remained unchanged.  
The main signals observed in the CHT/HNa/PEGE films were related to the bands at 2933 cm\({}^{-1}\) and 2886 cm\({}^{-1}\) where the latter increased with PEGDE concentration. Similarly, the band at 1461 cm\({}^{-1}\) increased and probably corresponded to pristine PEGDE but being displaced. Vibrations at 1376 cm\({}^{-1}\) and 1113 cm\({}^{-1}\) and 890 cm\({}^{-1}\) remained with medium intensity. Increases in intensity were expected for 1470 cm\({}^{-1}\), 1256 cm\({}^{-1}\), 1134 cm\({}^{-1}\), and 841 cm\({}^{-1}\) as PEGDE increased but this was not clearly observed.
## 3 Results and Discussion
### The Uncontaminated Water Analysis
#### 3.1.2 Dynamic Contact Angles Measurement  
The Gas/Solid/Water Three-Phase Dynamic Contact Angles Measurement  
Figure 5 shows the gas/solid/water dynamic contact angles of water drops onto the different plate surfaces with and without 300 Hz vibration. The results showed that the dynamic contact angles decreased by 10% with the increasing time. Furthermore, the vibration could make the contact angles lower.  
Figure 4: The vibration frequency on the dynamic contact angles of water drops onto the iron casting, steel, PVC and PE, with 7 days aging time.  
Figure 5: The gas/solid/water dynamic contact angles of water drops onto the (**a**) iron casting; (**b**) steel; (**c**) PVC; (**d**) PE, with or without 300 Hz vibration.  
The Water/Solid/Petroleum Three-Phase Dynamic Contact Angles Measurement  
Figure 6 shows the water/solid/toluene dynamic contact angles of water drops onto the different plates with or without 300 Hz vibration. The results indicated that the water/solid/toluene dynamic contact angles increased by 10-30%, which was due to the fact that the oil drops became easier to liberate from the solid's surface, and then the contact angles became higher. Moreover, the vibration made the contact angles become higher. However, different pipelines would have different effects.  
Figure 7 shows the water/solid/bitumen (0.1 wt%) dynamic contact angles of water drops onto the different pipeline materials. The results showed that the dynamic contact angles increased by almost 20% as the immersion time increased by 500%. In addition, the vibration made the dynamic contact angles higher. In addition, the equilibrium time for the system with 300 Hz vibration was higher than that without vibration. The reason was due to the fact that when the system was without vibration, the system was more stable, and then it was easier for the system to reach equilibrium. However, when the system was had the 300 Hz vibration, the system was not so stable, therefore, the equilibrium time was longer. For different pipeline materials, the dynamic contact angles were different when the interaction time was the same, and the dynamic contact angles followed the rule that \(\theta_{\text{PE}}>\theta_{\text{PVC}}>\theta_{\text{steel}}>\theta_{\text{iron  casting}}\). The reason was due to the fact that different materials' surfaces showed different surface energy.  
Figure 6: The water/solid/toluene dynamic contact angles of water drops onto the (**a**) iron casting; (**b**) steel; (**c**) PVC; (**d**) PE, with or without 300 Hz vibration.
# A Review of the Development and Future Challenges of Case-Based Reasoning
## 4 Application Fields of CBR
### Design and Planning  
Case-based design (CBD) [107; 108; 109] is a CBR-based application that develops new design solutions by adjusting previous solutions [30], allowing designs to be re-used and adapted to create new designs. For example, [PERSON] et al. [107] proposed a dual-cycle CBR model in the domain of recipe generation. The model combines the strengths of deep learning and similarity-based retrieval. The model learns the latent structure of the domain from the case base to generate a new object that deliberately flouts that structure and then reintegrates the new object with known cases through adaptation to generate novel and valuable recipes. [PERSON] et al. [109] proposed an intelligent design for remanufacture (DFREM) method based on a vector space model (VSM) and CBR, which can extract the features of customer requirement data from massive customer requirement data and quickly generate a design scheme meeting customer requirements.  
The planning problem relies on the experience of decision-makers to propose a reasonable planning scheme. As one of the important applications of CBR, planning tasks [5; 110; 111; 112], which are similar to design tasks, are not planned from scratch. For example, [PERSON] et al. [112] proposed using learning experience to solve motion planning problems and CBR technology to provide state samples associated with the problem to achieve path building and find the shortest path. [PERSON] et al. [5] proposed a CBR framework for assembly sequence planning (ASP). By establishing the overall architecture of CBR, the extraction, aggregation and re-use of assembly information of existing solutions are achieved, which can analyze the collection information in heterogeneous multimedia and re-use the existing assembly information scattered in multimedia solutions to solve assembly sequence planning.
## 2 Variables and Material Characteristics
### Chloride Resistance Performance  
To assess the chloride resistance performance of UHPFRC, the American Society for Testing and Materials (ASTM)-C1202 standard [51] was followed, as shown in Figure 1. The chloride resistance results are summarized in Figure 2 and Table 3. Each type of specimen has a value of 12.48, 13.30, and 17.62 coulombs, with an average of 14.47 coulombs. According to ASTM C1202, values below 100 Coulombs are deemed negligible, suggesting minimal chloride damage. The dense microstructure formed by the various mineral admixtures in UHPFRC acts as a barrier against external factors, contributing to the resistance to deterioration of the concrete. Therefore, the UHPFRC in this study could be considered to have outstanding durability owing to its dense structure.
# Dual-Dense Convolution Network for Change Detection of High-Resolution Panchromatic Imagery
## 3 Change Detection with the Proposed Dual-Dense Convolutional Neural Network
### Pre-Processing for Change Detection  
As mentioned previously, an atmospheric correction is required to remove scattering and absorption effects from the atmosphere to characterize the surface reflectance effects for a time-series image analysis. This work uses KOMPSAT-3 images with product level 1G. In these images, the radiometric correction has been done by converting the image pixel values (Digital Numbers/DNs) to surface reflectance values. It involves the conversion of DNs to a radiance value, and then to top-of-atmosphere (TOA) radiance. On the other hand, gain and offset values are provided by KOMPSAT-3 to derive the TOA reflectance values [26]. After the atmosphere errors are corrected, the geometric correction is performed in order to ensure that the pixels in the image are in their proper geometric positions on the Earth's surface. For our test images, geo-rectification and orthorectification are each conducted. For the geo-rectification, ground control points (GCPs) are identified in an unrectified image and correspond to their real coordinates to estimate the parameters (polynomial coefficients) of polynomial functions by the least square fitting. In addition, orthorectification can partly correct the image for image distortions caused by variations in the terrain topography in tandem with non-optimal satellite sensor viewing angle. Optical distortions are corrected, and terrain effects are corrected using coarse digital elevation model (DEM), namely shuttle radar topography mission DEM (SRTM DEM) for KOMPSAT-3 imagery [26].  
In general change detection systems, an image registration is applied in order to ensure that two images become spatially aligned. Even though the correction of geometric distortion is performed, the spatial alignment of two images could contain a relatively large error of up to \(\pm 6\) pixels. In order to overcome this distortion, automatic image registration is widely used. However, it requires high computational load, and is furthermore not easy to obtain perfect registration. They impact the performance of change detection algorithms, resulting in the possibility that a great deal of false change areas could occur. The proposed dual-DCN is proposed so as to handle distortion problems and simplify image registration. The dissimilarity distance of local characteristics is measured in order to identify a change with the dense dual-DCN model.
## 2 Materials and Methods  
### Dataset: Cardiovascular Health Study (CHS)  
As its dataset, this study used the Cardiovascular Health Study (CHS) available from BioLincc. The Cardiovascular Health Study (CHS) was an observational study to identify risk factors for the development and progression of cardiovascular disease (CVS) in older adults, initiated by the National Heart, Lung, and Blood Institute (NHLBI) in 1987. A total of 5201 men and women were recruited from four communities. The population from the Pittsburgh Field Center was entirely urban, and the other three Field Centers recruited a mixture of urban and rural populations. Eligible participants were sampled from Medicare eligibility lists.  
The dataset includes physical and laboratory measurements at baseline with the aim of identifying the presence and severity of CVD risk factors such as hypertension, hypercholesterolemia, and glucose intolerance, subclinical diseases. The database has 5888 patients, where 5125 correspond to the first recruitment (old cohort) and 763 correspond to the second (new cohort). In the study, 3329 patients were women, while 2466 were men. Follow-up was carried out between 1989 and 1999 using the old cohort, after which the new cohort was added. After 2000, 2 calls per year continued to be made to update patient data and monitor the evolution of different cardiovascular diseases. For the study, various annual physical and laboratory evaluations were performed along with biannual telephone calls to identify the presence and severity of risk factors for cardiovascular disease [22]. During these telephone calls, patients were given questionnaires to assess the evolution of the different cardiovascular variables measured in the previous medical examinations. Some of the examinations performed on CHS patients were:  
* The analysis of psychosocial factors, such as depression;
* Healthcare variables, such as medication, medical history, and hospitalizations;
* Blood pressure measurements, such as the ankle-arm ratio;
* Physical and functional examinations;
* Tests to measure neurological function;
* Electrocardiography;
* Laboratory tests;
* Lipid measurement;
* Ultrasonography;
* Echocardiography.  
The cardiovascular study is the most extensive NHLBI-funded study in older adults, with ten years of clinical measurements and approximately seven years of telephone follow-up. This dataset is appropriate for secondary analyses using machine learning models that generally require an adequate number of variables and a good sample size. Because the model in this study needs variables with an adequate information density and the least number of missing values, it was decided to use ten years of data (from 1989 to 1999) to build the machine learning models after an exploratory analysis.