esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Estimation of Bearing Capacity Using Multiple Regression Analysis
Although \(S_{r}\) is calculated as the ratio of \(L_{p}\) and \(d_{p}\), \(S_{r}\) was directly used in the sensitivity analysis because if \(L_{p}\) and \(d_{p}\) are used for \(S_{r}\), the range of \(S_{r}\) is overestimated in comparison with the actual range. From the simulation results, Spearman rank correlations were estimated, and their tornado diagram is shown in Figure 8.  
The tornado diagram exhibits the influence of the input variables on the bearing capacity, and \(s_{u}\) is the most influential variable in predicting the bearing capacity. The other variables were found to \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & \
\begin{tabular}{c} **Fitted** \\ **Coefficient** \\ \end{tabular} &
\begin{tabular}{c} **Coefficient** \\ **Standard Error** \\ \end{tabular} & **VIF** \\ \hline Intercept & \(-256.77\) & \(87.65\) & \(NA\) \\ \(1/a_{r}\) & \(67.83\) & \(17.46\) & \(2.30\) \\ \(\sqrt{s_{u}a_{r}}\) & \(169.25\) & \(11.62\) & \(2.92\) \\ \(d_{r}^{2}\) & \(271.42\) & \(128.93\) & \(1.89\) \\ \(1/S_{r}\) & \(-626.53\) & \(170.66\) & \(1.3\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary statistics for the proposed MLR model. Figure 7: Comparison of the observed and the predicted ultimate bearing capacity by the proposed MLR equation. influence the bearing capacity in the order of \(a_{r}\), \(S_{r}\), and \(d_{f}\). The rank correlations of input variables have positive values, thus indicating a positive relationship with the bearing capacity.
## 3 Results and Discussion
### Insectitical Activity of GEs  
_Aedes aegypti_ (the yellow fever mosquito) spreads dangerous human arboviruses including dengue, Zika and chikungunya. Consequently, control of yellow fever mosquitoes is a critical public health priority [63].  
The susceptibility of _A. aegypti_ larvae to GEs under laboratory conditions was tested by using dipping methods. The larvicidal activity of a compound is usually improved by increasing its concentration and exposure time, as [PERSON] et al. [64] reported for plant-derived bioactive products, such as essential oils, ethanol extracts and FAMEs. In the current study, _C. echinulata_-GEs showed strong insecticidal activity against _A. aegypti_ larvae with a LC50 of 0.541 mg/L, which could be probably attributed to the presence of GLA in significant concentrations, followed by EPA-GEs, olive oil-GEs and _N. gaditan_-GEs, demonstrating a LC50 of 10.24, 12.88 and 16.92 mg/L, respectively. On the contrary, _U. isabellina_-GEs were less active, presenting a LC50 equal to 39.62 mg/L (Table 6, Figure 8). On the other hand, the RR values indicated that _A. aegypti_ mosquitoes were much more susceptible to _C. echinulata_-GEs than to EPA-GEs, olive oil-GEs, _N. gaditan_-GEs and _U. isabellina_-GEs by about 20- to 70-fold. Overall, most of the GEs produced in this study, especially those of _C. echinulata_-GEs, had superior insecticidal activity. Likewise, FAAs synthesized using the GLA-rich lipids produced by _C. echinulata_ displayed a superior insecticidal activity against the same organism [36], suggesting that this FA is probably a key molecule responsible for the bioactivity of preparations.  
Figure 8: The larval mortality effect of GEs derived from _C. echinulata_, EPA concentrate, olive oil, _U. isabellina_ and _N. gaditan_ at different concentrations against _Aedes aegypti_ after continuous exposure for 48 h.  
[PERSON] [65] reported that SEs are useful as effective, environmentally safe pesticides for the control of soft-bodied arthropod pests. In addition, [PERSON] et al. [6] confirmed that the majority of the SEs exhibited higher insecticidal activity than insecticide soap. The nature of both the sugar and FA moieties determine the SEs' physicochemical properties, such as the solubility in water and stability of emulsions, and their insecticidal activity. However, changing the sugar or FA components from lower to higher carbon chains or the sugar from a monosaccharide to a disaccharide does not follow a consistent relationship with insecticide activity. C18 FAs, such as oleic, elaidic, linoleic, and linoleic acids, inhibited proliferation of malarial parasites in mice infected with _Plasmodium vinckei petteri_ or with _Plasmodium yoelii nigeriensis_[66].
## Nomenclature  
\begin{tabular}{l l} C\({}_{1}\) & constant (dimensionless) \\ c\({}_{\rm e}\) & volumetric heat capacity of the soil-fluid matrix (cal-m\({}^{-3}\).\({}^{\circ}\)C\({}^{-1}\)) \\ c\({}_{\rm e,w}\) & volumetric heat capacity of the water, (cal-m\({}^{-3}\).\({}^{\circ}\)C\({}^{-1}\)) \\ e & relative random error (\%) \\ g & acceleration of gravity (m/s\({}^{2}\)) \\ H & height (total depth) of the domain (m) \\ k & thermal conductivity of the soil-fluid matrix (cal-s\({}^{-1}\).m\({}^{-1}\)\({}^{\circ}\)C\({}^{-1}\)) \\ L & length of the aquifer (m) \\ l\({}_{\rm T}^{*}\) & characteristic length in which temperature profiles develop (m) \\ N & number of experimental measurements for the inverse problem \\ P-3, P-4 & piezometers in Agua Amarga coastal aquifer \\ t & time (s) \\ T & temperature (\({}^{\circ}\)C) \\ T\({}_{\rm s}\) & temperature at the soil surface (\({}^{\circ}\)C) \\ T\({}_{\rm b}\) & temperature at the bottom of the aquifer (\({}^{\circ}\)C) \\ T\({}_{\rm w}\) & temperature of the water at the inlet border (\({}^{\circ}\)C) \\ T\({}_{\rm min}\) & initial soil temperature (\({}^{\circ}\)C) \\ (T \(-\) y) & vertical temperature-depth profile (\({}^{\circ}\)C) \\ v\({}_{\rm o}\) & horizontal groundwater flow velocity (m/s) \\ v\({}_{\rm Draxy}\) & Darcy velocity (m/s) \\ x, y & spatial coordinates (m) \\ x\({}_{\rm a}\), x\({}_{\rm b}\) & horizontal locations within the interval \([0,\) l\({}_{\rm T}^{*}]\) (m) \\ x\({}_{\rm o}\) & x\({}_{\rm a}\)\(-\)x\({}_{\rm b}\) (m) \\ x\({}_{\rm sim}\) & horizontal location in simulation (m) \\ \(\alpha\) & thermal diffusivity of the soil-fluid matrix (m\({}^{2}\)/s), \(\alpha={\rm k}/\rho_{\rm e}\)c\({}_{\rm e}\) \\ \(\alpha_{\rm m}\) & \(\alpha_{\rm m}={\rm k}/\rho_{\rm e,w}\)c\({}_{\rm e,w}\) (m\({}^{2}\)/s) \\ \(\Delta{\rm T}_{\rm w}\) & temperature increment at the inlet boundary (\({}^{\circ}\)C) \\ \(\Delta{\rm v}_{\rm o}\) & velocity increment used in inverse problem (m/s) \\ \(\Delta{\rm x}\) & increment of the horizontal location (m) \\ \(\varepsilon\) & porosity (dimensionless) \\ \(\kappa\) & intrinsic permeability (m/s) \\ \(\mu\) & fluid dynamic viscosity (g/(m-s)) \\ \(\rho_{\rm e}\) & wet bulk density of the soil-fluid matrix (g/m\({}^{3}\)) \\ \(\rho_{\rm e,w}\) & fluid density of the water (g/m\({}^{3}\)) \\ \(\Psi\) & mathematical functional \\ \end{tabular}
## 3 Results
### Single Band Regression  
We present in this section the performance of the reconstruction of each spectral band by applying the neural network regression algorithm described in Section 2.2. As stated before, only the 20 m bands are reconstructed, and the following data are used as predictors:  
* the sines and cosines of the four observation angles,
* all 20 m bands except for the target one,
* the values of the four 10 m pixels for B2, B3, B4 and B8 associated with the 20 m target pixel,  
Figure 5: Normalized mutual information.  
Figure 6: Red edge and NIR bands.  
and, only for L1C, the value of the three 60 m bands interpolated (with a bicubic interpolator) to the coordinate of the center of the 20 m pixel. Each regression case is repeated 10 times using the protocol described at the end of Section 2.1.
## 2 Numerical Simulation Methods
### Simulated Working Conditions  
In this study, using experimentally verified model parameters, we conducted numerical simulation research on the characteristics of three-layer equidistant 304 or Q345 steel plates under reactive fragment impact conditions. The simulation's SPH smooth particle length was 0.04. The respective target plate material and structure were three layers of Q345S plate or three layers of 304 SS steel plate, both of which had a thickness of 2.5 mm, and the impact velocity of the cylindrical or spherical reactive fragment was 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, or 4.5 km/s.
## 4 Conclusions  
We performed a study of a Nano film of alumina-water flowing on a heated plate of a vertical channel. Validation and stability study of the mesh is given after mathematical analysis. Also, we have paid attention to prove the important influence of heating on the volume rate of the nanoparticle. Simulations provide the following conclusions:  
* The effect of the thermal conduction is significant and dominates the effect of density and the specific heat.
* An augmentation of the thermal conductivity induces an increase of the film temperature and the interfacial temperature, and consequently the vapor temperature.
* Augmentation of k exists because of the increase of \(\mathrm{x_{v}}\).
* The saturation vapor concentration is elevated when we elevate the nanoparticle volume rate.
* Augmentation of k naturally increases the evaporation rate.
* The augmentation of the film density, due to the increase of \(\mathrm{x_{v}}\), induces a deceleration of the film.  
**Author Contributions:** [PERSON], [PERSON] and [PERSON] developed the design strategy and design the interview questions. [PERSON], [PERSON] and [PERSON] collected the data and [PERSON], [PERSON] and [PERSON] analyzed the data. The results were finalized by [PERSON], [PERSON] and [PERSON] and [PERSON], [PERSON] and [PERSON] formulated the paper draft and wrote the manuscript [PERSON], [PERSON] and [PERSON] helped to review and finalize the manuscript. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflicts of interest.  
List of Symbols  
u axial velocity [m/s]  
v transversal velocity [m/s]  
p pressure [Pa]  
x axial coordinate [m]  
y transversal coordinate [m]  
X axial dimensionless coordinate  
Y transversal dimensionless coordinate  
T temperature [\({}^{\circ}\)K]  
g gravitational acceleration [m/s\({}^{2}\)]  
h heat transfer coefficient at the dry plate [J/m\({}^{2}\)-K]  
Figure 7: Effect of the nanoparticle volume rate on the film velocity – heated plate case.
## 2 Materials and Methods
### Statistics, Test Screening, and Analysis of Geographical Factors
#### 2.5.1 Database Generated from Digital Terrain Model Data: Topographic Factors  
The selection of factors likely to influence debris flows' commencement was based on previous studies [9; 12; 35; 37; 38; 39], as well as on the researchers' prior knowledge of the causes of debris flows in the study area. Using the QGIS nesting approach, they created a unique terrestrial factor database for each of the 33 potential debris-flow streams comprising its total area, average slope, length, average slope of the stream bed, and shape coefficient, among other variables. The calculation of each such variable is explained, in turn, below.  
Total area of catchment (A, unit: m\({}^{2}\)). The area of a given subdivision of the whole river catchment area pertinent to a particular potential debris flow is calculated as follows:  
\[\text{A}=\text{a}\times\text{n}\times 10^{-6}, \tag{2}\]  
where \(a\) is the area of the grid in m\({}^{2}\), and \(n\) is the number of grids the catchment subdivision contains.  
Average slope of the catchment (S, unit: degrees). The average slope of a given potential debris flow's catchment area is computed as follows:  
\[S=\frac{\sum\text{slope}}{\text{n}}, \tag{3}\]  
where slope is the slope value of each of its grids.  
Length (L, unit: m). The ArcMap program was used to nest the DTM with each potential debris-flow stream's catchment area. Then, using hydrological analysis, its length was established by adding the number of connected grids as follows:  
\[\text{L}=n\times W, \tag{4}\]  
where \(n\) is the number of grids, and \(W\) is the width of the grid in m.  
Average slope of the stream bed (S, unit: degrees). The average slope of each potential debris-flow stream bed is computed as follows:  
\[S=\text{tan}-1\;(H\text{L}), \tag{5}\]  
where \(H\) is the difference between the upstream elevation and the downstream elevation, in m, and \(L\) is the stream's length.  
Figure 11: Judgment-potential diagramming of debris flow DF041.  
Shape coefficient (F). The shape coefficient of a given potential debris-flow stream is calculated as the ratio of its length to the width of its watershed, i.e.,  
\[F=\frac{\text{A}}{L^{2}}, \tag{6}\]  
where \(A\) is the total area of its catchment in m\({}^{2}\), and \(L\) is its length in m.
## 4 System Architecture
### Software  
The software components adopted into the system include the development engine (Unity Technologies, Unity, San Francisco (HQ), CA, USA) and the distribution platform (Valve Corp., Steam, Bellevue, Washington, USA). Unity is a well-known real-time 3D development platform which enables users to develop both 2D and 3D games and models in Windows, iOS, Android, and over twenty types of systems. Microsoft C# is used as the programming language; users are allowed to control actions of virtual objects using specific C# scripts. Unity is compatible with other design software and gaming platforms that have external operating devices also compatible with Unity. Steam is one of the largest video game digital distribution service platforms in the world and also provides a virtual reality service called SteamVR to support the HTC VIVE virtual reality headset. Data collection and data management use SQLite (SQLite.org) as the database engine and BioGraph Infinite software to  
Figure 4: The system architecture for the driving phobia VRET prototype (refer to Figure 2 for Module 1 details).  
record bio-data. The VRET database management system (DBMS)'s entity-relation diagram is shown in Figure 5.
# Denoising Multiscale Back-Projection Feature Fusion for Underwater Image Enhancement
## 5 Experiment and Analysis
### Training Process  
For the underwater image enhancement task, the UIEB and EUVP datasets were randomly divided into 10 equal folds. During the training process, nine of these folds were randomly selected for training, with the remaining one used for testing. This process was repeated several times on the two datasets to compute the average values for the final results. Since the RUIE dataset only contains underwater images without corresponding clear images, we used the model trained on UIEB to enhance the images in the following results.  
For the multiple-type image enhancement task, we utilized the UIEB, RESIDE, Rain 1400, and CSD datasets for training and testing. Since these datasets contain varying numbers of images, we randomly selected 800 images from each dataset to ensure a balanced set. These subsets were denoted as \(Data_{UIEB}\), \(Data_{RESIDE}\), \(Data_{Rain}\), and \(Data_{CSD}\). Each of these four subsets was split into training and testing sets, with 80% used for training and the remaining 20% for testing. Initially, each subset was used to train a different teacher model. After training the teacher models, the training datasets were mixed together to serve as training data for the student model. The testing datasets were used to evaluate the performance of the student model.
###### Abstract  
Automatic detection of tire defects has become an important issue for tire production companies since these defects cause road accidents and loss of human lives. Defects in the inner structure of the tire cannot be detected with the naked eye; thus, a radiographic image of the tire is gathered using X-ray cameras. This image is then examined by a quality control operator, and a decision is made on whether it is a defective tire or not. Among all defect types, the foreign object type is the most common and may occur anywhere in the tire. This study proposes an explainable deep learning model based on Xception and Grad-CAM approaches. This model was fine-tuned and trained on a novel real tire dataset consisting of 2303 defective tires and 49,198 non-defective. The defective tire class was augmented using a custom augmentation technique to solve the imbalance problem of the dataset. Experimental results show that the proposed model detects foreign objects with an accuracy of 99.19%, recall of 98.75%, precision of 99.34%, and f-score of 99.05%. This study provided a clear advantage over similar literature studies.  
tire defect detection; foreign object detection; deep learning; XAI; tire X-ray; Grad-CAM
## 2 System Model
### Network Topology  
The HAPS-IMT network topology includes two layers. The first is the HAPS layer, in which there is only one HAPS node in the network topology. The HAPS node is the central node of the network, which can obtain all system information. The other is the 5G layer, containing several 5G ground base stations (GBS). Since the two layers adopt the OFDMA mechanism, every user can access one selected HAPS node or GBS of either layer during a period according to a scheduling algorithm. In this model, a ground station (GS) is set for communication with the outside network. The backhaul link is used for returning data traffic back to the GS. The two layers share the same spectrum resources, and dynamic TDD mode is adopted to minimize the inter-layer interference in this model. Resource allocation and transmission power control are two effective technologies to optimize the performance of the HAPS-IMT system in this paper. Users can access to HAPS node station or any one of the GBS as scheduled. We suppose two research cycles: a long cycle and a sub-cycle. The long cycle is 10 radio frames long. And one radio frame is 10 ms. The transmission protocol configuration is stable during the long cycle. One subframe, which is 1 ms long, is considered a sub-cycle. The system allocates time slots in each subframe to different users. The HAPS layer and 5G layer within the integrated system cover the same geographical area. The HAPS node could acquire all status information within the system in real-time since it has a connection with other GBSs and GS. Therefore, the HAPS node is also an RRM network element of the integrated network, which manages the system resources, the situation of channel link, resources remaining in the integrated network and allocated those resources (including resource block and power).  
Symbols and their meanings of essential parameters used in this paper are offered in Table 1. Superscript \"\(d\)\" represents downlink data, \"\(u\)\" represents uplink data. The superscript \"\(B\)\" indicates Best Effort service, and the superscript \"\(S\)\" indicates the soft quality of service (soft QoS). Symbol \(i\) is used for user index, and Symbol \(j\) is used for base station index. Superscript \"\(n\)\" and \"\(l\)\" denote normal subframe and low power almost-bank subframe (LP-ABS), respectively.  
Figure 1: System Model.
# Low-Cost RSAC and Adsorption Characteristics in the Removal of Copper Ions from Wastewater
## 2 Materials and Methods
### Study of Coexisting Metal Cations on Adsorption  
A series of 150 mL of 100 mg/L copper ion solutions were prepared with the coexistence of Na\({}^{+}\), Mg\({}^{2+}\), Ca\({}^{2+}\), and Fe\({}^{3+}\), respectively. To each sample 5 g RSAC particles was added and then mixed at room temperature (25 \(\pm\) 1 \({}^{\circ}\)C). The cooper ion concentration in the supernatant of each sample was measured when the adsorption time reached 48 h.
# Superimposed Perfect Binary Array-Aided Channel Estimation for OTFS Systems
## 3 Superimposed Perfect Binary Array-Aided OTFS Channel Estimator
### Rectangular Pulse Conditions  
In practice, ideal pulses cannot be achieved [11]. For better compatibility with OFDM systems, \(g_{\text{tx}}\) and \(g_{\text{rx}}\) are usually set as rectangular pulses. The accuracy of SPBA-OTFS channel estimation is decreased by the additional interference \(\alpha_{i}[k,l]\) introduced by the imperfect biorthogonality of the rectangular pulses according to (8).  
We now investigate SPBA-OTFS channel estimation with rectangular pulses. Note that \(\alpha_{i}[k,l]=e^{j2\pi lk_{i}/MN}\) when \(l_{i}\leq l<M\). That is, only phase shifts exist under this condition. Additionally, the maximum delay is much less than the symbol period, meaning that \(l_{m}\ll M\). Hence, for an SPBA-OTFS system with rectangular pulses, the correlation interval in (12) is reduced from \(0\leq l<M\) to \(l_{m}\leq l<M\), and (12) can be rewritten as  
\[z\big{[}k_{J},l_{J}\big{]}=\frac{\text{sum}\big{(}\mathbf{Y}\circ\mathbf{ \Gamma}_{J}\cdot\mathbf{R}\big{)}}{MN}\, \tag{32}\]  
where \(\mathbf{R}=\begin{bmatrix}\mathbf{0}&\mathbf{I}\end{bmatrix}^{\mathrm{T}}\in \mathbb{C}^{M\times(M-l_{m})}\), with \(\mathbf{I}\) being the identity matrix.  
Next, for \(\alpha_{i}[k,l]=e^{j2\pi lk_{j}/MN}\), when \(l_{i}\leq l<M\), we redefine the local PBA \(\mathbf{\Gamma}_{J}^{\text{Rec}}\) associated with search unit \(l\) in the case of rectangular pulses as  
\[\mathbf{\Gamma}_{J}^{\text{Rec}}=\frac{e^{-j2\pi\frac{\big{(}l-l_{j}\big{)}k_{ j}}{MN}}\mathbf{\Gamma}_{k_{J},l_{J}}^{*}}{\beta\rho}. \tag{33}\]  
Based on the analysis above, we need to modify only steps 4 and 5 in Algorithm 1 to adapt it to the rectangular pulse scenario. By adopting (33) to improve the local PBA generation in step 4 and reducing the correlation interval to \(l_{m}\leq l<M\) on the delay axis in step 5, Algorithm 1 can be modified for use in the rectangular pulse scenario.  
The relationship between the channel estimation MSE with rectangular pulses and that with ideal pulses is given by  
\[\text{MSE}(\hat{h})_{\text{Rec}}=\frac{M}{M-l_{m}}\text{MSE}(\hat{h}), \tag{34}\]  
where \(\text{MSE}(\hat{h})\) is the MSE with ideal pulses in (22). The threshold satisfies \(\gamma_{\text{Rec}}^{2}\propto\text{MSE}\left(\hat{h}\right)_{\text{Rec}}\). Equation (34) shows that due to the effect of the imperfect biorthogonality of rectangular pulses, the accuracy of SPBA-OTFS channel estimation with rectangular pulses is slightly lower than that with ideal pulses.