esa-sceva/satcom-chunk-collection huggingface dataset:
# An Optimized Load Balance Solution for Multi-homed Host in Heterogeneous Wireless Networks
## 2 Related Work
### Methods  
The heterogeneous multi-access network selection problem is considered as an NP-Hard problem [69; 70]. In order to pick the most appropriate network in terms of quality of service, several challenges need to be considered. Three main challenges can be mentioned to solve the problem of network selection and traffic load balancing. The first challenge is the suitable criteria that should be used for data distribution among networks while considering the QoS parameters. The second challenge is determining a suitable algorithm that enables each criterion to be weighted. The third challenge is ranking the optimal networks for these criteria.  
The load balance solution requires a ranking of the available multiple connections. This is performed using the MADM algorithm, which is a category of MCDM. The MADM can be commonly portrayed as the procedure of choosing one from many accessible choices, or positioning choices, in view of many criteria, which normally have an alternate essentialness. This section examines some of the most notable MADM methods in alternative decision making for providing a suitable solution for an issue with multiple criteria, such as the SAW, AHP, and TOPSIS methods. These methods are explained in the following subsection.
# An Intelligent Real-Time Driver Activity Recognition System Using Spatio-Temporal Features
## 3 Methodology
### Input Data Pre-Processing  
The pre-processing system comprises three minor steps including cropping, resizing, and normalizing. Recall that the original images were first stored with resolutions of 1280 \(\times\) 720 and 640 \(\times\) 480 for the RGB and IR images, respectively. Therefore, the raw images are first cropped to view the driver's side only. At this stage, the cropped image size becomes 640 \(\times\) 720 for the RGB images and 320 \(\times\) 480 for the IR images. Employing such high-resolution images inevitably consumes high storage and computational resources. Therefore, the images are further downsized to 128 \(\times\) 128 in terms of width and height without losing the essential driver body features to classify the driver activity. Note that the proposed input size image has a lower input size than that of the original pre-trained VGG16 [32] architecture, which is preferable in saving the computational source.  
Then, the datasets are structured and prepared for implementing a sliding window transformation throughout the whole dataset. Figure 5 illustrates the process of the sliding window. It is similar to the sliding window across a prior observation in which the window  
Figure 4: Part of the data samples for IR raw datasets.  
Figure 3: Part of the data samples for RGB raw datasets.  
width is fixed to five time steps. We also consider three-, four-, and six-time step inputs. However, the overall accuracy of the model witnessed a decrease when the number of frames was set to three, four, and six. The details of the number of frames' comparative results are presented in Appendix A, Table A. Initially, the window discovers the first five images and uses them as input to the classifier network. Then, as shown in Figure 5, the window slides to the right side by one and applies the next five series image as the second input. This process is repeated until the window covers the entire dataset.
## 3 Results
### C2 RCC Automatic Processor for Water Quality Products  
With the objective of making an independent validation of the automatic product of TSM provided by the atmospheric correction C2 RCC in SNAP, Figure 6 shows that the TSM results provided by the processor are positively distributed and show a good correlation with the TSM data measured in the field, presenting an error (RMSE) of 14.4 mg/L. This error can be reduced to 5.3 mg/L using the equation presented in Figure 6. Field data is approximately 1/3 of the data offered by the automatic TSM product.
# Design and Performance Analysis of Navigation Signal Based on OFDM
## 5 Performance Evaluation of OFDM Navigation Signals
### Communication Performance Analysis  
In this paper, when realizing the fusion of the communication and navigation signals, the selected communication signal also adopts the OFDM modulation method. The OFDM navigation signal and the OFDM communication signal are superimposed in the time domain, and the power ratio of the communication signal and the navigation signal is controlled at 20-30 dB. The sequence spread spectrum technology demodulates the navigation signal that is submerged in the communication signal to realize the communication and navigation functions.  
The following section focuses on analyzing the change in the bit error rate with the bit signal-to-noise ratio in the communication signal when under the influence of the navigation signal. In the simulation, the parameters of the navigation signal and OFDM(128) are the same, where the subcarrier spacing is 0.15 MHz, number of carriers is 128, and cyclic prefix length is 12 dB, and the simulation result of the bit error rate of the communication signal is shown in Figure 15. When the conduction power ratio is 20 dB, it can be found that the influence of OFDM(128) is slightly smaller than that of BOC(1,1) on the bit error rate of the communication signal. When the bit signal-to-noise ratio is 10 dB, it can be found that the bit error rate caused by BOC(1,1) is significantly higher than OFDM(128), indicating that when the EbN0 is large, the bit error rate is mainly caused by BOC(1,1), and it indicates that BOC(1,1) has a greater impact on the communication signal. When comparing the bit error rate caused by OFDM(128) to the communication signal under the conditions of 20 dB and 10 dB conduction power ratios, respectively, the difference between the two is very small. This indicates that when the conduction signal power value is changed from 20 dB to 10 dB, the change in communication bit error rate is small, and the influence of the OFDM navigation signal on the bit error rate of the OFDM communication signal is very small with respect to the change of the power ratio of the conduction signal. Therefore, the OFDM navigation modulation proposed in this paper can be used as the superimposed signal of the communication signal to integrate communication and navigation at the signal level.  
Figure 14: Multipath error of the navigation signal. (**a**) Multipath envelope error; (**b**) Multipath envelope average error.
# Super-Resolving Ocean Dynamics from Space with Computer Vision Algorithms
## 2 Materials and Methods
### Satellite Sea Surface Temperature Data  
Remotely sensed SST data are taken from the European Copernicus Marine Service ([[https://marine.copernicus.eu/access-data](https://marine.copernicus.eu/access-data)]([https://marine.copernicus.eu/access-data](https://marine.copernicus.eu/access-data)), last accessed 14 January 2022). They are Level-4 (L4) products, which means they provide gap-free estimates on a regular grid and are operationally produced and freely distributed in near-real time. We have used here 11 years (2008-2019) of the ultra-high-spatial-resolution (UHR) Mediterranean dataset, reaching a nominal 0.01\({}^{\circ}\)\(\times\) 0.01\({}^{\circ}\) resolution (Product ID: SST_MED_SST_L4_NRT_OBSERVATIONS_010_004_c_V2). This SST dataset is retrieved by first combining the night-time images collected by multi-platform infrared sensors, after specific quality control and cloudy pixel removal, and by successively running a two-step optimal interpolation algorithm [37]. Before using satellite SST L4 data to build our predictor tensor, we had to map them on the same grid used for the model training. To obtain a consistent prediction, we preliminary assessed the effective spatial scales resolved by the model SST, and eventually filtered the UHR to remove scales that have never been seen by the network. This was achieved by applying a low-pass Lanczos 2D filter (with window size = 9 and cutoff = 1/8) directly to the UHR data before remapping on the final 1/24\({}^{\circ}\) grid (through a basic bilinear interpolation). As discussed in the Results section, this pre-processing has the drawback of further smoothing the SST field in areas not covered by concurrent infrared satellite measurements due to cloud contamination or other coverage issues.
# Method of Suppressing Rayleigh Waves Based on the Technology of Time-Domain Differential Detection
## 2 Differential Detection Method for Suppressing Rayleigh Waves at Common Shot Points
### Analysis of the Impact of Operating Boundaries on Time-Domain Differential Technology  
In an ideal scenario where the operating surface is infinitely large or where the material properties of the medium are continuous, using time-domain differential processing to suppress Rayleigh waves can yield perfect results. However, in practical seismic exploration environments, various factors can interfere with this process, among which the effects of surrounding boundary reflection waves, particularly shear wave reflections, are especially significant. Technicians engaged in engineering seismic exploration commonly encounter this issue, warranting a qualitative analysis of boundary effects.  
Typical lateral boundaries can be categorized into two types: those parallel and those perpendicular to the exploration line. These categories can be examined individually to aid technicians in enhancing their differential detection techniques in future applications.
## 3 Materials and Methods
### Experimental Model  
Experimental validation of the analytical (mathematical) model was performed on the example of the SGTC with an air layer (S1 collector type, Chapter 2). The isometric view of the mentioned SC is shown in Figure 7a, while the complete experimental installation with the measuring chain is shown in Figure 7b.  
The experimental installation with a measuring chain (Figure 7b) is formed by two subsystems: solar construction (Figure 7a) and a meteorological station. The experimental installation operation principle with open circulation circuits (without circulation pumps) is based on hydrostatic pressure--the driving force for water flow through the SC. Accompanying measuring equipment for collecting appropriate parameters (solar irradiance intensity on a horizontal surface, ambient air temperature, wind speed, water inlet temperature, water outlet temperature, and water mass flow rate) required for thermal analysis consisted of a Kipp & Zonen SMP3 pyranometer (accuracy \(<5\%\)), a Kipp & Zonen data logger METEON (accuracy \(<0.1\%\)), WZP-035 \(\mathcal{O}5\times 50\) mm Pt-100 temperature probes (accuracy \(\pm\) 0.2 \({}^{\circ}\)C), and a HERZ STROMAX manual control valve [19].  
Experimental \(Q_{loss}\) value was measured indirectly by Equation (12):  
\[Q_{loss}=Q_{sun}-Q_{heat}=A_{abs(up)}I_{TOT}-\dot{m}_{w}c_{p}(T_{w,out}-T_{w,in}) \tag{12}\]  
where \(Q_{sun}\) [W] is the solar heat power, \(Q_{heat}\) [W] is the heat power of the SGTC with an air layer, \(A_{abs(up)}\) [m\({}^{2}\)] is the upside area of the absorber, \(I_{TOT}\) [W/m\({}^{2}\)] is the total incident solar irradiance (for the tilted surfaces, the value \(I_{TOT}\) is mathematically calculated according to the recommendations from [43]), \(\dot{m}_{w}\) [kg/s] is the mass flow rate of the water, \(T_{w,out}\) [K] is the outlet temperature of the water, and \(T_{w,in}\) [K] is the inlet temperature of the water.  
Figure 7: View of the SGTC with an air layer (**a**) and experimental installation with a measuring chain (**b**) [46].
## 3 Proposed Intelligent Weighting Factors Design Approach
### Proposed Weighting Factor Tuning Scheme Based on DRL
#### 3.2.4 Problem Formulation
With the state space \(\mathcal{S}\) and the action space \(\mathcal{A}\) described (albeit implicitly in the case of \(\mathcal{S}\)), one element remains to learn the weighting factor selection policy: the reward function. It plays a central role in guiding the agent's learning process and influencing its behavior. It has to be designed in order for the resulting weighting factors to balance multiple objectives in the asymmetric decision function, including accurate current tracking, minimizing switching frequency, maintaining stability in control actions, and ensuring smooth control transitions. These different objectives can be split into three reward terms:  
1. **The current tracking reward**\(R_{\text{current}}\) which incentivizes the SPMSM's phase \(a\) current to follow its reference within a given tolerance margin \(i_{\text{tol}}\) and is expressed as \[R_{\text{current}}(s(k),a(k))=\exp\left(-\left(\frac{\Delta i_{a}(k)}{i_{ \text{tol}}}\right)^{2}\right);\] (17)
2. **The switching frequency reward**\(R_{\text{switch}}\) ensuring that the switching frequency \(f_{\text{sw}}\) of the VSI follows its reference \(f_{\text{sw}}^{*}\) and is expressed as \[R_{\text{switch}}(s(k),a(k))=\exp\left(-\left(\frac{\Delta f_{\text{sw}}(k)}{ f_{\text{sw}}^{*}(k)}\right)^{2}\right);\] (18)
3. **The specific goal reward**\(R_{\text{goal}}\) increasing the reward if some specific performance thresholds are achieved, that is expressed as \[R_{\text{goal}}(s(k),a(k))= \begin{cases}0.5&\text{if }|\Delta i_{a}(k)|<0.05i_{\text{tol}}\text{ and }|\Delta f_{\text{sw}}(k)|<0.1f_{\text{sw}}^{*}(k),\\ 0&\text{otherwise};\end{cases} \] (19) and two penalty terms:The weighting factor variation penalty \(P_{\text{weight}}\) discourages rapid changes in the weighting factors' values to increase stability and is expressed as  
\[P_{\text{weight}}(s(k),a(k))=0.01\Big{(}(\lambda_{i}(k)-\lambda_{i}(k-1))^{2}+( \lambda_{f}(k)-\lambda_{f}(k-1))^{2}\Big{)}; \tag{20}\]
2. **The control unbalance penalty \(P_{\text{balance}}\)** penalizing the gap between the values of the two weighting factors, having for expression \[P_{\text{balance}}(s(k),a(k))=0.1\Big{|}\lambda_{i}(k)-\lambda_{f}(k)\Big{|}.\] (21) Combining these components results in the global reward function \[\rho(s(k),a(k))=R_{\text{current}}(s(k),a(k))+0.5R_{\text{switch} }(s(k),a(k))+R_{\text{goal}}(s(k),a(k))\\ -P_{\text{weight}}(s(k),a(k))-P_{\text{balance}}(s(k),a(k))\] (22) to achieve the objectives mentioned above. The content of Figure 6 is a rewriting of the structure of Figure 3, aiming to highlight the position of the RL agent and its interaction with the environment according to what we defined in this section.
# Study on Improvement Characteristics of a Novel Geotextile with Stitched Transverse Ribs
## 3 Experimental Results
### Comparison of Shear Strength of Stitched Rib Geotextile Reinforcement
The shear stress at the shear displacement of 10 mm under various working conditions is shown in Figure 9. It can be seen that the order of the shear stress value is: geotextile stitched with three ribs > geogrid > geotextile stitched with two ribs > geotextile stitched with one rib > flattening geotextile. When the shear displacement is small, the effect of the geotextile stitched with one or two transverse ribs is not obvious compared with that of the tiled reinforcement. The reason is the friction provided by the underlying geotextile increases rapidly, but the transverse ribs need more displacement to play their role. We can see that the reinforcement effect of the bottom geotextile is mainly produced by the friction between the bottom geotextile and the particles. In contrast, that of the geotextile with one stitched transverse rib is not only reflected in the friction with soil particles but also the extrusion on the transverse rib. The geotextile stitched with three transverse ribs has the best reinforcement effect and the most obvious increase in shear stress. The shear stress under four normal loads is greater than the geogrid. It can be seen that the geotextile stitched with three transverse ribs can provide greater shear resistance when the deformation is small. The relationship between the maximum shear stress and normal stress obtained from each test is shown in Figure 10. It shows that the shear strength of the structure can be significantly improved by sewing ribs. Under low normal stress (25 kPa), the maximum shear stress of the geotextile structure stitched with one, two, and three transverse ribs are 40 kPa, 55 kPa, and 62 kPa, respectively. Under high normal stress (150 kPa), the maximum shear stress of the geotextile structure stitched with one, two, and three transverse ribs are 100 kPa, 114 kPa, and 140 kPa, respectively. There is a good linear relationship between the maximum shear and the normal stress, and the pseudo-cohesion and friction angle are improved compared with the geogrid. The shear strength of one transverse rib is close to that of the geogrid, and the improved effect is mainly reflected in the pseudo-cohesion, and the improvement of the friction angle of three ribs is more obvious. According to the results shown in Figures 7, 9 and 10, the increase in the number of stitched transverse ribs can greatly improve the shear strength and enhance the constraint effect on the filler, making the structure exert a shear effect earlier. As the filler used in these tests is sand with small particle sizes, the thickness of the influence zone of the geogrid is small. The reinforcement mechanism is changed, and the influence range of the reinforcement is expanded by using the geotextile sewn with ribs, so the strength of the structure is greatly improved. Figure 9: Comparison of shear stress of different stiffened structures at 10 mm.
## 2 Reverse Logistics Facility Location Model  
### Problem Description  
#### 2.1.1 Assumptions  
To make the model easy to quantify and to make the optimization algorithm convenient to process, the following assumptions are made for the waste product recycling network:  
**Assumption 1**.: _Recyclers work with product sellers, so there is no need to build another collection point. Product sales points of each city are recycling points. This model is a simulation of waste product recycling in a province, so the recycling logistics in the city are not considered. That is, the logistics costs from the consumer to recycling points are ignored._  
**Assumption 2**.: _It is possible for a city to build both storage points and specialized treatment plants, and the recycling plant has the capacity to dispose of dismantled waste, so there is no need to build another waste treatment plant. The value products obtained through professional processing are sold separately, so the outflow logistics of the treatment plant are not studied, but only the simple reverse logistics process is studied here._  
**Assumption 3**.: _The whole recycling path of waste products can be roughly described as: recycling point-storage point-professional disposal point. The recycling operation period is one year, and the waste products produced during the recycling period will be recycled through this process._  
**Assumption 4**.: _This paper studies the product recycling treatment in the province, and the default transportation mode is road transportation. Therefore, the unit transportation cost between each facility is fixed, and the transportation cost is only related to the distance and transportation volume._  
**Assumption 5**.: _The fixed costs of building the same facilities should use uniform market prices, which do not vary from region to region. During the recycling period, no transportation activities are carried out between logistics facilities with the same function._  
**Assumption 6**.: _There is a quantitative limit on the amount of recycling accommodated by storage points and professional disposal points. However, the second-hand product market, component manufacturers and recycling material recyclers have no demand restriction on the recycled waste product flowing to them; that is, the amount of recycling of existing products has not reached market saturation._  
**Assumption 7**.: _Freight charges between different facilities in the same location are ignored by default._  
**Assumption 8**.: _Classified storage points have fixed unit storage costs, and professional disposal points have fixed operating costs, which are positively correlated with the amount of storage and processing, respectively._
## 3 Results and Discussion
### Modeling Based on the Effective Wavelength  
PLSR, MLR, and ANN methods were employed to model the SVM-PSO-selected effective wavelengths. Table S6 presents the R\({}^{2}\) (coefficient of determination), RMSE (root mean square error), and RPD (ratio of performance to deviation) values for the calibration and validation of PLSR and MLR models, respectively. Table S7 displays the corresponding values for the training, validation, and testing phases of the ANN model. The ANN model consistently outperformed PLSR and MLR in terms of validation R\({}^{2}\), RMSE, and RPD for all quality properties across the different bell pepper varieties. Specifically, the ANN model achieved the highest R\({}^{2}\) and RPD values (0.99 and 314.5, respectively) for total acidity (TA) in red and orange varieties, while recording the lowest RMSE (0.010677) for pH in the red variety. These results indicate that while all models developed using effective wavelengths demonstrated acceptable accuracy in predicting bell pepper quality characteristics, the ANN model exhibited superior predictive performance. This superiority can be attributed to the ANN's ability to learn directly from data without requiring the estimation of statistical characteristics, as reported in numerous studies across various fields [13; 61; 73]. A study utilized PLS and ANN methods for non-destructive, real-time monitoring of apple firmness during ultrasonic contact drying [74]. Employing portable NIR spectroscopy, they evaluated model performance using output cross-validation and an external dataset. To explore the impact of spectral changes on stiffness prediction, several pre-treatments were applied. Through weighted regression coefficients, seven significant wavelengths were identified and used to construct a simple MLR model for enhanced interpretability and noise reduction. Models incorporating PLS, MLR, and ANN with selected wavelengths predicted apple hardness with R\({}^{2}\)\(p\) values of 0.91, 0.91, and 0.95, respectively, and RMSEP values of 14.78 N, 14.85 N, and 12.46 N. These results demonstrate the superior performance of the ANN method compared to PLS and MLR [74]. Authors in another study applied a genetic algorithm neural network (GANN) to near-infrared spectral data for classifying oil palm ripeness [75]. Their findings revealed that the GA effectively reduced learning time and optimized the NN architecture by systematically minimizing identification errors and circumventing the conventional iterative approach. Dimensionality reduction through PCA generated new component variables, accelerating the GANN data analysis process and facilitating the identification of optimal solutions.