esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### BBD Method for PSA Process  
Within the predefined experimental design space, the response surface methodology (RSM) allows for the identification of the optimal combination of factors and their corresponding responses. RSM designs include Box-Behnken design (BBD) and the central composite design (CCD). Notably, BBD requires fewer experimental groups compared to CCD under identical factorial conditions, making it a more cost-effective and efficient option. Additionally, BBD is frequently employed for predicting nonlinear models [28, 29]. As a specific type of response surface methodology, BBD provides comprehensive equations to describe the relationship between factors and responses [30]. In this study, BBD was implemented using Design Expert software (version 10) to establish a correlation between PSA performance and operating parameters, with the objective of determining the optimal operating parameters for hydrogen purification.  
For the BBD method, a one-factor design was initially employed to determine the range of independent variables. In this setup, the independent optimization parameters are coded as \"-1\", \"0\", and \"+1\" to represent the levels of each factor. Desirability is used as the objective function, which ranges from 0 (indicating non-compliance) to 1 (indicating full compliance). A desirability value of 1 corresponds to the optimal scenario, whereas a value of 0 signifies that one or more responses fail to meet the desired criteria. The overall desirability (\(D\)) and the desirability for each individual response (\(d_{i}\)) are defined as follows:  
\[D=\left(d_{1}\times d_{2}\times\cdots\times d_{n}\right)^{\frac{1}{n}}=\left( \prod_{i=1}^{n}d_{i}\right)^{\frac{1}{n}}, \tag{12}\]  
\[d_{i}=\left(\frac{y_{i}-L}{T-L}\right)^{w}, \tag{13}\]  
where \(n\) represents the number of responses, \(T\) is the maximum possible values of responses, \(L\) is the minimum possible values of responses, \(w\) represents the weight, and \(y_{i}\) represents the optimal value of each response.
## 4 Error Analysis
### Error Caused by Geomagnetic Blind Area  
The geomagnetic blind area is another factor determining the error of roll angle measurement. Shown in Figure 18, during the flight, when the X-axis is parallel to geomagnetic vector \(f\), \(B_{\mathrm{Z}}\), the projection of \(f\) onto \(Z_{\mathrm{b}}\), is going to be zero or extremely small [22]. The range of the blind area depends on the location and attitude of projectile in flight.  
Figure 18 describes the cross-section of projectile and the geomagnetic vector. \(\mathbf{h}_{\mathrm{P}}\) is the projection of \(f\) on cross-section; \(\eta\) is the angle between X-axis and vector \(f\), \(\varphi\) is the roll angle; and \(\alpha\) is the angle between \(\mathbf{h}_{\mathrm{P}}\) and downward direction (Z-axis of NED coordinate). Thus, \(B_{\mathrm{Z}}\) in (5) can be also described as  
\[B_{\mathrm{Z}}=|\mathbf{f}|\sin\eta\cos(\alpha+\varphi) \tag{27}\]  
Referring to Zhang [24], there is a certain range of roll angle defined as the blind area of roll angle in which the detection module cannot satisfy the expected accuracy. It is determined by \(f\), the total intensity of local field; \(\Delta\varphi\), the expected precision of roll angle; \(\eta\), the angle between X-axis and \(f\), \(\lambda\), resolution of magnetoresistive sensor selected. The blind area of roll angle is in the range of [24]  
\[|\sin(\alpha+\varphi)|<\frac{\lambda}{\Delta\gamma|\mathbf{f}|\sin\eta} \tag{28}\]  
in which the sensor is not able to detect the weak signals.  
Thus, only when \(\varphi\) satisfies  
\[|\sin(\alpha+\varphi)|\geq\frac{\lambda}{\Delta\gamma|\mathbf{f}|\sin\eta} \tag{29}\]  
could the tracking loop obtain the rolling information satisfying the expected accuracy.  
One can make a conclusion that to reduce the effects from blind area, in the selection of sensor, more consideration should be given to the resolution to reduce \(\lambda\). Meanwhile, \(\varphi\) is determined by the attitude of projectile and local geomagnetic information. The period of blind area would not last too  
Figure 18: Cross-section and geomagnetic vector.  
long for attitude keeps changing in flight. Furthermore, the tracking loop enables the measurement system to regain rolling information after tracking loop unlocked for the weak signals.
## 2 Phenomenological Model  
The process of in-flight ice accretion includes several steps, each involving different physical phenomena, different scales and different regimes. The starting point is a cloud of supercooled droplets. These droplets, driven by the airflow field, impinge on the surface: the first impact location is entirely defined by the macroscopic scale droplet and airflow fields, and may be predicted via straight CFD modeling.  
On the other hand, the post-impact evolution of the droplet is defined by the solid-liquid interaction features at the microscale. In particular, following [11], at low velocity, small droplet size, and high surface tension (i.e., low Weber number), the droplets stick on the surface, after a brief unsteady spreading step, assuming a roughly spherical cap shape defined by the liquid-solid contact angle. Above a threshold _We_ number, they will rebound, re-emerging from the surface with a lower kinetic energy. Following the first rebound, the droplet will be driven again by the airflow drag forces, which may either move it far away from the solid surface or push it to a secondary impingement, possibly ending as a sticking drop further downstream. Furthermore, we have the possibility of rebounding droplets interactions with new incoming ones. As noted in the introduction, the physical mechanisms inducing super-hydrophobicity are quite complex, and at extreme low wettability rebound may occur even at the lowest _We_: thus, the proposed description and modeling of the process does not pretend to be appropriate for any possible coating design (which would be a task well beyond the scope of the present work), but aims to offer a suitable tool for the analysis of the most common standard super-hydrophobic isotropic surfaces.  
The actual quantity of water on the surface, in the form of a collection of spherical cap-shaped droplets, is thus a result of the initial collection efficiency, reduced by the rebounding mass flow, plus the water mass recovered from the secondary rebound and drop-drop collisions.  
The droplet's evolution on the surface is driven by aerodynamic drag, pressure gradient and surface tension, as well as by the growth via coalescence with new impinging drop and mass losses via evaporation or freezing. Thus, they may remain as sticking droplets, evolving into frozen beads, or they may run along the surface as individual droplets or merge into rivulets and possibly a film.  
In order to develop a complete phenomenological model, each of the above-mentioned steps and mechanisms must be described and taken into account with reasonable accuracy.
# Query Rewriting for Incremental Continuous Query Evaluation in HIFUN
## 4 Methods
### System Implementation
#### 4.2.1 Continuous HIFUN Queries over Micro-Batches  
In the micro-batching approach, as a dataset continuously grows and as new data become available, we process the tuples in discrete batches. The batches are processed according to a particular sequence. As a high volume of tuples can be processed per micro-batch, the aforementioned mechanism uses parallelization to speed up data processing. An initial dataset \(D_{i}\) is followed by a continuous stream of incremental batches \(\Delta D_{i}\) that arrive at consecutive time intervals \(\Delta t\). As we already explained, incremental evaluation would produce the query results at time \(t+\Delta t\) by simply combining the query results at time \(t\) with the results from processing the incremental batches \(\Delta D_{i}\). Two key observations should be made here. The first is that computations needed are solely performed within the specific batch, following the evaluation scheme described in the previous section. Therefore, for  
Figure 3: Incremental computing over an append-only dataset.  
every batch interval we calculate a result based on delta subset \(\Delta D_{i}\), e.g., \(inc_{i}\longleftarrow e(\Delta D_{i})\). The second observation is that the state should be kept across all batches to perform in-memory computations by wrapping the accessed data as distributive data collections. After the evaluation of each query is completed for each micro-batch, we need to keep the states across all batches. The previous state value and the current delta result are merged together, and the system produces a new state incrementally, e.g., \(state\longleftarrow u(inc_{i},state)\). Figure 4 illustrates this incremental approach.
# Review of GIS-Based Applications for Mining: Planning, Operation, and Environmental Management
## 3 Mine Planning Using GISs
### Potential Conflict Region Analysis  
In the potential conflict region analysis phase, GIS geoprocessing and hydrologic modeling can be utilized.  
[PERSON] et al. [24] conducted a case study of the ecological and economic zoning of Cajamarca in Peru, which is rich in gold, to analyze the land-use planning process in the face of conflicting interests regarding future land and water use. GIS techniques were used to evaluate the conflicting policies  
Figure 10: Result of the haul road design. (**a**) Open-pit mine with bench and haul road. 3D views: (**b**) pit 2 area and (**c**) pit 1 area [23].  
Figure 9: Study process of haul road design for open-pit mines using the proposed method [23].  
on land and water use between a powerful pro-mining association led by the central government and a conservation coalition (anti-mining) led by the local government. This study suggests how GIS technology can be used if there is a conflict between stakeholders concerning mine development, such as in Cajamarca, Peru.  
[PERSON] et al. [25] carried out a GIS-based spatial analysis considering social, environmental, and economic factors for the sustainable development of mineral and energy resources. In addition, a case study was conducted to identify potential sustainability conflict zones in mining design. They identified key parameters for consideration in decision-making through the GIS analysis of on-the-ground issues related to sustainability. The case study suggests that the use of this approach could have promoted better communication and planning. The adoption of such a process could assist in the transition to a new regulatory framework that is based on and promotes sustainable development principles [25].
# A CNN-Based Approach for Driver Drowsiness Detection by Real-Time Eye State Identification
## 3 Proposed Method
### CNN Training Experiments  
#### 3.4.1 Dataset Processing  
Before training the CNNs, image processing was performed. The extracted ROI had different pixel sizes with 3 layers of depth (mxnx3); therefore, resizing to a size accepted by the CNNs was necessary. All images were adjusted to a size of 112 \(\times\) 112 \(\times\) 3 pixels, then normalized by changing each pixel value from 0-255 to 0-1. Then, to avoid overfitting, data augmentation was applied with the following parameters: rotation range 20%, horizontal flip _True_ and fill mode _Nearest_, resulting in the creation of 5 images from each image of the training set.
# Signal Detection Based on Separable CNN for OTFS Communication Systems
## 4 Proposed SeCNN-OTFS Based Signal Detection Method
### SeparableBlock  
The SeparableBlock contains six separable convolutional layers, effectively reducing the computational complexity compared to traditional convolutional layers. The latter three convolutional layers mirror the structure of the first three layers. These tri-layered segments are interconnected through local residual and recursive residual connections, thereby preventing gradient explosion or vanishing gradient issues. The introduction of a channel attention mechanism effectively enhances the model's ability to identify critical channel features and improves its robustness against noise interference and Doppler spread. This design ensures training stability while maintaining computational efficiency. Furthermore, multiple SeparableBlocks can be stacked in different application scenarios, enabling reliable convergence even as the network depth increases.
## 6 Results and Discussion
The result is coincided with that in the reference [14; 18], and the highest growth temperature is about 3000-3100 K on the similar condition; (2) furnace top region owns lower temperature because of continuous heat release from top cooling hole, and then good temperature gradient is formed. Moreover, it can be found obviously that the temperature field of the spiral coil is non-axisymmetric, especially in the powder high temperature region. So it is necessary to simulate the temperature field in the SiC crystal growth furnace by three-dimensional models. Defects in SiC crystal--such as dislocation, microtubules, etc.--are generated mainly by non-uniform temperature gradient [2; 13; 19]. Figure 10 presents fields of temperature and temperature gradient (T_R, K/m) in r-direction on r0 cross section in the crystal. A good axisymmetric temperature field is shown in Figure 10a compared with non-axisymmetric temperature field of the spiral coil as shown in Figure 10b. It can be seen from Figure 10b that the highest temperature region deviates from  
Figure 8: Temperature profiles on SiC powder surface and SiC crystal surface along with r-direction. Figure 9: Temperature field at \(I\) = 1250 A and \(F\) = 16 kHz. (**a**) Axisymmetric coil; (**b**) spiral coil. the crystal center, which is as same as the results of [PERSON] et al. [13]. The axisymmetric T_R is also gotten for the axisymmetric coil. It is beneficial to grow SiC crystal with fewer defects. As seen from Figure 10d, the T_R is non-axisymmetric for the spiral coil, and the worse T_R are presented in the region far from the center of crystal which may generate more defects. To study the effect of coil type on the temperature and its gradient evenness (S\({}_{\text{T}}\), S\({}_{\text{T},\text{R}}\)) quantificationally, the date at r/R = 0.38 are extracted and processed by the sample standard deviation method  
\[\left\{ \begin{array}{l}S=\sqrt{\frac{\sum\limits_{i=1}^{n}\left(T_{i}- \overline{T}\right)^{2}}{n-1}}\\ \overline{T}=\frac{\sum\limits_{i=1}^{n}T_{i}}{n}\end{array} \right. \tag{10}\]  
where \(T_{i}\) is the temperature at mesh cell center, \(\overline{T}\) is the average temperature, and \(S\) means bad temperature or its gradient evenness. For the axisymmetric coil, both S\({}_{\text{T}}\) and S\({}_{\text{T},\text{R}}\) equal 0; for the spiral coil, S\({}_{\text{T}}\) and S\({}_{\text{T},\text{R}}\) are 1.59 and 170.93, respectively. So it is suggested that the coil is closed to axisymmetric structure.
# A DEM Study on Bearing Behavior of Floating Geosynthetic-Encased Stone Column in Deep Soft Clays
## 4 Conclusions  
A discrete element model of an FGESC in soft soil foundation was established. The stresses, particle movement, and failure mode of the FGESC with an encasement depth of 280 mm were analyzed. The stresses of the soil around the column and the soil in the underlying stratum were also analyzed. Meanwhile, the effect of the encasement depth on the bearing capacity, contact force, and failure mode of the FGESC was studied. The following conclusions can be drawn:  
1. The FGESC with encasement depth of 280 mm has large vertical and radial stresses at the top region of the column under load. The aggregates in the column top region become loose under load as the porosity increases, the coordination number decreases, and the sliding ratio increases. The force chain of the column is relatively complete and can effectively transfer the upper load downward. When the encasement depth is 280 mm, the failure mode of the FGESC is mainly in the form of penetration failure.
2. When the encasement depth is 280 mm, the vertical and radial stresses of the soil near the column body are not affected much by the column body. It indicates that the presence of geogrids limits the movement of the aggregates in the column. Therefore, the large stresses of the column cannot be effectively transferred to the soil around the column.
3. As the encasement depth increases, the bearing capacity of the FGESC increases, the force chain of the column body becomes more complete, and the column can transfer the load downward more effectively. However, when the encasement depth exceeds 280 mm, the bearing capacity of FGESC does not increase much.
4. The encasement depth to a certain extent controls the failure mode of the FGESC. As the encasement depth increases, the failure mode of the FGESC gradually transitions from swelling deformation to penetration failure.  
In this study, the load-bearing deformation characteristics and microscopic mechanism of the FGESC were mainly studied. The deformation of the aggregates under load and the influence of pore water were ignored. In future work, the Clump function of PFC will be used to simulate the aggregates; it is more realistic to reflect the irregular shape of the aggregates. The pore water will be taken into account to study the dissipation of pore water in the column and soft clay.  
Conceptualization, F.L., P.G. and X.G.; methodology, F.L. and B.L.; software, F.L. and X.H.; validation, P.G. and H.H.; investigation, F.L., H.H. and X.H. All authors have read and agreed to the published version of the manuscript.  
This research work was funded by the National Natural Science Foundation of China (51278438).  
Not applicable.  
Figure 20: Changes of column bottom with different encasement depths: (a) 100 mm, (b) 180 mm, (c) 280 mm, and (d) 360 mm.  
Data Availability Statement: The data supporting the findings of this study are not publicly available due to privacy.  
The authors declare no conflict of interest.
# TinyML Algorithms for Big Data Management in Large-Scale IoT Systems
## 3 Methodology
### Framework Architecture  
The proposed architecture outlines a systematic approach for managing Big Data in IoT environments. At the base is the IoT layer, composed of various devices such as sensors and wearables, which generate vast amounts of data. These data are directed towards Raspberry Pi devices, equipped with TinyML capabilities. Within the Raspberry Pi layer, three primarytasks are undertaken: data cleaning to remove inconsistencies, anomaly detection to identify unusual patterns, and feature extraction to select relevant data attributes. Once processed, the refined data are transmitted to the centralized Big Data system via the communication layer. Notably, the volume of data being transmitted is reduced due to the preliminary processing at the Raspberry Pi level. At the top layer, the centralized system performs further storage, analytics, and processing tasks. A feedback mechanism is incorporated, allowing the centralized system to send updates to the Raspberry Pis, ensuring continuous optimization. Overall, this architecture presents a structured methodology for efficient data processing and management in large-scale IoT settings. The illustration of this architecture is represented in Figure 1.
## 1 Introduction
The max-min PRF bounds in a spaceborne SAR system are important for determining the range resolution, unambiguous range, antenna area, swath coverage, and signal processing requirements [33]. Finding an optimal balance between these parameters is essential for designing an effective SAR system that meets specific imaging and operational requirements. When choosing the noise-equivalent sigma zero (NESZ), selected values represent different levels of system performance, with a lower NESZ indicating better sensitivity and a lower noise floor. However, the specific NESZ values to use depend on the application, desired performance, and system characteristics. To find the ideal size of a SAR satellite, the selected parameters are to be considered. Having explained the fundamentals of spaceborne SAR systems and the associated trade-offs, the paper continues with optimization. In employing the optimization methods, artificial intelligence (AI) processes presented in the literature may be of great aid in designing satellites with proper antenna and systems [34; 35]. Through the employment of optimization tools, our paper implies innovative processes can also be applied to SAR satellite design to cope with constraints and challenging trade-offs.
# Design and Analysis of Systematic Batched Network Codes
### Contributions Regarding Systematic Outer Codes
The decoding computation cost of the systematic outer code depends on the number of message packets received at the destination node. When all the message packets are received, no computation is required for decoding. When some of the message packets are not received, the decoding computation cost of the systematic outer code increases with the number of message packets that are not received and is at most 2 times the computation cost of the BATS outer code decoding.
## 3 Results and Discussion
### Volatile Compounds  
The results of the volatile compounds analysis of WBCs are shown in Table 10. A total of 94 volatile compounds were identified and grouped into the following chemical classes: organic acids (15), alcohols (16), aldehydes (10), esters (13), ketones (11), lactones (5), terpen (6), alcanes (8), and amines (3). Organic acids, alcohols, and esters, both in number and amount, are the dominant chemical groups. Regarding the individual identified volatile compounds, no significant differences were observed between the TC and SST cheeses. Organic acids were the most abundant chemical class in both cheeses (TC and SST) with acetic acid, decanoic acid, hexane acid, and octanoic acid representing about 78% of the total amount of organic acids. Each of them gives a characteristic flavor note [(21)]. In general, fatty acids, having between 4 and 20 carbon atoms, are formed through lipolysis by microbial lipases. The shorter fatty acids may come (originate) from the degradation of lactose and amino acids, as well as from the oxidation of ketones, esters, and aldehydes [(21; 29)]. As expected, the samples (TC and SST) had the highest content of total organic acids on the 180 th day (3828.29 mg/kg for TC and 3846.02 mg/kg for SST). From the chemical class of alcohols, the major alcohol was ethanol (both in TC and SST) throughout their ripening-preservation period, with the maximum value in both cases being noted on the 180 th day (530.41 and 549.44 for TC and SST, respectively), which was expected, considering that its formation is due to lactose fermentation, proteolysis, and reduction in acetaldehyde [(21; 29)]. Esters can be produced enzymatically or chemically through the reaction of short to medium chain fatty acids with primary and secondary alcohols that both derive from lactose fermentation and amino acid catabolism [(21; 30)].