esa-sceva/satcom-chunk-collection huggingface dataset:
# The Application of Fine Sand in Subgrades: A Review
## 3 Treatment Technology for Sand-Filled Subgrades
### Innovative Materials for Sand Subgrade Treatment and Engineering Cases  
Furthermore, scholars have proposed the utilization of materials such as shredded tires, sludge, and fly ash for the sand subgrade treatment [41; 42; 43; 44]. This approach not only enhances the stability and performance of sand subgrade but also promotes waste utilization, aligning with the prospects of sustainable development.  
[PERSON] [45] conducted inspections and monitoring on an experimental embankment constructed using a mixture of shredded tires and sand, with a weight ratio of 23/77 (volume ratio of 50/50). The results indicate that within one year of open traffic, the maximum settlement is only 12 mm. The maximum relative lateral displacement with respect to the embankment base was observed to be approximately 2 mm. No signs of internal heating were found, indicating no risk of spontaneous combustion. It is recommended to promote the use of mixtures of shredded tires and soil in embankment construction.  
The practical engineering cases of fine sand subgrade improvement are shown in Table 1. Although fine sand in coastal areas is not traditionally considered an ideal subgrade material, the rapid development of high-grade highways and the demands of engineering construction necessitate adherence to the principles of using local materials and environmental protection. Consequently, coastal areas inevitably face the questions of whether fine sand can serve as a subgrade material and how to utilize it.  
Traditional methods for detecting subgrade issues mainly rely on excavation and probing, which are costly, time-consuming, information-intensive, subjective, and can easily cause damage to the surveyed route. The traditional detection methods for fine sand subgrades may result in an overall instability of the subgrade due to sand leakage. Therefore, the development of a non-destructive detection and identification technology for defective subgrades is crucial for ensuring the long-term performance of fine sand subgrades during the operation and maintenance stages.
## 3 Results  
We identified, reviewed, and analyzed a total of twenty-one relevant articles for this study. The articles were selected based on our search methodology and contributed significantly to understanding NPH diagnosis using AI techniques. Table 1 presents a comprehensive summary of all included studies, their methodologies, and key performance metrics.  
Our analysis revealed distinct methodological distributions among the studies: eleven articles (52.4%) employed machine learning (ML) methods, including LogReg [32, 33, 34, 35, 36], SVM [37, 38], random forest (RF [XE \"RF\" \(\backslash\) t _\"Random Forest\"_)] [30, 37, 39, 40, 41, 42], XGBoost (XGB { XE \"XGB\" \(\backslash\) t _\"xgBoost\"_}) [37, 42], multilayer perceptron (MLP [XE \"MLP\" \(\backslash\) t _\"multilayer perceptron\"_) [42], gaussian naive bayes (GaussNB { XE \"GaussNB\" \(\backslash\) t _\"Gaussian Naive Bayes\"_)) [42], adaptive boosting (AdaBoost { XE \"AdaBoost\" \(\backslash\) t _\"Adaptive Boosting\"_}) [42] and other ML methods. Seven articles used DL methods, with deep convolutional network (DCNNs [XE \"DCNN\" \(\backslash\)t _\"Deep Convolutional Network\"_) [27, 28, 30, 39, 43, 44, 45] being the most common (used in six studies or 28.6% of the total). Notably, three studies (14.3%) implemented hybrid methodologies combining both ML and DL techniques, indicating an emerging trend toward integrated approaches.  
The distribution of AI methods reflects the evolving landscape of computational approaches for NPH diagnosis--from traditional ML algorithms that rely on predefined features to more advanced DL methods capable of automatic feature extraction. This methodological diversity underscores the complexity of NPH diagnoses and the varied approaches researchers are employing to address this challenge.
## 7 Estimating Jet Engine Temperature Distributions
### The Estimation Process  
From the matrix obtained in the previous section, a two-dimensional Discrete Fourier Transform is calculated using the FFT algorithm. The result is a matrix \(F_{m\,n}\) of equal size but containing complex values. In literature regarding the Fourier transform, many properties can be found. A good example is book [22]. We use symmetry and antisymmetry of the resulting matrix. General inversion of the Fourier transform is defined in the following way:  
\[f(i,j)=\frac{1}{\sqrt{m\cdot n}}\sum_{k=0}^{m}\sum_{l=0}^{n}F_{k,l}\exp({\bf j }\frac{k\cdot i}{m}\cdot\frac{l\cdot j}{n}) \tag{49}\]  
where \({\bf j}^{2}=-1\).  
Equation (49) is defined only for the original points of the matrix (48). The continuous surface can be obtained by changing the therms \(\frac{i}{m}\in[0,1]\), \(\frac{j}{n}\in[0,1]\) into \(\frac{r}{\max r_{i}}\in[0,1]\), \(\frac{T}{\max T_{i}}\in[0,1]\). We cannot guarantee that between grid points the result would be real. The sensible solution is to use the absolute value of a possibly complex number.  
We can ask the question of why use the FFT, if we reconstruct the same data again. The reason is smoothing the result. The reconstruction can use only a selected part of the spectrum--obviously lower frequencies. As mentioned before, they reside in the center of the matrix:  
\[f(r,T)=\left|\frac{1}{\sqrt{m\cdot n}}\sum_{k=K}^{m/2+K}\gamma_{k}\sum_{l=L}^ {n/2+L}F_{k,l}\exp({\bf j}\frac{k\cdot r}{r_{max}}\cdot\frac{l\cdot T}{T_{max} })\right|, \tag{50}\]  
where \(\gamma_{k}\) is the correction factor necessary for compensating for the omitted data. Its values should be selected so the reconstruction result is still p.d.f. and integrates to 1. Obviously, the size of part of the FFT matrix taken is \(4\cdot K\cdot L\), the careful selection of \(K,L\) is crucial. When those numbers are too small (see Figure 5), the result loses any resemblance to the original data (Figure 4). On the other hand, when the numbers are too large, this gives a detailed reconstruction (Figure 6) with all unnecessary details.  
The acceptable reconstruction in Figure 7 is made with \(K=16\) and \(L=4\). It is obvious that smaller size means faster calculations.  
The exact values can be obtained experimentally (as in this example) or by using some method like the Kronmal-Tarter rule.  
Another heuristic approach is to reduce the amount of the entry points (probabilities) \(f(X,\rho)\) by removing perimeter data. The result of such an approach can be seen in Figure 8. The removal of peripheral data results in reduction of over-fitting.  
Figure 4: Number of samples in the grid (reduced to 32 \(\times\) 32 for display’s sake).  
Figure 5: Insufficient amount of Fourier-transformed data, the result is too smooth (\(2\times 2\) in the **left**, \(4\times 4\) in the **right**).  
Figure 6: Use of too much Fourier-transformed data (\(64\times 64\) in the **left**) and full reconstruction (**right**).  
Figure 7: Good selection of data for smoothing, \(16\) in \(r\), \(4\) in \(T\).
# Improved Localization and Recognition of Handwritten Digits on MNIST Dataset with ConvGRU
## 4 Experiments
### Implementation Details  
The proposed model was implemented with the following specifications.  
* **Training configuration**:
* Optimizer: Adam (\(\mathrm{lr}=2\times 10^{-4}\), \(\beta_{1}=0.5\), \(\beta_{2}=0.999\)).
* Batch size: 32 samples per GPU.
* Training epochs: 100.
* Learning rate schedule: cosine annealing with warm restarts.
* **Model architecture:*
* ConvGRU hidden dimensions: [64; 128; 256].
* Attention heads: Eight.
* Feature fusion channels: 512.
* Total parameters: 2.8 M.
* **Data augmentation:*
* Random rotation: \(\pm 15^{\circ}\).
* Random scaling: [0.9, 1.1].
* Random translation: \(\pm 10\%\).
* Gaussian noise: \(\sigma=0.01\).
## 5 Case Study  
The proposed model was tested on a modified IEEE 24-node system over a daily time horizon whose data was taken from Reference [31]. The system comprises 34 transmission lines, illustrated in Figure 4. The wind farm is located at bus 20. The load profile used for testing was obtained as given in Figure 5. The 24-node system includes 17 loads. Both the nodal location of these loads and their contribution in percentage to the total system demand are listed in Table 1. It was assumed that there were smart appliances in 10 nodes, including nodes 3, 4, 5, 6, 8, 9, 10, 14, 19, and 20. The time interval was 1 min.  
Figure 3: Solution method’s flowchart. DCOPF: direct current optimal power flow.  
An ensemble of wind farms with a total installed capacity of \(W_{z}=600\) MW is connected to the base system as an example. The wind forecast power data is taken based on [32], and its forecasting error is distributed normally as shown in Section 2. Figure 6 shows the actual and prediction wind power.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Load** & **Node** & **Percentage** & **Load** & **Node** & **Percentage** \\ \hline
1 & 1 & 3.8 & 10 & 10 & 6.8 \\
2 & 2 & 3.4 & 11 & 13 & 9.3 \\
3 & 3 & 6.3 & 12 & 14 & 6.8 \\
4 & 4 & 2.6 & 13 & 15 & 11.1 \\
5 & 5 & 2.5 & 14 & 16 & 3.5 \\
6 & 6 & 4.8 & 15 & 18 & 11.7 \\
7 & 7 & 4.4 & 16 & 19 & 6.4 \\
8 & 8 & 6.0 & 17 & 20 & 4.5 \\
9 & 9 & 6.1 & & & \\ \hline \end{tabular}
\end{table}
Table 1: The 24-node system: node location and distribution of the total system load.  
Figure 4: The modified IEEE 24-node system.  
Figure 5: The daily load profile.  
To showcase the benefits offered from the proposed methodology, four distinct case studies were conducted as follows, in which the wind installed capacity was 600 MW and the DR percentage was 15%. The operator maximizes the wind accommodation for which, in Case 1, generating units provide energy and reserve and there is no DR participation to absorb as much wind power, but in Case 2, in addition to generators, smart houses can also participate in balancing the wind prediction error. The generator ramp constraint is not considered in Case 3, and the power flow transmission constraint is not taken into account in Case 4 in the condition of a wind percentage of 20% and DR percentage of 15%. The simulations were conducted in Matpower and Matlab [33]. The observations are reported in the following subsection.
## 3 Results and Discussion
### Load-Lateral Displacement Curves  
The lateral displacement of the specimen at the center of the column under the ultimate load in the test was taken as the ultimate deflection value. The load column mid-span deflection curves for each specimen are shown in Figure 8. Compared with the unreinforced specimens, the stiffness of the reinforced structure was significantly higher than that before reinforcement, and the deformation capacity was greatly improved. The curve trends are broadly divided into four stages. The elastic phase: when loaded to approximately 25% of the peak load, the slope of the curve was steeper, and the deflection grew at a slower rate because the crack resistance of UHPC is better than that of ordinary concrete. No cracks appeared on the surface of the specimen, and the curve of the specimen in this phase was approximately linear. The elastic-plastic phase: with the slow load increase, rapid deflection growth, and curve fluctuation, the specimen entered the elastic-plastic working state. The longitudinal reinforcement of the reinforced specimen did not yield. The deformation of the specimen increased faster, while the growth in the load slowed. The curve showed non-linear changes in this phase. The plastic phase: the load slowly grew, the deflection rapidly increased, the curve fluctuated, the specimen entered the elastic-plastic working state, the longitudinal stress reinforcement of the reinforced column did not yield, the deformation of the specimen grew faster, and the load grew slower. The descending phase: as the curve entered the descending phase, the vertical cracks in the UHPC in the compression zone became significantly wider, and their restraining effect on the core concrete in the compression zone decreased. When the load was reduced to approximately 75% of the peak load, the UHPC at the bottom of the compression zone was crushed, and the specimen was damaged but still retained good integrity. The descending section of the specimen curve after UHPC reinforcement was slower than that of the comparison column, which indicates that the late ductility of the UHPC-reinforced eccentric compression columns was improved, and the final deflection in the span was reduced to different degrees, which indicates that the UHPC reinforcement can improve the specimen's ability to resist deformation.
# Dynamic Single-Electron Transistor Modeling for High-Frequency Capacitance Characterization
## 4 Conclusions  
A model for SETs was proposed based on the time-dependent master equation and taking dynamic gate current into account, and it was realized by using the SPICE circuit simulator as a solver for simultaneous differential equations. Based on this model, intrinsic terminal capacitances and transcapacitances in the SET were discussed as a function of gate voltage, frequency, and temperature. It was concluded that, at low frequencies, the capacitances depended on the biasing but, at high frequencies, the transistor action of the SET was lifted, and it acted as a passive capacitance circuit. Such a transition from the low-frequency to high-frequency characteristics of the SET could be described quantitatively by the proposed model. Also, the model could analyze the behavior of the SET covering a wide range of temperature, in which the \(C_{\text{gg}}\) for small \(V_{\text{ds}}\) was inversely proportional to the temperature on the low-temperature side, and the \(C_{\text{gg}}\) assumed the constant value insensitive to the temperature or \(V_{\text{ds}}\) on the high-temperature side.  
In addition, the frequency response of the SET-based amplifier was investigated. The transformation from inverting amplifier at low frequencies to the capacitive divider at high frequencies was successfully described. It was also found that the Miller effect is not conspicuous in the SET-based amplifier due to the negligible feedback capacitance at the operation point.  
From these results, we can expect that the proposed model can be widely used to analyze the circuits, including SETs for high-frequency amplification, oscillation, detection, qubits readout by gate-based sensing, and so on.  
Conceptualization, A.S. and H.I.; Data curation, A.S.; Formal analysis, A.S. and H.I.; Methodology, A.S. and T.N.; Visualization, A.S. and H.I.; Writing original draft, A.S.; Software, T.N.; Investigation, H.S. and H.I.; Supervision, H.S. and H.I.; Resources, H.I.; Validation, H.I.; Writing--review and editing, H.I. All authors have read and agreed to the published version of the manuscript. This research was funded by the Cooperative Research Project of the Research Center for Biomedical Engineering with RIE, Shizuoka University, and the Cooperative Research Project Program of RIEC, Tohoku University. Not applicable. Not applicable. Not applicable. Data Availability Statement: The data that support the findings of this study are available from the corresponding author (H.I.) upon reasonable request. Authors are grateful to [PERSON] and [PERSON] of Shizuoka University for fruitful discussions. The authors declare no conflict of interest.
# Mine Intelligent Receiver: MIMO-OFDM Intelligent Receiver for Mine Information Recovery
## 3 Network Model
### Intelligent Receiver Model
#### 3.3.1 Implementation Method of Intelligent Receiver  
The network model structure of the VOVNet-based smart receiver designed in this paper is shown in Figure 7. The VOVNet-based intelligent receiver network model contains a total of 5 Stages, and each Stage is followed by a 1 \(\times\) 3 maximum pooling layer with a step size of 2 for downsampling. Stage1 contains 3 layers of convolution, Stage2-Stage5 consists of OSA modules, and the classifier contains a fully connected layer, so the model has a total of 40 layers.
## 3 Proposed Biased-Power Allocation (Bi-PA) for SM-LDM Systems
Considering that \(\beta_{L}\) and \(D_{L}\) are constant, the increase of SNR yields a decrement in \(\rho_{L}\). This reduction is significant to reduce the effect of _LL_ interference on the _UL_ receiver because the _UL_ does not apply SIC and considers the _LL_ signal as noise. Moreover, the low _LL_'s power ratio does not have a noticeable impact on its performance at the high SNR region. In contrast, the _UL_ layer starts with a relatively small power ratio at low SNR values and then increased gradually to deal with the interference from the _LL_ signal. Increasing the _UL_ ratio at high SNR is also crucial for successful SIC at the _LL_ receiver. Numerical comparison examples between the Pre-PA and Bi-PA will be given in Section 6.
# A Cryptography-Powered Infrastructure to Ensure the Integrity of Robot Workflows
## 4 Application and Evaluation in a Real Robot System  
In this section, we demonstrate how to apply our approach in a real robot system. We chose a particularly complex system which combines two separate robot systems that can be jointly programmed with workflows. We first demonstrate how the concept is implemented on top of the robot's software stack. Second, we evaluate it in a real use-case to investigate any additional overhead.
# Application of a Coupled Eulerian-Lagrangian Approach to the Shape and Force of Scientific Balloons
###### Abstract  
Scientific balloons provide an inexpensive and reliable platform for near-space scientific experiments. The analysis of the balloon geometry and forces has always been a major concern for balloon designers. Most previous studies have focused solely on the fully inflated shapes and forces of balloons, analyzing only the membrane structure and simplifying the effects of internal and external gases into a gradient pressure difference. This approach lacks consideration of the fluid-structure interaction (FSI) of scientific balloons. This paper utilizes the Coupled Eulerian-Lagrangian (CEL) method in the Abaqus/Explicit simulation environment to analyze the FSI effects of scientific balloons under the influence of internal helium and external air. Three typical working conditions of scientific balloons are selected for simulation analysis. First, a three-dimensional spherical balloon is simulated during the ascent process to verify the correctness of the CEL simulation framework. This also demonstrates the membrane folding characteristics during balloon ascent, which could not be calculated in previous two-dimensional axisymmetric simulations. Next, the study explores balloon shapes that deviate from quasi-static pressure distributions due to the motion of internal helium. These include the \"mushroom\" shape observed during the dynamic launching of the balloon on the ground and the \"sail\" shape caused by lateral airflow. The mushroom shape arises from the sudden loss of the bottom constraint, causing the internal helium to move upward while being resisted by the air at the balloon's top. The simulation successfully replicates the rapid waist transition and the downward concavity at the top due to air resistance, while also providing the corresponding force distribution. For the sail-shaped condition, the simulation analyzes the balloon's tilt angle and the characteristic upturn of its windward surface. By a comparison with no-wind conditions, this study quantifies the impact of wind on the forces acting on the balloon, offering practical guidance for balloon launching. The CEL simulation framework established in this study not only provides a new tool for the FSI analysis of scientific balloons but also enriches the mechanical analysis results of these balloons.  
s +
Footnote †: journal: applied sciences
# Sustainability Audit of University Websites in Poland:
## 2 Background
### Theoretical Framework  
Despite the growing body of literature on sustainable development in digital technologies, it still does not offer systematic analyses that would combine technical aspects and social conditions, including organisational, psychological, and motivational factors. Therefore, to gain a better understanding of why and to what extent universities engage in curbing the carbon footprint of their websites, one has to draw on established theoretical concepts from social sciences, such as the Theory of Planned Behaviour (TPB) or Stakeholder Theory (ST), which detect both instrumental and socio-ethical motives for decisions.  
The Theory of Planned Behaviour (TPB) by [PERSON] [22] is a valuable tool for analysing organisational behaviours aimed at sustainable development. It posits that an individual's or organisation's intentions to take a specific action hinge on three factors: (1) attitude toward the behaviour, (2) subjective social norms, and (3) perceived behavioural control. In the context of actions in the online ecosystem, such as practising sustainable design principles, the theory may help better understand the university's motivation to employ low-emission design techniques.  
Their readiness to curb the carbon footprint of their websites is influenced by factors such as declarative environmental attitudes, social expectations (including student pressure and institutional pressure), and technical capabilities, including available IT tools and competencies. The Theory of Planned Behaviour can identify key drivers of environmentally friendly decisions at universities and provide a groundwork for empirical analysis of their readiness to follow sustainable practices in the digital sphere. Stakeholder Theory can be as useful when analysing university attitudes towards environmental challenges. It proposes that organisations consider the needs and expectations of all affected groups, not only students and staff but also local communities, next generations, and the natural environment [23; 24]. Thus, as beneficiaries of public trust, universities have a moral obligation to consider environmental aspects in their operations, including those in the digital domain. Note that the pursuit of environmentally friendly technology can be motivated in diverse ways. Apart from classical instrumental and external motives, such as a better position on search engine results pages and brand image, ethical, social, and purpose-driven reasons--set to exert a positive influence and pursue a mission--grow more impactful [25; 26]. In this context, universities that make the effort to follow low-emission web design may be driven by social and environmental responsibility in addition to conversion rates.  
A combination of the TPB and ST, with the aid of organisational motivation typology, can identify rational causes and ethics-based intentions underlying the pursuit of green technologies. The study aims to empirically expand these theories by investigating low-emission web design efforts at specific universities.