esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Theory of seepage Calculation in Unsaturated Soil  
The seepage analysis of the dry-stack tailings under rainfall is carried out under the SEEP/W module in the Geostudio platform. The two-dimensional transient seepage theory is used, and the governing Equation (12) is adopted [18].  
\[\frac{\partial}{\partial x}\bigg{[}-k_{ux}\frac{\partial h_{w}}{\partial x} \bigg{]}+\frac{\partial}{\partial y}\bigg{[}-k_{uxy}\frac{\partial h_{w}}{ \partial y}\bigg{]}+q=m_{w}^{2}\gamma_{w}\frac{\partial h_{w}}{\partial t} \tag{12}\]  
where \(k_{ux}\) is the permeability coefficient in the x direction, \(k_{uy}\) is the permeability coefficient in the \(y\) direction, which is usually a function of matric suction or soil volumetric water content and can be determined according to the soil-water characteristic curve. \(h_{w}\) is the water head, \(q\) is the boundary flow caused by rainfall, \(m_{w}^{2}\) is the gradient of the soil-water characteristic curve, \(\gamma_{w}\) is the gravity of water, and \(t\) is the seepage time.  
As shown in Equation (13), the Fredlund and Xing model is used for the unsaturated soil-water characteristic curve [29].  
\[\left\{\begin{array}{l}\theta(\psi,\alpha,n,m)=C(\psi)\frac{\theta_{s}}{ \left[\ln\left[e+(\psi/\alpha)^{a}\right]\right]^{m}}\\ C(\psi)=\frac{-\ln(1+\psi/\psi_{r})}{\ln[1+(10^{\theta}/\psi_{r})]}+1\end{array}\right. \tag{13}\]  
where \(\theta\) is the volume moisture content, \(\theta_{s}\) is the saturated volume moisture content, \(e\) is the base of natural logs, \(m\) is the parameter related to residual water content, \(n\) is the parameter
## 5 Real-Time Experiments
### Experiment on Hydraulic Laboratory Plant  
The proposed tuning method was tested on a hydraulic laboratory plant, as shown in Figure 19. The plant consisted of three water tanks. Figure 20 presents a schematic diagram of the setup. The process input was voltage on pump P\({}_{1}\), and the process output was a filtered-water level in the first tank (h\({}_{1}\)). Valve V\({}_{12}\) was closed (see Figure 20). Since water was coming out of tank T\({}_{1}\) to the main reservoir placed below three tanks (valve V\({}_{1}\) was open), the process did not contain an integrator. However, since the main time constant was very large, the process could be considered as an approximation of an integrating system. Water levels were measured with pressure sensors. For data acquisition, a custom-made USB-DAQ card was used, and the closed-loop control was implemented in MATLAB and Simulink software packages. More information about this particular laboratory plant can be found in [69].  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(b\) & \(K_{i}\) & \(K_{P}\) \\ \hline \(0\) & \(-196.1\) & \(-59.2\) \\ \(0.5\) & \(-141.9\) & \(-58.17\) \\ \(0.7\) & \(-93.4\) & \(-57.2\) \\ \hline \hline \end{tabular}
\end{table}
Table 10: PI controller parameters for different values of factor \(b\).  
Figure 18: Closed-loop response of control system for charge-amplifier drift compensation.  
The open- and closed-loop experiments were performed on the setup. First, voltage step-change was applied to pump \(\mathrm{P_{1}}\). Voltage (process input) and water level in reservoir \(\mathrm{T_{1}}\) (process output) are shown in Figure 21. The actual water level (in mm) could be obtained by multiplying the measured process output value by 0.5.  
Figure 19: Hydraulic laboratory setup.  
Figure 20: Schematic diagram of hydraulic laboratory setup.  
From the measured open-loop responses, the following characteristic areas were calculated by using Expressions (17)-(19):  
\[A_{0}=0.476,\ A_{1}=0.994,\ A_{2}=1.57. \tag{45}\]  
Using Expressions (12) and (14), the PI controller parameters were calculated:  
\[K_{I}=0.069,\ K_{P}=0.54, \tag{46}\]  
where, to improve disturbance-rejection performance, factor \(b=0\) was chosen (\(b=0\) corresponds to a 2-DOF controller).  
The closed-loop responses on two reference changes and artificially added step-disturbances at process input (\(d\)) are shown in Figure 22. According to the response in Figure 22, it is evident that the proposed method results in a quite efficient control of the hydraulic plant. The nonlinear behaviour of the plant could be seen from different responses on positive and negative references, and input step-disturbance changes.  
Figure 21: Process input (voltage on pump in %) and output (water level in mm multiplied by a factor of 2) signals during manual experiment on hydraulic process.
# Internet of Spacecraft for Multi-Planetary Defense and Prosperity
## 5 Multi-Planetary Detection, Communications, and Defense: Live Long and Prosper
### Equipping the SCADN with Mitigation Technologies
Figure 19: (**a**) Illustration of the SCADN framework which monitors the space and detects and intercepts the hazardous space objects (dimension of celestial bodies, space objects, and orbits are not scaled); and (**b**) a brief flowchart of the SCADN framework. Moreover, some simulations and numerical analyses are conducted and presented in light of the channel physical characteristics of deep space wireless communications within the SCADN framework. The substantial path losses and latency vary as the positions among celestial bodies change significantly over time. Some parameters of interest, such as distance, free-space path loss (FSPL), and latency, are given and compared in Figure 20 for several exemplary cases. The distances are in the unit of AU and categorized into relatively large and small values (but not the maximum and minimum values). Last but not least, understanding the celestial bodies' movement and orbits and the channel characteristics on top of them will essentially help design the communication  
Figure 20: (**a**) Comparison of parameters of interest for Venus, Mars, Jupiter, Saturn, and Voyager 1; and (**b**) Comparison of free-space path loss (large distances for planets) at 2.3 GHz and 8.4 GHz, respectively. systems onboard the spacecraft/spaceships, plus the trajectory and orbital plans. This part of investigation can be put into a more detailed future study.
## 4 The Proposed DC-CAAE Method
### Deep Clustering Training Process  
The DC contains two modules, CAE and clustering. During training, the losses of the two modules are optimized together via Equation (7). In the backpropagation, the loss \(L_{joint}\) is fed back to the encoder to optimize the encoder's network parameters. After deep clustering, the features extracted by the coding layer are spatially friendly to clustering. This process uses the Adam optimizer, and the learning rate was set to 0.0001. The maximum epoch during DC training was set to 30. The autoencoder network parameters are then loaded from the previous training step into the neural network for deep clustering. The intermediate output \(\mathbf{z}\) and the sample reconstruction result \(\mathbf{x}^{\prime}\) are calculated by a forward-propagation to obtain the reconstruction error \(L_{BCE}\). Then, the cluster calculation is performed. It consists in selecting \(k\) initial cluster centers, calculating the distance \(d\) between each element in the dataset and each cluster center, obtaining the cluster loss \(L_{C}\), and dividing the samples into the nearest categories. The deep clustering error \(L_{joint}\) is computed jointly, and the network parameters are corrected by backpropagation until the loss function converges.
# Title-to-Length Coupling Analysis of an Off-Axis Optical Bench Design for NGGM
## 4 Global TTL Coupling
### Cross Coupling between Local and Global TTL
#### 4.3.1 Global TTL Coupling and Optical Bench  
The TX and LO beams of the off-axis layout originate from the same beam injected by the fibre coupler, which is then split by beamsplitter BS1. When the optical bench rotates, the optical pathlength of the TX beam passing through the BS1 changes, due to the change in the refractive index and the impact of the component wedge. The position of the TX RP then changes, resulting in an offset between the RP and the CoM.  
To evaluate the global TTL generated, the local optical bench is rotated around the RP point for \(\pm\)2 mrad in yaw and pitch degrees of freedom, respectively. LPS variations derived in the local and remote photodiode are calculated. The simulation results are shown in Figure 15. The figures depict the global TTL coupling generated by the rotation of the local optical bench. The parameters utilized in the simulation are shown in Table 1.  
Torque exerted by non-gravitational force on a satellite will generate jitter in the roll degree of freedom. Due to assembly errors, angular misalignment is generated between the line of sight vector joining the two centres of mass of the two satellite and the laser link. In addition, the principal axes of inertia of the satellite as a rigid body deviate from the satellite body frame, and cross talk exists among different rotational degrees or freedom. These all contribute to the TTL noise in the roll degree of freedom. To estimate the global TTL coupling in roll, an angular offset between the line connecting the RPs and the laser link between satellites is introduced by rotating the local optical bench around the RP point. The optical bench is rotated around the RP point first for 0.1 \(\upmu\)rad in the pitch degree of freedom and then \(\pm\)2 mrad in the roll degree of freedom. LPS variations are calculated and the global TTL coupling result is obtained, as depicted in Figure 16.  
Figure 16: Results of the global TTL coupling simulation in roll degree of freedom. The optical bench is rotated around the RP point first for 0.1 \(\upmu\)rad in the pitch degree of freedom and then \(\pm\)2 mrad in the roll degree of freedom. The LPS variation and the global TTL coupling are given.  
Figure 15: Results of the global TTL coupling simulation. The figures depict the global TTL coupling generated by the rotation of the local optical bench. The local optical bench is rotated around the RP point for \(\pm\)2 mrad in yaw and pitch degrees of freedom, respectively.
## 3 Methods
### Basic Processing of SAR Data for Multi-Temporal Analysis
These effects were removed by analyzing the temporal \(\sigma^{\circ}\) signature: anomalous peaks or troughs were identified, and the \(\sigma^{\circ}\) values were corrected by means of an interpolator. The correct application of this process relies strongly on _a priori_ knowledge of the rice crop calendar and the weather conditions when the image was acquired.
# LoS Theoretical and Experimental MIMO Study from 1-40 GHz in Indoor Environments
## 2 Description of the Experimental Setup
### Equipment--1-40 GHz  
All measurements have been performed using a commercial vector network analyzer (VNA) (Rohde and Schwarz ZVA 10 MHz-67 GHz). To reach all locations in the room an additional radio over fiber (RoF) link (EMCORE, Optiva OTS-2, 40 GHz [30]) has been used. The number of frequency points is 8192, and the intermediate frequency is 100 Hz. Ultra-wideband Omnidirectional Antennas (STEATITE Q-PAR ANTENNAS, 0.8-40 GHz, [31]) have been used at the transmitter (Tx) and receiver (Rx), with vertical polarization and omnidirectional radiation pattern in the horizontal plane. The antenna patterns were measured in an anechoic chamber at 17 different frequencies between 1 GHz and 40 GHz. The antenna gains range from \(-\)2.2 dBi to 6.9 dBi and the 3 dB beam width from 20 to 140 degrees. In Figure 3, the measured antenna pattern in dB is shown for four different frequencies. All remain details of the measurement setup can be found in [23; 32]. Table 2 summarizes all the configuration of the channel sounder parameters. All measurements are available online in the link included in the Supplementary Materials Section.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Tx Position** & **A (m)** & **B (m)** & **C (m)** \\ \hline
1 & 3.437 & 1.621 & 1.295 \\
2 & 2.264 & 2.041 & 2.571 \\
3 & 1.478 & 2.040 & 3.368 \\
4 & 1.449 & 3.226 & 3.380 \\
5 & 2.264 & 3.214 & 2.582 \\
6 & 3.071 & 2.891 & 1.780 \\
7 & 3.074 & 4.071 & 1.767 \\
8 & 2.276 & 4.421 & 2.564 \\
9 & 1.462 & 4.452 & 3.371 \\
10 & 1.469 & 5.703 & 3.359 \\
11 & 2.269 & 5.639 & 2.562 \\
12 & 3.085 & 5.283 & 1.748 \\
13 & 1.457 & 6.840 & 3.364 \\
14 & 2.259 & 6.856 & 2.566 \\ URA & 3.630 & 6.047 & 1.191 \\ \hline \hline \multicolumn{3}{c}{Heights (from top of the antenna)} \\ \hline URA & 1.44 m & - & - \\ ULA & 1.33 m & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Distance to the walls (A, C) and to either the cupboard or the door (B) of each measured position. The uniform rectangular array (URA) is the receiver. The uniform linear array (ULA) is the transmitter.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Frequency (GHz)** & **Bandwidth (GHz)** & **IF (Hz)** & **Number of Points** \\ \hline
1–40 & 39 & 100 & 8192 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Configuration of the channel sounder.
# Feature Extraction and Evaluation for Driver Drowsiness Detection Based on Thermoregulation
## 2 Materials and Methods
### Data Collection  
During the driving simulation, a number of different physiological variables were monitored. First, information about the state of the driver was obtained by means of a self-assessment of the mental state. The Stanford Sleepiness Scale (Stanford Sleepiness Scale: [[https://web.stanford.edu/~](https://web.stanford.edu/~)]([https://web.stanford.edu/~](https://web.stanford.edu/~))[]dement/sss.html) (SSS) was used for this purpose [25]. The SSS is a validated method for determining sleepiness and has been used in other studies on driver drowsiness [26; 27]. The scale consists of 7 scores/levels to indicate the degree of sleepiness with '1' being the most alert and '7' being almost asleep. The driver was asked to score his/her mental state every 5 min based on this scale. The timing of these moments was indicated by the controlling researcher present in the room by a small hand gesture (i.e., slowly raising their right hand). This way, distraction from the driving task was minimal and the driver did not experience any abrupt changes in his/her mental state. When the highest score of 7 was given 2 times in a row, the experiment was terminated prematurely because we assumed the driver to be too drowsy to continue driving in a normal way. In the SSS, a score of 7 corresponds to the following state: \"No longer fighting sleep, sleep onset soon; having dream-like thoughts\". Secondly, the skin temperature of the tip of the nose (T\({}_{\text{nose}}\)) was measured using a VarioCAM infrared thermal camera with a resolution of 640 by 480 pixels (InfraTec GmbH, Dresden, Germany). The camera pas pointed towards the face and from the thermal images, the position of the nose was manually determined. The frame rate of the camera was set to 1 frame per minute. Thirdly, the skin temperature at the wrist (T\({}_{\text{wrist}}\)) was measured continuously with an Empatica E4 wristband at a sampling rate of 4 Hz (Empatica Inc., Cambridge, MA, USA). Fourth, the heart rate (HR) was monitored using a Zephyr HxM chest strap (MedTronic, Annapolis, MD, USA). The HR, expressed in beats per minute (bpm), was sampled every second with a smartphone and app for logging the data (Samsung Galaxy J1). In this research, we did not consider raw ECG measurements because it was decided beforehand that only the HR would be used in the analyses (see Section 2.4. Data Analysis). Lastly, the ratings of the SSS were used to check whether participants who performed the first protocol actually became drowsy during the driving test and the second group of subjects remained alert. This decision was based on the sleepiness scores. If a score of 5 was given at any point during the test, the participant was labeled as \"drowsy\". If this was not the case, the subject was labeled as \"non-drowsy\".  
During one simulation, the data of T\({}_{\text{wrist}}\) were not stored correctly. Hence, only 18 measurements are available. In addition, the data of the heart rate were missing for 2 simulations.
## 1 Introduction
Educational environments often house personal and sensitive student or staff information; thus, any smart campus strategy must include encrypted communication, strong intrusion detection, and compliance with relevant regulations [18]. Adopting safe and equitable AI solutions additionally requires human oversight and stakeholder input, ensuring that algorithmic suggestions do not compromise educational equity or institutional integrity [19]. Despite the clear promise of AI-driven, networked, and IoT-based campus operations, widespread adoption is still in its infancy [11]. Many institutions lack a unifyingarchitecture to integrate emerging tools with legacy systems, and concerns persist about costs, infrastructural readiness, and organisational change management [7]. Moreover, administrators, faculty, and students must all learn to trust the outcomes of AI-based analyses; mistrust or confusion can nullify potential efficiencies, if stakeholders cling to established, manual procedures [12]. Addressing these issues requires a well-defined, scalable model, which specifies how IoT networks gather data, how AI algorithms process information for real-time decisions, and how secure channels disseminate those insights without jeopardising user privacy or system reliability. The present study introduces a conceptual framework which unifies AI-driven analytics with secure wireless networks to enhance educational resource management across institutional settings. While existing systems often remain fragmented, reactive, and technically inaccessible, this work addresses five critical gaps. Firstly, it responds to the absence of a holistic, cross-layered architecture which integrates sensing, analytics, and actuation within a coherent operational loop. Secondly, it challenges the inaccessibility of existing platforms for non-specialist administrators, particularly in institutions lacking dedicated IT support. Thirdly, it confronts the disconnect between data collection and real-time decision-making, whereby sensor data remain under-utilised and uncoordinated. Fourthly, it accounts for the limited scalability and adaptability of current solutions, offering a modular design suitable for both small and mid-sized institutions. Finally, it advances the marginal role of cybersecurity in campus digitisation by embedding AI-driven intrusion detection into the system's core architecture. Through the integration of machine learning techniques, IoT-based sensing, and interpretable design principles, the proposed framework can enable, when fully applied, dynamic resource allocation, predictive maintenance, and streamlined administrative decision-making--offering a practicable and unified alternative to ad hoc digital deployments.
## 3 AIS Sensor Data Acquisition and Preprocessing
### AIS Sensor Navigation Trajectory Data Extraction and Separation
The specific algorithm for extracting trajectory data is as follows:  
Step 1: In sequence \(T\), \(t_{i}\) is traversed until data points \(t_{i}\) and \(t_{j}\) satisfy \(\left|t_{i}-t_{j}\right|\geq 12h\). Then, the \(i\) and \(j\) data points in the time series are set as sequence tangent points and the steps are repeated until all data points in the sequence are traversed. Step 2: If there is an abnormality in the time data, this step is used for assistance. In sequence \(T\), \(v_{i}\) is traversed until the data point satisfies \(v_{i}\approx 0\); this point is then set to the tangent point in the sequence. Step 1 is then repeated until all data points in the sequence have been traversed.
## 3 Thermodynamic Modeling  
### Modeling Basis  
This study used the code PHREEQC to compute the model, which quantified the equilibrium state of the reaction products using \(SI\), with \(SI<0\) corresponding to mineral dissolution (undersaturation) and \(SI>0\) corresponding to mineral precipitation (supersaturation) [31]. The input data for the model calculation were the pore solution  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Properties** & **Index** & **Value** \\ \hline  & Liquid Limit (\%) & 60.6 \\  & Plastic Limit (\%) & 38.7 \\  & Plasticity Index, PI & 21.9 \\ Geotechnical & Optimum Moisture Content (\%) & 34.8 \\ Properties & Maximum Dry Density (g/cm\({}^{3}\)) & 1.65 \\  & Exchangeable Cation Amounts (meq/100 g) & \\  & Na\({}^{+}\) & 27.6 \\  & K\({}^{+}\) & 3.1 \\  & Ca\({}^{2+}\) & 9.7 \\ \hline  & Quartz (\%) & 28 \\ Mineralogy & Montmorillonite (\%) & 46 \\  & Muscovite (\%) & 17 \\  & Albite (\%) & 7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The geotechnical properties and mineralogy of the soil.  
compositions of the selected cement-stabilized soils. \(SI\) was calculated from the ion activity product (\(IAP\)) and the equilibrium constant (\(K_{eq}\)) of the phase using Equation (1) [32]:  
\[SI=\lg\frac{IAP}{K_{eq}} \tag{1}\]  
For a given dissolution reaction A\({}_{2}\)B\(\rightarrow\)2A\({}^{+}\)+B\({}^{2-}\), the \(IAP\) of A\({}_{2}\)B was obtained from Equation (2):  
\[IAP=\frac{\left[\text{A}^{+}\right]^{2}\cdot\left[\text{B}^{2-}\right]}{\left[ \text{A}_{2}\text{B}\right]^{0}} \tag{2}\]  
where A\({}^{+}\), B\({}^{2-}\) and A\({}_{2}\)B correspond to the activities of the aqueous species A\({}^{+}\), B\({}^{2-}\) and A\({}_{2}\)B, respectively. The activity \(i\) of any specie \(i\) was calculated using Equation (3):  
\[\left[\text{ }i\text{ }\right]=m_{i}\gamma_{i} \tag{3}\]  
where \(m_{i}\) is the mass molar concentration of ion \(i\) (mol/kg) and \(\gamma_{i}\) is the activity coefficient of ion \(i\), which was calculated with the well-known Debye-Huckel equation [33].  
Notably, Equation (2) suggested that the higher the dissolution magnitude of A\({}_{2}\)B, the higher the \(IAP\) value. Thus, the index lg\(IAP\) was used in this study to evaluate the dissolution magnitude of the reactants, following the conventions of cement chemistry and geochemistry [29].
## 4 Conclusions  
In this paper, the VIV responses of three different risers, i.e., conventional steel riser (Riser 1), composite riser with orthogonal reinforcements (Riser 2) and tailored composite riser (Riser 3) are investigated and compared considering the effects of 2D and 3D models, FSI and different modeling methods for the composite risers. The results show that:  
Figure 16: Stress distribution for every lamina with LSM: Riser 2 (**a**); and Riser 3 (**b**).  
Both 2D and 3D models lead to 2S vortex shedding modes for all three risers and VIV of a riser has obvious 3D characteristics and is affected by the depth of the flow field as well as the effect of FSI. As a result, a 3D model with FSI for the VIV is more accurate and realistic.
2. The amplitude of the cross-flow vibration is much larger than that of the flow-direction vibration, and the cross-flow vibration has a distinct periodicity while the flow-direction vibration tends to be at a relatively stable value. The deformation of the three risers are different due to their geometries, material properties, top-tension force, constraints, etc.
3. Different risers have different stress distributions, i.e., the stresses of composite risers are generally smaller than those of steel riser and occur in the middle and fixed end of the composite risers.
4. For the composite risers modeled by EMM and LSM: (i) EMM and LSM lead to similar VIV results for the same composite riser and both can be utilized to obtain the global performance of the composite risers; and (ii) EMM is easier and more efficient for the global calculation; however, to show the response of the each composite lamina, LSM is compulsory.  
[PERSON] designed and analyzed the experiments and collected data; [PERSON] and [PERSON] performed the simulation; [PERSON] provided the basic design parameters for the composite risers; [PERSON] analyzed the results; and [PERSON] wrote the paper.  
This work was supported by Shandong Provincial Natural Science Foundation (ZR2017 LEE031 and ZR2016 DL08), Science and Technology Development Project of Zibo (2017 KJ040017), and Promotive Research Fund for Excellent Young and Middle-aged Scientists of Shandong Province (BS2014 SF013).  
The authors declare no conflict of interest.