esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Design of In-Situ Test
### Cpt  
Figure 3a shows the schematic of the static CPT, consisting of a double bridge probe at the lower end of a high-strength and stainless steel pipe segments in succession. The double bridge probe comprised a cone tip, which was 10 cm\({}^{2}\) in basal area (35.7 mm in diameter) with an apex angle of 60\({}^{\circ}\), as presented in Figure 3b. The 10 CPT test points within 2 m and corresponding to SPT test points (CPT-1#, CPT-2#, CPT-3#, CPT-4#, CPT-5#, CPT-6#, CPT-7#, CPT-8#, CPT-9#, CPT-10#), were selected, respectively. Four ground anchors with a length of 1.5 m and a diameter of 35 cm of the anchor blade were penetrated into the ground. Two trucks provided counterforce for the CPT method, as shown in Figure 3c. The double bridge probe and the metal pipes were pushed down into 1 m below the ground and then raised 10 cm to observe the zero position movement. After that, the data were cleared, and the probe was pressed back to the original position to begin the formal penetration at a constant speed of 2 cm/s. Some soundings could not be penetrated to the long pile tip because of the dense over-consolidated sand of the coarse sand layers (2). Therefore, a drill machine was used to penetrate through the coarse sand layer (2) until the long probe toe could reach to the medium sand (2 & 2), and then the CPT could be conducted in the soil layers below. As penetration was ongoing, cone-tip resistance (\(q_{c}\) in MPa per unit area at the cone tip) and sleeve friction (\(f_{s}\) in kPa per unit area of the sleeve) were measured and recorded. The friction ratio (\(R_{f}\)) could be obtained as follows [53]:  
\[R_{f}=f_{s}/q_{c}\times 100\% \tag{5}\]  
The formula for determining the UBC of a single PHC pipe pile based on the CPT results of the double bridge probe is as follows [50]:  
\[Q_{uk}=Q_{sk}+Q_{pk}=u\sum l_{i}\cdot\beta_{i}\cdot f_{si}+\alpha\cdot q_{c} \cdot A_{p} \tag{6}\]  
\[\beta_{i}=5.05(f_{si})^{-0.45} \tag{7}\]  
where \(f_{si}\) is average unit skin friction of a double bridge probe induced by soil layer \(i\); \(\beta_{i}\) is the correction coefficient of skin friction induced by sandy soil layer \(i\); \(\alpha\) is correction coefficient of pile tip resistance (for saturated sand, \(\alpha=0.5\)); \(q_{c}\) is cone-tip resistance.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Soil Type**} & \multirow{2}{*}{\(q_{c}\)/N} & \multirow{2}{*}{
\begin{tabular}{c} **Friction Ratio** \\ **(\%)** \\ \end{tabular} } & \multicolumn{2}{c}{**Ultimate End**} & \multicolumn{2}{c}{**Ultimate Shaft**} \\  & & & **Bearing Capacity** & **Friction Capacity** \\ \hline Clean sand with various densities & 0.3745 & 0.60 & 342.4 N & 2.03 N \\ Mixed sand with clay and silt & 0.2140 & 2.00 & 171.2 N & 4.28 N \\ Plastic clay & 0.1070 & 5.00 & 74.9 N & 5.35 N \\ Shell-containing sand and soft & 0.4280 & 0.25 & 385.2 N & 1.07 N \\ argillaceous limestone & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Schmertmann method.
# Assessment of IRNSS-Only Data Processing: Availability, Single-Frequency SPP and Short-Baseline RTK
## 6 Discussion  
Although there are only eight available IRNSS satellites at present, the positioning performance of the IRNSS single-system SPP (with meter-level accuracy) and short-baseline RTK positioning (with a centimeter-level accuracy) is still acceptable in its service areas. As satellite-based positioning technologies, both SPP and RTK depend on the available satellites. In the future, with the continuous improvement of IRNSS constellation and the increased number of ground tracking stations, a better performance of IRNSS single-system navigation and positioning can be expected.  
Compared with GPS single-system results, the accuracy improvement coming with GPS/IRNSS integration is not obvious. This may be due to the satellite geometry being strong, and the positioning accuracy being satisfactory, even for GPS-only cases under an open sky environment. Despite this, the reliability of GPS/IRNSS integrated position solutions can be enhanced. When the users are in environments with limited satellite visibility such as in urban canyons, mountainous areas and open-pit mines, etc., the benefits from GPS/IRNSS integration for position determination are expected to be more significant.
# Experimental Study of Accuracy of High-Rate GNSS in Context of Structural Health Monitoring
## 2 Methods and Materials
### Experiments
#### 2.3.2 Data Collection and Processing  
Two Septentrio GNSS receivers (model: PolaRx5s, Figure 1b) were used in the experiments to record the GNSS observations at a sampling rate of 100 Hz (for both the rover and base stations). The Septentrio PolaRx5 can track multi-frequency and multi-constellations GNSS signals with 8 logging sessions (more technical parameters can be found at www.septentrio.com). The sampling rate of the LDT was also set to 100 Hz for easy comparison. The Phase Lock Loop bandwidth of the GNSS receiver was set to the default value of 15 Hz. The GNSS data were processed with GPSSHM [34] software and Net_Diff [35] software in RTK, PPP, and PPP-AR modes, respectively. Some details for the data processing are given in Table 1. The final ephemerids and 5-min clock products of the Multi-GNSS Experiment (MGEX) produced by the GeoForschungs Zentrum (GFZ) were used in the PPP processing [36]. The multi-GNSS rapid ephemerides and clock and phase bias products produced by Wuhan University (WUM) were adopted for PPP-AR processing [37]. Four computational schemes based on the different GNSS satellite systems were  
Figure 1: (**a**) Set up of experimental equipment: (**b**) Septentrio GNSS receiver (PolaRx5s) used; and (**c**) data logger with a GPS antenna.  
adopted. The GPS-only and BeiDou-only solutions were first considered (denoted by 'G' and 'C', respectively). The combined GPS/BeiDou and GPS/BeiDou/Galileo/GalONASS solutions were then computed (denoted by 'GC' and 'GCER', respectively). The accuracies of the estimated displacements were finally evaluated and compared.
# Spintronic RF-Direct on-off Keying Modulation Using a Frequency Division Multiplex
## 4 Design of the Spin Demodulator
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Parameters** & \multicolumn{2}{c}{**Transmitter**} & \multicolumn{4}{c}{**Receiver**} \\ \hline
**Signal Flow** & **STNO Output** & **TJunction** & **Antenna** & **Antenna** & **BPF** & **LNA** & **Demod Sensitivity** \\ \hline Specification & & & & & & & & \\ (3.5 GHz/42 GHz) & \(-71\) dBm & \(-71\) dBm & \(-30.0(\)(PF)/\(-3.5\) & 2. dB & \(20\log\frac{\mathrm{rad}}{\mathrm{rad}}-5.6\) & 2. dB & \(-15.5\)/ & 20/17.1 dB & \\  & & & & & & & (covering \(3.1\) to \(5.2\)) & \(-2.0\) dB & (\(12^{\circ}/17.1\) dB) & Min. sensitivity: \\ \hline Signal flow \(35.4\)/\(4.5\) & \(-71\) & \(-74\)/ & \(-72\)/ & \(-77\)/ & \(-75\)/ & \(-76.5\)/ & \(-64.5\)/ & \(-79.6\) dB/ \(-79.4\) dBm \\ GHz Level (dBm) & \(-71\) & \(-74.5\) & \(-72.5\) & \(-77.5\) & \(-75.5\) & \(-77.5\) & \(-60.4\) & \(64.2\) GHz \\ \hline System Gain (3.5 & & & & & & & \\ GHz/42 GHz) & & \(-1\) dB/\(-1.5\) dB & & & & & & \\ \hline Isolation at \(3.5\) GHz & & \(-10\) dB & \(2.4\) dB & \(0\) dB (on wired & \(2\) dBi & \(-14.8\) dB & \\   from \(4.2\) GHz & \(-71\) dBm & \(-81\) dBm & \((-79\) dBm) & \((-77\) dBm) & \((-91.8\) dBm) & \(12\) dB & \(-79.8\) dBm \\ \hline Isolation at \(4.2\) GHz & & \(-10.4\) dB & \(2.4\) dB & \(0\) dB (on wired & \(2\) dBi & \(-35.5\) dB (\(-112.5\) & \\   from \(3.5\) GHz & & \((-81\) dBm) & \((-79\) dBm) & condition) & \((-77\) dBm) & dBm) & \\ \hline \multicolumn{2}{c}{KTR \(-92.9\) dBm \(61\) 30 MHz Bandwidth} & & & & & \\ \multicolumn{2}{c}{G: Boltzmann’s constant, T. temperature, \(\mathrm{b}\), Bandwidth} & & & & & \\ \hline \multicolumn{2}{c}{LNA n: \(18.2/6.2\) dB} & & & & & \\ \multicolumn{2}{c}{System NF (3.5 & & & & & \\ GHz/42 GHz)} & & & & & & \\ \multicolumn{2}{c}{OR: SNR SNR \(9.6\pm 10.3-3.8\)} & & & & & \\ \multicolumn{2}{c}{Min. sensitivity: \(-79.6\) dBm \(9.55\) GHz/\(-79.4\) dBm \(64.2\) GHz (\(-92.9\) \(\pm\) \(4.5\) \(\pm\) \(9\))} & & & & \\ \hline \hline \end{tabular}  
* The \(-84\) dB attenuator is added right after the LNA only at a \(3.5\) GHz path to secure isolation.  
\end{table} 
Table 1: The specification and the signal flow in the receiver and transmitter.
## 4 Discussion
### Influencing Factors
#### 4.4.3 Temperature Changes  
The influence of temperature on aligner material behavior is evident across the studies that examined either controlled temperature conditions or thermal cycling. Materials such as those tested by [PERSON] et al. [27] and [PERSON] et al. [32] showed clear temperature-dependent force output and shape recovery, respectively, indicating that polymer response can be highly sensitive to even modest temperature variation within intraoral ranges. Thermocycling protocols were used to simulate daily temperature fluctuations in the oral cavity, with studies such as [PERSON] and [PERSON] [28] and [PERSON] et al. [25] reporting reductions in flexural strength following exposure. [PERSON] et al. [23], using a protocol of 1000 cycles between 5 \({}^{\circ}\)C and 55 \({}^{\circ}\)C, also found that overall force delivery decreased after thermocycling. However, they reported no significant change in delivered force within the deflection range considered optimal for tipping movement (0.2-0.5 mm), suggesting that mechanical degradation may not affect performance at clinically relevant deflection levels.  
These findings suggest that thermal exposure, both short-term and cyclic, can alter key mechanical properties such as modulus, force retention, and structural recovery. Notably, [PERSON] et al. [25] also identified a reduction in dynamic glass transition temperature following thermoforming and aging, suggesting temperature exposure may induce lasting molecular changes in some formulations.  
However, the omission of temperature-related testing in several studies limits the comparability of their findings to clinical performance. Given the wide range of intraoral thermal conditions encountered in practice, future research would benefit from adopting standardized thermal exposure protocols to improve the clinical relevance of laboratory data.
# Analysis of Facial Cues for Cognitive Decline Detection Using In-the-Wild Data
## 4 Experiments
### Performance by Feature Fusion  
The results obtained from feature fusion are presented in Table 9. The IQR approach is used for simplicity and because it provides the highest performance results in most cases. Feature fusion generally improves performance compared to individual features. However, for the three-way and four-way classification tasks, performance remains similar to using individual features, showing no significant improvement.  
In comparing SD and IQR as UBs, SD generally shows lower performance, particularly in classification tasks that distinguish between individuals with memory problems (ND/MCI, ND/FMD, MCI/FMD, and ND/MCI/FMD). In contrast, the IQR approach usually provides better results when classifying individuals with memory problems from HCs.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classification Task** & **Feature** & **Accuracy** & **Precision** & **Recall** & **F-Measure** \\ \hline ND/MCI/FMD/HC & \begin{tabular}{c} Feature fusion (137) \\ Feature selection (5) \\ \end{tabular} & **44\%** & **44\%** & **46\%** & 44\% \\ \hline ND/MCI/FMD & \begin{tabular}{c} Feature fusion (137) \\ Feature selection (5) \\ \end{tabular} & 32\% & 26\% & 30\% & 28\% \\ \hline ND/MCI/FMD & \begin{tabular}{c} Feature fusion (137) \\ Feature selection (6) \\ \end{tabular} & **48\%** & 47\% & **46\%** & **46\%** \\ \hline ND/MCI & \begin{tabular}{c} Feature fusion (137) \\ Feature selection (6) \\ \end{tabular} & **75\%** & **84\%** & **75\%** & **74\%** \\ \hline ND/FMD & \begin{tabular}{c} Feature selection (137) \\ Feature selection (20) \\ \end{tabular} & 76\% & **78\%** & **78\%** & **78\%** \\ \hline MCI/FMD & \begin{tabular}{c} Feature fusion (137) \\ Feature selection (7) \\ \end{tabular} & 65\% & **70\%** & **64\%** & **63\%** \\ \hline MP/HC & \begin{tabular}{c} Feature fusion (137) \\ Feature selection (116) \\ \end{tabular} & 62\% & **61\%** & **61\%** & **61\%** \\ \hline D/Non-D & \begin{tabular}{c} Feature fusion (137) \\ Feature selection (37) \\ \end{tabular} & 70\% & 68\% & 68\% & 68\% \\ \hline ND/HC & \begin{tabular}{c} Feature selection (37) \\ Feature selection (38) \\ \end{tabular} & 62\% & 59\% & 59\% & 59\% \\ \hline ND/HC & \begin{tabular}{c} Feature fusion (137) \\ Feature selection (38) \\ \end{tabular} & **85\%** & **85\%** & **84\%** & **85\%** \\ \hline MCI/HC & \
\begin{tabular}{c} Feature selection (38) \\ \end{tabular} & 60\% & 65\% & 64\% & 63\% \\ \hline FMD/HC &
\begin{tabular}{c} Feature fusion (137) \\ Feature selection (2) \\ \end{tabular} & **79\%** & **78\%** & **76\%** & **76\%** \\ \hline \hline \end{tabular}
\end{table}
Table 9: Classification accuracy of four-way, three-way, and two-way classification tasks for the IVA\({}_{\text{S2}}\) dataset, measuring the system performance using the interquartile range (IQR) approach as the upper boundary (UB) when features are fused and selected with the KNN classifier. The number of features is indicated in parentheses.
# Enhancing the Performance of SQL Injection Attack Detection through Probabilistic Neural Networks
## 3 Detection of SQL Injection Using Probabilistic Neural Network
### Smoothing Parameter Selection  
The performance of the proposed approach is based on the PNN for SQL injection attack detection and relies on the smoothing parameters, which show the distribution spread and define how many data are utilized to fit each local polynomial. Regardless of the complex nature of the PNN, it only has one training parameter in the form of a smoothing parameter. It is crucial that a smoothing parameter must have the best value for performance enchainment. Obtaining the best value for the smoothing parameters can be achieved through either a numerical or heuristic-based natural technique. Conventionally, the best value of soothing parameters was achieved through the hit and error method. The downside of using the hit and error method is that it is a very time-consuming procedure that will affect the neural network's performance. We employ the BAT algorithm in this approach, a nature-inspired optimization algorithm based on the bat developed by [32]. Compared to other optimization methods, this one can find nearly optimal solutions to hard optimization problems in a reasonable amount of time. This is because the BAT algorithm uses a well-balanced approach to exploration and exploitation to search the solution space and avoid local optima. The BAT algorithm can be used to solve many optimization problems because it is easy to set up, flexible, and doesn't need many tuning parameters. The BAT algorithm also has the ability to work in noisy and dynamic situations, which are typical of real-world issues. Overall, the BAT algorithm is a powerful method of optimization that has many benefits, such as being effective, adaptable, and resilient.  
A bat typically expands its sensing capabilities to detect prey efficiently, and this behavior guides it towards the articulation of an optimization algorithm and it can be employed for solving a real-time problem. Suppose a bat \(B\) at a certain position \(p_{i}\) flies with a velocity \(v_{i}\) to hunt prey, with a changing wavelength \(\lambda\) and a loudness \(A_{0}\) even in complete darkness [33]. Algorithm 1 defines the working of a standard bat algorithm obtained from [34].  
Figure 2: Architecture of PNN.
# Virtual Reality Collision Detection Based on Improved Ant Colony Algorithm
## 3 An Improved Ant Colony Algorithm Based on Random Collision Detection
### Optimization of the Ant Colony Algorithm
In this study, the heuristic factor was improved, and the influence of the position of the starting point and the end point on the probability of an ant selecting it is included in the formula as follows: \[\eta_{ij}=\frac{1}{d_{oj}+d_{j}+d_{js}}\] (12) where \(d_{oj}\) is the distance from the starting point to node, and \(d_{js}\) is the distance from the node to the end point. (4) The pheromone update rules greatly affect the efficiency of the ant colony in finding the optimal path, and all the ants in the traditional ant colony algorithm simply update their pheromones after reaching the end point, and the advantages of ants walking through shorter paths are not strongly reflected. Therefore, this study improved the pheromone update strategy and added a reward and punishment mechanism, strengthening the pheromone of an excellent path and weakening the pheromone of a poor path. Under the assumption that the optimal evaluation value is \(bt\) and the initial value is 0, the formula is as follows: \[bt=\left\{ \begin{array}{l}bt\quad,bt(i)\geq bt\\ bt(i),bt(i)<bt\end{array} \right.\] (13) where \(bt(i)\) is the local optimal evaluation of the \(ith\) generation of ants. When \(bt(i)\) is not better than \(bt\), the optimal solution remains unchanged as \(bt\). When \(bt(i)\) is better than \(bt\), the optimal solution is updated and \(bt(i)\) becomes the optimal solution. The pheromone is superimposed on the path that is superior to \(bt\), and the pheromone on the path that is not superior to the optimal solution \(bt\) is volatilized. The formula is as follows: \[\tau_{ij}(t+n)=(1-p)\cdot\tau_{ij}+\Delta\tau_{ij}+\Delta R\] (14) \[\Delta R=\left\{ \begin{array}{l}\theta\cdot\Delta\tau_{ij}\,,bt(i)\geq bt \\ \mu\cdot\Delta\tau_{ij}\,,bt(i)<bt\end{array} \right.\] (15) \[\theta=0.5,\mu=-0.5\]
## 4 Attacks and Misbehaviors
### Packet Number Discrepancies  
This type is the most existing attack that is very easy yet very effective. This attack includes packet dropping by uncooperative nodes, nodes lying about their counters, incompetent nodes due to malfunctions or noisy environments.  
As discussed in the Section 3, the BS always compares the number of packets received from a node to the number declared by this node; also it compares the number of packets declared to be forwarded by a node to the number declared to be forwarded by its DL. From these comparisons, the BS locates the problem in a link between two nodes; however, it can't specify exactly whether a node is lying or its neighbor is dropping or even there is noise in the link. So, the BS gives these nodes the benefit of the doubt and provides them another chance after removing the link between them. Then, based on a parameter set by the administrator, a node is isolated when the number of maximum allowed probations is reached.
###### Abstract  
Geostationary (GEO) satellites are employed in optical frequencies for a variety of satellite services providing wide coverage and connectivity. Multi-beam GEO high-throughput satellites offer Gbps broadband rates and, jointly with low-Earth-orbit mega-constellations, are anticipated to enable a large-scale free-space optical (FSO) network. In this paper, a power allocation methodology based on deep reinforcement learning (DRL) is proposed for optical satellite systems disregarding any channel statistics knowledge requirements. An all-FSO, multi-aperture GEO-to-ground system is considered and an ergodic capacity optimization problem for the downlink is formulated with transmitted power constraints. A power allocation algorithm was developed, aided by a deep neural network (DNN) which is fed channel state information (CSI) observations and trained in a parameterized on-policy manner through a stochastic policy gradient approach. The proposed method does not require the channels' transition models or fading distributions. To validate and test the proposed allocation scheme, experimental measurements from the European Space Agency's ARTEMIS optical satellite campaign were utilized. It is demonstrated that the predicted average capacity greatly exceeds other baseline heuristic algorithms while strongly converging to the supervised, unparameterized approach. The predicted average channel powers differ only by 0.1 W from the reference ones, while the baselines differ significantly more, about 0.1-0.5 W.  
atmospheric turbulence; deep reinforcement learning; deep neural network; free-space optical; optical satellite; power allocation; policy gradient; scintillation +
Footnote †: journal: Electronics
# Communication Network Standards for Smart Grid Infrastructures
## 2 Network Standards That Support Communication in Smart Grid Infrastructures
### E-Mobility  
E-mobility includes the use of fully electric or hybrid vehicles and is one of the main perspectives of smart grids both on its own (e.g., a vehicle-to-vehicle energy exchange scenario, based on cloud technology, can be found at [69]) as well as a method of energy storage, given that the full range of possibilities of this technology can be achieved only after smart grid architecture is fully implemented [7, 70].  
The specifications of the vehicle's batteries should be standardized and meet the minimum requirements of charging cycle and power stability, which are dictated by the specifications of the overall network to function as an integral part of it. The safety and electromagnetic compatibility (EMC) requirements must also be met in full [71, 72].  
While two-way communication is not required for electromobility, its integration into a smart grid requires information exchange between the smart grid and electric vehicles. This communication requires the use of appropriate communication protocols and data modeling within a framework that will allow the semantic understanding of the exchanged information.  
Although there is a lot of standardization activity as shown in Table 1, the data models and protocols within ISO/IEC 15118-1, -2, and -3 are not precisely identified. The same holds true for the IEC 61851 set of standards regarding the choice of plug and socket (1/3-phase host carrier, 400 V, 63 A) [7, 66].
# Electrification in Maritime Vessels: Reviewing Storage Solutions and Long-Term Energy Management
## 2 Electrification of Marine Vessels
### Artificial Intelligence and Applications in Marine Vessels
#### 2.5.1 Rule-Based Approaches  
These approaches focus on implementing predefined rules and logic to efficiently handle and optimize energy usage. These approaches are instrumental in naval environments with presumable, consistent, and explainable decisions. These approaches can be further divided into two main branches:  
Fuzzy logic is a type of rule-based AI dealing with uncertain and imprecise states and conditions. This nature makes fuzzy logic particularly useful for handling complex systems like EMSs and BMSs in naval-based electric vehicles (EVs) and hybrid electric vehicles (HEVs). Unlike classical binary logic, which separates input samples into strict true or false categories, fuzzy-logic-based approaches provide varying degrees of truth. Hence, these approaches can manage the approximate information suitable for real-world scenarios, such as fluctuating energy levels or ambiguous system statuses [66; 67; 54; 68; 69].  
Deterministic-based approaches operate under a clear, predictable framework where specific sample inputs give rise to specific outputs based on predefined rules. These approaches are highly structured, providing precision and consistency for the decisions made, which is crucial for the reliability and safety of naval operations. Although they offer important advantages to simplicity and consistency, their process can be limited by their rigidity, and manual updates may be needed to adapt to new scenarios or changing conditions [70; 71; 72; 73; 74].