esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analysis Methods
### Topic Modeling of mHTI Scholars Based on LLM's Semantic Latent Space
#### 3.2.2 Dimensionality Reduction to Construct a Latent Semantic Space  
Although the embedding vectors successfully preserve semantic information in numerical form, their high-dimensional nature complicates further analysis. To address the challenges posed by high dimensionality, we employ Uniform Manifold Approximation and Projection (UMAP, [36]) for dimensionality reduction. UMAP can not only preserve local relationships between data points but also partially maintain global structures in the reduced space. UMAP achieves this by determining local neighbors and constructing a proximity global graph among data points. This ensures that the clustering algorithm can operate effectively in a lower-dimensional space, avoiding the \"curse of dimensionality\" that often plagues high-dimensional data [37]. By reducing the 3076-dimensional embeddings to a more manageable number of dimensions, we strike a balance between computational efficiency and the preservation of meaningful semantic relationships. Additional parameter tuning details are provided in Appendix A.
# Research on Response Postures of Subway Train in Straight Line Collision
## 6 Response Postures Analysis
### Analysis of the Response Processes and Causes of Different Response Postures
#### 6.2.2 Large Lateral Movement of the Train Front  
When the impact direction is not perpendicular to the rigid wall, the train may deviate from the track due to the significant lateral movement of the front of the train. This results in the train slipping out of the impact zone and continuing to move with residual velocity, almost without any track constraint. The process and causes of the front movement can be analyzed as follows:  
1. Under these conditions, the buffer device at the front of the train cannot function normally, so the aluminum alloy frame at the front, which bears the impact and dissipates energy, becomes the primary component in this process. The impact point is subjected to both longitudinal force (\(F_{\mathrm{x}}\)) and lateral force (\(F_{\mathrm{z}}\)). The force exerted on the car body is transmitted to the axle through the bogie suspension system, leading to an increase in the wheel-rail contact force.
2. Oblique Impact and Nodding Movement: During an oblique impact, the front of the car, subjected to greater lateral forces, shifts along the inclined direction of the obstacle, accompanied by pitching. The axle of Wheelset-1 rotates slightly along the longitudinal axis at the point where the right wheel contacts the track, as shown in Figure 22. At 0.11 s, the left wheel is lifted more than 22 mm ahead of the right wheel, causing the carriage to overturn, although the right wheel remains on the track. At this point, the wheelset loses the track's constraint on the left side. Figure 22 illustrates the derailing process for an impact angle of 15\({}^{\circ}\) and an impact speed of 36 km/h. The time history curve of the impact force on the lower edge of Wheelset-1 prior to derailing is shown in Figure 23, with the force direction indicated in Figure 22. After 0.13 s of lateral impact, Wheelset-1 completely jumps off the rail, initiating a rail-jumping event [40].
3. Post-Derailment Behavior: Once Wheelset-1 is derailed, the front of the wheelset, no longer constrained by the track, slides forward along the inclined direction of the obstacle. As the lateral movement increases, C-1, tilted relative to the ground, becomes more prone to overturning.  
In the process of the train front sliding along the inclined direction of the obstacle, the rear carriage also moves sideways and flips at different angles because of the connection of the coupler buffer devices. If carriage C-1 is not completely overturned, the train will continue to move at a certain speed, accompanied by the continuous derails of the rear carriage wheelset, which is also easy to cause overturning. If C-1 is completely overturned, there will be multiple collisions between carriages.
## 5 Proposed Model
### MTD-Based Secure Two-Way Routing
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Routes**} & \multirow{2}{*}{**Alternatives**} & \multicolumn{5}{c}{**Route Criteria\(\rightarrow\)(\(\mathbf{q_{2}}\)\(\mathbf{q_{2}}\)\(\mathbf{q_{3}}\)\(\mathbf{q_{4}}\))**} & \multirow{2}{*}{**sim\({}_{\text{worst}}\)**} & \multirow{2}{*}{**Rank**} \\ \cline{3-3} \cline{5-8}  & & & **Is** & **dir** & **tru** & **ene** & \\ \hline ro (1) & \(\mathbf{e_{1}}\) & 30\% & Outwards & 0.1 & 10 J & 0.215 & 6 \\ \hline ro (2) & \(\mathbf{e_{2}}\) & 80\% & Outwards & 0.75 & 20 J & 0.842 & 3 \\ \hline ro (3) & \(\mathbf{e_{3}}\) & 97\% & Towards & 0.9 & 50 J & 0.971 & 1 \\ \hline ro (4) & \(\mathbf{e_{4}}\) & 60\% & Outwards & 0.5 & 15 J & 0.787 & 4 \\ \hline ro (5) & \(\mathbf{e_{5}}\) & 88\% & Towards & 0.8 & 30 J & 0.912 & 2 \\ \hline ro (6) & \(\mathbf{e_{6}}\) & 50\% & Towards & 0.3 & 12 J & 0.456 & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: MCDM-based optimal routes ranking. tor (DC\({}_{\rm x}\)) and generator (GN\({}_{\rm y\to x}\)), which are trained iteratively to perform MTD operation (i.e., active switch handling). The loss due to adversary in the CGAN can be formulated as:  
\[\mathbb{L}\big{(}\text{GN}_{\rm y\to x},\text{DC}_{\rm x}\big{)}=\underset{ \varnothing_{\text{CN}}}{\text{min}}\underset{\varnothing_{\text{DC}}}{\text{ max}}\{\varepsilon_{\rm y}[\text{logDC}_{\rm x}({\rm y})]+\varepsilon_{\rm y} \big{[}\text{log}\big{(}1-\text{DC}_{\rm x}\big{(}\text{GN}_{\rm y\to x}({\rm x}) \big{)}\,\big{]}\} \tag{36}\]  
From Equation (36), \(\varnothing_{\text{DC}}\) and \(\varnothing_{\text{GN}}\) denote the discriminator and generator parameters. The unpaired data for the target source can be denoted as \({\rm y}\in{\rm Y}\) and \({\rm x}\in{\rm X}\), respectively. Since we adopt CGAN, the consistency in backward and forward dependency in training data is defined. Henceforth, the cycle loss can be formulated as:  
\[\mathbb{L}\big{(}\text{GN}_{\rm y\to x},\text{GN}_{\rm x\to y},\text{DC}_{\rm x },\text{DC}_{\rm y}\big{)}=\mathbb{L}\big{(}\text{GN}_{\rm y\to x},\text{DC}_ {\rm x}\big{)}+\mathbb{L}\big{(}\text{GN}_{\rm x\to y},\text{DC}_{\rm x} \big{)}+\infty\,\mathbb{L}_{\rm c}\big{(}\text{GN}_{\rm y\to x},\text{GN}_{\rm x \to y}\big{)} \tag{37}\]  
From which the \(\mathbb{L}_{\rm c}\) is equated as,  
\[\mathbb{L}_{\rm c}\big{(}\text{GN}_{\rm y\to x},\text{GN}_{\rm x \to y}\big{)}=\big{\|}\text{GN}_{\rm x\to y}\big{(}\text{GN}_{\rm y\to x}({ \rm y})\big{)}-{\rm y}\big{\|}_{1}+\big{\|}\text{GN}_{\rm y\to x}\big{(}\text {GN}_{\rm x\to y}({\rm x})\big{)}-{\rm x}\big{\|}_{1} \tag{38}\]  
Equation (38) shows the cycle consistency loss of the proposed CGAN. On the whole, the MTD-based secure two-way routing resists against malicious routing attacks and also enables optimal routing in the SDWSN environment.
# Prediction of Ground Subsidence Risk in Urban Centers Using Underground Characteristics Information
## 5 Discussion  
The output data of the model, ground subsidence risk, was imbalanced. The datasets (numbers 3, 6, 9, and 12) wherein the imbalance of ground subsidence risk data was minimized resulted in better evaluation metrics. Similarly, the model wherein the imbalance  
Figure 4: Map of ground subsidence risk.  
Figure 3: Importance of influencing factors in the LGBM model.  
was addressed through the SMOTE sampling technique in the training data exhibited relatively better performance. Data imbalance is inevitable because of the partial occurrences of ground subsidence over a large area. Minimizing the data imbalance helps improve the performance of the ground subsidence risk prediction model.  
The RF-, XGB-, and LGBM-based ground subsidence risk prediction models developed in this study showed differences in performance metrics. Therefore, when utilizing these models for ground subsidence risk management, it would be efficient for practitioners to select the model that demonstrates the best performance based on a variety of models. Additionally, aside from the algorithms proposed in this study, SVM and AdaBoost models were also developed using dataset-3. However, the SVM model produced an unreliable AUC of 0.48 at Risk Level 2, and the AdaBoost model demonstrated lower performance compared to RF, XGB, and LGBM (accuracy: 0.58, F1-Score: 0.61).  
The optimal ground subsidence risk prediction model developed in this study achieved an accuracy of 0.658, an F1-score of 0.65, and an AUC of 0.83 for visualizing ground subsidence risk. The accuracy, F1-score, and AUC values are better than those of a previously reported ground subsidence risk prediction model [13]. Hence, the performance improvement of the ground subsidence risk prediction model can be attributed to the inclusion of the permeability coefficient, stratigraphic thickness, and height data. Ground subsidence is a phenomenon caused by multiple factors, wherein the loss of soil particles within the ground leading to the formation of voids is identified as a direct cause. Thus, the permeability coefficient, stratigraphic thickness, and height directly and indirectly influence groundwater flow within the subsurface, which is presumed to affect ground subsidence as well.  
Apart from the input data used in this study, additional data pertaining to underground spaces must be incorporated. Underground spaces have been developed for various purposes such as subways, underground tunnels, utility tunnels, and underground parking lots. The numerous excavations conducted to develop these underground spaces may disturb the ground and increase the risk of ground subsidence. Therefore, it is necessary to obtain data on various underground spaces, such as subways, underground tunnels, and underground parking lots, and use them as influencing factors.
# Chapter Detection in Robotic Milling Using Entropy Features
## 2 Theoretical Basis and Framework for the Chatter Detection System  
### Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN)  
The ICEEMDAN method, which is based on the EMD decomposition principle, can further reduce the residual noise in the intrinsic mode functions (IMFs) and improve the decomposition effect. Considering the original signal, \(x\), the main idea of the ICEEMDAN method can be expressed as follows [31]:  
Step 1: White Gaussian noise is added to the original signal, \(x\), and \(X_{1}{}^{(i)}=x+\beta_{0}E_{1}(w^{(i)})\) (\(i=1,2,3,\ldots,N\)) indicates the first set of the realization of the signal-plus-noise, where \(w^{(i)}\) denotes the noise, \(E_{k}(\cdot)\) denotes the \(k\)th mode obtained from EMD, and \(\beta_{0}=\varepsilon_{0}\ \sigma(x)/\sigma(E_{1}(w^{(i)}))\) means a desired signal-to-noise ratio (SNR) at first decomposition. Then, the first residue, \(s_{1}\), can be calculated by:  
\[s_{1}=\Big{\langle}M\Big{(}X_{1}{}^{(i)}\Big{)}\Big{\rangle}=\Big{\langle}X_{ 1}{}^{(i)}-E_{1}\Big{(}X_{1}{}^{(i)}\Big{)}\Big{\rangle}(i=1,\ 2,\ 3,\ \ldots,N) \tag{1}\]  
where \(M(\cdot)\) denotes the local mean of the signal and \(\langle\cdot\rangle\) denotes the action of averaging throughout the realizations.  
Step 2: The first mode can be calculated by:  
\[I_{1}=x-s_{1}. \tag{2}\]  
Step 3: For \(k\geq 2\), constructing the \(k\)th set of signal-plus-noise as \(X_{k}{}^{(i)}=s_{k-1}+\beta_{k-1}E_{k}(w^{(i)})\), where \(\beta_{k-1}=\varepsilon_{0}\ \sigma(x)/\sigma(s_{k-1})\). Therefore, the \(k\)th residue, \(s_{k}\), can be estimated as:  
\[s_{k}=\Big{\langle}M\Big{(}X_{k}{}^{(i)}\Big{)}\Big{\rangle}=\Big{\langle}X_{ k}{}^{(i)}-E_{k}\Big{(}X^{(i)}\Big{)}\Big{\rangle}(i=1,\ 2,\ 3,\ \ldots,N) \tag{3}\]  
Step 4: The \(k\)th mode can be calculated by:  
\[I_{k}=s_{k-1}-s_{k} \tag{4}\]  
Step 5: Repeat the process for the next \(k\), beginning at Step 3.
# Experimental Investigations on Laser Ablation of Aluminum in Sub-Picosecond Regimes
## 4 Results and Discussion
However, the frequency change strongly affects the crater profile. Crater depths obtained from 3D analysis varied from 10 to 30 \(\upmu\)m (Figure 5). Figure 6 presents the results obtained with a Ti:S laser, using different wavelengths and pulse energy. Comparable to the Yb:KYW laser, crater width decreases with decreasing pulse energy. Still, the effect of the wavelength change was different. For aluminum, the material reflectivity is very high (above 90%) in UV-VIS, with a dip at 700-900 nm, and then increases with the wavelength in the far infrared range [15]. It can be predicted that the laser beam will be best absorbed in the near infrared window, and not in the UV range. This effect was confirmed for the Yb:KYW sub-picosecond laser, whereas for the femtosecond Ti:S laser, machining with 266 nm and 800 nm beam resulted in a similar crater size. The VIS range was poorly absorbed by aluminum sample; however, better absorption occurred for 532 nm in shorter, fs pulses, than for 345 nm in longer, sub-ps pulses. Figure 7 shows a comparison between the results obtained for femtosecond and sub-picosecond laser. Vertical lines represent theoretical ablation thresholds, as calculated in Table 1. Other beam parameters were picked as close as achievable for both lasers. Pulse energy varied from 20 \(\upmu\)J to 80 \(\upmu\)J and the wavelength was in the IR range. For the sub-picosecond laser, the experimental ablation threshold was closer to the theoretical predictions; however, for both lasers, the calculated values were lower than the experimental results. Despite the fact that the ranges of crater dimensions were similar in both cases, there is a significant difference in the results obtained close to the ablation threshold. The sub-picosecond characteristic is more steep with lower fluence. For the femtosecond laser, once a certain energy level is achieved, the curve begins to flatten.
## 2 Materials and Methods
### Determination of Antioxidant Capacity
#### 2.3.3 ABTS Method  
Initially, the ABTS radical cation (ABTS\({}^{++}\)) was produced by mixing a 7 mmol/L ABTS solution with 2.45 mmol/L potassium persulfate at a 1:0.5 ratio. The mixture was kept in the dark at 22 \(\pm\) 2 \({}^{\circ}\)C for 16 h before use. The ABTS\({}^{++}\) solution was then diluted approximately 100 times with ethanol to an absorbance of 0.70 (\(\pm\)0.02) at 734 nm. Following this, spice extracts were added to the ABTS\({}^{++}\) solution, incubated at 40 \({}^{\circ}\)C for 1 min, and the absorbance was measured at 734 nm against a reagent blank (2.5 mL of ABTS\({}^{++}\) solution). The total volume of the extract and ABTS solution was always 2.5 mL, with the proportion of extract varying from 0.05 to 0.50 mL.  
The calibration curve was prepared using working TE solutions in methanol between 0.025 and 0.15 umol/mL. The equation for the calibration curve was %ABTS = (393.96 \(\pm\) 5.69)\({}_{\mathrm{CTE}}\) + (6.99 \(\pm\) 0.50), with a determination coefficient of R\({}^{2}\) = 0.9985.
# Semi-Empirical Satellite-to-Ground Quantum Key Distribution Model for Realistic Receivers
## 4 Satellite-to-Ground QKD Analysis
### Secret Keys for 300 mm and 600 mm Ground Stations  
We stress that only single photon states are completely secure in practical QKD with an imperfect single photon source [40]. The communication time of \(\Delta t\) corresponds to elevation angles greater than \(20^{\circ}\) and sifted key length \(l_{sift}\) is given:  
\[l_{sift}=\int_{\Delta t}R_{sift}\,dt. \tag{11}\]  
Therefore, we estimated the proportion of secret bits from obtained sifted keys using the classic BB84 decoy-state protocol, according to Refs. [28; 39]. The lower limit of the final secret key length \(l_{sec}\) is as follows:  
\[l_{sec}\geq Np_{s}\,q\big{\{}Q_{1}[1-H_{2}(e_{1})]-1.44Q_{\mu}H_{2}(E_{\mu}) \big{\}}, \tag{12}\]  
Figure 6: Comparison of peak sifted key rates simulated by a semi-empirical model with [PERSON]’s model data as a function of the shortest distance between satellite and ground stations. The experimental data collected by the Micius satellite for specific satellite passages are presented [13].  
where \(N\) is the overall number of pulses emitted by satellite, \(q=1/2\) is the BB84 protocol due to sifting procedure, the subscript \(\mu\) denotes the intensity of signal states, \(Q_{\mu}\) is the gain of signal states, \(E_{\mu}\) is the mean overall QBER across communication time, \(Q_{1}\) is the gain of single photon states, \(e_{1}\) is the error rate of single photon states, the \(H_{2}(x)\) is the binary Shannon entropy function, and 1.44 is the efficiency of error correction [41].  
Given the finite size of the sifted key length, the average values contain statistical fluctuations that can be estimated via Chernoff inequality [42] and the observed values. The failure probability for the Chernoff bound is equal to \(10^{-9}\).  
Table 4 shows the calculated sifted key length and the estimated secret key length, using Equation (12) and Chernoff inequality, for four satellite passages with the maximum elevation angles of 32.5\({}^{\circ}\), 42\({}^{\circ}\), 57.5\({}^{\circ}\), and 90\({}^{\circ}\).  
For typical satellite passages with various peak elevation angles, the secret key lengths differ by 3.2 to 4.6 times for the ground stations with 300 mm and 600 mm apertures, whereas telescope areas differ by four times. For some satellite passages, the 300 mm ground station is more cost-effective and efficient for space-based quantum communications.
# A Review of Neuro-ML Breakthroughs in Addressing Neurological Disorders
## 4 Results
### Review of ML on MS
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Objective** & **Metrics/AI Model** & **Strengths (1–5)** & **Weaknesses (1–5)** & **Reference** \\ \hline MS detection using ensemble of models & MSDNet (LSTM, DNN, CNN) & & & \\  & optimized by enhanced walrus & 5 & 3 & [182] \\  & optimization algorithm & & & \\ \hline MS classification using blood transcriptomics & CNN with VGG16 architecture: & & & \\  & Accuracy: 98.44\% & & & \\ MS lesion detection & CNN with VGG16 architecture: & & & \\  & Specificity: 99.4\% & 5 & 2 & [183] \\  & CNN with VGG16 architecture: & & & \\  & Sensitivity: 97.56\% & & & \\ \hline MS classification using blood transcriptomics & Binary classification model: & & & \\  & Accuracy: 97\% & 5 & 3 & [184] \\ \hline MS detection using metabolomics data & ANN: Accuracy: 87\% & & & \\  & ANN: Sensitivity: 82.5\% & 4 & 3 & [185] \\  & ANN: Specificity: 89\% & & & \\ \hline MS gait analysis & ResNet with regression-based & & & \\  & normalization: Accuracy 100\% & & & \\  & ResNet with regression-based & 5 & 3 & [186] \\  & normalization: F\({}_{1}\)-Score: 100\% & & & \\ \hline MS visual signs & NeuroVEP system: Agreement & & & \\  & evaluation & with standard tests: 91\% & 4 & 3 & [187] \\ \hline Central vein sign & CVSnet (3D CNN): Accuracy: & & & \\ detection in MS lesions & 91\% on test set & 5 & 3 & [188] \\ \hline MS diagnosis via & & & & \\ acceleration classifiers & Accuracy: 87\% vs. EMG & 4 & 3 & [189] \\ \hline MS biomarker identification in CSF & OCB+ Model: Sensitivity: 91\% & & & \\  & (training) & & & \\  & OCB+ Model: Specificity: 94\% & & & \\  & (training) & & & \\  & OCB+ Model: Sensitivity: 81\% & & & \\  & (validation) & & & \\  & OCB– Model: Specificity: 94\% & & & \\  & OCB– Model: Specificity: 87\% & & & \\  & (training) & & & \\  & OCB– Model: Sensitivity: 56\% & & & \\  & (validation) & & & \\  & OCB– Model: Specificity: 48\% & & & \\  & (validation) & & & \\ \hline MS and NMO & SVM \& KNN: Recall: 99\% & & \\ classification using MRI & SVM \& KNN: F\({}_{1}\)-Score: 99\% & 5 & 3 & [191] \\  & SVM \& KNN: Accuracy: 99\% & & \\  & (VGG16) & & & \\ \hline MS prediction using ANN & ANN: Training Accuracy: 100\% & & & \\  & ANN: Testing Accuracy: 75\% & 4 & 3 & [192] \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Summary of the MS discussed studies.
## 3 Context-Aware Positioning Model
### Context Sensing  
This stage involves sensing the physical environment of a device in order to determine which contextual elements are present at the time a positioning request is sent, as well as interacting with them (Figure 2). To this end, a device must be able to identify and characterize the contextual elements from both its physical environment and other participating devices. These elements are then used to describe and infer the users' location context.  
The contextual elements considered by the model include communication protocols and technologies, positioning strategies, neighboring access points, and neighboring devices. The protocols taken into account are TCP/IP and UDP (transmission control protocol/internet protocol and user datagram protocol, respectively). The technologies are GRPS (general packet radio service), HSDPA (high speed downlink packet access), LTE (long term evolution), Wi-Fi, and Bluetooth.  
This model assumes the existence of an _ad hoc_ communication infrastructure in the work scenario (in our case, in the affected area) that implements unstable communication links among the nodes, e.g., a mobile _ad hoc_ network. The model also considers that the networking issues (like nodes discovery and management of connections/disconnections) are addressed by the communication protocol that implements these links; for instance, the Optimized Link State Routing Protocol (OLSR) [25] or the  
Figure 2: UML sequence diagram of the model’s behavior.  
High Level MANET Protocol (HLMP) [26]. In other words, the positioning model runs into the application layer, and it uses the communication services provided by the networking layer.  
As for positioning strategies, several types are considered, including but not limited to fingerprinting, RFID, triangulation, and GPS. Note that the available positioning strategies of a device depend entirely on the device's capabilities; _i.e._, if no GPS transceiver is present, the model will not take GPS into consideration, even when it would be the preferred strategy otherwise.  
The devices' peripherals are also considered, since they determine which contextual elements can be sensed and accessed. When the model requests an interaction with the environment, the associated peripherals convey the required data. For example, if the model requests performing dead reckoning, the digital compass will return the device's current direction and the accelerometer the movement speed.  
During the context sensing stage, a device can perform the following tasks: (1) characterizing the physical environment and (2) interacting with that environment. These tasks are described in more detail in the following subsections.