esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Key Thermal Parameters: Convection Heat Transfer Coefficient and Heat Distribution Rate  
For solid discs, the convection heat transfer coefficient (\(h\)) in both turbulent and laminar flow can be determined using an empirical equation. Flow characteristics are turbulent and laminar based on the Reynolds number (Re) [14; 15; 18; 19; 20; 21]:  
\[\begin{array}{ll}h=0.04\left(\frac{k_{a}}{D_{d}}\right)\mathrm{Re}^{0.8}&\left( \mathrm{Re}\geq 2.4\times 10^{5}\right)\\ h=0.70\left(\frac{k_{a}}{D_{d}}\right)\mathrm{Re}^{0.55}&\left( \mathrm{Re}<2.4\times 10^{5}\right)\end{array} \tag{1}\]  
\[\mathrm{Re}=\frac{\rho_{a}VD_{d}}{\mu_{a}}. \tag{2}\]  
\(k_{a}\) represents the air's heat transfer coefficient, \(D_{d}\) represents the outer diameter of the brake disc, \(\rho_{a}\) signifies the air density, \(V\) corresponds to the speed of the railway vehicle, and \(\mu_{a}\) represents the air's coefficient of viscosity.  
For temperature analysis, precisely assessing the allocation of brake energy between the brake disc and brake pad is crucial. The heat distribution rate (\(\gamma\)) can be estimated using the following equation [14; 15; 18; 19; 20; 21]:  
\[\gamma=\frac{1}{1+\sqrt{\rho_{p}c_{p}k_{p}/\rho_{d}c_{d}k_{d}}} \tag{3}\]  
where \(\rho_{d}\) and \(\rho_{p}\) are the densities of the disc and pad, \(c_{d}\) and \(c_{p}\) are the specific heats of the disc and pad, and \(k_{d}\) and \(k_{p}\) are the thermal conductivities of the disc and pad, respectively.
## 2 Experimental Work
### Experimental Methodology  
The laboratory procedure is schematically represented in Figure 1. It starts with the dispersion of the MWCNTs in the surfactant solution, following the procedure described in [19] (add the required amount of surfactant (Glycerox or Amber 4001) and apply ultrasounds (20 kHz) for 5 min to the suspension). The quality of the dispersion was indirectly evaluated by statistical models that relate the mechanical properties (qu max and Eu 50) of the stabilized soil with the parameters of the CNT suspension. Afterward, all materials were mixed in a mechanical mixer Hobart N50 at a rotational speed of 136 rpm. Next, laboratory samples were produced and cured for 7 days. Finally, UCS performance tests were conducted in a universal testing machine (Wykeham Farrance--Tristar 5000, UK) to demonstrate the added value of using the MWCNT dispersion in soil stabilization.  
The performance tests were planned by changing the concentration of the surfactant (0, 0.5, 1, 2, and 3%), the type of surfactant (varying the hydrodynamic diameter one order of magnitude) and the MWCNT concentration (0, 0.001, and 0.01%, defined as the ratio of the weight of MWCNTs to the dry weight of Portland cement).  
For each different test condition, at least two specimens were tested. A conformity criterion was adopted based on compressive strength (qu max): a specimen test is only accepted when the deviation from the average value is less than 15% (in the absence of standards for this type of materials, the recommendations for concrete were adopted [40]).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**CaO** **(\%)** & **SiO2** **(\%)** & **Al2O3** **(\%)** & **Fe2O3** **(\%)** & **MgO** **(\%)** & **SO3** **(\%)** & **CI–**(\%)** & **Charge (mV)** \\ \hline
62.84 & 19.24 & 4.93 & 3.17 & 2.50 & 3.35 & 0.01 & \(-\)2.14 \\ \hline \end{tabular}
\end{table}
Table 4: Portland cement characteristics (average values).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Geotechnical Characterization** & & & & & & \\ \hline
**Sand** **(\%)** & **Silt (\%)** & **Clay (\%)** & **Water Content (\%)** & **Organic Matter** **Content (\%)** & **Unit Weight (kN/m3)** & **Specific Gravity (\%)** \\ \hline
22 & 66 & 12 & 80.87 & 9.3 & 14.6 & 2.555 & 67.8 \\ \hline \multicolumn{6}{c}{**Mineralogical Composition**} \\ \hline
**Quartz** **(\%)** & **Feldspar K + Masculive (\%)** & **Vermiculite (\%)** & **Ilite (\%)** & **Kaolinite (\%)** & **Chlorite Fe (\%)** & \\ \hline \(>\)60–65 & \(<\)25–30 & 4.6 & 2.4 & 1.5 & 1.5 & \\ \hline \multicolumn{6}{c}{**Chemical Composition**} \\ \hline
**CaO** **(\%)** & **SiO2** **(\%)** & **Al2O3** **(\%)** & **Fe2O3** (\%)** & **MgO** **(\%)** & **K2O** **(\%)** & **pH (\%)** & \\ \hline
0.74 & 62.00 & 16.00 & 4.80 & 1.10 & 3.00 & 3.50 & \\ \hline \end{tabular}
\end{table}
Table 3: Natural soil characteristics (average values).
## 3 T2G Communication Security, Vulnerabilities, and Threats
### Multi-Stage Advanced Attacks  
During the operation of EMU/DMU trains, the T2G system continuously uploads high-frequency data--including train status, location, fault parameters, and energy consumption--to ground control centers. Upon entering a depot state (such as a maintenance facility or parking area), the system transitions to batch data synchronization, device self-diagnostics, and firmware maintenance mode. Unlike traditional single-point attacks, real-world adversaries often orchestrate multi-stage or multi-vector attacks by combining several lightweight techniques to progressively infiltrate the system while remaining below the fault-tolerance threshold. These advanced attack strategies are typically characterized by high stealth and persistence, posing significant challenges for both detection and defense. Below are several representative combined attack scenarios specifically targeting T2G communication:  
1. Forgery-Jamming Combination During Online Operation: While the train is in active service, attackers may monitor T2G transmissions to infer data formats and forge onboard status messages with structurally identical characteristics. Short-term jamming--achieved via high-frequency interference or intentional data congestion--is then introduced during legitimate message transmission windows, preventing the ground system from receiving authentic data. As a result, the forged message may be erroneously accepted as the \"current train status\". This attack vector can be exploited to simulate faults, mislead train scheduling systems, or conceal actual anomalies.
2. Low-Frequency Injection with Replay: During normal operation, attackers intermittently inject low-frequency anomalies--such as borderline temperature, voltage, or current fluctuations--to mimic natural operational variations without triggering alarms. Once the train enters a depot for centralized data upload, historical \"normal\" messages may be replayed or combined with fabricated records, introducing logical inconsistencies in the dataset. This undermines predictive maintenance algorithms and health monitoring processes, potentially resulting in incorrect lifecycle estimations, false alarms, or unnecessary maintenance interventions.
3. Sequence Tampering and Timestamp Drift: T2G data packets transmitted during high-speed operation often contain critical temporal metadata such as timestamps and sequence numbers, which are essential for event reconstruction by ground-based systems. By subtly modifying timestamps (e.g., shifting by a few seconds) or scrambling the order of transmission, attackers can distort causal sequences (e.g., \"braking \(\rightarrow\) acceleration \(\rightarrow\) fault alarm\"), leading to misinterpretation of events and compromising situational awareness and decision-making on the ground.
# Recent Development of Aptamer Sensors for the Quantification of Aflatoxin B1
## 2 Colorimetric, Electrochemical, and Fluorescent Aptamer Sensors to Monitor AFB1
### SERS aptamer Sensor for AFB1
The GDADNTs were modified with the aptamer, which was employed as the  
Figure 14: Scheme representation of a SERS aptasensor for detecting AFB1 using exonuclease-aided recycling amplification. Reproduced from [33]. Copyright 2017, Elsevier. signal reporter. In the absence of AFB1, the GDADNTs were not able to assemble with the capture sensor, leading to no SERS signal in the precipitate after magnetic separation. Nevertheless, the signal reporter, as well as the capture sensor, were capable of assembling in the presence of AFB1, resulting in an increase of the SERS signal in the precipitate. An excellent linearity toward AFB1 was found in the range from \(1.0\times 10^{-9}\) to \(1.0\times 10^{-5}\) g\(\cdot\)L\({}^{-1}\). The limit of detection of the SERS aptasensor was estimated to be \(5.4\times 10^{-10}\) g L\({}^{-1}\), indicating excellent sensitivity of the sensor for AFB1. Therefore, the proposed aptasensor provided great potential for the quantification of other mycotoxins.
## 2 Materials and Methods
### Data Processing  
Data obtained from D-Flow 3.26 software were saved as *.mox files, and then imported into Matlab 2021a (MathWorks, Natick, MA, USA) using a toolbox developed by [PERSON], et al. [35]. Subsequently, they were transformed into OpenSim 4.2 input files: *.trc, which contained positions of markers placed on a subject at different times during a motion capture trial, and *.mot, which included ground reaction force data [36]. The gait2354_model, a three-dimensional, 23-degree-of-freedom computer model of the human musculoskeletal system with 54 musculotendon actuators representing 27 muscles in the lower extremities and torso, was adjusted to participants' anthropometry using the Scale Tool. Joint angles were determined using the Inverse Kinematics Tool (IK) based on marker positions during motion. Joint torques were obtained using the Inverse Dynamics Tool (ID) and ground reaction forces (GRFs).  
The first perturbed gait cycle for the left leg for each of the three trials, for each person, was determined in the OpenSim software, considering the visualisation of inverse kinematics and GRF. As previously described, it was around the 30 th second of gait. The  
Figure 5: Procedure for determining the gait cycle containing perturbations: **(A)** example of the vertical component of ground reaction force (vGRF) for a single participant, with perturbations occurring every ten seconds during the Pre-Swing phase; **(B)** example of vGRF without perturbations for comparison; and **(C)** vGRF containing perturbations at 30, 40, 50, 60, and 70 s of gait. The gait cycle with the first perturbation, highlighted by the bold line, was selected for further analysis.  
same process was applied to gait without perturbations, but only for the second trial. The gait cycle, which occurred around the 20 th second of the trial, was included for each individual. The gait cycle length for each perturbed trial and the gait without perturbations was estimated using the Euclidean distance equation based on the position of the left heel marker at the heel strike, as follows:  
\[\textit{stride length}=\sqrt{\left(x_{2}-x_{1}\right)^{2}+\left(y_{2}-y_{1} \right)^{2}+\left(z_{2}-z_{1}\right)^{2}},\]  
where \(x_{1}\), \(y_{1}\), \(z_{1}\) denote the position of the heel marker at the beginning of the gait cycle, and \(x_{2}\), \(y_{2}\), \(z_{2}\) denote the position of the heel marker at the end of the gait cycle, which was taken into consideration. For subsequent statistical analysis, the stride length, and extreme values (minimal and maximal) of kinematic and kinetic parameters were extracted for gait cycles both with and without perturbations.
# Improving Energy Efficiency in LoRaWAN Networks with Multiple Gateways
## 5 EE-LoRa Algorithm
### SNR Threshold Adjustment  
The required SNR values necessary to demodulate LoRa signals at different SFs are given in Table 4. For example, a node should have an SNR value between \(-10\) and \(-7.5\) dB to be assigned SF 8, and each node with an SNR value less than \(-20\) dB is considered uncovered. In our work, we adjust the required SNR thresholds based on the obtained optimal node distribution. We apply the adaptive SNR threshold method presented in [13] to obtain the new SNR threshold values for each SF \(s\), denoted by \(computed\_SNR\_thr(s)\).  
First, the network server arranges the received SNR from all nodes in descending order. Next, we deduce the optimal number of nodes at each SF from the calculated optimal ratios \(p(s)\). Then, we find the \(computed\_SNR\_thr(s)\) by adjusting the SNR thresholds at each SF \(s\) to the lowest SNR value in the corresponding optimal set of nodes. Finally, each node will be assigned a new SF based on the \(computed\_SNR\_thr(s)\). An illustrative example of this process is shown in Figure 7. For example, if the SNR of a node is \(-2\) dB, it is assigned SF 8 instead of SF 7, following the \(computed\_SNR\_thr(s)\).
# Local Dependency-Enhanced Graph Convolutional Network for Aspect-Based Sentiment Analysis
## 4 Experiment
### Ablation Study  
To investigate the effectiveness of each module in LDEGCN, extensive ablation experiments were conducted. The results are presented in Table 4, where \"w/o\" denotes \"without\". The ablation experiments clearly demonstrate that excluding any component from the complete model leads to a degradation in performance. This underscores the indispensability of all components, as each one contributes uniquely to the model's performance. It can be observed that (1) removing the semantic feature extraction module (w/o SEM) resulted in a noticeable drop in performance, indicating that the semantic feature extraction module plays a crucial role in reducing noise and learning the presentation of aspect-related semantic features; (2) the removal of SentiNet (w/o SenticNet) and dependency type (w/o type) both resulted in a decrease in model performance, indicating that they both assist the model in focusing on more informative dependencies. External sentiment knowledge provides additional information about emotions to better understand and express sentiment-related aspects. Meanwhile, specific sets of dependencies aid the model in emphasizing crucial dependency relationships when modeling connections between words; (3) the removal of the LCW method leads to a slight decline in model performance, suggesting that utilizing the local context weight method on the graph can reduce attention to irrelevant features and alleviate issues related to long-distance dependencies; and (4) the significant degradation in performance was observed when the entire LDEGCN module was removed underscores the effectiveness of LDEGCN in providing sentiment information to the dependencies between context and aspect. This enables the model to obtain more accurate sentiment features and enhance the prediction performance for aspect-specific sentiment. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Twitter**} & \multicolumn{2}{c}{**Lap14**} & \multicolumn{2}{c}{**RestI4**} & \multicolumn{2}{c}{**RestI5**} & \multicolumn{2}{c}{**RestI6**} \\ \cline{2-10}  & **Acc** & **F1** & **Acc** & **F1** & **Acc** & **F1** & **Acc** & **F1** & **Acc** & **F1** \\ \hline W/o SEM & 75.73 & 74.10 & 80.13 & 76.81 & 85.62 & 80.07 & 84.87 & 71.42 & 90.8 & 77.65 \\ W/o SenticNet & 75.02 & 73.07 & 80.09 & 76.64 & 85.09 & 77.96 & 83.23 & 68.54 & 89.98 & 76.71 \\ W/o type & 75.43 & 74.22 & 80.88 & 77.52 & 85.26 & 78.74 & 84.56 & 70.88 & 90.59 & 77.45 \\ W/o LCW & 76.16 & 74.94 & 80.41 & 77.12 & 85.68 & 79.20 & 84.98 & 71.24 & 91.07 & 78.37 \\ W/o LDEGCN & 74.49 & 72.44 & 79.62 & 75.65 & 84.82 & 77.14 & 82.29 & 67.59 & 88.97 & 73.26 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ablation experiment.
# An Efficient Location Verification Scheme for Static Wireless Sensor Networks
## 8 Conclusions  
This paper provides an efficient location verification scheme by utilizing the MSR token. For this, we defined three properties of the MSR token: mutual symmetry, independent computation and anti-deduction. The mutual symmetry means that the same token should be acquired only by two participants, i.e., the claimant and verifier. The independent computation means that the participants can calculate the token without an explicit message exchange. The anti-deduction is that the token cannot be deduced by an attacker. For the source of the token that qualifies these properties, we have chosen the packets from the mutually-shared region between claimant and verifier. The analysis and evaluation substantiate that our scheme provides an efficient location verification in terms of communication cost, computation cost and verification performance. Furthermore, it would be possible to consider the effect of the piggybacking strategy, the side-effect of D-filtering, the frequency of location verification being invoked, the unclear communication range and the mobility, as mentioned in the Section 7, to make the verification process more effective. Those considerations may give a chance to improve the performance of the proposed scheme. We leave them for future work.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(NRF-2015R1D1A1A01058928). [PERSON] sketched and developed the idea. [PERSON] and [PERSON] conceived of and designed the simulation. [PERSON] analyzed the simulation data. [PERSON] and [PERSON] the paper. [PERSON] guided the paper, including the concept of the idea, simulation and analysis methods. [PERSON] and [PERSON] contributed analysis tools. [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript; nor in the decision to publish the results.
## 2 Control and Estimation under Information Constraints
### Coding/Decoding Schemes
#### 2.3.1 Static Binary Quantizer  
Let \(\sigma[k]\) be a scalar information signal to be transmitted over the digital communication channel in discrete instants \(t_{k}=kT_{0}\), where \(k=0,1,\cdots\in\mathbb{Z}_{+}\) is a sequence of natural numbers, and \(T_{0}\) is the sampling interval. Let us introduce the following static quantizer:  
\[q(\sigma,M)=M[k]s,\quad s=\text{sign}(\sigma), \tag{4}\]  
where \(\text{sign}(\cdot)\) is the _signum_ function: \(\text{sign}(\sigma)=1\), if \(\sigma\geq 0\), \(\text{sign}(\sigma)=-1\), if \(\sigma<0\). Parameter \(M\) is referred to as a _quantizer range_. The output signal of the quantizer is represented as one-bit information symbol from the coding alphabet \(\mathcal{S}=\{-1,1\}\), and is transmitted to the decoder. Note that for the binary coder, the transmission rate is as \(R=1/T_{0}\) bit per second. It is assumed that the _equi-memory condition_ is fulfilled, i.e., the coder and decoder make decisions based on the same information [67; 68]. The binary output codeword \(s\in\mathcal{S}\) is transmitted to the decoder.
## 3 Results and Discussion
### \(pH\)
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Marinade Flavoring** & **Piperine Enrichment** & **HHP Treatment** & **Storage Time [day]** & **Mean of pH [-]** & **Std. Deviation of pH [-]** \\ \hline Raw & & 0 & 5.43 & 0.02 \\ \hline \multirow{8}{*}{0} & & 0 & 5.75 & 0.02 \\ \cline{3-5}  & & 4 & 5.73 & 0.03 \\ \cline{3-5}  & & 7 & 5.68 & 0.03 \\ \cline{3-5}  & & 12 & 5.66 & 0.03 \\ \cline{3-5}  & & 14 & 5.63 & 0.03 \\ \cline{3-5}  & & 0 & 5.79 & 0.03 \\ \cline{3-5}  & & 4 & 5.72 & 0.04 \\ \cline{3-5}  & & 7 & 5.73 & 0.03 \\ \cline{3-5}  & & 12 & 5.70 & 0.03 \\ \cline{3-5}  & & 14 & 5.71 & 0.01 \\ \cline{3-5}  & & 0 & 5.79 & 0.02 \\ \cline{3-5}  & & 4 & 5.73 & 0.02 \\ \cline{3-5}  & & 7 & 5.75 & 0.02 \\ \cline{3-5}  & & 12 & 5.77 & 0.02 \\ \cline{3-5}  & & 14 & 5.76 & 0.04 \\ \cline{3-5}  & & 0 & 5.82 & 0.06 \\ \cline{3-5}  & & 4 & 5.75 & 0.05 \\ \cline{3-5} Herb marinade & 600 & 7 & 5.75 & 0.03 \\ \cline{3-5}  & & 12 & 5.75 & 0.03 \\ \cline{3-5}  & & 14 & 5.75 & 0.02 \\ \hline \multirow{8}{*}{300} & & 0 & 5.59 & 0.03 \\ \cline{3-5}  & & 4 & 5.62 & 0.09 \\ \cline{3-5}  & & 7 & 5.60 & 0.03 \\ \cline{3-5}  & & 12 & 5.58 & 0.02 \\ \cline{3-5}  & & 14 & 5.57 & 0.03 \\ \cline{3-5}  & & 0 & 5.65 & 0.03 \\ \cline{3-5}  & & 4 & 5.67 & 0.03 \\ \cline{3-5}  & & 7 & 5.64 & 0.02 \\ \cline{3-5} BA+ & 450 & 7 & 5.64 & 0.02 \\ \cline{3-5}  & & 12 & 5.64 & 0.02 \\ \cline{3-5}  & & 14 & 5.63 & 0.04 \\ \cline{3-5}  & & 0 & 5.69 & 0.03 \\ \cline{3-5}  & & 4 & 5.71 & 0.04 \\ \cline{3-5}  & & 7 & 5.64 & 0.02 \\ \cline{3-5}  & & 12 & 5.65 & 0.02 \\ \cline{3-5}  & & 14 & 5.63 & 0.01 \\ \hline \multirow{8}{*}{600} & & 0 & 5.69 & 0.03 \\ \cline{3-5}  & & 4 & 5.71 & 0.04 \\ \cline{3-5}  & & 7 & 5.64 & 0.02 \\ \cline{3-5}  & & 12 & 5.65 & 0.02 \\ \cline{3-5}  & & 14 & 5.63 & 0.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: pH [-] results of meat sample groups made with different marinade flavoring (herbs or paprika), without or with piperine enrichment (BA\(-\) or BA+), with different HHP treatment (0 MPa, 300 MPa, 450 MPa or 600 MPa) and with different storage time (0 day, 4 days, 7 days, 12 days or 14 days). Other authors have obtained similar results for pH when post-mortem beef was treated at 200-300 MPa at room temperature, resulting in a slight increase of 0.2 units in pH [46; 47]. The change in pH was explained by a change in protein conformation and the release of the imidazolium group of histidine under pressure.
# Primitive Shape Fitting in Point Clouds Using the Bees Algorithm
## 3 Primitive Fitting Methods
### Representation Scheme  
A primitive in a 3D scene is unequivocally described by a finite set of parameters that determine its position (the geometric centre, or centroid, henceforth simply referred to as centre), rotation and other geometrical properties. A solution \(I\) is thus naturally encoded as a vector of real values representing the primitive parameters. The size of the vector is shape-specific:  
**Sphere**: Four parameters: three to locate the centre, and one to represent the radius;
**Box**: Ten parameters: three to locate the centre, four to describe the orientation (rotations are described using quaternions), and three parameters to encode the width, depth, and height;
**Cylinder**: Nine parameters: three to locate the centre, four to describe the orientation, and two parameters to encode the radius and height;  
For the sake of clarity, henceforth a distinction will be made between pose parameters which include the position and, when applicable, the orientation, and the size parameters, namely: the radius of the sphere; the width, depth, and height of the box; and the radius and height of the cylinder.