esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Evaluation Experiment  
### Experiment Setup  
Acoustic data acquisition experiments were conducted at the Honjo Campus of Akita Prefectural University (Yurihonjo City, Akita Prefecture, Japan). This campus, located at 39\({}^{\circ}\)23\({}^{\prime}\)37\({}^{\prime\prime}\) north latitude and 140\({}^{\circ}\)4\({}^{\prime}\)23\({}^{\prime\prime}\) east longitude, has a size of 204,379 m\({}^{2}\). The left panel of Figure 6 portrays an aerial photograph of the campus. The surroundings comprise an expressway to the east and a public road to the south. The experiment was conducted at the athletic field on the west side of the campus, as depicted in the right panel of Figure 6.  
We used a pre-recorded sound of a mallard as the sound source. We played this sound on a Bluetooth wireless speaker that had been loaded on a small unmanned ground vehicle (UGV) to move to the respective measurement positions. In our earlier study, we developed this UGV as a prototype for mallard navigation, particularly for remote farming [2]. We moved it remotely via a wireless network. Our evaluation target was the detection of stationary sound sources. While the UGV was stopped at an arbitrary position, we played a mallard call sound file for 10 s. The sound pressure level produced from the speaker was \(75\pm 10\) dB.  
We conducted experiments separately on 19, 26, and 28 August 2020. Table 5 shows the meteorological conditions on the respective experimental days. All days were clear and sunny under a migratory anticyclone. Experiments were conducted during 14:00 to 15:00 Japan Standard Time (JST), which is 9 h ahead of the Coordinated Universal Time (UTC). The temperature was approximately 30 \({}^{\circ}\)C. The humidity was in the typical mean range for Japan. The wind speed was less than 5 m/s.  
Figure 5: Photographs showing the appearance of M1 (**left**) and M2 (**right**) after the setup.  
Figure 6: Aerial photograph showing the experimental environment (**left**) and UGV used to move the sound source positions (**right**).  
We conducted three evaluation experiments, designated as Experiments A-C. Experiment A comprised a sound source orientation estimation experiment using a single microphone array. The objective of this experiment was to verify the angular resolutions. Subsequently, Experiments B and C provided position estimation experimental results obtained using two microphone arrays. The difference between the two experimental setups was the field size and the orientations of the respective microphone arrays.
## 4 Results and Analysis
### Stepwise Regression Model
#### 4.2.3 Uncertainty Analysis  
An analysis of the model's effectiveness was conducted, as illustrated in Figure 8. Residual analysis revealed slight deviations; however, the distribution of residuals was largely consistent with the theoretical normal distribution. This consistency supports the validity of the model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Real-Predicted** & **T** & **Relevance** & **Significance** \\ \hline Number of fires real-predicted & \(-\)0.095 & 0.532 & 0.000 \\ Real value of area affected by fire-predicted value & \(-\)0.264 & 0.809 & 0.002 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Paired \(t\)-test for true and predicted value samples.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Implicit** & **Introducing Variable** & **R2** & **Significance** \\
**Number** & **Variable** & & & \\ \hline
1 & Number of fires & Average annual rainfall, average wind speed & 0.244 & 0.008 \\
2 & Fire area & Average annual rainfall & 0.572 & 0.00 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Stepwise regression fitting results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Independent** & **Ratio** & **Standard Error** & **Significance** \\
**Number** & **Variable** & & & \\ \hline \multirow{3}{*}{1} & Constant & 1601.75 & 383.98 & 0.000 \\  & Average annual rainfall & \(-\)0.481 & 0.191 & 0.018 \\ \cline{1-1}  & Average wind speed & \(-\)190.484 & 86.744 & 0.037 \\ \cline{1-1}  & Constant & 9964.326 & 1288.626 & 0.000 \\ \cline{1-1}  & Average annual rainfall & \(-\)7.493 & 1.170 & 0.000 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameter values of forest fire occurrence model in Yunnan Province.
## 4 Training and Validating/Testing the Dataset
### Discussion on Validating the CICIoT2023 Dataset Utilizing the DLMIDPSM  
We used validation/testing of the dataset to ensure that our proposed DLMIDPSM was fed with the trained model and ran through 80 epochs. Based on this, we achieved a model accuracy of 85 percent for both the training and validation of the complete model, using 80 epochs, as shown in Figure 10. The model incurred a validation loss of only 1% in 80 epochs, as shown in Figure 11. The pre-trained model, including large-scale IoT devices, was finalized and optimized on the benchmark dataset. The optimization solution considered feasible computation costs and the maximum accuracy obtained through our  
Figure 9: Ten numerical features of the trained dataset for detecting and preventing intrusion.  
proposed DLMIDPSM model. In addition, this marked the end of the intrusion attacks initially present in the CICIoT2023 dataset used.  
We further interpreted the plots of the complete model accuracies, most of the simple model accuracies, and the no-regularization model accuracies in the graphs below in Figures 12-15. Thus, from Figures 12 and 13, the model used the most straightforward accuracy. In Figures 12 and 13, the model accuracy dropped for both training and validation based on the simple accuracy model of 78% at 80 epochs, whilst the training and validation loss was only 5% at 80 epochs, leading to the model accuracy dropping. In Figures 14 and 15, the model used no-regularization accuracy for training and validation, with an accuracy of 82% and a loss of 1% at just 80 epochs. These figures and their corresponding values showed the progress and the capability of our proposed deep neural network DLMIDPS. Based on Table 1 below, the learning curve plots indicate the best-fit models, because the training loss plot decreases to the stability point, and the plot, including the validation loss, decreases to the stability point with a small gap with the training loss. The codes are in [[https://www.kaggle.com/code/samerskine/seguridad-informatica-iot](https://www.kaggle.com/code/samerskine/seguridad-informatica-iot)]([https://www.kaggle.com/code/samerskine/seguridad-informatica-iot](https://www.kaggle.com/code/samerskine/seguridad-informatica-iot)) (readers should have kaggle account to access link).  
Figure 11: Complete model accuracy loss vs. number of epochs.  
Figure 10: Complete model accuracy in 80 epochs.  
Figure 12: Most simple model accuracy vs. number of epochs.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**DLMIDPS Models (Proposed)** & **Accuracy for Training/Validation (\%)** & **Loss for Training/Validation (\%)** \\ \hline Complete\_model & 85 & 2 \\ \hline No\_regularization\_model & 80 & 1 \\ \hline Most\_simple\_model & 78 & 0.5 \\ \hline \end{tabular}
\end{table}
Table 1: Showing the training and validation accuracies and their corresponding losses of the DLMIDPS models.  
Figure 13: Most simple model accuracy loss vs. number of epochs.
## 2 Materials and Methods
### FR Calculation from Site Index
#### 2.3.2 Determine Relationship between Stand Volume and Site Index  
Most of the plots in the study areas were thinned at age 12, so volume at age 11 was used to derive the relationship between SI and yield. Volume for each tree was calculated using the equation: Volume = 0.1365 + 0.0024 \(\times\) DBH\({}^{2}\) H [44], where volume is in cubic feet, DBH is in inches, and H, total tree height, is in feet. Values were calculated in English units and converted to metric units. Stand volume was determined by summing the volume of individual trees in the plot and using the plot area to adjust to a per hectare value. The relationship between SI and stand volume in the plots was fit using three different equations: linear, exponential, and sigmoidal with the following forms.  
1. Linear (\(\mathrm{Volume}=\beta_{0}+\beta_{1}\mathrm{SI}\))
2. Exponential (Volume = e\({}^{\mathrm{o}_{0}+\alpha_{1}\mathrm{SI}}\))
3. Sigmoid (Volume = \(\dfrac{\gamma_{1}}{1+e^{\gamma_{2}+\gamma_{3}\mathrm{SI}}}\))  
where \(\beta_{0}\) and \(\beta_{1}\) represent the intercept and slope parameter for the linear model; \(\alpha_{0}\) and \(\alpha_{1}\) represent parameters for the exponential function; and \(\gamma_{1},\gamma_{2}\), and \(\gamma_{3}\) represent parameters for the sigmoid function. Nonlinear least square fits of sigmoidal and exponential equations were accomplished by using 'nls' procedure in \"R version 3.1.1\". To ensure the solution was global and not local, different initial values for the parameters were provided for the fits. _R\({}^{2}\)_ and the leave-one-out cross validation method were used to determine the equation with the best fit using the following selection criteria: Root Means Square Error (RMSE), Mean Absolute Error (MAE), and Predicted Residual Sums of Square (PRESS) statistics.  
RMSE was calculated as the standard deviation of the difference between y\({}_{i}\)-\(\hat{y}_{i,-i}\), where y\({}_{i}\) is observed volume per hectare and \(\hat{y}_{i,-i}\) is the predicted value for the volume using the model with all observation in the fitting data except the observed volume on that plot. MAE was calculated as:  
\[MAE=\sum_{i=1}^{n}\dfrac{\mid y_{i}-\hat{y}_{i,-i}\mid}{n}\]  
where n is the number of observations and y\({}_{i}\) and \(\hat{y}_{i,-i}\) are as described above.  
Similarly, the PRESS statistic was calculated as:  
\[PRESS=\sum_{i=1}^{n}(y_{i}-\hat{y}_{i,-i})^{2}\]
# Towards Adaptive Multipath Managing:
## 1 Introduction
### Motivation  
Although applying MPTCP to the multihomed mobile computing devices towards multipath data transmission provides numerous potential benefits for data delivery [12; 13; 14; 15; 16; 17; 18; 19], the MPTCP path management mechanism is very simple in RFC6824 [7], and this, in turn, leads to MPTCP being vulnerable to the path quality differences of the multiple paths [20]. With the regular path management mechanism, the MPTCP data traffic can be split and assigned to all the available end-to-end paths for concurrent multipath transmission. Such a full-MPTCP mode may be a useful way of providing a \"best-effort\" bandwidth aggregation service, but this, in turn, will determine data to be received out-of-order because asymmetric paths in an MPTCP connection are commonly encountered with different transmission characteristics. The out-of-order data arrival phenomenon can be especially problematic when applying MPTCP to a multihomed mobile computing device because today's mobile computing devices commonly have a poor receiver buffer, due to their very limited memory space. When a large number of out-of-order packets are maintained in the overloaded receiver buffer for reordering, buffer blocking will occur, and thereby, the MPTCP's performance will encounter a serious degradation (as we will discuss in more detail in the next section).  
Figure 1: A typical Multipath TCP (MPTCP) based multipath communication among multihomed mobile devices.
# A Feasibility Study of the Drive-By Method for Damage Detection in Railway Bridges
## 1 Introduction
A numerical model for simulating the train-track-bridge interaction [8] is used, including the bridge structure, the track, and a multi-body model of the vehicle. Several conditions are simulated, considering different kinds of bridges, different train models, train speeds, damage levels, and locations of the damage along the bridge. The optimal sensor layout is investigated, to define the sensor position on the train that can deliver the most meaningful output to analyze the structural status of the bridge. Finally, the effect of track irregularity and ballast degradation on the reliability of the damage detection method is addressed. Two different approaches can be investigated for data-processing, basically differing in the frequency range of the acceleration data considered. The first approach considered in this paper originates from the work described in [PERSON], & [PERSON] [9; 10], focused on a bridge for road transportation. In the mentioned work, data generated by the vehicle passage are filtered to cancel the dynamic components due to the bridge modes of vibration, so that the remaining signal is representative of the quasi-static deflection of the bridge in response to the moving loads. The authors then define a damage component index that is suitable for damage identification, obtained by subtracting from the acceleration measured the reference response of the healthy structure. A second approach may be intended to consider the entire dynamic response of the train-bridge system, in as wide a frequency range as possible. The presence of a defect alters indeed not only the quasi-static response, but also the dynamic response of the vehicle when the train passes by the structural defect, as well as the dynamic response of the bridge itself. In this paper, an analysis of the spectral components of train accelerations resulting from healthy and damage bridge is performed, and the results of a damage identification method based on moving window root mean square (RMS)are commented. Discussion on the applicability of the two methods, and limitations and influence of realistic operational parameters is carried out. The paper is organized in the following points: in Section 2, the complete model for the train-track-bridge system is described; the bridge is modeled through the finite element method (FEM), while the full train is modeled as a multibody system. In Section 3, the simulation plan is described, and the two different data processing techniques are presented. Section 4 presents the simulation results in terms of the best positioning of sensors on the train, the acceleration levels to be measured, accuracy in the localization of the damage, and the effects of track irregularity on the feasibility of the method. Further necessary researches are mentioned, needed before the method could be tested in practice. Finally, in Section 5, conclusions are drawn, and the feasibility of the drive-by method is discussed.
## 4 Reed-Solomon Codes Effectiveness against Single-Frequency EMD
### Impact of the Message Length  
Increasing the message length has no effect on the number of mentioned CTP and CFN peaks and the peak frequencies in which happen. However, an increased message length decreases the minimum Hamming distance among code words. This means a smaller number of bit-flips would be required to generate a CTP or CFN. Accordingly, due to the smaller Hamming distance, CTP and CFN peaks in Figure 2 are wider in comparison with Figure 3. In these cases, the message length increased to three symbols, which in turn decreased the minimum Hamming distance among code words.  
In case that \(k=3\) (i.e, Figure 2c), there are other CTPs and CFNs in addition to the mentioned peaks. These peaks are caused by multi-symbol-value code words. Compared to the required number of bit-flips to generate valid one-symbol-value code words, which are responsible for the main peaks, smaller numbers of bit-flips are needed to generate valid multi-symbol-value code words. Note that more intense disturbances (i.e., greater induced voltages) are able to push more data points beyond the specified threshold, and they generate bit-flips in great numbers (i.e., tend to generate one-symbol-value code words).  
As can be seen in Figure 3, there is no CTPs or CFNs with a multi-symbol value for the symbol size of 4 under the considered electromagnetic variables. This is due to the fact that the required number of bit-flips in this case (i.e., 16 bit-flips) is greater than the required number of bit-flips for the symbol size of 3 (i.e., 6 bit-flips).
## 3 Results
### Compatibility
#### 3.5.3 Overall Compatibility  
The system demonstrated robust compatibility across all tested environments, GPU configurations, driver versions, and CUDA versions. The absence of setup issues and the ability to deploy efficiently in diverse setups, contingent on the installation status of containerization tools, underscore the system's versatility and reliability in real-world scenarios. Ensuring that the NVIDIA Container Toolkit and Docker are pre-installed can optimize setup efficiency, facilitating quicker and more seamless deployments.
# Error Correction Method of TIADC System Based on Parameter Estimation of Identification Model
## 2 Research on the Calibration Method
### Frequency Characteristic Analysis of the TIADC System Identification Model
 \tag{23}\]  
According to Equation (23), the expressions of real part \(\text{Re}[G(j\omega)]\) and imaginary part \(Im[G(j\omega)]\) of frequency characteristics can be obtained:  
\[\text{Re}[G(j\omega)]=\frac{[n_{0}+\psi^{T}(\omega)\beta_{2}]\left[1+\psi^{T} (\omega)\alpha_{2}\right]+\varphi^{T}(\omega)\beta_{1}\varphi^{T}(\omega) \alpha_{1}}{[1+\psi^{T}(\omega)\alpha_{2}]^{2}+[\varphi^{T}(\omega)\alpha_{1} ]^{2}} \tag{24}\]  
\[Im[G(j\omega)]=\frac{\left[1+\psi^{T}(\omega)\alpha_{2}\right]\varphi^{T}( \omega)\beta_{1}-\left[n_{0}+\psi^{T}(\omega)\beta_{2}\right]\varphi^{T}( \omega)\alpha_{1}}{\left[1+\psi^{T}(\omega)\alpha_{2}\right]^{2}+[\varphi^{T}( \omega)\alpha_{1}]^{2}} \tag{25}\]  
The real part \(\text{Re}[G(j\omega)]\) and imaginary part \(Im[G(j\omega)]\) of the frequency characteristics contain all the parameters \(n_{0}\), \(\alpha_{1}\), \(\alpha_{2}\), \(\beta_{1}\) and \(\beta_{2}\) of the system to be identified, which are also all the unknown parameters contained in the system transfer function. The frequency characteristic can also be expressed as the plural form of the sum of the real part and the imaginary part, namely:  
\[G(j\omega)=M(\omega)+jN(\omega) \tag{26}\]where \(M(\omega)\) Called the real frequency characteristic, \(N(\omega)\) Called virtual frequency characteristic, \(j^{2}=-1\). For the identification problem, using the observed data to construct the criterion function for estimating the unknown parameters is the key to realizing the parameter estimation, and the principle of establishing the criterion function is to realize the minimum error between the observed output and the model output. It can be seen from the frequency characteristic expression that the expressions of the real part and the imaginary part contain all the parameters to be identified in the system, so we can construct the criterion function through the expressions of the real or imaginary frequency characteristics.
## 4 The Energy Balance Problem
### A Closed Form  
Under specific assumptions (that we discuss and motivate) we can make the calculation of probabilities \(p_{i}\) simpler. Combining lemma 10 and lemma 12 we have that  
\[E[h_{i}]=\lambda_{i}N+E[f_{i}]=\frac{2i-1}{n^{2}}N+E[f_{i}] \tag{6}\]  
By the corresponding relation for \(E[h_{i-1}]\) it must be:  
\[\frac{\frac{2i-1}{n^{2}}N+E[f_{i}]}{(2i-1)\frac{\phi}{2}R^{2}}[p_{i}+(1-p_{i}) i^{2}]R^{2}=\frac{\frac{2i-2}{n^{2}}N+E[f_{i-1}]}{(2i-3)\frac{\phi}{2}R^{2}}[p_{i -1}+(1-p_{i-1})(i-1)^{2}]R^{2}\]  
But \(2i-1\simeq 2i-3\) and \(\frac{\phi}{2}\), \(R^{2}\) cancel. Dividing by N we get:  
\[\left[1+\frac{E[f_{i}]}{N}\right][p_{i}+(1-p_{i})i^{2}]\simeq\left[1+\frac{E[ f_{i-1}]}{N}\right][p_{i-1}+(1-p_{i-1})(i-1)^{2}]\]  
If \(E[f_{i}]\simeq E[f_{i-1}]\) then the previous relation becomes:  
\[p_{i}+(1-p_{i})i^{2}=p_{i-1}+(1-p_{i-1})(i-1)^{2} \tag{7}\]  
In [16] we show how to solve the above recurrence.  
**Theorem 6**: If \(E[f_{i}]\simeq E[f_{i-1}]\), \(3\leq i\leq n\), then the one-hop forwarding probability, guaranteeing energy balance, is  
\[p_{i}=1-\frac{3x}{(i+1)(i-1)}\]  
where \(p_{2}=x\in(0,1)\) a free parameter and \(p_{1}=0\).  
We remark that the assumption \(E[f_{i}]\simeq E[f_{i-1}]\) is quite reasonable and well motivated. We provide the following intuitive explanation of why this happens. Remark indeed that the area sizes of adjacent sectors (and thus the number of events generated in such sections) are more or less the same, especially when \(i\) increases. Furthermore, the probability \(p_{i}\) of forwarding to the adjacent (towards the sink) sector increases very fast with \(i\) and becomes 1 in most sectors of the network (in the middle territory).
# Combating Fake News with Cryptography in Quantum Era with Post-Quantum Verifiable Image Proofs
## 6 Cryptographic Foundations and Preliminaries
### Lookup Tables, Polynomial Testing, and Non-Interactive Proofs
#### 6.3.3 Comparison with Plonk  
While Plonk was groundbreaking in providing an efficient and general-purpose framework for zero-knowledge proofs, HyperPlonk enhances this model by improving both performance and flexibility:  
* Efficiency: HyperPlonk's use of multivariate polynomials reduces proof size and speeds up the verification process compared to standard Plonk, making it more suitable for large-scale applications.
* Gate Flexibility: HyperPlonk's ability to define custom gates and constraints allows for the encoding of more complex operations; whereas, Plonk is more constrained to standard gate types.
* Prover Optimization: The optimizations in HyperPlonk make it significantly more efficient for proof generation, which is critical in systems requiring high throughput.
* Post-Quantum Security: Unlike traditional Plonk, which relies on cryptographic assumptions vulnerable to quantum attacks (e.g., elliptic curve cryptography), HyperPlonk integrates post-quantum secure primitives. This makes it resilient to quantum attacks, securing future-proof applications where long-term data confidentiality is required.
## 2 Culvert Extension Project Profile
### Culvert Extension Conditions and Monitoring Section  
As shown in Figure 1, the foundation treatment of the existing line in Site 1 is a lime-soil compaction pile having a 40 cm pile diameter, 12 m pile length, 95 cm pile distance, and a plum-shaped arrangement. The foundation treatment of the existing line in Site 2 is in the form of a cement mixing pile of 50 cm pile diameter, 14 m pile length, 120 cm pile distance, and a plum-shaped arrangement. The foundation treatment of the culvert extension section is a high-pressure rotary jet pile of 50 cm pile diameter, 140 cm pile distance, and a square arrangement. The pile length at Site 1 is 12 m, and that at Site 2 is 13 m.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Stratigraphic Type**} & \multicolumn{2}{c}{**Thickness/m**} & \multicolumn{2}{c}{**Poisson’s**} & \multirow{2}{*}{**Cohesion/kPa**} & \multirow{2}{*}{**Angle of Internal**} \\  & **Site 1** & **Site 2** & & & **Ratio** & \\ \hline \The culvert transition section and the surface layer on the foundation bed of the existing line and the new line were filled with respective layers of 3% cement-graded crushed stone and 5% cement-graded crushed stone (0.6 m) (shown in Figure 2). The length \(\times\) width \(\times\) height of the principal culvert part at Site 1 was 15.75 m \(\times\) 6.0 m \(\times\) 6.0 m. The extension on the uplink side was 7 m, and the extension on the downlink side was 9 m. The length \(\times\) width \(\times\) height of the principal culvert part at Site 2 was 15.0 m \(\times\) 4.0 m \(\times\) 4.0 m. The extension on the uplink and downlink sides was 13 m. In the operation stage of the existing line, a monitoring section was set at a distance of 1m from the culvert structures and the settlement observation markers were used for monitoring the settlement of line center and the road shoulder.(shown in Figure 2). A level gauge was also set longitudinally at the transition section of the existing line and at the toe of the ballast slope at the top of the culvert before the construction of the new subgrade.  
Figure 1: Schematic diagram of the culvert extension project: (**a**) Site 1; (**b**) Site 2.