esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulation Results  
To test [PERSON]'s correct guessing probability \(p\) for the LH situation, we assumed that [PERSON] and [PERSON] selected \(R_{\text{L}}=1\) k\(\Omega\) and \(R_{\text{H}}=10\) k\(\Omega\). During these experiments, the DC voltage was kept at a constant level of 0.1 V (see Figures 2 and 3). To generate noise, we used the white Gaussian noise function (wgn) from the Matlab communication system toolbox to test the success statistics of the attack scheme while varying the temperature. The effective bandwidth \(\Delta f\) and the range of temperatures were 1 MHz and \(10^{8}<T<10^{18}\) K, respectively. At lower temperatures \(p\) was 1, within the statistical inaccuracy of simulations; at the high-temperature limit it converged to 0.5. The duration of the secure bit exchange period was characterized by the number \(N\) of _independent_ noise samples used during the exchange of the particular bit.  
We tested secure key length \(M=700\) bits at different bit exchange durations represented by sample/bit numbers \(N=1000\), 500, and 200, respectively. Figure 4 shows [PERSON]'s correct guessing probability (\(p\)) of a key bit versus temperature. With temperature approaching infinity, the effective noise voltage on the wire also approaches infinity and the Gaussian density function is symmetrically distributed around the threshold voltage \(U_{\text{th}}\). Thus, the probabilities of finding the noise amplitude above or below \(U_{\text{th}}\) are identical (0.5) Therefore, [PERSON]'s correct guessing probability represents the perfect security limit, \(p=0.5\).  
Figure 3: [PERSON]’s threshold scheme to guess the bit situation LH versus HL.  
The observed dependence can be interpreted by the behavior of the error function (see also Equations (8) and (12))  
\[p\{U(t)\ \geq U_{\rm th}\}=0.5\bigg{[}1-erf\bigg{(}\frac{U_{\rm th}-U_{\rm DCW}}{U_{ \rm eff}\sqrt{2}}\bigg{)}\bigg{]} \tag{16}\]  
where \(U(t)\) is the instantaneous voltage amplitude in the wire and the error function is  
\[erf(x)=\frac{1}{\sqrt{\pi}}\int\limits_{-x}^{x}\exp\left(-y^{2}\right)\,dy. \tag{17}\]  
The noise in the KLJN scheme is a bandlimited white noise, and thus, in accordance with the Johnson formula, the effective noise voltage scales as  
\[U_{\rm eff}\propto\sqrt{T\ \Delta f} \tag{18}\]  
Therefore, when temperature \(T\) is converging towards infinity, \(p\) converges to the perfect security limit of 0.5 (see Figure 4).
# A Multinomial DGA Classifier for Incipient Fault Detection in Oil-Impregnated Power Transformers
## 3 Methods and Modeling
### Random Forests  
The Random Forest, also known as Random Decision Forest, presents a powerful way of exploring and analyzing data with the potential to perform predictive modeling. The Random Forest is made up numerous discrete decision trees that create a forest and hence the name Random Forest. The fundamental principle underlying the Random Forest is that individual constituent models cannot outperform numerous delinked trees working as a group. To make a prediction, each classifier in the assembly submits a prediction [57; 58; 59]. The votes are then tallied and the class that garners the highest ballots is presumed to be the prediction of the overall model as shown in Figure 11. Some of the reasons why the Random Forest is a powerful algorithm is that it does not suffer from overfitting and it is highly accurate, maintaining this accuracy even when some of the data is missing.  
A Random Forest is defined as a class predictor made up of an ensemble of autonomous tree structures \(\{t(x,\theta_{n}),n=1,2,\ldots\}\) where \(\{\theta_{n}\}\) are random vectors and \(x\) is the input vector [58]. A distinctive phenomenon of the Random Forest is that each tree \(\{\theta_{n}\}\) is independent of all past random vectors \(\{\theta_{1},\ \ldots\ \theta_{n-1}\}\) but have a similar distribution. In creating the tree structures, a random attribute is selected upon which the split takes effect. The pool of potential attributes to split on is kept limited by calculating the first integer less than \(\log_{2}B+1\)[59].  
In growing its trees, the Random Forest begins by first selecting randomly at each node a small group of input vectors on which to split then secondly it calculates a suitable split point. The best feature to split on is determined randomly.  
The Random Forest algorithm works by executing the following three steps [60]:  
1. Selection of the set used for training. Using an indiscriminate sampling technique, several training sets are selected from the first dataset such that the magnitude of each is equal to that of the original.
2. Construction a Random Forest model. For each of the bootstrap training set, a forest of classification trees is created to produce a similar number of decision trees.
3. Form a simple voting. The training of the Random Forest can proceed simultaneously because the process of training its members is independent of each other, thus considerably enhancing its efficiency. To decide on some sample input, every decision tree  
Figure 11: Tally: Class A—6 votes, Class B—3 votes; Prediction Class A.  
submits a vote. The Random Forest algorithm decides the ultimate category of the submitted sample in accordance to the voting pattern.
## 4 Experiments
### Comparative Experiments on Rice Grain Dataset
#### 4.7.3 Statistical Validation  
To address the concern of statistical significance with a small sample size, we conducted patch-level significance testing using the Wilcoxon signed-rank test. Each of the 10 high-resolution rice images was partitioned into multiple non-overlapping patches (512 \(\times\) 240), resulting in a total of 40 low-resolution test samples. We compared the PSNR and SSIM values of our proposed DRFAN model against representative lightweight baselines.  
As shown in Table 4, the \(p\)-values for both PSNR and SSIM are less than 0.01 in all comparisons, confirming that the DRFAN's superiority is statistically significant. This indicates that even with a small dataset, the performance gains of the DRFAN are consistent and reliable, confirming that its superiority in dense-grain texture reconstruction is not incidental.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Comparison** & **Metric** & \(p\)**-Value** & **Significance (\(p\) \(<\) 0.01)** \\ \hline \multirow{2}{*}{DRFAN vs. CARN} & PSNR & 0 & Yes \\  & SSIM & 0.00195 & Yes \\  & PSNR & 0 & Yes \\ DRFAN vs. IMDN & SSIM & 0.00195 & Yes \\  & PSNR & 0 & Yes \\ DRFAN vs. SAFMN & SSIM & 0.00195 & Yes \\  & PSNR & 0 & Yes \\ DRFAN vs. ShuffleMixer & SSIM & 0.00195 & Yes \\  & PSNR & 0 & Yes \\ DRFAN vs. SwinIR-S & SSIM & 0.00195 & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistical significance tests (Wilcoxon signed-rank test) between DRFAN and other models on the rice dataset.  
Figure 11: Visual comparisons between our model and mainstream SR models on the rice dataset (\(\times 4\)).  
Figure 10: Enhancement results of our model on rice images from an actual processing environment.  
Additionally, we present box plots in Figure 12 to visualize the distribution of PSNR scores across models. These results validate the robustness of our method in restoring fine-grained textures under industrial conditions, despite the relatively limited number of source images.
# The Influence of Feature Representation of Text on the Performance of Document Classification
## 4 Results
### Accuracy Rankings  
Table 6 summarizes the rankings of the document representation models according to accuracy. The average ranks according to accuracy suggest that bow-TF is the top-ranking model followed by doc2 vec-25 and bow-TFIDF. The variants of doc2 vec corresponding to low-dimensional embedding dimensionality (\(\leq\)75) outperform all the word2 vec variants, while the GOW model variants (together with high-dimensional variants of doc2 vec) are the worse performers. Note again that _gov-avg_ is the top-ranked GOW model variant, which is consistent with the results obtained on AUROC.  
For the classification tasks including longer documents, BOW and doc2 vec lead to the maximal accuracy; for medium sized documents, the best accuracy is obtained using BOW and word2 vec variants, while for the tasks including short documents BOW variants are clearly outperforming all the other representation models. The same descendant ordering of ranked representation models BOW, doc2 vec, word2 vec, GOW is preserved regardless of the size of vocabulary or the number of class labels. The only slight deviation (in which word2 vec and doc2 vec exchange the ranking positions) is noticed for the large-vocabulary data sets and the moderate number of class labels.  
In summary, following the graphical representation of the accuracy rankings in Figure 4, when interested in classification accuracy, the two variants of the BOW model were the most advisable choices regardless of the vocabulary size or the number of class labels; for tasks involving long documents, doc2 vec could also be a recommended option.  
Figure 3: Radar-chart comparison of the multi-objective rankings by task (**left**) and their averages (**right**) for the four top-performing variants of the document representation models in each of the four groups with respect to AUROC.
## 3 Results and Discussion
### Results of BT-Based Models  
Excerpt of input variables is the initial step in developing BT-based models. In the present paper, the model was established using pH, P, Ca, Mg, and C. Model development is a similar process as that followed by the MLP-based model development. Figure 8 indicates the BT model-based tree graphs for Co, Cu and Pb, respectively. The BT regression trees  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{3}{c}{**Training**} & \multicolumn{3}{c}{**Testing**} & \multicolumn{3}{c}{**Validation**} \\ \cline{2-7}  & **Run** & **CC** & **RMSE** & **CC** & **RMSE** & **CC** & **RMSE** \\ \hline MLP 2-3-1 & 25 & 0.8561 & 0.0938 & 0.3529 & 0.1078 & 0.7132 & 0.0128 \\ MLP 2-5-1 & 25 & 0.8465 & 0.0104 & 0.3490 & 0.1084 & 0.7113 & 0.0142 \\ MLP 2-5  
were obtained for Co, Cu and Pb using soil properties as forecasters. The root nodes of the regression tree in Co, spilt on phosphorus and pH, were also splitting variables into trees. It is assumed that lower phosphorus content is allied with greater Co retention, and pH is also an imperative variable in the retention of Co [48]. In the BT regression model of Cu and Pb, the root nodes of the regression tree also spilt on phosphorus and pH, and both these variables are imperative in maintaining Cu and Pb [49]. The agreement plots for target values and predicted values of Co, Cu and Pb are shown in Figure 9, respectively, using a validation dataset. CC and RMSE values of Co, Cu and Pb using BT-based models are listed in Table 5.  
Figure 8: Boosted regression tree graph of Co (**a**), Cu (**b**) and Pb (**c**) (_Mu_, mean; _Var_, variance).  
Figure 9: Scatter plot of target vs. predicted Co (**a**), Cu (**b**) and Pb (**c**) using the boosted regression tree method in validation stage.  
Among the predicted level of metals, the highest CC values were found for Cu in training (0.960) and testing (0.8646), while in the validation of models, the highest CC values were found for Co (0.9062). The RMSE values were recorded as the highest for Pb in training (0.1175), testing (0.0791) and validation (0.1000). [PERSON] et al. (2005) used boosted regression, random forest and support vector machine techniques for predicting the PTE concentration, mainly arsenic, and found that among all applied models, boosted regression is the best model for predicting the arsenic concentration with RMSE (0.6007). [PERSON] et al. (2019), in their studies for predicting the PTEs (Zn, Cu, Cr, Ni, Hg, Cd, As, and Pb) concentrations, applied numerous modelling techniques such as boosted regression, random forest and generalized linear models. Among the applied models, random forest is the best, followed by boosted regression and generalized linear models for predicting the concentration of these PTEs with RMSE values Zn (0.067), Cu (0.059), Cr (0.033), Ni (0.044), Hg (0.021), Cd (0.229), As (0.103) and Pb (0.004).
# Introduction to Reconfiguration
## 6 Connectivity and Diameter for \(k\)-Coloring
### \(k\)-Coloring Reconfiguration
Subsequently, the conjecture was proved for \(k=\Delta(G)+1\) and \(\Delta(G)\geq 3\); in this case, the reconfiguration graph consists of isolated vertices and one more component, with diameter \(O(|V(G)|^{2})\)[81]. It was further observed that by increasing the number of colors, it is possible to obtain linear diameter, in particular, for every \(k\geq 2\mathrm{col}(G)+2\)[86]. For \(\mathrm{mad}(G)\), defined to be the maximum average degree of a non-empty induced subgraph of \(G\), \(\mathrm{col}(G)\leq\mathrm{mad}(G)\leq 2\mathrm{col}(G)\)[86], it has been shown that, for every integer \(d\geq 1\) and every \(\epsilon>0\), there exists \(c=c(d,\epsilon)\geq 1\) such that for every \(G\) such that \(\mathrm{mad}(G)\leq d-\epsilon\) and every \(k\geq d+1\), the diameter is in \(O(|V(G)|^{c})\)[86]. As a consequence of the result on maximum average degree, since every planar graph has \(\mathrm{mad}(G)\leq 6\), the diameter is polynomial for every \(k\geq 8\)[86], providing support for the planar graph version of [PERSON]'s conjecture, which states that, if \(G\) is planar, then for any \(k\geq 7\), the diameter is polynomial [85]. Algorithmic results focus on both reachability and finding a reconfiguration sequence. For bounded-degree graphs, reachability can be solved in constant time when \(\Delta(G)\leq k-2\), linear time when \(k\geq 3\) and \(\Delta(G)=k-1\), and quadratic time when \(k=3\) and \(\Delta(G)\geq 3\)[81]. With respect to parameterized complexity, the problem is fixed-parameter tractable when parameterized by \(k+\ell\) (where \(\ell\) is the length of the reconfiguration sequence) [79; 87] but \(W[1]\)-hard when parameterized by \(\ell\) alone [79]. The problem of finding a path for \(k=\Delta(G)+1\) was shown to be solvable in quadratic time for any \(G\) such that \(\Delta(G)\geq 1\) and \(\mathrm{col}(G)=\Delta(G)-1\)[80]; by a recent result applying to \(\Delta(G)\)-regular graphs, the result was extended to hold for any connected graph with \(\Delta(G)\geq 3\)[88].
# A Method for Estimating the Resultant Expanded Uncertainty Value Based on Interval Arithmetic
## 5 Example of the Proposed Analysis Method Application
### First Example  
In the example, it is assumed that the time-varying physical quantity \(s(t)\) undergoes processing by a measuring transducer, which outputs a representation of this quantity as a time-varying electrical voltage \(y_{1}(t)\). Subsequently, this voltage is amplified by an amplifier and input as a signal \(y_{2}(t)\) to the A/D converter. The converter then takes consecutive implementations of the input quantity and converts them into discrete samples represented as \(x(i)\). The diagram depicting the discussed measurement pathway is illustrated in Figure 7.  
It was assumed that the measuring transducer introduces white noise disturbance into the output signal, with the variance transferred to the value \(x(i)\) being \(\sigma_{w}^{2}\), and the realization values of this signal following a normal distribution. The measurement amplifier introduces an error related to zero shift due to temperature variations into the output quantity, with the variance transferred to the quantity \(x(i)\) being \(\sigma_{z}^{2}\), and subsequent implementations following a triangular distribution. Subsequently, the A/D converter introduces quantization-related error into the value \(x(i)\), with a variance of \(\sigma_{q}^{2}\), and succeeding implementations following a uniform distribution.  
Given the outlined assumptions, the vector comprising expanded uncertainties for the specified quantity \(x(i)\) can be expressed as:  
\[\mathbf{U}=\begin{bmatrix}U_{w}&U_{z}&U_{q}\end{bmatrix}=\begin{bmatrix}c_{w} \sigma_{n}&c_{z}\sigma_{z}&c_{q}\sigma_{q}\end{bmatrix}, \tag{19}\]  
Figure 7: Diagram of an example measurement chain.  
while the coherence matrix takes the form:  
\[\mathbf{h}=\begin{bmatrix}1&h_{w,z}&h_{w,q}\\ h_{z,w}&1&h_{z,q}\\ h_{q,w}&h_{q,z}&1\end{bmatrix}, \tag{20}\]  
whereby:  
\[h_{w,z}=h_{z,w} =s_{n,t}\sqrt{\frac{\min(U_{w},U_{z})}{\max(U_{w},U_{z})}}\left( \frac{U_{w}^{2}+U_{z}^{2}}{U_{w}^{2}+U_{z}^{2}+U_{q}^{2}}\right), \tag{21}\] \[h_{w,q}=h_{q,w} =s_{n,u}\sqrt{\frac{\min(U_{w},U_{q})}{\max(U_{w},U_{q})}}\left( \frac{U_{w}^{2}+U_{q}^{2}}{U_{w}^{2}+U_{z}^{2}+U_{q}^{2}}\right),\] (22) \[h_{z,q}=h_{q,z} =s_{t,u}\sqrt{\frac{\min(U_{z},U_{q})}{\max(U_{z},U_{q})}}\left( \frac{U_{z}^{2}+U_{q}^{2}}{U_{w}^{2}+U_{z}^{2}+U_{q}^{2}}\right), \tag{23}\]  
and finally, based on Equation (7):  
\[U_{\Sigma}=\sqrt{\mathbf{U}\cdot\mathbf{h}\cdot\mathbf{U}^{T}}. \tag{24}\]
# Influence of Xanthan Gum-Based Soil Conditioners on the Geotechnical Properties of Soils
## 4 Results and Discussion
### Saturated Hydraulic Conductivity  
In Table 6, the mean values and corresponding standard errors of the measured saturated hydraulic conductivities are given for all the specimens of untreated soils and mixtures. In general, the addition of SCs decreases the saturated hydraulic conductivity of the investigated soils. The magnitude of the decrease in saturated hydraulic conductivity due to the addition of the high dosage of the SCs was observed to be significant (\(\alpha\) = 0.05), i.e., by a factor of 100 to 10,000 m/s. Although untreated soils exhibit significantly different saturated hydraulic conductivities, the addition of a high dosage of SCs results in similar saturated hydraulic conductivities in the mixtures, which was confirmed with an ANOVA (\(\alpha\) = 0.05). The saturated hydraulic conductivity of mixtures with high dosages of SCs was found to be unaffected by the effective vertical stresses for soil S3. In the case of S1 and S2, the saturated hydraulic conductivities of high dosage mixtures at an effective vertical stress of 25 kPa were significantly (\(\alpha\) = 0.05) higher compared to the ones at an effective vertical stress of 50 kPa. In general, the type of the SC was not recognized as an influential parameter.  
The results are in agreement with those in the literature [35], where for similar dosages of XG, a similar magnitude of decrease in saturated hydraulic conductivity was reported, from 1.42 \(\times\) 10\({}^{-7}\) m/s for untreated soil to 4.46 \(\times\) 10\({}^{-10}\) m/s for the addition of 1% of XG. It is also established that the decrease is highly dependent on the soil type [14; 16; 35].
## 3 System Development
### Communication Protocols  
Communication between the devices and the EDGE server node is managed using different IoT communication protocols. The use of the HTTP protocol allows connection and exchange with any device that has the appropriate configuration and registration on the general network, thus respecting a stable and reliable communication standard as required by DT infrastructures. MQTT, on the other hand, is a messaging protocol designed for IoT networks. The use of the MQTT protocol requires a specific Broker service to be run separately as a service within server nodes or centralizers. Where possible, the use of HTTP is preferred to reduce the number of software services active on the server. Modbus is a communication protocol used to transfer information over serial lines between industry-standard electronic devices. ONVIF is a standard dedicated to the management of video streams from IP cameras and is preferable to data access via FTP as it allows individual frames to be requested directly from the device and has low latency. REST handles data calls over http via APIs used to communicate with local or external servers to access the available data. Another advantage is the ability to request traffic from the management platform as needed to reduce network traffic, determine the update frequency for each device and ensure system stability.  
During the design of the infrastructure, an attempt was made to standardize the type of data protocols used as much as possible, preferring hardware from the same manufacturer (where possible) and equipped with open communication libraries already integrated in the main existing DT platforms. For example, Shelly peripherals are equipped internally with a web server to allow direct access to the network without the need to configure a dedicated MQTT broker service. These devices are therefore able to send traffic measurements over a standard connection. The Modbus protocol is used to connect Schneider electricity meters; these devices are wired and physically integrated into the network via Ethernet cable. The weather station works via REST API through the manufacturer's Davis service. Finally, the cameras operate via ONVIF. Each peripheral device has a static IP assigned to it when it connects to the network segment; these IPs are used to connect the specific peripheral device to the management platform and ensure its continuity in the event of a system reboot.  
Schneider's internal power meter management uses an internal radio protocol for wireless communication between the current clamps and the power meter. The system is configurable via proprietary ECO Struxure 4.0. When integrating other devices that use MQTT and do not have an internal web server, the Mosquito Broker is used. This open source message broker implements the above protocol and is widely used for lighting message management in various communication scenarios.