esa-sceva/satcom-chunk-collection huggingface dataset:
# QuietPlace: An Ultrasound-Based Proof of Location Protocol with Strong Identities
## 2 Characteristics of PoL Protocols  
PoL protocols have different characteristics depending on their purpose and design. Having many of the characteristics defined below does not necessarily make a protocol more secure or efficient. We describe below the main characteristics of PoL protocols, as these occur in the relevant literature. The specific characteristics have been the basis for the definition and direction of the current research effort as well.  
* Proof creation architecture: Witnesses can either belong to users or be controlled by a centralized authority. In the first case, we have a peer-to-peer architecture and in the second a centralized one. The above approaches can be combined, resulting in a hybrid architecture.
* Proof verification architecture: The verification process between the prover and the verifier can be executed directly between the two parties or indirectly with the involvement of other entities.
* Use of a trusted third-party/certificate authority: Many proof of location protocols include a certificate authority (CA) in their system model. Depending on its duties, the CA may pose a single point of failure.
* Physical layer: Communication between prover and witness is achieved using a physical layer such as Wi-Fi [1], Bluetooth [5], ZigBee [18], or even low-power wide-area networks like LoRa [3].
* Geocoding: The way location is represented. Examples include geographical coordinates, a geocoding system such as Plus codes [19], or arbitrary values (country, city, etc.).
* Geolocation: A proof of location protocol may provide a system for calculating the position of the nodes involved or, more commonly, just verify the location provided by other sources such as GPS.
* Proximity test: A procedure between prover and witness that certifies that they are close to each other.
* Incentive to participate: An incentive such as a cryptocurrency reward can be given to the users to continue providing resources (i.e., install witness nodes) to the system.
* Location granularity levels: The prover can choose the granularity in which their location is known to the verifier. For example, the prover can choose to reveal their exact coordinates or just provide the name of the city or country where their location belongs to.
* Verifier-agnostic proofs: The PoL produced during a protocol session can be verified by different verifiers. This characteristic is necessary to support proactive proofs of location, which are proofs that are produced by the prover without being asked to do so by a specific verifier. In this case, the prover does not know from the beginning to whom the specific proof should be addressed.
* Proof of location chains: The prover can create chains of proofs that are either tamper-proof or tamper-evident. By using chains, the verifier can track the location history of a prover. Different schemes have been proposed [2; 9; 20; 21] and some of them allow the prover to hide information from a proof in the chain without being able to hide the existence of a proof completely.
# A Quantitative Study of Axial Performance of Rockbolts with an Elastic-Debonding Model
## 4 Parameter Study and Discussion  
After the ED model was successfully validated, a quantitative parameter study was conducted to analyse the axial performance of rockbolts. The following tensile test scenario was created. A rockbolt with a diameter of 16 mm was installed in a borehole whose diameter was 22 mm. The rock diameter was 1 m. The installed length was 2 m. Bolts had a modulus of elasticity of 220 GPa, and rock had a modulus of elasticity of 30 GPa. For the b/g face, the bond coefficient was 0.1 m/GPa. The shear strength, slippage at the ultimate load, and residual strength were 3 MPa, 1 mm, and 2 MPa. These parameters are tabulated in Tables 1 and 2. Attention is paid that in this section, the parameters were not set based on a specific experimental test scenario.
# Non-Orthogonal eMBB-URLLC Radio Access for Cloud Radio Access Networks with Analog Fronthauling
## 5 Non-Orthogonal Multiple Access (NOMA)
### eMBB Rate by Successive Interference Cancellation
_where \(\mathbf{H}_{AE}=((\mathbf{I}-\mathbf{A}\mathbf{E})\mathbf{H})\otimes\mathbf{H}_{c}^{\eta}\) is the equivalent wireless plus cable channel in case of SIC; \(\mathbf{E}=\text{diag}(E_{1},E_{2},...,E_{M})\) is a diagonal matrix whose \(k\)-th entry \(E_{k}\sim\mathcal{B}(qe_{U}^{D})\) accounts for the probability that the URLLC signal is not successfully decoded at \(EN_{k}\); and \(\mathbf{R}_{AE,z_{\text{eq}}}=\mathbf{R}_{z_{\text{eq}}}+\rho^{2}\beta^{2}P_{U }\left(\left(\mathbf{A}(\mathbf{I}-\mathbf{E})\right)\otimes\mathbf{H}_{c}^{ \eta}\mathbf{H}_{c}^{\eta}\right)\) is the overall noise plus residual URLLC interference._  
Proof. : Lemma 5 can be proved by following similar steps as for the proofs of the previous Lemmas. SIC describes a more complex ENs architecture in which the URLLC signals, if successfully decoded, are successively canceled from the eMBB signals at the EN. However, in the case of imperfect interference cancellation, i.e., \(\rho>0\), the eMBB signal is still impaired by some residual URLLC interference, which is accounted for by the overall noise covariance \(\mathbf{R}_{AE,z_{\text{eq}}}\) in (42), similarly to TIN. The URLLC arrival probability and the probability of successful decoding of the URLLC packets are reflected by random matrices \(\mathbf{A}\) and \(\mathbf{E}\), respectively.
## 4 Case Study and Applications
### A Classification Problem: Binary Prediction of Smoker Status Using Bio-Signals
#### 4.3.3 Relative Risk Calculation  
Risk ratio (RR) is a statistical measure used to assess the relationship between the probability of a particular outcome in a group exposed to an event compared with a group not exposed. It is given by the following equation:  
\[RR = \frac{\textit{Incidence in the of exposed Group}}{\textit{Incidence in the unexposed Group}} \tag{4}\]  
The results of the RR calculation are to be interpreted in a manner analogous to that employed for the OR calculation. In the event that the RR is equal to 1, it can be concluded that there is no association between exposure and outcome. A value of RR greater than 1 indicates a positive association, suggesting that exposure is associated with an increased risk of the outcome. A value of RR less than 1 indicates a negative association between exposure and outcome.  
The principal findings in Table 8 with regard to the RR values, are corroborated by those in Table 7 and can be summarised as follows: (1) As with the previous analysis, there is an association between smoking and higher body weight. (2) Furthermore, there is an increase in blood glucose levels, although less pronounced than that observed in the odds ratio calculation. (3) As in the previous table, there is a significant increase in triglycerides and haemoglobin and a large decrease in HDL. (4) There is also an increase in serum creatinine. (5) There is also a significant increase in alanine aminotransferase (ALT) and a large increase in guanosine triphosphate (GTP).  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Variable** & **Odds Ratio** \\ \hline weight(kg\_Low & 0.20961654947072159 \\ weight(kg\_Medium\_Low & 1.218644191656455 \\ weight(kg\_Medium\_High & 2.097852237464934 \\ weight(kg\_High & 2.8299577733161576 \\... & \\ fasting blood sugar\_Low & 0.6308223587839904 \\ fasting blood sugar\_Medium\_Low & 0.9541383540661215 \\ fasting blood sugar\_Medium\_High & 1.173636730372843 \\ fasting blood sugar\_High & 1.4547915548094907 \\... & \\ triglyceride\_Low & 0.23407715488962197 \\ triglyceride\_Medium\_Low & 0.7200827234227996 \\ triglyceride\_Medium\_High & 1.63706272479650663 \\ triglyceride\_High & 3.716787070580836 \\ HDL\_Low & 2.365531709348869 \\ HDL\_Medium\_Low & 1.4317551296401139 \\ HDL\_Medium\_High & 0.7715794823329627 \\ HDL\_High & 0.3282304510981655 \\ LDL\_Low & 1.1629428483574764 \\ LDL\_Medium\_Low & 1.1890457266489936 \\ LDL\_Medium\_High & 1.0438026307432897 \\ LDL\_High & 0.6844025582281992 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Odds ratio for categorical variables.
# Characterizing the Impact of Data-Damaged Models on Generalization Strength in Intrusion Detection
## 5 Results of Attack-Specific Two-Class Models
### Internal Retest  
This section describes the results of making the pretrained models reclassify the samples of the attack class on which they were trained. This is included to test whether the newly trained models do not suffer from a regression in their performance. The classification performance should mirror the results described in this earlier work [11]. The fresh models tested less points of vertical data reduction over a larger range (13 points between 0.1 and 50% of data used for training, versus 35 points between 0.1 and 33%). No other variables were altered in the training methodology.  
After comparing the original classification results to the new set of results, no performance regressions were found in the class-specific models. A short reiteration of the results is in order to have a baseline for comparison. CIC-IDS2017 has three classes of attack traffic that were universally well recognized by the tested algorithms.  
Models trained on the DDoS, HTTP-DoS and port-scanning traffic subsets are insensitive to reduction in training volume and removal of discriminative features. Put another way: increasing the learning difficulty by scaling back the amount of data for training on while also removing the best features from the data, did not hurt the models' classification scores much or at all. It logically follows that these models are expected to perform well on the samples of these classes from CSE-CIC-IDS2018.  
Models trained to recognize FTP/SSH brute force attacks, web attack and botnet traffic are extremely well recognized by tree-based methods, but algorithms from other families have mixed results. Recall tends to stay high, but precision is lost. For all learners, these classes were more sensitive to data reduction, with horizontal data reduction having the biggest negative impact.  
The final class, infiltration, is problematic because the subset in CIC-IDS2017 contains a mere 36 positive samples out of a total of 288,602. Results on the CSE-CIC-IDS2018 of these models will be reported, but are unlikely to be good.
## 2 Related Works
### Crowd Counting  
Crowd counting is a crucial application, particularly for smart cities, with significant challenges in the domains of computer vision and deep learning. The development of a  
Figure 2: Three different methods of tensor network decomposition of a deep neural network.  
comprehensive computational model capable of analyzing and monitoring high-density crowds is a primary objective for many smart urban environments. Building these models is important, especially in high-risk environments such as stadiums, spiritual gatherings, and music concerts, where preventing crowd crushing and managing blockages are paramount concerns. Furthermore, accurate analysis of crowd density and movement patterns contributes to enhanced security services and facilitates the development of improved logistics and infrastructure for efficient crowd flow management.  
Crowd counting is a challenging task, and its challenges come from different sources, such as background noise, the non-uniform location of people, blurred images, and distorted and affected images [61]. The deep learning literature has introduced numerous deep neural network models and various types of datasets to address this problem. Crowd images often come from the surveillance camera video feeds, and most analysis is conducted in the cloud instead of within the surveillance camera itself.  
In this paper, we use crowd counting as a case study for our method. Specifically, we employ the following two models:  
1. MCNN (Multi-Column Convolutional Neural Network) [62]: This model captures different receptive fields using multiple-column convolutional layers with different kernels and fuses them to generate a density map.
2. CSRNet [63]: This model uses convolutional neural networks (CNNs) as the front end and dilated CNNs for the back end. The use of dilated CNNs allows the model to retain spatial information effectively.  
We evaluate these models across four different datasets, namely ShanghaiTech B, UCF_CC_50, WorldEXPO'10, and UCF-QNRF [63]. Our analysis compares the performance of our compressed models to that of their floating-point counterparts using various metrics, including mean absolute error (MAE), root mean square error (RMSE), and storage costs.  
In this paper, we apply our method to MCNN and CSRNet and compare them to the floating-point versions of MCNN and CSRNet using different metrics, such as mean absolute error (MAE), root mean square error (RMSE), and storage costs.
# Cascaded Dual-Inpainting Network for Scene Text
## 4 Experiments and Discussion
### Experiments on Real-World Scene Text Images  
To validate the effectiveness of the proposed method in practical scenarios, experiments were performed on real-world scene text images in two ways:  
Controlled degradation: A subset of TII-ST real images [30] (originally collected from the ICDAR2015 benchmark) was randomly selected and subjected to structural degradation patterns. Figure 3 shows representative inpainting results generated by the proposed method, along with corresponding recognition results.  
Wild degradation: The proposed method was further evaluated on naturally degraded scene text images obtained from open-source web repositories. The reconstruction and recognition results for these challenging scenarios are demonstrated in Figure 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Parameter Count** & **GFLOPs** & **Inference Time** \\ \hline SGM & 1.85 M & 15.06 GFlops & 0.001 s \\ SRM & 15.14 M & 16.25 GFlops & 0.004 s \\ \hline \hline \end{tabular}
\end{table}
Table 6: Computational performance of models used in the proposed method.  
Figure 3: Qualitative results of scene text inpainting and recognition on the ICDAR 2015 dataset. (**a**) Corrupted images; (**b**) ground truth text; (**c**) reconstruction results achieved using CDINST; (**d**) CRNN-based recognition results achieved using CDINST.  
These experiments demonstrate that the proposed method can effectively reconstruct incomplete scene text across diverse real-world scenarios while maintaining compatibility with mainstream text recognition architectures.
## 2 Materials and Methods
### Pose Estimation
The difference in our approach is that we additionally incorporate the principal direction error \(\sum_{i}^{n}\left\|\mathbf{e}^{\prime}_{ij}\right\|_{\Sigma}\) into the error equation. We also apply constraints to the error boundary to mitigate the influence of outliers. The final pose estimation is obtained by minimizing the reprojection error and the principal direction error. After incorporating the principal direction projection error, although both error components share the same unit of pixel distance, it is inevitable that optimizing one objective function may deteriorate the other. In practice, it is necessary to identify the Pareto optimal solution. The specific description is as follows. Assume  
\[f_{1}(\mathbf{\upxi})=\sum_{i}^{n}\left\|\mathbf{e}_{ij}\right\|_{\Sigma} \tag{9}\]  
\[f_{2}(\mathbf{\upxi})=\sum_{i}^{n}\left\|\mathbf{e}^{\prime}_{ij}\right\|_{\Sigma} \tag{10}\]  
If there exists a solution \(\mathbf{\upxi_{1}}\) that satisfies the following formula:  
\[\left\{ \begin{aligned} & f_{1}(\mathbf{\upxi_{1}})\leq f_{1}(\mathbf{ \upxi_{2}})\\ & f_{2}(\mathbf{\upxi_{1}})\leq f_{2}(\mathbf{\upxi_{2}})\\ &(f_{1}(\mathbf{\upxi_{1}})<f_{1}(\mathbf{\upxi_{2}}))\vee(f_{2}( \mathbf{\upxi_{1}})<f_{2}(\mathbf{\upxi_{2}}))\end{aligned} \right. \tag{11}\]  
where \(\vee\) denotes a logical OR. Then, \(\mathbf{\upxi_{1}}\) is considered to dominate the solution \(\mathbf{\upxi_{2}}\). Solutions not dominated by others are referred to as solutions on the Pareto Frontier. The pose-estimation process is executed multiple times, and after each execution, we obtain an estimation solution and remove suspicious outlier feature points. Indeed, some of these points might be accurate but classified as outliers due to estimation errors. This is one of the reasons for employing the Pareto approach. All solutions on the front form a solution set, from which the Pareto optimal solution is selected. We choose the solution on the Frontier that minimizes \(f_{1}(\mathbf{\upxi})\) as the Pareto optimal solution. The principal direction projection is shown in Figure 4. We first calculate the principal direction of the feature points, then project the feature points onto the principal direction, and finally use them in pose estimation along with the reprojection error. Figure 4: The illustration of principal direction feature projection. The purple rectangles represent feature points, while the pink lines indicate the principal directions of the feature points.
## 3 Algorithm Flow
### Polarization Image and Spectral Decoupling and Spectral Reconstruction
#### 3.1.2 Polarization Interference Image Registration and Polarization Spectral Reconstruction
The gradient histogram of 16 sub-regions is constructed in 8 directions, and the 128-dimensional SIFT descriptor can be obtained. Then, the SIFT feature matching is completed. Finally, the outlier points are removed using the Random Sample Consensus (RANSAC) algorithm, to complete the image fine matching. In order to make sure the obtained feature points are more accurate and more representative and the best are selected, the intersection of the feature point sets obtained through SUSAN corner detection and SIFT feature matching are taken as the final feature point set. Then, image registration is carried out according to the position of feature points in image units to obtain the cube set of polarization interference intensity data, \(I_{\theta}[x,y,\Delta(m,n)]\), and then the polarization interference intensity sequence of feature points is obtained. After addressing baseline correcting and apodization by using the Gaussian function, a discrete Fourier transform is carried out to obtain the polarization spectrum cube \(B_{\theta}(x,y,\
u)\). The reconstructed polarization spectrum can be obtained by Equation (4).
## 3 Results and Discussion
### Fast Cooling Behavior of P3 HBHHx and Nucleated Samples  
Figure 21 shows the DSC thermograms of the P3 HBHHx samples with different nucleating agents during fast cooling (100 \({}^{\circ}\)C/min) and subsequent reheating (10 \({}^{\circ}\)C/min). This experiment was designed to evaluate the influence of rapid cooling on the crystallization behavior of P3 HBHHx by comparing the crystallization temperatures and enthalpies of the samples.  
During the fast-cooling step, only the sample containing boron nitride (PHH_05 BN) exhibited a crystallization exotherm, with a peak at 57.4 \({}^{\circ}\)C and an associated enthalpy of 16.8 J/g. This result confirms that BN substantially enhances nucleation efficiency, even at high cooling rates. However, the relatively low enthalpy suggests that while nucleation is initiated, crystal growth is limited by the insufficient time available during rapid cooling, resulting in partial crystallization.  
In contrast, neat P3 HBHHx, PHH_05 PHB, and PHH_2 Talc did not exhibit any exothermic peaks during cooling, indicating that crystallization was not triggered under these conditions. This suggests that while PHB and talc are effective at promoting crystallization under moderate cooling rates, they are not sufficiently active to induce nucleation during fast cooling at 100 \({}^{\circ}\)C/min.  
Figure 20: Simulated dependence of the growth rate on the crystallization temperature of (**a**) P3 HBHHx, (**b**) PHH_05 BN, (**c**) PHH_05 PHB, and (**d**) PHH_2 Talc.  
Upon reheating (Figure 21b), all the samples exhibited a cold crystallization peak, confirming that most of the crystallization occurred during this second step. The cold crystallization temperatures (\(T_{\rm cc}\)) ranged from 42.6 \({}^{\circ}\)C to 44.4 \({}^{\circ}\)C, with PHH_05 BN showing the lowest \(T_{\rm cc}\). The cold crystallization enthalpy (\(\Delta H_{\rm cc}\)) ranged from 22.7 to 37.6 J/g, reflecting the extent of crystallization that occurred during reheating. Surprisingly, the total cold crystallization enthalpy was approximately 20 J/g lower than the melting enthalpy (approximately 53 J/g for all samples; see the dashed zone in Figure 21b). This discrepancy suggests that full crystallization was only achieved during the high-temperature ramp (see the dashed line in Figure 21), likely due to kinetic limitations, restricted chain mobility, or the inability of certain amorphous domains to reorganize into crystalline structures.  
Despite the differences in crystallization behavior, all samples, including the neat polymer, exhibited a melting peak around 150 \({}^{\circ}\)C with similar melting enthalpies (\(\sim\)53 J/g). This consistent melting temperature confirms that the addition of nucleating agents does not alter the fundamental crystalline structure of P3 HBHHz, reinforcing that their primary role is to influence the crystallization kinetics.
## 5 Simulation Results and Discussion
### Model Adaptation Logic and Window Clustering Behaviour  
Figure 3 illustrates the decision step in the DE-ACSW-R model when an abrupt AoA trajectory jump is detected within the sliding prediction window. The plot shows the AoA values inside the prediction window at time t = 5, along with LR fits for the entire window and the dynamically clustered window after a trajectory jump is detected.  
* Window AoA: The blue points and line indicate the sequence of recent AoA measurements (window history), which the model uses for prediction.
* Full LR fit and prediction: The blue dashed line is the Linear Regression fit across the full window. The corresponding blue dot at index 6 is the predicted AoA for the next time slot, assuming no change in trajectory.
* Jump detection and clustering: At window index 4, the model detects a sudden, significant change in AoA identified as a trajectory jump. The DE-ACSW-R logic immediately re-clusters the window, discarding older points that do not reflect the new direction.
* Clustered LR fit and prediction: The red dashed-dotted line represents the LR fit applied only to the most recent cluster of AoA points after the jump. The red square at index 6 is the resulting predicted AoA for the next slot, based solely on the recent post-jump samples. The key model behaviour may be described as follows:
* When the trajectory is smooth, the model uses the full window to fit and predict the next AoA, leveraging maximum history for noise averaging.
* Upon detecting a jump, the window is clustered so that only the recent (relevant) points influence the LR fit and prediction. This avoids \"lag\" and prevents stale trajectory data from biassing the result.  
Figure 3: Illustration of adaptive window clustering and prediction logic in DE-ACSW-R.  
* The clustered LR fit and its prediction are visibly better aligned with the new AoA trend than the full-window fit, demonstrating the model's ability to adapt in real-time.
## 2 Mathematical Formulation  
This section introduces the mathematical model that represents the problem regarding the sizing and placement of D-STATCOMs in meshed or radial power distribution systems. As this problem contains discrete variables (i.e., the location of D-STATCOMs at specific nodes) and continuous variables (i.e., the size of the D-STATCOMs), it can be expressed with a mixed-integer nonlinear programming (MINLP) model [38]. Additionally, the sum of the costs of energy losses during a whole year of operation and the annualized investment costs associated with installing the D-STATCOMs are considered the objective function. The investigated problem is shown below.