esa-sceva/satcom-chunk-collection huggingface dataset:
# Achieving Sender Anonymity in Tor against the Global Passive Adversary
## 1 Introduction
This is an impractical solution resulting in intolerable communication latency in a large network (such as the Internet). Instead, our approach allows us to modulate the size of the anonymity set to a value that fulfills reasonable anonymity requirements, without introducing intolerable latency times, and importantly, relying on the existing Tor system. The feasibility of our approach was tested through careful experimental analysis conducted by simulations. Therefore, this research involved both theoretical and experimental analysis. To the best of our knowledge, there is no proposal in the literature aimed at equipping the Tor protocol with sender anonymity against a global passive adversary. A proposal with some similarities to our paper (as both papers take inspiration from the original idea of buses [10], as discussed above) is given in [11]. However, their method [11], as clearly stated in the paper, does not provide protection against a global passive adversary, because the observation of the initiator of the ring construction breaks sender anonymity. On the contrary, sender anonymity against a global passive adversary in all the (even preliminary) phases of the communication is the objective achieved by our approach, which purposefully advances the state of the art. Moreover, our paper treats and solves the problem for the case of complete bi-directional communication, i.e., a request from the sender to the recipient and a response from the recipient to the sender. Observe that, in general, this is not trivial when anonymity should be maintained. Indeed, the response cannot be simply implemented as a different forward tunnel directed from the recipient to the sender; otherwise, simple intersection attacks would break anonymity. The method in [11] does not facilitate a response. This makes it applicable only for unidirectional communications, which is a very strong limitation. The structure of the paper is the following. In Section 2, we review the related literature. We provide some basic notions needed to understand the proposed protocol in Section 3. The protocol is presented in Section 4. The introduction of a certain degree of fault tolerance is described in Section 5. The computational complexity of our solution is discussed in Section 6. The security of the protocol is analyzed in Section 7. In Section 8, we report the results of an experimental validation of our approach. Finally, in Section 9, we draw our conclusions.
## 5 Performance Evaluation
### Comparison of BSMH and Other Benchmarks
#### 5.4.2 Average End-to-End Delay  
Figure 14 depicts the average end-to-end delay of all successfully delivered packets. Since LVGR attains the best performance in terms of the average routing path stretch, it also results in the smallest average end-to-end delay. Following LVGR, BSMH with small values of \(\epsilon\) (i.e., \(\epsilon=0.3\) and 0.5) gains the second-best performance. Moreover, the performance gap between LVGR and BSMH is insignificant when \(\epsilon\) is small. For examples, when \(\epsilon=0.3\), the average end-to-end delay resulted by BSMH is smaller than 1.1 times that caused by LVGR in both 1-n and n-n communication. Similar to the routing path stretch, the end-to-end delay caused by BSMH increases with the increase of \(\epsilon\). When \(\epsilon\) reaches 1.2, BSMH may cause an end-to-end delay higher than 1.7 time that of LVGR in the worst cases (i.e., Topo 3, 4 in 1-n communication). In comparison with EDGR, it can be seen that the end-to-end delay caused by BSMH with \(\epsilon=0.3\) and 0.5 are smaller than that of EDGR in all topologies. Specifically, BSMH with \(\epsilon=0.3\) results in an average routing path stretch smaller than 71% and 75% regrading 1-n and n-n communications, respectively. When \(\epsilon\) increases to 0.5, the ratio between the routing path stretch of BSMH to that of EDGR is smaller than 0.78 and 0.81 with respect to 1-n and n-n communications, respectively. The performance of GPSR strongly depends on the network topology. Specifically, its end-to-end delay is extremely higher than those of the other protocols in some cases such as Topologies 1 and 3 despite the fact that its average routing path stretch is not much higher than that of the other protocols. We observe that the end-to-end delay is affected not only by the transmission time between the hops, but also is contributed by the processing time in the hops. While the former one closely related to the routing path routing path stretch, the latter one has a strong relationship with the load balance. Hence, there are two main reasons causing the worse performance of GPSR in terms of end-to-end delay. The first reason is due to the long route path (as described in Section 5.4.1), and the second reason is due to the traffic concentration around the hole boundaries (which has been shown in Section 5.4.6).  
Figure 13: The average routing path stretch of successfully delivered packets [38]. (**a**) 1-n communication.
###### Abstract  
Heart attack is a leading cause of mortality, necessitating timely and precise diagnosis to improve patient outcomes. However, timely diagnosis remains a challenge due to the complex and nonlinear relationships between clinical indicators. Machine learning (ML) and deep learning (DL) models have the potential to predict cardiac conditions by identifying complex patterns within data, but their \"black-box\" nature restricts interpretability, making it challenging for healthcare professionals to comprehend the reasoning behind predictions. This lack of interpretability limits their clinical trust and adoption. The proposed approach addresses this limitation by integrating predictive modeling with Explainable AI (XAI) to ensure both accuracy and transparency in clinical decision-making. The proposed study enhances heart attack prediction using the University of California, Irvine (UCI) dataset, which includes various heart analysis parameters collected through electrocardiogram (ECG) sensors, blood pressure monitors, and biochemical analyzers. Due to class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied to enhance the representation of the minority class. After preprocessing, various ML algorithms were employed, among which Artificial Neural Networks (ANN) achieved the highest performance with 96.1% accuracy, 95.7% recall, and 95.7% F1-score. To enhance the interpretability of ANN, two XAI techniques, specifically SHapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME), were utilized. This study incrementally benchmarks SMOTE, ANN, and XAI techniques such as SHAP and LIME on standardized cardiac datasets, emphasizing clinical interpretability and providing a reproducible framework for practical healthcare implementation. These techniques enable healthcare practitioners to understand the model's decisions, identify key predictive features, and enhance clinical judgment. By bridging the gap between AI-driven performance and practical medical implementation, this work contributes to making heart attack prediction both highly accurate and interpretable, facilitating its adoption in real-world clinical settings.  
machine learning; heart attack prediction; explainable AI (XAI); deep learning; classification algorithm; neural networks +
Footnote †: journal: _arXiv preprint arXiv:1907.03390_  
_Article_
# Magnetic Communication Using High-Sensitivity Magnetic Field Detectors
## 3 Sensitivity Analysis
### Communication Range for Lossless Transmission Mediums
#### 3.2.6 Coil-to-Detector Transmission Range for Different Detection Thresholds  
Now, the influence of the sensor's detection limit, \(B_{\text{min}}\), on the maximum transmission distance is investigated. Figure 7 shows the transmission range for coil-to-detector communication as a function of \(B_{\text{min}}\). The range is strictly monotonically increasing for increasing sensitivity. Therefore, it is recommendable to use the most sensitive sensors that are available and applicable for the communication scenario of interest. This allows much longer transmission distances for the same remaining system parameters. In the figure, the maximum communication distance for the three investigated bandwidths is marked. For \(\Delta f=1.47\) kHz the maximum distance is 2 m, for \(\Delta f=100\) kHz the communication range is 1 m, and if the maximum bandwidth \(\Delta f=999.9\) kHz exploited the range results in 0.65 m.
# Accurate Modeling of Conductor Rough Surfaces in Waveguide Devices
###### Abstract  
To address the effects of surface roughness on wave propagation in the microwave and millimeter-wave bands, this paper studies electromagnetic wave propagation and focuses on the propagation loss within an inner environment featuring surface roughness of the metallic waveguide structures. The conductivity gradient model is first developed to treat surface roughness with inhomogeneous conductivity, and then the concept of a frequency-dependent effective conductivity is introduced to model the effects of surface roughness in waveguide structures. With the effective conductivity used in the commercial 3D field solver, High Frequency Structure Simulator (HFSS\({}^{\text{th}}\)), a surface impedance boundary condition strategy is applied on ideally smooth surfaces to model the actual behavior of rough metallic waveguide surfaces. Finally, the propagation performance is predicted by comparing the effects of surface roughness in metallic waveguide structures and the results from the models with ideally smooth surfaces, using rectangular waveguide, cylindrical resonator and coupling cavity filters models. Overall, this effective conductive concept can be applied using various commercial field solvers to handle surface roughness effects accurately and efficiently in any work involving conducting microwave structures.  
surface roughness; effective conductivity; waveguide structures; conductor loss Article
## 5 Case Study
### Preliminary Analyses: Sensors Spatial Distribution and Orientation
#### 5.1.1 Analysis of the Functions of Spaces  
The analysis of the building through the BIM model allowed the identification of number and type of rooms of the building, as shown in Table 3. The BIM model had been previously defined and modeled, as described in [PERSON] et al. [75], who also performed a complete survey to update the as-built documents and to ensure the correspondence between the BIM model and the building. Equipment rooms, storage closets, and archives were excluded, since the only users are cleaning services employees or technicians in charge of maintenance activities. The analyses highlighted that sensors installed in common spaces, i.e., corridors, would be sufficient to monitor room occupancy, i.e., the count of users entering and leaving rooms. Anonymous real-time agent movements are detected only in corridors and can be visualized in the online platform, but are not stored in the DB, to ensure and protect the privacy of users. On the contrary, as regards rooms, the count of number of users (O) and time of occupancy (T) is recorded, as shown in Table 3. The occupancy of critical rooms is monitored to optimize their use, cleanness, and maintenance, while corridors are considered only as circulation areas. As shown in Table 3, 70 rooms out of 87 were selected as critical areas to be monitored.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Building Level** & **Space Type** & **Quantity** & **Monitored/Not Monitored** \\ \hline \multirow{4}{*}{Underground Level} & Laboratory/Office & 5 & Monitored \\  & Bathroom & 2 & Monitored \\  & Classroom & 1 & Monitored \\  & Equipment Space & 3 & Not monitored \\  & Storage Room & 10 & Not monitored \\ \hline \multirow{4}{*}{Ground Level} & Laboratory/Office & 22 & Monitored \\  & Bathroom & 3 & Monitored \\  & Meeting Room & 1 & Monitored \\  & Equipment Space & 1 & Not monitored \\ \hline \multirow{4}{*}{First Level} & Laboratory/Office & 21 & Monitored \\  & Bathroom & 3 & Monitored \\  & Storage Room & 1 & Not monitored \\  & Terrace & 1 & Not monitored \\ \hline \multirow{4}{*}{Second Level} & Laboratory/Office & 10 & Monitored \\  & Bathroom & 1 & Monitored \\ \cline{1-1}  & Meeting Room & 1 & Monitored \\ \cline{1-1}  & Equipment Space & 1 & Not monitored \\ \hline \hline \end{tabular}
\end{table}
Table 3: Type and quantity of rooms and necessity to be monitored.
# A Survey on Adversarial Perturbations and Attacks on CAPTCHAs
## 3 Deep Learning Attacks against CAPTCHAs
### Deep Learning Attack
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Study** & **Year** & **Approach** & **Accuracy \({}^{*}\)** \\ \hline
[19] & 2014 & ML & 51.09\% \\ \hline
[10] & 2016 & Leveraging DL technologies for the semantic annotation & 83.5\% \\ \hline
[11] & 2017 & Recursive cortical network (RCN) & 94.3\% \\ \hline
[22] & 2017 & Simple image processing techniques for the segmentation and recognation & 53\% \\ \hline
[23] & 2017 & A neural network trained using synthetic data & 90\% \\ \hline
[8] & 2018 & A GAN & 96\% \\ \hline
[24] & 2018 & A generic attack based on DL techniques & 90\% \\ \hline
[27] & 2018 & Conditional deep convolutional generative adversarial networks and CNN & 98.4\% \\ \hline
[26] & 2019 & Pre-processing, segmentation and recognition & 96.8\% \\ \hline
[25] & 2019 & Utilising a CNN and an attention-based RNN & 97.3\% \\ \hline
[28] & 2020 & Unsupervised learning and representation learning & 94.5\% \\ \hline
[21] & 2020 & Generative adversarial network (GAN) & 92.08\% \\ \hline
[29] & 2020 & Using an automated DL based solution & 98.94\% \\ \hline
[30] & 2020 & CNN and LSTM & 85.97\% \\ \hline
[31] & 2020 & An efficient CNN model that uses attached binary images to recognize CAPTCHAs & 92.68\% \\ \hline
[12] & 2020 & Incremental learning & 87.37\% \\ \hline
[32] & 2020 & A transfer learning-based approach & 96.9\% \\ \hline
[34] & 2021 & Combining a brute-force attack with transfer learning & 80\% \\ \hline
[33] & 2021 & Filtering and enhancing the accuracy attack rate & 8.31\% \\ \hline
[9] & 2021 & Cycle-consistent generative adversarial networks & 97\% \\ \hline
[35] & 2021 & A CAPTCHA transformation model based on GAN & 96\% \\ \hline
[37] & 2022 & The adversarial training strategy & 87\% \\ \hline
[38] & 2022 & CNN and RNN-based automatic speech recognition systems & 49.76\% \\ \hline \hline \end{tabular} \({}^{*}\) If the study contains multiple experiments, the highest accuracy was used.  
\end{table} 
Table 1: Summarizing recent automatic CAPTCHA attack studies armed with different DL techniques.
## 2 Basic Concept
### Fundamental Principles of the Utilized Cryptographic Framework  
Elliptic curve cryptography, abbreviated as ECC in the world of cryptography, is a public key cryptographic system. This encryption system is designed by using algebraic structural features and implementing elliptic curves on finite fields. Among the features of this encryption system, we can point out the short key length and provide higher security, which is used in key-exchange protocols, digital signature mechanisms, and pseudo-random generators. In the following, the ECC process and theories related to this encryption system are described.
## 4 System Performance Analysis
### Analysis of Signal Transmission Rate  
Signal transmission rate is used to measure the amount of data transmitted per unit time. The transmission rate is usually expressed by baud rate and bit rate. The numerical relationship between the two is: C = B-log\({}_{2}\)N, where C is the bit rate, B is the baud rate and N is the base. There are two ways to increase the bit rate from the equation. One way is to increase the baud rate, which can theoretically be up to one-tenth of the carrier frequency. For wireless power transmission systems, the baud rate is limited by the response time of the system itself. Passive devices such as inductors and capacitors can affect the response time of the system. Theoretically, the response speed can only be improved by changing the system device or structure.  
The variation law of resonant circuit voltage amplitude with time is:  
\[U_{\text{P}}(t)=U_{\text{pm}}\cdot e^{-\frac{t}{t}}. \tag{8}\]  
\(U_{\text{pm}}\) is the inverter output voltage peak. In Figure 1a, \(\tau\) can be viewed as the \(L_{\text{d}}\) discharge time constant in conventional amplitude modulation  
\[\tau=\frac{R_{\text{P}}C_{\text{p}}L_{\text{d}}}{L_{\text{p}}}. \tag{9}\]  
The whole signal '0' modulation process \(t\), voltage change is:  
\[U_{\text{P}}(t)=U_{\text{pm}}\bigg{(}1+e^{-\frac{t}{t}}-e^{-\frac{t-T_{\text{ off}}}{t}}\bigg{)}. \tag{10}\]  
\(T_{\text{off}}\) is the voltage source disconnection time. It can be seen that the existence of \(L_{\text{d}}\) affects the time constant \(\tau\), and the DC-DC modulation circuit increases the system response time. The MASK modulation simultaneous transmission method based on PSFB improves the system  
Figure 7: Receiving end voltage diagram of power and signal synchronous transmission system.  
response speed because there is no DC-DC modulation circuit. It controls the voltage amplitude variation at 5% and reduces the amplitude variation, which can reduce the required response time and improve the baud rate of the system itself. As shown in Figure 8, respectively, the baud rate is 750, 1500 and 1875 for data transmission. The results show that when the baud rate is 1875, the rising and falling edges of the signal coincide. After measurement, the response time of the signal is 280 us. The overall response time of the rise and fall has exceeded the signal time period of 533 us, which is beyond the limit. The final limit baud rate of the experimental system is about 1500, and the experimental parameters are shown in Table 1.  
Another way to increase the transmission rate is to change the transmission system \(N\). By increasing the transmission base, the signal is no longer limited to '0' and '1', increasing the bit width, thereby increasing the signal transmission rate. As shown in Figure 7, using 8 ASK signal modulation, compared with the traditional signal transmission method, the signal transmission rate increased to three times.
## 3 Results and Discussion
### Leaf Area and Yield Components  
As a direct consequence of the removal of the upper part of the canopy, the primary, lateral and total leaf area of the vine was reduced in both years via severe shoot trimming (SST) as compared to the high canopy (HC) treatment, whereas cluster thinning (CT) did not have any impact on vine leaf area (Table 1). The photosynthetically active radiation (PAR) in the fruit zone was, in both years, higher in SST vines because more sunlight reached the clusters from the upper part of the canopy; this finding is also corroborated in some other studies with late severe shoot trimming [30; 32; 37]. Yield per vine, clusters per vine and clusters per shoot were affected only with CT treatment as a direct consequence  
Figure 1: Average air temperature and monthly rainfall in 2017 and 2018 growing seasons.  
of the removal of clusters, while late source limitation had no effect on these variables, as observed in other studies where severe shoot trimming was applied after the onset veraison [9; 28; 37]. Berry weight was higher in CT in both years and was reduced via SST in 2017, presumably because of the differences in assimilates' availability among treatments [11] as the source/sink (leaf area/yield) ratio was significantly increased by CT and decreased via SST in both years. The absence of difference in berry weight between HC and SST in 2018 was possibly due to more favorable conditions for berry ripening in this season because of higher water availability during the summer months.
# A Survey of Machine Learning Models in Renewable Energy Predictions
## 3 Measurements of Forecasting Performance  
Measurements of forecasting accuracy are investigated in this section. In total, 41 types of measurements of forecasting accuracy were gathered in this study. Table 6 lists measurements of forecasting accuracy used by more than 10 studies. Mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) are three measurements most frequently used. Renewable energy can be represented in diversified units, and values of renewable energy fluctuate a lot for different studies. To avoid influences of units and values of renewable energy, MAPE is specified here to depict forecasting accuracy. In Table 7, a total of 39 collected studies used MAPE to measure forecasting performances. Generally, many models were introduced in each study. Thus, Table 7 shows the best performance result in each study. According to [PERSON] [149], MAPE values less than 10 percent are highly accurate predictions. Thus, most forecasting accuracies of collected studies are high in terms of MAPE. In addition, the coefficient of determination (R\({}^{2}\)) is another measurement specified in this study for analysis. The coefficient of determination represents the proportion of the variance in the dependent variable that is explainable by the independent variables. Table 8 illustrates 23 collected studies employing R\({}^{2}\) as a measurement in forecasting renewable energy. It can be observed that most values of R\({}^{2}\) are larger than 0.8. Besides, eight articles used R\({}^{2}\) and MAPE simultaneously as forecasting performance measurements. Table 9 lists values of R\({}^{2}\) and MAPE, and a correlation coefficient of \(-0.7869\) was calculated from the numerical data. Thus, this study revealed that machine-learning models with higher R\({}^{2}\) values result in more accurate renewable-energy prediction in terms of MAPE. When employing machine-learning models in forecasting renewable energy, the coefficient of determination could be calculated before further steps of modeling are performed.  
Figure 2: Metaheuristics for machine-learning parameter selection in renewable-energy predictions.