esa-sceva/satcom-chunk-collection huggingface dataset:
# TOAR: Toward Resisting AS-Level Adversary Correlation Attacks Optimal Anonymous Routing
## 8 Conclusions  
In this work, we propose TOAR, an optimal anonymous routing mechanism that incorporates SDN, designed to resist correlation attacks from AS-level adversaries in anonymous communication using Tor. TOAR addresses the challenges of reduced anonymity and increased communication delays caused by AS-level attackers. It employs a two-stage optimization strategy. In the first stage, it identifies routes that adhere to policy guidelines while avoiding AS that could link traffic between the source and destination, thus ensuring anonymity and minimizing communication costs. The second stage includes a programmable interface for SDN inter-domain routing, allowing users to query routing policy information and validate their routing selections. This feature supports flexible end-to-end source routing and extends the SDN anonymity mechanism across multiple domains. Anonymity analysis and experimental results reveal that the proposed TOAR provides strong anonymity without significantly degrading communication performance.  
Our security policy targets specific objectives without general comparison or discussion. Although we propose a software-defined source routing method, suggesting it could enable source routing over BGP with added programmable interfaces (e.g., route query and confirmation), the route discovery process is only simulated in code, and no dynamic routing protocol such as BGP has been deployed in an SDN test environment.  
In future work, based on BGP speakers with extended programmable interfaces and segment routing, we will deploy anonymous routing across networks with multiple SDN controllers in an SDN test environment, which will allow us to discuss the scalability and efficiency of SDN AS-level routing. Additionally, we are considering a generic framework for anonymous routing, with some issues such as a consistency attack model, consistent anonymity objectives, and unified model abstraction methods as our future research direction. Furthermore, we consider a generic framework for anonymous routing, with some issues such as a consistency attack model, consistent anonymity objectives, and unified model abstraction methods as our future research direction.  
Conceptualization, H.Z.; methodology, H.Z.; software, H.Z.; validation, H.Z. and X.S.; formal analysis, H.Z. and X.S.; investigation, X.S.; resources, X.S.; data curation, H.Z.; writing--original draft preparation, H.Z.; writing--review and editing, H.Z. and X.S.; visualization, H.Z.; supervision, X.S.; project administration, X.S.; funding acquisition, X.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The datasets presented in this article are not readily available due to privacy, as the data are part of an ongoing study.  
We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work and that there is no professional or other personal interest of any nature or kind in any product, service, and/or company.
# Analysis of Recent Deep Learning Techniques for Arabic Handwritten-Text OCR and Post-OCR Correction
## 2 OCR Steps [22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36]  
Generally, the major steps in OCR are as follows, as depicted in Figure 1.  
Image acquisition: Obtain the photograph that is captured from an exterior origin through a tool such as a camera or a scanner;  
Pre-processing: Techniques that are applied to the acquired images in order to enhance their quality. For example, removing the noise or the skew, thinning, and binarization, such as in Figure 2. In addition, sometimes, lossy filtering is performed as a preprocessing step to create a graph representation of characters by discarding non-isomorphic pairs based on simple graph properties such as the number of edges and degree distribution before moving to fine-grained filtering using deep neural networks;  
Segmentation: The splitting of the image into the characters, words, or lines constituting it. Basic techniques include component analysis and projection profiles. Moreover, histograms can be utilized to study segmentation. An example is shown in Figure 3. In addition, thinning and segmentation are performed after making the character rotation invariant;  
Figure 1: Key OCR steps.  
4. Feature extraction: The extraction of features that help in learning the representation. The features can be geometrical, such as points and loops, or statistical, such as moments. The most closely related features that are representative and not redundant should be chosen after the extraction;
5. Classification: Used to predict the mapping between the character and its class. Different classification techniques can be used and, recently, deep-learning-based techniques have used models that are based on deep neural networks;
6. Post-processing: Further corrections of the output of the OCR to reduce the number of errors. Different techniques can be used for post-processing. Some are based on devising a dictionary to check spellings; others are based on utilizing probabilistic models, such as Markov chains and N-grams. With the advancement in natural language processing, in the case of word misclassification, using dictionaries to identify alternatives can provide multiple options. To determine the word that is most likely to be accurate, the context at the sentence level of the text needs to be explored, which requires the techniques of NLP and semantic tagging. Currently, text-embedding models and techniques, such as BERT, and their variations can be utilized for this step.
## 3 Case Study: Entire Frame
### Impact of Masking and a Network Gap  
Here, we investigate the impact of masking during Step 0-4. Without masking the northwestern area at Step 0-4, many unwrapping errors remain in the interferograms, and 47 interferograms are excluded during the loop closure at Step 1-2, generating a gap in the network between 18 and 30 June, 2017 (Figure S7). This means the masking has improved the network refinement and avoided the gap at the cost of a much-reduced extent of spatial coverage.  
We compare the time series and velocities derived from the network, with and without the gap (i.e., with and without masking at Step 0-4). The time series at 950179 with the gap is consistent with the no-gap time series and the GNSS data, but slightly (\(\sim\)2 mm) biased across the gap (Figure 6c  
Figure 6: (**a**) Line-of-sight (LOS) velocity of InSAR (background colors) and GNSS (circles). Positive values mean displacement toward the satellite (i.e., uplift and/or eastward displacement, in this case). A black square denotes the reference GNSS point (950217). (**b**) Difference of the LOS velocity between InSAR and GNSS (colors in circles) and the estimated best-fit quadratic polynomial ramp. Note that the color scale is different from Figure 6a. (**c**) Displacement time series at 950179 (150 km away from the reference). The same ones for all the GNSS stations, and after removing the constant velocity (35.1 mm/yr) at 950179, are shown in Figures S5 and S6, respectively. (**d**) STD of the difference of the time series between InSAR and GNSS as a function of the distance from the reference point.  
and Figure S6). This bias is caused by the interpolation associated with SB inversion linear temporal constraint (see Section 2.4.3). In this case, the bias is very small, because the overall trend is almost linear (i.e., constant velocity). However, if the actual displacement is highly nonlinear, the bias would become large and need to be carefully considered in the interpretation of the derived time series. The impact differs from point to point (Figure 6d and Figure S5).  
The overall velocity, with and without the network gap, is also broadly similar, although significant differences up to \(\sim\)5 mm/yr exist in some areas (Figure S8). The STD of the difference of the LOS velocity between InSAR with the gap and GNSS is 2.9 mm/yr, which is significantly larger than that without the gap (1.9 mm/yr). This is because the time length of the connected network, which has an impact on the precision of the velocity, is shortened from 4.6 years to 2.6 years by the gap (see Section 3.5).
# Detecting Banana Plantations in the Wet Tropics, Australia, Using Aerial Photography and U-Net
## 1 Introduction
### Deep-Learning Classifications  
Neural networks have been around for many decades (see review by [PERSON] [22]). However, only since the recent advancements in GPU technology have they been able to be trained with large amounts of data in a reasonable amount of time [23]. Neural networks simulate the processes of the human brain--interconnected neurons which process incoming information [24]. The solution is obtained by nonalgorithmic and unstructured methods, and by the adjustment of weights connecting the neurons in the network [25]. They can adaptively simulate complex and non-linear patterns [24; 26] such as those found in high-resolution aerial photography.  
Deep learning methods are based on neural networks [22]. These networks consist of many layers, which can transform images into categories through learning of high-level features [27]. ConvolutionalNeural Networks (CNN) are situated at the fringe between machine learning and computer vision, combining the power of deep learning with contextual image analysis. CNNs have been used in applications such as number-plate reading, facial recognition, and aerial image classification [28; 29]. CNNs have gained momentum for image classification since the AlexNet architecture won the ImageNet contest by a wide margin in 2012 [30].  
[PERSON] [31] and [PERSON] et al. [32] found that deep CNNs outperform shallow CNNs (with fewer hidden layers), Support Vector Machines (SVM), Kernel-based Principal Component Analysis (KPCA), and spectral classifications for land use classification in aerial photography, multispectral and hyperspectral imagery. As CNN classifications integrate spatial as well as spectral information, they achieve higher accuracy compared to SVM and Random Forest (RF) classifications [23].  
CNNs evaluate large amounts of contextual information over multiple scales that can result in classifications at a lower resolution than the original image. To overcome this, the review article by [PERSON] et al. [23] suggests using U-Net [33] or an ensemble of models trained with different variables or different architectures. U-Net was originally developed for image segmentation problems in biomedical imaging [33] and has been adopted for use with optical earth observation data with overall accuracies exceeding 90% [34; 35; 36]. Issues occur with U-Net at the edges of inference areas and vanishing gradient problems where the network becomes difficult to train and has insufficient learning [37]. To overcome this, [PERSON] et al. [37] suggests using an ensemble model approach to counter the edge effects and the use of concatenation operations and activation functions such as rectified linear units (ReLU) to reduce the vanishing gradient problem.
## 3 Proposed System Model
### Generation of Single BS  
Figure 2a depicts the simulated signal strength observed by a UE moving along a road segment in the coverage area of a legitimate BS. When the platoon leader is within range of the legitimate BS, the Reference Signal Received Power (RSRP) is calculated once every second. RSRP is an average power received and typically ranges from \(-44\) dBm (good signal strength) to \(-140\) dBm (poor signal strength) [40].  
As illustrated in Figure 2b the modelled RSRP of the mobile UE is calculated every second by averaging adjacent values to reduce the effect of noise. In our experiment, the RSRP begins at an initial value of approximately \(-105\) dBm and increases as the vehicle approaches its closest point to the BS at a peak value of about \(-68\) dBm before attenuating as the car moves away.
## 3 Results
### Particle Velocity  
A range of models have been trialed to obtain the best distribution fittings for the streamwise instantaneous particle velocities including Beta, exponential, Gamma, normal, and Weibull. Table 3 shows the mean particle velocity goodness of fit results for different distributions, for the minimum and maximum flow rates used in the experiment.  
Using the goodness of fit metrics, the exponential distribution is found to offer the best fit to the particle velocities data. Instantaneous streamwise particle velocity (v) was fitted by the exponential function, as follows:  
\[f_{v}(v)=\frac{1}{V_{mean}}e^{\frac{V_{mean}}{v}},\ v>=0 \tag{7}\]  
where \(V_{mean}\) is the average particle velocity.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model Distribution for** & **Goodness of Fit** & **Goodness of Fit** & **Rank** \\
**Particle Velocities** & **(for Q = 56 L/s)** & **(for Q = 42 L/s)** & **Rank** \\ \hline Weibull & 4.4 & 4.7 & 4 \\ \hline Beta & 2.9 & 3.1 & 3 \\ \hline Gamma & 5.6 & 6.2 & 5 \\ \hline Normal & 1.7 & 2.1 & 2 \\ \hline Exponential & 0.6 & 0.7 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Particle velocity goodness of fit results for minimum and maximum flow rates used in the experiment.  
Figure 9: Distribution of empirical hop travel time data and corresponding best fit model (exponential probability density function) for the flow rate of 52.5 (L/s), particle density of 1180 (kg/m\({}^{3}\)), and bed surface B.  
Figure 10 shows the instantaneous particle streamwise velocities modeled probability density function (PDF) for the flow rate of 49 (L/s), particle density of 1132 (kg/m\({}^{3}\)) and bed surface A, where the x-axis is the magnitude of instantaneous particle velocities, the y-axis shows the event counts, and the red line represents the exponential distribution best fitting the experimentally observed data.  
As shown in Figure 10, the exponential function was the best fitting of measured streamwise particle velocities distributions. The findings are consistent with those reported by [30; 31; 58], which also identified the exponential probability distribution models for the streamwise instantaneous velocities as the best fitting. In contrast, other studies [64; 65; 66; 67] found that the Gaussian distribution offered the closest match to the empirical data of streamwise particle velocities. The differences in these findings are probably best attributed to the different range of particle mobility assessed in these studies: herein, the lowest particle mobility conditions are found to be best fit by exponential particle velocity models, while for flow conditions near the continuous or bulk bedload transport rates, the normal model may yield a better predictive value.
# Physical Layer Authentication in Wireless Networks-Based Machine Learning Approaches
## 3 Background
### Wireless Network
#### 3.1.2 Wireless Physical Layer Protocols  
Recently, massive of advanced wireless technologies and dozens of different wireless protocols meet the needs, each with its performance characteristics and optimized for a specific task and context. However, various wireless protocols exist, such as WiFi, Bluetooth, ZigBee, NFC, WiMAX, LoRa, 5G, satellite services, and more. Therefore, it is necessary to be aware of the system's constraints and performance requirements when choosing protocols. Power, data rate, reliability, and range are essential metrics for distinguishing between protocols [27].  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Study** & **Publication Year** & **Publisher** & **Topics** & **Review Type** & **Covers ML** & **Environment** & **Focused Issues** \\ \hline
[2] & 2018 & IEEE & PLS & Survey & No & Wireless Network & The optimization and design in PLS \\
[3] & 2018 & IEEE & PLS & Survey & No &
\begin{tabular}{c} 5G Wireless \\ Networks \\ \end{tabular} & PLS techniques provide for 5G wireless networks \\
[4] & 2016 & IEEE & PLS & Survey & No & Wireless Network & The PLS techniques, and challenges \\
[10] & 2021 & Springer & PLS/PLA & Survey & Yes & Wireless Network & PLS techniques based on machine learning \\
[25] & 2013 & IEEE & PLS & Survey & No &
\begin{tabular}{c} Cognitive Radio \\ Networks \\ \end{tabular} & PLS in cognitive radio networks \\
[26] & 2019 & IEEE & PLS & Survey & No &
\begin{tabular}{c} 5G Wireless \\ Networks \\ \end{tabular} & The PLS on enabling technologies for 5G \\
[27] & 2021 & IEEE & PLS & Survey & No & \
\begin{tabular}{c} IoT \\ Communications \\ \end{tabular} &
\begin{tabular}{c} PLS techniques in the \\ IoT communication protocols \\ \end{tabular} \\
[28] & 2020 & IEEE & PLA & Survey & Yes & Wireless Network & PLA techniques \\
[29] & 2021 & IEEE & PLA & Survey & No & Wireless Network & PLA techniques \\
[30] & 2020 & IEEE & PLA & Survey & Yes & Networks and IoT & machine learning for the Communications & 5G-based IoT \\
[31] & 2022 & IEEE & PLS & Survey & No & Wireless Network &
\begin{tabular}{c} Physical layer \\ security challenges \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of related works.
# On Deploying the Internet of Energy with 5G Open RAN Technology including Beamforming Mechanism
## 4 Implementation of Beamforming in Open RAN Networks
### System Model and Simulation Results
The results show that MMSE performance is higher than the zero-forcing algorithm in case of low SNR, whereas, for high values of SNR, the difference between zero-forcing and MMSE clearly disappears. This is because Equation (9) approximates to Equation (7) when SNR is very high. From the practical point of view, this means that in scenarios where the antenna may reach high power transmission, MMSE is not necessary (which may reduce system complexity). In the case of O-RAN, the implementation of the O-RU hardware implies that the SNR is reduced. In that case, the use of MMSE is recommendable, even when this introduces more complexity in the transmission system. Figure 5: Impact of the number of users (mobile devices) into the spectral efficiency: (**a**) transmission with ZF-BF precoding; (**b**) transmission without ZF-BF precoding. Our results show that precoding (both ZF-BF and MMSE-BF) may obtain a high reduction of interferences in the channel interface, so that high bitrate and low latency requirements are reachable. This is also achieved thanks to the implementation of precoding in the O-RU, jointly to the digital beamforming, so that the data transported in the front-haul are minimized.
## 4 Experimental Result
### YU TIR Pedestrian Tracking Dataset  
FLIR T620 was used for the IR camera sensor in this experiment. Figure 7 shows the appearance of the T620 model. Additionally, Table 1 lists the specifications of the T620. The video sequence was obtained mainly in the campus and urban traffic road to collect diverse single pedestrians. In Table 2, the data was captured mostly from 3 to 6 pm in February-March. The total shooting time was approximately one to three minutes and eight effective video sequences were selected from a total of 6864 frames that were not mirrored. For the radiometric calibration, FLIR atlas SDK 3.0 for Matlab, thermo-vision toolbox, was used.  
Figure 7: FLIR T620.  
**Ground truth annotation.** A total of 6864 frames were annotated manually to make the ground truths. At most, a single pedestrian was in the scene and the bounding boxes for the annotations were set manually [33].
# Construction of Geosynthetic-Reinforced Pavements and Evaluation of Their Impacts
###### Abstract  
Geosynthetic materials (i.e., geogrids, geogriles and other geocomposites) act as an inter-layer system and are widely used in construction applications. In pavement structures, geosynthetic layers provide potential benefits such as reinforcement, reflective cracking mitigation, increased fatigue life, and improved drainage and filtering. However, few studies have addressed the installation and construction practices of geosynthetics in pavements. Furthermore, the study of geosynthetics and their contribution during construction are limited. In this paper, a full-scale field study was conducted and three trial sections were constructed; two types of geosynthetics, a fibreglass geogrid and a geogrid composite, were installed in the asphalt binder course and at the interface between the subgrade and base layer, respectively, to be compared with a control section without geosynthetic reinforcement. Trial sections were instrumented to monitor the pressure applied on the subgrade, the strain in the base lift of the asphalt binder course, the temperature, and the moisture within the pavement structure during construction. In addition, post-construction field testing was performed to measure the stiffness of the pavements after construction. The results indicated that geosynthetic-reinforced pavements can maintain pavement resilience during construction and significantly mitigate the disturbances caused by construction activities. The geogrid embedded in the asphalt layer was demonstrated to reduce the pressure at the subgrade caused by paving equipment by 70% compared with the control section, while simultaneously reducing the longitudinal and transverse strain at the bottom of the asphalt layer by 54% and 99%. Observations from the geogrid composite test section also demonstrate the potential to minimize the impacts of future freeze-thaw at the subgrade due to the improved drainage and indirect insulation effect.  
geosynthetics; fibreglass geogrid; geogrid composite; pavement; roads; full-scale tests; construction +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Ultrasonic Velocity Tests and Characteristics  
To grasp the main points of the interaction between microstructural change and ultrasonic signal, the ultrasonic velocity and attenuation measurements were carried out. Figures 11 and 12 show the variations in ultrasonic velocity with aging time and deformation. There was an increase trend in ultrasonic velocity both HF and LF. In addition, the velocity increases both of the deformed samples.  
Figure 10: The hardness values with 10% deformation for HF and LF samples.  
According to the hardness test and velocity test, the increase in the hardness was observed to increase with aging time up to the 30,000 h. These results are coherent with the results reported by [PERSON] et al. [21].  
The experimental measurements by [PERSON] et al. indicated that the increase in ultrasonic velocity is due to the increase of the Young's modulus and attributed to the spinodal decomposition. This result reveals that the ultrasonic velocity may have the potential to monitor the microstructural change.