esa-sceva/satcom-chunk-collection huggingface dataset:
# Defacement Detection with Passive Adversaries
## 5 Results: Experiments, Full-Scale Deployment and Discussion
### Production Environment
#### 5.3.2 Performance and Results (Deployment Phase)  
We first analyze an initial period of four months, when the site was set up, tested, and filled with content. Since no real defacements have occurred, we measure the number of false positives that caused an alarm, due to their higher \"evaluation\" result (called \"notification\" events), and we count the number of critical messages and warnings. Let us start by analyzing just one month (Table 4).  
The three events mentioned in Table 4 are caused by a known bug in the Selenium WebDriver, that occurs when HTTPS is used: for self-signed certificates the page may be loaded incorrectly and the WebDriver returns an empty page. They caused a notification.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Period** & **Critical Events** & **Warning Events** & **Notification Events** \\ \hline M4 & 3 & 0 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Single month.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Adversarial Knowledge** & **FAR** & **UDR** \\ \hline \(k\) known & 11.11\% & 88\% \\ \(k\) not known & 10.81\% & 25\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results for the adversarial news scenario.  
A graphical representation is given in Figure 8, where we report, for every day: (1) the total number of critical messages, (2) the total number of warnings and (3) the day's average for the combined evaluation of fixed and adaptive tests. The three critical messages of Table 4 are visible in the first part of the month.  
On day 16, the CMS users made some changes that affected all the pages, as highlighted by a relative peak in the graph. However, every evaluation for a single URL was below threshold, so no event was triggered and no message or warning was generated.  
For the complete deployment period (four months: M1, M2, M3, M4), a total of 975 events (warnings or critical messages) were generated, as reported in Table 5.  
Only 72 events caused a notification. Again, a graphical representation with average daily evaluation and total number of messages is reported in Figure 9. One can observe the small peak on the right end side of Figure 9, corresponding to the main peak of Figure 8.  
With reference to Figure 9, we identified four major events that have a semantic relevance, where the system reasonably generated several alerts (see Table 6).  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Event** & **Description** \\ \hline Evt.\#01 & The development environment is targeted: most pages are empty or filled with mock content, \\  & many changes and rollback \\ Evt.\#02 & Website go-live; after a CMS error (peak on month M2, day 27), content insertion began \\ Evt.\#03 & Website responding with an error \\ Evt.\#04 & CMS temporary shutdown \\ \hline \hline \end{tabular}
\end{table}
Table 6: Events.  
Figure 8: Aggregated evaluation graph and relevant events (production scenario—month M4).
## 4 Experimental Results
### Experimental Setup  
#### 4.1.1 Datasets  
We used two mainstream medium and long-term datasets of the SOT field, i.e., LaSOT [54] and GOT-10k [28], to evaluate the performance of the object tracking module in TRTrack. The LaSOT dataset consists of 1400 sequences, totaling over 3.5 million frames. Each sequence of it includes various challenges originating from the wild, such as target occlusion, deformation, etc. We also divided the training set and the test set according to the 4/1 ratio given by LaSOT. The GOT-10k dataset includes over 10,000 video segments with  
Figure 5: Effectiveness of the backtracking identification module. It shows the visual effect of using the object tracking model to track images of cropped shot boundary objects. It can be seen that the tracker will accurately track the target rather than other objects.  
Figure 6: Flow-process diagram of the backtracking identification module. It can be seen that when the target is judged to have left the shot, the backtracking identification module will crop out the objects at the shot boundary and input them back into the object tracking model. The output of the tracking model includes the prediction bounding box and the corresponding confidence score. If the score is greater than the parameter \(\lambda\), it indicates that the target is re-identified.  
more than 1.5 million manually labeled bounding boxes. It contains a total of over 560 types of moving objects and 87 motion modes. We follow the split of GOT-10k, approximately 9.34 k of training data, and 420 test data in GOT-10k.  
The benchmark dataset MS COCO 2017 was selected to evaluate our object detection model. MS COCO 2017 is an object detection dataset built by Microsoft, with a total of 80 object categories. The dataset contains objects of various sizes, some of which are noisy or obstructed, making it challenging. There are over 118 k and 5 k images in the training and testing sets, respectively. We follow this split in experiments.  
For target re-identification, considering the target re-identification problem of objects in videos studied in this paper, we found that the classroom videos fit our research needs very well. In classroom videos, because the interaction between teacher and student is crucial in a lecture, the behavior of a teacher leaving the podium to communicate with students can easily lead to the teacher being out of view.  
On this basis, we established a classroom video dataset CLV, which is selected from actual classroom videos with prominent classroom video features, such as targets' frequently moving in and out of the shots. It includes 16 main classroom video segments, with more than 6400 manually labeled bounding boxes when the target leaves the shot and reappears. The video information of the dataset is shown in Table 1.
## 4 Results and Discussion
### Rockfall Risk Assessment  
According to [63], the risk is defined in terms of the exposure of the elements at risk, their vulnerability, and most importantly, the hazard. In RHRS, which is the most well-known rock hazard evaluation method, the risk parameters are associated with traffic conditions, mainly AVR, PDSD, and ditch effectiveness, etc. Furthermore, the risk is directly linked to the exposure of vehicles on the road, which is rated through assigning scores to various parameters [27]. Risk assessment of an area assigns it a level of risk, which provides a way for planners to minimize, control, and monitor the probability of future geohazards [64]. The final rockfall risk assessment map along the highway is shown in Figure 9. The hazard and risk zones are more or less similar in pattern along the entire highway stretch. The zone falling in the very high- to high-risk category starts from the Jijal complex to Kuwde, Pattan, Leo, Kiru, and Mandraza. Starting from Besham and passing through Khenai and Dubair are the zones of moderate to low risk. About 50% of the highway stretch is occupied by very high- to high-risk zones. It is also seen that 26% of the highway is under the low hazard zone and about 35% is under the low-risk zone. (Figure 8 and 9). Based on our results, it is clear that a major part of the highway falls under the unacceptable risk category that requires urgent remedial work. According to [22], the road must be prioritized based on the risk scores, but [27] mentioned that this prioritization should vary from place to place because, in our study area, the entire section does not fall under the same risk category. The Frontier Works Organization (FWO) has managed the KKH for decades and is responsible for removing any blockage due to natural hazards and for building and repairing roads. Different risk levels demand appropriate risk control measures. For risk level I, continuous monitoring is suggested daily and demands effective engineering measures for slope stability. Daily inspection, warning boards, and speed limit boards are required for risk levels II and III. Although risk levels IV and V fall within a relatively safer zone, an inspection of the slope once a month is recommended. Despite all safety measures, it is strongly recommended that the local community should be provided with information through social media campaigns, newspapers, and personal visits to enable them to tackle the natural hazards.
###### Abstract  
As a technological advantage of ultrasonic non-destructive testing, fully focused imaging can accurately feedback the defective characteristics of the inspected object, greatly improving the detection efficiency. This article aims to address the challenges of outdated and low detection rates in the detection technology of branch pipe filled welds. The full matrix acquisition (FMC) and total focus method (TFM) ultrasonic detection technology are used for detection and defect image feature analysis. Firstly, a multi-mode, fully focused real-time imaging software system was developed to address the specificity of the detection object; secondly, a phased array detection system based on 64 elements was constructed; finally, a region wavelet denoising method based on TFM images was proposed to solve the problem of artifacts caused by poor coupling; and based on the feature extraction method for a minimum rectangle, we analyzed the size, position, angle, and other information regarding defects. Through experiments, it has been found that this technology can effectively improve the detection efficiency of branch pipe weld defects, with a detection rate of 100%. Based on the partition fusion denoising method, the defect imaging quality can be further improved, at the same time, based on the feature extraction method, the error is 0.1 mm, the length range of various defects is 2.3 mm-6.3 mm, the width range is 0.6 mm-0.8 mm, and the angle range is \(\rm 52^{\circ}\)-\(\rm 75^{\circ}\), which can provide an application basis for the localization, classification, and risk assessment of corner weld defects in branch pipes.  
FMC-TFM; weld inspection; noise reduction; feature extraction Article
# Energy Efficient Multicast Communication in Cognitive Radio Wireless Mesh Network
## 2 Related Work
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Ref** & **Year** & **Objective** & **CR** & **WMN** & **Multicast** & **Approach** & **Remarks** \\ \hline \multirow{4}{*}{[6]} & \multirow{4}{*}{2015} & \multicolumn{3}{c}{Automata based} & & & A multi-path on-demand routing & Increased \\  & & multipath multicasting & ✓ & & ✓ & protocol based on the Artificial & throughput and \\  & & & in CRN & & & Intelligence Reinforcement & reduced \\  & & & & & & Learning Method & end-to-end delays \\ \hline \multirow{4}{*}{[7]} & \multirow{4}{*}{2010} & \multicolumn{3}{c}{Assisted-multicast} & & & A multicast scheduling technique & & \\  & & scheduling in & ✓ & ✓ & ✓ & where client nodes assist the base & Reduced the total \\  & & wireless cognitive & & & station in delivering the multicast & multicast time \\  & & mesh networks & & & data over orthogonal channels & \\ \hline \multirow{4}{*}{[8]} & \multirow{4}{*}{2017} & \multicolumn{3}{c}{Spectrum-aware} & & & A multicast scheduling technique & \\  & & network coded multicast & & & where client nodes assist the base & Reduced the total \\  & & in mobile cognitive & & & station in delivering the multicast & multicast time \\  & & radio adhoc networks & & & data over orthogonal channels & \\ \hline \multirow{4}{*}{[9]} & \multirow{4}{*}{2018} & \multicolumn{3}{c}{Packet multicast in} & & & Random network coding based & Reduced the \\  & & cognitive radio & ✓ & & & multicasting strategy & total \\ \cline{1-1}  & & adhoc networks & & & & of transmissions \\ \hline \multirow{4}{*}{[10]} & \multirow{4}{*}{2010} & \multicolumn{3}{c}{Multicast scheduling} & & & Optimization method for & \\ \cline{1-1}  & & with cooperation and & & ✓ & & multicast scheduling using & Protected the PU \\ \cline{1-1}  & & network coding in CRN & & & network coding & and fairness \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of Related Work.
# Multibyte Electromagnetic Analysis Based on Particle Swarm Optimization Algorithm
## 2 Related Works
Thus, it is meaningful to explore multiple bytes to recover the key. In addition, there is very little research that focuses on the PSO optimization algorithm to recover cryptographic devices' key by electromagnetic radiation. The genetic algorithm has been applied to electromagnetic analysis in [20] to recover the secret key, but the genetic algorithm is a more complicated optimization algorithm. Compared with particle swarm optimization, there are more steps and more parameters in genetic algorithms, such as coding, selection, crossover, and mutation. The PSO algorithm is simplified and effective, which has had great achievements in various fields. A particle swarm optimization (PSO) algorithm was associated with ESN to pre-train some fixed weights values within the network, and experimental results showed obvious enhancement of ESN learning results [24]. An improved PSO algorithm was used to optimize the parameters of least squares support vector machines (LS-SVM) in order to construct an optimal LS-SVM classifier. The improved PSO algorithm can effectively improve the classification accuracy of LS-SVM [25]. An improved PSO called Selectively informed PSO (SIPSO) was used as a leak detection and localization method for liquid pipelines, which was shown to outperform the other global PSO algorithm and local PSO algorithm [26]. In this paper, combining the advantages of multiple bytes of the key and PSO algorithm, we propose a multibyte correlation electromagnetic analysis based on particle swarm optimization to recover the cryptographic devices' key. The major contributions are summarized as follows:  
1. Multiple bytes of the secret key are used in our method to take full advantage of available information. 2. We transform the key into multidimensional form, each byte of the key is considered as a dimension. Thus, each point in the coordinate is a key candidate, and we transform the problem of the right key searching into the problem of optimizing correlation coefficients of key candidates. 3. The PSO algorithm is particularly suited to solve the optimization problems with high dimensions and complex structure, so our method applied the PSO algorithm into the optimization process. In addition, we also add a mutation operator to the PSO algorithm to improve the result.
## 3 Related Work
There are several caching mechanisms were proposed based on the ICN concept. A caching mechanism was proposed named as \"Breadcrumbs\" [35], where they used some metadata related to cached content. These metadata help the end-users to find out the relevant and appropriate content from the network. The hash-routing mechanism was proposed as a mapping mechanism and later five different types of hash-routing techniques were applied in the ICN caching mechanism to show the impact of these strategies [24]. These caching mechanisms increase the content availability from the nearby content providers, but the efficiency of the memory usage is reduced a lot because of redundant content. The combination of on-path and off-path caching is known as hybrid techniques. Scalable content routing for content-aware networking (SCAN) [36] is a hybrid caching technique where the bloom filter was used to cache the useful content and on-demand delivery of the cached contents. They compared their routing mechanism with the IP routing mechanism and showed that content transfer time was reduced compared to the existing Internet architecture. But, the combination of ICN and EC mechanisms provides faster data transfer environments than the hybrid SCAN mechanism. The coexistence of ICN and EC is not a new area of research. Several works already being done to get the mix features outcomes into a single architecture. A fog-enabled edge learning mechanism was proposed [7] to design a cognitive CCN in the 5G environment to associatively learn and control the states of edge devices and the network resources. They used the caching capabilities and other network resources of edge devices. It ensures that the edge device network resources are useable for the betterment of network performance. Other works showed that collaborative edge caching [8] enable mobile users to fetch the contents from cache servers so that the user experience will be improved, and end-to-end communication latency will be reduced. These works indicate the coexistence of EC and ICN will improve the user experience and reduce the end-to-end communication latency based on the combined characteristics of the ICN and EC.
## 4 Experiment Result and Analysis  
To comprehensively evaluate the effectiveness of our proposed method, we carried out comprehensive experiments on both simulated [66] and real image sequence datasets [67]. The simulation image data sequences include some synthesis image sequences data via applying complex background images and a small target generated by the computer, which presents the scene of the small target moving in complex backgrounds. All simulation image data sequences are sampled at a frequency of 1000 Hz, that is, 1000 frames of video images are generated per second. The video sample in our experiment is 800 frames. The  
Figure 5: Upper figure depicts the process of extracting the motion trajectory of small features. We continuously record the positions of small objects, and when two consecutively detected positions are close enough, we consider them to belong to the same trajectory. Using this approach, we can track the movement path of an object over a period of time. The following figure displays the complete trajectory extracted by the system.  
RIST dataset contains some real videos obtained by capturing the various moving targets and environments with a high-definition camera. The RIST dataset covers several types of scenarios, including significant clutter, camera motion, and variations in overall brightness. The real video sample in our experiment is 750 frames. The experiments are conducted using MATLAB R2017a on a machine with Windows 10, featuring an i7 CPU at 3.10 GHz and 16 GB of memory. As shown in Table 1, the parameters of the \(\mathcal{CISTMD}\) network are presented, and the motion detection Chennel parameters are determined via sensitivity analysis in [17]. These variables match the speed and size requirements of STMD neurons when responding to moving stimuli.
## 3 Communication System Analysis in the Presence of Fading  
### Single-Correlator Receiver  
The flat fading channel results in multiplicative distortion of the transmitted signal \(s(k)\). Furthermore, the condition that the channel fades slowly implies that the multiplicative process may be regarded as a constant during one symbol interval. When fading and noise are present in the channel the fading coefficient in Figure 1 is different from zero, _i.e._, \(\alpha\
eq 1\). Consequently, the received signal \(s_{R}(k)\) can be expressed as [13]  
\[s_{R}(k)=\alpha e^{-j\phi}s(k)+n(k) \tag{29}\]  
where \(\alpha\) is the fading factor, \(\phi\) is the phase shift and \(n(k)\) is pass-band noise. The channel fading is sufficiently slow that the phase shift \(\phi\) can be reduced to zero by the receiver phase locked loop. For this case the random sample \(w_{1}(k)\) in Equation (7), can be expressed as  
\[w_{1}=\sum_{i=1}^{2\beta}z_{i}c_{i1}=\alpha 2\beta\sqrt{E_{c}}c_{i1}^{2}+2 \beta\sqrt{E_{N}}n_{i}c_{i1} \tag{30}\]  
having the mean  
\[\eta_{w1}=E\{w_{1}\}=\alpha 2\beta\sqrt{E_{c}}E\{c_{i1}^{2}\}=\alpha 2\beta \sqrt{E_{c}}P_{c} \tag{31}\]and the variance  
\[\sigma_{w1}^{2}=\alpha^{2}2\beta E_{c}E\{c_{i1}^{4}\}-\alpha^{2}2\beta E_{c}E^{2} \{c_{i1}^{2}\}+2\beta E_{N}\sigma_{ni}^{2}E\{c_{i1}^{2}\} \tag{32}\]  
\[=2\beta E_{c}[E\{c_{i1}^{4}\}-E^{2}\{c_{i1}^{2}\}]\alpha^{2}+2\beta E_{N}\sigma _{ni}^{2}E\{c_{i1}^{2}\}\]  
Then, using the general expression (14), the probability of bit error can be calculate as  
\[p_{be}=\tfrac{1}{2}erfc\left(\tfrac{\
u_{e}-1}{\beta}+(\tfrac{\alpha^{2}E_{b}} {N_{0}})^{-1}\right)^{-1/2}. \tag{33}\]  
If the source generates binary bits and the spreading sequence is in binary form, we may have  
\[p_{be}=\tfrac{1}{2}erfc\left(\sqrt{\tfrac{\alpha^{2}E_{b}}{N_{0}}}\right)\]  
For this case the closed form expression for the probability of error is [13]  
\[p_{be}=\tfrac{1}{2}\left(1-\sqrt{\tfrac{\bar{\
u}_{b}}{1+\bar{\
u}_{b}}}\right) \tag{34}\]  
where \(\bar{\
u}_{b}\) is the average signal-to-noise ratio defined as \(\bar{\
u}_{b}=E\{\alpha^{2}\}E_{b}/E_{N}\).  
This expression was derived by finding the mean values and variance for \(\alpha\) as a random variable and analyzing \(w_{1}\) as a random function, as it was done in [15]. The probability of error then can be derived in this closed form  
\[p_{be}=\tfrac{1}{2}erfc\left(\tfrac{2(4-\pi)}{\pi}+\tfrac{4}{\pi}(\tfrac{E_{b} }{N_{0}})^{-1}\right)^{-1/2}, \tag{35}\]  
where the energy of a bit is related to the energy of a chip as \(E_{b}=2\beta E_{c}\).
## 3 Materials and Methods
### Applications and Performance
#### 3.2.3 Application in Behavioral Bioassays  
A behavioral assay was performed with the two stingless bee species. Bees of both species were collected from four colonies each of \(M\). _quadrifuscata_ and _Partamona heller_ in Vicosa, State of Minas Gerais, Brazil (20\({}^{\circ}\)45\({}^{\prime}\) S and 42\({}^{\circ}\)52\({}^{\prime}\) W). The collected bees were kept for 1 h in the laboratory under conditions similar to those found in their colonies (28 \({}^{\circ}\)C and 80% relative humidity in total darkness) [37]. Subsequently, bee behavior was recorded in the arenas (Petri dish, 9 cm diameter, 2 cm height) for 15 min with a digital video camera (HDR-XR520V, Sony Corporation) at 30 fps and high definition (1920 \(\times\) 1080 pixels). Behavioral bioassays were performed in a room with artificial fluorescent light at 25 \(\pm\) 3 \({}^{\circ}\)C and 70 \(\pm\) 5% relative humidity. Bioassays were performed with 37 replicates, with each replicate corresponding to a group of five bees of each of the two species. The kinematic variables measured with Ethoflow included centrality, polarization, milling, resting, meandering, and tracked distance. In the centrality response, the interaction was considered when the individuals approached a distance \(\leq\)1.41 cm. An instantaneous tracked distance \(\leq\)0.046 cm frame\({}^{-1}\) was counted as resting. Centrality was the response variable in the model with interaction between polarization and bee species, or model with interaction between milling and bee species. Meandering was the response variable in the model with interaction between resting and bee species. Besides, the tracked distance between the bee species was compared. These models were fitted with generalized linear models (GLM) with a gamma distribution, displaying adequate distribution for continuous data in which the variance increases with the square of the mean [38]. When an explanatory variable had no significant effect, the model was simplified, and the results were plotted as a function of the significant variable.  
A toxicological bioassay was also performed with _M. quadrifuscata_ to demonstrate trophallaxis recognition under pesticide stress conditions. The acclimated bees were orally exposed to the commercial formulation (cf) (water-dispersible granules at 700 g a.i. \(\text{Kg}^{-1}\), Bayer CropScience, Sao Paulo, SP, Brazil) of the neonicotinoid imidalcoprid in a sublethal concentration (0.2 mg cf L\({}^{-1}\)). This concentration is 300\(\times\) smaller than that recommended for controlling the whitefly _Bernisia tabaci_ (60 mg cf L\({}^{-1}\)) [39]. The pesticide imidalcoprid is commonly associated with bee decline and causes motor impairments in bees [40]. After 3 h of exposure, the bees were filmed as previously described, and trophallaxis behavior was quantified using Ethoflow. Trophallaxis response (\(n\) = 60) to the pesticide was assessed using a GLM with a Poisson distribution, a suitable distribution for count data [38].