esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Experimental Results
#### 4.4.4 Event Detection and Tracking  
Detecting events and tracking them is, indeed, one of the objectives of the methodology. In broad terms, the events taking place can either be desired (when they are part of the context under study) or undesired (when they are external phenomena). In Figure 13, three different events are analyzed. The first event, hashtagged with #notredame, refers to the fire that devastated Notre-Dame de Paris cathedral on the afternoon of 15 April 2019. Congeneration ensued, and most politicians quickly expressed their lament on social media. Notice, however, how the tracking happens only during the highest point of global dismay (on the 16 th, with the images of destruction after the fire was extinguished) and swiftly fades afterwards. As soon as the authorities change the subject, the topic--undesired for the political tracking--disappears. A similar case but with a routine event happens with #selectividad, a hashtag devoted to the university entrance exams that take place in June in Spain. There are a few periods where the hashtag gains some relevancy among the top topics, corresponding to the dates where the test is held in different regions (so, each region's politicians direct their posts to their own region's students), but it also fades right afterwards.  
A different case takes place with #pride2019 and #orgullo2019, two linguistic variants (English and Spanish) of the same event, the annual LGBT pride festival. Such a topic is not exempt from political content and discussion on social media, especially between conservative and progressive parties. This is reflected in the length of the topic tracking, which lasts for nine days in the first case and two weeks in the second (as expected, the Spanish version is used for a longer time). These would be two examples of events that are somehow related to the conversation that is being tracked so such lengthy tracking represents the desired behavior.
## 4 Implementation
### Hardware  
The DM hardware was developed using a Raspberry Pi 3 Model B, installed in a vehicle. The Raspberry Pi 3 features Bluetooth 4.1 and IEEE 802.11 ac communication capabilities, allowing BLE and DTN protocols over Wi-Fi in the proposed architecture, respectively. Figure 4a depicts our final DM prototype based on a commercial UAV.  
Figure 3: Flowchart of the “FloatingBlue Manager”.  
Figure 2: Protocol stacks defined for the FloatingBlue architecture.  
The sensor node was designed to function as an environmental monitoring device. The device was implemented using a BLE beacon nRF52840 dongle from Nordic Semiconductor, a BME680 gas sensor, and a soil moisture sensor, as illustrated in Figure 4b.
# Research on the Application of THz-TDS in Coal-Rock Interface Recognition
## 3 Results and Discussion
### THz Spectral Properties of Coal-Rock and Coal-Rock Mixtures
#### 3.1.1 THz Spectral Properties of Coal and Rock
Figure 2d illustrates the absorption coefficient of coal and rock samples, showing that the absorption coefficient of each coal and rock sample increases steadily with the increase of frequency. The absorption spectrum of each sample exhibits slight fluctuations due to internal absorption or surface scattering of the sample. The differences in optical parameters, such as refractive index and attenuation coefficient between coal and rock, allow for the accurate identification of coal and rock in the THz band, which also demonstrates that THz time-domain spectroscopy technology can be well applied to coal and rock identification.
# Security Challenges of Location Privacy in VANETs and State-of-the-Art Solutions: A Survey
## 2 Background
### Location Privacy  
In vehicular ad-hoc networks (VANETs) it is possible and a great problem for vehicles to be tracked while in transmission with other vehicles or during communication with road-side infrastructure which can become a dangerous situation, this type of threat can lead to location privacy of vehicles users. Figure 2 shows location privacy assessment methods.  
Several protection controls have been suggested in the literature [17]. However, this study focuses on the privacy measures used in the assessment of the following specific approaches to the position of privacy addressed in this paper.
## 6 Experimental Results and Evaluation
### Effect of BOW Size on Bug Classification  
In parallel with the focus on stop-words/lemmatization selection, we have also evaluated the impact of the BOW size for the selected ML algorithms; the results are shown in Figure 6. From the figure, we can observe that the SVM linear kernel performs the best.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{4}{c}{**F1-Measure (\%)**} \\ \hline \multirow{2}{*}{**Classifier**} & **No SW** & **No SW** & **SW** & **SW** \\  & **No LEM** & **LEM** & **No LEM** & **LEM** \\ \hline Naïve Bayes & 58.49 & 59.05 & 60.05 & 59.30 \\ SVM-linear kernel & **70.92** & 70.60 & **71.66** & **71.30** \\ SVM-poly kernel & 56.47 & 56.43 & 56.42 & 55.76 \\ SVM- RBF kernel & 67.92 & 68.97 & 69.48 & 68.71 \\ SVM- sigmoid kernel & 70.24 & **70.88** & 70.36 & 70.30 \\ KNN & 57.57 & 57.72 & 58.48 & 55.93 \\ Logistic regression & 63.11 & 64.24 & 65.63 & 65.15 \\ Decision tree & 55.04 & 55.78 & 58.03 & 54.40 \\ Random forest & 66.05 & 66.42 & 65.48 & 65.93 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Effect of bilingual preprocessing using stop-word removal (SW) and lemmatizing (LEM) for the case study.  
The F1 measure values obtained for varying BOW size using SVM linear kernel are plotted in Figure 7. The vertical axis represents the F1 measure, while the horizontal value shows the BOW size. From the figure, we can conclude that the BOW size of 265 seems to be optimal. BOW sizes greater than 265 do not seem to have a further positive impact on the accuracy of the ML algorithm.
# Routing Protocols for UAV-Aided Wireless Sensor Networks
## 4 Routing Protocols for UAWSNs
### Tree-Based Routing
#### 4.1.3 Location-Based Routing  
Energy-efficient localization and UAV-based WSN (EEJLS-WSN-UAV): In energy-efficient joint localization in a UAV-based WSN (EEJLS-WSN-UAV) [51], localization and synchronization for UAWSNs are exploited. The information of localization and synchronization is required for UAVs to know the position of the sensor nodes and global time to relate event detection at a specific location and time. One easy way to provide this information to all the sensor nodes is to equip them with a GPS. This method is simple but costly. In Reference [51], a method of three-dimensional localization and synchronization was introduced, where a UAV equipped with GPS roams around the sensor field broadcasting its geographical position and clock time. In this way, the sensor nodes in the sensor field can estimate their geographical position and global time without having a GPS of their own. As a result, the number of beacon nodes required in the network is decreased. This scheme also provides higher accuracy of estimating localization information and local timing compared to earlier algorithms. One drawback of EEJLS-WSN-UAV is that it does not consider the time taken for a GPS-equipped roaming UAV to empower other nodes in the sensor field. Route optimization of UAVs is necessary if huge numbers of sensor nodes are deployed. In a future improvement of EEJLS-WSN-UAV, it may be possible to optimize the flight path to reduce the time required to cover the entire network area.  
Location-based UAV-aided WSN (LS-UAV-WSN): Event-based WSN requires a faster response in data processing and offloading. Location service in a UAV-based WSN (LS-UAV-WSN) [52] is based on a distributed algorithm, which autonomously and independently drives a mobile sink node toward the static sensor nodes in a WSN for data acquisition. Mobile sinks directly infer and compute the network trajectory. In addition, all the nodes are assumed to be position-aware. When a node has data to transmit, it broadcasts a source advertisement packet at a certain time interval that contains its identifier (ID) and exact position. Nodes that receive the packet must check its ID and broadcast it again after a certain timeout. Mobile sinks also can send a sink advertisement after the expiration of a particular time interval. The energy consumption is significantly reduced and, thus, the network lifetime is prolonged.
# Deep Learning-Based Denoising in Brain Tumor CHO PET: Comparison with Traditional Approaches
## 3 Results
### Influence of Denoising on Tumor Features  
SUVmax, SUVmean, TLA and T/N ratio are the most crucial parameters for malignancy evaluation in clinical settings, and minimum changes were identified in SUVmax, SUVmean, and TLA. SUVmax measures the maximum radioactivity of the tumor region, which represents the highest malignancy, and this remained unchanged after denoising (change ratio of 6.29% for N2N, 4.77% for N2V, 3.02% for BM3D, and 4.86% for NLM). Little changes were noticed in the SUVmean, which reflects the average radioactivity of the tumor region, with the change ratio of 0.85% for N2N, 0.58% for N2V, 2.25% for BM3D, and 1.52% for NLM. N2N and N2V reflected smaller changes than conditional methods in TLA, while all these methods exhibited little changes within 3% compared to the original images. In accordance with the denoising purpose, the T/N ratios increased for all algorithms, with the improvement ratio of 20.93% and 5.46% for N2N and N2V, while fewer changes were identified in conventional methods (2.54% for BM3D and 1.51% for NLM). Table 4 showed the mean improvement ratio of tumor features with outliers detected and removed. Figure 6 showed the distributions of tumor features for denoised data.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Change Ratios** & **N2N** & **N2V** & **BM3D** & **NLM** \\ \hline SUVmax & 6.29\% & 4.77\% & 3.02\% & 4.86\% \\ SUVmean & 0.85\% & 0.58\% & 2.25\% & 1.52\% \\ TLA & 0.85\% & 0.58\% & 2.25\% & 1.52\% \\ T/N ratio & 20.93\% & 5.46\% & 2.54\% & 1.51\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: The change ratios of SUVmax, SUVmean, TLA and T/N ratio for N2N, N2V, BM3D and NLM.  
Figure 6: Distributions of the improvement ratios of SUVmax (**A**), SUVmean (**B**), TLA (**C**) and T/N ratio (**D**).  
There is also no correlation of CNR improvement ratio with tumor size, SUVmax, SUVmean and TLG, with the Pearson correlation coefficient ranging from \(-0.110\) to \(-0.031\) for CNR improvement ratio. Correlation coefficients of CNR and CNR improvement ratio with tumor features are displayed in Table 5.
# LoRa Communication Using TVWS Frequencies: Range and Data Rate
###### Abstract  
Low power wide area network (LPWAN) is a wireless communication technology that offers large coverage, low data rates, and low power consumption, making it a suitable choice for the growing Internet of Things and machine-to-machine communication applications. Long range (LoRa), an LPWAN technology, has recently been used in the industrial, scientific and medical (ISM) band for various low-power wireless applications. The coverage and data rate supported by these devices in the ISM band is well-studied in the literature. In this paper, we study the usage of TV white spaces (TVWS) for LoRa transmissions to address the growing spectrum demand. Additionally, the range and data rate of TVWS-based LoRa, for different transmission parameter values using different path-loss models and for various scenarios such as free space, outdoor and indoor are investigated. A path-loss model for TVWS-based LoRa is also proposed and explored, and the evaluations show that TVWS offers a longer range. This range and data rate study would be useful for efficient network planning and system design for TVWS-based LoRa LPWANs.  
LPWAN; LoRa; ISM band; TVWS; path loss Article
# COX2-Inhibitory and Cytotoxic Activities of Phytoconstituents of _Matricaria chamomilla_ L.
## 3 Results
### Inhibition of COX2 Enzyme Activity In Vitro  
To exemplarily verify the in silico predicted interaction of the compounds with COX2, we measured the enzymatic activity upon treatment with the selected seven phytochemicals. Figure 4A illustrates the rest activity of COX2 after treatment with \(\beta\)-sitosterol, \(\beta\)-amyrin, \(\beta\)-eudesmol, daucosterol, apigenin, myricetin, and farnesol at concentrations of 0.1 and 1 \(\mu\)M. \(\beta\)-Sitosterol exerted the highest inhibitory effect on COX2, with percentages of 92% and 98%, respectively. By contrast, apigenin had the lowest inhibitory activity (42% and 90%, respectively). Then, we corrected the percentages of COX2 rest activity upon treatment with 0.1 \(\mu\)M of the compounds in vitro with the LBE values in silico and found a significant correlation (Pearson collection test, \(p=0.030\); \(r=0.736\)) (Figure 4B). We also observed a significant correlation upon treatment with 1 \(\mu\)M (\(p=0.042\); \(r=0.692\)) (Figure 4C).
# Mechanical Analysis of Ground Sleeve
## 3 Test Results
### Strength Evaluation  
It is evident from the test results in Table 1 that the tensile capacity of the specimens that failed by bar tensile fracture was close to that of the bare bar in tension.  
According to _ACI-318_[36] specifications, the tensile strength of a splice should be at least 125% of the nominal yield strength of the spliced bar. Therefore, the ratio of the tensile strength of the splice to the yield strength of the spliced bar, \(R_{\mathrm{s}}\), should satisfy: \(R_{\mathrm{s}}\geq 1.25\). The strength ratings following this criterion are shown in Table 1. It is shown that the specimens of the grouted sleeve lapping connector with lap lengths greater than or equal to 150 mm satisfied the previously mentioned criterion.
## 5 Performance Evaluation  
### Performance Evaluation of ICN Hierarchical Cache System  
#### 5.1.1 Simulation Setup  
We implemented an ICN HCS based on a hierarchical NRS on a simulation platform named Icarus [41], which is a lightweight simulator that allows users to easily test customized caching and routing policies. Table 1 shows the fundamental parameters we tested in our experiment. A simple binary tree topology was chosen to verify the accuracy of our modeling of the HCS we discussed in Section III. TISCALI, an ISP-like topology depicting the realistic internet environment of Europe, was used to evaluate the performance of HCS under different caching strategies. Each cache node (indexing content in chunk level) deployed LRU as the default replacement policy. We divided the topology into a hierarchical nested structure through a graph-partitioning-based algorithm named latency-aware hierarchical partitioning (LHP) [32]. In order to facilitate the gathering of statistics of signaling overhead in the HCS, we created some new nodes to deploy distributed RNs and bound them to specific attachment nodes chosen by the above algorithm. Both stationary and YouTube Video traffic in real world were used as the tested traffic workload. The stationary workload extracted from a catalog of \(N=1\times 10^{6}\) content objects followed the Zipf distribution (\(\alpha=0.8\)). The requests of this workload were designed up to \(2\times 10^{6}\) with the IRM model. The YouTube Video traffic records total \(3.8\times 10^{6}\) of requests [42] for \(1.76\times 10^{6}\) of videos, coming from 31,124 distinct IP addresses. Half of the total requests were used to warm up cache system, and the remaining were used to gather statistics. Each experiment in same scenario was independently run ten times, and we analyzed the results with a 95% confidence interval.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Traffic Workload & Stationary/YouTube Video traffic \\ catalogue size & \(1\times 10^{6}/1.76\times 10^{6}\) \\ Topology & Tree, TISCALI \\ Cache size & Uniform \\ Replacement & LRU \\ Request number & \(2\times 10^{6}/1.76\times 10^{6}\) \\ Request distribution/rate & Possion/10 req/s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulator environment. LRU: least recently used.
## 4 Experiments and Results
### Experiment Settings  
**Hardware setup**: All our experiments were conducted on a computer that was equipped with an Intel(R) Core(TM) i9-10920X CPU and an RTX 3080 GPU.  
**Model**: The pretrained DNN model structure used for MNIST and SVHN was the same, that is, a Convnet with \(3\times 3\times 16\), \(3\times 3\times 32\), and \(3\times 3\times 64\) convolutional layers followed by \(2\times 2\) max pooling layers and two 200-unit fully connected layers. They achieved an accuracy of 99.34% and 87.39% on MNIST and SVHN, respectively. For CIFAR10, we trained a fine-tuned Resnet20 with an additional linear layer. This model reported an accuracy of 87.09%. Refer to Table 1 for the detailed training parameters.  
**Adversarial examples**: We implemented five attacks based on an open uniform platform for security analysis [29], including: fast gradient sign method (FGSM) [3], projected gradient descent (PGD) [7], Jacobian based saliency map attack (JSMA) [22], DeepFool [23], and CW\({}_{2}\)[24].  
* **FGSM**: We set the perturbation amplitude \(\epsilon\). For MNIST, we set the amplitude \(\epsilon\) as 0.3. For SVHN and CIFAR10, we set it as 0.1.
* **PGD**: There were two parameters to set: the number of iterations \(it\) and the perturbation amplitude \(\epsilon\). In the experiment, we set \(it\) as 1000 for the three datasets and we set \(\epsilon\) as 0.3 for MNIST and 0.1 for both SVHN and CIFAR10.
* **JSMA**: The perturbation coefficient \(\theta\) was set to 1 and the modified pixel number was limited by the parameter \(\gamma\), which was set to 0.2 for the three datasets.
* **DeepFool**: We set the number of iterations \(it\) as 50 and the overshoot coefficient as 0.02 for all datasets.
* **CW\({}_{2}\)**: There were four parameters that could affect the adversarial examples: the number of iterations \(it\), the confidence coefficient \(c\), the number of search step \(n_{s}\), and the learning rate \(lr\). We set \(c=0,it=1000\), \(n_{s}=10\), and \(lr=0.002\) for all datasets.  
For each attack, we chose 1000 candidate samples from the test dataset (which were classified correctly by the target classifier) and generated the adversarial examples. We also chose an equal number of test samples as baseline data.
## 5 Simulation Results and Discussion
#### Problems and Disadvantages  
1. It is a challenge to catch the animals from the environment and implant sensors into them in order to set up the system.
2. When specially-trained animals are used for this system, it can create problems related to animal rights.
3. There may be an insufficient number of animals particular to the region.
4. Since the power of sensors is generally provided by batteries, lack of battery power or physically damaged devices can disrupt the regular data collection.
5. The cost of equipment used for the decomposition of human voice signals is considerable (although this may not be a constraint where national security is concerned). The natural reactions of animals equipped with sensors should always be examined before. There is also a necessity of using more than one group of animals to detect multiple human groups.
6. As soon as the audio signals have been recorded, it is essential keep a very short data flow interval because it is likely that the target (voice owner) will move.
7. The high cost of and difficulty in installing the data collection tower infrastructure especially HVD method.
# A Study of Network Intrusion Detection Systems Using Artificial Intelligence/Machine Learning
## 3 IDS Performance Evaluation  
To evaluate IDS's, which use machine learning methods, we use performance metrics. A confusion matrix is a table which describe the performance of a classification model, also called a \"classifier\", on a set of test data for which the true values are known. The matrix compares the actual target values with those predicted by the machine learning model.  
To describe a confusion matrix the following terms are used:  
* True Positive (TP): An attack sample has been correctly identified as an attack.
* True Negative (TN): A normal sample has been correctly identified as normal traffic.
* False Positive (FP): A normal sample has been incorrectly identified as an attack.
* False Negative (FN): An attack sample has been incorrectly identified as normal traffic IDS should have a low false positive rate to avoid false alarms in the system, as this creates network disturbances. In addition, it is necessary to have a low false negative rate to avoid undetected attacks entering your network.  
When using the aforementioned terms and matrix from Table 1, we can evaluate the various metrics that are frequently used to assess the performance of an IDS. A list of the main metrics that are used by researchers follows here:
## 3 Selected Research Results
### Analysis of Signals in the Time Domain
The coefficient of correlation and sensitivity between the cylinders was calculated by determining the relationships between the state features and diagnostic parameters or by comparing the time waveforms and spectra for the reference cylinder and the other cylinders. Correlation and sensitivity coefficients for individual cylinders were found based on the relationships between diagnostic parameters and input values, such as the technical state or load of the internal combustion engine. The most useful symptoms are those whose correlation coefficient values between the state of suitability and developing damage were closest to 0.00 and reached the highest sensitivity coefficient values. Optimization was performed due to the selected criteria [9]. The most significant values of these coefficients were obtained for windowed time analysis and decimation, followed by Haar wavelet statistical measures. In the study of damage development, comparisons were made between current and reference time waveforms, and the correlation coefficients between them were determined. It is only possible to effectively evaluate signals with the time-domain visualization of the raw signals, and various advanced signal processing like decimation is possible.
## 3 Mapping Lithologies in the Kohat Plateau Using SVM and ANN
### Spectral Features of Lithologies in the Region  
The reflectance values of all the training samples of Sentinel-2 data sampled from GEE for each class are shown in Figure 4. The plot shows the normalized value, dividing the samples' reflectance values with peak reflectance values. The highest peak value of 1 is shown by alluvium at 1.61 \(\upmu\)m, while water shows the high absorption/smallest reflectance value of 0.05 at 2.2 \(\upmu\)m, as compared with other classes. The normalized reflectance value of each class increased from 0.50-0.83 \(\upmu\)m; all formations have a positive gradient (slope) from 0.83-1.61 \(\upmu\)m, except negative gradient of water in that range. The Kohat Formation also slightly declines the reflectance value from 0.56-0.66 \(\upmu\)m. There is a sharp decline in the reflectance value of the Jatta Gypsum Formation after 1.61 \(\upmu\)m. The overall average reflectance value of Kamlial Sandstone, Dhok Patan, alluvium, and Jatta Gypsum was higher than the Nagri, Kohat, Muree, Mami Khel, and Chinji formations. These reflectance values could not distinguish between the lithologies of interest and therefore invited the use of machine learning algorithms for better delineation and further analysis.