esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Evaluation System
### Implementation of Differences between Human-Driven and Self-Driven Vehicles
#### 4.1.2 Inter-Vehicle Distance Used to Decide Whether to Enter the Intersection  
The low-priority vehicle decides whether to enter the intersection based on how far it will be from the priority vehicle in the back after it has passed through the intersection, as shown in Figure 6. The value we set to the inter-vehicle distance threshold was such that the priority vehicle does not need to brake even when the low-priority vehicle moves into a position in front of it. The inter-vehicle distance thresholds for the two types of vehicle were as follows:  
* Self-driven vehicle: The minimum inter-vehicle distance.
* Human-driven vehicle: The minimum inter-vehicle distance + extra inter-vehicle distance.  
In the case of a human-driven vehicle, the higher the speed of the priority vehicle, the greater the inter-vehicle distance that we set. The extra inter-vehicle distance was the distance that the priority vehicle moves per second. For example, if the priority vehicle is running at a speed of 10 km/h = 2.78 m/s, the extra inter-vehicle distance is 2.78 m. If the priority vehicle is running at a speed of 40 km/h = 11.11 m/s, the extra inter-vehicle distance is 11.11 m.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Self-Driven Vehicle** & **Human-Driven Vehicle** \\ \hline Response time [s] & 0.1 & 0.9 \\ Max. rate of acceleration [G] & 0.15 & 0.25 \\ Max. rate of deceleration [G] & \(-\)0.175 & \(-\)0.25 \\ Min. inter-vehicle distance [m] & 3 & 2.5-3.5 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Parameter values that cause differences in operations between self-driven and human-driven vehicles.
## 5 Quality Attribute Outcomes
### Privacy and Security
#### 5.1.2 Decentralized Data Storage  
A blockchain's decentralized storage prevents risks associated with centralization, such as single points of failure. [PERSON] et al. [26] and [PERSON] et al. [31] each describe a blockchain-based platform that stores user activity data securely across a distributed network. This approach allows users to engage in physical activity challenges with guaranteed data protection and reduces vulnerabilities typically associated with centralized servers.  
In a multi-hospital setting for rehabilitation, [PERSON] et al. [27] describe how a blockchain supports decentralized data sharing. Data from IoMT sensors, such as bio-anke sensors used in recovery, can be securely shared among hospitals without compromising patient privacy, providing real-time access while preserving decentralized data integrity.
# Towards an Alternate Evaluation of Moisture-Induced Damage of Bituminous Materials
## 2 Challenges  
Various, predominantly empirical, mechanical test methods to evaluate the moisture susceptibility of asphalt mixtures have been developed and are discussed elsewhere [8]. The AASHTO T 283 test method is frequently used by highway agencies worldwide for the sake of evaluation of moisture damage of compacted asphalt concrete mixtures. Basically, in the T 283 test, the TSR, which is calculated as the ratio of the indirect tensile strength (ITS) of conditioned specimens to that for dry specimens, is used to determine the resistance of the asphalt mixture to moisture damage. A minimum TSR of 80% is usually used as a threshold to ensure that the mixture is moisture-resistant, while the mixture is considered moisture-prone if the TSR is any lower. However, several concerns of this ratio have been discussed [9; 10]:  
* Does this value guarantee a satisfactory resistance to moisture damage in the long-term? The current practice of the TSR concept is based on short-term aged samples, but the moisture susceptibility is a concern throughout the service life of asphalt pavement.
* What is the proper conditioning method to mimic the moisture damage in real life? Freeze-thaw is used in AASHTO T 283, and the moisture induced stress tester (MIST) was introduced in recent years. Can these methods, or some other method, better mimic real moisture damage?
* As the unconditioned or dry compacted specimens are different from the conditioned or wet specimens, does specimen-to-specimen variability affect the results? Even for the same mixture, the prepared compacted samples would have different distributions of aggregate particles and/or air voids, which affects the measured property. Therefore, the test still lacks repeatability, and its accuracy is questionable.
* Does the saturation level while conditioning affect the repeatability of the results?
* Based on previous studies on the AASHTO T 283 test, a number of successful cases that pass in the laboratory would fail in the field.
# A Survey on the Impact of Intelligent Surfaces in the Terahertz Communication Channel Models
## 2 IS in Wireless Communication  
This section provides an overview of the IS developments during the last decades, the IS architecture, and the main IS attributes that support SRE.
## 3 Results and Discussion  
### Effect of pH  
In order for the adsorption to proceed with a high intensity, the surface charge of the adsorbent and the electrical potential of the adsorbate should be opposite. For many solids, the potential-forming ions are H\({}^{+}\) and OH\({}^{-}\) ions, which entails that the surface potential can be influenced by changing the pH of the solution in which a given solid is found. In the case of a very strong adsorption of potential-forming ions on the surface of the solid (especially for oxides) it may happen that the charge of the adsorption layer will change its mark. This means that the surface of the solid body with which the adsorption layer is closely related will also change the sign. Therefore, depending on the pH of the solution in which the adsorbent is located, it will take up a positive or negative surface electric charge [5; 25].  
Within the neutral range, the inherently amphoteric character of the activated carbon surface has a positive surface charge. At a higher pH of the solution, the negative chromate ions significantly reduce their carbon-related affinity, as a negative surface charge may form on the surface of the particle and inside the carbon pores [26].  
The solution pH effect on chromium (VI) adsorption is shown in Figure 1. For WD (HNO\({}_{3}\)) the adsorption decreased with increasing the solution pH. For carbons WDA and WD (HCl), the reaction effect on chromium adsorption capacity was greater and definitely more beneficial at lower pH. Another work has also reported similar behavior using other adsorbents [22; 24; 27].  
Further studies were carried out at pH = 2.
# Bilinear CNN Model for Fine-Grained Classification Based on Subcategory-Similarity Measurement
###### Abstract  
One of the challenges in fine-grained classification is that subcategories with significant similarity are hard to be distinguished due to the equal treatment of all subcategories in existing algorithms. In order to solve this problem, a fine-grained image classification method by combining a bilinear convolutional neural network (B-CNN) and the measurement of subcategory similarities is proposed. Firstly, an improved weakly supervised localization method is designed to obtain the bounding box of the main object, which allows the model to eliminate the influence of background noise and obtain more accurate features. Then, sample features in the training set are computed by B-CNN so that the fuzzing similarity matrix for measuring interclass similarities can be obtained. To further improve classification accuracy, the loss function is designed by weighting triplet loss and softmax loss. Extensive experiments implemented on two benchmarks datasets, Stanford Cars-196 and Caltech-UCSD Birds-200-2011 (CUB-200-2011), show that the newly proposed method outperforms in accuracy several state-of-the-art weakly supervised classification models.  
fine-grained classification; B-CNN; weakly supervised localization; loss function +
Footnote †: journal: applied sciences
## 2 Materials and Methods
### NO\({}_{x}\) Detection in the Gas and Liquid Phases  
After passing through the plasma reactor, the gas composition was examined using a gas analyzer system (Testo 350, Testo SE & Co. KGaA, Titisee-Neustadt, Germany), allowing us to observe NO and NO\({}_{2}\) species in the range of 0-5000 ppm with an accuracy of 5 ppm. For this purpose, the gas analyzer was connected in series with the plasma reactor outlet. In the case of the Me-L electrode configuration, liquid samples of 2 mL were collected after 15 min of plasma exposure, and the nitrite (NO\({}_{2}\)-) and nitrate (NO\({}_{3}\)-) concentrations produced in the liquid phase were measured employing two different methods. The NO\({}_{2}\)- concentration was determined using the Griess assay, a standardized method for examining water and wastewater [40; 41]. The Griess reagent was prepared according to the procedure described in the SI. Meanwhile, the NO\({}_{3}\)- concentration was quantified employing a Nitrate 3021 combination ion selective electrode immersed into a plasma-exposed liquid sample. All gas and liquid phase measurements were carried out in triplicate. It is worth noting that the gas treatment time for the Me-Me system was kept consistent with that of the water system, ensuring uniformity in the measurements.
## 4 Case Study
### Machining and Accuracy  
Shown in Figure 14 is a diagram of the herringbone gear machining process for a special gear-shaping machine. Firstly, the NC program should be developed according to the flow chart of the actual detection and compensation process, including the right- and left-hand gear machining programs and symmetry error detection programs. Secondly, the preparation work should be performed before the machining of herringbone gears, including gear clamping, positioning, alignment, program debugging, etc. Then, the numerical control program controls the gear shaper to complete the processing of the right-handed gear and controls the Marposs probe to locate and determine the symmetrical position of the calibration slot.  
After that, the herringbone gear is turned over and clamped, and the above steps are repeated to complete the processing of the left-handed gear (without cutting to the tooth depth). The numerical control program controls the Marposs probe to complete the measurement of the left-handed tooth space position corresponding to the position of the right-handed gear calibration slot. Finally, the calculation of the spatial symmetry error is  
Figure 14: Diagram of machining process for herringbone gear with a special-gear shaping machine.  
completed, and the phase compensation processing is carried out. Based on the designed special fixture, specific NC machining programs are developed and debugged, and three types of herringbone gears are manufactured on this special NC gear-shaping machine.  
The machining process for the measurement and compensation of herringbone gear spatial symmetry error is shown in Figure 14, including NC program debugging, preparation work before machining, without shaping to the tooth depth, symmetry error detection program debugging, detection of right- and left-hand gear errors, symmetry error compensation machining and precision detection of spatial symmetry error.  
Based on the proposed machining method, the results of the machining experiments indicated that the symmetry can be reliably controlled within 0.015 mm. As shown in Figure 15a, the machining instances for herringbone gear were extended to other sizes. As shown in Figure 15b, the machining accuracy of symmetry detected by the coordinate measuring machine can be controlled within 0.02 mm with SSEDC. Comparing the traditional and novel machining methods, the contrasting results are shown in Table 2.
# Development of an Ontology-Based Solution to Reduce the Spread of Viruses
## 1 Introduction
For this reason, we opted for developing this solution to cope with the emergence of strange viruses that require a long time to be identified accurately. This accurate identification is needed to develop appropriate vaccines. Following this, the appearance of strange symptoms in a person automatically leads to informing official institutions, which in turn take urgent measures, such as applying a policy of distancing to limit the spread of the virus or providing preventive treatments. The advantages of the developed solution can be summarized in the following:  
* Using the ontology enables the necessary updates to be made in the database without causing a bug in the solution;
* With the emergence of new viruses, new concepts can be added at the right place and time without re-developing the entire solution;
* This solution is characterized by accuracy, speed, and extensibility;
* This solution is open to all future developments. The major research contribution of our work may be summarized as follows:  
* We propose a smart solution to provide preventive guidance about an individual's health. The inputs to this solution are data about the person and his surroundings in terms of health status and virus spread. * The proposed solution is based on information and communication technologies; in addition, from a media point of view the field of health and diseases is an open and distributed system that includes a large amount of information, which requires good structuring. An ontological approach is advanced to develop the proposed solution. The solution's knowledge base includes information about the general symptoms of viruses and how to prevent them. Ontology technology has many advantages, such as flexibility in the use and updating of information and its reliance on collaborative and intelligent thinking, through a set of pre-developed rules that work in concert with each other to provide immediate guidance and advice to people to reduce the spread of infection. To accomplish this work, a series of steps was followed: * The first step represents a field study of everything related to the viruses. The results of this step are the extract of information, which includes concepts, concepts' attributes, relationships between concepts, examples related to concepts, and rules of intelligent reasoning. * In the second step, all this information obtained with the \"Protege\" program is edited and checked using syntax checking to display edited information. * Finally, the final step is to implement the proposed solution using several scenarios. The remainder of the article is organized as follows: After this introduction, Section 2 is devoted to presenting the study environment. In Section 3, the proposed methods and obtained results are presented. This includes the design and implementation of the proposed solution. Various scenarios are addressed. A detailed description of the rule is given; analysis and discussion follow in Section 4, and concluding remarks are then given in Section 5.
## 4 Statistical Testing of ENMRA Algorithm
However, as indicated in [45], these underground environments create significant wireless communication challenges through conventional electromagnetic (EM) waves. Especially, the main problems with EM communication are caused by large antenna sizes, highly unreliable channel conditions, and concise communication ranges. The magnetic induction (MI) technique is a promising alternate wireless communications solution to deal with the underground challenges [45]. This technique uses a near magnet field of coils to spread information, thereby attaining stable channel conditions through small coil sizes. Numerous significant contributions to underground wireless sensor networks were extensively reported in the literature [46; 47; 48; 49]. MI communication is ideal for underground environments with these great features. The majority of physical layer work has been completed to date, including signal analysis and antenna design. However, there is currently no protocol for MI-based WUSNs. This work aimed to provide a cross-layer solution for optimizing MI communication within the network via a distributed approach. In this article, we presented a distributed cross-layer solution for WUSNs based on MI using an enhanced naked mole-rat algorithm (ENMRA), called the DECEN protocol framework. Our solution satisfied the quality requirements (i.e., packet delay and transmission reliability) of various applications while achieving optimal energy efficiency and computational complexity, resulting in increased throughput. These characteristics contribute to the practicality of our solution. The rest of this section is arranged accordingly. Section 5.1 presents classification of MI techniques used in WUSNs. The system model is presented in Section 5.2. A cross-layer framework using ENMRA is proposed for the optimal energy-throughput performance in Section 5.3. Performance evaluation is provided in Section 5.4.
## 3 Experiments and Analysis
### t-SNE Clustering Visualization  
To explain and visualize the working process of the network model, the t-SNE [26] clustering algorithm was used to explain the cause of the failure. t-SNE can map high-dimensional features into low-dimensional spaces to visualize high-dimensional data. A total of 375 samples from 5 categories were randomly selected for demonstration, as shown in Figure 10a. After the proposed model's processing, the separation between the dense clusters of mapped feature points was greater. In Figure 10b, after the Vgg11 model's processing, the separation between scratches and stains was not obvious and there were overlapping areas indicating that Vgg11 found it difficult to classify these two types of defects. This also explains why scratches and stains were prone to misdetection in the normalized confusion matrix of Vgg11. The t-SNE study helped improve the model. Our future work will be focused on improving the model's performance on these similar defect types.
## 2 Materials and Methods
### Brain Machine Interface
#### 2.4.2 Pre-Processing  
The pre-processing stage started with two frequency filters: a notch filter at 50 Hz to remove the contribution of the power line and a high-pass filter at 0.1 Hz. In order to reduce motion artifacts, electrode wires were fixed with clamps and a medical mesh. The movement of jaw muscles can generate signal artifacts, so subjects were asked to not swallow or chew while they were performing MI, regressive count or were in a idle state.  
The \(H_{\infty}\) denoising algorithm was applied to mitigate the presence of eye artifacts and signal drifts [5]. This algorithm estimates the contribution of the EOG and a constant parameter to the EEG signal and removes it. Afterwards, there were two different pre-processing lines, one for each paradigm.  
For the MI paradigm, a filter bank comprising multiple band-pass filters was applied to the data after the \(H_{\infty}\) denoising algorithm. Four band-pass filters were employed to obtain data associated with alpha and beta rhythms.  
Regarding the attention paradigm, EEG signals from each channel were first standardized following the process presented in [20]. For each channel, the maximum visual threshold was computed as the mean of the 6 highest values of the signal. This value was iteratively updated for each epoch and it was used to standardize the data as  
\[SV(t)_{ch}=\frac{V(t)_{ch}}{\frac{1}{Ch}\sum_{j=1}^{Ch}MVThreshold_{j}}. \tag{1}\]  
The signal of each channel, \(V(t)_{ch}\), was normalized taking into consideration the maximum visual threshold (\(MVThreshold\)) of all the EEG channels. Subsequently, the surface Laplacian filter was used to reduce spatial noise and enhance the local activity of each electrode [21].  
Figure 3: Brain–machine interface (BMI) scheme. During training, the exoskeleton was in opened-loop control, and for testing, it was in closed-loop control. The BMI used two different paradigms: one based on motor imagery of gait and another one based on the user’s level of attention to gait. Both paradigms shared some steps of pre-processing but there were additional different steps for each one. Then, two different feature extraction methods were employed. Trials from the training phase were used to train the BMI classifiers for testing.
# Enhancing Botnet Detection in Network Security Using Profile Hidden Markov Models
###### Abstract  
A botnet is a network of compromised computer systems, or bots, remotely controlled by an attacker through bot controllers. This covert network poses a threat through large-scale cyber attacks, including phishing, distributed denial of service (DDoS), data theft, and server crashes. Botnets often camouflage their activity by utilizing common internet protocols, such as HTTP and IRC, making their detection challenging. This paper addresses this threat by proposing a method to identify botnets based on distinctive communication patterns between command and control servers and bots. Recognizable traits in botnet behavior, such as coordinated attacks, heartbeat signals, and periodic command distribution, are analyzed. Probabilistic models, specifically Hidden Markov Models (HMMs) and Profile Hidden Markov Models (PHMMs), are employed to learn and identify these activity patterns in network traffic data. This work utilizes publicly available datasets containing a combination of botnet, normal, and background traffic to train and test these models. The comparative analysis reveals that both HMMs and PHMMs are effective in detecting botnets, with PHMMs exhibiting superior accuracy in botnet detection compared to HMMs.  
network analysis; botnets; malware detection; Hidden Markov Models; Profile Hidden Markov Models +
Footnote †: journal:  
The structure of this paper is organized as follows. Section 2 provides background details on the topics covered in the project's implementation. Section 3 explores relevant work related to this research. Section 4 delves into the work's implementation, dataset, and methodology, covering steps such as data pre-processing, feature extraction, HMM and PHMM training, and evaluation. Section 5 consists of a comparison and analysis of the results obtained from the experiment. Finally, Section 6 proposes our conclusions.