esa-sceva/satcom-chunk-collection huggingface dataset:
# Malware Detection Issues, Challenges, and Future Directions: A Survey
## 5 The Taxonomy of Feature Extraction and Representation Methods
### Feature Representation Methods
#### 5.2.3 Weight-Based Vector  
The distribution of the extracted features is one of the biggest differences between legitimate and malware classes, and even between malware families. The features that are related to performing malicious behavior have to be heavily distributed in malware samples and are not or rarely distributed in legitimate samples. Furthermore, malware families are determined based on assumptions about how the malware acts and what techniques are used to carry out their own malicious actions [83]. Therefore, among the whole set of extracted features, there are specific features that can be related to particular families and are rarely distributed in others. Based on the above concept, some of the previous studies have represented the extracted text features as numerical weights using a weight-based vector with the help of statistical methods such as Term Frequency-Inverse Document Frequency (TF-IDF), Information Gain (IG), and entropy value.  
A weight-based vector consists of the extracted features and their weights. Specific weights are assigned to each feature as their data values using statistical methods such as information gain and TF-IDF. Therefore, the proposed models can distinguish between malware and benign files based on the assigned weights. The authors of [43] calculated the frequency of each extracted word, and then the frequency values were normalized. Further, the Information Gain method has been employed to assign the calculated weights to each feature. Those weights were used to determine where the testing files should be placed, whether in malware or benign files. Another study [81], computed weights for each extracted feature using TF-IDF to select only the most significant features and then represented their samples using the selected features along with their weights as an Attribute Relation File Format (ARFF), with each row in this file representing a weight vector. However, the statistical methods which aim to convert text data into numerical data to be fed into ML techniques do not capture any contextual relationship between features [85].
# Internet of Things in Marine Environment Monitoring: A Review
###### Abstract  
Marine environment monitoring has attracted more and more attention due to the growing concern about climate change. During the past couple of decades, advanced information and communication technologies have been applied to the development of various marine environment monitoring systems. Among others, the Internet of Things (IoT) has been playing an important role in this area. This paper presents a review of the application of the Internet of Things in the field of marine environment monitoring. New technologies including advanced Big Data analytics and their applications in this area are briefly reviewed. It also discusses key research challenges and opportunities in this area, including the potential application of IoT and Big Data in marine environment protection.  
Internet of Things; Big Data; wireless sensor networks; marine environment monitoring 1
# Reliability Enhancement Driven by ANN for Lighting Control System in Highway Tunnels
## 6 Discussion
### Application of This Network Model  
(1). For the tunnel lighting system that has been in actual operation, the trained neural network model can run in parallel with the actual lighting control system to realize the parallel verification function.  
(2). For the tunnel lighting system that has been in actual operation, the trained neural network model can partially replace the physical lighting control system when the line fault of the lighting control system occurs.  
(3). For tunnel cluster systems that meet certain conditions (artificial lighting systems between different tunnels do not influence each other, climate and natural light conditions are relatively close), the trained neural network model and physical lighting control system can be deployed in different tunnels in an alternating or hybrid manner. Such deployment can reduce the cost of installation and maintenance of light sensing equipment and tunnel control lines, and also improve the overall economy of tunnel cluster systems.  
(4). If artificial lighting sensing modules are added inside the tunnel, deviations between actual lighting parameters (actual color temperature and luminance) and predicted values of this network model (predicted color temperature and luminance) can be tracked dynamically. When these deviations exceed a certain limit, early warnings are a great help in reporting and diagnosing the failure of lighting fixtures, line failures, or other equipment malfunctions in a timely manner. Additionally, if target lighting parameters can be adjusted rapidly to compensate for this probable degradation of lighting quality caused by these deviations, there will be great benefits from this quasi closed loop lighting control.
## 5 Experiment and Evaluation
### Experimental Setup
#### 5.1.3 Skeleton-Based Methods  
Skeleton-based action recognition methods focus on the action characteristics of the key points of the human skeleton. They have high efficiency and accurate recognition results.  
The skeleton data provided by the NPU RGB+D dataset contain the original 3D positions of the basketball player's body key points in the corresponding camera coordinate system.  
At present, the most popular skeleton-based action recognition methods are based on GCN. We chose the best baseline methods ST-GCN and 2s-AGCN as the experimental methods and built the model framework on the basis of open-source code.  
To set up a unified evaluation benchmark, we uniformly reserved approximately 5% of the training data as the verification set. Considerable iterative training was carried out on the network. The network with the smallest verification error was selected from all iterations, and its performance is reported in the test data. All experiments were run on NVIDIA 2080 ti GPU.
# Nanomaterials in Photocatalysis: An In-Depth Analysis of Their Role in Enhancing Indoor Air Quality
## 5 Titanium-Based Nanomaterials
### Nanotubes  
One-dimensional structures like TiO\({}_{2}\) nanotubes (TNTs) have been studied due to their high photocatalytic efficiency. These structures have a high specific surface area which provides a high number of adsorbates and channels that enhance electron transfer leading to a reduction of charge carrier recombination especially for nanotubes with thin walls. Furthermore, the length of the TiO\({}_{2}\) nanotubes can increase the photocatalytic efficiency since a longer nanotube implies a greater specific surface area. Moreover, their characteristic structure which contains many hydroxyl groups on the interlayer region of their walls reduces the recombination rate of electron and hole pairs. The recombination rate is lower also because the electrons and positively charged holes can move freely throughout the length of the TNTs [118,119].  
In a recent study [119], [001] facet-exposed TiO\({}_{2}\) nanotubes were synthesized and installed on a commercial air cleaner. This study demonstrated that 001-TNT has a high degradation activity for acetaldehyde and formaldehyde under visible light being the first air cleaner that satisfied the Korean air cleaner standards protocol. On the other hand, another sample of TNT whose surface is composed of mainly [101] facets, exhibited only moderate degradation of these pollutants. Furthermore, 001-TNT exhibits a higher efficiency of degradation of toluene than TNT. It has been observed that the 001-TNT also has a greater charge transfer resistance than TNT implying that the charge carriers are transferred at the interface with a higher efficiency. Despite it having been proven that [001] facet exposure is beneficial for photocatalysis, there is an optimum value of exposure of [001] facets on TiO\({}_{2}\) which is about 70%. Over this value, the separation between the charge carriers is not efficient due to the lack of [101] facet TiO\({}_{2}\). It has also been investigated the durability of the two semiconductors, and researchers found that the durability of 001-TNT was almost 9 times greater than that of TNT. This is due to the fact that the photocatalytic activity of 001-TNT has been regenerated by UV irradiation in clear air.
## 3 Methodology
### Models
#### 3.2.2 GPP Model  
The MODIS land product algorithm (MOD17A2) [77] for daily _GPP_ is calculated as:  
\[GPP=\varepsilon\times APAR\ =\ \varepsilon\times IPAR\times FPAR, \tag{6}\]  
\[APAR\ =\ IPAR\times FPAR, \tag{7}\]  
\[IPAR\ =\ SWRad\times 0.45, \tag{8}\]  
\[FPAR\ =\ NDVI\times 1.24-0.168, \tag{9}\]  
where _APAR_ is the absorbed photosynthetically active radiation. _IPAR_ can be estimated from the incident shortwave radiation (_SWRad_) using Equation (8), and _FPAR_ is the fraction of photosynthetically active radiation. Here, _FPAR_ from satellite-derived data in Equation (9) is used instead of the direct quantization of spectral vegetation indexes, such as the _NDVI_ in the _MODIS_ algorithm [78]. The \(\varepsilon\) is a conversion factor, which varies widely for different vegetation types [79; 80; 81]. It is calculated as  
\[\varepsilon=\varepsilon_{\text{max}}\times T_{\text{min}Scalar}\times VPD_{Scalar}\, \tag{10}\]  
where \(\varepsilon_{\text{max}}\) is the maximum conversion factor, obtained from the Biome Property Look-Up Table (BPLUT) parameters [77], and \(T_{\text{min}Scalar}\) and \(VPD_{Scalar}\) are the linear attenuation scalars from the daily minimum temperature (\(T_{\text{min}}\)) and vapor pressure deficit (\(VPD\)), respectively.
# An Enhanced Fusion Strategy for Reliable Attitude Measurement Utilizing Vision and Inertial Sensors
## 3 The Enhanced Fusion Strategy Based on RBF and CKF  
### Basic Filter Process of CKF Algorithm  
For multi-frequency fusion issues, an enhanced fusion strategy based on RBF and CKF is mainly discussed in this paper. To start, the basic filter process of CKF is given. Instead of applying the linearization operation on the nonlinear equation, CKF approximates the probability density distribution of nonlinear function using a set of cubature points. Thus, this results in good estimation accuracy with an acceptable computation complexity. Furthermore, in CKF, the cubature points and weights are determined uniquely by the dimension of the state vector so that the complex process of parameter selection can be removed. The filter process is given as follows.  
Figure 2: Vision and inertial integrated attitude measurement model. RBF: radial basis function; CKF: cubature Kalman filter.  
(1) Calculating volume point:  
\[\xi_{i}=\sqrt{n}[1]_{i} \tag{4}\]  
\[\omega_{i}=\frac{1}{2n},i=1,2,\ldots,2n \tag{5}\]  
\[\omega_{i,jkk-1}=\sqrt{P_{k-1|k-1}\xi_{i}}+\hat{x}_{k-1|k-1} \tag{6}\]  
where \([1]_{i}\) denotes the column \(i\) of \([1]\), and \([1]\) is a symmetric matrix with a dominant diagonal line of 1. (2) Time update:  
The estimated attitude and covariance matrix at time \(k\) can be described as:  
\[\hat{x}_{k|k-1}=\frac{1}{2n}{\sum_{i=1}^{2n}{X_{i,jkk-1}}} \tag{7}\]  
\[P_{ik|k-1}=\frac{1}{2n}{\sum_{i=1}^{2n}{X_{i,jkk-1}X_{i,jkk-1}^{T}}}-\hat{x}_{ ik|k-1}\hat{x}_{ik|k-1}^{T} \tag{8}\]  
(3) Measurement update:  
Calculating volume point:  
\[X_{i,ikk-1}=\sqrt{P_{ik|k-1}\xi_{i}}+\hat{x}_{ik|k-1} \tag{9}\]  
Using measurement equation to propagate the volume point:  
\[Z_{i,ikk-1}=h(X_{i,ikk-1}) \tag{10}\]  
\[\hat{x}_{ikk-1}=\frac{1}{2n}{\sum_{i=1}^{2n}{Z_{i,jkk-1}}} \tag{11}\]  
The predicted measurement and covariance matrix at time \(k\) are in the following forms:  
\[P_{xz,jkk-1}=\sum_{i=1}^{2n}{\omega_{i}X_{i,jkk-1}Z_{i,jkk-1}^{T}}-\hat{x}_{ik |k-1}\hat{x}_{ik|k-1}^{T} \tag{12}\]  
\[P_{zz,jk|k-1}=\sum_{i=1}^{2n}{\omega_{i}Z_{i,jkk-1}Z_{i,jkk-1}^{T}}-\hat{x}_{ ik|k-1}\hat{x}_{ik|k-1}^{T} \tag{13}\]  
Calculating Kalman filter gain:  
\[K_{k}=P_{xz,jkk-1}P_{zz,jkk-1}^{-1} \tag{14}\]  
Attitude estimates and covariance estimate of the attitude error at time \(k\) are expressed as follows.  
\[\hat{x}_{ikk}=\hat{x}_{ikk-1}+K_{k}(z_{k}-\hat{x}_{ik|k-1}) \tag{15}\]  
\[P_{k|k}=P_{ik|k-1}-K_{k}P_{zz,jk|k-1}K_{k}^{T} \tag{16}\]
## 3 Framework
### Requirements and Assumptions
#### 3.2.1 Requirements  
To build the set \(\mathcal{N}(N)\), we have to consider all the types of communication provided by the network and identify collisions that should be avoided. In this paper, we consider the following requirements:  
* _Req1 for general communications_: Any node can communicate in unicast mode with any other node.
* _Req1' for tree communications_: Any node can communicate in unicast mode only with its parent or its children in the data gathering tree.
* _Req2 for immediate acknowledgement of unicast transmissions_: since radio transmission is unreliable, the receiver must transmit its acknowledgement immediately after receipt of the MAC frame. In other words, the receiver uses the slot of the sender to transmit its acknowledgement. Thus, the sender can use its slot to retransmit if needed.
* _Req3 for broadcasts_: Any node can broadcast information to its neighbors.  
Consequently, SERENA must meet requirements \(Req1\), \(Req2\) and \(Req3\) for a general application, while it must meet \(Req1^{\prime}\), \(Req2\) and \(Req3\) for a data gathering application. In the following, we abusively use the terms general coloring for the first case and tree-based coloring for the second one.
## 3 Measuring System, Method, and Scope of Research
### Methods of Research
#### 3.2.2 Determination of the Value of Surge and Limited Current and Voltage on the HTS Tape  
The HTS tapes were subjected to test current impulses with amplitudes significantly exceeding the critical current value. Due to the transition of the HTS tape from the superconducting state, the expected short-circuit current was limited to the value of \(I_{min}\).  
There was a gradual increase in the voltage on the HTS tape and a decrease in the current value due to the increase in temperature and, thus, the tape resistance.  
On the basis of the recorded waveforms, the characteristic values \(I_{0 max}\), \(I_{min}\), \(U_{max}\), and the energy released in the belt for various values of the test pulse were determined:  
\[E=\frac{\Delta t}{n}\sum_{n=1}^{k}u_{n}\cdot i_{n} \tag{1}\]  
where: \(E\)--the amount of energy released on the sample, \(\Delta t\)--impulse duration, \(u\)--instantaneous voltage on the tape, \(i\)--the instantaneous value of the current, \(n\)--number of samples.
## 4 Results and Discussion
### Predictive Capability of the Models  
#### 4.3.1 Prediction of Temperature \(T_{f_{out}}\) at the End of the Roadway  
The predicted temperature data on the testing set were compared with the actual temperature data, as shown in Figure 9. Among the 182 pieces of data, 80% were used as the training set, while the other 20% served as the testing set.  
The hyperparameters of the LSTM-GA and PCA-LSTM models are disclosed in Table 2.  
As presented in Figure 9, the values of MSE and \(R^{2}\) of the LSTM model are 0.0011 and \(-\)0.5724, respectively; those of the LSTM-GA model are 1.6417 \(\times\) 10\({}^{-5}\) and 0.9762; and those of the PCA-LSTM-GA model are 5.8713 \(\times\) 10\({}^{-6}\) and 0.9915. Moreover, it can be observed from Figure 9 that the predicted curve of the PCA-LSTM-GA model is the closest to the actual temperature curve.
## 4 Discussion
### Models and Measurements for Setup Validation
When the wave crosses an interface with an inclination (water to polystyrene), the change of acoustic impedances provokes more deviations that are reverted to the original inclination when the wave crosses the next interface (polystyrene to culture medium). At certain opposite inclinations of the studied boundaries, the effect of the resonances is still present, for instance, when both relative inclinations are the same (\(\xi=\mathit{v}\mathit{x}\mathit{r}\mathit{n}\)). In this case, the wave returns to the transducer following the same path, which produces constructive interference with the next generation of waves and creates standing waves. This did not occur when both boundaries were relatively inclined in the same direction, because the waves were continuously deviating from the transducer after crossing each interface. LIPUS heating in the culture medium and the dish was modeled for a geometry with no resonant effect by standing waves, in accordance with the experimental setup (\(TD=10\) mm, \(CM=5\) mm). The temperature increment produced in the dish after 5 min of LIPUS with the largest intensity used in experiments was 0.76 \({}^{\circ}\)C at the very center of the well (Figure 4). We can notice the correspondence of this temperature distribution with the acoustic field pattern of Figure 3b. Heat propagation in liquids, although influenced by the thermal conductivity, has a predominant contribution of natural and force convections. In this model, it was omitted the effect of agitation produced by the ultrasound radiation force that could act as an external force convection and should permit a more distributed heat. Although nature convection was included based on [27], the effect of acoustic agitation should be more significant. Therefore, real-temperature increments in the dish produced by ultrasound absorption should be smaller and more uniform than those shown in Figure 4 and can be comparable to the measured increments of 0.3 \({}^{\circ}\)C reported by [13].
# Emerging Data-Driven Calibration Research on an Improved Link Performance Function in an Urban Area
## 4 Case Study
### Results
#### 4.2.2 Calibration of the VOT  
(a) VOT of travel for work  
From a macroeconomic perspective, it is considered that labor is one of the factors of production, and the savings in travel time spent by travelers can be used in national production, which is equivalent to an increase in the factors of production and, thus, leads to an increase in GDP. Based on this approach, the formula for calculating the VOT is  
\[VOT=GDP/(P\times T). \tag{24}\]  
Here, \(GDP\) is the gross national product, \(P\) is the average annual employment, and \(T\) is the average annual number of hours worked by an individual.  
The city's gross regional product per capita based on the household population was CNY 172,912 (equivalent to USD 26,802 at the average annual exchange rate).  
The hourly wage rate per capita is  
\[VOT_{\text{twork}} =172912/250/8 \tag{25}\] \[=86.456.\]  
Existing studies have shown that this production method is applicable for reflecting the time value of the flow of the group of passengers who travels with working time (or productive time), i.e., it is more applicable to work trips. At the same time, travelers should theoretically be regular occupants.  
(b) VOT of travel for non-work  
For non-work travel by fixed and non-fixed occupants, the income method was used to measure their VOT. The income method reflects the opportunity cost of the time taken by the trip relative to the individual, i.e., the reduction in income caused by the time being consumed by the travel process and resulting in the traveler not being able to work. The calculation formula is  
\[VOT=INC/T. \tag{26}\]  
Here, \(INC\) denotes the annual personal income in RMB (yuan).  
In contrast to the production method, the income method is applicable for reflecting the VOT of the flow of a group of passengers who pay for trips with personal income, and this is more suitable for non-work (or non-production) trips.  
As there is a strong correlation between the VOT and income, the monthly income was divided into intervals, and the correspondence between the time value and individual monthly income was obtained, as shown in Table 4.  
Figure 4: Results of calibration (with DE_par algorithm added).
# A New String Edit Distance and Applications
## 1 Introduction  
String similarity is an important tool for many applications, including web search completion suggestions [1], spelling correction [2], and ontology alignment [3]. Some of the tools developed in the field of string similarity carry over well to DNA applications, such as DNA-protein matching, sequence assembly, and local similarity searches [4], as well as Longest Common Substring and Longest Common Subsequence algorithms [5]. Determining similarity of DNA strands is important in phylogenetics, and the volume of available data continues to increase over time. Alignment-free methods have been developed, in part to account for issues that arise when traditional sequence alignment is pushed to its limit. These include the use of geometric representations in various dimensions, as well as graph theory [6].  
Most string edit distances consider only single character edits and are variations of Levenshtein distance. These standard string edit distances are inadequate for analyzing similarity between DNA sequences in forensic applications where the goal is to identify the individual who is the source of the DNA in a crime sample. To fill this gap, a new string edit distance is defined, referred to as Restricted Forensic Levenshtein (RFL) distance, that accommodates the addition or deletion of one or more motifs (consecutively repeating sequences of nucleotides 2 to 6 base pairs long) as edit types separate from single-nucleotide insertion, deletion, and substitution. This makes the problem of computing the distancemuch more difficult. To address this, a dynamic programming algorithm for computing the RFL distance between sequences has been developed. Forensic examples are used to illustrate the purpose and use of RFL distance but the applications extend to sequence alignment and string similarity in other biological applications. The algorithm's novel contribution is in efficiently handling the complex interactions that arise between multiple- and single-character edits.
# A Coaxial Dielectric Barrier Discharge Reactor for Treatment of Winter Wheat Seeds
## 3 Results and Discussion
### Impact of the DBD Reactor Operation Time on the Temperature Evolution  
Figure 5a presents the curves of temperature increase during 10 min of plasma on for argon (black curve) and helium (red curve) with a discharge power of 6.5 W. Both curves rapidly increase with time clearly tending to saturation, where the helium discharge temperature rises to about 5 \({}^{\circ}\)C more than for the argon. This difference might be explained by the higher thermal conductivity of helium heating up the entire discharge volume more homogeneously, while for the argon plasma, the biggest temperature gradient occurs in the filaments (Figure 2c), leading to an average lower temperature value. Running the plasma source for up to 10 min led to an increase of temperature to around 55 and 60 \({}^{\circ}\)C for argon and helium, respectively. Thus, in order to avoid the overheating of biological targets, a limitation of 5 min of maximum treatment time was established for such discharge parameters. Switching off both discharges (Figure 4a) after 10 min of plasma on causes the temperature to drop, reaching physiological temperatures after 5 min. Therefore, for seed treatments longer than 5 min using this coaxial DBD, a minimum 5-min pause in between treatments was taken.  
Winter wheat seeds were exposed to both plasmas (argon and helium) in order to check the temperature evolution at the seed coat. The seed's temperature was measured using an IR camera directly after plasma treatment for different exposure times. The curves are shown in Figure 5b where the same trend for the gas temperature can be observed. However, the temperature values at the seed coat tend to saturate at shorter treatment times, remaining below 41 \({}^{\circ}\)C. The seed's temperature is comparable when wheat is treated with both gases, reaching around 37 \({}^{\circ}\)C after 5 min of plasma treatment. Therefore, no heat damage caused by the plasma is expected when keeping 5 min as the maximum treatment time using 30% of voltage duty cycle and a discharge power of around 6.5 W.