esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Material and Methods
### Variable Information and Data Preprocessing  
The data provided by NFA consists of continuous and categorical types. As listed in Table 1, continuous variables were divided into seven types, including property damage, number of floors, number of basement floors, total floor area (TFA), and building area. Categorical variables were divided into eight types, including the ignition heat source and ignition factor. Among the continuous variables, one target variable (property damage) was set, and those without property damage were used as predictive variables in the analysis.  
For the categorical variables, the model performance decreased when the frequency (or ratio) of each variable level was low. Thus, recategorization was performed by integrating and modifying the number of levels of the categorical variables. Recategorization removes outliers and refers to reducing the number of levels (classes) of the categorical variables. It is considered when one categorical variable has ten or more levels. Rare events that are difficult to occur or levels with a low frequency are eliminated, and the number is adjusted to approximately four to improve the performance of the classification model. Recategorization was performed for the final 12,223 data items. Twelve months were recategorized into four seasons for the month of fire and 24 h into four 6 h sections for the time of fire. For facility location information, 226 locations were recategorized into the metropolitan management area (MMA), metropolitan cities (MCs), and provinces/regions (PRs). This is shown in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline
**Category** & **Variable** & **Number** \\ \hline Continuous type & Property damage, number of floors, number of basement floors, TFA, building area, burnt area, and number of casualties & 7 \\  & Facility location information, structure, industry & 8 \\ Categorical type & type, month of fire, time of fire, ignition factor, ignition material, and ignition point & 8 \\ \hline \end{tabular}
\end{table}
Table 1: List of data variables.
# A Packet Content-Oriented Remote Code Execution Attack Payload Detection Model
## 4 Methodology
### ELi and IDSER Detector
#### 4.4.3 Java Code Feature Extraction  
It is easy to locate the Java bytecode by checking whether the original packets (in hexadecimal form) contain the segment beginning with cafebabe (cafebabe is the magic number used to indicate the class file). We intercept the part that begins with cafebabe from the original packet, and then use Javassist to extract the class name, and dump it into a class file. Next, we use Soot to convert the dumped class file into Jimple code and analyze it.  
Different from expression language, Java code has a more complex structure and execution logic. So we cannot simply extract features based on AST. In response, we use a lightweight code slicing algorithm based on data dependency as shown in Algorithm 3. For a class file, we first use Soot to parse it and obtain a list of Soot classes. Next, we analyze all the Soot methods of each Soot class to build code slices. Specifically, we define a set of sensitive methods and perform intraprocedural analysis within the method body containing sensitive method invocations to build the basic code slices. Then, based on these basic code slices, we perform interprocedural analysis across all methods to construct the complete code slices. Finally, we extract features on the basis of the constructed code slices.  
```
Input: Class file classFile Output: Code slices codeSlices
1\(\mathit{sootClassList}\gets\mathit{SootClassFile}\)// Use Soot to parse the class file
2 foreachootClass in sootClassListdo
3 sinkMethods \(\leftarrow\) init();
4 foreachootMethod in sootClass.getMethods()do
5 ifsootMethod in sinkListthen
6//sinkList is a set of sensitive methods manually defined by security experts.
7 sinkMethods.add(sootMethod);
8
9 end if
10
11 end
12 foreachsinkMethod in sinkMethodsdo
13 Intraprocedural Analysis: find out all variables (relatedVar) and method invocations (relatedFunc) that have data dependencies with sinkMethod.
14 Interprocedural Analysis: find out all method invocations (newRelatedFunc) that have data dependencies with relatedVar and relatedFunc in all sootMethods of sootClass.
15 codeSlices.addAll(newRelatedFunc)
16
17 end if
18
19 end for
```  
**Algorithm 3**Code slice.
# Experimental and Modeling of Residual Deformation of Soil-Rock Mixture under Freeze-Thaw Cycles
## 5 Discussion  
In this study, we investigated the residual deformation of soil-rock mixture under the action of freeze-thaw cycles with the rock content as a variable. It was found that the boulders in the soil-rock mixture assumed the main skeletal role. With the increase in the rock content, the residual deformation after the freeze-thaw cycle gradually decreased, and a smaller amount of residual deformation was produced when the rock content was about 80%. This conclusion is similar to the results of [PERSON] et al. [37]. However, when the rock content of the soil-rock mixture reached 90%, a larger residual deformation was produced again. This phenomenon may be related to the \"lubricating\" effect of the soil filling between the boulders and destroying the skeletal structure, as mentioned in [PERSON] et al. [38]. In addition, the residual deformation of all groups of specimens was settling. According to [PERSON] et al. [39], the water migrates upward when the soil thaws, and thus the excess pore space generated leads to the redistribution of fine particles in the soil and the appearance of softening of the soil, which fits with the experimental results. We also tried to build an LSTM model using deep learning methods. The model was validated based on the results of freeze-thaw cycling tests on soil-rock mixture, and the numerical simulation results were relatively small in error with the actual test results, which proved the feasibility of the LSTM model in simulating freeze-thaw tests.  
However, there are still some limitations in this study on the freeze-thaw cycle mechanism of soil-rock mixture. First, due to the test equipment, the changes inside the soil-rock mixture during the freeze-thaw process cannot be clearly and directly observed. Secondly, the deep learning method has been little involved in the study of the freeze-thaw cycle mechanism, and the constructed LSTM model is not mature yet. Given the above problems, it is hoped that future research can be combined with high-precision CT scanning equipment to visualize and monitor the stages of freeze-thaw of the soil-rock mixtures so that more specific experimental data can be obtained. It can also provide more references for deep learning models.
## 3 Understanding Biofilms
### Biofilm Formation
#### 3.3.3 The Process of Maturing or Reaching Full Development  
One crucial aspect of biofilm maturation is the development of water channels that function like a circulatory system. These channels efficiently transport nutrients and eliminate waste to ensure the viability of cells within the inner regions. These channels also contribute to the biofilm's resilience by maintaining a harmonious environment within the structure [71; 72].  
Throughout the maturation process, a biofilm does not simply grow in size, it forms an intricate and advanced three-dimensional structure. The arrangement of this structure is meticulously organized, maximizing the biofilm's effectiveness in utilizing resources and protecting itself from external dangers. The spatial organization inside the biofilm enables efficient resource allocation and confers a tactical benefit in defending against antagonistic chemicals, such as antibiotics or immunological reactions [73].  
A dynamic alteration in gene expression distinguishes the maturation phase. The upregulation or downregulation of certain genes reflects the biofilm's dynamic requirements. These genes are crucial in EPS production, stress response, nutrition acquisition, and cell-cell communication facilitation [74; 75]. Differential gene expression plays a pivotal role in enabling biofilms to adapt and thrive under diverse environmental conditions. Recent studies have identified specific genes exhibiting increased activity during the maturation stage of biofilm development. For instance, in Bifidobacterium longum FGSZY16M3, genes such as _epsH_, _epsK_, _eff_, _frr_, _pheT_, _rfbA_, _rfbJ_, _rfbP_, _rpmF_, _secY_, and _yidC_ were found to be significantly upregulated, contributing to polysaccharide biosynthesis and structural integrity of the biofilm matrix [76]. Similarly, the elevated expression of functional amyloid genes _fapABCDE_ in fully matured biofilms of Pseudomonas fluorescens underscores their critical role in biofilm formation, facilitating adhesion and stability. Notably, the _fapC_ mutant exhibited defects in forming mature biofilms, further emphasizing the importance of these genes in biofilm development [77; 78]. These findings highlight the diverse genetic adaptations that support biofilm maturation and resilience.  
The maturity period of biofilms varies across bacterial species and is affected by environmental conditions, including nutrition supply, temperature, and pH. Biofilm maturation often occurs over hours to days, depending upon the organism and ecological circumstances. In _P. aeruginosa_, biofilm maturation starts within 13 h under laboratory conditions, evolving into a well-organized and densely populated community by 24 to 48 h [79]. This phase entails the significant creation of EPS, which provides structural integrity and protection. Likewise, _E. coli_ biofilms on urethral catheters achieve maturity within 24 h, underscoring the fast formation of biofilms in clinical environments [80; 81].
## 3 Push-Off Tests on Joints between Segments  
### Test Design  
#### 3.1.1 Specim and Geometry  
Push-off specimens similar to those used by other researchers [33; 34] to study the shear transfer and aggregate interlock behaviour of pre-cracked concrete were adopted. Similar tests for dry and epoxied joints have been performed by several researchers [35; 36; 37; 38]. The main research activities on the field of bridge segments with dry or epoxied connections are compiled in [39]. Further, grouted joints were also investigated in various experimental studies, primarily focusing on large precast panels' connection in building construction [40; 41]. The specimens used in these tests consisted of pairs of concrete blocks (Figure 12) with a height of 260 mm and a width of 100 mm. These blocks were cast with a self-compacting concrete mixture with a target compression strength of 60 MPa, typical for precast elements. Four bent bars with a diameter of 10 mm were used to realize the corbels, which formed the load introduction points. Those were necessary to suppress bending effects in the tested joint.  
The connection between the separate blocks was achieved by a 10 mm thick layer of mortar, resulting in a joint area of 100 \(\times\) 140 mm (types 2-4 in Figure 12). The layer thickness was identified as the minimum required tolerance in the construction of a full-scale prototype in [36]. Besides the connected specimens, a reference test without mortar was tested (type 1 in Figure 12).  
Specimens of type 1 and 2 were plain joints with and without mortar. The surface was pre-treated with sandpaper (P40) for better adhesion. Specimens of type 3 had two shear keys with a depth of 5 mm and a width of 30 mm. The shear keys were designed to comply with the geometry rules of keyed surfaces in Eurocode 2, resulting in a total key surface \(A_{Key}\) = 6000 mm\({}^{2}\). The shear transfer of type 4 was realised using a half-round vertical groove with a profiled surface, represented by a negative profile of a hollow-core drill IBO32 (rib height 1.6 mm and screw pitch of 12.7 mm, \(A_{Groove}\) = 4480 mm\({}^{2}\)). When two elements were put together, the two grooves formed a channel, which simplified the grout filling. This new joint-design was developed at the TU Wien, specially to address the proposed method in Section 1 and [36]. The surface of the specimens of type 3 and 4 had not been pre-treated before the grouting of the joints to spare this step with the  
Figure 12: Geometry of push-off specimens with the joint geometry.  
intention of saving construction time in the actual application. Since the separation along the groove in two specimens of type 3 occurred, an entirely untreated surface could not be recommended.
# A Review on Upper Limb Rehabilitation Robots
## EMG-Driven Exoskeleton Robots
### Hand Exoskeleton  
A hand exoskeleton was developed by researchers from the University of Berlin, Germany to increase hand mobility [60]. This type of exoskeleton uses the principal of blind source separation as a controlling algorithm, which provides an accurate motion for the finger joints [55]. Blind source separation has its own limitations, where adding additional degrees of freedom requires additional sensors, which ultimately increases the complexity of placing the electrodes [60; 61].  
An orthotic hand exoskeleton that was developed by a researcher from Carnegie Mellon University, Pittsburgh, Pennsylvania, USA is characterized by its low weight [55]. Two controlling algorithms were used in the orthotic exoskeleton [62]. Firstly, a binary controlling algorithm, which provides two states: either \"on\" or \"off\", which turn on and off the actuators [28]. This type of controlling algorithm cannot predict the intermediate state between the on and off states [55]. Secondly, a variable controlling algorithm that provides the information of the intermediate state in addition to the \"on\" and \"off\" states [55]. A variable controlling algorithm is beneficial for patients with one impaired limb and the other limb is functional. It was concluded that the controlling algorithm has to meet the requirements needed by the patient to say it is efficient [55; 63].
# The Detection of Aggressive Driving Patterns in Two-Wheeled Vehicles Using Sensor-Based Approaches
## 2 Literature Review
Thus, in this study, an attempt is made to detect rapid lane change using trajectory data obtained solely from individual two-wheeled vehicle sensors like GNSS and GPS without utilizing image-based algorithms or information about surrounding traffic. Additionally, this study aims to enhance detection accuracy through data acquisition from simulations.
# Testing a Vision-Based Autonomous Drone Navigation Model in a Forest Environment
###### Abstract
The optical positioning module takes the input from the front-view video camera of the drone, extracts the visual object information of each frame, and feeds this information to the proposed empty space-masking algorithm to guide the drone position in the visual scene's context, as shown in Figure 1.  
Figure 1: Sample frame from the drone vision system depicting the detected objects and the guided localization point in the frame. In this study, a deep learning-based model is adopted for object identification, and the model is pre-trained offline with a set of tree objects that appear in the context of the particular application. The dynamics of high-level semantic representations are learned from video streams via state-of-the-art object detection networks. The spatial relationships among objects are extracted to form segments in the scene. A field of view is used to pass through the segments as a masking process to identify empty space for the drone path. Our approach enables drones to self-navigate, especially in remote environments. The scenario used in this study is based on a front-view drone camera, which is different from those in other studies that are more focused on the aerial or top view. In summary, the proposed study contributes the following:  
* The localization of visual objects is performed via object detection to give the algorithms we parse in Section 3 contextual information of the scene for further processing. In Section 3, scenes are described using color-based segmentation with a masking approach to provide a comprehensive semantic representation of their relative environments. * The vision-based navigation model can autonomously guide a drone in a dynamic and remote environment. * The masking model is innovatively employed on the basis of vision to autonomously navigate the drone in a remote environment and without any connection with the center. * The masking model is innovatively used based on the vision to autonomously navigate. * A drone can navigate in remote environments without any connection with the center. This paper is structured as follows: Section 2 provides an overview of the technical challenges encountered in vision-based navigation systems and examines related research efforts. The algorithm and data pertaining to the proposed vision-based navigation model are detailed in Section 3. Subsequently, Section 4 showcases the simulation results of drone pathing based on the proposed model, while Section 5 offers concluding remarks for the paper.
## 5 Comparison of Different Decomposition Methods
### ZYZ Decomposition  
For a one-qubit gate, no CNOT gates are necessary, and if rotation gates around any axis are possible, only one such gate is needed to apply any one-qubit operation. However, when using standard elementary gates, such as rotations around the Pauli \(X\), \(Y\) or \(Z\)-axis, the decomposition of an arbitrary one-qubit gate results in three rotation gates using ZYZ decomposition [15].  
One way to do this is with two rotation-\(z\) gates and one rotation-\(y\) gate. For this decomposition, the angles \(\Phi,\alpha,\beta,\gamma\) can be found so that the following equation is satisfied:  
\[U(2) =e^{-i\Phi}\begin{bmatrix}A&B\\ C&D\end{bmatrix} =e^{-i\Phi}R_{z}(\alpha)R_{y}(\beta)R_{z}(\gamma) \tag{6}\] \[SU(2) =\begin{bmatrix}A&B\\ C&D\end{bmatrix} =R_{z}(\alpha)R_{y}(\beta)R_{z}(\gamma) \tag{7}\]  
These angles can be calculated using the eigenvalues of the matrix and are used in the circuit shown in Figure 5. This is a universal decomposition for a one-qubit \(SU(2)\) gate [15].
## 6 Investigating Several RL-Based Routing Methods
### [PERSON] et al. in [65] have offered the reliable self-adaptive routing scheme (RSAR) for VANETs. In this method, it is assumed that vehicles are distributed at the one-way highway according to a log-normal distribution. The first step in RSAR is to present a connection lifetime model with regard to the features of the vehicle movement such as speed, acceleration, direction, and distance. This model is applied for computing the link reliability, which is used in the learning model. In the Q-learning-based routing scheme, each vehicle expresses a learning agent. The state set consists of all vehicles except the vehicle considered as the agent. The action space also indicates a beacon message that is sent from the current vehicle to the next vehicle. Figure 20 shows the learning structure in RASR. In this optimization issue, each vehicle stores a Q-table, and the size of this table depends on the number of single-hop neighboring nodes and the number of destination nodes. Q-value is periodically freshened by broadcasting beacon messages. Each beacon message includes information about the vehicle, including its speed, location, and Q-value. In this method, the learning process is performed using a decentralized technique, which increases the convergence rate. Furthermore, the scalability of this method is also improved. In this approach, the learning parameters are computed with regard to the number of hops, bandwidth, and link reliability. RSAR has two parts: path development and route maintenance. In the path development operation, the source vehicle searches its Q-table to obtain the most suitable relay vehicle with the highest Q-value. If it finds such a vehicle, it forwards this data packet to this relay vehicle. Otherwise, the source vehicle begins the path discovery operation by broadcasting a route request on the network. RREQ stores the list of all the intermediate nodes passed to reach the destination node. Next, the destination vehicle produces a RREP message and transfers it to the source vehicle. When a vehicle receives RREP, it refreshes its Q-table. After forming the path and updating the Q-value in neighboring tables, the route maintenance process is started by exchanging beacon messages periodically to ensure the validity of the created routes. This process is responsible for dynamically maintaining the Q-table and solving the network segmentation problem. To update Q-table effectively, the beacon message must be exchanged at a certain time interval. If the time duration to reach the destination through a certain path is longer than this beacon time interval, the path is not updated and is known as an invalid path and is deleted from Q-table. When the network is segmented because vehicles are moving in the network, RSAR utilizes the store-and-forward strategy and re-start the route discovery process to build a new path.  
Figure 19: Learning structure in GLS.
## 2 Materials and Methods
### RNA Extraction, Expression Analysis and Gene Copy Number Determination  
Total RNA from 50 mg lyophilized plant material was extracted using the Plant RNA Isolation Kit (Stratec, London, ON, Canada) but also subjected to DNA digestion step [RNase Free DNase Set (Qiagen, Hilden, Germany)], and RNA integrity was checked on an agarose gel. Same amounts of total RNA, template cDNA for subsequent PCR reactionswas generated using SuperscriptTM III (Invitrogen, Waltham, MA, USA). The absence of genomic DNA contamination was confirmed using an equivalent amount of the total RNA for each sample without reverse transcription. A no-template control was included for each primer pair. Quantitative real-time PCR was performed with SsoAdvanced Universal SYBR Green Supermix (BIO-RAD, Hercules, CA, USA), with 0.5 uM of each primer. Relative RNA levels were calibrated and normalized to the level of the housekeeping gene actin. Sequence alignments of published cDNAs from the NCBI database ([[http://www.ncbi.nlm.nih.gov/](http://www.ncbi.nlm.nih.gov/)]([http://www.ncbi.nlm.nih.gov/](http://www.ncbi.nlm.nih.gov/)), accessed on 15 January 2018) were used to design primers for gene isolation and specific primer pairs were designed based on the conserved regions of cDNAs. Primer sequences for Actin (Act), Limonene synthases (_Lim_ s), and Pulegone reductase (_Pr_) are shown in Table 1[20]. PCR was performed using a CFX Connect Real-Time PCR System (BIO-RAD). The thermal cycling conditions were 5 min at 95 \({}^{\circ}\)C, followed by 40 cycles of 15 s at 95 \({}^{\circ}\)C, 15 s at 58-62 \({}^{\circ}\)C, and 30 s at 72 \({}^{\circ}\)C. Transcript abundance was normalized to the transcript abundance of the actin. The double-delta CT (\(\Delta\)ACT) method was used for data analysis [21]. All reactions were run in duplicate, and average values were used in the analysis.