esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards Automatic Detection of Social Anxiety Disorder via Gaze Interaction
## 4 Analysis, Results, and Discussions
### Relationship between Gender-Based Gaze Interaction/Avoidance and SAD Classes  
The data were divided based on gender groups and processed for each SAD class separately. Figure 6 describes the relationship between SAD and the gaze interaction of female participants. The graph in Figure 7 describes the relationship between SAD and the gaze interaction of male participants. From the results, the gaze interaction trend becomes negative as SAD class severity increases in both male and female cases. Thus, it can be said that the SAD prediction method, as described in this research, applies to all genders.  
Figure 5: Overall view of gaze interaction and SAD relationship.  
Figure 6: Relationship between SAD and the gaze interaction of female participants.
## 4 Results and Discussion  
In this study, Volve field data (well-logs and seismic) have been investigated for petrophysical properties, and qualitative and quantitative methods were used to identify the hydrocarbon-bearing reservoirs. In all of the wells, the reservoir sands have an average thickness of about 40 ft. They are characterized by low bulk densities, low relative P-wave velocities, high resistivities, and high porosities, compared to the overlying shale. Figure 4 shows that the Hugin Formation has good quality reservoir properties, with an average porosity of 0.23 porosity units, a thickness of about 100 m, a net-to-gross ratio of 0.9, and hydrocarbon saturation of about 80%.
# Dynamic Routing in Flying Ad-Hoc Networks Using Topology-Based Routing Protocols
## 2 Dynamic Routing Protocols in FANETs
### Hybrid Routing Protocols
#### 2.3.2 Temporarily Ordered Routing Algorithm (TORA)  
TORA is highly adaptive and the most suitable on-demand routing protocol for multi-hop networks. Here, each UAV can only update routing tables about the neighboring UAVs. The key feature of using this protocol is to cater the transmission of signaling overhead t highly mobile scenarios and thus minimize the response to topological changes [36]. Moreover, it removes invalid paths and searches for new ones in a single-pass of the distributed algorithm. TORA mostly uses reactive routing, but it also uses a proactive approach in some cases. These routing protocols construct and maintain a directed acyclic graph (DAG) from the source UAV to the destination. Multiple paths are created among the UAVs, based on the DAG, to transmit packets. TORA is selected for the fast computation of updated paths in the case of disconnected paths, and to enhance adaptability [36]. TORA does not use the shortest path algorithm, and lengthy paths are normally selected to minimize signaling overhead. In this routing protocol, each UAV has a parametric value known as \"height\" and no two UAVs have similar height values in DAG. Data packets move from the upper UAVs to lower UAVs in a top-down approach. In TORA, data packets flow towards the higher-placed UAVs, offering loop-free routing. In the route discovery phase, this height parameter is reverted to the requesting UAV, and in this approach the central UAVs maintain their routing tables according to the incoming path and height information. Table 1 shows the comparative characteristics of all the aforementioned topology-based routing protocols.
# AR-Assisted Guidance for Assembly and Maintenance of Avionics Equipment
## 3 Materials and Methods
### Dynamic Assembly Pose Tracking
#### 3.2.5 Optimization  
For the posterior probability of region modality, depth modality, and texture modality, this paper uses the Newton method modified by Tikhonov regularization to maximize the probability.  
Assuming that the measurements of the three modalities are independent, the pose of the tracked assembly can be estimated by maximizing the joint probability function. The joint probability density function of the three modes is defined as follows.  
\[p(\theta\mid\mathcal{D})=\prod_{i=1}^{n_{c}}\ p(\theta\mid\omega_{si},l_{i}) \prod_{i=1}^{n_{d}}\ p(\theta\mid P_{i})\prod_{i=1}^{n_{t}}\ p(\theta\mid \mathcal{D}_{li}) \tag{6}\]  
where \(n_{c}\) is the number of data for the region modality, \(n_{d}\) is the number of data for the depth modality, and \(n_{t}\) is the number of data for the texture modality.  
To maximize the joint probability function, several iterations are performed at each pose estimation, where the change vector \((\hat{\theta})\) is calculated and the pose is updated. In each iteration, a Newton optimization method with Tikhonov regularization is used.  
\[\hat{\theta}=\left(-H+\begin{bmatrix}\lambda_{r}I_{3}&0\\ 0&\lambda_{t}I_{3}\end{bmatrix}\right)^{-1}g \tag{7}\]  
where \(H\) is the Hessian matrix of \(6\times 6\), \(g\) is the gradient vector, \(\lambda_{r}\) and \(\lambda_{t}\) are the regular parameters of rotation and translation, and \(I_{3}\) is the identity matrix.  
The gradient and Hessian matrix of the three modalities can be added directly for optimization.  
\[g=\sum_{i=1}^{n_{c}}\ g_{ci}+\sum_{i=1}^{n_{d}}\ g_{di}+\sum_{i=1}^{n_{t}}\ g_{ti} \tag{8}\]  
\[H=\sum_{i=1}^{n_{c}}\ H_{ci}+\sum_{i=1}^{n_{d}}\ H_{di}+\sum_{i=1}^{n_{t}}\ H_{ti} \tag{9}\]  
Finally, considering that \(\hat{\theta}_{r}\) corresponds to the rotation vector represented by the axis angle, exponential mapping can be used to update the pose of the tracking object.  
\[{}_{C}T_{M}^{+}=\ _{C}T_{M}\begin{bmatrix}exp\left(\begin{bmatrix}\hat{\theta}_{r} \end{bmatrix}_{skru\_symmetric}\end{bmatrix}&\hat{\theta}_{t}\\ 0&1\end{bmatrix} \tag{10}\]  
where \(\ {}_{C}T_{M}^{+}\) is the updated transformation matrix from model coordinate system \(M\) to camera coordinate system \(C\).  
In order to maximize the joint probability function, our method in this paper conducts 4 iterations in each pose estimation. We calculate the change vector \((\hat{\theta})\) in each iteration and update the tracking object's pose. The confidence degree of the algorithm with respect to the current data and the range of the RGBD data considered can be defined by adjusting the standard deviation, threshold, and other parameters.
###### Abstract
In the second test, the comparison of two comparatively distinct vowels with the semivowels corresponding to them was carried out. The two tests demonstrated that the detection of English vowels was difficult in comparison with stop consonants but easy in comparison with semivowels. A novel method to segment phonemes by utilizing multilayer perceptron (MLP) was suggested in [PERSON] and [PERSON] [23]. The suggested segmenter's structure includes three parts, i.e., a preprocessor, an MLP-based phoneme segmenter, and a postprocessor. The pre-processor utilized feature parameters for every speech frame in accordance with the acoustic phonetic information. The MLP-based phoneme segmenter was utilized for the purpose of learning the ability to determine the boundaries in question and segment continuous speech into corresponding phonemes. In post-processing, the positions of the phoneme boundaries were decided by utilizing the MLP output. The best performance of phoneme segmentation was found to be approximately 87% with an accuracy of 15 ms. In recent years, more researchers have effectively combined CNN and RNN models [24]. These models were applied to different speech processing tasks to improve the accuracy [25; 26; 27; 28; 29]. In general, it is beneficial to use CNN and RNN methods together. Therefore, in our work we used this model. This paper presents an extensive and first study for finding optimal feature parameter sets by using the GRU for Kurdish speech segment detection. Also, it is the first time speech detection has been developed for the Kurdish language. The effects of varying hybrid features, window type, window size, and different classification methods were investigated for Kurdish speech segment detection. The identified optimal parameter features were analyzed using a novel Kurdish speech dataset collected from a television broadcast. Kurdish represents the language that is spoken by an estimated seventeen million speakers in Turkey, Armenia, Syria, Azerbaijan, Iran, and Iraq. It is the most widely spoken language in Middle East after Arabic, Turkish, and Persian. Therefore, the speech segment detection study to be conducted on Kurdish is important.
## 5 Methodology
### Features Selection
#### 5.5.1 Search Methods  
As stated, we use two searching methods: The Multi-Objective Evolutionary Search Strategy and also a Ranker Strategy.  
* Multi-Objective Evolutionary Search Strategy (MOES): In particular, we execute the multi-objective evolutionary algorithm known as the Evolutionary NOn-dominated Radial slots-based Algorithm (ENORA) as a selection strategy for a random search method, which minimizes the selected features and also the RMSE [66].
* Ranker: This search strategy makes ranks of features one by one by utilizing their evaluations [67].
## 2 System Model
During the learning process, the SU senses set of licensed channels \(S_{ch}^{t}=\{Ch_{z}|z=1,2,3,...,n_{z}\}\) at every episode \(t\), sends its local decision \(LD_{i}^{t}=\{D_{z}|z=1,2,3,...,n_{z}\}\) to the clusterhead \(CH_{j}\) for the final cooperative decision \(CD_{j}^{t}=\{D_{z}|z=1,2,3,...,n_{z}\}\) and then compares its local decision \(LD_{i}^{t}\) with the cooperative decision \(CD_{j}^{t}\) to determine the local decision accuracy \(LDA_{j}^{t}\) in respect to cluster \(cl_{j}\). It also determines the energy consumption for communicating data \(E_{dcost,j}^{t}\) and cooperative sensing \(E_{pcost,j}^{t}\). In addition, during the process, favourable clusters which offer minimum energy consumption and better spectrum hole detection would be selected, while excluding the less favorable clusters. The maintenance phase involves coordination of cluster members. In this phase, the clusterheads specify the set of channels \(S_{ch}\) to be sensed based on their availabilities and control access to the free licensed channels for data communication. Upon energy depletion of any clusterhead, the clusterhead initiates re-clustering process and a new clusterhead would emerge among the member nodes. The main objective of the algorithm is to achieve an optimal policy for selecting optimal cluster or clusterhead that satisfies the pairwise constraint conditions, minimizes cooperative channel sensing energy consumption and data communication energy consumption while enhancing spectrum hole detection. Let \(EH=\{CH_{j}|j=1,2,3,...,hn\}\) denote a set of clusterheads and \(H_{n}=\{CH_{j}|j=1,2,3,...,n_{hg}\}\) denote a set of neighbouring clusterheads such that \(H_{n}\subset EH\). And let \(CL_{n}=\{cl_{j}|j=1,2,3,...,n_{hg}\}\) and \(E_{TC}^{set}(B_{dt},d)=\{E_{TC,j}|j=1,2,3,...,n_{hg}\}\) denote the corresponding set of neighbouring clusters and set of energy consumed for transmitting \(B_{dt}\)-bits data packet to the respective clusterheads at distance \(d\) and for cooperative sensing of set of channels \(S_{ch}=\{Ch_{z}|z=1,2,3,...,n_{z}\}\). Each cluster \(cl_{j}\) consists of a clusterhead \(CH_{j}\) and member nodes \(MN_{i}|i=1,2,3,...,m_{n}\}\) such that \(cl_{j}=\{CH_{j},MN_{i}|i=1,2,3,...,m_{n}\}\). If \(M_{V,i}=\{Ch_{z}|z=1,2,3,...,n_{mv}\}\) and \(H_{V,j}=\{Ch_{z}|z=1,2,3,...,n_{hw}\}\) denote sets of vacant channels detected by the member node \(MN_{i}\) and the selected clusterhead \(CH_{j}\), respectively, then the problem of finding optimal clusterhead \(CH_{j}^{*}\) can be formulated as a Markov Decision Process while the energy minimization problem can be formulated as pairwise constraint Sum of Square Error (SSE) minimization problem subject to one hop transmission constraint. This is given as:  
\[\arg min\sum_{j=1}^{q}\sum_{t=1}^{m_{n}}d^{\beta}(i,j)\] (2a) Subject to: \[\begin{array}{l}\text{C1:}\ M_{V,i}\cap H_{V,j}\
eq\emptyset\\ \text{C2:}\
## 3 Results
### Workflow Using Commercial FEA Software
#### 3.2.1 FEM Model and Linearization  
A FEM model of the mechanism shown in Figure 1 is made in Abaqus. Figure 9 shows the model with all part names.  
Figure 9: FEM model of the flexible manipulator: (**left**) input and output, (**right**) couplings, (**bottom**) bottom view.  
The input of this model is the motor torque and the outputs are the y-displacement at the tip of the arm and the angle and angular velocity of the motor.  
The arm, base plate, spacer, and the two disk-coupling plates are modelled as flexible components using shell elements. The shaft and hub are also flexible, but modelled using beam and continuum elements, respectively. All flexible parts are made of steel. A linear elastic material model is used; initially without damping.  
The mass, flywheel, and clamp are rigid and modelled with discrete rigid elements. The rotor and stator of the motor are rigid too, but modelled with point inertias. A connector element between stator and rotor keeps these parts aligned, and allows one to specify the motor torque and conveniently extract relative rotation (or vice versa). The model is input torque which is directly proportional to motor current. Besides this gain, no further motor parameters are needed.  
Bearings are also modelled using connectors. Torque is zero for these of course. The location of the bearings can be seen in the coupling plot, Figure 9 (right). The couplings are the red lines. From top to bottom: clamp to shaft and mass to arm; upper bearing to hub; lower bearing to hub; disc coupling plates to shaft, to each other, and to the rotor; and last, stator to cage. The model does not use contact interactions.  
The model is clearly non-linear because of the large rotations that are possible. Linearization of the model is done in the position shown in Figure 9 (left). The velocity is zero for this model state. Therefore, the linearized model will be accurate for low rotational velocities only.  
The fifty lowest eigenfrequencies and eigenmodes are calculated in Abaqus. The first eigenmode is the rotational rigid body mode. Mode 2 and 3 are shown in Figure 10.  
If measuring the system is not possible, then damping can be added to the FEM model. However, modelling damping is not trivial; see for example [35]. Therefore, a practical shortcut is taken here. Damping was not included in the FEM model (before linearization and reduction). Instead, modal damping was added to the reduced model. Realistic  
Figure 10: Illustration of mode shapes for eigenmode 2 (**top**) and 3 (**bottom**).  
damping values are identified later from experimental measurements acquired from the system.
# Radiation Qualification by Means of the System-Level Testing: Opportunities and Limitations
## 4 Limiting Factors of Qualification
### Irradiation Source Issues  
Typical issues with the irradiation source to be used, are related to the beam penetration and the size of the SUT that might be irradiated with the homogenous beam.
# Drone-Based Parcel Delivery Using the Rooftops of City Buildings: Model and Solution
## 3 Model Development  
### Parcel Delivery Issues in Cities  
As we previously mentioned, freight shipment activities within urban areas are critical because emissions from parcel-delivery trucks comprise a large share of toxic air pollutants and greenhouse gases in most metropolitan areas worldwide. In addition to the environmental impact, freight shipment activities also lead to traffic congestion and traffic accidents within cities. It seems that one of the most prominent factors for the increase in freight shipment activities is the growth of online shopping. In a recent study, eMarketer estimated that worldwide retail e-commerce sales will increase from $2.3 trillion in 2017 to $4.5 trillion by the end of 2021, representing approximately 16.1% of total retail sales [31].  
Before the proliferation of online shopping, people would visit local stores to purchase products that they needed in their daily lives. In addition, they tended to visit large warehouse stores usually located far from their home to purchase their daily necessities on weekends. However, following the development of ICTs, people started to purchase their daily necessities not only from local shopping stores but also from online shopping stores through the internet. In this environment, customers can obtain their items within several hours during the working day, providing customers with a notable sense of convenience [18]. However, the explosive growth of online shopping has placed unprecedented pressure on the logistics industry, especially for last-mile parcel delivery [32].  
Logistics and distribution channels are attempting to overcome such issues by optimizing their logistics and transport activities with the help of advanced technologies and information systems in urban areas. Better forecasting and planning can be conducted by adopting machine learning and big data analytics. Additionally, from the viewpoint of transport modes, drones are widely considered as support for conventional delivery modes. As the importance of last-mile delivery increases and the online shopping market grows, parcel delivery issues within cities will become one of the top priorities for improving the convenience and quality of city life. In line with this trend, drone-based parcel delivery using the rooftops of city buildings could be considered a good alternative to resolve such issues.
## 4 Conclusions  
This is the first report on the green synthesis of GNPs using ethyl acetate extract of _Quercus incana_. GNP synthesis was confirmed by UV-visible spectra, IR spectra, and by TEM analysis. GNPs showed a strong broad absorption peak at 543 nm. The synthesized nanoparticles were clumpy agglomerates of polydispersed particles with an average size of 5-15 nm. The antimicrobial efficacy of the nanoparticles depends on the shape and concentration of the nanoparticles. This can be confirmed by studying the inhibition of bacterial growth by differentially shaped nanoparticles [51]. Nanoparticles have large surface area available for interactions, which enhances bactericidal effect compared to the large-sized particles; hence, they impart cytotoxicity to the microorganism [52]. _Quercus incana_ appears to have significant antimicrobial capacity resembling a broad spectrum antibiotic against the human pathogenic _Pseudomonas pickettii_, _Salmonella setubal_, _Staphylococcus aureus_, _Bacillus subtilis_, _Aspergillus flavus_, and _Aspergillus niger_.  
The author is grateful to International Center for Chemical and Biological Sciences, Hussain Ebrahim Jamal Research Institute of Chemistry, University of Karachi, Higher Education Commission (HEC) Pakistan and COMSATS Institute of information technology, Pakistan for providing financial support for this work.  
[PERSON] and [PERSON] conceived and designed the experiments. [PERSON] performed the experiments; [PERSON] analyzed the data; [PERSON] and [PERSON] carried out biological activities; [PERSON] and [PERSON] were involved in writing, editing of manuscript. All authors have read and approved the final version of the manuscript.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
GNPs Gold Nanoparticles TEM Transmission Electron Microscopy FT-IR Fourier Transform Infrared Spectroscopy SPR Surface Plasmon Resonance
## 4 Results
### Case I: Network Performance Analysis for DDoS Attacks (24 Nodes)
#### 4.1.1 Step 1: Data Pre-Processing Phase  
Creation of the Network Topology  
The ns-3 software was deployed with BRITE to generate 24 nodes at integer coordinates over a rectangular area of 300 \(\times\) 300, as displayed in Figure 11. As depicted in Figure 11, the simulated network topology consisted of two local area networks (LANs). Simulated hosts and routers were configured using a BriteTopologyHelper class. Moreover, each of the four LANs had host nodes, one switch node, one router node and the relay nodes of the internet. The attack sites were compromised IoT devices and were host0-host9 in LAN1. The switch node was designated as switch1. The victim was an online game server (host11) in LAN2 (host10-host19). We used the 'networkx,' which is a Python package to build the network topology. Each pair of adjacent nodes was an edge which was assigned a weight or cost in all paths. Then, we used the function attribute res_cost(x,y) to indicate the bandwidth and quality of service (QoS) of a path.  
One must set the routing cost of each route in the network topology to decide the next hop path using the command res_cost = array (\([x,y]\), weight = \(x\)). The lower the routing cost is on a path, the higher priority a packet has on that path. A high-priority packet can traverse a low-cost path with a relatively small delay. In the experiments, equal cost (i.e., res_cost was set as 1) was adopted to examine the performance of the proposed mode.  
Step 1.2: Data Pre-Processing for DDoS Threats  
The attack paths were constructed using the following two-step procedure.  
Figure 11: Simulated network topology specified by the Boston University Representative Internet Topology Generator (BRITE) framework (number of nodes = 24).  
Step 1.2.1: Attack on the victim  
In this step, the attack nodes host0-host2 (IP address of 192.158.1.2-192.168.1.4) launched a DDoS attack using UDP floods against the online game server (host11) in LAN2. The victim (IP address of 192.168.2.3) listened by default on port 5001. Six cycles of attacks were conducted in 120 s to generate routing information on the victim node for the IPTBK problem. A total of 1800 attack packets (\(m=1800\)) were sent to host4 using UDP floods. The average packet quantity of the visited node was the basis of updating the number of particles and assisting particle swarms to trace the sources of attacks by reconstructing route routes, as illustrated in Figure 12.  
In the experiments, we used Wireshark to collect samples of network traffic flows on port 5001 of the victim. The traffic flows were recorded in the Pcap format. Once the attack flow packets had been collected, a set of tools (scavetool) were applied to convert the recording files to the comma-separated values (CSV) format for the reconstruction of attack paths, as depicted in Figure 13.
## 4 Experimental Result and Evaluation
### Experimental Results in a Real Case Study of Robot Coverage Path Planning
As the robot moves perpendicular to the wall, whenever it meets an obstacle and the wall, both sensors should be ON, and this will notify the robot that there is an irregular obstacle. The robot may not move to any cell if the robot is faced with static and moving obstacles. To consider dirt in the floors, the degree of importance of the function, corresponding to the means of cleaning approaches in Hedge algebras, based on the dirt that is shown in how the robot can start working on the identified and mapped directions, helps the robot to move quickly. To consider robot directions (moved straightway, left, right, stop,..., etc. ), rules considered by experts are obtained from historical data that can be applied in optimal robot path planning. In the case study of experiments, we used 50 rules for considered robots in dynamic environments to avoid complex obstacles.