esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology and Solution
### Adaptive Bandwidth Adjustment for Notch Filter
If the utilized notch filter is smaller than the optimum \(r\), residual power of interference will decrease the received SNR and if the gap bandwidth is larger than what is required, the unnecessary spectrum from the input signal will be removed and that will decrease the SNR of the system. Figure 10b displays the optimum \(r\) parameter value based on the input JSR that maximizes the SNR in the receiver. Based on this figure, the mitigation stage can adaptively adjust the \(r\) parameter based on the input JSR. Figure 9: Utilized scenario for optimum \(r\) parameter calculation. One may ask this question: \"How can the detection system estimate the input JSR?\" The answer is: The detector is also capable of the interference power measurement. Based on the proposed structure for the detector bank, the detector can simply estimate the power and its average. Figure 11 displays the input JSR based on the measured interference power in the detector. These two figures are references for adaptive notch filtering and can be simply saved inside the software (or FPGA) as a look up table and be utilized as the reference for adaptive \(r\) adjustment. Figure 10: Optimum \(r\) parameter selection based on the input Jamming to Signal Ration (JSR); (a) Three dimentional representation; (b) Optimum \(r\) based on the input JSR
## 4 Drilling Vibrations
### Torsional Vibrations  
Torsional vibrations are considered as the most dangerous vibrations to which the drillstring is vulnerable to. The phenomenon responsible for the regeneration of these vibrations is known as'stick-slip' (Figure 4c). Stick-slip occurs when the drill bit is frictionally engaged with the formation in a non-linear manner; the non-linearity causes the bit to switch between two phases; an adhesion phase and a sliding phase. Therefore, the velocity value undergoes intense variations; the severity of this phenomenon can go as far as completely stopping the drilling process, wherein, rotational speed variations are considered a strong indicator of stick-slip [23; 24].  
Recordings from MWD (measurement while drilling) of well-1 located in Algerian hydrocarbons field indicate that the rotational speeds at the bit are irregular, more precisely; the curve representing this speed contains peaks and valleys (Figure 5). The bit speed corresponding to a peak is six times the value of the speed measured at the surface. Also, the speed corresponding to stuck phase can reach the value zero, which means that the bit is completely blocked; contrary to the surface where nothing is indicated. The duration of the stuck phase varies from 1 s up to 5 s. While the bit is stuck at the bottom of the borehole, the top drive system continues to rotate at a constant velocity; this implies that the drillstring is twisting. This torsion makes the pipes to store elastic energy, however, above a certain quantity, the torque applied on the bit becomes greater than the resisting torque (static torque), which consequently releases the bit, and the cycle is repeated again.  
Figure 4: Types of vibrations: (**a**) axial, (**b**) lateral, (**c**) torsional.
# Energy-Efficient Opportunistic Routing Algorithm for Post-Disaster Mine Internet of Things Networks
## 5 Performance Evaluation
### Performance for Varying Number of Nodes
Figure 16 illustrates the influence of the number of nodes on the network lifetime. From Figure 16, it can be seen that the network lifetime of the four routing algorithms increases as the number of network nodes increases. This is because DEOR designs a candidate forwarding zone (FZ) for all senders to make appropriate routing decisions. DEOR can limit the number of nodes involved in packet forwarding based on the current network density \(D\), reducing the number of redundant packets generated in the network and thus reducing network energy consumption. Furthermore, DEOR utilizes a routing quality function to balance the traffic load of forwarding nodes, overcoming the problem of hot spots caused by critical routing nodes near the sink and thus improving the network lifetime.
## 2 Optimal Trajectory Formulation
### General Problem Formulation
Therefore, this is a multi-criteria (or multiple objective) design problem. There are a number of techniques to handle multiple objectives (or cost functions; see, e.g., [26]). In engineering cases where computational effort is high or where the computational time should be kept to a minimum, the problem is transformed into a single-objective problem by either constraining all objectives except one, i.e. :  
\[ \begin{split}&\min\ F_{1}\\ &\min\ F_{2}\\ &\vdots\\ &\min\ F_{n}\end{split} \quad \begin{split}&\min\ F_{1}\\ &\quad g_{1}=F_{2}-F_{2,\text{allow}}\\ &\quad\vdots\\ & g_{n-1}=F_{n}-F_{n,\text{allow}},\end{split}  \tag{6}\]  
or, as in this work, by creating a composite objective function using weighting factors:  
\[ \begin{split}&\min\ F_{1}\\ &\min\ F_{2}\\ &\quad\vdots\\ &\min\ F_{n}\end{split} \quad \begin{split}&\min\sum_{i}\alpha_{i}F_{i}.\\ \end{split}  \tag{7}\]  
As such, the problem can then be solved with a wide range of optimization techniques. In our specific case, a min-jerk trajectory could lead to a very large arc-length. In fact, since the curvature is proportional to the second derivative, the jerk minimization creates a smooth trajectory with low curvature values. As the curvature is the rate of change of the direction of the curve with respect to the arc-length, it follows that a minimum jerk trajectory will require a curve with a significant arc-length to join a sequence of non-collinear via-points. We can conclude that minimum jerk motion has a larger arc-length than other kinds of trajectories such as the trapezoidal velocity profile. It follows that a limit on the velocity will penalize a minimum jerk motion more than other motions in terms of execution time. To overcome these issues, we formulate a problem that grasps the balance between smoothness and arc-length. Our approach consists of recognizing that the velocity cost function is proportional to the arc-length. It follows that we can construct a cost function that integrates the smoothness and arc-length with a convex combination of the velocity and the jerk:  
\[F(\mathbf{\dot{q}},\dddot{\mathbf{q}})=\alpha\|\mathbf{\dot{q}}\|^{2}+(1- \alpha)\|\dddot{\mathbf{q}}\|^{2}, \tag{8}\]  
where \(\alpha\) varies from zero, i.e., only considering the jerk-related objective, to unity, i.e., only considering the arc-length related objective.
## 2 Materials and Methods
### Mapping Units and Landslide Conditioning Factors  
Considering the type of phenomena analyzed and the scale of the landslide susceptibility evaluation, we decided to employ slope units as mapping units (SLU). In fact, for the purpose of the project, we needed to detect the activation area but also include the potential area of propagation and arrest of the phenomena. According to the literature [6; 9; 32], SLUs have been demonstrated to be more geomorphologically adequate to represent all landslide phases (for the flow and slide phenomena) as it is assumed the complete landslide kinematic (initiation, propagation, and accumulation) occurs inside. For this research, SLUs were delimited by applying the r.watershed [33; 34] GRASS GIS module using the 2000 contributing area threshold. By overlapping the SLUs with the landslide inventories, the stable/unstable status with respect to the slide and flow phenomena was defined for each slope unit depending on whether it hosts at least one LIP.  
Geo-environmental predictors were selected on the basis of the expected direct or proxied role in landslides [7; 27; 35] (Table 1): outcropping lithology (LITO), land use (obtained by the Corine Land Cover 2018-USE), elevation (ELE--10 m), landform classification (LCL), steepness (SLO), aspect (expressed as northerness and easterness), plan (PLN), and profile (PRF) curvatures, topographic wetness index (TWI), and stream power index (SPI).  
Figure 3: (**a**) Rotational slide/flow landslide affecting the slope of the A19 motorway; (**b**) multiple rotational slide/flow landslides affecting the slope of the SS120 national road.  
For the continuous variables, a multicollinearity analysis was carried out using the variance inflation factor (VIF) obtained by applying the \"usdm\" R-package [36]. No multicollinearity emerged between the selected predictors. However, considering that specific modeling procedures were implemented separately for flow and slide, the SPI predictor was excluded for the slide model, while the TWI variable was excluded from the flow model.  
Each variable was then characterized inside the SLUs by zonal statistics as deciles for the continuous variables and as relative frequencies for the categorical ones.
## 2 Problem Definition
### Variable Structure Optimization  
In the problem of GS deployment optimization according to the objectives of number and placement (coordinates of GSs), the variables are the geographic coordinates of GSs and their total number. Therefore, the variable structure (referred to as a chromosome in evolutionary algorithms) could be formed as a vector of \(2n_{GS}+1\) components, where \(n_{GS}\) denotes the number of GSs used. Figure 2 shows the variable (chromosome) structure used in this optimization problem.  
The coordinates of each GS are considered as variables in this optimization problem, which means the number of decision variables changes for each individual in a population. Each population has a different number of decision variables corresponding to the number of considered stations. Each variable vector \(X_{k}\) can be expressed as  
\[X_{k}=\left[n_{GS}^{k},\lambda_{1}^{k},\varphi_{1}^{k},\ldots,\lambda_{n_{GS}} ^{k},\varphi_{n_{GS}}^{k}\right], \tag{1}\]  
where \(n_{GS}\) is the number of GSs, \(\lambda_{i}\) and \(\varphi_{i}\) are the longitude and latitude of the \(i^{th}\) ground station, respectively.
## 3 Hypotheses and Model Development
### Vendor Support  
Top management literature suggests that IT service provider or vendor also plays a very important role in the decision of IT services adoption [142; 143]. Vendor support in the case of CC services is far more crucial because cloud-based IT services from a capable vendor may enhance the internal capabilities of an organization [144]. Vendors provide cloud-based services, which can be dynamically priced and can be scaled up/down according to the requirements. This flexibility enables the client institution to develop and enhance their capabilities. However, only capable service providers will be able to provide these benefits. Therefore, it is hypothesized that:  
**Hypothesis 5 (H5).**_Vendor support positively influences CC adoption in HEIs._
## 1 Introduction
In [23], trust in automated systems is defined as \"an attitude that a user is willing to be vulnerable to an action from an automated system\". Trust is seen as a key factor in the acceptance and usage of automated driving technology. Therefore, a considerable amount of literature focuses on understanding trust in the context of automated driving and how it can be increased to meet user expectations [7,24,25,26]. Due to the small number of studies investigating driving style preferences for automated driving and their varying focus on different driving scenarios and maneuvers, it is not yet possible to derive a clear picture of how users want to be driven in an automated vehicle (AV). Consequently, this study aims to further contribute to answering the question of how users want to be driven by analyzing the influence of personal driving styles on the perception of comfort, safety, and trust in conditional automated driving. In addition, a new approach is introduced to identify user preferences for automated driving by giving participants the opportunity to demonstrate their desired automated driving style through manual showcasing and comparing it to their personal driving style. Furthermore, it is investigated if the perceived anthropomorphism and driving strategy influence whether a presented automated driving style is preferred or not. To complement existing driving simulator studies, a two-part real-world driving study with 42 participants was performed on a German motorway. During the first part of the study, the personal driving style (PDS) and the desired driving style (DDS) for conditional automated driving were recorded on a given route. In the second part, participants experienced a defensive automated driving style (ADS) while seated in the passenger seat of an AV, traveling on the same route as before. Participants were then asked to rate their level of perceived comfort and safety in various driving situations. By comparing PDS with ADS and DDS, it is examined whether a user-like driving style is preferred or rejected for CAD. The ratings for anthropomorphism and driving strategy are collected through a questionnaire. By determining the preferred driving style for CAD and identifying key factors that influence the choice of a presented automated driving style, important implications for the design of future Level 3 driving systems can be derived. This paper is organized as follows: Section 2 illustrates previous work that serves as the basis for the present study. Section 3 describes the experimental setup and method for analyzing user preferences in automated driving. The results are presented in Section 4, followed by the discussion. The paper ends with a conclusion and an outlook for future research.
# Void Hole Avoidance for Reliable Data Delivery in IoT Enabled Underwater Wireless Sensor Networks
## 3 Background
### System Model
#### 3.1.3 Packet Types  
In these schemes, there are three types of packets: namely _neighbor_, _ack_ and _data_packet_. _neighbor_ consists of three fields: type ID, source node ID and depth. The type ID denotes the packet type; source ID represents the address of the source node; and depth illustrates the depth of the source node.  
The _ack_ packet has three data fields: type ID, source node ID and depth. The type ID represents the _ack_ packet ID; source ID represents the source node ID; and depth is the depth of the source node.  
On the other hand, _data_packet_ is composed of: type ID, source node ID, destination ID, depth and PID. The type ID, source node ID and depth represent the same as for the packet types _neighbor_ request and _ack_. The destination ID represents the ID of the destination node, and PID (Packet ID) represents the order of packets.  
_neighbor_\(table\) consists of the neighbor ID, depth, distance and time stamp. The neighbor ID indicates the location of the neighboring node. The distance shows the distance from the neighboring nodes. The time stamp represents the time for updating neighbors entry, and depth is the depth of the source node.  
_packet_queue_ is generated to keep the record of all the sent and received data packets in order to restrain the replication of data packet transmission and for saving energy. The _Packet_queue_ consists of the fields of source ID, PID and flag. The source ID is the ID of the source node; PID is the Packet ID; and flag indicates whether the data packet has been sent or not.
# Survey on 5G Physical Layer Security Threats and Countermeasures
## 10 Summary
\begin{tabular}{c c} NSA & Non Stand Alone \\ NTN & Non Terrestrial Networks \\ OFDM & Orthogonal Frequency Division Multiplexing \\ PBCH & Physical Broadcast Channel \\ PC-CVR & Principal Components of Channel Virtual Representation \\ PCFICH & Physical Control Format Indicator Channel \\ PCI & Physical Cell ID \\ PDCCH & Physical Downlink Control Channel \\ PO & PDCCH Order \\ PDSCH & Physical Downlink Shared Channel \\ PDF & Probability Density Function \\ PHY & Physical \\ PHICH & Physical Hybrid ARQ Indicator Channel \\ PRACH & Physical Random Access Channel \\ PSS & Primary Synchronization Signal \\ PT-RS & Phase Tracking Reference Signal \\ PUCCH & Physical Uplink Control Channel \\ PUSCH & Physical Uplink Shared Channel \\ QAM & Quadrature Amplitude Modulation \\ QPSK & Quadripphase Phase Shift Keying \\ RACH & Random Access Channel \\ RAN & Radio Access Network \\ RB & Resource Block \\ RE & Resource Element \\ RF & Radio Frequency \\ RIS & Reconfigurable Intelligent Surfaces \\ RNN & Recurrent Neural Network \\ RRC & Radio Resource Control \\ RA & Random Access \\ RAN & Radio Access Network \\ RS & Reference Signal \\ RNTI & Radio Network Temporary Identifier \\ RS & Reference Signal \\ RSS & Received Signal Strength \\ RSRP & Reference Signal Received Power \\ RSRQ & Reference Signal Received Quality \\ SA & Stand Alone \\ SCA & Successive Convex Approximation \\ SCS & Sub-Carrier Spacing \\ SW & Software \\ SDR & Software Defined Radio \\ SFI & Slot Format Indicator \\ SIB & System Information Block \\ SINR & Signal to Interference and Noise Ratio \\ SNR & Signal to Noise Ratio \\ SP & Semi-Presistent \\ SP-SRS & Single Panel Sounding Reference Signal \\ SR & Scheduling Request \\ SRS & Sounding Reference Signals \\ SS & Synchronization Signal \\ SSB & Synchronization Signal Block \\ SRS & Sounding Reference Signal \\ CSI-RS & Channel State Information Reference Signal \\ SSS & Secondary Synchronization Signal \\ STAR-RIS & Simultaneous Transmitting and Receiving RIS \\ STFT & Short-Time Fourier transform \\ TA & Timing Advance \\ TDD & Time Division Duplex \\ TD-LTE & Time Division Long Term Evolution \\ TDoA & Time Difference of Arrival \\ TPC & Transmit Power-Control \\ UCI & Uplink Control Information \\ UE & User Equipment \\ UL & Uplink \\ \end{tabular}
# Deep-Feature-Based Approach to Marine Debris Classification
## 4 Results
### Deep Feature Classification with Conventional ML Classifiers  
The neural network classifier on top of the deep feature extractor can be replaced with any conventional ML classifier. Table 6 provides the classification results of RF, SVM, NB, LR, and KNN classifier trained on deep features extracted with the best performing feature extraction scheme (see Table 5) for a given CNN architecture. As we can see from Table 6, by replacing the NN classifier by SVM or LR classifier, the performance on new data in many cases improves, e.g., SVM classifier with Inception-ResNetV2 obtains 91.61% accuracy, 92.27% macro F1-score, and 0.90 Kappa coefficient, as opposed to 91.40% accuracy, 92.08% F1-score, and 0.89 Kappa of the NN classifier. The most significant improvement was observed with the MobileNetV2 feature extractor, which obtains the best classification result with an SVM of accuracy 85.32% compared to 82.60% accuracy of the NN classifier. Figure 19: Predictions on new images from the realistic marine environment of the Adriatic Sea. Misprediction of the first and fourth image in the first column can be attributed to the coverage of waste objects with the seabed, making it difficult to distinguish the litter from the seafloor. Moreover, we can notice how the network associates a round shape with the _Rubber_ class, e.g., although it correctly classifies metal can in the class _Metal_, it has assigned a certain probability to the class _Rubber_, making it the second choice. Figure 20 shows the confusion matrices for the best-performing Inception architectures (InceptionV3 and Inception-ResNetV2) and a lightweight MobileNetV2 architecture designed to meet the resource constraints of mobile and embedded devices. Although replacing the NN classifier by an ML classifier did result in a slight boost of model's overall performance on new data, in several cases the performance on some classes dropped, e.g., _Other trash_ for InceptionV3 and _No trash_ for the Inception-ResNetV2. The confusion across all models (in NN and ML classifiers) is the most pronounced at the _Other trash_ class with corresponding images often assigned to classes _Metal_ and _Plastic_, and vice versa. Furthermore, misconceptions of plastic as metal and metal as plastic often occur. The confusion of _No trash_ images with different categories of marine litter can be attributed to the various non-waste objects found in such images, such as other marine life objects, seagrass, and rocks.
## 4 Results and Discussion
### Test Systems
#### 4.2.3 Test System 3
According to the convergence curve, the ALGWO algorithm needs 60 iterations to find the local optimal solution and 90 iterations to depart from it. Compared to the 150 iterations needed by the GWO method, this is a huge reduction. As a result, ALGWO can eliminate local optimal solutions more quickly.
## 2 System Framework for GIS-Based Map Construction
### Intersection Connection Layer  
The intersection connection layer can be regarded as a connection layer between road-level information and lane-level information, which matches the lane-level information with the traditional road-level model. This layer includes road-level intersections and lane-level intersections.  
A single intersection \(c_{r}\) on a road can be expressed as Equation (6):  
\[c_{r}=\left(P_{r,i},T_{r}\right) \tag{6}\]  
where \(P_{r,i}\) is defined as the road segment node on the intersection, and \(T_{r}\) denotes the topological relationship of different road nodes at the same intersection, which describes the connection between two roads, including two-way connections, one-way connections, and no connection.  
A single intersection \(c_{l}\) at the lane level can be expressed as Equation (7):  
\[c_{l}=\left(P_{l,i},T_{l}\right) \tag{7}\]  
where \(P_{l,i}\) is the set of lane nodes at the intersection, and \(T_{l}\) denotes the topological relationship of different lane nodes at the same intersection. The lane-level topology relationship is represented according to the traffic rules at the intersection, including straight ahead, left turn, and right turn as well as unconnected lanes, as shown in Figure 2.  
Figure 2: Lane-level topology relationship.  
The topological relationship between different lanes for the same road segment can be defined by using the inclusion relationship, as in Equation (8).  
\[P_{r}=(P_{l,1}\ \cdots,\ P_{l,i}) \tag{8}\]  
where \(P_{r}\) is a single road segment node, the lane segment nodes \(P_{l,i}\) contained in that road segment node \(P_{r}\).