esa-sceva/satcom-chunk-collection huggingface dataset:
# The Impact of Pressure on the Fingerprint Impression: Presentation Attack Detection Scheme
## 6 Experimental Results
### Experimental Protocols  
In order to carry out the proposed experiments, the following protocols are provided to ensure obtaining reliable and comparable results.  
**Protocol I**  
The purpose of this protocol is to evaluate the proposed PAD mechanisms by conducting Experiments I. We thus utilize the database portion which consider the pressure scenario. This portion was collected to investigate the influence of pressure on bona fide and attack presentations. In all of the experiments, each sensor's data are studied individually because of the differences between the acquired data, i.e., frame rate, image size, resolution, noise, etc. Then sensors' data are divided into 55% training set and 45% testing set. The division is performed by randomizing capture subjects, such that all the presentations (bona fide and attacks) of an independent capture subject are either in the training or testing data. The randomization of partitioning is performed to ensure that the machine learning model has never seen presentations that correspond to the tested capture subjects in the training phase. Finally, the PAD subsystem is evaluated through performing the PAD feature extraction, training/testing the PAD classifier, and assessing the obtained results following the evaluation methodology.  
Figure 4: Illustration of \((SSIM)_{m=1}^{n}\) for a bona fide and 7 attack presentations. The presentations are acquired using the optical sensor.  
**Protocol II**  
The aim of the second protocol is to compare the PAD performance of the proposed mechanisms in the scenarios of ordinary presentations and presentations with pressure. The comparison is performed in two parts: (i) Accuracy comparison: we apply the steps of protocol I to the database portion which includes ordinary fingerprint presentations, then we compare the results to those obtained in Experiments I; (ii) Generalizability comparison: in order to test the mechanisms' generalizability, we perform 'leave-one-out' cross-validation to the machine learning model. The cross-validation is performed for each sensor individually for the reason mentioned in protocol I. Then, each sensor's data are split into k folds, where k represents the number of individual capture subjects. The learning algorithm is performed k times by taking one capture subject as a testing set and all other capture subjects as a training set. At each testing phase, the PAD subsystem is evaluated, and results are reported. Once the cross-validation is done, results are demonstrated.  
Table 7 summarizes the proposed experiments, the corresponding protocol and database, and the experiment objective.
# Temporal Variations Dataset for Indoor Environmental Parameters in Northern Saudi Arabia
## 4 Dataset Description
### Measures of Central Tendency  
The central tendency is measured through:  
* Mean: The arithmetic average of the data;
* Median: The middle value that separates the data into two halves;
* Mode: The value that appears most frequently in the data.  
Table 4 represents the descriptive statistics (mean, median, and mode) for five different measures: light, temperature, humidity, pressure, and attitude.  
Light: The average light intensity is about 265.87 units, but the median value is much lower, at 102 units, indicating that the light distribution is skewed to the right, with a few high-intensity readings pulling the mean upwards. The most frequent (mode) light reading is 0.0 units, which could indicate periods of darkness or the device is turned off or blocked.  
Temperature: The mean and median values are quite close (36.96 and 37.1, respectively), suggesting that temperature distribution is approximately symmetrical. The mode, or the most frequently occurring value, is 36.6.  
Humidity: The mean humidity is 11.78 units, slightly higher than the median of 11 units, implying a slight right skewness in the data. The mode, like the median, is 11.0 units.  
Pressure: The mean and median pressure is nearly the same (89,448.6 and 89,455.0, respectively), suggesting a symmetrical distribution. The mode is 89,326.0, which may be a common baseline or ambient pressure reading.  
Attitude: The mean attitude measure is 1039.16 units, slightly higher than the median of 1038.56 units, implying a slight right skewness in the distribution. The mode of 1050.45 suggests that this specific attitude value occurs more frequently than others.  
In general, for all measures, the proximity of mean and median values suggests that, except for light, the distributions are approximately symmetric with no significant outliers. However, it would be beneficial to visualize this data using plots (like histograms or box plots) to understand the distribution better and identify any potential outliers or trends.
## 5 Outer and Inner Loop Optimization Algorithm with Online Learning
### Outer Loop Optimization: Novel Dec-MPMAB Algorithm  
#### 5.1.1 General Single Player MAB Algorithm  
In our scenario, when there is only one device-to-device (D2D) pair serving as a player, the multi-armed bandit (MAB) problem simplifies to a situation where the player seeks to maximize their reward among multiple options, referred to as \"arms\". The fundamental  
Figure 2: Overall outer and inner network structure 1.  
goal of the MAB problem is to identify and select, within a restricted number of attempts, the arm that results in the greatest long-term rewards. This problem assumes that the rewards associated with each arm follow independent and identical distributions (i.i.d.), and these distributions are unknown to the player.  
At the outset, the player initiates an exploration phase by experimenting with as many arms as possible to gather information about each arm's characteristics. There are \(MJ\) potential actions, denoted as \(a\in a_{1},a_{2},\ldots,a_{MJ}\), and a total of T rounds. In each round \(t\), the algorithm chooses one of the available arms \(a(t)\in a_{1},a_{2},\ldots,a_{MJ}\) and receives a reward associated with this arm, denoted as \(r_{a}(t)\). This information is then utilized to refine the player's strategy for the selection of actions in subsequent rounds.  
After this exploration phase, the player shifts to exploitation, concentrating on interacting with the arm that seems to offer the highest expected reward. The accuracy of this estimation process is contingent on the duration of the estimation period. If the estimation time is sufficiently long, the player can make precise estimations of the expected rewards for each arm. Conversely, if the estimation time is too short, the player may not collect enough data, potentially leading to the selection of an arm with a lower reward and yielding imprecise results.  
Various multi-armed bandit (MAB)-based algorithms have been devised to strike a balance between exploration and exploitation. Notable examples include the upper confidence bound (UCB) and Thompson sampling (TS) algorithms [25]. In the subsequent section, we introduce a distributed UCB algorithm based on the current work to address the multi-player MAB problem.
## 3 Discussions of Experimental Results
### Effect of the Obstruction Factor
Comprehensive comparisons of the aerodynamic characteristics of the DCFJ312 airfoil with various _OF_s show that: as the _OF_ increases, the effect of lift enhancement and drag reduction of the DCFJ312 airfoil first increases and then decreases. The greater the _OF_, the closer the two adjacent taps and the stronger the turbulent mixing caused by the spanwise vortex structures of the DCFJ airfoil. Thus, DCFJ312-OF1/2 airfoil has better aerodynamic performance than DCFJ312-OF1/3 airfoil. However, as the _OF_ increases, more power consumptions of the ducted fans are needed to inject the air through the smaller injection slot. When _OF_ is 2/3, the power of the ducted fans in the experiment is not enough to inject the air through the injection slot, resulting in the insufficient mass flow. Therefore, the aerodynamic performance of the DCFJ312-OF2/3 airfoil becomes worse and the aerodynamic performance of the DCFJ312-OF1/2 airfoil is the best.
# Effects of Hybridizing the U-Net Neural Network in Traffic Lane Detection Process
### Traffic Lane Detection  
As a fundamental part of ADAS systems, lane detection systems are a critical computer vision task that involves identifying and tracking lane boundaries in real time, providing crucial information for driving, navigation, and safety. Traffic lane detection (TLD) is a cornerstone for various intelligent transportation systems and automated driving features. Currently, there are four major categories of advanced driver assistance systems related to traffic lane detection:  
* LDW--Lane departure warnings that alert the driver if the vehicle unintentionally leaves its lane, using visual, audible or haptic alerts.
* LKA--Lane keeping assist, which actively intervenes in steering to help keep the vehicle in its lane.
* LCA--Lane centering assist, acting on the steering system to keep the vehicle centered in the lane.
* ALKS--Automatic lane keeping systems that are designed to follow lane markings with minimal driver intervention.  
By analyzing lane occupancy, optimizing vehicle flow, and reducing congestion based on traffic patterns, TLD systems can help monitor and manage traffic, in addition to their primary responsibilities related to road safety [12]. Due to their ability to transmit real-time images to assess the condition of road markings and potentially identify areas in need of repair, TLD systems can also be used for infrastructure condition monitoring and road maintenance.  
In terms of the types of existing traffic lane detection systems, lane detection systems can be broadly classified according to the underlying detection methodology and technology, and their main classification is shown in Figure 1. The process of detecting lane lines generally involves the key steps shown in Figure 2.
# Channel Characterization and SC-FDM Modulation for PLC in High-Voltage Power Lines
## 3 Noise in PLC Channels  
Due to the restrictions on the transmitted power of communication signals, one of the main limitations to the PLC system performance is the noise present in the channel. In particular, for these systems, noise can be divided into two categories [1; 2]:  
\begin{table}
\begin{tabular}{c c} \hline \hline  & \(Ac_{1}=\left[1,-1,0\right]/\left[1,-1,0\right]\) \\  & \(Ac_{2}=\left[1,-1,0\right]/\left[1,0,-1\right]\) \\ Couplings & \(Ac_{3}=\left[1,0,-1\right]/\left[1,0,-1\right]\) \\  & \(Ac_{4}=\left[0,1,-1\right]/\left[1,-1,0\right]\) \\  & \(Ac_{5}=\left[1,-1,0\right]/\left[0,1,-1\right]\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Coupling schemes.  
\begin{table}
\begin{tabular}{c c} \hline \hline \(\mathbf{Parameter}\) & \(\mathbf{Value}\) \\ \hline Coupling capacitor & 4246 pF \\ Blocking inductor & 0.8166 mH \\ Central frequency & 210 kHz \\ Bandwidth & 400 kHz \\ \hline Wave Trap & \\ \hline \(L_{1}\) & 0.8166 mH \\ \(C_{1}\) & 703 pF \\ \(L_{2}\) & 0.2026 mH \\ \(C_{2}\) & 2835 pF \\ \hline Line Tuner & \\ \hline \(L_{1}\) & 0.1353 mH \\ \(C_{1}\) & 4.246 pF \\ \(L_{2}\) & 0.5454 mH \\ \(C_{2}\) & 1053 pF \\ \hline \hline \end{tabular}
\end{table}
Table 2: Coupling Equipment.  
Figure 6: Frequency response of PLC channel for each coupling \(A_{c}\) proposed in the study case.  
* The same HV line as a source: corona noise and impulsive noise due to power-switch operations and faults in the line. Other factors such as surge and fault protection circuitry can be added to the PLC-channel model in order to improve it. Furthermore, the study of these components on the channel response can be found in the literature, such as in [3; 22].
* Interference with other electronic equipment that operates in the same frequency band: radio and navigation stations, other PLC equipment, etc. In this work, we will focus on the study of corona noise, as it represents the predominant contribution in HV lines under different weather conditions [2].
# Modelling and Stability Analysis of Articulated Vehicles
## 2 Modelling of Non-Linear Systems for Articulated Vehicles  
Articulated vehicle structures are relatively complex, and different simplification methods for establishing articulated vehicle dynamics models can be broadly divided into two-dimensional planar models [10; 19] and three-dimensional spatial models [20; 21]. Two-dimensional planar models study the lateral, longitudinal and transverse motions of vehicles on a plane. Tire forces are obtained through the vertical distribution of gravity, axial load transfer and relevant experienced or semi-experienced tire models. Three-dimensional spatial models involve substantially complex effects, such as roll motion and suspension, and the models can directly calculate tire forces based on tire vertical displacement and associated tire models. The vehicle coordinate system can be divided into a one-coordinate [19; 20] and two-coordinate [22; 23] systems. The one-coordinate system often takes the articulated centre as the coordinate origin. The relevant research parameters can be obtained by dereferencing the excursions of the rotational inertia and dynamic balance equations of the front and rear vehicles. The two-coordinate system often considers the front and rear plasmas as the coordinate origin. The relevant research parameters are obtained through coordinate transformation or constructing kinematic relationships between the front and rear vehicles. One of the mathematical means for modelling global dynamics is to construct generalised coordinates and generalised forces, and derive the Lagrange equations [4; 24]. The other method uses Newtonian mechanics to construct mechanical equilibrium equations. According to dissimilar research questions, the reasonable simplification of models and selection of appropriate modelling methods can improve efficiency and obtain superior results.  
The vehicle dynamics model is the research foundation of vehicle handling and stability characteristics. However, owing to the special steering mechanism of articulated vehicles, front and rear vehicles coupling, non-linear influence of tires, axle load transfer and other factors, accurate articulated vehicle nonlinear system models should be established, including vehicle dynamics, tire and hydraulic steering models.
# RF Energy Harvesting Wireless Communications:
###### Abstract  
Radio frequency (RF) based wireless power transfer provides an attractive solution to extend the lifetime of power-constrained wireless sensor networks. Through harvesting RF energy from surrounding environments or dedicated energy sources, low-power wireless devices can be self-sustaining and environment-friendly. These features make the RF energy harvesting wireless communication (RF-EHWC) technique attractive to a wide range of applications. The objective of this article is to investigate the latest research activities on the practical RF-EHWC design. The distribution of RF energy in the real environment, the hardware design of RF-EHWC devices and the practical issues in the implementation of RF-EHWC networks are discussed. At the end of this article, we introduce several interesting applications that exploit the RF-EHWC technology to provide smart healthcare services for animals, wirelessly charge the wearable devices, and implement 5G-assisted RF-EHWC.  
RF Energy harvesting wireless communication (RF-EHWC); RF environment; RF-EHWC hardware; practical issues in RF-EHWC network design +
Footnote †: journal:  
1
## 4 Simulation Evaluation
### Simulation Setup
#### 4.1.4 Evaluation Metric  
The following metrics are common to both scenarios.  
* Total energy consumption: Total energy consumed by all sensor nodes in one data collection.
* Frame delivery ratio: The number of data frames reaching the sink node divided by the number of data frames that should reach the sink node, where the latter is equal to the number of sensor nodes.
* The following metric is only used for the fixed sink scenario.
* Data collection time: The period from the time the sink node transmits the first WuC to the time the sink node returns the ACK frame for the last-received sensor data.
* The following metrics are only used for the mobile sink scenario.
* Maximum energy consumption: Energy consumption of the node with the highest energy consumption within 24 h.
* Network lifetime: The time when first dead node appears (Computed by dividing the initial energy (10,000 (J)) with the maximum energy consumption).  
\begin{table}
\begin{tabular}{c|c} \hline \hline
**Parameter** & **Value** \\ \hline Method & SC-Sched, WuR-TDMA & ContikiMAC \\ \hline Network simulator & ns-2.34 & ns-3.26 \\ \hline Number of sensor nodes & 86/196/400/600 (fixed sink scenario), 100/200/300 (mobile sink scenario) \\ \hline WuC size & 11 byte \\ \hline Data frame size & 50 bytes (frame header 17 bytes + sensor data 33 bytes) \\ \hline Frequency band & 920 MHz \\ \hline Bit-rate & 100 Kbps \\ \hline Modulation & OOK (WuRx), GFSK (comm. module) \\ \hline Propagation model & Friis + Nakagami & Two Ray Ground \\ \hline Area & 1600 m \(\times\) 1600 m (fixed sink scenario), 800 m \(\times\) 800 m (mobile sink scenario) \\ \hline Position of sink node & Center of area (fixed sink scenario), Midpoint of each side (mobile sink scenario) \\ \hline Transmission range & 800 m (sink), 200 m (sensor) \\ \hline Transmission power & 24 dBm (sink), 0 dBm (sensor) \\ \hline WuC detection time & 7 ms & - \\ \hline Wake-up latency & 5 ms \\ \hline Simulation times & 300 (fixed sink scenario), 100 (mobile sink scenario) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**State** & **Power Consumption (WuRx + Communication Module + MCU)** \\ \hline Sleep & 20.7 uW (2.4 uW + 3.0 uW + 15.3 uW) \\ Detecting WuC & 25.5 uW (2.4 uW + 3.0 uW + 15.3 uW) \\ Transition & 24.4 mW (2.4 uW + 19 mW + 5.4 mW) \\ Idle & 57.2 mW (2.4 uW + 57 mW + 163.5 uW) \\ Receiving & 62.4 mW (2.4 uW + 57 mW + 5.4 mW) \\ Transmitting & 74.4 mW (2.4 uW + 69 mW + 5.4 mW) \\ \hline \hline \end{tabular} \({\rm WuRx}\) = wake-up receiver; MCU = micro controller unit.  
\end{table}
Table 3: A sensor node’s power consumption at each state.  
In the following, Sections 4.2.1 and 4.2.2 describe the evaluation of the two functions of SC-Sched in the fixed sink scenario, respectively. Section 4.2.3 shows the performance of SC-Sched in the mobile sink scenario. Section 4.3 compares the performance of the three methods in the two scenarios.
## 7 Performance Evaluation
### On Packet Loss  
Underwater acoustic networks are prone to packet loss. If we consider the packet loss (PL) as the ratio between the average number of lost packets and the number of sent packets, then PL = \(1-\mathrm{ADR}\) (Section 7.1). It follows that in our experiments we experienced a packet loss that ranges from \(32.8\%\) to \(61.5\%\) (see Figure 10).  
As it turns out, the problem of reliable communication is certainly important in UASNs but it is largely orthogonal to the problem of secure communication and thus we do not address it here. Interested readers can refer to the literature [1; 38]. Notwithstanding, the security suite has been designed in such a way that it does not negatively interfere with reliable communication. In order to achieve this goal we have adopted three main solutions. First of all, we have limited the ciphertext expansion phenomenon by properly designing the cryptographic primitives (Section 5).  
Second, we have used the User Datagram Protocol (UDP) in key management protocols of the security suite, namely, the Group Key Management Protocol (Section 5.3) and the Cluster Key Distribution Protocol (Section 6.3). In an underwater acoustic network, UDP presents several advantages with respect to connection-oriented protocols such as TCP. Mainly, UDP does not require an end-to-end connection, and therefore entirely avoids all the connection management issues that are typical of connection-oriented protocols and that cause them to perform poorly in a networking environment that is characterized by long delays and large losses. Of course, the main drawback of UDP is that it does not provide the same level of reliability in message delivery. Therefore, services such as packet re-transmission have to be moved to the upper layers. Here, it comes the third solution we have adopted.  
As to the Group Key Management Protocol, if any node misses any of the rekeying messages then the node fails to receive the new group key \(g^{+}\). However, upon the next group communication, the node will be able to detect the missing event because it is not able to decrypt the received messages. The node can thus recover the key directly from the gateway GW.  
Similarly, in the Cluster Key Distribution Protocol, if any node misses any key distribution message, the node fails to receive a cluster key \(c\). However, upon the next cluster communication, the node will be able to detect the missing event because it is not able to decrypt the received messages. The node can thus recover the cluster key directly from the cluster head.
## Materials and Methods
### Equipment and Data Collection Methods
In the second experiment (Figure 4b), different position and nozzle spacing were tested in the second experiment, and the VMD values and deposition values were obtained, the zero points of \(z\)-axis was at midpoint of the two nozzles, the measuring points were mainly composed of the overlapping area points, points out of the overlapping area and between the vertical lines of two nozzles (red measuring points) were also included, group B and group C in Table 1 represented the VMD values and deposition values obtained by the second experiment. The measuring points of overlapping area were shown in Figure 4c, group D in Table 1 represented the VMD values obtained in this area. Group A, B, C, and D represented the data in Sections 3.1-3.4. Table 2 shows the result of the mean values, standard deviation, and significant difference for VMD values and deposition values under different spray heights and nozzle spacing. From the saliency analysis, the VMD values in group A showed a significant difference under different spray height. For group B, C and D, the VMD values and deposition values showed significant difference under different nozzle spacing and spray height. On the whole, different experimental conditions in this study can change the droplet size and deposition of the UAV spray nozzle (TEEJET XR110015 VS). Figure 4: Distribution diagram of measuring points (**a**) experiment for single nozzle (x, spray height, mm; y, horizontal position, mm); (**b**) experiment for twin nozzles (x, spray height, mm; y, nozzle spacing, m; z, horizontal position, mm); (**c**) measuring points of overlapping area for twin nozzles condition (x, spray height, mm; y, nozzle spacing, m; z, horizontal position, mm).