esa-sceva/satcom-chunk-collection huggingface dataset:
# Pulse Peak Delay-Total Focusing Method for Ultrasonic Tomography on Concrete Structure
## 3 Simulation
### Array Performance Evaluation  
#### 3.2.1 Potential Damage Curve  
The potential damage curve is the collection of the possible damage locations. If information of a time-of-flight for a reflector with transducers is given and the same wave speed in the wave field is assumed as the wave speed in the algorithm, the potential damage curve will look like an half ellipse passing the location of the reflector, as shown in Figure 4a. Since the potential damage curve shows possible damage locations, a corresponding B-scan is similar to the potential damage curve, as shown in Figure 4b. Thus, a resulting B-scan image can be approximated by drawing all potential damage curves for all pair of transducers.  
Figure 3: B-scan for Simulation by Total Focusing Method (TFM) (**left**) and Pulse Peak Delay—Total Focusing Method (PPD-TFM) (**right**).  
The potential damage curves for the B-scans of the simulated signal by the TFM and the PPD-TFM are illustrated in Figure 5. Since the PPD is not calibrated in the TFM, the potential damage curves are crossing down shifted from the exact reflector location. On the other hand, the potential damage curves for the PPD-TFM are exactly passing through the exact reflector location. Similar reflection shapes can be seen in the corresponding B-scans in Figure 3.
# Secure and Lightweight Cloud-Assisted Video Reporting Protocol over 5G-Enabled Vehicular Networks
## 5 Details of Proposed Protocol
### System Setup  
In setup phase, TA generates global system parameters and all the entities register to the TA as follows:  
1. TA selects an elliptic curve group \(\mathbb{G}_{1}\) of order \(q\) with \(P\in\mathbb{G}_{1}\) as a generator.
2. In order to get the master secret \(sk_{TA}\) and public key \(pk_{TA}\), TA executes \(\mathrm{CertS.Setup}()\) and sets \(\left\langle sk_{TA},\mathit{CertS.params}\right\rangle\leftarrow\mathrm{CLS.Setup}()\), then output \(\mathit{CertS.params}\).
3. In order to keep a record of each vehicle \(v_{i}\), TA set a pseudonym \(alias_{v_{i}}\) to every \(v_{i}\) based on its real identity \(5 GID_{v_{i}}\).
4. Each \(RSC_{j}\), \(DV_{j}\) and \(DMV_{k}\) registers to TA, then computes \(\mathrm{CertS}\) private keys as follows: * \(RSC_{j}\), \(DV_{j}\) and \(DMV_{k}\) computes \(\mathit{Sec}_{1,RSC_{j}}\leftarrow\mathrm{CertS.Secret}(RSC_{j})\), \(\mathit{Sec}_{1,DV_{j}}\leftarrow\mathrm{CertS.Secret}(DV_{j})\) and \(\mathit{Sec}_{1,DMV_{k}}\leftarrow\mathrm{CertS.Secret}(DMV_{k})\), and makes a request for partial private key to the TA, respectively. * TA provides \(\mathit{Sec}_{2,RSC_{j}}\leftarrow\mathrm{CertS.PartialK}(sk_{TA}\), \(RSC_{j}\), \(P_{RSC_{j}})\); \(\mathit{Sec}_{2,DV_{j}}\leftarrow\mathrm{CertS.PartialK}(sk_{TA}\), \(DV_{j}\), \(P_{DV_{j}})\) and * \(\mathit{Sec}_{2,DMV_{k}}\leftarrow\mathrm{CertS.PartialK}(sk_{TA}\), \(DMV_{k}\), \(P_{DMV_{k}})\) to each entity securely. * \(RSC_{j}\), \(DV_{j}\) and \(DMV_{k}\) set \(\left\langle sk_{RSC_{j}},pk_{RSC_{j}}\right\rangle\leftarrow\mathrm{CertS.SKey}( \mathit{Sec}_{1,RSC_{j}}\), \(\mathit{Sec}_{2,RSC_{j}})\); \(\left\langle sk_{DV_{j}},pk_{DV_{j}}\right\rangle\leftarrow\mathrm{CertS.SKey}( \mathit{Sec}_{1,DV_{j}}\), \(\mathit{Sec}_{2,DV_{j}})\) and \(\left\langle sk_{DMV_{k}},pk_{DMV_{k}}\right\rangle\leftarrow\mathrm{CertS.SKey}( \mathit{Sec}_{1,DMV_{k}},\mathit{Sec}_{2,DMV_{k}})\), respectively.
5. Likewise, every \(v_{i}\) computes \(\mathit{Sec}_{1,v_{i}}\leftarrow\mathrm{CertS.Secret}(alias_{i})\), TA provides \(\mathit{Sec}_{2,v_{i}}\leftarrow\mathrm{CertS.PartialK}(sk_{TA}\), \(\mathit{Alias_{i}}\), \(P_{v_{i}})\), then \(v_{i}\) sets \(\left\langle sk_{v_{i}},pk_{v_{i}}\right\rangle\leftarrow\mathrm{CertS.SKey}( \mathit{Sec}_{1,v_{i}},\mathit{Sec}_{2,v_{i}})\).  
Figure 2: Protocol description.  
\(DMV_{k}\) selects a given universe of attributes \(U=\{1,...,N\}\), computes ABE parameters as \(\langle ABE.amk,labe.params\rangle\leftarrow\) ABE.Setup(), then avails \(labe.params\) to the whole system. Note that \(DMV\)s will later send \(ABE.amk\) to TA in non busy hours.
# An Analysis of Methods and Metrics for Task Scheduling in Fog Computing
## 2 Fog Computing: Architecture and Characteristics
### Characteristics  
The main fog computing characteristics are geographical distribution [11], decentralization [1], heterogeneity [12], real-time interaction, low latency [13], and mobility [14].  
Figure 1: Fog computing architecture.  
Fog computing is a decentralized computing infrastructure in which data, computing, storage, and applications are geographically distributed and located between the data source and the cloud [11]. There is no central server to manage computing resources and services. Therefore, fog nodes are self-organizing and collaborate to provide end users with real-time IoT applications [1].  
Fog nodes are deployed and operated independently, forming a heterogeneous computation and storage network. Fog computing exhibits versatility in operating across multiple platforms [12].  
Fog computing sustains real-time interaction rather than batch processing. Real-time refers to a system required to respond within a specific time frame. This category of applications includes real-time e-health, traffic data transmission, aviation operations, and critical industrial process monitoring systems [13]. Fog computing distinguishes itself from cloud-based paradigms by offering efficient and rapid real-time responses.  
Mobility is an intrinsic trait of several fog applications to improve users' experience [14]. Many IoT devices are moving objects (like vehicles), demanding connected service through their path. Hence, decision engines should oversee the process of migrating application components and select appropriate replacement servers as necessary. Due to the mobility of fog nodes, the selection of tasks for fog nodes has been made dynamically.  
Low latency is critical for delay-sensitive applications such as video surveillance, live streaming, and data analytics. Fog computing enables the emergence of the latency-sensitive Internet of Things network to support real-time applications. The fog is closer to end devices. Therefore, the latency in response is much less than that of cloud computing [13].
## 2 Review of Model Construction and Parameter Determination
### Range of Motion Constraints
#### 2.1.2 Functional Constraints  
In forward-dynamics simulation models, time-dependent constraints are required to mimic the role of the action potential and regulate the activation of each muscle or torque generator incorporated within the system. Typically, a time-dependent function is employed where activation varies on a scale from 0 (no activation) to 1 (maximal activation). This function is multiplied by the maximum voluntary muscle force (muscle-driven) or joint torque (torque-driven) available at each time point, based on the kinematic joint conditions, to determine the force or torque exerted. Movements within forward-dynamics simulation models are initiated and controlled via parameters within a function governing the activation profile of each muscle or torque actuator. This enables activation timings to be varied within realistic bounds (e.g., guided by literature or electromyography) to optimize performance within individual-specific strength capabilities [4].  
While a simple \"bang-bang\" or \"on-off\" approach to activation profiles has been adopted [95; 125; 126; 127], instantaneous changes in activation result in unrealistically fast rates of force development. This is a limitation as the optimal performance of sporting movements has been shown to be sensitive to muscle activation rise times [32; 128]. More recently, a ramped activation method has been employed, utilizing and varying a limited number of activation level rise and fall onsets, rates and magnitudes [9; 35; 36]. Future simulation of more complex tasks may require more sophisticated muscle activation constraints than have previously been used.  
Joint Kinematic Constraints  
Where strength is not a limiting factor in a movement, joint angle time histories have sometimes been used as drivers for forward-dynamics simulation models, with the resulting motion specified by the whole-body mass center and orientation kinematics. Examples include aerial phases of high jumping [129], trampolining [37], and diving [130]. Where strength may influence performance, constraints have occasionally limited joint torques within angle-driven models to prevent unrealistic movements [58; 131]. In instances where this approach is deemed suitable, angle-driven models benefit from the ease of control and superior computational speed compared with muscle-driven or torque-driven approaches, as well as potentially avoiding the need for realistic individual-specific strength parameters.  
Joint-angle time histories have also been used to drive specific joints within mostly torque-driven forward-dynamics simulation models of maximal effort sporting movements, particularly where actuator strength is not a limiting factor and the kinematics at the joint are considered to have a negligible effect on model outcomes [10; 13]. While it is possible to manipulate the joint angle-time histories within optimization procedures, this is not recommended as the joint angles, and associated torques within any optimized solution may not be feasible [13].
# State-of-the-Art Survey on Deep Learning-Based Recommender Systems for E-Learning
## 3 Classification Method
### Classification Based on Recommendation Techniques
#### 3.1.2 Content-Based Technique  
Unlike collaborative filtering, which uses similar rating patterns over users to make predictions, the content-based technique does not require the ratings of other users to make valuable recommendations. It is designed to use users' profiles to recommend items due to their similarities with items the user was already interested in the past. The word similarity used here does not necessarily refer to the same similarity used to define collaborative filtering, which was based on rating correlations, but the similarity between attributes of the current item and those of items the user liked previously. Content-based filtering systems try to match features of items stored in the user's profile. At the same time, the references are kept with the features of the newly presented items to make recommendations. Therefore, only items that the user previously liked will be recommended.  
Contrary to collaborative-based systems that recommend items based on preferences from other users with similar tastes, content-based systems always make recommendations in a personalized manner. Whether the user will like the item or not relies on matching the features of the user's profile and the item's attributes. Case-based reasoning and attribute-based techniques are the most common content-based techniques [7].
# Developing a Fire Monitoring System Based on MQTT, ESP-NOW, and a REM in Industrial Environments
## 3 Methodology
### YOLOv5
However, the trade-off between speed and accuracy may be more noticeable on Raspberry Pi 4 compared to more powerful hardware like a GPU, with slightly longer processing times during real-time detection. YOLOv5 works by dividing the image into grids and assigning bounding boxes to regions where it detects fire. Thanks to YOLOv5's speed, this detection process happens in real-time. The model outputs the coordinates of the fire, along with a confidence score, typically within milliseconds. If YOLOv5 detects fire, indicated by a high confidence score, an alert is immediately triggered and sent to the central monitoring server via MQTT. This ensures that appropriate safety measures (e.g., fire suppression systems or alarms) can be activated. Figure 3 shows the application of fire detection using YOLOv5 on the Raspberry Pi 4 module. The fire was detected in videos and images. For each bounding box, YOLOv5 assigns a confidence score representing the likelihood that the identified object is indeed a fire. Next, the accuracy of the model is displayed as well.
# Optimization Environment Definition for Beam Steering Reflectarray Antenna Design
## 1 Introduction
This approach has been applied successfully in [19] for a single optimization problem and in [20] for a multi-objective one. A recent problem in reflectarray antenna design is to improve their scanning capabilities, i.e., the possibility to modify the direction of the radiation pattern main beam [21]. In our paper this capability is achieved by steering the feeder with respect to a fixed reflector. Thus, the latter must be designed to reach good reflection proprieties with different scan angles. This system is much simpler than electronic scanning, because it does not require a complex biasing system for the reflector; however, the radiation pattern worsens quickly with increasing scan angles. The lack of optimal deterministic solutions to this problem make it suitable to be addressed by an Evolutionary Optimization approach. The aim of this paper is to analyze the entire Optimization Environment that has been implemented for the design of a beam-scanning RA. The problem is characterized by a large number of design variables (148) and is highly non-linear. The Optimization Environment has been designed to be easily adaptable to different EA: to prove this, seven EAs have been tested on this problem. With respect to other papers available in literature, here the entire optimization process has been analyzed to maximize the EAs performances. The paper is structured as follows: in Section 2 the optimization problem is described and the optimization environment presented. Section 3 presents a brief description of the adopted Evolutionary Algorithms. In Section 4, the results of the antenna optimization are presented and, finally, in Section 5 some conclusions are drawn.
# Curcumin Innovative Delivery Forms: Paving the Yellow Brick Road' of Antitumoral Phytotherapy
## 2 Biological Actions of Curcumin
### Molecular Targets of Curcumin
#### 2.2.2 Curcumin Decreases Tumor Angiogenesis  
Curcumin has anti-angiogenic properties by inhibition of vascular endothelial growth factor (VEGF) and fibroblast growth factor (FGF) [79]. Several in vitro and in vivo studies have demonstrated the association between the suppression of VEGF expression by curcumin with its inhibitory action on tumor growth [115; 116; 117]. In vivo studies in mice showed that VEGF expression and angiogenesis suppression is mediated through suppression of the osteopontin gene expression and the NF-kB/ATF-4 pathway [115].
## 2 Materials and Methods
### Instruments  
This research comprised a kinematic methodology and anthropometric model of the human body consisting of 15 body segments, and 16 anthropometric reference points calculated by [PERSON] [21]. An absolute spatial reference system, as presented by [PERSON] [22], was utilized, and the CG was calculated using this model. The movements on uneven bars are two-dimensional and are performed in the sagittal plane (xy-plane). Any movement along the \(z\)-axis is considered a deviation from the established technique or an error in performing the analyzed exercises. Since these \(z\)-axis displacements are minimal, they were ignored and not considered for further analysis, as correct execution of the technique should not involve \(z\)-axis movement. Given that this is a symmetrical two-dimensional movement, only the points and segments on the right side of the gymnast's body wereanalyzed, since body movement speeds are the same on both sides, and the side closer to the camera lenses was prioritized for exercise analysis.  
After selecting the recorded material (without technical errors), further data processing was carried out through the following stages:  
1. Extracting positions (frame grabbing)--As the execution of the two exercises selected for analysis takes 2-3 s, 50-100 frames were extracted for each execution.
2. Image digitization--Analog signals were converted to digital using a digitizer connected to a computer.
3. Creating a kinogram--Defining the desired configuration for each gymnast and each position of the selected movement was performed using a fifteen-segment measurement model, with 16 anthropometric points and 1 point representing the body's center of gravity. The verification of anatomical positions and reference points and segments was achieved electronically by the same measurer (E.P.). The advantage of the APAS program is that it is a non-invasive method for athletes, meaning it does not require the placement of any markers on the skin of the subjects. The edges of the apparatus, i.e., in this case, the edges of the upper and lower parts, were also marked with four points. The APAS program enabled the video camera to record the desired movement in the field previously measured for the given measurement and numerically determined the movement of all body parts or the body as a whole. By projecting each frame of the video onto the monitor and locating the pointer, each position of the plotted reference point was assigned a numerical value from the selected coordinate system. Kinograms were created using stick figures, which represent simplified models of the human body and provide a minimalistic visual representation of the gymnasts' motion.
4. Transformation of two-dimensional data into three-dimensional data--This step was possible because the recording was carried out with two video cameras. To harmonize the images from both cameras, an internal synchronizer was used, which digitized an adequate number of reference points.
## 3 Proposed Intrusion Detection System
### Extract the Local Proximity Features by Using Packet2 vec Learning
#### 3.4.1 Overview
The updated weight is the new transition probability \(n_{vx}\); (3) Then select a packet for next step of the penalty-based random walk, which is equivalent to simulating the Alias sampling with time complexity \(O(1)\) according to the updated transition probability \(n_{vx}\)[10]; (4) The above steps (2) and (3) are continuously repeated until the local proximity sampling list \(N_{S}\left(v_{i}\right)\) of length \(l\) is obtained. The local proximity sampling list of \(v_{i}\) obtained by the above algorithm is \(N_{S}\left(v_{i}\right)\), also known as _walk_. In detail, \(N_{S}\left(v_{i}\right)\subset V\), and the sampling range of penalty-based random walk is not limited to the direct neighbor, but can be sampled by the proximity sampling strategy \(S\) within the low-order proximity of the source network packet \(v_{i}\). Next, we use Skip-gram [28] to optimize the proximity sample list \(N_{S}\left(v_{i}\right)\) to obtain continuous \(d\)-dimension features. The specific process is as follows. We seek to optimize the following objective function, which maximizes the log-probability of observing a network proximity \(N_{S}\left(v_{i}\right)\) for a network packet \(v_{i}\) conditioned on its feature representation, given by \(f\)[10]. The objective function is shown as (1). In particular, the Skip-gram [12] aims to learn continuous feature representations for source network packet \(v_{i}\) by optimizing a proximity preserving likelihood objective. The network packet feature representations are learned by optimizing the objective function using SGD with negative sampling [28]. Finally, we obtain a continuous \(d\)-dimensional local proximity feature that accurately characterizes the similarity between network packets to optimize the local neighbor proximity sample list \(N_{S}\left(v_{i}\right)\) motivated by node2 vec algorithm in [10]. \[\max_{f}\sum_{u\in V}\log\Pr\left(N_{s}\left(v_{i}\right)|f\left(v_{i}\right) \right), \tag{1}\]  
\[\Pr\left(N_{s}\left(v  
Figure 4 is a flow chart of the packet2 vec learning algorithm. Algorithm 2 describes in detail the algorithm flow of obtaining the local proximity feature of the source network packet by using packet2 vec preprocessing. A description of several key operations involved in Algorithm 2 is as follows. ```
0: Relational graph on packets similarity \(G^{\prime}(V,E,W)\), Walk length of proximity sampling \(l\), Probability of returning to the previous node \(p\), Probability of moving away from the source node \(q\), Penalty value \(\eta\). 0: Local proximity features of \(i^{th}\) network packet \(v_{i\_packet2 vec}\), which is \(d\) dimensional. 1: Initialize _walk_ to Empty. 2:for each node \(v_{i}\in V\)do
3: walk = packet2 vecWalk\((G^{\prime},v_{i},\eta,l)\). 4: endfor
5:\(featur\_embedding=\) Skip-gram optimization(walk). 6:return feature_embedding\(v_{i\_packet2 vec}\). ```  
**Algorithm 2** Packet2 vec learning algorithm
## 2 Materials and Methods
### Data and Processing
#### 2.2.2 Remote Sensing Image Data  
In the present study, Landsat-8 satellite data were selected as the data source. The aforementioned satellite was successfully launched by NASA on 11 February 2013, and it is one of the most commonly used satellites for water quality inversion and ground object recognition [27]. Landsat-8 carries two sensors covering a total of 11 bands (Table 2), providing global satellite data every 16 days [28,29]. The Landsat-8 Land Imager (OLI) dataset was obtained for the period of May 2021 to September 2023 via the Geospatial Data cloud platform. A total of 68 OLI images with less than 10% cloud cover were retrieved. These images provide high-resolution spatial data for water quality monitoring and facilitate accurate water quality inversion and dynamic change analysis.
## 3 Multi-Objective Dynamic Mission Scheduling Algorithm (MODMSA)
### Profit-oriented Local Search Algorithm
#### 3.5.1 Destroy Operation  
In the destroy operation, several scheduled missions are removed to facilitate the insertion of high-profit missions. Two removal operators are defined in the destroy operation, and each scheduled mission is removed with probability \(P_{D}\) in two removal operators:  
(1) Random removal: The random removal removes scheduled missions randomly from the single-orbit observation sequence \(\Pi_{ij}^{O}\) and adds the removed missions into the unscheduled mission set \(\Pi_{ij}^{Ul}\), where \(|N_{ij}|\) is the number of scheduled missions in the current observation plan \(\Pi_{ij}^{O}\).  
(2) Indicator removal: In the indicator removal, we define the mission fitness value to guide the removal of the scheduled missions. The mission fitness value \(tf_{k}\) is calculated by:  
\[tf_{k}=\frac{p_{k}}{d_{k}\cdot L_{u_{k}}\cdot cf_{k}} \tag{15}\]  
where \(p_{k}\) and \(d_{k}\) are the profit and duration of the scheduled mission \(t_{i}\), \(L_{u_{k}}\) is the length of the VTW for mission \(t_{k}\) on the current orbit, and \(cf_{k}\) is the VTW conflict degree between the scheduled mission \(t_{k}\) and all missions in the unscheduled missions set \(\Pi_{ij}^{U}\). The VTW conflict degree \(cf_{k}\) is calculated by:  
\[cf_{k}=\sum_{l}^{N_{u}^{ij}}\frac{p_{l}\cdot\Phi_{kl}}{L_{up_{l}}} \tag{16}\]  
where \(N_{u}^{ij}\) is the number of missions in the unscheduled mission set \(\Pi_{ij}^{U}\), \(p_{k}\) and \(d_{k}\) are the profit and duration of the unscheduled mission \(t_{l}\), and \(\Phi_{kl}\) is the length of the overlap time between the VTW of mission \(t_{k}\) and the VTW of mission \(t_{l}\). The mission with high profit and low duration should be prioritized for observation as a high profit can be achieved with less observation time cost. The longer length of the VTW indicates that the mission has more opportunities to be observed again after it is removed, and the scheduled mission with a larger conflict time degree is more likely to hinder the insertion of other unscheduled missions. Thus, missions with a small mission fitness value are prioritized for removal in the indicator removal.