esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Two Examples of Application  
### Simulation of Phase Objects in a Droplet for Detection of Nanoparticles  
We performed recently Digital In-line Holography experiments to reconstruct inclusions in a droplet [21,34]. In such experiments, the droplet itself induces a magnification factor which is added to the magnification introduced by the imaging system. Using our formalism, the two sides of the droplet and propagation through the droplet can be taken into account as effective optical elements by introduction of their own transfer matrices in the global optical transfer matrices \(M_{1}\) and \(M_{2}\). Let us consider the experimental set-up of Figure 7. The beam emitted by a fibered-laser diode is collimated using a first lens and then focused in the vicinity of a suspended water droplet with a microscope objective. Calibrated microspheres (diameter \(20\)\(\upmu\)m) are introduced in the droplet. Figure 8 (on the left) shows optimal reconstruction of some of the microspheres. The wavelength of the incident beam is \(642\) nm. The diameter of the water droplet is \(1.5\) mm and its refractive index is \(1.33\). The other parameters are \(e_{1}=42.8\) mm, \(f_{1}=42.8\) mm, \(e_{2}=135.6\) mm, \(f_{2}=5.5\) mm, \(e_{3}=10\) mm, \(z_{1}=0.825\) mm, \(z_{2}=1.125\) mm, and \(z=9.7\) mm. Let us now present in Figure 8 (on the right) the optimal reconstruction issued from a simulated hologram. In the case of this reconstruction, we have considered 7 circular particles: 4 are opaque objects, 3 are transparent phase objects (phase shift introduced \(\pi\)). The fact that the particles are located in a droplet is not a problem. The different optical elements of the droplet are considered through the appropriate definition of matrices \(M_{1}\) and \(M_{2}\). The different inclusions are clearly reconstructed. The opaque and non-opaque objects are easily identified. The possibility to simulate this experiment is particularly attractive for us. This set-up can indeed be used to detect \(50\) nm nanoparticles [22]: the droplet is seeded with nanoparticles. The droplet is then heated using a second laser (a frequency-doubled Nd:YAG laser emitting \(4\) ns, \(10\) mJ pulses in our case). Heating a nanoparticle creates a bubble which is reconstructed using Digital In-Line Holography [22]. For more details concerning the description of spherical micro-bubbles in this experiment, please refer to [22]. This indirect detection method is particularly efficient. With this new simulator that can describe arbitrarily-shaped phase objects as arbitrarily-shaped bubbles, we should be able to obtain information on morphology of nanoparticles through analysis of the shape of microbubbles in the future [35]. Our numerical model allows this.
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Measurement** & **Pre** & **Post-Period 1** & **Post-Period 2** & _p-Value_ & \
\begin{tabular}{c} **Sequence *** \\ **MRG–CEG** \\ \end{tabular} &
\begin{tabular}{c} **Sequence *** \\ **CG–MRG** \\ \end{tabular} & _p-Value_ \\ \hline DCFET (s) & 16.46 \(\pm\) 3.76 & 23.53 \(\pm\) 8.16 & 23.47 \(\pm\) 5.83 & 0.98 & \(-2.88\pm\) 3.61 & \(-11.11\pm\) 8.97 & 0.055 \\ Fatigue & 3.29 \(\pm\) 2.13 & 3.32 \(\pm\) 2.31 & 3.14 \(\pm\) 2.44 & 0.64 & 0.60 \(\pm\) 0.96 & \(-0.30\pm\) 2.16 & 0.34 \\ DCFET & & & & & & \\ Flexion (\({}^{\circ}\)) & 3.28 \(\pm\) 1.52 & 3.45 \(\pm\) 1.94 & 3.18 \(\pm\) 1.51 & 0.54 & 0.12 \(\pm\) 1.91 & 0.08 \(\pm\) 2.30 & 0.98 \\ CJPET & & & & & & \\ Extension (\({}^{\circ}\)) & 5.15 \(\pm\) 1.81 & 4.29 \(\pm\) 2.40 & 3.55 \(\pm\) 1.20 & 0.19 & 1.42 \(\pm\) 1.59 & 1.79 \(\pm\) 1.79 & 0.69 \\ CJPET & & & & & & \\ Rotation Left (\({}^{\circ}\)) & 4.57 \(\pm\) 2.07 & 3.70 \(\pm\) 1.20 & 3.36 \(\pm\) 1.29 & 0.43 & 1.76 \(\pm\) 2.60 & 0.68 \(\pm\) 2.43 & 0.44 \\ CJPET & & & & & & & \\ Rotation Right (\({}^{\circ}\)) & 4.04 \(\pm\) 1.56 & 3.88 \(\pm\) 1.79 & 3.26 \(\pm\) 1.88 & 0.26 & 0.84 \(\pm\) 1.83 & 0.71 \(\pm\) 1.79 & 0.89 \\ \hline \hline \end{tabular}  
* Mean differences between pre- and post-period for each variable were used to analyze the sequence effect. DCFET, deep cervical flexor endurance test; s, seconds; cm, centimeters; CJPET: cervical joint position error test; \({}^{\circ}\), degrees; SD, standard deviation; MRG, mixed-reality group; CEG, conventional exercise group.  
\end{table} 
Table 2: Table showing data for the analysis of period and sequence effect. Expressed in mean \(\pm\) SD. In the present study, both groups showed improvements in the DCFET. These findings for the MRG are relevant due to the fact that there is no precedent for the use of mixed reality for this purpose. Previous studies only used specific exercises that have been shown to produce improvements in the variables of resistance and strength of the cervical muscles [33; 34; 40; 41]. Finally, in terms of satisfaction, the results obtained on both questionnaires were positive. A pilot study conducted by [PERSON] et al. involved participants with chronic musculoskeletal pain and reported a high degree of usability, satisfaction, and motivation with the use of VR [42]. Another study compared VR with conventional treatments and both showed effectiveness. However, a greater degree of satisfaction with the therapy were obtained in the VR group as in this study [43].
# Results and Achievements of the ALLIANCE Project: New Network Solutions for 5G and Beyond
## 2 Network Architecture and Project's Main Achievements
### Achievements
#### 2.2.3 Trust Element  
We have investigated how to transfer routing information between different administrative domains, such as different operators. Taking into account that they do not necessarily trust each other, we have analysed the advantages and disadvantages of blockchains as an alternative to classical RPKI to distribute, control and authenticate this information. We have built and evaluated two prototypes, one that acts as a distributed mapping system, and another to perform access control.
## 4 Results
### Overall Architectural Design  
Figure 9 illustrates the platform's overall architecture, which is composed of four key layers, structured in a bottom-up approach: the Infrastructure Layer, Data Service Layer, Application Service Layer, and User Entity Layer. The Infrastructure Layer, forming the platform's foundational base, consists of IoT devices and basic networking equipment, crucial for operational support and fundamental functions like data gathering and analysis. At the core of the platform's governance is the Data Service Layer, which consolidates essential data from both the Carbon Generalized System of Preferences platform and additional sources, thereby enabling efficient recording of community residents' low-carbon activities. The Application Service Layer acts as the interactive interface for the community, encompassing elements such as the Community Showcase Platform, the Management Platform, and user-centric mini-apps. The User Entity Layer represents the active participants within the platform's ecosystem, including community residents practicing carbon reduction, government entities, community management bodies, carbon finance organizations, businesses, and utility providers like electricity, gas, and water companies.
# GNSS Spoofing Identification and Smoothing Localization Method for GNSS/Visual SLAM System
## 2 System Model  
### Model Description  
The GNSS spoofing identification and smoothing localization model is divided into front-end and back-end. The front-end refers to the acquisition of data from each sensor and modeling, and the back-end refers to the processing of the data from each sensor. At the front-end of the system, the measurement inputs of GNSS and visual SLAM system are processed separately. First, model the acquired GNSS measurement data, perform pseudorange calculation and coordinate conversion, and initialize the absolute coordinate of the system. Then, model the acquired visual SLAM measurement data and perform pose calculation and optimizing. At the back-end of the system, use the time and pose information of visual SLAM system to invert the relative time error and localization offset of GNSS data, and perform time and localization verification on GNSS measurement data to analyze whether GNSS spoofing has occurred. The GNSS data frames that if time-localization verification passes, it will be input into the back-end optimization process to optimize the cumulative error of the visual SLAM, and the GNSS data frames that have not passed the time-localization verification will be discarded. Finally, two strategies of the GNSS/Visual SLAM back-end smoothing localization method are selected according to the results of the time and localization verification to perform smoothing localization results.  
The following of this section is the modeling and error analysis of the measurement data of the GNSS and visual SLAM system. In Section 3, the time-localization mutual verification, localization verification threshold and smoothing localization method are analyzed. In Sections 4-6, parameter analysis and experiment simulation are carried out. The GNSS spoofing identification and smoothing localization model is shown in Figure 1.
# Pores Evolution of Soft Clay under Loading/Unloading Process
## 5 Conclusions  
Based on the fractal theory and microscopic analysis, the evolution of pores under loading/unloading process was investigated. The following conclusions can be drawn:  
1. Small pores with diameters smaller than 0.2 \(\upmu\)m had intrinsic characteristics, their \(D_{f}\) and size distribution was basically unchanged with pressure. Large pores, bigger than 0.6 \(\upmu\)m, changed greatly under loading/unloading, and the pore-size distribution was mainly influenced by large pores.
2. Based on the evolution of the fractal characteristics of pores, the loading/unloading process could be divided into three stages: the natural structural stage (<100 kPa), the structural adjustment stage (from 100 kPa to 400 kPa), and the new equilibrium stage (>400 kPa).
3. The state of microstructure of soft clay before unloading influenced the change of \(C_{s}\) and the pore evolution under unloading. The \(C_{s}\) increased greatly as the \(D_{s}\) and large pore area decreased, and the \(D_{f}\) increased, under loading; and the \(\Delta D_{s}\), \(\Delta D_{f}\), and large pore area increased under each unloading process.
4. The \(C_{c}\) changed nonlinearly with the pore evolution under loading. Below 100 kPa, fractal dimensions and the area of large pores increased slightly under loading, and the \(C_{c}\) changed by a small degree. From 100 kPa to 400 kPa, the \(D_{s}\) and area of large pores decreased and \(D_{f}\) increased, and \(C_{c}\) increased greatly to the peak value of 0.464. Above 400 kPa, the change rates of the above parameters slowed down.  
**Author Contributions:** [PERSON] conducted the loading/unloading and the FESEM tests and wrote the manuscript. [PERSON] contributed to the general frame, critical revision of the manuscript at multiple stages. [PERSON] conducted the laboratory geotechnical tests. [PERSON] and [PERSON] provided a useful modification of the manuscript. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the National Nature Science Foundation of China under Grant Nos.41572255 and 51678361.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Around-View-Monitoring-Based Automatic Parking System Using Parking Line Detection
## 3 Proposed Method
### Motion Planning and Tracking
#### 3.2.2 Motion Tracking  
Motion tracking tracks the car along the generated path. During motion tracking, acceleration, braking, steering, and gear control are required to move the vehicle [36].  
Automatic parking is performed at low speeds. Therefore, the target value of drive control was set to 10 kph. Based on this value, the drive control was performed using PI control, which is expressed as follows:  
\[MV(t)=K_{p}e(t)+K_{i}\int_{0}^{t}e(t)\,dt. \tag{5}\]  
As shown in Equation (5), \(u(t)\) is the amount of control, \(e(t)\) is the difference in the target speed, and \(K_{p}\) and \(K_{i}\) represent the gain.  
In this study, electronic stability control (ESC) was used for braking. When the target point is reached using ESC, the vehicle is stopped by braking. In an automatic parking situation, because the vehicle moves at a low speed, it is possible to stop at the target point with a certain amount of braking. Therefore, when the target point is reached, this method is employed.  
Gear control uses an electronic gearbox. The gear control is configured with forward, reverse, and parking gears.  
The steering control is configured to follow the generated path, and for automatic parking, this is obtained as follows [10]:  
\[\phi(t)=\phi(t-1)+K_{p}e_{1}(t)+K_{d}(e_{1}(t)-2e_{1}(t-1)+e_{2}(t)). \tag{6}\]  
Here, \(\phi\) is the steering angle, \(K_{p}\) and \(K_{d}\) represent the gain, \(e_{1}\) represents the closest difference between the curve at the target point and the straight line connecting the center point of the vehicle, and \(e_{2}\) represents the closest difference between the curve at the target point and the curve of the vehicle.  
Figure 16 shows the results of vehicle speed control and steering control. Figure 16a shows the speed control result, and Figure 16b shows the steering control result. As shown in Figure 16, the velocity and steering angle are tracked to the target.  
Figure 15: Calculation of \(r\) in the angle parking trajectory.
## 2 Materials and Methods
### Patient Enrollment  
We adhered to the CONSORT (Consolidated Standards of Reporting Trials) guidelines for presenting the results. A flowchart following these guidelines shows patient grouping and progression in our clinical study (Figure 1). Patients recruited from those with a surgical indication for the treatment of TMJ OA were randomly divided into two groups: Group A comprised patients undergoing AST, and Group B consisted of patients undergoing arthroplasty. For patients surgically treated between 2019 and 2022 in the Unit of Orthopaedics, Department of Life, Health and Environmental Sciences, University of L'Aquila for TMJ OA (Eaton-Littler stage III), conservative treatment previously failed (consisting of either cortisone or hyaluron injections, analgesics, patient education, strengthening exercises, aids, and orthoses). Inclusion criteria were age between 40 and 75 years, isolated TMJ OA with scaphotrapeziotrapezoid joint integrity, and Eaton-Littler stage III. Exclusion criteria included having (1) undergone previous TMJ surgery; (2) severe systemic diseases, rheumatoid arthritis or post-traumatic arthritis; (3) an active infection, osteomyelitis colitis, sepsis, or distant infections, which may spread to the site of operation; (4) osteomalacia or other metabolic bone disorders; (5) Eaton-Littler stages I-II-IV.  
Figure 1: This study’s flowchart follows the CONSORT (Consolidated Standards of Reporting Trials) guidelines for clear reporting and visualization of participant progression.
## 2 Materials and Methods
### Model's Architecture
In this study, the electrochemical properties HOMO and LUMO were utilized to enable each model to construct a latent space that represents these properties. The objective of this study is to generate new molecules with the desired HOMO and LUMO properties and to facilitate easy and accurate prediction of the HOMO and LUMO values for newly generated molecules. This summary outlines the purpose and functionality of each step, supporting the ability of NP-VAE to accurately reconstruct chemically valid and stereochemically consistent molecular structures. The loss function is constructed as a weighted sum of individual components, following the approach used in previous studies. Each component includes the Cross-Entropy Loss (\(CE\)) of the decoder, the KL Loss (\(D_{KL}\)) representing the difference between the Gaussian prior and the latent variable distribution, the Binary Cross-Entropy Loss (\(BCE\)) for three-dimensional structure prediction, and the Mean Squared Error (\(MSE\)) for molecular property prediction. Each of \(y_{r}\), \(y_{\tau}\), \(y_{s}\), \(y_{b}\), \(y_{c}\), and \(y_{p}\) represents the ground truth for Root Label prediction, Topological prediction, Label prediction, Bond prediction, ECFP prediction, and molecular property prediction (HOMO or LUMO), respectively. The loss function \(L\) is defined as follows:  
\[L =\alpha\cdot CE(y_{r},u_{L_{r}})+\beta\cdot\sum_{i}CE(y_{\tau,i},u_{ \tau})+\gamma\cdot\sum_{j}CE(y_{s,j},u_{L_{s}})\] \[\quad+\delta\cdot\sum_{j}CE(y_{b,j},u_{L_{b}})+\epsilon\cdot BCE (y_{c},u_{L_{c}})\] \[\quad+\epsilon\cdot MSE(y_{p},u_{L_{p}})+\zeta\cdot D_{KL}[Q(z|X) \|P(z)] \tag{1}\]  
\[CE(y,\hat{y})=-y\log\hat{y} \tag{2}\]  
\[BCE(y,\hat{y})=-[y\log\hat{y}+(1-y)\log(1-\hat{y})] \tag{3}\]  
\[MSE(y,\hat{y})=(y-\hat{y})^{2} \tag{4}\]  
Figure 2: Example samples from the constructed dataset for electrolyte additives. Each sample includes the 2D visualization of the molecule, along with its properties such as SMILES, HOMO, LUMO, \(E_{0}\) Energy, and Zero Point Energy. \[D_{KL}[Q(z|X)\|P(z)]=-\frac{1}{2}\sum_{d}\Bigl{(}1+\log\sigma_{d}^{2}-\mu_{d}^{2}- \sigma_{d}^{2}\Bigr{)} \tag{5}\]  
where \(\alpha,\beta,\gamma,\delta,\varepsilon,\varepsilon,\zeta\) are hyperparameters used to adjust the contribution of each term.
# Overlap Avoidance of Mobility Models for Multi-UAVs Reconnaissance
## 3 Proposed Model
### Overlap Avoidance for Random Waypoint Mobility Model  
#### 3.2.1 Random Waypoint Mobility Model  
In MANET, Random Waypoint model [23] has been used as a basic model due to its simplicity. A node generates a random destination in scanning area and travels toward the destination with a random speed between \(V_{min}\) and \(V_{max}\). When the node reaches the destination, it pauses the time between \(T_{min}\) and \(T_{max}\) as shown in Figure 2a.  
In the UAV reconnaissance scenario, however, Random Waypoint model is modified so that the UAV speed is fixed as \(V_{max}\) and there is no pause time after arrival. Another modification is considering the smooth turn at the destination, which is required for a constant-speed flying of the UAV during its reconnaissance, as shown in Figure 2b. In Figure 2a, the node in MANET is a mobile object such as cellular telephone, laptop computer, handheld, etc. Since the speed is slow and there is a pause time, the node changes its direction sharply. However, in the reconnaissance scenario, as the node is a UAV with fast speed and no pause time, we consider its smooth turn as shown in Figure 2b.  
In this model, UAVs mainly reconnoiter the center of the scanning area, although they move randomly. It results in insufficient search in the boundary of the scanning area. This is one of main reasons of the low reconnaissance coverage rate in the Random Waypoint model [15]. Another  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Specifications** \\ \hline Type of aircraft & Fixed-wing \\ Rotation radius & 500 m \\ Communication range & 8000 m \\ Scan area of camera & 2000 \(\times\) 1000 m \\ Flight speed & 150 km/h \\ Altitude & 3500 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: The simple Unmanned Aerial Vehicle (UAV) model.  
limitation is the overlapped reconnaissance. As this model does not consider avoiding overlap, UAVs tend to reconnoiter the same area simultaneously.
# Biosorption of Pb(II) Using Coffee Pulp as a Sustainable Alternative for Wastewater Treatment
## 3 Results and Discussion
### Determination of Optimum pH of Pb(II) with CP  
The removal percentages that we obtained for each pH were: pH 1 (25.85 \(\pm\) 2.11%), pH 2 (76.15 \(\pm\) 2.66%), pH 3 (71.36 \(\pm\) 1.44%) and pH 4 (54.37 \(\pm\) 0.90%). The free software XLSTAT was used to obtain Figure 2 (boxplot). The optimum pH determined was 2.0 units, obtained with a size of 180 \(\upmu\)m, 0.500 g of biomass (CP), room temperature, agitation for 60 min and 100 RPM.  
According to [PERSON] et al. [32] and [PERSON] (1996, cited in [PERSON], 2007) [42], with respect to the optimum pH exposed, it should be clarified that the chemical species of lead found in aqueous solution corresponds to Pb(II) and to a pH higher than 8 units, and based on the Pourbaix diagram, lead precipitates as Pb(OH)\({}_{2}\).
## 5 Results and Discussion  
### Acquisition of Data from Sensors  
The WSS, with the implemented control algorithm, transmitted two types of frames: frames containing complete data from the MEMS sensors sent with a frequency of 119 Hz and frames modified by the algorithm. The study of the hand motion mapping quality based on data modified by the transmission control algorithm was carried out on the basis  
Figure 12: View of the hand when performing gestures: (**a**–**f**) defined measurement gestures and (**g**) control gesture.  
of measurements with one volunteer. A series of measurements was performed, consisting of the previously described three repetitions of a predetermined series of gestures. Then, the data obtained from the frames created by the algorithm were compared with the complete data from the MEMS sensors.  
Examples of measurement signals (from complete data and after applying the algorithm) from one series of gestures, obtained from the accelerometer and the gyroscope, are presented in Figure 13a,b, respectively. The presence of subsequent gestures 1-6 (Figure 12a-f) and the control gesture C (Figure 12g), visible as a flattened signal between individual gestures (Figure 13), have been marked.  
It can be observed in Figure 13 that the use of the algorithm does not significantly change the nature of the recorded signal. However, the areas under the curves (AUC) were compared to determine the differences between the time waveforms obtained. The entire recorded measurement cycle was separated into individual gestures, and for each of them (for each component: \(a_{x}\), \(a_{y}\), \(a_{z}\), \(\omega_{x}\), \(\omega_{y}\), and \(\omega_{z}\) from 16 sensors), the AUC was calculated.
# A Multi-Gateway Behaviour Study for Traffic-Oriented LoRaWAN Deployment
## 4 Simulations and Results Analysis
### Studied Scenarios
#### 4.2.1 Effect of the Payload Length
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Data Rate** & **DR5** & **DR4** & **DR3** & **DR2** & **DR1** & **DR0** \\ (\(SF\), \(BW\)) & (\(7\), \(125\)) & (\(8\), \(125\)) & (\(9\), \(125\)) & (\(10\), \(125\)) & (\(11\), \(125\)) & (\(12\), \(125\)) \\ \hline airtime (ms) & 189.7 & 338.4 & 615.4 & 1108 & 2461.7 & 4431.9 \\
1\% DC (s) & 19.0 & 33.8 & 61.5 & 110.8 & 246.2 & 443.2 \\ msg/hour & 189 & 106 & 58 & 32 & 14 & 8 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Airtime and number of allowed messages according to different data rates for a payload of 100 bytes [27].
# An Enhanced Message Distribution Mechanism for
## 1 Introduction
The main contributions are as follows:  
* we abstract the message processing of the NBI into a mathematical model, provide an analysis in a qualitative way, and compare the performance changes of several controllers in the case of multi-app running (Section 3);
* ee propose an enhanced message distribution mechanism, named EMD, which adds the message queue to the NBI as an enhancement function of SDN to improve the performance of northbound communication (Section 4.1);
* we propose a protocol-independent method for message feature extraction and classification to enhance the compatibility of apps, and additionally add the network invariants checker for high availability and robustness (Sections 4.2 and 4.3); and,
* we evaluate the model from three aspects: north-south communication efficiency (Section 5.1), data plane performance (Section 5.2), and compatibility (Section 5.3) through experiments, and point out the app's scope of the model (Section 5.4).