esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Model
### Internal Components
The high-level and detailed workings of the proposed model are shown in Figures 3 and 4, respectively. The object falls on the midsection inside the bin, and the impact weight is read by the load cell upon contact. The subsequent weights are also read until the reading of the object weight is constant. A set of 30 continuous weight readings were measured in the previous step. The readings are split into two sets: the first set is the impact and rebound readings of the object. These are variable readings as the object rebounds upon impact. The highest reading value is obtained from this set. The second set is obtained when the object has a stable set of readings. The average of this set is determined and divided by the highest reading value of the first set. If the ratio of the two is greater than a threshold, the servo motor will be turned 90 degrees, or else 270 degrees, from its origin point. Thishelps calibrate the waste segregation system and accurately read all the weight values of the waste products placed on top of the smart garbage board. The garbage is placed in the smart bin, which the load cell picks up, determines the weight of the trash, and then segregates it. The ultrasonic sensor keeps track of the garbage level. If the bin level is more than half full, the information is sent to the cloud for alert generation, which allows the client to empty the bin. This work is still made smarter by the use of technology in terms of garbage collection, segregation, and indication to the concerned authorities (municipal authorities are referred to as clients). Furthermore, at the administrator's end, this system provides a control to provide a warning signal when the garbage reaches 60, 70, 80, or 90 percent of the trash box, avoiding sanitary issues. This implementation enables the smooth and uninterrupted service of the smart container. Only authorized users can login to and utilize the system. Hence, cloud computing provides functional reliability for the city's infrastructure with regard to security. Smart systems can analyze a problem and make decisions, such as alerting the appropriate client to perform waste collection in the appropriate city location, which is a key concept, developed in the proposed system for effective problem solving. Figure 5 shows the proposed image classification model. Figure 4: Detailed design of the GA–fuzzy inference model. Figure 3: High-level design of the proposed system.
# A Hybrid Encryption Scheme for Quantum Secure Video Conferencing Combined with Blockchain
## 2 Preliminaries  
### Notations' Definition  
All the notations used in this paper are summarized in Table 1.
## 5 Conclusions  
The paper has proposed analytical method for determining optimal location, capacity, and power factor of DGs for minimizing losses in power distribution networks. Validity of the proposed method are tested and verified on two test systems. In addition, a comparison has been conducted with hybrid, IA, and PSO methods. Based on the results, the main observations are summarized as follows:  
* The proposed method can provide a simple and accurate solution and do not required exhaustive computations.
* Locations, capacities, and power factor of DG are crucial factors in the planning of the DG-integrated distribution systems for loss minimization.
* DG of type 1 with optimal power factor reduces losses more than the DG of other types.
* The voltage profile will improve when DGs are optimally connected to the distribution networks.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **DG** & **DG** & **Penetration** & **PF\({}_{\text{DG}}\)** & **Total Power** & **PLR (\%)** \\  & **Number** & **Location** & **Level (\%)** & **(Lagging)** & **Losses (kW)** & **PLR (\%)** \\ \hline \multirow{2}{*}{Proposed method} & DG1 & 11 & 12.688 & 0.814 & 4.27 & 98.10 \\  & DG2 & 18 & 9.861 & 0.833 & & \\  & DG3 & 61 & 44.091 & 0.814 & & \\ \hline \multirow{3}{*}{Hybrid} & DG1 & 18 & 10.300 & 0.770 & 4.30 & 98.09 \\  & DG2 & 61 & 44.204 & 0.830 & & \\  & DG3 & 66 & 11.373 & 0.820 & & \\ \hline \multirow{3}{*}{PSO} & DG1 & 11 & 12.875 & 0.830 & 4.61 & 97.95 \\  & DG2 & 18 & 9.871 & 0.810 & & \\ \cline{1-1}  & DG3 & 61 & 44.204 & 0.810 & & \\ \hline \multirow{3}{*}{IA} & DG1 & 17 & 13.347 & 0.820 & 4.95 & 97.80 \\ \cline{1-1}  & DG2 & 50 & 17.789 & 0.820 & & \\ \cline{1-1}  & DG3 & 61 & 44.483 & 0.820 & & \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparison between methods for adding three DGs using 69-bus test system.  
Figure 17: Voltage profile of 69-bus distribution test system.  
Finally, the proposed method is based on simple algorithm via basic mathematical models. Therefore, it can be easily extended to consider the load variation and intermittent nature of RES-based DGs.  
All authors contributed extensively to the work presented in this paper. [PERSON] proposed the topic, conceived the solution, and supervised the whole work. [PERSON], [PERSON] ([PERSON]), and [PERSON] ([PERSON]) studied and programed the proposed approach. [PERSON] and [PERSON] contributed materials and reviewed the results.  
This research received no external funding.  
The present work has been realized in the framework of the Research Cooperation Agreement between the Engineering Department of the University of Palermo and the Department of Electrical and Computer Engineering of Birzeit University, for the project DGWG--Distributed Generation integration in Weak Grids.  
The authors declare no conflict of interest.
# Deep Convolutional Neural Network for HEp-2 Fluorescence Intensity Classification
## 3 Results  
The fluorescence intensity classification method, described in Section 2, was analyzed using all images (2080 images) in the public AIDA database. Performance analysis was differentiated for each pre-trained network used.  
Table 1 shows the accuracy obtained for the various CNN used. Furthermore, the table shows the network layer numbers and the layer that has returned the most discriminating features (best layer).  
The best configuration, obtained with the densenet201 CNN, showed a sensitivity in the recognition of positive images equal to 96.1%, while with regard to the ability to identify the negatives, this configuration showed a specificity of 84.4%. The ROC (Receiver Operating Characteristic) curve in the Figure 2 was obtained by plotting the true positive rate (TPR) against the false positive rate(FPR) at various threshold settings. The area under the curve value obtained was A\({}_{\text{Z}}\) = 0.974 \(\pm\) 0.003. The accuracy value obtained was Accuracy = 92.8%.  
Table 2 shows the results obtained and the performance comparison with other notable intensity fluorescence classification methods proposed in the literature in the last years. Due to the greater statistical weight of our result, Table 2 also shows that our method of fluorescence intensity classification performs better than the other methods analyzed.  
From the comparison of the system proposed here with the other method [9] of using the same database, it is easy to deduce the quality of the analysis carried out and the potentialities of the method.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Pre-Trained CNN** & **Depth** & **n. Layers** & **Best Layer Results** & **Accuracy** \\ \hline alexnet & 8 & 25 & conv3 & 90.3\% \\ googlenet & 22 & 144 & incep\_3a-output & 90.1\% \\ vgg16 & 16 & 41 & drop7 & 90.3\% \\ vgg19 & 19 & 47 & drop7 & 90.5\% \\ resnet18 & 18 & 72 & res5b\_relu & 92.3\% \\ resnet50 & 50 & 177 & avg\_pool & 92.2\% \\ resnet101 & 101 & 347 & res5c\_relu & 92.2\% \\ sequeezenet & 18 & 68 & drop9 & 89.2\% \\ densenet201 & 201 & 709 & bn & 92.8\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification accuracy for the pre-trained convolutional neural networks (CNNs) analyzed.  
Figure 2: Receiver Operating Characteristic (ROC) curve of fluorescence intensity classification method.
# Wireless Strain Gauge for Monitoring Bituminous Pavements
## 4 Validation of Measurement Process
### Outdoor Test with Real Load Condition and Comparison with Numerical Result  
We used a mobile load simulator (MLS10) to test our system with a real moving load. This device involves simulating real-world traffic conditions on a full-scale section of pavement. This type of testing allows the evaluation of the performance of new pavement materials and technologies under realistic conditions, and can provide valuable information about the durability, longevity, and sustainability of the pavements. The MLS applies 65 kN loads on tandem axles (32.5 kN per axle) to the pavement surface at an 8 km per hour speed [11]. Figure 11 introduces the pavement structure and the MLS measurements. The studied pavement is a classic structure of the roads of the French network. The material of the wearing course is composed of 60 mm of asphalt concrete. The second layer is composed of a grave bitumen (GB3) 80 mm thick, then 3 layers of unbound granular material (UGM) and granitic sand. The mechanical parameters of the structure are those given by the pavement designer and have been verified by numerous FWD tests beforehand.  
Figure 11: MLS10 testing system and pavement structure.  
Figure 10: Comparison between innovative wireless method and wired reference method.  
The remaining drilling core is used to determine the Young's modulus of the layer where the gauge is glued. After MLS installation and starting cycles, we compared the measured strains from wireless and wired data acquisition systems and numerical values, both measurements are recorded simultaneously. A comparison with our measurement of the longitudinal strain under the bituminous layer is carried out using a semi-analytical calculation model. This numerical tool is presented in the work of [PERSON] et al. [33]. This multi-layer tire-road contact model has already been validated in the MLS tests. The structure of the modeled pavement is the same as shown in Figure 11. The load is modeled by the contact surface of the real dual tire loaded at 2 \(\times\) 32.5 kN traveling at a speed of 8 km per hour. Figure 12 shows a comparison of the obtained numerical results and the response of our sensor. This result then confirms the quality of our measurement to find the maximum strain and the shape of the signal.
# Interface Direct Shear Tests on JEZ-1 Mars Regolith Simulant
## 3 Experimental Setup  
### Oedometer Test  
A one-dimensional oedometer test was performed on a dry JEZ-1 simulant, aiming to provide a first insight into its properties of compressibility. The effective vertical stress loading steps were 12.5 kPa, 25 kPa, 50 kPa, 100 kPa, 200 kPa, 400 kPa, 800 kPa, finally unloaded to 50 kPa, which is similar to [15; 17]. The initial sample height and corresponding deformation were measured before and during the test, respectively.
###### Abstract  
Radio frequency energy harvesting (RF-EH) is a potential technology via the generation of electromagnetic waves. This advanced technology offers the supply of wireless power that is applicable for battery-free devices, which makes it a prospective alternative energy source for future applications. In addition to the dynamic energy recharging of wireless devices and a wide range of environmentally friendly energy source options, the emergence of the RF-EH technology is advantageous in facilitating various applications that require quality of service. This review highlights the abundant source of RF-EH from the surroundings sources, including nearby mobile phones, Wi-Fi, wireless local area network, broadcast television signal or DTS, and FM/AM radio signals. In contrast, the energy is captured by a receiving antenna and rectified into a working direct current voltage. This review also summarizes the power of RF-EH technology, which would provide a guideline for developing RF-EH units. The energy harvesting circuits depend on cutting-edge electrical technology to achieve significant efficiency, given that they are built to perform with considerably small current and voltage. Hence, the review includes a thorough analysis and discussion of various RF designs and their pros and cons. Finally, the latest applications of RF-EH are presented.  
radio frequency energy harvesting; antenna; rectenna; rectifier; impedance matching network; voltage multiplier +
Footnote †: journal: Sensors
## 3 Related Works
### Concept Drift Detection  
Variability in new data from cyber-physical systems can be caused by various factors such as aging sensors, system updates or external events affecting the data distribution, leading to the problem of concept drift. Addressing this requires robust mechanisms for both detection and adaptation [14]. Several methods exist for drift detection. Window-based methods like Adaptive Windowing (ADWIN) [34] use sliding windows to track the mean values of data over time. ADWIN adjusts window sizes dynamically, comparing the means of two sub-windows. If their difference exceeds a set threshold, ADWIN detects a change and resets the window to adapt to the new data conditions. These methods are quick and simple to implement. Performance-based methods, such as the drift detection method (DDM) [35], monitor the error rate of a classifier, adjusting it based on how well the classifier predicts new data [36]. If the classifier correctly predicts an instance, the DDM lowers the error rate; if incorrect, it increases the rate. The DDM signals a warning or identifies a drift when the error rate crosses a predefined threshold. However, these approaches have their drawbacks. Window-based methods might discard useful historical data, which can be critical for maintaining context in data analysis. On the other hand, performance-based methods, while effective in identifying sudden drifts do not perform well for slow and gradual drifts [14; 36; 37]. [PERSON] et al. [38] provide a benchmark evaluation for existing drift detection algorithms, where their experimental results show that the DDM algorithm's most pronounced limitation is its incapability to detect incremental (or gradual drifts).
## 5 Simulation Experiments and Numerical Results Analysis
### Results Analysis  
Vehicles entered the road with random velocities in the experiments. To mitigate the effects of random errors, 20 consecutive experiments were conducted for each combination of traffic flow rate and CAV penetration level, with the data from these simulations being compiled for analysis.  
Impact of Traffic Flow  
With a CAV penetration rate of 40%, the lane traffic volume was set from 1000 veh/h to 2200 veh/h.  
Average Number of Stops  
Average delay  
As shown in Tables 2 and 3, when the penetration rate was 40% and the traffic flow rates were 1000 veh/h, 1200 veh/h, 1400 veh/h, 1600 veh/h, 1800 veh/h, 2000 veh/h, and 2200 veh/h, the average numbers of stops before and after guidance decreased by 0.130,  
Figure 11: Intersection simulation scenario diagram.  
0.180, 0.249, 0.34, 0.331, 0.396, and 0.374, respectively. The average delays decreased by 1.362 s, 1.777 s, 2.422 s, 2.653 s, 3.079 s, 2.532 s, and 2.481 s, respectively. Additionally, at a constant penetration rate, as the traffic volume increased, the reductions in average stops and average stop delay were more significant. However, when the volume reached a certain level, congestion intensified, and the effectiveness of the guidance gradually diminished.
## 3 Proposed Sensor Fusion Framework Model Using PDR and W-Fi Localization Systems
### Wi-Fi Positioning  
The classical localization approaches for Wi-Fi positioning are Wi-Fi trilateration and Wi-Fi fingerprinting. Of these approaches, Wi-Fi fingerprinting is the most popular and it does not require the coordinates of Wi-Fi APs in the experiment area. However, the fingerprint approach has two drawbacks when used in practical applications. The fingerprint approach needs a _priori_ knowledge of the local area and thus requires a lot of time for location survey and manpower to generate the fingerprints. The second drawback is the position accuracy. As opposed to other localization approaches, the position accuracy of the fingerprint approach depends on the amount of fingerprint data that is generated for a specific coverage area. If the amount of data used in the fingerprint maps is not sufficient, it is difficult to estimate the user position with high accuracy. The computation time for estimating user position in fingerprinting algorithm is very high as compared to other Wi-Fi localization approaches. In the case of Wi-Fi trilateration algorithm, we estimated the distance from the Wi-Fi AP to the user using a free-space path loss model [36]. The localization algorithm uses the distance values from path loss model to estimate the current user position. The performance of trilateration algorithm depends on the channel conditions between APs and the user. For position estimation using trilateration algorithm, the user should be within a limited Wi-Fi signal coverage area with LOS conditions. In order to improve the localization accuracy of Wi-Fi systems and to compensate the position errors of classical Wi-Fi localization approaches, we propose a Wi-Fi fusion algorithm for indoor localization. The proposed Wi-Fi fusion algorithm improves the indoor position accuracy by utilizing the advantages of fingerprint and trilateration algorithms.
## 7 Simulations and Numerical Results
For example, at 6 dB SNR and \(m=6.7\), the ergodic capacity is 1.0219 bits/s/Hz for the IM/DD technique under strong turbulence, while the ergodic capacity significantly increases to 1.8193 bits/s/Hz for heterodyne detection under the same atmospheric turbulence. The ergodic capacity decreases as the pointing errors become severe. For example, the ergodic capacity decreases from 2.8938 bits/s/Hz to 1.6904 bits/s/Hz when the parameter \(m\) is 0.2217. Figure 4: Average BER performance under variable-gain relaying and strong turbulence. Figure 3: Outage probability performance under various \(C\). decreases from 6.7 to 1 in the case of the HD method under strong turbulence at the 14 dB SNR. Moreover, the system performance becomes worse when the atmospheric turbulence becomes severe. For instance, under heterodyne detection, \(m=1\), and at 4 dB SNR, the ergodic capacity is 0.94833 bits/s/Hz in strong turbulence, while the ergodic capacity is 2.3058 bits/s/Hz in moderate turbulence. It shown that the simulation results closely match the analytical results.
## 2 Materials and Methods
### Biome-BGC Model  
The BIOME-BGC model is a well-established ecosystem process model widely used both in China and globally due to its accurate depiction of carbon, nitrogen, and water cycles within forest and grassland ecosystems [13; 29]. The BIOME-BGC model serves as a powerful tool for researchers to simulate vegetation ecosystem productivity, with numerous studies reporting its strong performance across various habitats worldwide [30; 31; 32]. The Biome-BGC model is capable of simulating four types of forest ecosystems (evergreen needleaf, evergreen broadleaf, deciduous broadleaf, and deciduous needleaf), two types of grasslands (C3 and C4 grasslands), and one type of shrubland. Through its detailed calculations of biogeochemical processes within vegetation ecosystems, the model generates a wide range of outputs, including GPP and NEP.  
The BIOME-BGC model provides a detailed and precise depiction of material cycling and energy flow processes within ecosystems. Its core ecological processes include carbon fixation through photosynthesis, plant respiration and transpiration, leaf transpiration, soil water discharge, and runoff formation. The input data for the BIOME-BGC model comprise three main components: geographic data, meteorological data, and vegetation ecophysiological parameters [33]. The sources and processing methods for these data in this study are outlined in the previous sections. For a more comprehensive explanation of the operation and mechanisms of the Biome-BGC model, readers are referred to the relevant studies by [PERSON] et al. [33].  
The Biome-BGC model was originally developed by [PERSON] and has undergone several iterations under the maintenance of [PERSON] [18; 34]. The most recent official version of the model, Biome-BGC v4.2 is currently maintained by the Numerical Terradynamic Simulation Group (NTSG) at the University of Montana ([[http://www.ntsg.umt.edu/project/biome-bgc.php](http://www.ntsg.umt.edu/project/biome-bgc.php)]([http://www.ntsg.umt.edu/project/biome-bgc.php](http://www.ntsg.umt.edu/project/biome-bgc.php)), accessed on 23 August 2024) [23]. The version utilized in this study is also Biome-BGC v4.2.
## 4 Experimental Results
### Case Two  
As another reference value, the moving table moved from the starting coordinate of \(x=0\), \(y=0\), \(z=15\) cm, and \(\theta=0^{o}\) to the reference coordinate of \(x=15\) cm, \(y=0\), \(z=15\) cm, and \(\theta=0^{o}\) in 1.5 s. In other words, the movable table was intended to move 15 cm to the right only on the x-axis at a height of \(z=15\) cm without any orientation. In order for this reference to be realized by the system, according to the kinematic equations of the system, the first and third motors must turn leftward at an angular position value of \(-18.23\) radians, and the second and fourth motors must turn rightward at an angular position value of 15.87 radians. According to the experimental results, the angular position changes of the motors are presented in Figure 10a,b. In this experiment, in order to compare the performance of the proposed sliding mode controller with the classical PID controller, the MSE and ISE values of the response occurring after the settling time were calculated, and these values are presented in Table 3. Although the torque values falling on the motor shafts are higher in this translational movement, a faster and more robust response was provided because the sliding mode controller is model based. As a result, lower average error values and integral error values were obtained in the SMC controller compared to the PID controller.  
Figure 9: (**a**) Position change of the motors for case one and (**b**) position change of the motors for case one.
## 3 Experiments and Results Analysis
### Ablation Experiment
This experiment verifies the impact of gradually merging features with different dilation factors on the network. As shown in Table 3, after the replacement, the PSNR values on both datasets decreased by 0.06 dB. The results of the ablation experiments fully demonstrate the effectiveness and necessity of the entire PCCSTSR network.