esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Testbed Implementation,Prototype, and Software Development
### Database and Display Chart  
Database management is most useful in providing a centralized view of the data that can be accessed by multiple users from multiple locations, in a controlled manner [29]. It can limit the data that the end user can see, as well as how that end user can view the data, providing many views of a single database schema. End users and software programs are free from having to understand where the data is physically located or on what type of storage media it resides because the database handles all the requests. The greatest advantage of using a database is that it lets end users access and use the same data while managing data integrity. Instead of creating new iterations of the same data stored in new files for every new application, data is better protected and maintained when it can be shared using a database. The database provides a central store for data that can be accessed by multiple users in a controlled manner.  
For our proposed battery monitoring system, we have designed the database using MySQL [30]. The divided data in the TCP/UDP server GUI is automatically saved into our database where multiple battery data can be easily maintained and stored over a period of time, as shown in Figure 15. Also, the different parameters of the battery can be chosen and plotted in an appropriate bar, column, line, spline, pie form using the display chart for a meaningful visual representation of the battery data, as shown in Figure 16.  
Figure 14: User datagram protocol (UDP) server socket GUI for our proposed system.  
Figure 15: Database for our proposed system.
###### Abstract  
This study aimed to evaluate the effect of different manufacturing techniques and thermal aging on the fracture resistance of provisional crowns and fixed partial dentures. Methods: A total of 60 provisional crowns and 60 provisional fixed partial dentures were fabricated using three manufacturing techniques: conventional manufacturing (CM), subtractive manufacturing (SM), and additive manufacturing (AM). An index created from SM-manufactured restorations was used to produce the CM group. Artificial abutments were created by duplicating scanned phantom teeth using model resin. Half of the restorations (\(n=10\) per group) were subjected to thermal aging (5-55 \({}^{\circ}\)C, 5000 cycles). The fracture resistance values of the specimens were tested using a universal testing machine. Data were analyzed using a two-way ANOVA and [PERSON]'s post hoc tests (\(\alpha=0.05\)). The highest mean fracture resistance was observed in the SM group without aging, both for crowns (1645.4 \(\pm\) 346.8 N) and fixed partial dentures (1291.13 \(\pm\) 564.15 N). The two-way ANOVA revealed statistically significant differences among the groups, and thermal aging significantly reduced the fracture resistance (\(p<0.05\)). Both the manufacturing method and thermal aging significantly influenced the fracture resistance of provisional crowns and fixed partial dentures. In fixed partial dentures, a significant effect of aging was associated with the reduced durability of restorations fabricated using the subtractive manufacturing method.  
provisional restorations; fracture resistance; CAD-CAM; 3D printing; aging +
Footnote †: journal: Applied sciences
# Power Quality Issues and Mitigation for Electric Grids with Wind Power Penetration
## 4 Simulation of Voltage Mitigation  
### Voltage Mitigation and Control  
As the voltage variation is considered the main problem associated with intermittent wind-based energy sources [9]. The voltage mitigation is achieved using the available reactive of the inverter-based WECS as shown in Figure 2. The system involves WECS connected to an arbitrary node \(k\) of the study distribution network which may involve various loads. A control signal is generated from the management system which is sent via power line communication (PLC) and extracted at node \(k\) by the WECS control circuit. A developed control circuit assesses both the available reactive power from the inverter circuit as well as the voltage at the controlled node and hence a suitable amount of reactive power is compensated at the point of common coupling (PCC).  
The general representation of the two-quadrant operation of the interface inverter is shown in Figure 3, which is possible for Type 3 and Type 4 WECSs [19]. As shown in the figure, the available reactive power depends on the generated real power from the WECS. For a specific power generation \(P_{g}\) the available reactive power \(Q_{i}\) for the purpose of compensation is calculated in real time as follows:  
\[Q_{i}=\sqrt{S_{i}^{2}-P_{g}^{2}} \tag{1}\]  
However, the actual amount required for compensation to mitigate the voltage and keep it within a preset code could be different from the available from the inverter \(Q_{i}\). The actual amount is computed using control methods such as drop control or fuzzy logic controllers. In this paper, the voltage deviation is evaluated, and fuzzy rules are implemented to compute the required reactive power \(Q_{c}\) to maintain the voltage at the PCC of the WPP. The developed control depends on a reference signal sent by the management system. In distribution systems, two objectives are defined to control the voltage level as shown in Figure 4.  
1. the compensation is computed to minimize the deviation between the actual positive sequence and the reference voltage, hence the overall voltage profile could be improved
2. the compensation is computed per phase based on the deviation among the three phases and the reference voltage, hence both the voltage profile and unbalance factor are improved.  
Figure 3: Reactive power control capability of inverter-based energy systems.  
Figure 2: Reactive power compensation of inverter-based WECS.  
In the case of \(Q_{c}>Q_{i}\), the value of \(Q_{c}\) is set to equal the available reactive power from the inverter \(Q_{i}\). If the inverter size is higher than the installed generator capacity, the available \(Q_{i}\) is increased by a considerable amount which could be invaluable for voltage mitigation [40]. The figure shows that if the inverter size is increased by 10%, the available \(Q_{i}\) changes from 0 to be 42% of the inverter capacity \(S_{i}\) as shown in Figure 3.
## 1 Introduction
This frequent updating elevates the risk of the RA becoming trapped in local optima [15], thereby complicating the search for a globally optimal solution. Our study observes that when transitioning the DR strategy to a time-triggered approach, the involvement of CAVs in crossing sequence adjustments is confined within a finite range. At this juncture, the CSP bears a striking resemblance to the Car Reordering Problem (CRP) encountered in automotive paint shops. Both seek to minimize the cost associated with a specific sequence dependency within a finite retrieval sequence [16]. Drawing upon the methodology of the CRP, we establish a centralized CSP model and introduce state transition networks to centrally enhance the RA, effectively mitigating the challenge of local optima. Expanding on this, we integrate the enhanced RA with the Platoon Forming Algorithm (PFA) to formulate the Resequencing Platoon Algorithm (RPA). This innovative approach allows CAVs to pre-organize batches, enabling the formation of CAV platoons capable of collectively navigating intersections [17]. During the calculation of the optimal crossing sequence for CAVs, the RPA extensively leverages historical computation results, thereby minimizing unnecessary crossing sequence updates and further evading local optima traps. Addressing the TOP, we persist in employing a decentralized energy-optimal control framework, refining the recursive algorithm for controllable arrival times to accommodate the requirements of CAV platoons. This innovative strategy is designated as the Dynamic Resequencing and Platooning (DRP) strategy, presenting fresh perspectives and methodologies for alleviating traffic congestion. This paper is organized as follows. Section 2 delves into the design of signal-free intersections and outlines the framework of the DRP strategy. It establishes both a centralized CSP model and a decentralized TOP model. Section 3 introduces an enhanced RA that capitalizes on state transition networks. This algorithm is amalgamated with the PFA to devise the RPA, aimed at achieving the global optimal solution for the centralized CSP within a finite time. In Section 4, an improved recursive algorithm is proposed for determining controllable arrival times tailored to CAV platoons. This algorithm imposes temporal constraints to address the energy-optimal control problem [18] while solving the TOP. Section 5 presents a series of experiments comparing the performance and fairness of CAVs at both traditional intersections and signal-free intersections under various strategies: the traffic light strategy, FIFO strategy, LDR strategy, and DRP strategy. Subsequent to this, a detailed analysis and discussion of each strategy's performance ensue. Finally, in Section 6,the paper culminates with a comprehensive summary, offering research conclusions and outlining avenues for future exploration and development.
## 6 Conclusions  
The induction motor's single-phasing fault is presented and illustrated theoretically and practically through practical tests, theoretical calculations, and simulation. Single phasing reduced the motor speed while increasing the currents in the other phases under certain loading conditions. To ensure that the motor currents in the experimental settings do not exceed the permissible limits, a protection scheme was introduced and implemented.  
It is important to note the following:  
Figure 17: Current and temperature under healthy and single-phasing fault for the light load.  
Figure 18: Current and temperature under healthy and single-phasing fault for the half load.  
* The proposed protection scheme is practical, and a fault-tolerant control scheme can be used to make the three-phase induction motor run more reliably and safely under the single-phasing fault.
* It has been discovered that the stator winding temperature should be considered in any advanced research on motor operation during single phasing.  
The preliminary findings of this study (based on extensive experimental and practical tests) indicate that the three-phase induction motor could continue to operate safely at 30% of full load under a single-phasing fault, which can be advantageous in induction motor applications such as cooling systems, ventilation, and maybe some elevator applications. Overall, these findings have the potential to significantly impact industries that rely on three-phase induction motors for critical operations. This level of resilience to single-phasing faults could save companies time and money by avoiding unexpected downtime and maintenance costs.  
The vibration signal shown in Figure 8 and the temperature signal shown in Figure 12 are collected in healthy and faulty conditions. These data could be used for further research on fault detection based on artificial intelligence (AI). We plan to develop a machine learning algorithm that accurately detects faults based on these signals.  
Conceptualization, A.A. ([PERSON]); Methodology, A.A. ([PERSON]) and R.M.; Software, J.S.; Validation, H.S.; Formal analysis, J.S. and A.A. ([PERSON]); Investigation, J.S., A.A. ([PERSON]) and R.M.; Data curation, H.S.; Writing--original draft, A.A. ([PERSON]); Writing--review & editing, A.A. ([PERSON]), H.S. and R.M. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Research Committee, Birzeit University.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflicts of interest.
## 6 Rhizobial AHL in Interkingdom Communication
The finding that a comparable increase in nodulation was achieved with L-homoserine, a product resulting from the cleavage of the amide bond present in AHLs, suggests that some responses to AHLs in _M. truncatula_ probably require signal hydrolysis by plant acylases or related enzymes. AHL amidolysis by a plant-derived fatty acid amide hydrolase (FAAH) has been demonstrated to mediate growth regulatory effects of AHL on _A. thaliana_[114]. The fact that rhizobial AHLs can have an impact on non-legume plants should be taken into account when trying to understand how AHLs impact nodulation. Again, it raises the question of how AHLs impact symbiosis, i.e., through direct, specific pathways or/and through general improvements in plant health.
###### Abstract  
Knowledge of key variables driving the top of the atmosphere (TOA) radiance over a vegetated surface is an important step to derive biophysical variables from TOA radiance data, e.g., as observed by an optical satellite. Coupled leaf-canopy-atmosphere Radiative Transfer Models (RTMs) allow linking vegetation variables directly to the at-sensor TOA radiance measured. Global Sensitivity Analysis (GSA) of RTMs enables the computation of the total contribution of each input variable to the output variance. We determined the impacts of the leaf-canopy-atmosphere variables into TOA radiance using the GSA to gain insights into retrievable variables. The leaf and canopy RTM PROSAIL was coupled with the atmospheric RTM MODTRAN5. Because of MODTRAN's computational burden and GSA's demand for many simulations, we first developed a surrogate statistical learning model, i.e., an emulator, that allows approximating RTM outputs through a machine learning algorithm with low computation time. A Gaussian process regression (GPR) emulator was used to reproduce lookup tables of TOA radiance as a function of 12 input variables with relative errors of 2.4%. GSA total sensitivity results quantified the driving variables of emulated TOA radiance along the 400-2500 nm spectral range at 15 cm\({}^{-1}\) (between 0.3-9 nm); overall, the vegetation variables play a more dominant role than atmospheric variables. This suggests the possibility to retrieve biophysical variables directly from at-sensor TOA radiance data. Particularly promising are leaf chlorophyll content, leaf water thickness and leaf area index, as these variables are the most important drivers in governing TOA radiance outside the water absorption regions. A software framework was developed to facilitate the development of retrieval models from at-sensor TOA radiance data. As a proof of concept, maps of these biophysical variables have been generated for both TOA (LIC) and bottom-of-atmosphere (L2A) Sentinel-2 data by means of a hybrid retrieval scheme, i.e., training GPR retrieval algorithms using the RTM simulations. Obtained maps from LIC vs L2A data are consistent, suggesting that vegetation properties can be directly retrieved from TOA radiance data given a cloud-free sky, thus without the need of an atmospheric correction.  
radiative transfer models; global sensitivity analysis; emulation; machine learning; top-of-atmosphere radiance data; PROSAIL; MODTRAN; retrieval; Sentinel-2
## 3 Processing Method
### The Proposed Method
#### 3.2.1 Data Rearrangement  
In the traditional TDCM [35], the cross-correlation operations are performed between adjacent channels at the same slow time sample point or between a certain moment of the last channel and the next moment of the first channel. The data sorting is shown in the top row of Figure 4. The TDCM operation is performed between adjacent data. However, as Figure 4 shows, the azimuth interval between adjacent data for the data sorting shown in the top row of Figure 4 is large, and the correlation between the data is poor, thus the correlation coefficient cannot be estimated. Therefore, before the phase error estimation, the data are rearranged based on the spatial distribution, as shown in the bottom figure of Figure 4, to fit the Nyquist sampling theorem.
## 3 Results
### Aroma Description Based on Odor Activity Values (OAV) and Sensory Evaluation  
The types of aromas and the OAVs contributed by each compound were listed in Table S3. The OAVs of the different compounds that contribute the same odor description were summed up. Herein, the aroma variation of the wines was mainly concerned with fruity, floral, kerosene, and honey smells (Figure 4).  
Figure 3: Correlation analyses between the pH value and the concentration of individual aroma compounds and between different aroma compounds. The “_”, “_”, and “_”_” represented that the correlations were statistically significant, which means \(p<0.05\), \(p<0.01\), \(p<0.001\), respectively.  
At 0 and 6 months of bottle storage, the OAVs of both the fruity and floral smells had no significant difference between the cold treatment wines and the control group except for the floral OAV at 6 months of bottle storage. After bottle storage for 12 months, all cold treatments significantly increased the fruity and floral OAVs, which was related to the increase in the concentrations of ethyl esters, \(\beta\)-damascenone, \(\beta\)-ionone, and others (Table S1). These compounds provide the fruity and floral aroma [20]. Compared with the control group without cold treatment, the OAV corresponding to the kerosene smell was significantly increased in the T14 and T15 wines at the end of the cold treatment (R0), and in the wines with cold treatments over 12 days (T12-T15) after bottle storage for 6 months, respectively. Moreover, the OAV of kerosene steadily increased during bottle storage and no statistical difference existed among the treated wines at the 12-month point of bottle storage, which was closely related to the increase of TDN concentration (Table S1). The kerosene was provided by TDN in Riesling wines [22]. The honey smell is characterized by phenylethyl alcohol in this study [20]. The OAV corresponding to the honey smell increased in the T11, T13, and T15 wines at the end of cold treatment (R0), and in the cold treatment wines, except for T11 and T13, 6 months after bottle storage. But at 12 months of bottle storage, these differences all disappeared, even though the OAV corresponding to the honey aroma was too low, so the variation did not influence the wine aroma sensory characteristics. Regarding aroma sensory evaluation, various aroma characteristics did not show statistical differences between the different groups at the 6-month and 12-month points of bottle storage (Figure 5).  
Figure 4: Effect of cold treatment duration on the OAVs of four aroma types ( floral, fruity, honey, and kerosene). Samples (\(n=3\)). Different lowercase letters represent significant differences in the OAV between different groups at the same bottle-storage period. Different capital letters represent significant differences in the OAV between different bottle-storage durations for the same treatment group (Duncan, \(p<0.05\)).
# Learning Swimming Techniques by Means of Real-Time Monitoring with Embedded Devices
## 2 Materials and Methods
### Software Analysis
#### 2.2.1 Master Device  
As illustrated in Figure 4, Device_4 serves as the master device in the proposed system. It has the capability to connect to nearby Wi-Fi sources, such as a router, a mobile phone hotspot, or any other device that functions as a Wi-Fi access point with user authentication. Additionally, it can connect to multiple devices using the implemented ESP-NOW wireless communication protocol [15].  
Master-slave communication is established using the ESP-NOW protocol. This peer-to-peer wireless communication protocol enables devices to exchange small data packets, with a maximum size of 250 bytes per packet. During the setup, the master device stores the MAC address of each slave device, and each communication session is initiated by sending data to the specific broadcast address (MAC address) of the targeted slave device. A significant advantage of this system is that after each message is sent, feedback is received from the slave, confirming the successful transmission of data.  
For control and visualization, the Async Web Server library is utilized to create a local webpage. This webpage allows for the control and analysis of data. It is flashed onto the ESP32 microcontroller and runs automatically whenever the device is powered on.  
The device is bidirectional, it sends data to the slave devices as requests. The received information consists of specific data for each request. The following actions are used for requests and received information for Calibration, Training, and Vote (endless monitorization):  
* Is triggered from the webpage interface;
* Sends simple requests for each slave in the system to start the requested routine;
* After the requested routine is performed, each slave sends confirmation of the finished task with the desired information;
* The received information is listed in the feedback section from the webpage.  
The master device consists of a single ESP32 module, which operates with a power consumption of approximately 150 mA when the Wi-Fi is continuously enabled. Given a fully charged battery capacity of 500 mAh, the estimated operational time can be calculated as follows:  
\[\frac{500\;\mathrm{mAh}}{150\;\mathrm{mA}}\;\approx 3.33\;\mathrm{h} \tag{1}\]  
This provides an approximate battery life of 3.33 h under continuous operation.
# Few-Shot Charge Prediction with Data Augmentation and Feature Augmentation
## 3 Method
### Data Augmentation
#### 3.1.1 Encoder  
Given a sequence of fact descriptions \(X\) containing \(L\) sentences, we chose bi-directional gated recurrent unit (Bi-GRU) [25] to calculate the hidden state of words. Bi-GRU not only effectively solves the problems such as gradient disappearance of traditional RNN, which is widely used in the field of LJP for case coding, but also has a simple structure compared with pre-trained language models such as BERT [26] and is less prone to over-fitting in the few-shot classification task. Specifically, based on each sentence \(S_{i}=\{x_{i1},x_{i2},\ldots,x_{i\_m}\}\) (\(m\) represents the number of words contained in the \(i-\) th sentence and \(x_{i\_j}\) represents the \(j-\) th word in the \(i-\) th sentence) of fact description, we used a forward GRU and a reverse GRU to calculate the hidden states \(h_{ij}\).  
\[h_{i,j}=[\overset{\rightarrow}{GRU}(e(x_{i,j})),\overset{\leftarrow}{GRU}(e( x_{i,j}))],j=1,2,\ldots,m, \tag{1}\]  
where \(h_{i,j}\in R^{2d_{w}}\) (\(d_{w}\) is the dimension of the hidden layer) denotes the hidden representation of the \(j-\) th word of the \(i-\) th sentence in the sequence and \(e(;)\) is the word embedding code. This paper used a skip-gram algorithm of word2 vec [27] to model the word vector. However, not all words in the fact description are equally important to the sentence expression, so an attention mechanism was used to extract words that are mean  
Figure 2: The structure of the charge prediction model.  
ingful to the sentence and calculate the weights \([\alpha_{i,1},\alpha_{i,2},\ldots,\alpha_{i,m}]\), where \(\alpha_{i,j}\) is the weight of \(x_{i,j}\). The combination of the attention mechanism can obtain deeper text features, so that Mixup can produce augmented data with diverse features. \(\alpha_{i,j}\) is calculated as follows:  
\[\alpha_{i,j}=\frac{\exp(\tanh(W_{w}h_{i,j})^{T}u_{i})}{\sum_{j}\exp(\tanh(W_{w }h_{i,j})^{T}u_{i})}, \tag{2}\]  
where \(W_{w}\in R^{d_{w}\times 2d_{w}}\) are trainable weight parameters and \(u_{i}\in d^{d_{w}}\) is the word-level context vector. \(u_{i}\) was used to measure the importance of words generated by random initialization and jointly learned during the training process. Then, we obtained the charge information word-level hidden states \(h^{\prime}_{i,j}=\alpha_{i,j}h_{i,j}\).