esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Damage Model and Numerical Test
### Numerical Test
Through the simulation results, it was found that excavation construction resulted in the formation of isolated coal pillars between the goat of the 21,201 working face and the 21,181 working face. The coal and rock mass around the isolated coal pillar is damaged greatly, and the two sides of the coal are the positions with the highest damage level. The damage evolves from both sides of the coal body towards the center and gradually spreads  
Figure 21: Damage and vertical stress evolution results on the 5 th day. (**a**) Damage evolution results. (**b**) Vertical stress evolution results. Figure 17:0 Damage and vertical stress evolution results on the 15 th day. (**a**) Damage evolution results. (**b**) Vertical stress evolution results. Figure 17:1 Damage and vertical stress evolution results on the 30 th day. (**a**) Damage evolution results. (**b**) Vertical stress evolution results. towards the adjacent rock mass, ultimately forming an arched damage area. However, the damage cannot continue to evolve towards the deep rock mass, and a large amount of elastic properties accumulate around the coal rock mass, which poses a risk of inducing rockburst. From the 5 th day to the 30 th day, the vertical stress above the coal body gradually increases and a stress concentration phenomenon occurs, with a stress concentration coefficient of about 2.4. Based on the above analysis, it can be concluded that the stress concentration phenomenon is obvious after the formation of the isolated coal body, and the elastic performance accumulates significantly in the arched damage area, which poses a risk of inducing rockburst. In addition, in the case of a rockburst accident at the Yimaqianqiu Coal Mine, the presence of the F16 reverse fault subjects the coal body to horizontal pressure from the hanging wall of the fault, which is also one of the potential triggering factors of an accident. In the above calculations, the influence of faults and protective coal pillars was not considered. However, by simulating the dynamic evolution of the damage field and stress field of isolated coal pillars, reference can be provided for the safety prevention and control of rockburst.
## 4 SD-EAR in Detail
### Optimum Route Selection
#### 4.2.4 Sleeping Strategy  
Whenever a node \(\rm n_{i}\) wants to go to sleep, it needs to ask for permission from the SDN controller of that zone. It gets a sleep grant provided that the following conditions are true:  
(i) (\(\rm r\)-\(\rm en_{i}/dep_{i}\)) < TH where TH is a threshold.  
(ii) If \(\rm n_{i}\) is part of any live communication route, then it must have at least one valid alternative. The strategy of computing an alternative is present in (27a)-(27c).  
Also, there is one forceful sleep grant mechanism. When all of the neighbors of a node \(\rm n_{i}\) are sleeping, then \(\rm n_{i}\) is instructed by the SDN controller in that zone to go to sleep if it does not want to transmit anything. It has practically no reason to listen to the network, because it has no way of receiving any traffic.  
The attributes of a sleep request are:  
(i) message-type-id (5)  
(ii) node-id (\(\rm n_{i}\))  
Similarly, the attributes of a sleep grant are:  
(i) message-type-id (6)  
(ii) node-id (\(\rm n_{i}\))
## 2 Related Works
### Charging Coordination for EVs and Home Energy Management
To mitigate issues arising from chaotic EVs scheduling, they proposed a real-time energy management system utilizing multi-agent deep RL optimization, where the aggregator and prosumers act as smart agents that interact to make optimal decisions. The goal is to reduce electricity costs for prosumers through efficient EVs battery scheduling. The aggregator employs real-time pricing to enhance prosumer behavior and calculate revenues from energy trading. The given experimental results indicate that the proposed method reduces mean power consumption by 9.04% compared to a system without EVs usage and by 39.57% compared to one using conventional energy prices. Additionally, it decreases prosumer costs by 1.67% to 24.57% and allows the aggregator to generate daily revenue of 0.065 USD, surpassing earnings from conventional energy pricing. Additionally, [PERSON] et al. [20] propose a novel home energy management system designed to optimize electric battery operation in real-time using RL based on data-driven methods for demand-side management in residential settings. They utilize advanced load forecasting techniques to enhance decision-making robustness using Convolutional Neural Networks (CNN) and LSTM. Additionally, various RL agents are trained to minimize electricity purchase costs and maximize photovoltaic self-consumption for a PV-battery residential setup, including Double DQN, Dueling DQN, Rainbow, and Proximal Policy Optimization. The given experimental results demonstrate that the best RL agent achieves a 35% reduction in total costs compared to a traditional optimization-based agent.
## 6 Model Results
### Comparing with Other Machine Learning Models
When \(p=1\), this is equivalent to using manhattan_distance, and euclidean_distance for \(p=2\). The choice of KNN and SVM as benchmarks is based on the principle of diversity in model comparison. KNN and SVM are indeed examples of single-learner algorithms, where a single hypothesis is learned from the training data and used for predictions. By comparing XGBoost with these single-learner models, we aim to demonstrate the advantages of using an ensemble method. Ensemble methods like XGBoost are known for their superior performance in terms of accuracy and robustness because they aggregate the predictions of multiple models to minimize the variance and bias. This comparison will highlight these benefits of XGBoost over single-learner algorithms. Figure 7 provides a summary of the three metrics used to evaluate the predictions on the test sets for the three models. R\({}^{2}\), RMSE, and MAE are measures of the prediction error among the three models: BO-XGBoost, BO-SVR, and BO-KNN. BO-XGBoost demonstrated the least prediction error, followed by BO-SVR, then BO-KNN. Specifically, BO-XGBoost yielded an RMSE of 2.3525 and an MAE of 1.7737, signifying it has the smallest prediction error. The BO-SVR model's prediction error was less than that of the BO-KNN model. Considering R\({}^{2}\), which gauges how well the models fit the independent variables, the models ranked as follows: BO-XGBoost performed the best, followed by BO-SVR, and then BO-KNN. In particular, BO-XGBoost achieved an R\({}^{2}\) of 0.9756, implying the best fit. Overall, based on the aforementioned prediction metrics, BO-XGBoost provides the most accurate predictions for CS of eco-friendly concrete. The reason may be that both SVR and KNN are individual learning algorithms, while XGBoost is an ensemble learning method. In essence, XGBoost constructs multiple weak learners (in this case, decision trees) and combines their  
output to create a single, more accurate prediction. This methodology allows it to correct its errors with each iteration, progressively enhancing the model's performance. Furthermore, a Taylor plot was applied to compare the realistic characteristics of the three models by measuring the distance between each model and the point marked \"actual\" (see Figure 8). It can be seen that the BO-XGboost model was situated closest to the \"actual\" point with a comparatively higher correlation coefficient and lower RMSE value, indicating that this model performed better in predicting CS values of eco-friendly concrete. As for the other two models, KNN lay farthest from the \"actual\" point due to a larger RMSE and standard deviation as well as lower correlation coefficient, suggesting poorer predictive performance of this model.
# Current Biomedical Applications of 3D Printing and Additive Manufacturing
## 4 Healthcare Applications
### Surgical Tools  
Advancements in radiological imaging have allowed the creation of CAD reconstructions of patient anatomy, providing potential for designing and fabricating patient-specific, customized surgical instruments [152]. Most surgical tools are designed to work with most patients. However, unique anatomical features or complex procedures could benefit from customized tools that allow for a more-controlled and simplified operative experience, in turn decreasing risk of complications [153]. AM is a very effective tool in rapidly converting CAD designs into usable tools that could serve the ongoing needs of high-output institutions such as hospital operating rooms. AM-fabricated tools have been effectively sterilized and used for drill and cutting guides [154], retractors [155] and jigs. One study showcased how a 3D-printed PLA-based retractor was able to perform as well as its stainless counterpart, while costing 10 times less [155]. One can imagine the large savings for institutions if this technology were implemented broadly. Interestingly, some studies have suggested the feasibility and effectiveness of 3D printing surgical tools for emergencies on long-term space missions, such as a trip to Mars [156; 157]. More realistically, the cost-effectiveness of using 3D-printed surgical tools or casts in low-income countries and institutions has high feasibility [155].
# Grape By-Products in Sustainable Cosmetics: Nanoencapsulation and Market Trends
## 4 Grape By-Products and Their Environmental Impact  
Grapevine (_Vitis vinifera_ L.) is a perennial woody fruit crop used for the production of wine, juice, dried fruit, and distilled liquor, and for fresh consumption (table grapes) [23]. Grapes are one of the world's biggest fruit crops, being one of the most popular fruits consumed worldwide, with approximately 80% of its production being destined for wine-making [24; 25]. High levels of organic waste are produced during the winemaking process, which primarily entails pressing and macerating grapes. Those wastes are primarily made up of grape pomaces (62%), lees (14%), stalks (12%), and dewatered sludge [26], reaching approximately 14.5 million tons per year in Europe [27]. In line with this, a significant amount of these by-products (Figure 2) is produced in a short amount of time, posing ecological issues, since 35.9 million tons of grapes are pressed worldwide each year to make wine. Grape pomace is composed of 25% seeds, 25% stalks, and 50% skins [28]. The main applications of these by-products have been composting and applications on agriculture and animal feeding; however, relevant quantities are still dumped in landfills. As an agro-industrial by-product, grape seeds may be responsible for environmental problems, when improperly managed [26; 29], due to the low pH and high concentration of phenolic compounds, which can resist biological degradation [26], having the potential to unbalance the ecosystems, by modifying organic compound pathways and nutrients cycle [28]. In addition, the high organic carbon content of pomace, between 31 and 54%, can cause water pollution and bad odours.  
Notwithstanding, it is possible to effectively valorise these wastes by means of extraction of their bioactive compounds, especially antioxidant phenolic molecules, but also vitamin E and fatty acids, using a safe, green, and environmentally friendly extractive medium (i.e., water-glycerol solution) [26; 29].  
Phenolic compounds are secondary plant metabolites that can be used as adjuvant treatment for a number of illnesses, including diabetes, diseases associated with the ageing of the brain, and hypertension [30]. Structurally, they have an aromatic ring with one or more hydroxyl groups, and are mainly found in fruits, vegetables, nuts, seeds, and cereals. In particular, the phenolics produced by grapevine range from structural cell wall compounds, such as lignin and tannins, to specialised ones such as phenolic acids and flavonoids, exhibit several properties such as antioxidant, anti-inflammatory, antibacterial, anticarcinogenic, antidiabetic, and cardioprotective effects, having great potential for several industries, in particular the nutraceuticaltial and cosmetic ones [23; 30; 31].
## 4 Challenges in Rainfall Estimation by CMLs
### Reconstruction of the Rainfall Spatial Distribution  
In reconstructing the continuous spatial distribution of rainfall using sparse rainfall estimates from CMLs, uncertainties are inevitable. Although [PERSON] et al. [173] demonstrated that. In the Netherlands. the error in estimating rainfall by CMLs was greater compared to rainfall mapping, rainfall mapping is likely to be more critical over a larger area. The main sources of uncertainty in rainfall mapping include the representation of path-averaged rainfall estimates, systematic errors in mapping methods, and the density and distribution of CMLs. Firstly, the majority of studies simply treat the path-averaged rainfall estimates for CMLs as the rain rate at the midpoint of their path [45; 126; 127], which is feasible for short CMLs; but, for long CMLs, this undoubtedly ignores the spatial variability of rainfall on the path.  
Secondly, mapping methods, regardless of geostatistical interpolation algorithms, tomographic reconstruction techniques, stochastic mixing or compressed sensing, each have their own limitations in their principles. When applying these methods, these limitations are, of course, reflected in the errors of the rainfall maps.  
Thirdly, CMLs are not always distributed in high densities. Due to their purpose of communication, the density of CMLs in sparsely populated areas is usually much lower than in densely populated areas. This results in the poor ability of CMLs to capture the spatial distribution of rainfall in these areas. In addition, the unique topology of CML networks is not designed for measuring rainfall and does not facilitate rainfall mapping [161].
## 4 SMS Configurations Setup
### Testing and Validation
#### 4.3.5 Result Validation by Field Engineers  
In this section, the validation performed by field engineers on each of the submitted test results is discussed further. A total of nine test configurations and results were submitted to field engineers for validation and feedback purposes. A summary of the feedback is shown in Table 8.  
As shown in Table 8, field engineers have strongly agreed on the test configuration and result from Configuration 1 Test 2 and Test 3, Configuration 2 Test 2, and Configuration 3 Test 2 (C1 T2, C1 T3, C2 T2, C3 T2), respectively. This agreement from field engineers proves that the configurations used for these test cases are suitable for use in the manufacturing production environment. Engineers have mixed responses for Configuration 2 Test 3 and Configuration 3 Test 3 (C2 T3, C3 T3), respectively, as Engineer 1 has chosen to be neutral. In contrast, Engineer 2 and Engineer 3 have disagreed and strongly agreed on the configurations' results.  
In the case of Configuration 1 Test 1 (C1 T1) and Configuration 2 Test 1 (C2 T1), engineers have stated their agreement with the results with either strong agreement or just basic agreement, as shown in the table above. On the other hand, the engineers recorded a  
Figure 15: Final Summary.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test Results** & **Engineer 1** & **Engineer 2** & **Engineer 3** & **Average** \\ \hline C1 T1 & 5 & 4 & 5 & 4.7 \\ C1 T2 & 5 & 5 & 5 & 5 \\ C1 T3 & 5 & 5 & 5 & 5 \\ C2 T1 & 4 & 5 & 5 & 4.7 \\ C2 T2 & 5 & 5 & 5 & 5 \\ C2 T3 & 3 & 2 & 5 & 3.3 \\ C3 T1 & 1 & 2 & 2 & 1.7 \\ C3 T2 & 5 & 5 & 5 & 5 \\ C3 T3 & 3 & 2 & 5 & 3.3 \\ \hline \multicolumn{5}{c}{Satisfaction Rate (\%)} & \multicolumn{3}{c}{(4.19/5) 83.7\%} \\ \hline \hline \end{tabular}
\end{table}
Table 8: Engineer Validation Result (1-Strongly Disagree, 2-Disagree, 3-Neutral, 4-Agree, 5-Strongly Agree).  
trend of disagreement for the Configuration 3 Test 1 (C3 T1), as it may not be a practical configuration based on field expertise. This configuration was set up using high lead time, high quality, and medium cost. The final cost justification was considered to be low based on the set Fuzzy rules. However, engineers have disagreed on this, as even though the product may be of good quality, the high lead time used will cause a high final cost. This is also influenced by the resources and materials used during the increased lead time process duration. Based on the validation by the engineers, it is proven that this evaluation model by using Fuzzy Logic Rules has an accuracy of 83.7%, as shown in the table above. This has been achieved by summarizing all the obtained results and feedback from actual field engineers.
## 3 Methodology
### Training Datasets and Experimental Setup  
Our research objective encompassed a main use case: resume classification enhanced with augmented data. At the core of our study, we aimed to explore and assess the feasibility and advantages of enriching real-world job resume data, sourced from Indeed with synthetic resumes created with ChatGPT. Our focus then shifted to performing the multiclass classification task on this real-world data, particularly in categories characterized by sparse samples.  
To perform the multiclass classification of resumes, five datasets were created:  
* One synthetic dataset from ChatGPT (ChatGPT, Section 3.3).
* One dataset (Indeed, Section 3.2) with real-world data from Indeed.com.
* A training dataset (Indeed_60) derived from the Indeed dataset (approximately 60%).
* An evaluation dataset (Indeed_40) derived from the Indeed dataset (approximately 40%). The selection of instances for both Indeed_60 and Indeed_40 was performed randomly, ensuring against a biased distribution or the exclusion of difficult-to-learn or classify instances.
* An augmented dataset (Indeed augmented) was constructed by combining the \"Indeed_60\" dataset, which contained 919 resumes, with an additional 1331 resumes generated with ChatGPT. This resulted in a total of 2250 resumes in the augmented dataset. A primary objective behind this augmentation was to ensure a balanced representation across resume categories within the dataset.  
This balance was realized through a thorough curation process, where each job class received an approximately equal number of entries. The primary motivation behind this augmentation was twofold: firstly, to enhance the model's training, promoting consistent prediction accuracy and, secondly, to address potential inconsistencies arising from overlaps in similar resumes that might describe different job categories. This reinforced the model's ability to discern subtle differences and distinctions among professions.  
In our experiments, we used occupation abbreviations for clarity and simplicity in presenting the procedure and results. Table 1 provides an overview of the datasets we constructed. The term \"Job category/class\" refers to the class represented in the multiclass classification task.
# Remote Speckle-Based Measurements of Backward Brillouin Acoustic Vibrations in Optical Fibers
## 2 Theoretical Background
### Speckles Analysis  
The random self-interference pattern known as speckle, allows us to track changes in the phase of the light being scattered from a rough surface while being illuminated and vibrated [24; 25]. By tracking the position of the time-varying correlation peak, it is possible to extract the movement trajectory. In this way, very small temporal shifts and movements of an object can be measured [16]. The shift of the speckle pattern is related to the changes in the physical properties of the tissue being tested [25]. By recording the temporal speckle pattern, and by using correlation analysis of the video, we can calculate the relative shift between the frames which stands for the temporal oscillation profile. By calculating the Fourier transform of the shifts' vector, the oscillation frequencies of the captured speckle pattern can be computed [26]. The speckles analysis has already been tested successfully for several kinds of measurements such as extraction of multiple speech sources and heartbeats [27], breathing, heart rate, pulse pressure, blood Coagulation, blood oxygenation [28], alcohol concentration [29], detection of pigmented lesions, and human skin hydration [30].
## 2 Related Work
propose a new reliable transport scheme based on coding and ACK, aiming at optimizing round-trip delay of file under energy constraints [23; 24]. The transmission is organized within cycles. During each cycle, the source transmits a number of redundant packets based on the last ACK received from the destination, and then stops transmitting packets to wait for the ACK. They assume that each node can only store one packet, and consider the buffer management strategy that source packets and acknowledgements have to compete for storage. A \"half-duplex feature\" is used to describe this contention. What they focus on is how to allocate the limited storage space of redundant packets and ACKs to minimize the delay of the file. Although the limited energy is taken into consideration, their optimization objectives are still the delivery delay of the file, and to simply give an upper bound to energy consumption. Hence, an optimal trade-off between energy consumption of relays and the delivery delay remains to be further studied. In this paper, what we focus on is how to improve the reliability of the network by reducing the network energy consumption. We assume that there is no competition between redundant packets and ACKs at the node cache, and a reliability transport scheme is proposed though the optimization of both the energy consumption and the file round-trip delay.
## 5 Radiometric Calibration Processing Results  
### Point Target SCR Analysis  
When using point targets to complete SAR image radiometric calibration, it is necessary to ensure that the point targets in SAR images are not markedly affected by background clutter. When SCR is greater than \(20\) dB, it can be inferred that the point target is effectively involved in the subsequent response energy calculation.  
Using Equation (3) to calculate the SCR of each CR for the two modes' data, the results are presented in Table 5. The SCRs of CRs on the UFS mode data are all greater than \(36\) dB, and the mean is \(38.128\) dB. The SCRs of CRs on the FSI mode are all greater than \(32\) dB, and the mean is \(33.230\) dB. This implies that each CR can be used for the subsequent calculation of response energy and calibration constant.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**CR**} & \multicolumn{2}{c}{**Resolution (m)**} & \multicolumn{2}{c}{**PSLR (dB)**} & \multicolumn{2}{c}{**ISLR (dB)**} \\ \cline{2-7}  & **Range** & **Azimuth** & **Range** & **Azimuth** & **Range** & **Azimuth** \\ \hline CR-1 & \(1.651\) & \(4.881\) & \(-23.047\) & \(-24.345\) & \(-21.831\) & \(-19.536\) \\ CR-2 & \(1.616\) & \(4.760\) & \(-23.361\) & \(-24.533\) & \(-17.211\) &