esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Method
### Translation Augmentation  
On construction sites, movement by either the camera or the subject can cause objects to appear at different locations within the frame, resulting in translational variation. For example, a camera might capture a worker from various horizontal or vertical viewpoints depending on its position, or the subject could shift between panoramic shots. Translation augmentation, which alters the whole object's position in an image by shifting it along the \(x\) or \(y\)-axis, enriches a dataset by mimicking these positional changes [26]. This approach bolsters a model's capacity to detect non-PPE items regardless of where they appear in the image. The translation transformation can be mathematically expressed by Equation (6):  
\[I^{\prime}_{translation}\big{(}x^{\prime},y^{\prime}\big{)}=I\big{(}x-t_{x},y-t_ {y}\big{)} \tag{6}\]  
Figure 5: Examples of images with perspective augmentation.  
Figure 6: Examples of images with rotation augmentation.  
where \((x,y)\) denote the original coordinates, \(t_{x}\) and \(t_{y}\) are the translation distances, and \(I^{\prime}_{translation}(x^{\prime},y^{\prime})\) is the pixel intensity at the shifted coordinates. Figure 7 presents an example: the left image is shifted right by \(t_{x}\) = 0.7 and \(t_{y}\) = 0, while the right image is shifted both right and downward with \(t_{x}\) = 1.1 and \(t_{y}\) = 1.5.
## 3 Results
### In-Cylinder Temperature and NO Emission
Figure 12: In-cylinder average temperature diagrams in all simulation cases: (**a**) IP = 0.01 m\({}_{2}\) (**b**) IP = 0.015 m; (**c**) IP = 0.02 m.  
Figure 13: In-cylinder peak average temperatures in all simulation cases. It is widely known that NO (nitric oxide) is the main component of NOx emissions generated from ICEs and usually occupies more than 90% of NOx. Three typical chemical mechanisms are involved in NOx formation: the thermal NOx mechanism, the prompt NOx mechanism, and the fuel NOx mechanism [47]. However, in ICEs that operate with nitrogen-free fuels, only the NOx thermal mechanism must be considered. This is why the Extended Zelodovich mechanism was used to model NO formation in this study. According to this mechanism, NO formation is mainly influenced by the in-cylinder peak temperature, the residual time, and the oxygen concentration inside the engine cylinder. NO formation occurs strongly in very high-temperature (\(\geq\)1800 K) regions in the cylinder, and the formation rate significantly increases according to the increase in the in-cylinder peak temperature [10; 47]. Since NO formation is closely related to the peak temperature in the engine cylinder, it had the same trend as the peak temperature in the cylinder when the SA and IP varied. This can be verified by comparing Figures 13 and 14a. However, when expressing the NO emission in terms of specific values (the amount of NO emission (gram) generated per unit time (1 h) per unit engine power (1 kilowatt)), the NO emission had a different trend, as shown in Figure 14b. It can be seen from Figure 14b that the IP of 0.01 m with an SA of 50 degrees produced the lowest specific NO emission in both diesel and DF modes. This set of injection parameters decreased the specific NO emissions of the engine by 18.92% and 18.97% compared to when using the original set of injection parameters (IP = 0.01/SA = 45) in the diesel and DF mode, respectively. It is thus considered to be the optimal parameter that should be set for the fuel injector to reduce NO emissions in both diesel and DF modes.
## 6 Evaluation
### BERT Model Evaluation
#### 6.5.2 Analyzing the Effect of the Amount of New Words to Be Included  
A more thorough and complementary analysis of the performance of our vocabulary extension method can be derived from Figure 3 where results for different values of N were presented (i.e., the number of new tokens or words included as new words) for the three alternative approaches. First, baseline performances obtained when the three different pretrained models are simply fine-tuned without explicitly adding any new word were also included at the beginning of the series for easier comparison (i.e., first 0 columns on the left). Results are then reported starting from 25 new tokens at first and up to 100, increasing the amount by 25 on each different and independent run. Further results for experiments with values of 200, 300, 400, and 500, respectively, are also reported.  
The figure shows that our vocabulary extension method consistently outperforms the prior approach based on general vocabulary. Particularly, our vocabulary extension method demonstrates to be effective achieving a top performance of 79.48% (achieved by the 50 new tokens configuration) and an improvement over the baseline performance obtained when our model is simply fine-tuned without explicitly adding any new word (i.e., 78.16%, as also previously reported in Table 3). However, results become worse when the amount of new tokens exceeds a certain small limit, which suggests the importance of finding an adequate balance between the increased complexity of our target model (i.e., the number of new unit embeddings to be learned) and the available training data. Finally, we can clearly confirm again that fine tuning the cased version of the BETO pretrained model is the top-performing approach when adopting our vocabulary extension method.  
Figure 3: BERT models comparison when fine-tuning with our vocabulary extension strategy.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Pretrained Model** & **Extension** & **F1 (\%)** \\ \hline
4 & bert-base-multilingual-cased & & 75.27 \\
**5** & **dcuchile/bert-base-spanish-wwm-cased** & No & **78.16** \\
6 & dccuchile/bert-base-spanish-wwm-uncased & & 76.85 \\ \hline
7 & bert-base-multilingual-cased & & 77.64 \\
**8** & **dcuchile/bert-base-spanish-wwm-cased** & & **79.48** \\
9 & dccuchile/bert-base-spanish-wwm-uncased & & 77.50 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of results for bidirectional encoder representations from transformers (BERT) model (named entity recognition (NER)-based inputs in all cases).
## 3 Proposed Scheduling Scheme
### Recurrent Learning
The sharing interval is estimated for the power requirement and allocation and shows less processing than the power requirement. If the power allocation decreases, the failed request from the user is enhanced. The training iterates define the sharing intervals and increase if the failed request increases (Table 2). In Table 3, the service dissemination factor for different factors is presented. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scheduling** & **Sharing** & **Queued** & **Service** & **Failed** \\
**Intervals** & **Intervals** & **Requests** & **Dissemination** & **Requests** \\ \hline
10 & 11 & 23 & 0.96 & 0 \\
20 & 19 & 25 & 0.92 & 19 \\
30 & 15 & 20 & 0.94 & 12 \\
40 & 25 & 75 & 0.93 & 20 \\
50 & 38 & 98 & 0.91 & 25 \\
60 & 49 & 103 & 0.87 & 36 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Failed requests in scheduling intervals. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sharing** & **Power** & **Power** & **Failed** & **Training** \\
**Intervals** & **Requirement (J)** & **Allocation (J)** & **Requests** & **Iterates** \\ \hline
10 & 80.37 & 30.1 & 39 & 107 \\
20 & 80.54 & 40.58 & 34 & 98 \\
30 & 78.14 & 46.34 & 32 & 75 \\
40 & 98.36 & 54.25 & 29 & 68 \\
50 & 102.87 & 60.48 & 22 & 38 \\
60 & 107.98 & 75.37 & 20 & 41 \\
70 & 111.25 & 80.69 & 18 & 39 \\
80 & 118.8 & 91.33 & 12 & 23 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Training iterates for sharing intervals. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Users** & **Power** & **Predicted (J)** & **Deficiency (J)** & **Service** \\  & **Requirement (J)** & **Predicted (J)** & **Deficiency (J)** & **Dissemination** \\ \hline
40 & 80.37 & 86.5 & \(-\)6.13 & 0.87 \\
80 & 82.36 & 93.58 & \(-\)11.22 & 0.89 \\
120 & 92.47 & 86.74 & 5.73 & 0.96 \\
160 & 102.58 & 119.47 & \(-\)16.89 & 0.91 \\
200 & 118.8 & 126.3 & \(-\)7.5 & 0.94 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Service dissemination for different factors. User density determines power requirement, and it is enhanced appropriately. The prediction is made by mapping the current and prior service and range increases. If the prediction increases, the deficiency decreases, and service dissemination is performed for the different types of users in the network. Service dissemination also increases if the deficiency is higher (Table 3). The article suggests the CS3, an advanced SDN resource management method. By integrating the SDN's adaptable control and data plane operations with deep learning's predictive capabilities, the CS3 efficiently handles high-user-request densities while minimizing latency. Network performance and customer satisfaction are enhanced by integrating these technologies, which provide efficient and resilient power allocation and service scheduling.
## 2 Optical Inspection Technologies in Railways
### Sensor Systems
#### 2.3.3 Vegetation  
For the detection of vegetation, bvSys has developed an inspection system that inspects the entire track area at speeds of up to 100 km/h. The area is recorded using four digital color line-scan cameras, lenses with fixed focal lengths and artificial lighting. The evaluation is carried out online and automatically. Linking with a spraying system is possible [26]. Fraunhofer IPM has developed a Weed Detection System (WDS) for the detection of vegetation at speeds of up to 100 km/h. The system works with RGB and NIR cameras. Due to the characteristic fingerprint of green vegetation, it is automatically detected. Thanks to artificial lighting, the system can also work at night. When combined with a spray nozzle, speeds of up to 50 km/h can be achieved [27]. The use of this measurement technology can significantly reduce the consumption of herbicides [56].  
Geometric accuracy is not a particular challenge with these systems. Instead, attention must be paid to the required speed of image processing, signal evaluation, and signal forwarding to the existing technology for applying the herbicide [56]. The disadvantage is the dependence on sufficient ambient light or additional lighting.
# Fertilization and Soil Microbial Community: A Review
## 1 Introduction
A possible alternative is the use of controlled-release fertilizers (coated and uncoated fertilizers with a low solubility) [15], but they are expensive and, therefore, used mainly for high-value crops (e.g., vegetables, fruits, flowers, ornamentals) [8]. Inhibitors of nitrification and urease processes can also be used for maintaining N in its soil-stable form by slowing its conversion to nitrate or delaying the first step of degradation of urea [16]. For these compounds (such as clayandiamide, thiosulfates, 3,4-dimethylpyrazole phosphate), there is a lack of correlation between laboratory testing data and the actual field data [17]; there is also some concern about the potential for some of them to enter the food chain [18]. Phosphorus availability to plants after chemical fertilization can vary depending on the type of fertilizer used and, even under the best conditions, only about 25% of applied P is taken up by plants during the first cropping season [19]. Depending on the pH and moisture of soil, P can than precipitate (at high pH due to the presence of calcium and magnesium and at low pH due to an iron and aluminum presence) [20] or can be immobilized in soil [21]. The use of P fertilizers also leads to eutrophication (when P runs off to surface waters) [22]. Potassium has several beneficial roles in plant physiological and metabolic processes, including resistance to biotic and abiotic stresses and absorption and utilization of N and P by crops [23]. On the other hand, fertilization with KCl does not increase crop yields and has detrimental effects on the quality of major food, feed and fiber crops, with serious repercussions for soil ecosystem and human health [24]. Conversely, organic farming using organic fertilizers that are environmentally friendly amendments (e.g., microbial fertilizers [25; 26], manure, compost) can be a good alternative and can reduce the consequences of environmental pollution from synthetic fertilization. In fact, organic fertilizers for example gradually release primary and micronutrients into the soil, maintaining a nutrient balance for a healthy growth of crop plants. They can also be an effective source of soil microbes, while also improving soil structure [27]. Table 1 shows a list of the main organic fertilizers.
# Through-Wire Microstrip-to-Empty-Substrate-Integrated-Waveguide Transition at Ka-Band
## 4 Conclusions  
In this paper the design method for the through-wire microstrip-to-ESIW transition has been validated at the Ku- and Ka-bands, showing that it is not frequency dependent. The results obtained have shown a very similar behavior of the transition independently of  
Figure 5: Comparison of simulated and measured transmission and reflection parameters of the Ka-band back-to-back transition.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Transition** & **Manufacturing Process** & **Freq. (GHz)** & **IL Max (dB)** & **RL (dB)** \\ \hline
[4] & medium & 12 & 1.5 & 13.5 \\
[5] & medium & 15 & 1.2 & 20 \\
[6] & easy & 15 & 1.08 & 21 \\
[7] & difficult & 33.25 & 1.26 & 14.7 \\
[8] & easy & 15 & 0.31 & 20.8 \\
[8] & easy & 33.25 & 1.36 & 14.75 \\
[9] & easy & 10.25 & 0.9 & 12 \\
[10] & easy & 10 & 1.3 & 15 \\
[15] & medium & 12 & 1.5 & 13.5 \\
**This work** & easy & 33.25 & 3.7 & 11.7 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison of the proposed back-to-back transition with other microstrip-to-ESIW back-to-back transitions.  
the frequency band, being this proof of its robustness. Moreover, a back-to-back transition has been accurately manufactured in the Ka-band, obtaining measured insertion losses lower than 3.7 dB and return losses higher that 11.7 dB. Taking into account the advantages shown in [9] (easiness of manufacturing, independence of microstrip and ESIW heights, possibility of implementation of planar circuits in top and bottom layers, feeding of the ESIW from any angle, and possibility of designing broadband and narrowband devices) jointly with the advantages shown in this paper (frequency-independent design method and good behavior of the transition at any frequency band), it is possible to conclude that it is the most suitable microstrip-to-ESIW transition independent of the intended application.  
[PERSON] and [PERSON] designed, simulated, and optimized the device; [PERSON] and [PERSON] manufactured the device and characterized it in the laboratory; [PERSON], [PERSON] and [PERSON] wrote the manuscript; all the authors revised the manuscript; [PERSON] supervised the whole work. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Coordinated Research and Development Project PID2019-103982 RB, Subprojects C44 and C41, funded by the Ministerio de Ciencia e Innovacion--Agencia Estatal de Investigacion/10.13039/501100011033. This work was also supported under grant TED2021-129196B-C44 funded by MCIN/AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR.  
Not applicable.  
All the data are available upon reasonable request.  
The authors declare no conflict of interest.
# Ecosystem Water Use Efficiency in the Three-North Region of China Based on Long-Term Satellite Data
## 3 Results
### The Sustainability Analysis of GPP, ET, and WUE  
As shown in Figure 7, the sustainability of GPP, ET, and WUE is divided into five categories, as shown in Table 2. For GPP, in the Three-North region, approximately 38.04% of the total area showed sustainability and degradation, mainly in the western parts (Xinjiang Province). Additionally, approximately 56.56% of the total area showed sustainability and improvement. For ET, the corresponding numbers were 30.20% and 58.02%, respectively. For WUE, 43.82% of the total area showed sustainability and degradation, and 51.15% showed sustainability and improvement. For spatial distribution, the sustainability and improved areas of GPP and WUE were mainly located in the eastern and southeastern border regions, while the sustainability and degraded areas were mainly located in the western regions, suggesting great ecological pressure in the western arid regions of China. For ET, the situations were obviously different from the sustainability and degradation regions, mainly in Qinghai Province and parts of the eastern regions. The sustainability analysis further indicated that Xinjiang Province and Inner Mongolia should pay more attention on vegetation growth conditions in the future. In southeastern parts, the effects of ecological restoration projects are obvious, with large areas of sustainability and improvement in the future. These results all suggested that the ecological restoration projects in the eastern parts effectively improved the local vegetation conditions in the present and in the future. However, in the western parts, the effects were not as good as in the eastern parts. Furthermore, the variation trends in the future are not satisfying.  
Figure 7: Sustainability analysis of GPP (**a**), ET (**b**), and WUE (**c**) of the Three-North region of China.
# Design and Testing of a LUT Airfoil for Straight-Baded Vertical Axis Wind Turbines
## 1 Introduction
In addition, the Technical University of Denmark (DTU) has been dedicated to the research of airfoils for VAWTs. Its studies [20; 21] depict some very thick airfoils that were designed by considering the structural stiffness of the blade and the aerodynamic characteristics. For instance, the AIR001 airfoil (max. thickness of 25%) can improve the aerodynamic performance in the case of a larger tip-speed ratio, while the DU12W262 airfoil (max. thickness of 26.2%) can increase blade strength. Other research [9; 22; 23] has explored specially purposed airfoils for VAWTs, such as the DU06-W-200, EN0005, WUP1615, and \"TWT series\" airfoils from Tokai University. In brief, there are different design aims for different special-purpose airfoils. As mentioned above, a great deal of investigation has been done to understand the aerodynamic performance of VAWTs and the characteristics of the airfoil. However, compared to HAWTs, the research on special-purpose VAWT airfoils, which affect the aerodynamic performance of VAWTs, is limited. In addition, there exists room for improvement of many aspects, such as the maximum lift coefficient, maximum lift-drag coefficient, wide drag bucket, and stall performance, in the aerodynamic performance of the existing airfoil. Additionally, it is very important that the airfoil has good performance at a low \(R_{e}\) due to the operating environment of VAWTs. Therefore, the objective of this paper is to design an airfoil--named the LUT airfoil, a reference to Lanzhou University of Technology--that shows good aerodynamic characteristics for VAWTs and to investigate the characteristics of the airfoil at low \(R_{e}\) values by wind tunnel experiments. This paper is divided into three main parts: illustrating the design basis and optimization method in Section 2, describing the experimental equipment and procedures in Section 3, and reporting the profile and the aerodynamic performance of the LUT model in Section 4.
## 6 Numerical Results
### Validation of Wind Load Test Model  
Comparisons of the global behavior of the two sandwich elements, represented as wind load versus mid-span deflection at locations \(d1\)-\(d3\), are shown in Figure 13 for both single and double connector configurations. It should be noted that the deflection related to FEA was measured in the middle of the inner facing. Compared to the experimental results, the stiffness during the loading sequences is captured rather well. The hysteresis effects in the unloading sequences are not fully captured in the model. The incremental damage in the facings due to cracking was included. However, in the experiments, a major part of the hysteresis effects can most likely be attributed to unforeseen movements in the anchoring positions, which was excluded in the developed FE model. Another factor which could influence the numerical results, is the fact that linear elastic material models were assigned for all materials, except for the RPC facings. As such, these materials recover perfectly after unloading in the model, which is not the case in the experiments. It is also important to note that pre-cracks existed in the facings which could have also likely influenced the presented experimental behavior. Despite these discrepancies, the outcome of the analysis is deemed suitable since the aim of the model was primarily to simulate the behavior under static wind loading (load increments).  
Figure 14 depicts contour plots at a wind pressure of 2.0 kPa of the out-of-plane displacement for both single and double connector cases. From these figures, it can be seen that the elements mainly bend about the x-axis, as per Figure 10, which confirms the experimentally observed behavior.  
At a wind pressure of 2.0 kPa, corresponding to the maximum wind pressure of the climate chamber, the compressive forces in the most stressed diagonal connectors were checked for both single and double connector cases. For both cases, it was observed that the maximum compressive force was -1.8 kN at this wind pressure. This compressive force very close to the experimentally yielded critical buckling load (1.7 kN), see Table 2. At a wind suction of 2.0 kPa, the maximum tensile force in the most stressed diagonal was 3.0 kN and 2.8 kN for single and double connector configurations, respectively. These loads are well below the average value of  
Figure 13: Comparison between experimental and finite element analysis (FEA) global behavior for the two sandwich element configurations with single (S) connectors (**a**–**c**) and double (D) connectors (**d**–**f**). Data pertaining to deflection locations \(d\)1–_d_3 are indicated in the figures.
###### Abstract
Section 4 optimizes the distance-adaptive multi-carrier modulation scheme by jointly optimizing the modulation order, subcarrier bandwidth, and transmit power and proposes the BCD method to solve the non-convex optimization problem. In Section 5, the adjustment rules of the modulation scheme are designed when the user moves and the adjustment probability is also derived. In Section 6, numerical results are presented to evaluate the performance of the proposed algorithm and modulation schemes. The paper is concluded in Section 7. Notations involved in this paper are included in Table 1. \begin{table}
\begin{tabular}{c l} \hline \hline
**Parameter** & **Definition** \\ \hline \(h_{l}\) & Path gain coefficient \\ \hline \(h_{p}\) & Beam misalignment fading coefficient \\ \hline \(h_{f}\) & Multi-path fading coefficient \\ \hline \(G_{T}\) & Transmit and receive antenna gains \\ \hline \(G_{R}\) & Receive antenna gains \\ \hline \(P_{T}\) & Transmit power \\ \hline \(P_{w}\) & Total noise power at the receiver \\ \hline \(M_{1}\) & Modulation order at the overlapped frequencies of farther user \\ \hline \(M_{2}\) & Modulation order at the overlapped frequencies of closer user \\ \hline \(M_{3}\) & Modulation order at the remaining frequencies of closer user \\ \hline \(P_{1}\) & Transmit power for the overlapped frequencies of closer user \\ \hline \(P_{2}\) & Transmit power for the overlapped frequencies of farther user \\ \hline \(P_{3}\) & Transmit power for the remaining frequencies of closer user \\ \hline \(P_{b,1}\) & BER requirement for the overlapped frequencies of farther user \\ \hline \(P_{b,2}\) & BER requirement for the overlapped frequencies of closer user \\ \hline \(P_{b,3}\) & Transmit poBER requirement for the remaining frequencies of closer user \\ \hline \(h_{1}\) & Channel amplitude gain for the overlapped frequencies of closer user \\ \hline \(h_{2}\) & Channel amplitude gain for the overlapped frequencies of farther user \\ \hline \(h_{3}\) & Channel amplitude gain for the remaining frequencies of closer user \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the Involved Notations.