esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Test of Seeding-Picking Mechanism of Fully Automatic Transplanting Machine
## 2 Materials and Methods  
### Overall Structural Design  
The overall structural design of the seedling-taking and casting device, as depicted in Figure 1, is grounded in the operating principles of each component and their corresponding action flows. The device is primarily composed of several parts, including the frame, seedling feeding mechanism, seedling extraction mechanism, and seedling cup mechanism [17]. The seedling feeding mechanism comprises a seedling topping device and a tray feeding device. The topping device mainly includes an electromagnetic top bar and a mounting box, which facilitates the horizontal arrangement of seedlings in hole trays for potting. The tray feeding device consists of two pairs of roller devices driven by four stepper motors; the rollers grip the fluent strip device and slide it along the seedbed to achieve longitudinal feeding of the burrow tray. The seedling extraction mechanism mainly comprises an electric actuator, connecting plate, slider slide device, electric rotary table, seedling extraction arm, variable distance slide, and flexible seedling extraction claw [18]. The electric actuator, powered by a servomotor, extends and contracts to enable the horizontal movement of the entire seedling extraction arm. The connecting plate links the motorized actuator to the slider and motorized rotary table. The slider slide device works in conjunction with the electric actuator to facilitate the overall horizontal movement of the seedling-picking arm. The motorized rotary table, controlled by a servo motor, allows for angular adjustments of the seedling-picking arm. The flexible seedling extraction claw, functioning as the seedling-picking actuator, is mounted on the variable distance sliding table slider and can adjust the distance between adjacent clamping jaws according to different workstation requirements. The variable-pitch slide and seedling-picking arm are integrated into a single unit, enabling simultaneous seedling-picking operations for an entire row of six holes. Furthermore, the spacing of the seedling claws can be adjusted according to the center distance between adjacent seedling cups during the seeding process, ensuring precise seedling placement [8].
## 1 Introduction
[PERSON] proposed an improved Genetic Algorithm-Back Propagation (GA-BP) model to enhance the accuracy and computational efficiency of temperature field prediction in metro tunnel fires, providing data support for tunnel fire prevention designs [9]. Since the late 1980s, Chinese scholars have also applied numerical simulation techniques to evaluate geothermal fields, and they have achieved remarkable research results. [PERSON] applied finite element analysis to solve the geothermal field's heat conduction equation, developed a program for simulating the two-dimensional geothermal field, and examined how geological anomalies affect the spatial distribution pattern during the simulation [10]. The influence of geological anomalies on the spatial distribution law of geothermal field was analyzed during the simulation process. [PERSON] predicted the temperature of surrounding rocks around the Lari Railway Tunnel and gave a suggestion for the selection of the route [11]. By predicting the temperatures of the surrounding rocks in the Lari Railway Tunnel, [PERSON] offers route selection advice. [PERSON], [PERSON] et al studied the impact of various heat sources on the surrounding rock temperature during tunnel construction [12]. Based on convective heat transfer theory, they proposed key parameter optimization methods for ventilation to improve the thermal safety of the construction environment. [PERSON], [PERSON] proposed a self-modeling method to accurately predict the steady-state temperature distribution of tunnel cables in a natural convection environment, offering a reliable thermal environment assessment tool for the safe operation of tunnel power systems [13]. High-temperature rock tunnels often encounter heat damage due to excessive burial depths and complex geological conditions, which can jeopardize the health of construction workers and compromise the stability of engineering equipment. However, the existing heat damage prediction models primarily rely on average geothermal flow empirical methods that do not fully account for the region's intricate geological background, making it challenging to accurately predict heat damage distributions in high-temperature rock areas. This issue is especially pronounced in regions such as the northeastern edge of the Qinghai-Tibet Plateau, where fractures are abundant, thermal anomalies are active, and effective numerical simulation and evaluation methods are lacking. In this study, we focus on the deeply buried water diversion tunnel of the \"Yellow River to Jining\" project and propose a method that combines Kriging interpolation with two-dimensional steady-state heat conduction numerical simulation. Unlike traditional empirical approaches, our study comprehensively incorporates the thermodynamic properties of the rock and the geological structural features along the tunnel. We establish an accurate geothermal field prediction model, validated using on-site logging data. The research findings not only provide a scientific basis for predicting heat damage in high-temperature rock tunnels but also offer specific recommendations for subsequent tunnel design and construction.
## 4 Discussions  
In practice, in-situ soil tests produce soil parameters with physical meanings for practical engineering applications. However, the soil samples used in this study were created in the laboratory. Thus, the results cannot be directly compared with in-situ soils,  
Figure 11: Statistical analyses of the amplification factors of all studied soil specimens.  
without referencing the relative densities at which the tests were executed and applying the relevant similitude laws for the most suitable application interpretations. Moreover, a well-known issue is the problem of establishing correct model confining stresses relative to the high confining stresses inherent in prototype in-situ soils [18]. On this basis, model shaking tests are performed at low confining stresses (i.e., extremely loose deposits) to compensate for the effects of reduced geostatic stresses compared to in-situ soils. Therefore, the scaling or similarity laws, known as the similitude laws, are relevant to the logical interpretations of physical test results. As it is practically impossible to meet all the scaling laws requirements, most researchers only select a few key model parameters for scaling. The key elements for the investigation in the current study are itemized as follows:  
1. Investigating the dynamically generated excess porewater pressures (PWP) of the studied soil specimens, as a result of the varied clay (kaolinite) contents embedded in the fabrics of the ECS,
2. Computing the amplification factors (AF) of all the studied soil samples,
3. Observations and records of the seismically induced immediate settlements based on the sensitivity influences of the varied kaolinite contents in ECS,
4. Studying the effects of recurring earthquakes on liquefaction extents by successively varying the intensities of base input motions.  
The scenario in item 4 is mainly referred to as aftershocks, which are most common during actual earthquake occurrences in real-life situations. It is interesting to understand the response of the investigated soil samples under this condition.  
As can be inferred from Figure 11, the soil sample with the highest amount of kaolinite exhibited the lowest AF, suggesting that the PGA would likely travel slower in soils containing clays. The granular structural arrangements of cohesionless materials, such as sand, with particle-to-particle contact would experience faster travel of the PGA emanating from bedrocks. Due to limited research funds and resources, other soil dynamic deformation properties were considered out of scope in the current study but are recommended for further studies. Surface settlement, measured with the printable rulers on the soil container, indicated that the immediate soil settlement was highest in the clean ECS sand and soil samples with more clay. This is because clean sands and clays are highly contractive; the highly contractive soil samples are ECS00, ECS20, and ECS30. A reduced immediate settlement was observable with moderate contents of kaolinite.
## 2 Materials and Methods
### Protocol  
We used an extracorporeal shock wave pain treatment device (STORZ Medical, UK) to deliver the f-ESWT to the pain-inducing area. We gradually increased the f-ESWT energy transfer in the range of 0.01-0.25 mJ/mm\({}^{2}\) according to the pain, delivering 2500 shots per session for 4 Hz. The subjects were irradiated with f-ESWT twice, at the beginning of the study and after 2 weeks. We evaluated the pain, rest pain and tenderness of the patellar tendon. Rest pain was mapped on the Numeric Rating Scale (NRS) [30] and the degree of pain was rated on 11 levels. We then used a pressure pain meter (FP meter, SN-402, Navis, Japan) to evaluate the pressure pain and recorded the pressure value at the time the participant experienced pain [31; 32; 33]. When the pressure pain meter was applied to the pain area and pushed in, the value increased from 0 to 10 (kg). The examiner pushed it in until the study participant felt pain and recorded the value at the time of pain as the pressure value. In other words, it was shown that the higher pressure value indicated a decrease in pain. To examine the effect of f-ESWT on the affected area, we evaluated the tendon thickness and blood flow of the patellar tendon using an ultrasound imaging system, Toshiba Aplio scanning machine (Canon Medical Systems Corporation, Japan). Ultrasound evaluations of the tendon thickness and blood flow were performed in the long axis direction using an ultrasonography device equipped with a high-frequency (5-14 MHz) linear probe. To measure the tendon thickness (mm), we recorded a still image of the area of maximum swelling using B-mode (Figure 1). For blood flow evaluation (Doppler), we recorded still images during the beating phase when the blood flow area was large (cm\({}^{2}\)). Each image was captured by an orthopaedic surgeon who examined the subject and selected the images to be used. Four sessions in total were taken for evaluation by the ultrasound imaging system before and after the f-ESWT in each subject. Three images of tendon thickness and blood flow evaluation were recorded in each imaging session, and the average of the 3 images was adopted as the value. These recorded images were used to calculate the blood flow area on the ultrasound image using the open-access image analysis software Fiji (Fiji is Just ImageJ) (Figure 2). The shock wave irradiation site, pain evaluation site and ultrasound evaluation site were evaluated and determined by a physician using an ultrasound imaging device, and markings were applied thus that measurements could be performed at the same site. The measurements were performed 4 times: before f-ESWT (PRE(1)), immediately after f-ESWT (POST(1)), before f-ESWT 2 weeks after the first f-ESWT (PRE(2)) and immediately after the second f-ESWT (POST(2)).
# Towards Efficient Wireless Body Area Network Using Two-Way Relay Cooperation
## 3 Packet Loss Probability  
The packet error rate of a link transmitting packet length of \(L\) bits from one node to another, along with header of \(H\) bits, given the bit error probability \(P_{b}\) of the link, is expressed as,  
\[PER=1-(1-P_{b})^{L+H}. \tag{2}\]  
In two-way relay communication, a packet error occurs in one of the following conditions:  
1. Error occurs in both \(S-D\) link and \(S-R\) link.
2. The direct link (\(S-D\)) fails, the relay link (\(S-R\)) is error free, data is transmitted from source to relay, but the destination to relay (\(D-R\)) link fails.
3. The direct transmission fails i.e., \(S-D\) link in error, the relay links from source to relay (\(S-R\)) and destination to relay (\(D-R\)) are error free, but the broadcast by the relay node fails i.e., either \(R-D\) or \(R-S\) is in error.  
The final equation of PER for the two-way relay link is given below:  
\[\begin{split} PER_{TW}=PER_{SD}PER_{SR}+PER_{SD}(1-PER_{SR})PER_{ DR}\\ +PER_{SD}(1-PER_{SR})(1-PER_{DR})(1-PER_{RS})PER_{RD}.\end{split} \tag{3}\]  
The error in any transmission is dependent on the prior transmission or one can say that the successful execution of a phase depends on the previous phase. The broadcast phase in the case of two-way relay scenario is basically protocol dependent and conditioned. Either both links i.e., \(R-S\) and \(R-D\) fails or any one of them fails to transmit the data. The system would be erroneous in both cases. If both broadcast links fails, no data is received at either end. If one of the links fails, data is collected at one node but again it would not accomplish the two-way cooperation task fully. For a successful accomplishment of two-way relay transmission, both broadcast links should be successful.
## 2 Computational Simulation Models
### MV and LV DC Networks  
The hybrid AC/DC network modeled in this work comprises a MV and an LV DC section, as represented in Figure 7. The MV and LV DC networks comprise controllable EV charging systems (including controllable EV fast chargers [38]), controllable PV generation units and other non-controllable DC loads connected in different nodes.  
The feeders of the DC networks were modeled considering only the resistance of the cables. Non-controllable loads were modeled in the DC networks in order to evaluate the exploitation of its voltage sensitivity for the provision of frequency support to the main power system. No relevant studies addressing the estimation of DC load voltage sensitivity or the use of DC load voltage sensitivity for the provision of frequency support were found in the literature. There are several references suggesting that a large portion of the load in DC systems, being based on power electronic converters, may behave as constant power loads [39; 40]. An acceptable compromise can be found in [41], where a scenario with a considerable share of constant power loads is evaluated using the exponential load model. The aforementioned scenario is adopted in this work to represent the non-controllable DC loads, the modeling equation being adapted here to consider DC voltages, as expressed in Equation (18).  
\[P=P_{0}\left(\frac{V_{DC}}{V_{DCn}}\right)^{0.45} \tag{18}\]
# Energy Demand Forecasting Scenarios for Buildings Using Six AI Models
## 2 Methodology
### Model Development  
AI can be applied to energy demand forecasting; there is a need for robust predictive models that can estimate demand. The ANNs are very effective here due to their strength in capturing complicated nonlinear relations inherent in data. Training ANN models with historical data in energy consumption helps in deducing patterns that could provide useful insights for demand in the future. In addition, ensemble methods such as Random Forest and XGBoost enhance the predictive accuracy by combining the results of a large number of Decision Trees. These models are fit for handling large datasets with several features, which in turn makes them suitable for a variety of factors that may influence energy demand, such as temperature fluctuations, occupancy rates, and building characteristics. Other useful approaches include Radial Basis Networks and Autoencoders, which have advantages in energy demand modeling. RBFs use radial basis functions for activation, which are very effective in capturing the local patterns of the data and, hence, useful in specific energy consumption scenarios. Autoencoders are especially useful for feature extraction and dimensionality reduction. By training an Autoencoder on energy demand data, we can find the latent features contributing to the fluctuation in demand that can then be fed into other models to improve the forecasting accuracy [40; 41; 42]. Decision Trees, including advanced variants such as Gradient Boosted Trees, remain highly relevant in energy demand forecasting due to their interpretability and ease of use. Decision trees, by splitting the data into subsets based on feature values, can show with clarity how various factors contribute to energy demand. Linear Regression models can make simple predictions, which are easy to interpret and present, by considering historical trends and the integration of external factors. While it does not capture more complex nonlinear relationships as well as more advanced AI techniques, it is a good bench  
Figure 2: Energy demand forecasting workflow.  
mark and has often been used in conjunction with more sophisticated models to validate results [43; 44].
# High-Frequency Artifacts-Resistant Image Watermarking Applicable to Image Processing Models
## 3 Experimental Results and Analysis
### Importance of Clean Loss  
We have reason to believe that omitting the clean loss from the loss functions could lead to the problem of overfitting in the watermark extraction network, potentially allowing it to erroneously extract watermarks from any image. To empirically investigate this  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Robustness against Pre-processing Attacks**} & \multicolumn{2}{c}{**Imperceptibility**} \\ \cline{3-4}  & & **Spatial Domain** & **Frequency Domain** \\ \hline Ref. [17] & Yes & Partially & \\ Ref. [19] & Yes & Yes & \\ Proposed & Yes & Yes & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison between different model watermarking methods in terms of robustness and imperceptibility.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Task** & **Watermark** & **Size** & **Mean PSNR** & **FP** & **BER** \\ \hline method [36] & DE & \(MDPI\) & \(128^{2}\) & 54.59 dB & 0.1622 & 0.0010 \\ Ours & DE & \(MDPI\) & \(32^{2}\) & 47.62 dB & 0.1790 & 0.1291 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparative results of the proposed method with the method [36]. “DE” means “de-raining” and BER measures the quality of binary watermarks.  
hypothesis, we conducted an experiment where we trained two watermark extraction networks: \"\(E\)\", which incorporated the clean loss, and \"\(E\)1\", which excluded the clean loss. Subsequently, we input various clean images into \(E\)1 and observed that it mistakenly retrieved watermarks from these clean images, as illustrated in Figure 7. In contrast, \(E\) adeptly extracts watermark and noise from watermarked and clean images. Consequently, clean loss prevents the problem of overfitting in the watermark extraction network, making our copyright protection highly effective and practically relevant.
## 3 Method of Sign Word Recognition System
### Hand Segmentation Technique
#### 3.1.1 YCbCr Segmentation  
In this section, we segment the hand gesture from the RGB color space based on the chrominance-red (Cr) component of the YCbCr color space. The input image was converted from the RGB color space to a grayscale image of the YCbCr color space, which contained the components of luminance (Y) and the blue and red different components (Cb and Cr). The Cr component was extracted from YCbCr and used for further processing. Then, the extracted Cr image used the threshold method to process binary images. However, the converted image was exposed with two colors, black and white. The image grayscale value was 0-255; usually, zero is black, and 255 is white. In the segmentation process, we defined a threshold value of 128, which redefined the pixel values from 0-127 to zero and 128-255 to 255. Thereafter, we could process the grayscale image as a binary image. Then, erosion was applied to remove the boundary of the foreground pixels. As a result, the hole area became larger because the size of the foreground pixels may shrink. Finally, we achieved segmented images by filling the hole. Figure 2 represents the block diagram of the segmentation steps, and Figure 3 depicts the YCbCr segmentation process of an input image.  
Figure 3: YCbCr segmentation process of an input image.  
Figure 2: Block diagram of the YCbCr segmentation process.
## 4 Proposed Trust-Based Optimized Reporting Scheme
### Trust Propagation
#### 4.3.2 Cost Optimization through Delta-Based Strategy  
This subsection elaborates on how the delta-based isolation strategy presents a cost-effective approach to trust management. Unlike immediate isolation upon falling below a threshold, the methodology minimizes unnecessary isolation events by focusing on the trust degradation trend over time. This reduces the potential for false positives, ensuring that nodes are isolated based on sustained untrustworthiness rather than momentary fluctuations. By adopting this strategy, the proposed methodology optimizes costs associated with unnecessary node isolation, enhancing the overall efficiency of trust management in IoT networks.
## 2 Related Work
### Context-Free Grammar (CFG)
The Stanford parser utilizes a treebank [16], similar to the dependency parsers described later in Section 2.3. A treebank consists of sentences manually parsed by human annotators. While dependency parses establish relationships between individual words, context-free grammar parses focus on constituents--groups of words that form grammatical units such as noun phrases. The replacement rules required for probabilistic context-free grammar parsing are derived from the human-annotated treebank, and the probability of a replacement rule corresponds to its frequency of use within the treebank. For example, if two replacement rules for the start symbol S, S -> NP VP and S -> VP, are observed in 70% and 30% of cases, respectively, their assigned probabilities would be 0.7 and 0.3. A sufficiently large treebank ensures that one can find appropriate replacement rules for a derivation of each \"real\" written sentence. However, these replacement rules are not derived from first linguistic principles; they are learned from human annotators. It is sufficient that solely one human annotator introduces a replacement rule while constructing a specific constituent parse to include it into the set of replacement rules, but it occurs with a low probability. Therefore, the probabilistic variant of CFG blurs the line of whether a rule is included in the set of rules or not. This circumvents the problem of constructing a fixed set of replacement rules, where each rule is underpinned by linguistic principles. The correctness of a PCFG parse is not guaranteed in this way. As summarized in Table 1, this approach is classified as \"mixed logical and statistical\", as it combines rule-based and statistical methods. In this paper, we address three grammatical questions, of which the Stanford parser fully resolves the first, determining word classes, and partially answers the second, identifying main and subordinate clauses. Clauses can be recognized based on the constituents identified within the sentence, which delineate the parts of the sentence belonging to each clause. However, the parser does not distinguish between the main and subordinate clauses.