esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Prediction of NPK Content of Fruits Based on MLR  
The PCR and PLSR models were constructed using MLR. As a result of conducting the analysis and identifying the optimal factors, the N, K, and P levels were considered as independent variables (x) and the measured chemical levels were considered as dependent variables (y) and were entered into the MLR model separately. Figure 3 presents the correlation plots, a visual method for evaluating the models fit with experimental data. As shown in Equations (1) and (2), the model obtained by MLR can predict the amount of nitrogen-based on the optimal factors of PCR and PLSR:  
\[\text{Nitrogen }_{\text{(PLSR)}}=3.4+0.317\text{ X}_{1}+0.002 \tag{1}\]  
\[\text{Nitrogen }_{\text{(PCR)}}=3.4+0.316\text{ X}_{1}+0.002 \tag{2}\]  
The MLR prediction models for N fertilizer values, shown for calibration and validation sets, are presented in Figure 7. Based on the MLR model, the correlation coefficients for predicting nitrogen levels based on the optimal PCR and PLSR parameters were 0.905 and 0.905, respectively, and for the calibration sets, the coefficients were 0.900 and 0.900, respectively, for the validation sets.  
To build the model, the K content was calculated as presented in Equations (3) and (4), based on the optimal factors of PCR and PLSR:  
Figure 7: MLR prediction models for nitrogen fertilizer based on optimal factors of (**a**) PCR model and (**b**) PLSR model.  
\[\text{Potassium (PLSR)}=4.7+0.648\,\text{X}_{1}+1.861\,\text{X}_{2}+0.018 \tag{3}\] \[\text{Potassium (PCR)}=4.7+0.648\,\text{X}_{1}-0.909\,\text{X}_{2}-0.958\, \text{X}_{3}-1.512\,\text{X}_{4}+0.018 \tag{4}\]  
The MLR prediction models for K fertilizer values for calibration and validation are shown in Figure 8. Based on the optimal parameters of PCR and PLSR, the MLR model achieved correlation coefficients of 0.847 and 0.849 for calibration and 0.835 and 0.825 for validation, respectively.  
The final step for constructing a potassium prediction model with optimal PCR and PLSR factors is presented with the calculations indicated in Equations (5) and (6), respectively:  
\[\text{Phosphorus (PLSR)}=0.62+0.098\,\text{X}_{1}+0.0004 \tag{5}\] \[\text{Phosphorus (PCR)}=0.62+0.098\,\text{X}_{1}+0.0004 \tag{6}\]  
The MLR prediction models for P fertilizer values for calibration and validation sets are presented in Figure 9. In this model, the correlation coefficients for predicting phosphorus, based on the optimal parameters of PCR and PLSR, were 0.814 and 0.813, respectively, for the calibration sets and 0.806 and 0.805, respectively, for validation sets.  
The N model showed good performance criteria and may be considered a reliable and acceptable predictive mode based on the relatively high coefficients of determination.  
Figure 8: MLR prediction models for potassium fertilizer based on optimal factors of (**a**) PCR model and (**b**) PLSR model.
# Performance Analysis of BDS-5G Combined Precise Point Positioning
## 2 Methodology
### Data Processing Strategy  
As shown in Table 1.
## 4 Results and Discussion  
### Definition of Design Factors for Modeling  
The images of the representative sports shoes in this study had two types of design factor--form factors and color factors--which were also divided into sublevels (Table 5). For example, the number of colors \(X_{2}\) was subdivided into two feature levels: single color (\(X_{21}\)) and two colors (\(X_{22}\)). The sole color \(X_{4}\) was also subdivided into two feature levels: white (\(X_{41}\)) and black (\(X_{42}\)). The body color index was represented by \(X_{51}\)-\(X_{526}\). In the data type and data values, the form presented must be observed to facilitate the numerical data format and conversion. Table 6 displays the coding format used for ANN training, and the average Konsei evaluation ratings of 12 selected sports shoe samples, presented in a matrix.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Design Factors** & **Design Factor Levels** \\ \hline \multirow{4}{*}{\(X_{1}\): Form type (Data type: discrete)} & \(X_{11}\) Basketball shoes \\  & \(X_{12}\) Jogging Shoes \\  & \(X_{13}\) Running Shoes \\  & \(X_{14}\) Casual Shoes \\  & \(X_{15}\) Other type \\ \hline \multirow{2}{*}{\(X_{2}\): Number of colors (Data type: continuous)} & \(X_{21}\) Single Color \\  & \(X_{22}\) Two Colors \\ \hline \multirow{2}{*}{\(X_{3}\): Direction of color gradient} & \(X_{31}\) Horizontal Gradient \\  & \(X_{32}\) Vertical Gradient \\ \hline \multirow{2}{*}{\(X_{4}\): Sole color} & \(X_{41}\) White \\  & \(X_{42}\) Black \\ \hline \multirow{2}{*}{\(X_{5}\): Experimental color index Body color} & \multirow{2}{*}{Color index} & \(X_{51},\ldots,X_{526}\) 26 Experimental color index \\ \cline
# Effect of Temperatures on Polyphenols during Extraction
## 2 Thermal Degradation
### Solvent Concentration and pH  
The solvent concentration also has a significant impact on the optimal extraction temperature [16; 41]. [PERSON] and [PERSON] [16] studied the impact of ethanol concentration on the optimal extraction temperature of polyphenols from blackcurrants. They found that extraction could be performed at lower temperatures when low ethanol concentration was used. For example, at 85% ethanol concentration (wt basis), the maximum anthocyanin extraction is between 30-35 \({}^{\circ}\)C, while at 20% ethanol (wt basis), the highest anthocyanin yield was observed at 25 \({}^{\circ}\)C.  
The pH of the extraction media also plays an important role in determining the effect of temperature. [PERSON] and [PERSON] [65] investigated the stability of anthocyanins under various pH and temperatures. At lower temperatures (50-60 \({}^{\circ}\)C), pH plays a significant role in anthocyanin's thermal stability, but at temperatures above 70 \({}^{\circ}\)C, the pH does not have a significant effect. The pH during extraction did not affect the stability of the anthocyanins when the oxygen concentration during treatment was negligible.
## 3 Implementation of Automatic 2D Drawing Generation Program  
### Overview of Automatic 2D Drawing Generation Program  
#### 3.1.1 Overview of IFC-Based 2D Drawing Generation  
The IFC-based 2D drawing generation process is shown in Figure 1. First, for the visualization of the IFC file, IFC information is parsed to classify the property information of an object and presentation information for expressing the shape of a model. When the presentation information is separated and stored in memory, solid information, namely shape information, can be generated from it. The Open Cascade library is used to convert the mesh to a 3D shape representation, and the user can use the Direct X library to visualize the 3D shape. It is implemented so that IFC files can be viewed on a 3D screen. For the creation of plan and sectional views, a visualization screen for 2D drawings is generated using a mesh cutting algorithm. Since the lines remaining on the opposite side of the section generated through mesh cutting are unnecessary for drawing, they are removed through the hidden line removal algorithm, and vector information for the output is generated. Subsequently, for the creation of annotation elements (dimensions, grids, levels, etc.) required in the generated drawings, information is generated in the IFC model and depicted in the generated drawings. The generated drawings can be extracted and stored in dwg, pdf, and bcf formats, and they facilitate the exchange of information required during the licensing process.  
In addition to this, through the Explorer function, it is possible to view the IFC information and property information of each object, not just drawing information, and visualize schema and hierarchy information for each entity. In other words, the amount and quality of information in the existing drawings can be improved by inserting and viewing the annotation information in the existing drawing format in the units of each object.  
Figure 1: Process diagram depicting the process of the program for the openBIM-based automatic generation of 2D drawings.
## 4 Experiments
### Relational Knowledge Distillation
#### 4.2.2 Loss Function Comparison  
Various loss functions can be used to distill relational knowledge. We constructed and evaluated the LR-Distillation Loss function using the frequently used regression loss function (MAE, MSE). Using the previous method, we build a student model with half layers and large teacher models in each base and proceed with distillation. The Shannon information content change in the vector generated in the last layer of LR-ALBERT distilled in each method was measured. The Shannon information quantity is a unit for measuring the quantity of information in terms of information theory. If the amount of information is high, it can be indirectly confirmed that the corresponding vector captures complex or various features well. \(softmax\) probability \(p(x)\) is converted to for the model's output vector \(x\), and the Shannon entropy H is measured as  
\[H(x)=-\sum_{x}p(x)\log_{2}p(x) \tag{5}\]  
Figure 4: Distillation Results of ALBERT-Base and ALBERT-Large with Reduced Layer Reuse.  
We prepared 10,000,000 data samples and proceeded with distillation for one epoch. One epoch comprises six steps, and the Shannon entropy of the learned model output at each training step was measured and recorded in Figure 5.  
Overall, the base model has more information in the model's output trained by MSE. However, the large model had more information in the model's output trained with MAE up to the fourth step. This reflects the tendency of the MAE function to distill a model quickly but ignore some complex patterns. MSE takes more time to learn complex patterns and sparse features but can have a richer amount of information. Since the output of the language model is significant for very sparse pattern features, MSE may be more appropriate. We constructed an additional experiment to see if the model with a large amount of information performed excellently in the fine-tuning task. The GLUE benchmark performance of the Base and Large models trained up to six steps with MAE and MSE was measured and compared. Table 1 presents the measurement results.  
Even in the GLUE benchmark performance, the average score of the model trained by MSE is higher. In particular, tasks for small-scale learning data environments such as RTE and CoLA were much higher. This shows that the model trained with the MSE function can grasp the complex patterns of the language model and simultaneously have outstanding generalization ability for new data.
## 1 Introduction
It was found unable to account for the response of isoprene emissions to the severe drought during the 2012 MOFLUX campaign, and the low value of \(\theta_{W}\) assumed for the site was designated the main culprit [32]. In addition to errors in the formulation and \(\theta_{W}\), uncertainties have been pointed out in relation to the MEGANv2.1 drought parameterization. The reduction in global annual isoprene emissions entailed by this drought factor ranges between 7 and 50% for different model configurations [6; 49; 50; 51]. The range is even wider (10-70%) when varying the soil moisture input datasets ([52; 53]). In addition, MEGAN is typically run using reanalysis data, and thus does not benefit from the four-decades of progress in the field of microwave surface soil moisture retrievals [54]. The objective of this study is to update the drought parameterization in MEGANv2.1 [5] using (i) the 2011-2012 MOFLUX field measurements, and (ii) remotely sensing-based root-zone soil moisture from the Global Land Evaporation Amsterdam Model (CLEAM; [55; 56]). GLEAM assimilates surface soil moisture retrievals from the ESA Climate Change Initiative soil moisture (ESA CCI SM; [57]) into a multilayer soil model driven by observation-based precipitation. GLEAM soil moisture recently proved to outperform the reanalysis estimates used in MEGAN in comparison to in situ measurements [58]. The influence on isoprene emissions of the updated MEGAN parameterization using GLEAM data is evaluated at different scales using the models (biogenic emission and chemistry-transport)and observations (in situ and remotely sensed) described in Section 2. The assessment of the re-adjusted algorithm at the site-, regional- and global-scales is presented in Section 3, with the regional study performed over the contiguous U.S. for 2011-2012. The evaluation method used here is based on spaceborne HCHO and a chemistry-transport model, and relies on the high HCHO yield from the oxidation of isoprene. This method has been widely used in past studies [42; 59; 60; 61; 62]. Discussion and conclusions are drawn in Section 4.
## 2 Materials and Methods
### Model and Parameters Description  
DRAINMOD is a deterministic field-scale model to simulate hydrology, nitrogen, carbon, and phosphorus dynamics in poorly drained soils. The model was scaled up to the watershed scale by incorporating the surface runoff routes from the field and projecting the flow rates and stages in the drain channels and receiving streams [2; 20]. DRAINMOD-H is the module of DRAINMOD for hydrological analysis that performs water balance on hourly and daily time scales, and the hydrologic variables such as surface runoff, infiltration, subsurface drainage, water table depth, and drained pore space in the soil profile can be projected on yearly, monthly, or daily scales as per the need. In addition, relative crop yield and irrigation water requirement can be predicted [21]. The fundamental relationship of the  
Figure 1: Study area showing the field partition, monitoring outlets, and soil classification.  
model is the water balance in the unit area of soil layers that extend from the surface to the impermeable layer (Figure 2). For the time step of \(\Delta\)t, the water balance can be equated as  
\[\Delta\text{Va}=\text{F}-\text{ET}-\text{D}-\text{DS}\]  
and  
\[\text{P}=\text{F}+\text{RO}+\Delta\text{S}\]  
where,  
\(\Delta\)Va = Soil storage or change in the air volume (cm);  
F = Infiltration (cm);  
ET = Evapotranspiration (cm);  
D = Lateral drainage (cm);  
DS = Deep seepage (cm);  
P = Precipitation (cm);  
RO = Surface runoff (cm);  
\(\Delta\)S = Change in surface water storage (cm).  
Characterization of infiltration was conducted using Green-Amp equation [22] and potential evapotranspiration (PRT) was determined using Thornthwaite method [23] using temperature as the sole climatic data. Similarly, subsurface drainage flow (cm/h) was computed using [PERSON]'s steady state equation [24] with correction for the convergence near drains given by  
\[q=\frac{4\;\text{{Ke}}\times m\times(2 de+m)}{L^{2}}\]  
where \(q\) is the lateral subsurface drainage flux (cm/h), _Ke_ is the effective lateral hydraulic conductivity (cm/h) under water table, \(m\) represents the midpoint of the height in water table above the drain (cm), _de_ is the equivalent depth which is the depth of impermeable layer below the base of drain (cm), and \(L\) is the spacing between drains (cm).  
Maximum surface storage (SS) characterizes the intensity of surface drainage which indicates the average depth of depression storage that needs to be filled before surface runoff takes place.  
Figure 2: Schematic representation of sub-surface hydrological components used for DRAINMOD (Source: [25]).
# Efficient Implementation of PRESENT and GIFT on Quantum Computers
## 3 Proposed Method
### Quantum Circuit for GIFT Block Cipher  
In the presented GIFT\(-n/128\) quantum circuit, only (\(n\)+128)-qubits are allocated respectively to assign plaintext (\(n\)-bit ) and key (128-bit). Therefore, it is optimized without additional qubits. All operations, including AddRoundkey, Sbox, Permutation, Constant XOR, and Keyschedule, were optimized in terms of quantum resources.  
Figure 9: Quantum circuits for Sbox of PRESENT block cipher.
## 3 Proposed Approach
### Situation Awareness Data
Further, we can also calculate whether other aircraft are reachable through intermediate friendly nodes, thus enabling a multi-hop routing protocol. The position of adversary aircraft allows us to determine if we would leak packets if transmitting towards a specific node. When using directional antennas, the radiation pattern will include a main lobe and a set of sidelobes. The region of 3D space where the signal can be detected, through the either main lobe or sidelobes, is referred to as a _forbidden sector_, as described in Section 3.4. HDARP avoids leaking packets by selecting routes outside forbidden sectors if such routes are available. An important aspect of the topology control and routing scheme is that aircraft can both enter and leave a configuration. The enter/leave information should be present in the situation awareness data, thus enabling the routing information to be updated with new/disappearing nodes (aircraft).
## 1 Introduction
The GSAT+Weight and the Breakout algorithm became a building block for many clause weighting-based algorithms in SLS due to their efficacy in handling enormous problems and their simplicity w.r.t implementation. Therefore, a large number of weighting SLS algorithms were proposed in the last three decades. The breakout algorithm and the GSAT+Weight both assign weights (penalties) to all the clauses (constraints) in the formula \(\mathcal{F}\). Initially, the weights are set to 1, then searching for cost improving moves begins such that the objective function (which measures the total number of unsatisfied clauses or violated constraints) is improving, that is, the number of violations is reducing. When the search could not find any cost improving moves, the weights of all violated constraints (unsatisfied clauses) are increased additively as in Reference [23] or multiplicatively as in Reference [24]. This paradigm of using weights while searching for a solution is a milestone in local search and artificial intelligence in general due to its implications of handling real-world problems. In recent years, handling real-world problems through stochastic local search (SLS) techniques has evolved dramatically, which has lead to the production of more sophisticated general-purpose stochastic local search algorithms. The evolution of SLS varies from proposing simple yet efficient approaches such as tabu search [25; 26], and simulated annealing [18; 19; 20]), to more sophisticated approaches such as scatter search [27], evolutionary algorithms [28], dynamic local search [23; 24; 29; 30], and hybrid SLS [31; 32]. In this paper, we are interested in dynamic local search weighting algorithms, as this family of algorithms forms one of the four general-purpose mechanisms for solving SAT [33]. More specifically, we are interested in investigating the setting of the initial weights, as, to the best of our knowledge, the first and only try in this direction was made in Reference [34] where a new algorithm is proposed by [PERSON] et al., known as DDFW(Divide and distribute fixed weights), in which the initial weights were set to 8, instead of 1. Therefore, a crucial question appears here: is there a way to allocate the initial value of the weights so that it optimizes the search process's performance and reduces the time it takes to reach a solution with higher efficiency than the currently existing algorithms. In the next Section 2, we discuss the preliminaries that are used within the paper. In Section 3, we discuss a general overview of the similarities and differences among the state-of-the-art clause weighting SLS solvers. In Section 3.1, we broadly investigate the initial weight setting impact on the state-of-the-art clause weighting SLS. Then, in Section 4, we propose a new novel mechanism to assign the clauses' weights dynamically. We show that changing the current method of weight initialization is dramatically improved by our novel method. Then, in Section 5, we illustrate the improvements experimentally gains by our approach, and then we analyze the results, and then, the conclusion is drawn in Section 6.
## 6 Performance Analysis by Monte Carlo Simulation  
In this section, a Monte Carlo simulation experiment is carried out to analyze the performance of point estimates in terms of the bias and MSE, as well as the performance of the interval estimates in terms of the CI length (LCI). With various parameter values and sample sizes in mind, the simulation study was carried out. This section is broken into two sections, the first of which is a simulation study and the second of which outlines findings of the simulation.
## 4 Results and Discussion
### Analysis of Influencing Factors on Resistivity  
The influencing factors on electrical resistivity were determined by utilizing the test specimens shown in Figure 1 and the test set-up represented in Figure 4a. Before testing, the conductive silver paste EMS 12640 (Electron Microscopy Sciences, Hatfield, PA, USA) was applied and dried for at least 12 h.
# Electromyogram in Cigarette Smoking Activity Recognition
## 5 Discussion  
This study shows that only using sEMG signals is underperformance compared to a single IMU to recognize cigarette smoking. This performance difference may arise because the collected sEMG signals only monitor finger and hand movement but do not provide information about forearm motions (elbow flexion and elbow extension) as the muscles (biceps brachii, brachialis, and coracobrachialis) that are responsible forearm gestures are located in the upper arm [55]. Using another Myo armband on the upper arm could be a solution. However, this means an extra device which not suitable for long-term studies. The difference may also be due to the fact that the sEMG patterns and sEMG sensor locations vary from person to person. On the other hand, the usage of sEMG signals with an IMU has increased classification performance on cigarette smoking recognition. The previous studies increased this performance by using multiple IMUs on other parts of the arm/body (Table 1). However, multiple device usage is obstructive for smokers and not suitable for free-living studies. In this study, we proposed an approach that allows the motion of the forearm and hand and finger motions with a single device.  
This present study has some limits which ought to be considered in the interpretation of its results. The Myo armband has dry electrodes. Compared to gel electrodes, dry electrodes are not accurate enough and less resistant to motion artifacts [56]. Additionally, the Myo armband allows a maximum of 200 Hz sampling rate, which is less than the preferred value of 1 kHz [57]. One of the other limitations of the study is that the Myo armband was not calibrated individually for each participant. At the beginning of the study, the device was calibrated by a researcher, and the same setting was used for all participants. Future work with individual calibration may enhance the detection performance of the armband. One of the other limitations of the study is that the study is performed with limited participants and for a limited duration. As a future study, we are planning to conduct a new  
Figure 4: Sequential forward selection results on individual sEMG channels.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & **TPs** & **FPs** & **FNs** & **TNs** & **Rec** & **Prec** & **F1** & **Acc** \\ \hline sEMG (all 8 channels) & 4329 & 1312 & 1897 & 10,959 & 0.69 & 0.76 & 0.70 & 0.82 \\ sEMG (selected channels: 6, 1, 7, 4, 5) & 4711 & 1294 & 1515 & 10,977 & 0.75 & 0.78 & 0.75 & 0.84 \\ IMU & 4765 & 854 & 1351 & 11,292 & 0.79 & 0.85 & 0.81 & 0.87 \\ IMU+sEMG (selected channels) & 5091 & 697 & 1135 & 11,574 & 0.82 & 0.88 & 0.84 & 0.90 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Confusion matrices and performance metrics of the proposed detection algorithm for different sensor combinations.  
study in a free-living condition with higher participants. This study did not investigate optimal hyperparameters for the proposed model. In future research, these parameters could be optimized by using grid search, Gaussian processes, or genetic optimizer.