esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Different Droop Functions in Active Power Curtailment Method
#### 4.1.2 Quadratic Droop  
In this case, the PV inverters are controlled with quadratic droop-based APC method, where the droop values are guided by a quadratic function.  
Figure 9 shows the voltage profile of the twelve houses and the power curtailment with the droop-based APC method with a quadratic function. The maximum voltage of  
Figure 8: Voltage profile of twelve houses (House 1 to House 12) and curtailed power in APC method with linear droop.  
1.056 p.u. occurs at 1 pm in houses 11 and 12, which does not exceed the critical value \(V_{cri2}\) (1.058 p.u.). The maximum voltages in houses from 7 to 12 is higher in the APC method with quadratic function than in APC method with linear droop. This happens because the droop function is guided by a quadratic function rather than a constant value, which better fits the objective to lower the curtailment. The voltages in houses are pushed to the boundary limits which facilitates lower curtailment. The maximum power curtailment is 1.98 kW at houses 11 and 12, which is less than that in constant droop-based APC method. The power curtailments for houses 7 to 12 are less than that of APC method with linear droop. The total energy loss due to PV curtailment is 15.27 kWh.
## 7 Performance Evaluation and Results
### Results and Discussion
#### 7.3.3 Complex Segment 3  
Traffic flow patterns in this complex segment were mostly normal for all the segments. The DMLRF performed more efficiently than other base-line models in terms of all evaluation metrics for all segments as represented in Table 3. however, the performance in this complex segment of the proposed model was lower than the previous complex segments in terms of \(R^{2}\) and \(AR^{2}\). The \(R^{2}\) and \(AR^{2}\) results were 59% and 60% respectively. However, MAE, MSE and RMSE results were very low especially in Segment B. The predicted average speed values and actual average speed values were very close. We observed that there is no curved road in this complex segment and the actual average speed were mostly over 65 mph. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Complex Segment** & **Algorithm** & **Segment** & **MSE** & **RMSE** & **MAE** & \(\mathbf{R^{2}}\) & \(\mathbf{AR^{2}}\) \\ \hline \multirow{8}{*}{Complex Segment 1} & DMLRF & Segment A & 7.9 & 2.8 & 2.1 & 61\% & 61\% \\ \cline{2-7}  & LR & Segment A & 8.4 & 2.9 & 2.2 & 59\% & 58\% \\ \cline{2-7}  & LogR & Segment A & 9.9 & 3.1 & 2.3 & 15\% & 14\% \\ \cline{2-7}  & KNN & Segment A & 10.9 & 3.3 & 2.6 & 7\% & 7\% \\ \cline{2-7}  & DMLRF & Segment B & **1.3** & **1.1** & **0.9** & 75\% & 75\% \\ \cline{2-7}  & LR & Segment B & 4.3 & 2.0 & 1.7 & 18\% & 17\% \\ \cline{2-7}  & LogR & Segment B & 6.5 & 2.5 & 1.4 & 22\% & 22\% \\ \cline{2-7}  & KNN & Segment B & 1.5 & 1.2 & 1.0 & 71\% & 71\% \\ \cline{2-7}  & DMLRF & Segment C & 3.5 & 1.8 & 1.5 & **85\%** & **85\%** \\ \cline{2-7}  & LR & Segment C & 10.5 & 3.2 & 2.7 & 56\% & 55\% \\ \cline{2-7}  & LogR & Segment C & 21.1 & 4.5 & 3.5 & 12\% & 8\% \\ \cline{2-7}  & KNN & Segment C & 4.9 & 2.2 & 1.7 & 79\% & 79\% \\ \cline{2-7}  & DMLRF & Segment D & 5.0 & 2.2 & 1.8 & 71\% & 71\% \\ \cline{2-7}  & LR & Segment D & 7.9 & 2.8 & 2.2 & 54\% & 54\% \\ \cline{2-7}  & LogR & Segment D & 9.2 & 3.0 & 2.2 & 46\% & 13\% \\ \cline{2-7}  & KNN & Segment D & 10.5 & 2.6 & 3.2 & 38\% & 38\% \\ \hline Complex Segment 2 & DMLRF & Segment A & 7.0 & 2.6 & 2.0 & 52\% & 52\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The Prediction Results of the DMLRF Model and Baseline Models for Segments A, B, C, and D in CS1, CS2, CS3, and CS4. Figure 9: DMLRF predicted average speed compared to actual SRI values for Segments A, B, C, and D of complex Segment 2 (tested on one week with more abnormal traffic flow cases).
## 2 Literature Review
### Price Forecasting
#### 2.2.2 Machine Learning Methods  
Traditional statistical methods are often built upon assumptions and still exhibit limitations when tasked with analyzing intricate non-linear data sets. In contrast, machine learning approaches offer superior flexibility in correlation analysis, dealing with overfitting and regularization, among other aspects. [PERSON] et al. [14] harnessed the XGBOOST algorithm to forecast the price volatility of gold, followed by comparing its performance with six alternative machine learning models to confirm the efficacy of the algorithm. [PERSON] [15] predicted high-frequency, multi-seasonal time-series data sets using a forward feature selection based on the SVR. Meanwhile, [PERSON] et al. [16] have integrated the Ensemble Empirical Mode Decomposition, Least Squares Support Vector Regression, and K-means clustering methods, which proved valuable in forecasting foreign exchange rates. Advancements in deep learning technology coupled with the better computational capabilities of hardware make it possible to reduce the need for manual intervention, process vast amounts of multi-source heterogeneous data concurrently, and rapidly perceive market exigencies.
## 2 Materials and Methods  
### Field Trial Site and Design  
A field trial commenced in April 2015 in Gongzhuling (Jilin Province, China; 42\({}^{\circ}\)40\({}^{\prime}\) N, 124\({}^{\circ}\)88\({}^{\prime}\) E), with a temperate humid continental climate. The annual average air temperature in this area is approximately 5.6 \({}^{\circ}\)C, with a maximum air temperature of 23 \({}^{\circ}\)C in July and a minimum air temperature of \(-\)15 \({}^{\circ}\)C in January. The annual mean rainfall reaches 600 mm. Meteorological data, including maximum air temperature, minimum air temperature, and rainfall at this study site during maize growth period from May to October in the five experimental years (2015-2019), are graphed in Figure 1. The field was planted with a maize monoculture with 60,000 plants per hectare. The maize variety used was Fumin985,and approximately 90% of the root was distributed between 0\(-\)40 cm. The initial properties of the soil (Mollisol by USDA Soil Taxonomy) were determined using the recommended procedures [17], and the findings are given in Table 1.  
The trial consisted of three treatments with three replications for each treatment using a randomized complete block design: (1) control without straw return (CK): straw was removed manually from the field after maize harvesting; (2) straw return by mulching (SMU): straw was chopped and mulched evenly on soil surface following maize harvesting; and (3) straw return by deep ploughing (SDP): straw was chopped, covered evenly on soil surface, and then plugued to approximately 20-30 cm soil depth using a hydraulic overturning plow (Jindachuan Machinery Co., Ltd., Kaifeng, China). Each experimental plot was 702 m\({}^{2}\). The mineral fertilizers for each treatment were applied annually at 225.0 kg N as urea and diammonium phosphate, 82.5 kg P\({}_{2}\)O\({}_{5}\) as diammonium phosphate, and 82.5 kg K\({}_{2}\)O as potassium chloride per hectare, respectively. A part of mineral N fertilizer (90.0 kg N per hectare) and all mineral P and K fertilizers were used as base fertilizers, and the remaining mineral N fertilizer was used as topdressing at jointing stage of maize. Maize sowing (flat sowing with a no-tillage planter) and harvesting were in late April and early October, respectively. No additional irrigation was conducted out of the rainfall. Following mechanized harvesting of maize on 21 October 2019, grain yield per hectare (GY), hundred grain weight (HGW), ear number per hectare (EN), and grain number per ear (GNPE) were determined. The grain yield was expressed on 14% moisture basis.
# Tip Vortex Cavitation and Induced Noise Characteristics of Hydrofoils
###### Abstract  
Tip vortex cavitation is one of the most classical themes in fluid mechanics. Although many experimental and theoretical studies have been performed, unsolved problems still remain. In particular, the trailing vortices at the tip of the hydrofoil directly affects the hydrodynamic and acoustic performance of submerged objects such as the marine propeller, rudder and various foil-shaped appendages of the ship. In this study, the experimental results from the measurements of the vortex cavitation from the tip of two different three-dimensional hydrofoils are presented. Experiments have been carried out in Chungnam National University-Cavitation Tunnel (CNU-CT). By high speed imaging technique, the development process of vortex cavitation is observed in detail. Based on the high-speed images, physical features of the cavity inception and the swirling motion of the tip vortex cavity flow are examined. In addition, the induced noise characteristics in the vortex development process are examined by unsteady pressure measurements. The forces exerted on the hydrofoil were also measured using a dynamometer with a view to verify the scaling relation between the inception cavitation number and the non-dimensional parameters namely, the coefficient of lift, \(C_{L}\) and the Reynolds number, \(Re\). The results further shed light on the cause of the intense noise induced by tip vortex cavitation.  
hydrofoil; cavitation; cavity inception; tip vortex cavitation (TVC); sound pressure level (SPL) +
Footnote †: journal: Applied sciences
## 5 Model Evaluation
### Similarity Score  
Since the primary goal of the embedding models is to capture the unique linguistic characteristics of Arabic, we utilize the similarity score to test if the model can determine the similarity between words. This is performed using tasks such as SemEval-2017 (semantic textual similarity task). SemEval-2017 provides pairs of Arabic sentences along with their assigned similarity scores, which are determined by human experts. In this task, the tested model is used to predict the similarity score, which is then compared to the assigned scores.  
However, we found this task unsuitable for testing the efficiency of our models for three reasons. Firstly, the pairs are not well-constructed, as they include repeated terms and some pairs have sentences that cover two different areas. Secondly, the assigned similarity scores are not fully explained, as the mechanism used to calculate the score is not provided, whether it is an average score for all words or only for the important words. This absence of such calculation creates a gap in matching them with the generated scores from the models. Additionally, our main focus is to represent the words in the models; therefore, we will focus on (and test) using words instead.  
To achieve this goal, we used our dataset named ArabicSynonymPairs. We constructed the dataset by compiling a list of synonyms sourced from the Arabic book [31] titled _Almojam Almofasal_, which elaborates on Arabic words and their synonyms. In addition to this book, we utilized the Almaany ([[https://www.almaany.com/](https://www.almaany.com/)]([https://www.almaany.com/](https://www.almaany.com/)) (accessed on 28 September 2024)) dictionary to collect the synonyms as well.  
To compute the scores, we used the cosine similarity score for each pair and then calculated the average score for each model. The score ranges from 0 up to 10, where 10 represents a full match, and 0 represents no relatedness between the words.
## 3 Results and Discussions
### Chlorophylll Determination
#### 3.4.2 Chlorophyll Fluorescence Measurements  
From the results obtained using the HandyPea fluorometer, it is possible to observe the photosynthetic efficiency (Figure 3B). The only significant differences were observed at t1 in Treat 2, which showed a lower photosynthetic efficiency, thus demonstrating the importance of microelements. In fact, Treat 2 was the only treatment (together with Untreat) where microelements were not supplied. Treat 2 denoted an interesting behavior: since Zn is involved in the photosynthetic capacity [47], and in the first step Treat 2 did not comprise Zn (Table 1), the effect at t1 was the lower photosynthetic efficiency when compared to the other treatments; in the second step, Zn was added to Treat 2, thus causing an increase in photosynthetic capacity to the levels of other treatments at t2.
###### Abstract  
Spatio-temporal mismatches between Remote Sensing (RS) and Eddy Covariance (EC) data as well as spatial heterogeneity jeopardize terrestrial Gross Primary Production (_GPP_) modeling. This article combines: (a) high spatial resolution hyperspectral imagery; (b) EC footprint climatology estimates; and (c) semi-empirical models of increasing complexity to analyze the impact of these factors on _GPP_ estimation. Analyses are carried out in a Mediterranean Tree-Grass Ecosystem (TGE) that combines vegetation with very different physiologies and structure. Half-hourly _GPP_ (\(GPP_{\text{hh}}\)) were predicted with relative errors \(\sim\)36%. Results suggest that, at EC footprint scale, the ecosystem signals are quite homogeneous, despite tree and grass mixture. Models fit using EC and RS data with high degree of spatial and temporal match did not significantly improved models performance; in fact, errors were explained by meteorological variables instead. In addition, the performance of the different models was quite similar. This suggests that none of the models accurately represented light use efficiency or the fraction of absorbed photosynthetically active radiation. This is partly due to model formulation; however, results also suggest that the mixture of the different vegetation types might contribute to hamper such modeling, and should be accounted for _GPP_ models in TGE and other heterogeneous ecosystems.  
Gross Primary Production (_GPP_); Remote Sensing (RS); footprint; mismatch; light use efficiency; \(f_{\text{PAR}}\); \(PRl\); semi-empirical _GPP_ model; MODIS _GPP_
# Re-Keying Scheme Revisited: Security Model and Instantiations
## 6 Security Considerations
### Security of the SCA-Resistant Re-Keying Encryption Scheme  
Let us discuss the security of the scheme in Figure 11. Assume that an \(\mathsf{SCA}\) adversary mounts the attacks on the encryption device, whereas the \(\mathsf{SCA}\) on the decryption is restricted. Similar to the previous scheme, this scheme is also \(\mathsf{SCA}\) resistant if it is used in the encryption device.  
Let us discuss the \(\mathsf{SCA}\) resistance of the decryption device. Without restriction, it is vulnerable to the message recovery attack with the \(\mathsf{SCA}\), because the output of \(\mathsf{BC}^{-1}\) is unprotected. If we restrict the adversary not to receive \(m\), which is the output of \(\mathsf{BC}^{-1}\), the attack is difficult only with the leakage information.  
The security of our second scheme, by ignoring the side channel information, can be proven in a similar way as Theorem 1.
# Influence of Social Overhead Capital Facilities on Housing Prices Using Machine Learning
## 3 The Proposed Scheme
### Preprocessing Data
#### 3.2.7 Interplay between Housing Prices and Financial Market Dynamics  
The dynamics of housing prices are influenced by a myriad of factors, one of which is the prevailing financial climate [1; 2; 3; 4; 5; 6]. Among various indicators representing the financial health of an economy, the policy rate emerges as a pivotal gauge reflecting a nation's monetary policy and the broader economic sentiment. In South Korea, the Bank of Korea is responsible for setting and adjusting this rate. Recognizing the significance of the policy rate in our study, we incorporated it as a primary variable to understand the intricate interplay between the monetary policy landscape and housing prices.  
In light of the time-series nature of our foundational dataset, it is essential to consider the timing of housing transactions. By combining the 'CTRT_YRMTH' (Contract  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Line** & **Operating Institution** \\ \hline Incheon Subway Line 1 \& 2, Urban Railway & Incheon Transit Corporation \\ Line 7 & \\ \hline Everline & Yongin Light Rail Corporation \\ \hline Incheon International Airport Line & Airport Railroad Corporation \\ \hline Ui LRT & Ui LRT Corporation \\ \hline Shinbundang Line & DX Line \\ \hline Greater Capital Area Light Rail Sillim Line & ROTEM SRS Co., Ltd. \\ \hline Maglev & Incheon International Airport Terminal \\ \hline Gimpo Gold Line & Gimpo Gold Line Co., Ltd. \\ \hline Jinjeop Line & Namyangju City Corporation \\ \hline Seoul Metro Lines 1–8 (part of Line 9) & Seoul Metro \\ \hline Greater Capital Area Metro Line 9 & Seoul Metro Line 9 Corporation \\ \hline Uijeongbu LRT & Uijeongbu Light Rail Co., Ltd. \\ \hline Gyeonggang Line, Gyeongbu Line, Janghang & \\ Line, Gyeongwon Line, Gyeongui Central Line, & \\ Gyeongin Line, Gyeongchun Line, Bundang & Korea Railroad Corporation \\ Line, Suin Line, Gwacheon Line, Ansan Line, & \\ Ilson Line, West Sea Line, ltx-Gyeongchun Line & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Subway operating institutions by line.  
Year and Month) and 'CTRT_DAY' (Contract Day) variables, we introduce a new variable, 'PLC_RATE' (Policy Rate), which corresponds to the policy rate prevailing on the specific date of the transaction. This integration offers a nuanced reflection of the financial context at the time of each housing transaction. However, an inherent limitation must be acknowledged in that the funding mechanisms employed for housing purchases may not always align with the contemporaneous policy rate; for instance, prospective homeowners might secure financing well in advance, potentially under different interest rate conditions. Thus, solely examining the interplay between the policy rate and housing prices might provide an oversimplified perspective, highlighting the need for more comprehensive studies that delve deeper into this complexity.
# Research on Energy Storage Optimization Operation Schedule in an Island System
## 2 Problem Formulation
### System Constraints  
#### 2.5.1 Power Balance Constraint  
At any given time, the power generated by the PV and diesel generators, plus the discharging and charging of the battery, should be equal to the load demand. Equation (13) shows the power balance constraint where \(P_{RE}\) is the power from the renewable energy, in this case PV, and \(P_{L}\) is the load demand.  
\[P_{RE}(t)+P_{In}(t)+\sum_{n}^{N}P_{n}(t)=P_{L}(t) \tag{13}\]
# The Design and Implementation of a Novel Open Source Massive Deployment System
## 2 Related Works
### Automated Installation Method  
Among the automated installation methods, Kickstart Installation [1] and Fully Automatic Installation (FAI) [14; 15] are two well-known open source projects. Both use a configuration file to define the steps in the system installation. Once the installation on a given computer is ready to be done, the Kickstart or FAI program takes over and performs the automatic installation without any manual intervention. Cobbler [11] is open source software that is revised based on the Kickstart program. The major enhancement of Cobbler is the provision of a web user interface that allows for the provision of more features, so system administrators can automate many Linux-related tasks. Metal as a Service (MaaS) [16] is another open source solution that is provided by Ubuntu Linux that was designed specifically for deploying Ubuntu Linux. All of the software mentioned here can only be used to deploy the Linux system. The deployment of other OSs (e.g., MS Windows, MacOS, or FreeBSD) requires other solutions. One typical example is open source software called Razor [3], which can deploy both Linux and MS Windows systems. Another open source program is Crowbar [17], which is supported by Dell Inc. and it also provides the ability to deploy both Linux and MS Windows systems.
## 4 Discussion  
### Single Loading Cases  
Figure 4 shows the equivalent plastic strain (EPS) results for the single loading cases. In this paper, the fracture or maximum deformation occurred at the four corners, which are referred to as top-left (TL), top-right (TR), bottom-left (BL), and bottom right (BR) for convenience. In the shear loading case shown in Figure 4a, fractures occurred at the BL and TR corners of the solder. Although the EPS at the BR and TL corners rose significantly, no fractures were observed. In the case of compression shown in Figure 4b, the largest plastic strain was evenly concentrated at all four corners, but no fracture occurred. For the bending case shown in Figure 4c, severe deformation occurred at the TL and TR corners, but fracture occurred only at the TL corner. These results clearly indicate that fracture in the solder ball cannot be determined solely based on the magnitude of the equivalent plastic strain. For more understanding, deformation history should be analyzed. Figure 4: Equivalent plastic strain contour of the single loading cases: (**a**) shear, (**b**) compression, and (**c**) bending. Figure 5 shows the critical elements surrounded by red squares for each loading condition, and Table 4 lists the deformation gradient **F**, rotation **R**, and stretch tensor **U**, as defined in Section 2. For the shear loading shown in Figure 5a, the element at BL was contracted horizontally but stretched vertically with rotation to counterclockwise direction. For the compression shown in Figure 5b, four corner elements were almost equally stretched horizontally but contracted vertically. They also rotated clockwise for TL and BL but counterclockwise for TR and BR, respectively. The bending case shown in Figure 5c caused the most deformation near the top region, wherein the critical element of TL was contracted horizontally but stretched in a vertical direction. The same element was rotated in a clockwise direction. Because all cases showed different tendency, a quantitative comparison in a normalized state is necessary. Figure 6 shows the undeformed and deformed configurations for the normalized critical elements. The deformed configuration was obtained from the Lagrangian strain **E** to calculate the pure deformation without the effect of the rotation and the values are listed in Table 5. For the shear and bending loading cases, the critical elements were compressed in the x-direction by 3.28% and 6.99%, respectively, and stretched in the y-direction by 1.56% and 9.47%, respectively. Although the shear loading caused the predominant shear deformation of the critical element, the bending showed a uniaxial tensile shape with a small shear deformation. Meanwhile, under the compressive loading, the critical element was stretched by 24.02% in the x-direction and compressed by 16.46% in the y-direction. In summary, when the effect of rotation is neglected, the shear deformation was dominant in the shear loading case, while the normal deformation in the x-direction and y-direction was dominated in the compression and bending cases, respectively.