esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis and Prioritization of the Floating Photovoltaic System Potential for Reservoirs in Korea
## 2 Study Area and Data
### Design and Evaluation of the PV System  
In this study, it is assumed that the solar PV panels are installed in a fixed-tilt array with a 20\({}^{\circ}\) slope facing south. The annual power production of the PV system was calculated using the formula of RETScreen software developed by Natural Resources Canada (NRC, Ottawa, Canada). RETScreen evaluates the power of the PV system via Equation (4),  
\[E_{A}=H_{t}\times S\times\eta_{r}\times\eta_{int}\times\left[1-\beta_{p}\times \left(T_{c}-25\right)\right]\times\left(1-\lambda_{p}\right)\times\left(1- \lambda_{c}\right) \tag{4}\]  
where \(E_{A}\) is the amount of power (kWh/h) produced by a PV system, \(H_{t}\) is solar irradiation per unit area per unit time (kWh/m\({}^{2}\)/h), \(S\) is a surface area (m\({}^{2}\)) of the solar array, \(\eta_{r}\) is conversion efficiency (0-1) of the solar cell module, \(\eta_{int}\) is conversion efficiency of the inverter, \(\beta_{p}\) is temperature coefficient related to the efficiency of the solar cell module, \(T_{c}\) (\({}^{\circ}\)C) is average temperature of the solar cell module, \(\lambda_{p}\) is loss coefficient of the solar cell module, and \(\lambda_{c}\) is loss coefficient of the inverter [26]. Table 1 shows the parameters for the solar panel and inverter used in this study. In this study, the number and surface area of the panels that can be installed are calculated, considering the area of each reservoir,  
Figure 6: Irradiance prediction: (**a**) interpolated irradiance map; (**b**) shadowless ratio map; (c) irradiance map considering shadow by terrain.  
the area of one panel, and array spacing. Then, this result is substituted into equation (4), and finally, the power production is estimated.  
The parameters of solar panel and inverter in this study are the values of the products that are commercially available and are produced on an industrial scale. If more efficient equipment is installed in the reservoirs than Table 1, then the total power production and environmental benefit will increase further. In that case, however, the overall cost will increase, and the economic burden will also increase. Therefore, appropriate selection should be made according to technology level and economic conditions at installation. According to [PERSON] et al. [27], current silicon solar cells technology achieved over 25% of efficiency, and several advanced concepts have been proposed to overcome single-junction solar cells. In addition, silicon hetero-junction solar cell achieved over 26% of efficiency [28]. Therefore, more efficient equipment is expected to be installed at a lower cost in the future.
# Challenges and Potential in Increasing Lutein Content in Microalgae
## 2 Lutein Sequestration Versus Structural Function
### Estimated Storage Capacity of Lutein Lipid Droplets  
It has been found that carotenoids could be massively concentrated in variety of lipid environments [59; 60; 61]. For example, over 50% (w/w) of \(\beta\)-carotene was stored in the specialized plastoglobules of _Dunaliella paradoxi_. _Haematococcus pluvialis_ was found to accumulate 40% of the dry cell weight (DCW) as cytoplasmic lipid droplets (CLD); 4% DCW of the astaxanthin was concentrated in the CLD. Similarly, the majority of lutein was also found concentrated in the plastoglobules of marigold petals [62]. Unfortunately, the mass ratio between lutein and the plastoglobules in the marigold is not yet apparent. Considering the effect that 20 wt % lutein could be sequestrated in corn oil and 10% astaxanthin has been stored in the CLD of _H. pluvialis_[63], it can be assumed hypothetically that lutein could have 10-20% storage capacity in the \"oil form\" because of the similarity in lutein and astaxanthin's chemical structures.
## 4 Experiments and Results  
### Datasets  
To comprehensively validate the effectiveness of the improved algorithm, we conducted experiments using two public dense pedestrian detection datasets: CrowdHuman [38] and WiderPerson [39].  
The CrowdHuman dataset is a large-scale human detection benchmark containing 24,370 high-resolution images (15,000 training, 4370 validations, and 5000 test images). It includes approximately 470,000 pedestrian instances, averaging 23 instances per image,with extensive multi-scale targets and occluded objects across diverse environments and complex poses. Each pedestrian instance is annotated with three bounding boxes: head box, visible-region box, and full-body box. Our experiments used the most challenging full-body annotations. As shown in Figure 4, the dataset contains images with significant challenges, including crowd density, occlusions, and scale variations.  
The WiderPerson dataset is a widely used benchmark for human detection in real-world complex scenarios. It contains 13,382 images selected from diverse environments, comprising approximately 400,000 pedestrian instances with varying occlusion levels. The dataset features complex occlusion scenarios, dense crowds, and diverse human poses, as shown in Figure 5. While it originally included five categories (pedestrians, cyclists, crowds, etc.), this study exclusively used the pedestrian category for experimentation.
###### Abstract  
Aquaculture effluents are a growing source of water pollution, releasing suspended solids, organic matter, nitrogen, and phosphorus into aquatic environments. Recirculating aquaculture systems (RASs) have emerged as a more sustainable solution, allowing water to be continuously treated and reused. Within RASs, coagulation-flocculation is a key treatment step due to its simplicity and effectiveness. Tannin-based coagulants have gained attention as natural alternatives to traditional chemical agents. Although natural coagulants have been studied in aquaculture, only a few works explore their use in continuous-flow systems. This study evaluates a chestnut shell-based (CS) coagulant applied in continuous mode for the post-treatment of aquaculture effluent. The performance of CS was compared with Tanfloc, aluminum sulfate, and ferric chloride in removing color and dissolved organic carbon (DOC). At natural pH (6.5) and 50 mg\(\cdot\)L\({}^{-1}\), CS and Tanfloc achieved color removal of 61.0% and 65.5%, respectively, outperforming chemical coagulants. For DOC, Tanfloc and chemical coagulants removed 45-50%, while CS removed 32%. All coagulants removed over 90% of phosphorus, but nitrogen removal was limited (30-40%). These results highlight the potential of tannin-derived coagulants, particularly from agro-industrial residues, as sustainable solutions for aquaculture wastewater treatment in continuous systems.  
coagulation-flocculation; natural coagulants; continuous flow treatment; recirculating aquaculture systems 2020
# A Survey of Preservatives Used in Cosmetic Products
## 4 Discussion
When parabens are used on healthy, undamaged skin, their sensitizing effect is low at around 1%, one of the lowest rates among all preservatives. Immediate allergic reactions that can lead to urticaria are very rare and often involve the use of preparations containing parabens on damaged skin [4; 8; 29]. In animal studies, methylparaben has not shown carcinogenic or teratogenic effects. There are reports of effects of parabens on estrogens, but these are minor and mainly involve butylparaben [1; 29]. To sum up, the use of four parabens and their salts is safe when applied in permitted concentration ranges according to Anne V of the Regulation (EC) No. 1223/2009. Maximum concentrations of methylparaben (MeP) and ethylparaben (EtP) in ready-for-use products (0.4% (as acid) for single ester and 0.8% (as acid) for mixture of esters) are higher than for propylparaben (PrP) and butylparaben (BuP) (0.14% (as acid) for the sum of the individual concentrations and 0.8% (as acid) for mixtures of substances (MeP, EtP, PrP, and BuP)). These preservatives should not be used in leave-on products for application on theappy area of children under three years of age [30]. The present survey also examined the numbers of combined uses of preservatives in a single product. Two-preservative preparations (34.8%) were most commonly used, followed by single-preservative products (25.5%) and three-preservative products (19.4%). Higher numbers of preservatives (from 4 to 8) per single product occurred less often, from 8.9% to 0.9%, respectively. Preservatives are combined to broaden the spectrum of their antimicrobial activity by synergistic effect. The use of combinations of benzyl alcohol with chlorobutanol and benzyl alcohol with methylparaben may enhance antifungal activity of the formulation compared to the single use of preservatives. The combination of methylparaben and propylparaben increases the antibacterial effect of the preservative system, but also affects the solubility, and enables the protection of both phases of the emulsions [31]. It is speculated that alcohols (benzyl alcohol and pentyl alcohol) exhibit synergistic effects with benzalkonium chloride [32].
## 4 Phase Denoise Experiment and Result
### Result of Synchronization Phase Denoising
In summary, ground experiments verify the effectiveness of the proposed synchronization phase denoising method based on CS, which has the potential to realize high accuracy phase synchronization in the LT-1 system in orbit.
# Apparatus and Method of Defect Detection for Resin Films
## 1 Introduction
Figure 3a,b depicts the transparent-background and white-background plastic resin film surfaces under a 2K-resolution camera. The transparent and white plastic resin film results vary considerably under the 2K-resolution camera compared with the data from the microscope. The defects depicted in Figure 3(a1,b1) are mostly small and dot-like, whereas the scratch and dust are shown in Figure 3(a2,b2) and Figure 3(a4,b4), respectively, are similar to lines. Bubbles depicted in Figure 3(a3,b3) are circular. The difference between each type of nonbackground objects is not as obvious in the microscale version. However, only a few images are required for composing one plastic resin film. Figure 2: Plastic resin film under the microscope. (**a**) Defect, (**b**) scratch, (**c**) bubble and (**d**) dust. A microscale defect inspection architecture that could automatically identify defects for plastic resin films has been presented in this paper. The proposed method on the 2K-resolution camera was modified based on the microscope method. The microscale defect inspection architecture consists of two steps. In the first part of the architecture, an image preprocessing method for the detection part, segments, and local nonbackground objects was proposed. In the second part, a classification module is used to classify the objects. Compared with traditional procedure, the proposed method has the following advantages:  
1. With the same standards for defect detection, the proposed method can obtain the precise area and location of defects. 2. The proposed method has high precision and high speed, which speeds up the inspection process and reduces labor cost. The average amount of plastic resin films that can be checked in a day increases. The rest of paper is organized as follows: In Section 2, the overall system and proposed approach are described in detail. Experimental results are presented in Section 3 and the discussion is presented in Section 4. Finally, the conclusion is provided in Section 5.
# Efficient SMC Protocol Based on Multi-Bit Fully Homomorphic Encryption
## 4 Key Homomorphism of NFHE Scheme
### Proof of Key Homomorphism  
In the NFHE scheme, \(s\in\mathbb{Z}^{n\times l}\), \(\mathbf{sk}=\mathbf{\tilde{t}}=\left(\begin{array}{c}-s\\ 1\end{array}\right)\in\mathbb{Z}_{q}^{n+1}\) is the private key, and \(pk=\mathbf{\overline{K}}=(\mathbf{A},\mathbf{b}=\mathbf{A}\cdot\mathbf{s}+\mathbf{e})\in\mathbb{Z}_{q }^{n\times n}\times\mathbb{Z}_{q}^{n}\) is the public key. Denote it as \(pk=\mathbf{\overline{K}}=\frac{1}{N}\sum\limits_{i=1}^{N}\mathbf{\overline{K}}_{i}\). If \(pk\) is used to encrypt the plain-text \(\mu\), \(\mathbf{C}=\text{mbFlatten}(\mathbf{\mu}\cdot\mathbf{I}_{N}+\text{mbDpt}(\mathbf{C}^{\prime}))\)\(=\text{mbFlatten}(\mathbf{\mu}\cdot\mathbf{I}_{N}+\text{mbDpt}(\mathbf{r}\cdot\mathbf{K}))\) is obtained.  
\(sk=\mathbf{\tilde{t}}=\mathbf{\frac{1}{N}}\sum\limits_{i=1}^{N}\mathbf{\tilde{t}}_{i}\) can be used to decrypt the ciphertext. In other words, if \(\mathbf{A}\) remains unchanged, the scheme will satisfy the linear homomorphism of the key.  
**Proof.**\(\mathbf{\overline{K}}=\frac{1}{N}\sum\limits_{i=1}^{N}\mathbf{\tilde{t}}_{i}\cdot\frac{1}{ N}\sum\limits_{i=1}^{N}\mathbf{\overline{K}}_{i}\)  
\(=\left(\begin{array}{c}-\frac{1}{N}\sum\limits_{i=1}^{N}\mathbf{s}_{i}\\ 1\end{array}\right)\left(\mathbf{A},\frac{1}{N}\sum\limits_{i=1}^{N}b_{i}\right)\)  
\(=\frac{1}{N}\sum\limits_{i=1}^{N}e_{i}\approx 0\)  
is right. Therefore, there is still \(\mathbf{\tilde{t}}\mathbf{C}=\text{mbFlatten}\big{(}\mathbf{\mu}\cdot\mathbf{I}_{N}+\text{mbDpt} \big{(}\mathbf{r}\cdot\mathbf{\overline{K}}\big{)}\big{)}\mathbf{\tilde{t}}=\mu\cdot\mathbf{ \tilde{t}}+\mathbf{r}\cdot\mathbf{\overline{K}}\cdot\mathbf{\tilde{t}}=\mu\cdot\mathbf{ \tilde{t}}+\mathbf{r}\cdot\mathbf{e}=\mu\cdot\mathbf{\tilde{t}}+\mathbf{\overline{\tilde{t}}}+ \mathbf{\overline{\tilde{e}}}\). and \(\mathbf{\overline{\tilde{e}}}=\mathbf{r}\cdot e\)  
Therefore, plain-text \(\mu\) can be obtained by decryption according to the original scheme. Therefore, when \(A\) is unchanged, the scheme will satisfy the linear homomorphism of the key. \(\Box\)
# XR Technology Deployment in Value Creation
## 4 Deployment Purposes of XR Technologies in Value Creation  
### Data Acquisition  
XR technologies have the capability to capture data with either their tracking system or the input devices of the selected setup [28; 29; 30]. The two major data types can be acquired from the user or the environment.  
Furthermore, the content can be designed accordingly to enable the user to enter the required data or to acquire data by interacting with the solution or the provided content [78]. [PERSON] et al. executed a VR-based analysis, observing the users in a simulated environment with observation and tracking systems to acquire consumer behavior data and identify the buying action triggers [5]. Within the context of Digital Twin (DT) data in a production environment, the input unit of an AR solution supports monitoring the  
Figure 2: Meta-characteristics for the taxonomy [27; 28; 29; 30].  
DT's physical element [79]. Further application scenarios of AR technologies suggest their deployment for monitoring processes through data recording, documentation of process execution or damages, object recognition, barcode and QR code scanning, object measurement, and automated inventory recording [16]. AR technologies are additionally deployed to monitor the workers' performance and count orders to be loaded on trucks in the shipping process [80]. It can be subsidized that XR technologies that generate both AR and VR are utilized to acquire relevant data for value creation.  
Data acquisition in the context of XR technologies can be understood as collecting data from an individual or an ecosystem relevant to value creation. This includes acquiring environmental data, data from process flows, or data from the user itself. The collection can be either through hardware technologies, i.e., tracking systems, or through the user experience design, which enables users to provide data to generate value-adding information for value creation.
# A Review of SATis5: Perspectives on Commercial and Defense 5G SATCOM Integration
## 6 Outlook Perspectives on SATis5 for Commercial and Defense Applications
### Commercial SATis5 Applications  
As discussed in Section 4, civil and commercial applications for current SATis5 architectures for eMBB and mMTC-NB-IoT use cases are of the most interest [13, 14, 15, 16, 17, 18, 19, 20, 21]. Recently, Reference [30] provides a good high-level summary of these SATis5 architectures. It focuses on SATis5 architectures that are 5G access agnostic and applicable for both eMBB and mMTC-NB-IoT with either fixed or mobile user equipment and/or terminal. Figure 14 describes a top-level perspective on a potential SATi5 architecture for civilian and commercial applications. This architecture perspective fully complies with ETSI TR 103 611 for scenario A3 [30]. The key interfaces shown in Figure 14 are specified in the 3 GPP/5G-NTN technical report (ETSI TR 103 611) as follows [31]:  
* N1 interface: This is for UE service, it is implemented between the UE and \"Access and Mobility Management Function\" (AMF) at the core network through gNB located at satellite customer provided equipment (CPE);
* N2 Interface: This interface is for RAN and UE service, and it is implemented between the gNB and the core AMF;* N2 Interface: This interface is for the NTN-NT-UE service, and it is implemented between the NTN-LTE-gNB and the core AMF;
* N3 Interface: For NTN-NT-UE service, it is implemented between the NTN-LTE-gNB and the core \"User Plane Function\" (UPF);
* N3 Interface: For RAN and UE service, it is implemented between the gNB and the core UPF.  
The outlook for the potential SATis5 architecture described in Figure 14 is consistent with the roadmap discussed in Section 3.2. In the long-term, the integration of satellites and 5G-TN requires adopting the same 5G-NTN architecture recommended by 3 GPP, including transmission, and switching technologies. The outlooked SATis5 architecture, shown in Figure 14, also requires modifying existing air interface protocols of the satellite access networks using existing 3 GPP/5G-TN air interface protocols without changing the current recommended 3 GPP/5G-UE air interface protocol stack. As mentioned earlier, the outlook perspective on commercial SATis5 can be extended to civilian SATis5.
# Enhancing Discoverability: A Metadata Framework for Empirical Research in Theses
## 5 Documentation Standards
### The Data Documentation Initiative (DDI)  
#### 5.1.1 Overview  
The DDI is an international standard for documenting empirical research in the social sciences [18]. First released in 1995, the standard is written in XML and supports the preservation and dissemination of research data. Its primary function is to facilitate the creation of structured \"codebooks\" describing datasets in detail.  
Today, the DDI is actively used by data specialists and archive managers worldwide. According to the official DDI website (ddialliance.org), it \"provides a standard for describing statistical and social science data, enabling both human and machine interpretation of datasets.\"
## 3 Proposed Methods
### The Real-Time Doppler Estimation Algorithm  
Using the designed preamble signal, a real-time Doppler estimation algorithm is proposed in this work.  
Figure 3 illustrates the structure diagram of the proposed Doppler estimation method, where \(t_{1}\) and \(t_{2}\) are the correlation peak positions of correlator 1 and correlator 2, respectively; and \(t_{0}\) is the start position of the received preamble signal without the Doppler factor.  
Figure 2: The communication frame structure.  
As shown in Figure 3, two correlators are used to estimate the Doppler factor. In this paper, we consider a realistic Doppler factor level (\(\alpha<0.01\)). Hence, for correlator 1, according to (8), the time offset between the correlation peak position and the start position of the received preamble signal, \(\Delta t_{1}\), can be obtained as  
\[\Delta t_{1}=t_{1}-t_{0}=-\frac{\alpha(f_{\rm L}+f_{\rm H})T_{\rm P}}{2(f_{\rm H }-f_{\rm L})}. \tag{19}\]  
Similarly, for correlator 2, the time offset between the correlation peak position and the start position of the received preamble signal, \(\Delta t_{2}\), is  
\[\Delta t_{2}=t_{2}-t_{0}=-\frac{\alpha(f_{\rm L}+f_{\rm H})T_{\rm P}}{2(f_{\rm L }-f_{\rm H})}=\frac{\alpha(f_{\rm L}+f_{\rm H})T_{\rm P}}{2(f_{\rm H}-f_{\rm L })}. \tag{20}\]  
Solving (19) and (20), the estimation of the Doppler factor can be presented as  
\[\hat{\alpha}=\frac{(f_{\rm H}-f_{\rm L})(\Delta t_{2}-\Delta t_{1})}{(f_{\rm H }+f_{\rm L})T_{\rm P}}=\frac{(f_{\rm H}-f_{\rm L})(t_{2}-t_{1})}{(f_{\rm H}+f_ {\rm L})T_{\rm P}}. \tag{21}\]  
Furthermore, by substituting (21) into (19), the estimation of the start time of the received signal can be calculated as  
\[\hat{t}_{0}=t_{1}+\frac{\hat{\alpha}(f_{\rm L}+f_{\rm H})T_{\rm P}}{2(f_{\rm H }-f_{\rm L})}. \tag{22}\]  
One finds from (22) that communication frame synchronization is realized.
# Research on Intrusion Detection Based on an Enhanced Random Forest Algorithm
## 2 Research Method
### The Random Forest Algorithm  
The Random Forest Algorithm is an ensemble learning method that models large-scale ensembles by combining multiple independent decision trees. The principle lies in combining multiple decision trees together, with the dataset randomly having put back selected each time, while randomly selecting some of the features as input; therefore, the Random Forest Algorithm can also be regarded as a Bagging (1) algorithm with a decision tree as an estimator [31]. Each decision tree is trained on a randomly sampled subset of training data. Ultimately, the classification decision is made by a voting mechanism among the decision trees [32]. The specific construction process is as follows:  
(1) Sample selection: Select randomly from the original data set using Bootstrap sampling to form \(N\) independent data subsets \(\{D_{N}\,n=1,2,\ldots,N\}\)  
(2) Feature selection: \(M\) features are randomly selected from \(m\) features.  
(3) Decision tree construction: Build a decision tree according to the samples of the data set. At each node, the best partition feature is selected according to Gini impurity.  
\[\text{Gini}(t)=1-\sum_{i=1}^{\text{c}}p(i|t)^{\,2}\]  
Integrated prediction: For a new sample, a majority vote is conducted through the prediction results of each decision tree to finally determine the classification result of the sample.  
In the context of intrusion detection, applying the Random Forest method can be challenging due to high-dimensional data in the dataset and class imbalance, which leads to suboptimal performance in metrics, such as classification accuracy. Furthermore, the algorithm's classification performance is heavily dependent on two factors: the accuracy of individual decision trees and the bias in the voting results among multiple decision trees. Therefore, to enhance the overall performance of Random Forest in intrusion detection, we are considering the integration of data dimensionality reduction algorithms and cost-sensitive learning methods. The critical steps and improvement strategies are depicted in Figure 1.  
This paper addresses the enhancement of the Random Forest algorithm from three key perspectives:  
Data Dimensionality Reduction for Classifier Input: in the first aspect, we focus on reducing the dimensionality of the data fed into the classifier.  
Construction of Cost-Sensitive Base Classifiers: The second aspect involves the creation of cost-sensitive base classifiers.  
Weighted Majority Voting at the Decision Stage: In the third aspect, we introduce weighted majority voting techniques at both the leaf nodes of decision trees and the ensemble decision stage. These improvements collectively aim to elevate the performance and effectiveness of the Random Forest algorithm in the context of intrusion detection.