esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Integration of RDF and LPG in SKATEBOARD
### Usage Example: The History of Computing  
To gain a deeper insight into this integration, let us delve into a practical example centred on the evolution of computing. Imagine we possess an RDF ontology defining various computer hardware classes (e.g., DBpedia). Concurrently, we employ an LPG managed by GraphBRAIN to elucidate the intricate connections and interactions between these hardware components.  
Through RDF, we define fundamental classes such as \"Processors\" and \"Memory\", establishing a foundational knowledge structure in a comprehensible and interoperable format. Simultaneously, within the LPG governed by GraphBRAIN, we painstakingly capture the nuances of interactions among specific hardware components, harnessing the reasoning capabilities inherent to LPGs.  
For instance, we can designate a processor as an RDF class and leverage the GraphBRAIN interface to oversee the ontology as an LPG. This lets us visually depict how that processor interfaces with a specific RAM module or motherboard. This methodology facilitates the detailed description of relationships and definitions for each hardware component in an unprecedented manner. By adopting this approach, we embark on a journey to explore the fundamental ontology of computer hardware (RDF) while maintaining precision in delineating the connections between these components (LPG). The outcome is a holistic view of knowledge within the computer hardware.  
The seamless integration of RDF and the LPG managed by GraphBRAIN in SKATEBOARD epitomises a potent and versatile approach for representing, enriching, and querying knowledge. This unique integration empowers users to interpret data within specific contexts while preserving a foundational ontological structure. In this way, SKATEBOARD serves as an encompassing platform for extracting, managing, and exploring knowledge grounded in semantic technologies, thereby providing a substantial advantage in analysing and discovering pertinent new information.
# Potential of Homomorphic Encryption for Cloud Computing Use Cases in Manufacturing
## 3 Encryption for Cloud Computing in Manufacturing
### Potentials and Challenges of Homomorphic Encryption for Cloud Computing  
#### 3.2.1 Potentials of Homomorphic Encryption  
Homomorphic encryption is an evolution of existing cryptographic approaches that has as its central feature the usability of encrypted data. As data backup in the cloud (scenario 1, Section 2.2.2) is realizable without limitations and does not include data usage, it is not further considered for homomorphic encryption. However, the potential of homomorphic encryption for data analysis in the cloud (scenario 2) is considered. As shown in Figure 4, the database is first encrypted at the company (on premise) and stored in the cloud. Once the company wants to perform an analysis, this encrypted database can now be used without any decryption in the cloud. The complete analysis is executed in the cloud, and the result is also encrypted. This encrypted result is then sent back to the on-premise IT, where it is decrypted by the company, to make the result visible. Compared to classic encryption, homomorphic encryption tackles both major limitations (see Section 3.1), as demonstrated by the color code in Figure 4. First, homomorphic encryption enables data security over the whole data life cycle. Second, as this continuous data security allows analyzing data in the cloud with any risks, homomorphic encryption allows revealing the full potential of cloud computing and flexibly using the cloud resources.
# Development and Application of a Novel Non-Iterative Balancing Method for Hydronic Systems
###### Abstract
The same authors in 2022 [21] indicated the evaluation of the mean square deviation of indoor temperatures as a valuable criterion for the selection of buildings for which water system balancing would result in high potential energy savings. [PERSON] et al. in 2018 [22] presented the results of a long-term study on the energy benefits of introducing balancing among different control systems in a set of buildings for a period of six years, resulting in energy savings of between 14.6% and 23.8% and payback time between 1.4 and 4.9 seasons of heating. This can sometimes also be obtained by the transformation of constant flow-borne systems into variable rate flow systems; however, automatic balancing devices such as thermostatic valves, differential pressure control valves, and pressure-independent flow controllers result in increased pump work and significant installation costs. The methods traditionally proposed by network balancing associations such as the Testing, Advancing and Balancing Bureau (TABB) and National Environmental Balancing Bureau (NEBB) are iterative methods. The first proposal of a non-iterative method was made in the 1990s by [PERSON], who presented the compensated method (CM) [23] based on the use of calibrated balancing valves. Many applications of the CM are available; among these, in 2001 [PERSON] [24] reported the application of the CM in six buildings in Hungary (each characterized by a number of flats from 20 to 354), showing experimentally that only a correct balancing procedure made it possible to achieve the required performance at the lowest consumption. In 2010 [25], CIBSE (the Chartered Institution of Building Services Engineers) included the CM among the suggested methods for hydronic systems balancing. Research and interesting insights about balancing have also come from the field of air systems balancing; the proposal by [PERSON] et al. in 2013 [26] of a non-iterative method called the progressive flow method (PFM) presented an approach for air systems similar to the one considered via the CM for water systems. The method was based on combined operations on dampers, fan velocity control, and proper flow measurements at the terminals. In addition to that, the PFM included some conceptual enhancements derived from a more general and theory-based approach. [PERSON] et al. in 2016 [27] proposed a non-iterative fan pressure-based method for air systems balancing which also partly used the procedures adopted by the PFM. This method suffered from some declared limitations related to the uncertainty of the flow rate estimation carried out by the fan curves and did not consider the extent of air leakages in airducts. However, the interest in this method lies in the fact that it made use of an assessment of the flow rate to the fan, introducing the possibility of drastically reducing the number of local measures; this concept is largely adopted in the proposal presented here.
## 2 Site Characterisation  
### The Industrial Study Site  
The industrial chemical complex of Estarreja (ICCE) occupies an area of approximately 2 km\({}^{2}\) (Figure 1), and is located 1 km east of Estarreja and 20 km north of Aveiro city in northern Portugal.  
This industrial complex started operations in the 1950s and, since then, has gone through different production cycles. The more relevant products have been [25]: ammonium sulphate (1952-1990s) manufactured from sulphuric acid (obtained by pyrite roasting) and ammonia; nitric acid and ammonium nitrate (1974-1990s); nitric acid, aniline, and nitrobenzene (1978-present); sodium and chlorate compounds using mercury cathodes (1956-2002); polyvinyl chloride--PVC (1963-1983); and isocyanide polymers of aromatic base (1978-present). Consequently, large amounts of solid wastes, some enriched in metal(loid)s (e.g., As, Cu, Pb, Hg, and Zn) were produced, which were latter deposited in a sealed landfill, while others were deposited in a calcium hydroxide mud dam (Figure 1). In addition, effluents are also responsible for a high pollution load containing organic compounds (namely, aniline, benzene, monochlorobenzene, and monointobenzene) as well as metal(loid)s (e.g., As, Hg, Pb and Zn). Until 1975, these effluents were transported by artificial (open and permeable) ditches (Vala de S. Filipe, Vala da Breja, and Vala do Canedo), which discharged into the natural surface water streams [25]. In the present, they are transported by an emissary pipe to a water treatment plant and only then discharged into natural water systems.  
The main environmental remediation interventions were undertaken from 1994 onwards by a joint effort of the local council, industries, and national environmental authorities. Since that time, chemical plants have also adopted procedures to control the emission of effluents and solid wastes to reduce their environmental impacts. Examples of the interventions carried out at the ICCE include the replacement of Hg cells with membrane cells in 2002 and the containment of about 300,000 tons of waste and contaminated soils in a confined landfill (2003-2005), supported by the ERASE project, an association between industry and local authorities [26].  
Groundwater contamination studies in the ICCE started in the last quarter of the 20 th century [27; 28; 29], and since then many other works have been carried out, devoted either to the environmental impacts [25; 30; 31; 32; 33; 34] or to the potential associated health risks [35; 36; 37].  
However, in parallel to the intense industrial activity, agriculture and farming have also been intensive and must be accounted for in terms of the contamination process in the area. In fact, these activities exert further pressure on local ecosystems because of the widespread use of agrochemicals and the effluents from cattle breeding.
# Camera-Based Continuous Heart and Respiration Rate Monitoring in the ICU
## 2 Materials and Methods
### Heart Rate Extraction
#### 2.4.3 Heart Rate Estimation from Remote PPG  
Both the remote RGB and the NIR based PPG signals were segmented into non-overlapping segments of 60 s using a hanning window. The windowed segments were then zero padded such that the Fourier spectrum of the segments had a resolution of 0.1 BPM. For each segment, the heart rate was chosen as the peak of the Fourier spectrum between 40 and 220 BPM, which we consider as the range of valid human heart rates. Then, based on the video annotations, any segments which contained frames that were marked as privacy sensitive, out of view, etc., were discarded, and the remaining segments were marked as valid time. In Table 3, we have reported the valid recording time per patient.
# Performance Comparison of H.264 and H.265 Encoders in a 4K FPV Drone Piloting System
## 4 Experimental Results
### Obtained Data  
We represent the obtained data by drawing parallel coordinates, using the python package Plotly (Figure 7). The drawn plot is interactive (Figures 8 and 9) and very distinct, but is not clear in the form of an image. This is why we extracted crucial data and visualize in the form of tables and more simplistic graphs in the following paragraphs. Instead of showing QP and target bitrate values, we only consider the actual bitrate of the encoded videos. We do this as the results indicated that the QP and target bitrate parameters affect each other too much, resulting in redundant data. Note that the last parallel represents a BRISQUE score of 100 to help make the chart look more intuitive. The percentages shown in Table 2 represent libx265 scores relative to libx264 scores for subjective \(S1\) and \(T1\) approximation, and Table 3 presents the results for objective approximation. For example, if the libx264 PSNR score is 35 and the libx265 PSNR score is 40, the table will display a value of 14.3%. Considering the relevance of the data, only the libx264 bitrate will be displayed with corresponding relative percentages of libx265 bitrates in parentheses. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sequence ID** & **Sequence alias** & **Class** & **Duration (s)** \\ \hline S1 & A1 & A & 5.71 \\ S2 & A2 & A & 12.57 \\ S3 & A3 & A & 3.95 \\ S4 & A4 & A & 12.62 \\ S5 & B1 & B & 2.95 \\ S6 & B2 & B & 7.05 \\ S7 & B3 & B & 21.18 \\ S8 & B4 & B & 7.58 \\ S9 & C1 & C & 30.97 \\ S10 & C2 & C & 7.80 \\ S11 & C3 & C & 12.38 \\ S12 & C4 & C & 16.35 \\ S13 & D1 & D & 4.22 \\ S14 & D2 & D & 12.32 \\ S15 & D3 & D & 19.17 \\ S16 & D4 & D & 8.82 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Basic sequence information. Figure 6: Motion vectors—high temporal complexity example. Figure 8: x264 filtered purple color parallel coordinates (filtered with magenta line). Figure 7: Video encoding results for x264 purple color and x265 teal color with various parameters, encoding times, and quality data, interpreted using parallel coordinates. Figure 9: x265 filtered purple color parallel coordinates (filtered with magenta line).
# Candidate Term Boundary Conflict Reduction Method for Chinese Geological Text Segmentation
## 4 Experiments and Discussion
### Experiment and Results
The experiment proves that the F1-value of the HFCR algorithm in general text segmentation is higher than 87%, which is slightly better than Jieba. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Data** & **Text Length** & **Tools** & **Accuracy** & **Precision** & **Recall** & **F1-Value** \\ \hline \multirow{6}{*}{Rencun} & \multirow{6}{*}{241k} & HFCR & 92.38\% & 95.85\% & 95.90\% & 95.88\% \\  & & Baidu LAC & 80.63\% & 86.62\% & 89.86\% & 88.21\% \\  & & Pkuseg & 83.35\% & 95.55\% & 83.91\% & 89.35\% \\  & & Hanlp & 81.49\% & 88.23\% & 89.18\% & 88.70\% \\  & & Jieba & 78.95\% & 87.94\% & 84.99\% & 86.44\% \\ \hline \multirow{6}{*}{Nanpanjiang} & \multirow{6}{*}{346k} & HFCR & 93.91\% & 96.89\% & 96.62\% & 96.76\% \\  & & Baidu LAC & 78.13\% & 80.12\% & 95.77\% & 87.25\% \\  & & [PERSON] & 86.18\% & 97.60\% & 86.07\% & 91.48\% \\  & & [PERSON] & 79.31\% & 83.00\% & 92.90\% & 87.69\% \\  & & [PERSON] & 83.99\% & 91.46\% & 89.27\% & 90.35\% \\ \hline \multirow{6}{*}{Xiaoshan} & \multirow{6}{*}{576k} & HFCR & 93.98\% & 97.18\% & 96.39\% & 96.78\% \\  & & Baidu LAC & 80.62\% & 86.47\% & 90.06\% & 88.23\% \\ \cline{1-1}  & & [PERSON] & 82.72\% & 92.55\% & 86.04\% & 89.18\% \\ \cline{1-1}  & & Hanlp & 80.73\% & 89.20\% & 86.62\% & 87.89\% \\ \cline{1-1}  & & Jieba & 77.28\% & 87.62\% & 82.17\% & 84.80\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of accuracy between HFCR algorithm and other tokenizers (geological text). Figure 5: Variation under the different percentages of candidate term calculation range of the full text. (**a**) Rencun; (**b**) Nanpanjiang; (**c**) Xiaoshan. Note: The \(x\)-axis is the percentage of the currently calculated candidate term set in the full text, and the \(y\)-axis is the variation in accuracy.
# Recognition of Vehicle License Plates Based on Image Processing
## 3 Experimental Results  
To evaluate the proposed license plate recognition algorithm performance, we acquired vehicle images using a network camera with a resolution of 3-Mega pixels installed on the road. Still images that can be identified with the naked eye were selected and used as experimental data. The experimental image data consist of 2500 image sheets. The experiment was conducted using license plates with various sizes and colors of characters.  
Figure 12 shows the experimental results of using the proposed image improvement algorithm. The experimental results showed that characters misrecognized in existing low-resolution images were successfully recognized using the proposed method. However, there is a problem in that the characters are not normally recognized when the perspective distortion of the license plate is severe among images of the license plate restored in high-resolution. To solve this problem, the distortion-correction technique proposed earlier was applied to images. Figure 13 shows the image to which the distortion-correction technique is applied.  
Figure 11: (**a**) Input image; (**b**) License plate recognition; (**c**) Recognition result.  
Figure 12: (**a**) Input image and Korean string; (**b**) low-resolution result; (**c**) super-resolution result.  
Besides the distortion-correction technique, the experimental results verify that the proposed algorithm improves the recognition rate. Table 2 presents the experimental data result using the proposed algorithm. The license plate recognition rate of the original image was 72.6% which was, however, improved to 73.2% by using the low-resolution image reconstruction technique described in Section 2.3. When the distortion correction technique described in Section 2.4 was used in conjunction, the recognition rate became 81.4%, which verified the validity of the proposed license plate detection algorithm.
## 3 Proposed Method
### Message Extraction Algorithm
#### 3.2.2 Message Extraction by Single Base and Weight Modulus  
In this phase, the recovered image, \(R^{I^{\prime\prime}_{ouect}}\), is processed pixel-by-pixel to extract messages hidden within it. Given \(R^{I^{\prime\prime}_{ouect}}\), we process each pixel, \(\mathbf{RP^{\prime\prime}_{ouect}}\), and derive the \(m\)-ary secret message, \(d_{um}\), using the 3-tuple weight vector, \(\mathbf{W_{a}}\), and modulus operation, as shown in Equation (13). We then extract the secret message vector, \(\mathbf{d_{sb}}\), using the parameter \(n\) and the modulus operation, as shown in Equation (14).  
\[d_{um} =\mathbf{RP^{\prime\prime}_{ouect}}\mathbf{\cdot W_{a}}\bmod m \tag{13}\] \[\mathbf{d_{sb}} =\left(d_{sb}^{1},\,d_{sb}^{2},\,d_{sb}^{3}\right)=\mathbf{RP^{ \prime\prime}_{ouect}}\bmod n \tag{14}\]  
Figure 2: The flowchart of message extraction.  
We next describe an example of message extraction. Following up on Example-2, the recipient inputs the pixels \(\mathbf{P}_{\text{st}}^{\prime\prime}\) = (98, 138, 158) and \(\mathbf{P}_{\text{s2}}^{\prime\prime\prime}\) = (97, 139, 158) in dual stego images, \(I_{s1}^{\prime\prime}\) and \(I_{s2}^{\prime\prime\prime}\), respectively. The recipient acquires the 3-tuple weight vector, \(\mathbf{W_{a}}\) = [1, 2, 3] and the 2-tuple weight vector, \(\mathbf{W_{b}}\) = [1, 4] as secret keys delivered in prior by the sender, along with parameters \(n\) = 3, \(m\) = 7, \(k\) = 5. The secret messages are extracted in reverse order: \(\mathbf{d_{du}}\), first, followed by \(d_{wnn}\), and finally \(\mathbf{d_{sb}}\). First, the recipient extracts the secret message vector, \(\mathbf{d_{du}}\), using Equation (11), thus producing \(\mathbf{d_{du}}\) = (1\({}_{5}\), 3\({}_{5}\), 0\({}_{5}\)). Next, an image is restored, and using Equation (12), the recovered pixel is produced from \(\mathbf{P}_{\text{st}}^{\prime\prime\prime}\) and \(\mathbf{P}_{\text{s2}}^{\prime\prime\prime}\), leading to \(\mathbf{RP}_{\text{o}uct}^{\prime\prime}\) = (97, 138, 158). Furthermore, the recipient extracts the 7-ary secret message using Equation (13) and the 3-tuple weight vector \(\mathbf{W_{a}}\), thus producing \(d_{wnn}\) = (13, 5, 21)-(1, 2, 3) mod 7 = 2\({}_{7}\). Finally, the recipient extracts the 3-ary secret message vector using Equation (14), thus producing \(\mathbf{d_{sb}}\) = (1\({}_{3}\), 0\({}_{3}\), 2\({}_{3}\)). All of the other secret messages can be extracted in a similar approach from the corresponding pixels in dual stego images, \(I_{s1}^{\prime\prime}\) and \(I_{s2}^{\prime\prime}\), respectively.
# Estimating Global Gross Primary Production Using an Improved MODIS Leaf Area Index Dataset
## Appendix A The Structure of the Model
### Two-Leaf Model  
We adopted the two-leaf model to estimate the radiation absorbed by the canopy, which divides the canopy into shaded and sunlit leaves that receive different components and portions of incoming shortwave radiation [49]. It is assumed that the sunlit leaves receive both direct and diffuse solar radiation, while shaded leaves absorb diffuse radiation only [48]. We obtained the LAI of shaded and sunlit leaves of the canopy by using the LAI [107].  
\[LAI_{sun}=\frac{1}{K_{b}}(1-e^{-K_{b}\times LAI\times CI}) \tag{12}\] \[LAI_{shade}=LAI-LAI_{s}un \tag{13}\]  
where \(CI\) is clumping index.  
The total solar radiation flux density absorbed by the sunlit leaves in the canopy (\(AS_{sun}\)) is the sum of the direct component of direct radiation (\(AS_{b,b}\)), the scattered component of direct radiation (\(AS_{b,s}\)), and diffuse radiation (\(AS_{d}\)).  
\[AS_{sun}=AS_{b,b}+AS_{b,s}+AS_{d} \tag{14}\]  
The total solar radiation flux density absorbed by shaded leaves in the canopy (\(AS_{shade}\)) is the sum of the scattered component of direct radiation (\(AS_{b,s}\)) and diffuse radiation (\(AS_{d}\)):  
\[AS_{shade}=AS_{b,s}+AS_{d} \tag{15}\]  
We coupled the canopy radiative transfer model with the two-leaf model to calculate the absorption of different radiation components for sunlit and shaded leaves. The absorbed diffuse radiation (\(AS_{d}\)) is expressed as follows:  
\[AS_{d}=S_{d}(1-\tau_{d}-\beta_{d}) \tag{16}\]  
The absorbed direct component of direct radiation (\(AS_{b,b}\)) is expressed as follows:  
\[AS_{b,b}=S_{b}K_{b} \tag{17}\]  
The absorbed scattered component of direct radiation (\(AS_{b,s}\)) is expressed as follows:  
\[AS_{b,s}=S_{b}(1-\tau_{b}-\beta_{b})-AS_{b,b} \tag{18}\]
# A Knowledge Graph Embedding Approach for Polypharmacy Side Effects Prediction
## 3 Experiments and Results
### Experimental Setup  
A model for predicting multidrug side effects was trained using the labeled data of drug pairs and single drugs. In addition, virtually generated drug pairs from the 284 drugs were employed without labels to regularize the model. For model training, Adam optimizer [29] was used at the learning rate of 0.01. The Hu method [30] was used to set the initial values of learning parameters. The learning was stopped if performance on validation data did not improve after 500 consecutive epochs.  
As targeted side effects, we selected 200 side effects that were used chosen by [PERSON] et al. [10]. The prediction performance was measured for each targeted side effect \(s\), while drug pairs containing single drugs that cause the targeted side effect \(s\) were excluded from the test data, as done in a previous study [14]. To handle the data imbalance issue betweenpositive and negative samples in test data, we randomly selected as many negative drug pairs as positive drug pairs for each side effect.  
For evaluation, we considered two types of drug pair inputs, termed DPU and non-DPU, respectively, and conducted experiments for each of term separately. The DPU and non-DPU inputs are defined as follows:  
* DPU: Drug pair including drugs unused in labeled multidrug combinations included in training data.
* Non-DPU: Drug pair consisted of only drugs used in labeled multidrug combinations included in training data.  
The prediction performance for DPU inputs indicates how well the model predicts side effects for drug pairs including new drugs for which multidrug clinical information is unknown. Thus, this performance will be of interest to clinicians who want to use new drugs in combination with other drugs.
## 2 Materials and Methods
### MODIS Imagery  
The MODIS MOD13Q1 products with a spatial resolution of 250 m, from 2001 to 2009, were collected and produced at 16-day interval using bidirectional reflectance distribution function (BRDF), constrained-view angle-maximum value composite (CV-MVC), and maximum value compositing  
Figure 1: **(a)** The location of the Khingan Mountain region in China. **(b)** Land cover in the Khingan Mountains [19].  
(MCV) methods [20]. MOD13Q1 includes two vegetation indices (VIs), normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), and quality data that indicate the quality of the VIs and input reflectance data. Recent comparison analysis of NDVI and EVI showed that EVI had much stronger relationship with towered gross primary production (GPP) and better depiction to the seasonality of GPP [21]. In this study, EVI data were used and reprojected from native Sinusoidal projection to the Albers Conical Equal Area projection.