esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The organic Rankine cycle (ORC) has been demonstrated to be an effective method for converting low-grade heat energy into electricity. This paper proposes an improved analysis method for the ORC system. A coupling model of the ORC system with a radial-inflow turbine efficiency prediction model is presented. Multi-objective optimization was conducted for a constant turbine efficiency ORC system (ORC\({}_{\text{CTE}}\)) and a predicted turbine efficiency ORC system (ORC\({}_{\text{DTE}}\)), and the optimization results were compared. Additionally, a sensitivity analysis was conducted with respect to the heat source temperature and the ambient temperature. It can be found that the predicted turbine efficiency decreases with the increasing evaporation temperature, and increases with the increasing condensation temperature. The turbine efficiency is not constant and it varies with operating conditions. The distribution of the Pareto frontier for ORC\({}_{\text{CTE}}\) system and ORC\({}_{\text{CTE}}\) system is different. Compared with the ORC\({}_{\text{CTE}}\) system, the ORC\({}_{\text{DTE}}\) system has a lower optimal evaporation temperature, but a higher optimal condensation temperature. The deviation between the predicted turbine efficiency and the constant turbine efficiency increases with the increasing heat source temperature but decreases with the increasing ambient temperature. Thus, the difference in the theoretical analysis results between ORC\({}_{\text{CTE}}\) system and ORC\({}_{\text{DTE}}\) system increases with the increasing heat source temperature but decreases with the increasing ambient temperature.  
organic Rankine cycle; predicted turbine efficiency; turbine efficiency prediction model; multi-objective optimization; sensitivity analysis +
Footnote †: journal: Applied Sciences
# Cloud-Based Single-Frequency Snapshot RTK Positioning
## 3 Experiment Setup and Results
### Results and Discussions
#### 3.3.3 Integration Time Impact  
The fix rate and mean LRF results for different integration times at different baselines are collected in Tables 1 and 2, where a signal bandwidth of 31.8 MHz was used to generate these results.  
As expected, both tables show that the performance of longer integration time is generally better compared to those shorter ones, although its impact is still not as significant as the baseline distance. By looking at Table 1, an 8% improvement is observed when increasing the integration time from 40 ms to 100 ms, while this improvement becomes less noticeable when the baseline distance grows larger than 40 km. This table also shows that the fix rate can still reach 90% for all the integration times used when the baseline distance is smaller than 10 km, and a fix rate of 80% can be assured for 20 km baselines. Results in Table 2 further confirm that the mean LRF result improves when a longer integration time is used. An overall 20% improvement in the mean LRF value can be seen when integration time increases from 40 ms to 100 ms, except for the scenarios with baseline distances over 40 km.
# Improving Symbolic System-Level Synthesis by Solver Coordination and Domain-Specific Heuristics
## 5 Answer Set Programming (ASP)--Binding & Routing
### Domain-Specific Heuristics in ASP  
While ASP provides a rich modeling language together with highly performant yet general-purpose solving techniques, these general-purpose solving capabilities can further be boosted by utilizing domain-specific heuristics that are exploited at non-deterministic choice points [17]. Thus, the search heuristics already implemented in answer set solvers can be enriched with the knowledge of domain experts and potentially a better solution may be obtained within a shorter amount of time.  
In the paper at hand, we focus on the heuristic presented in [27] which contains the most domain-specific knowledge and has also turned out to be most effective. In this heuristic, the answer set solver decides the binding of all computational tasks of one application before it decides the binding of any other computational tasks. Furthermore, the sending and the receiving computational task of a message should be bound to the same tile. If it is not possible to to bind all communicating tasks of one application to the same tile, e.g., due to load balancing computational load, we encode that next neighboring tiles should be preferred.
## 3 Results and Discussion
### Geographical Dependence  
Figure 7 shows the corrosion rates of zinc at different thicknesses and environments. It is reported that repair or replacement is needed when rusting of the metal surface is at 5%. For example, at a zinc thickness of 75 \(\upmu\)m, the steal will last for approximately 56 years in industrial areas, or more than 100 years in the rural area [12; 13; 14]. It is known that corrosion is affected by sea salt and pollution. In coastal areas, NaCl accelerates corrosion and, in industrial areas, corrosive gases (SO\({}_{2}\), NO\({}_{\times}\), and Co) are released, which cause acid rain. Normal rainfall has a relatively low pH of 5.6 because of dissolved carbon dioxide. However, most rainfall is pH less than 5.6 due to SO\({}_{\times}\) and NO\({}_{\times}\). The strict definition of acid rain is rain with a pH lower than 5.6. However, rain is more commonly referred to as acid rain when the pH is 5 or more. In 2006, the average monthly pH values of rainfall in Gangneung, Daejeon, Daegu, Busan, and Ulsan were 5.52, 4.81, 5.61, 5.69, and 4.90, respectively [8; 9; 10; 11; 12; 13].  
Figure 8 shows the caps and pins of insulators gathered from Daejeon, Ulsan, and Busan. Although the average pH values of rainfall in Daejeon and Ulsan are similar, it is notable that rainfall in Ulsan is very acidic during the winter since Ulsan is an industrial city. The considerable progress of erosion found in Ulsan can be attributed to acid rain caused by pollutants--such as SO\({}_{\times}\) and NO\({}_{\times}\)--from factories and automobile exhaust. Comparatively, the corrosion found in the insulators used in Busan  
Figure 6: (**a**) Pin corrosion and (**b**) severe crevice corrosion.  
Figure 7: Corrosion rate of zinc in different environments.  
was due to NaCl, since Busan is a coastal city. From the images in Figure 8b collected from the Ulsan site with rainfall having a pH of 4.90 is less rusted than the other two samples taken from Daejeon with a pH of 4.81 and Busan with a pH of 5.69. On the other hand, erosion is more pronounced on the Ulsan sample. Thus, NaCl causes less rust but more erosion. For the insulator used in Daegu for 48 years, there was substantial rust on one side of the cap and a smaller amount at the pin. This is due to the direction of wind flow. The contaminants will deposit on one side of the porcelain insulator [15]. Moreover, the direction of wind flow also decides the direction of corrosion propagation as observed in the previously mentioned sample.
## 6 Implementation and Evaluation
### Implementation
The simulation environment allows both a functional and performance evaluation, with the demo box complementing the functional evaluation. The simulation environment enables the initial evaluation of the performance of our solution for varying entity sizes and number of hops to the entity resources. Evaluation on larger real-life testbeds will prove useful for validating the simulation experiments and for conducting experiments in more dense and more realistic (e.g., Wi-Fi interference) environments. However this will take a significant amount of time and is beyond the scope of this work.
## 3 System Model
### Proposed Deployment Framework Architecture  
The proposed deployment of the multi-tier clustering framework for the smart WSN-IoT network is demonstrated in Figure 1. The framework considers a rectangular network area where all the nodes in the network are stationary and uniform. Each sensor node is equipped with GPS and a temperature sensor and they are randomly distributed across all network areas. Each node may communicate with the Base Station by varying its transmission range. The Base Station is present at the top in the uppermost layer and is responsible for collecting information from network nodes and sending it further to the IoT cloud for processing. The sensor nodes periodically send data to BS. The network is divided into various regions and each region is further divided into various sub-regions, which are commonly referred to as clusters. To ensure even load distribution across all network nodes, area covered by regions keep on decreasing towards BS and the number of sub-regions in each region keeps on increasing towards BS. As a result, the lowest region is the one with the maximum occupied area and the least number of sub-regions (clusters), while the highest region is with the least covered area and the maximum number of sub-regions. The motivation behind this is listed as the highest region. It not only sends aggregated data towards BS but also sends traffic coming from lower regions to BS, thereby utilizing more energy and network resources. This implies reduced intra-cluster communication distance in higher regions with increased number of clusters. Moreover, the sub-regions present in any particular region occupy the same area.
# A Comparative Analysis of Machine Learning Techniques for Cyberbullying Detection on Twitter
## 1 Introduction
* We evaluated the classifiers investigated in this work, and test their usability and accuracy on a sizeable generic dataset. * We developed an automated detection model by incorporating feature extraction in the classifiers to enhance the classifiers' efficiency on the sizeable generic dataset. * We compared the performance of seven ML classifiers that are commonly used in the detection of cyberbullying. We also used the Frequency-Inverse Document Frequency (TF-IDF) and Word2 Vec models for feature extraction. This comparison analysis helped to understand the limitations and advantages of ML in text classification models. Accordingly, we formulated and aimed to answer the following research questions in this work:  
* What types of existing machine learning techniques/methods are being used extensively to detect cyberbullying in social media platforms? * How can an automatic cyberbullying detection model be developed with high accuracy and less processing time? * How can feature extraction be used to enhance the detection process? The proposed approach detects cyberbullying by extracting tweets, classifying the tweets using text analysis techniques based on predefined keywords, and then classifying the tweets as offensive or non-offensive. Therefore, the outcomes of the current evaluation will help other researchers to choose a suitable and sufficient classifier for the datasets of global cyberbullying tweets collected from [12; 13], because improvements are necessary to further increase the classification accuracy. This paper is structured as follows. Section 2 is dedicated to the background and related work wherein the examined classifiers will be described. Section 3 provides an overview of the methodology adopted for the proposed research and a description of the dataset utilized for the experiment. Results are discussed in Section 4, and conclusions and future work are provided in Section 5.
## 2 Materials and Methods  
### Search Strategy and Study Selection  
This study was developed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines [22]. Multiple databases were searched to retrieve relevant studies, including PubMed, Web of Science (WOS), SPORTDiscus, and Scopus. The following search string was used to retrieve studies: (1) \"basketball\" OR \"basketball players\" AND (2) \"selection\" AND (3) \"gaze behavior\" OR \"quiet eye\" OR \"QE duration\" OR \"final fixation duration\" AND (4) \"shooting performance\" OR \"shooting accuracy\" AND (5) \"visual attention\" OR \"focus\" OR \"visual control\".  
Database searches were initially conducted on 12 November 2022, and an updated search was completed on 13 October 2024. The searches covered all available records from their inception up to the respective search dates, ensuring a comprehensive review of the relevant literature. In addition to database searches, manual reference list checks and searches in sports research portals and organizational websites were performed to identify articles not indexed in academic databases and cited studies related to the research topic.  
To streamline the screening process, Rayyan software (Rayyan for Systematic Reviews, Qatar Computing Research Institute, Doha, Qatar, [[https://www.rayyan.ai](https://www.rayyan.ai)]([https://www.rayyan.ai](https://www.rayyan.ai)), accessed on 27 March 2025) was used to collate search results from all databases into a unified spreadsheet, automatically remove duplicate studies, and facilitate blinded independent screening. The titles and abstracts of the retrieved studies were first screened by H.M., who assessed them against the inclusion and exclusion criteria. Full-text versions of studies that met the initial inclusion criteria were then retrieved and independently reviewed by both [PERSON] and J.L. to determine their final eligibility. Each researcher flagged studies in the Rayyan software as included, excluded, or possibly included.  
Whenever conflicts arose regarding the inclusion decisions, they were resolved through a structured process, first through discussion between [PERSON] and J.L. If disagreements persisted, a third independent researcher ([PERSON]) was consulted to make the final decision.
## 4 Conclusions  
Herein, a solvent-free process, called reactive gelation, towards macroporos polymer-protein hybrid materials is presented. Using copper-mediated click chemistry, a ter-polymer based on styrene, divinyl benzene, and 4-vinylbenzyl azide was modified to attain macroporous polymeric microclusters with active sites for protein-conjugation in form of epoxides or \(N\)-hydroxysuccinimide. In a subsequent step, _Staphyloloccous aureus_ protein A was immobilized and the obtained protein-polymer hybrid materials were packed in a chromatographic column. As protein A is commonly used for downstream processing of monoclonal antibodies, the capture ability of the produced materials was investigated revealing that the single-point attachment gave up to three times higher static binding capacities than the multi-point coupling through amidation reactions. However, the highest static binding capacity of 15 mL mL\({}^{-1}\) is still far below those of commercial products and requires further optimization. Nevertheless, it was shown that, due to their peculiar macroporous stucture, the developed materials show superior intraparticle mass transfer compared to the typical behavior of commercial protein A products, and, therefore, provide a promising alternative the purification in industrial processes.  
Conceptualization, M.L.; Methodology, M.L., C.P.; Validation, M.L., G.S. and M.M.; Formal Analysis, C.P.; Investigation, M.L., C.P.; Resources, M.M.; Data Curation, C.P.; Writing-Original Draft Preparation, M.L.; Writing-Review & Editing, M.L.; Visualization, C.P., M.L.; Supervision, M.L., G.S.; Project Administration, M.L., G.S.  
This research received no external funding  
[PERSON] is acknowledged for providing the herein used base material.  
The authors declare no conflict of interest.  
Figure 11: Breakthrough curves of produced protein A resins using microclusters with either NHS–ester or epoxide groups on their surface.
# Interaction Strength Analysis to Model Retweet Cascade Graphs
## 8 ISN, ISN-AF and Baseline Comparisons
### Research Implications  
As shown in the comparison figures (Figure 7, Figure 8, Figure 9 and Figure 10), the proposed models give completely different reconstructions of retweet cascades with respect to the traditional method described in [11]. The difference mainly lies in the way in which the edges are derived. In fact, previous work mainly focused on user social networks, whereas we adopted a user social interactions approach combined with \(root_{author}\) information, and as a residual step, retweeters' friendship networks. Moreover, the proposed algorithms are able to assign to each cascade graph a metric (CAS) that is indicative of the strength of the solution found. The proposed approach is able to link a higher number of nodes, thereby creating denser graphs. Derived cascades diverge not only in their outline, but also in terms of evaluation metrics, especially for the SNI value. When the number of nodes involved in the cascade is relatively small, the priority is given to the root, which assumes a more relevant role (Figure 7). In contrast, when the number of nodes is higher, the proposed approach highlights specific nodes that shared the information across different communities (Figure 10). For instance, Figure 9(c) shows important differences with respect to the traditional approach, which was centered on the tweet's root author. Differently, Figure 8(a),b shows the presence of important nodes that were involved in the cascade and that spread the information within communities, and nodes that were able to spread the tweet across different communities. Thus, this information can represent a starting point for different future applications, such as social bot community detection [36].  
Figure 10: Retweet cascade graph comparison #4.  
Figure 9: Retweet cascade graph comparison #3.
## 3 Results and Discussion
### X-ray Photoelectron Spectroscopy (XPS)  
The surface analysis and oxidation states of the elements in pure TiO\({}_{2}\) sample and NT-3 samples are analyzed using XPS. The high resolution XPS spectra with peak fitting for pure TiO\({}_{2}\) and NT-3 sample are shown in Figures 8 and 9, respectively. The high resolution XPS of Ti 2p for both samples (Figures 8a and 9a) exhibits the corresponding peaks of Ti 2p\({}_{3/2}\) and Ti 2p\({}_{1/2}\) binding energies, indicating the presence of Ti\({}^{4+}\) ions in both, pure TiO\({}_{2}\) and NT-3 the samples [14,15]. Figure 8b displays the high resolution O 1s XPS spectrum with three fitted peaks for pure TiO\({}_{2}\) sample, the peaks appearing around 529.18 eV, 531.58 eV and 534.68 eV are attributed to lattice Ti-O bonds [54], and non-lattice O-H and C-O bonds, respectively. The O 1s XPS spectrum for the NT-3 sample (Figure 9b) exhibits similar peaks with a slight shift towards higher binding energy that can be attributed to the effect of N doping. Figure 8c shows N 1s XPS spectrum for pure TiO\({}_{2}\) which exhibits only noise rather than any peak and state of N, whereas, for the NT-3 sample, the N 1s XPS spectrum (Figure 9c) clearly displays peaks appearing around the  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Photocatalyst**} & **BET Surface Area** & **Pore Size** \\  & **[m\({}^{2}\)/g]** & **[nm]** \\ \hline Pure TiO\({}_{2}\) & 42.98 & 0.6427 \\ NT-1 & 38.87 & 0.5947 \\ NT-2 & 30.18 & 0.5788 \\ NT-3 & 23.93 & 0.5776 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Textural properties of pure TiO\({}_{2}\), NT-1, NT-2 and NT-3, synthesized by using 0.0, 0.983, 1.965 and 2.940 of urea, respectively.  
Figure 7: PL spectra of (**a**) Pure TiO\({}_{2}\), (**b**) NT-1, (**c**) NT-2 and (**d**) NT-3.  
binding energy of 398.48 eV and 407.48 eV. The former peak is associated to the Ti-N bonds formed after replacement of O atoms with N atoms within the TiO\({}_{2}\) lattice [55], whereas the later peak indicates the presence of N atoms chemisorbed on the surface of the NT-3 sample [56]. Figure 8d shows the C 1s XPS spectrum for pure TiO\({}_{2}\) samples with the peak fitting showing three peaks. The peak appearing at binding energy of 284.68 eV, is associated to the elemental carbon [57], whereas the peaks appearing at 288.48 eV and 289.88 eV are ascribed to C-O and C=O [58], indicating the presence of carbonaceous species within the samples. The C 1s XPS spectrum for NT-3 sample with peaks fitting is shown in Figure 9d. The peak appearing at 284.98 eV can be attributed to elemental carbon [57], whereas the peaks appearing at 287.78 eV and 289.58 eV can be associated to the C-O and C=O species [58], respectively. In both samples, pure TiO\({}_{2}\) and the NT-3 sample, in addition to elemental carbon peaks, the peaks of carbonaceous species can be ascribed to the adsorption of carbon during the synthesis procedure and/or probable formation of Ti-O-C bonds.
# Benchmarking of Load Forecasting Methods Using Residential Smart Meter Data
## 2 Materials and Methods
### Models' Calibration and Feature Selection  
Initially, a _Naive_ model was planned as a baseline when comparing different forecasting tools. This _Naive_ model assumes that the half-hourly electricity consumption records being predicted for the day-ahead (day D) are simply the reproduction of the half-hourly electricity consumption records of the same day one week before (day D-7).  
In order to allow a fair comparison between the different forecasting approaches, and for each individual consumer, a strategy was used of splitting the dataset into two subsamples: training and test subsamples. Along with this, the following aspects were considered:  
* Training data: from 28 December 2011 to 21 September 2013 (80% of the whole available dataset);
* Test data: from 22 September 2013 to 27 February 2014 (20% of the whole available dataset)  
The careful selection of input data to be used is one of the most important stages in data-driven forecasting approaches. Based on similar studies [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22], this quite often involved the use of historical data of the variable being predicted (electricity consumption, hourly active power or peak values of active power), the use of auxiliary variables (such as the month number, the weekday code, or even the hourly period code) or the dependence on exogenous variables (weather variables being very common in this context or variables related with household lifestyle).  
Figure 3: Example of a trained ANN model with 5 neurons in the hidden layer.  
Due to the lack of information available regarding weather conditions and knowing that in the UK, the dependence of heating/cooling needs does not significantly rely on electric devices (electric heating present a combined share of 5% and only 3-5% of households is estimated to have a cooling unit [29; 30]), this type of information was not here included. In addition, [15] also proved that after several experiments involving an ensemble method, better results were obtained only using past values of load (excluding date/time variables and weather conditions).  
Based on the available type of information, it was decided to only use historical values of the variable being predicted (electricity demand). In addition, two supplementary variables to characterize the data periodicity, in this case to distinguish the weekday being predicted and the specific hourly period, were considered to be used. For the different forecasting approaches, the same inputs were chosen, based on the information available in Table 2.  
After several experiments were made, and as a major part of the features are related with electricity consumption data (no exogenous variables were here used), it was decided to avoid data normalisation.
## 4 Data Quality Control in TasMAN Using Fuzzy Rules
### Sensor Q4 QC Inputs
#### 4.1.3 Rate of Change of Output Values  
The rate of change of the sensor values (\(RoC(t)=x(t)-x(t\)-1)) was used as an input. Given the large-scale physical processes under study, rapid changes in sensor output are typically due to faulty sensors and not to sudden changes in the environment (e.g., given the high specific heat capacity of water and the large volume present, it is unlikely that water temperature in the estuary would rise by 1 \({}^{\circ}\)C over 5 min). Instead, typical timescales for changes in physical parameters are hours or days.