esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Numerical Analysis of the Erosion Model
### Theoretical Analysis of Erosion Wear
#### 2.2.3 Turbulence Model Selection  
The division of turbulence is based mainly on the size of the Reynolds number distinguished. For tube flow, a Reynolds number of over 2300 liquid flow is considered turbulence. The formula is as follows [21]:  
\[R_{e}=\frac{\rho VD}{\mu} \tag{6}\]  
where \(\rho\) is the fluid density; \(V\) represents the fluid velocity; \(D\) denotes the diameter of the pipeline; and \(\mu\) is the fluid viscosity.  
Turbulence is a very complex flow condition. There is currently no way to correctly describe its status. The accuracy of the Laminar model is not guaranteed. The relevant parameters of the k-omega model are difficult to obtain. The RNG k-\(\epsilon\) model has the following form [22]:  
\[\left\{\begin{array}{l}\frac{\partial(\rho k)}{\partial t}+\frac{\partial( \rho ku_{i})}{\partial x_{i}}=\frac{\partial}{\partial x_{i}}\left[\left(\mu+ \frac{\mu_{t}}{\varphi_{t}}\right)\frac{\partial k}{\partial x_{i}}\right]+G_ {k}-\rho\varepsilon+S_{k}\\ \frac{\partial(\rho\varepsilon)}{\partial t}+\frac{\partial(\rho ku_{i})}{ \partial x_{i}}=\frac{\partial}{\partial x_{i}}\left[\left(\mu+\frac{\mu_{t} }{\varphi_{t}}\right)\frac{\partial k}{\partial x_{j}}\right]+G_{1\varepsilon }\xi_{k}-C_{2\varepsilon}\rho\frac{\varepsilon^{2}}{\varepsilon}+S_{e}\end{array}\right. \tag{7}\]  
where \(u_{t}=\rho C_{u}\frac{k^{2}}{\varepsilon};k\) is the kinetic energy of liquid flow; \(\mu\)m represents the velocity of the liquid phase along the axis gradient; \(\rho\) denotes the density of the liquid phase; \(\eta\) stands for the viscosity of the liquid phase; \(G_{k}\) is a derivative of k; \(\varepsilon\) represents the dissipation of energy;  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameters** & **Symbol** & **Units** \\ \hline Liquid phase density & \(\rho\) & kg/m\({}^{3}\) \\ Time & t & s \\ Thermal conductivity & \(\lambda\) & \\ Stress tensor & \(\tau_{nm}\) & Pa \\ Liquid phase velocity & v & m/s \\ Turbulent dissipation rate & E & m\({}^{2}\)/s\({}^{3}\) \\ Volume & V\({}_{\text{m}}\) & m\({}^{3}\) \\ Pressure & p & MPa \\ Energy source & \(S_{e}\) & W/m\({}^{3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters in the equation.  
xm and xn are the space coordinates; \(\sigma_{k}\) represents the Prandtl number corresponding to the dissipative active function, which is generally 1; and \(\sigma_{\varepsilon}\) is the Prandtl number corresponding to the power dissipated by turbulent kinetic energy, which is generally 1.2. In Fluent, the default Cu = 0.09, G\({}_{1\varepsilon}\) = 1.44 and C\({}_{2\varepsilon}\) = 1.92.
## 3 Research Model and Hypotheses  
### Research Model  
The majority of factors examined in this study were drawn from the updated Information System Success Model (the updated D&M). This model was first established in 1992 by [PERSON] and [PERSON] and subsequently revised in 2003 to accommodate for the evolving roles and advancements in information systems [38]. The revised model suggests that service quality, information quality, and system quality directly impact user satisfaction, willingness to use, and actual utilization. As the focus of this research was on student satisfaction, these three factors--service quality, information quality, and system quality--will be examined in this study.  
[PERSON] and [PERSON] [38] emphasized that the updated D&M model should not be viewed as a universally applicable measure to assess information systems. It was suggested that researchers modify the number of factors as necessary to ensure the model's relevance to their specific context. Various research has adapted the revised D&M model and subsequently utilized it in the domain of distance learning to assess the success and satisfaction of online learning systems [15; 39].  
This study introduced a framework derived from the updated D&M model, with two additional factors affecting student satisfaction: student-student interactions and self-directed learning. The rationale behind the inclusion of student-student interactions or a peer interaction factor was due to their significant role in terms of impacting student satisfaction with online learning. In distance learning environments, students encounter feelings of isolation, lack of motivation, and a lack of belonging to an academic community, which has a negative effect on their satisfaction [40; 41]. However, these feelings could potentially diminish if e-learning systems incorporate features designed to promote interaction and collaboration among students.  
With regard to self-directed learning, it was added as a context-specific factor that was mainly related to online learning. Self-directed learning has been viewed as a factor that impacts student satisfaction with online learning [13]. In traditional educational settings, students rely heavily on instructors to guide the learning process, including setting objectives, choosing teaching methods, and assigning tasks. However, in online learning environments, the reliance on instructors is reduced compared to face-to-face education. Consequently, students must possess the skills to independently direct and manage their learning experiences [42]. Therefore, assessing self-directed learning abilities is a key element when assessing satisfaction with online learning (see Figure 1).
# Fuzzy-Based Time Series Forecasting and Modelling: A Bibliometric Analysis
## 3 Research Methodology  
This work was carried out following the PRISMA statement [31], which constitutes a guideline to report a systematic review while offering enough information to make it reproducible. The steps of performing a systematic review include the formulation of the research questions and methods, the development of a research protocol (methods for literature searching, screening, data extraction and analysis) and the definition of strict inclusion and exclusion criteria for studies, the search of literature papers using bibliographic databases, the extraction of relevant data, the analysis of data and the interpretation of outputs by summarizing the findings and discussing the strengths and weaknesses of the included studies.  
This study has been addressed and organized to answer the following research questions:  
RQ1 Which journals are used the most as sources to disseminate the research results?  
RQ2 What is the evolution of publications per year?  
RQ3 Which countries are the most active?  
RQ4 What are the main research domains?  
RQ5 Who are the most cited authors?  
RQ6 What is the co-occurrence of words?  
RQ7 What are the most influential works?  
With the support of the Web of Science (WoS) database, the collection of records was carried out by cross-searching a comprehensive set of keywords (time series AND fuzzy AND (forecast* OR predict* OR analysis OR modelling)) appearing in the title or keywords of an article. In addition, the abstracts of all identified papers were checked to verify that they were directly related to the topic addressed here. To be included in the review, the articles had to report on the development, adaptation or important additions to existing models and be published in peer-reviewed journals indexed in the 2020 Journal Citation Reports (JCR) of the WoS between 1 January 2017 and 31 December 2021. Papers were excluded if they met one or more of the following criteria: (i) written in a language other than English; (ii) reported in conference proceedings, letters, books, technical reports or dissertations; (ii) published in a non-JCR journal; and (iv) reviews, early access papers or studies without any new proposal.  
Using the sample of articles given by the protocol just described, the data extraction step was designed to gather data pertinent to the systematic review. The data items recorded were the characteristics of the articles (title, authors' name and affiliation, publication year, journal, number of citations received and research domain). Apart from these general study items, we also put down the forecasting models used and some characteristics of the experimental design. These data were then organized in a standardised table to make the analysis of outputs easier. The PRISMA flowchart of the research protocol is shown in Figure 2.
## 3 Method
### Multi-Scale Spatiotemporal Feature Extraction Framework  
#### 3.3.1 Transformer Attention Mechanism Efficacy (Figure 1)  
The attention heatmap reveals asymmetric interactions between query and key tokens (0-15 indices), with high-attention regions (dark purple, values \(>\) 0.85) concentrated along the diagonal (x = y \(\pm\) 2). Statistical analysis identifies 12.7% of token pairs exhibiting significant attention weights (\(p\) < 0.001, Bonferroni-corrected \(t\)-tests), primarily in temporal feature channels (columns 5-9). The off-diagonal sparse activation pattern (light blue, values \(<\) 0.25) confirms the model's capacity for cross-scale feature integration, achieving 98.4% temporal alignment accuracy in ISO 13264 gesture trials. Notably, quadrant III (x = 10-15, y = 0-5) shows 3.2\(\times\) higher attention variance (\(\sigma\) = 0.17 vs. 0.05 baseline), correlating with emergency gesture detection latency improvements (r = 0.79, n = 1204 samples).
## 4 Dataset and Methodology
### Dataset  
The dataset employed in this study spans from 1 January 2021, to 14 September 2023, and is provided by the Energy Exchange Istanbul (EXIST). It includes hourly SMP values, which serve as the primary target variable for prediction. A wide array of predictor variables were incorporated into the models to capture the various factors influencing SMP fluctuations. These predictors include  
* Date Information: Year, month, day, and hour, capturing the time series dynamics;
* Calendar Effects: Categorical variables distinguishing weekdays, weekends, and special days, such as holidays, to capture demand shifts during these periods;
* Macroeconomic Indicators: Financial indicators like USD/TRY and EUR/TRY exchange rates, and the BIST 100 index.  
The dataset was preprocessed to handle missing values, detect and correct outliers, and standardize the scale of predictor variables. The statistics of the dataset show that the SMP ranged from a minimum of 0 TL/MWh to a maximum of 4800 TL/MWh, with an average value of 1713.34 TL/MWh. The standard deviation was 1301.16 TL/MWh.
## 2 Materials and Methods
### Extraction and Analysis of Volatile Compounds  
Volatile compounds were extracted using the headspace solid-phase microextraction (HS-SPME) method. For withtered tea leaves, 2.000 g of freeze-dried powder was weighed into a 20 mL headspace bottle, and 100 mL of ethyl decanoate (1 mL/L) was added as an internal standard. The bottle was sealed, and an SPME fiber (65 um PDMS/DVB) was inserted and heated in a 60 \({}^{\circ}\)C water bath for 60 min to adsorb the aroma components. For black tea, 2.000 g of crushed tea was mixed with 10 mL of boiling distilled water and 100 mL of ethyl decanoate (4 mL/L, internal standard) and extracted using a 50/30 um CAR/DVB/PDMS SPME fiber for 60 min in a 60 \({}^{\circ}\)C water bath [18]. Each sample was analyzed in triplicate.  
Volatile compounds were analyzed using a GC-MS. After extraction, the fiber was immediately inserted into the GC injector for thermal desorption at 250 \({}^{\circ}\)C for 5 min. The analysis was performed under the following conditions: chromatographic column: TG-5 MS (30 m \(\times\) 0.25 mm \(\times\) 0.25 um, Thermo Fisher Scientific, USA); injection mode: splitless; carrier gas: high-purity helium (purity \(\geq\) 99.99%); column flow rate: 1.2 mL/min; inlet temperature: 250 \({}^{\circ}\)C; heating program: initial temperature of 35 \({}^{\circ}\)C held for 2 min, increased to 180 \({}^{\circ}\)C at 4 \({}^{\circ}\)C/min and held for 2 min, then increased to 280 \({}^{\circ}\)C at 15 \({}^{\circ}\)C/min and held for 5 min; ion source (EI) electron energy: 70 eV; ion source temperature: 300 \({}^{\circ}\)C; mass scanning range (m/z):35-400 amu. Volatiles were identified by matching the National Institute of Standards and Technology mass spectral database (NIST 17, USA) and retention index (RI) [21]. The RI for each compound was calculated using the linear formula for C3-C25 N-alkanes. Compounds were further screened based on the retention index difference compared to the NIST database.
# Numerical Study on Cooling Performance of a Steam-Cooled Blade Based on Response Surface Method
## 2 Numerical Methods
### Data Reduction  
The cooling efficiency formula of the blade is as follows:  
\[\varepsilon=\frac{T_{\text{gi}}-T_{\text{w}}}{T_{\text{gi}}-T_{\text{ci}}} \tag{7}\]  
where \(T_{\text{gi}}\) is the inlet temperature of mainstream/K; \(T_{\text{w}}\) is the temperature of blade surface/K; \(T_{\text{ci}}\) is the inlet temperature of steam flow/K.  
The average cooling efficiency formula of the blade is as follows:  
\[\varepsilon_{\text{ave}}=\int\limits_{A}\varepsilon dA/A \tag{8}\]  
where \(A\) is the surface area of the blade.  
The dimensionless temperature formula of the blade surface is as follows:  
\[\eta=T_{\text{w}}/T_{\text{gi}} \tag{9}\]  
The average temperature of the blade is as follows:  
\[T_{\text{wave}}=\int\limits_{A}T_{\text{w}}dA/A \tag{10}\]  
The average dimensionless temperature formula of the blade is as follows:  
\[\eta_{\text{ave}}=\int\limits_{A}\eta dA/A \tag{11}\]  
The parameter to measure the temperature gradient of the blade is the temperature non-uniformity. The higher the temperature non-uniformity is, the more uneven the temperature distribution on the blade surface is. The formula is as follows [29]:  
\[\zeta=\sqrt{\int\limits_{A}(T_{\text{w}}-T_{\text{wave}})^{2}dA/A}/T_{\text{ wave}} \tag{12}\]  
Variation range of the working parameters for the steam-cooled blade is shown in Table 2. It can be seen from the table that the working parameters include mainstream inlet temperature \(T_{\text{gi}}\), mainstream outlet pressure of the \(p_{\text{go}}\), mainstream inlet to outlet  
Figure 5: Numerical method validation of the steam-cooled blade channel: (**a**) Local cooling efficiency distribution; (**b**) Average cooling efficiency.  
pressure ratio \(p_{\rm r}\), temperature ratio of steam to mainstream \(T_{\rm r}\), and flow ratio of steam to mainstream multiplied by 100 \(M_{\rm r}\). The variation range is 680-710 K, 120-160 kPa, 0.6-0.7, 1.3-1.5, 3-8 respectively.
## 2 Materials and Methods
### Calculation of the Interfacial Tension by Contact Angle Measurements  
The contact angle measurements of the unfilled polymers (HDPE00 and AAHDPE00) were conducted using compression molded plates. A plate of each material was produced in the hydraulic vacuum press P200 PV (Dr. Collin GmbH, Maitenbeth, Germany). A steel frame with a thickness of 2 mm and flat polytetrafluoroethylene (PTFE) plates were used during the pressing. New PTFE plates were used for each material in order to obtain the same surface roughness, as this is known to influence the contact angle measurements [36]. The compression molding started with the pre-heating of the material at 180 \({}^{\circ}\)C during 10 min at a pressure of 1 bar, followed by the compression at 50 bar during 10 min and finally by the cooling down to 30 \({}^{\circ}\)C at a pressure of 50 bar.  
The contact angle measurements were conducted at room temperature with the goniometer Kruss DSA100 (Kruss GmbH, Hamburg, Germany) using deionized water and diiodomethane as the test liquids. Fifteen repetitions were performed per combination of polymer and liquid. For the zirconia, we used the contact angle values measured by [PERSON] et al. [37] for plates of pure zirconia, tetragonal zirconia stabilized with yttria and zirconia with 3% of yttria. The contact angle values were employed for the determination of the surface energy components of each material using the Owens, Wendt, Rabel and Kaelble (OWRK) method [38; 39; 40]:  
\[\frac{(1+\cos\theta)\cdot\left(\sigma_{l}^{P}+o_{l}^{D}\right)}{2\cdot\sqrt{ \sigma_{l}^{D}}}=\sqrt{\sigma_{m}^{P}\cdot\sqrt{\frac{\sigma_{l}^{P}}{\sigma_ {l}^{D}}}}+\sqrt{\sigma_{m}^{D}}, \tag{1}\]  
where \(\theta\) is the contact angle between the evaluated material (zirconia, AAHDPE or HDPE) and the test liquid; \(\sigma_{l}^{P}\) and \(\sigma_{l}^{D}\) are the polar and disperse fractions of the surface energy test liquids, respectively, both known; \(\sigma_{m}^{P}\) is the polar fraction of the material surface energy, which is calculated as the slope of the fitted line; and \(\sigma_{p}^{D}\) is the disperse fraction of the material surface energy, calculated as the intercept between the fitted line and the axis. The interfacial tension between the zirconia and each polymer was calculated [41; 42] with the equation:  
\[\gamma_{\text{zp}}=\sigma_{z}^{P}+\sigma_{z}^{D}+\sigma_{p}^{P}+\sigma_{p}^{D }-2\left(\sqrt{\sigma_{z}^{D}\cdot\sigma_{p}^{D}}+\sqrt{\sigma_{z}^{P}\cdot \sigma_{p}^{P}}\right), \tag{2}\]  
in which the subscript \(z\) refers to the zirconia and the subscript \(p\) to the polymer (HDPE or AAHDPE).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Polymer** & **Acrylic Acid Content (mol\%)** & **Zirconia Content (vol\%)** \\ \hline HDPE00 & HDPE & 0 & 0 \\ HDPE30 & HDPE & 0 & 30 \\ AAHDPE00 & AA-HDPE & \(>\)5 & 0 \\ AAHDPE30 & AA-HDPE & \(>\)5 & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Compounds prepared and nomenclature.
# Camera Geolocation Using Digital Elevation Models in Hilly Area
## 4 Propose Method
### Building DEM Skyline Feature Database
#### 4.3.2 Divide into 24 Subsections  
Similar to the method described by the authors of [25], at each sample point, we rendered twenty-four 30\({}^{\circ}\) images at 15\({}^{\circ}\) offsets, covering the full 360\({}^{\circ}\) panorama at the location. Thus, the DEM skyline database was divided into 24 subdatabases according to angles, thereby accelerating the speed of retrieving and matching of the corresponding skyline.
# Hydrogen from Depleted/Depleting Hydrocarbon Reservoirs: A Reservoir Engineering Perspective
## 6 H\({}_{2}\) Generation in DORs
### Reservoir Engineering Aspects
#### 6.2.3 Associated Risks  
The risks are primarily due to the geomechanical issues caused by thermal effects as the reservoir temperature is increased up to 1000 \({}^{\circ}\)C [93]. Under elevated T and P, large changes in porosity, permeability, and compressibility occur, which may potentially create leakage pathways to the surface by inducing existing fractures and/or initiating new fractures both in the reservoir as well as in the cap rock. Since only air is injected in ISC, the injection facilities are of less importance than in SF in terms of thermal effects.  
If the reservoir rock contains carbonates, the thermal decomposition of carbonates is typical, creating one gaseous and one solid reaction product (CaCO3\(\leftrightarrow\)CaO + CO2). The reaction is reversible and the equilibrium shifts to the right when the temperature increases due to a thermal operation, e.g., ISC, or when the partial pressure of CO2 in the gas decreases [94]. These phenomena are crucially risky with respect to maintaining the integrity of the reservoir and cap rock for storing CO2 in situ.  
H2S is the most reported gas component of ISC operations [77; 80; 82; 87; 89; 95]. The sulfur content of crude oil and/or of the reservoir is converted to H2S, which is an HSE issue for field management.  
In addition to the risks discussed above, the ISC operation itself is a complex process to operate; the issues in connection with ignition and the up-scaling of O2 needs encapsulate some of the questions to be clarified specific to the fields under consideration. Over the course of the 60 years until 2013, a total of more than 270 ISC field pilots were conducted, with over 200 of them having taken place in the USA. However, it is likely that only 10-20% of these pilots have reached the commercial phase, of which very few have achieved sustainable success [69; 96]. The declining interest in ISC in recent years is probably a consequence of the large number of early failures in field trials. However, most of these failures have been retrospectively attributed to inappropriate reservoir selection [52; 75]. In order to minimize the risks, laboratory and numerical studies should be carried out to quantify the above-mentioned critical processes and operating parameters as is common practice in reservoir engineering [16; 79; 80; 97; 98].
# Testbed for Experimental Characterization of Indoor Visible Light Communication Channels
## 2 Indoor Visible Light Channel
The contribution of a ray after its \(b\)th bounce to the total received power is [14]:  
\[P_{\rm R}^{(b)}=R_{s}^{\prime}(\phi^{\prime},\theta^{\prime})A_{\rm eff}( \phi^{\prime})\frac{P_{E}}{N}\times\int_{\lambda}\rho_{1}(\lambda)\rho_{2}( \lambda)\dots\rho_{k}(\lambda)p^{\prime}(\lambda)d\lambda/d^{\prime 2}. \tag{4}\]  
Here, \(\rho_{1}(\lambda)\rho_{2}(\lambda)\dots\rho_{k}(\lambda)\) are the reflection coefficients of the corresponding surfaces against which it has collided, \(d^{\prime}\) is the distance between the receiver and the last collision point (see Figure 1), \(R_{s}^{\prime}(\phi^{\prime},\theta^{\prime})\) is the normalized reflection pattern of this last surface (independent of \(\rho(\lambda)\) and \(P_{l}\)) and \(A_{\rm eff}(\phi^{\prime})\) the effective area at observation angle \(\phi^{\prime}\). The remaining parameters keep the same meaning as in Equation (2). Each lamp is composed of \(N_{\rm LED}\). Therefore, the total received power due to the \(l\)th WLED lamp can be calculated as the sum of the LOS component and contributions of the reflected light of rays generated from each of its LEDs:  
Figure 1: Illustration of the indoor VLC environment with LOS contribution of a single LED and its power contribution after off a wall. \[h_{l}(t)=\mathcal{R}\sum_{k=0}^{K_{M}-1}p_{k}^{(l)}\delta(t-k\Delta t). \tag{5}\]  
Here, \(\mathcal{R}\) is the detector _responsivity_, \(\delta(t)\) is the Dirac's delta function and \(p_{k}^{(l)}\) is the total power contribution (of all the \(N_{\text{LED}}\) LEDs composing \(l\)th lamp) received at \(k\)th time interval of the total of \(K_{\Delta t}\) (of duration \(\Delta t\)) into which simulation time is discretized. From Equation (5) is inferred that the distribution of LEDs in the lamp affects the obtained impulse response. In this work, we have considered small square-shape lamps with uniform distribution of their LEDs, thus the combined effect of LEDs is practically identical to having a single LED source of total emitted power \(P=N_{\text{LED}}\times P_{\text{E}}\), but any other distributions in larger lamps could be possible in practice. The noise in a VLC link is mainly composed of shot noise due to the own transmitted signal and the ambient light if this reaches the interior of the room during daylight hours, plus a component of thermal noise [18]. Theoretically, for a well-designed receiver, the shot noise dominates and signal-to-noise ratios (SNR) above 60 dB can be achieved for room illumination standard levels above 400 lx [14; 23]. However, experimental tests have usually demonstrated SNR up to \(\sim\)30 dB due to thermal noise and design impairments [24; 25], which limits the average channel capacity to 4-5 bit/s/Hz [26].