esa-sceva/satcom-chunk-collection huggingface dataset:
# From Ethnographic Research to Big Data Analytics--A Case of Maritime Energy-Efficiency Optimization
## 4 Applying Big Data Analytics for Maritime Energy Efficiency
### Constructing Prediction Models  
Two approaches on how machine learning is used to process the data described in Section 4.1 are considered, in order to provide a predictive-analytical foundation for future energy-efficiency monitoring tools. The selection of the input and output parameters from these data sets are described in this section.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dimension** & **Total** & **Removed** & **Remaining in \%** \\ \hline Marine Diesel Oil (MDO) Main Engine (ME) Group 1 & 50,958 & 44 & 99.912 \\ Marine Diesel Oil (MDO) Main Engine (ME) Group 2 & 50,958 & 144 & 99.716 \\ Apparent Wind Speed (AWS) (m/s) & 50,767 & 35 & 99.993 \\ Speed Through Water (STW) (knots) & 45,419 & 31 & 99.993 \\ Speed Over Ground (SOC) (knots) & 50,785 & 20 & 99.996 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Amount of remaining data points per series after cut-off at 47%.  
Figure 4: Number of remaining samples per dimension after glitch cut-off.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dimension** & **Min** & **Max** & **Mean** & **Std** \\ \hline AWS (m/s) & 0.000000\(|\)\(-\)1.918170 & 43.038380\(|\)4.194465 & 13.505619\(|\)0.000000 & 7.040889 1.000000 \\ normalized SOG (knots) & 0.000000\(|\)\(-\)1.490091 & 359.99000
# GUI for Analysis of Parameters, Accurate Design and Optimization of Microstrip Filters
## 6 Results and Discussion
### Additional Insights  
This is the first version of MicroStripOptim, a software tool designed for the study of low-pass microstrip filters. It offers several options to analyze various design results, based on the permissible limits of \(W_{\text{C}}\) and \(W_{L}\), enabling users to visualize the allowed ranges for filter design. In terms of the design process, every filter generated automatically includes frequency shift compensation. The software generates scattering parameter graphs, and all relevant filter parameters are displayed in a table for easy reference. Additionally, the GUI provides an option to obtain an optimized filter, starting from a fixed filter size.  
Some limitations of the software are the following:  
* This version of the GUI only considers the design of low-pass microstrip filters. Future versions will include high-pass, band-pass and band-stop filters.
* The GUI lacks an option to generate a sketch of the designed filter.
* Only six types of filters were included, three Butterworth and three Chebyshev.  
Figure 21: Comparison of the transmission response of Filter 1 and Filter 2 (optimized filter). Filter 2 was designed to have the same total length as Filter 1 while maximizing the maximum attenuation.  
* The values obtained in the \"TL const\" optimization are based on the data from the \"Simulate\" button. Therefore, if smaller values are set for \(W_{Lstep}\) and \(W_{Cstep}\), a different set of optimal parameters can be obtained.  
To conclude, an additional advantage is that the code is available in a repository (see Section 5), allowing readers to download it and make modifications as needed.
## 2 Literature Review
### Storyliving Scenario Structure  
The SL scenario structure is designed based on interactions between the user and the narrative flow [26; 27]. Five types of structure are considered here: String of Pearls, Parallel, Interpolated, Dynamic, and Branching (Table 1).  
The String of Pearls structure is easy to understand but has weak connectivity and consistency between episodes [26; 29]. The Parallel structure can lead to confusion as multiple episodes progress simultaneously [26; 30]. The Interpolated structure struggles to maintain length and consistency in the narrative [26; 31]. The Dynamic structure, although  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Type** & **Structural Diagram** & **Description** & **Advantages** & **Disadvantages** \\ \hline String of Pearls & & Each episode within the narrative is independent, similar to pearls on a string. & Narrative is easy to understand. & Weak connections and poor consistency between episodes. \\ \hline Parallel & & Each episode is independent yet related. & Maintains interest through a complex narrative. & Can be confusing because of simultaneous progress. \\ \hline Interpolated & & Composes a larger narrative from smaller stories. & Offers various choices and paths. & Difficult to maintain narrative length and consistency. \\ \hline Dynamic & & Narrative evolves with real-time interaction and feedback. & Provides a tailored experience based on audience choices and actions. & High technical requirements. \\ \hline Branching & & Leads to multiple endings based on choices. & Provides high immersion and engagement through diverse choices. & Complex to design and manage. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Types of storyliving scenario structures (Adapted from [26; 28]).  
based on real-time interaction and feedback, has high technical demands [26; 31]. These requirements can be challenging to implement in HMD VR environments because of problems such as VR sickness.  
Of these options, the Branching structure was selected for this study. This structure enables the narrative to develop in different directions based on user choices, which enhances immersion and engagement. It also offers new experiences in repeated playthroughs because of its multiple possible endings [32]. Although the Branching structure can be complex to design and manage, it serves this study's focus on the creation of diverse and personalized user experiences. Through the development of a nonlinear SL VR scenario using the Branching structure, this study seeks to provide an immersive experience that maximizes users' control over the progression of the story.
## 3 Numerical Simulations
### Mathematical Model
#### 3.2.5 Definitions of Characteristic Parameters  
For the convenience of analysis, the production temperature and accumulated heat energy were used to characterize the heat extraction performance of EGS-E. The temperature at the end of the tunnel and the SRV can be considered as the production temperature of the working fluid [39]. The accumulated thermal energy refers to the cumulative amount of heat extracted by the working fluid during operation [40], which is calculated as:  
\[Q=\int_{0}^{t}{(q_{\text{out}}\;\rho_{w,\text{out}}\;c_{p,w,\text{out}}\;T_{ \text{out}}\;-q_{\text{in}}\;\rho_{w,\text{in}}\;c_{p,w,\text{in}}\;T_{in})dt} \tag{22}\]  
where \(Q\) represents the accumulative thermal energy, \(q\) is the flow rate, and \(T\) is the production temperature. The subscripts in and out represent the inlet and outlet.
# Eight Element Wideband Antenna with Improved Isolation for 5G Mid Band Applications
###### Abstract  
Modern wireless communication systems have undergone a radical change with the introduction of multiple-input multiple-output (MIMO) antennas, which provide increased channel capacity, fast data rates, and secure connections. To achieve real-time requirements, such antenna technology needs to have good gains, wider bandwidths, satisfactory radiation characteristics, and high isolation. This article presents an eight-element CPW-fed antenna for the 5G mid-band. The proposed antenna consists of eight symmetrical, modified circular monopole antennas with a connected CPW-fed ground plane that offers 24 dB isolation over the operating range. The antenna is further investigated in terms of the scattering parameters, and radiation characteristics under both the \(x\) and \(y\)-axis bending scenarios. The antenna holds a volume of 83 \(\times\) 129 \(\times\) 0.1 mm\({}^{3}\) and covers a measured impedance bandwidth of 4.5-5.5 GHz (20%) with an average gain of 4 dBi throughout the operating band. MIMO diversity performance of the antenna is performed, and the antenna exhibits good performance suitable for MIMO applications. Furthermore, the channel capacity (CC) is estimated, and the antenna gives a value of 41.8-42.6 bps/Hz within the operating bandwidth, which is very close to an ideal 8 \(\times\) 8 MIMO system. The antenna shows an excellent match between the simulated and measured findings.  
MIMO; sub-6 GHz; isolation; bending; channel capacity Article
## 2 Theoretical Model
### Optical-Electrical-Thermal Model of Non-Uniform Concentration
The other is the area that is blocked by the metal grid line where the concentration cannot reach on the cell surface. In this case, only two diodes exist and the current source equals zero. The equivalent circuit network model is calculated by NGSPICE. The input parameters include the series resistance Rs, the reverse saturation current of three sub cells I\({}_{011}\) and I\({}_{022}\) and the photogenerated current source \(I_{Li}\). The reverse saturation current can be obtained by fitting the relationship curve between the open-circuit voltage and light intensity. The series resistance of triple-junction cell can be unified with one resistance Rs determined according to the light and dark characteristic curve. As the short-circuit current of each junction cannot be measured separately, in this paper the photogenerated current is obtained indirectly through the external quantum efficiency (EQE) of the three sub cells [29]. The external quantum efficiency is defined as the ratio of the number of photogenerated carriers that contribute to the short-circuit current generated by illumination to the number of photons incident on the surface of the cell. \[\text{EQE}(\lambda)=\frac{\text{I}_{\text{L}}(\lambda)}{\text{q}\varphi( \lambda)}, \tag{9}\]  
It can be seen from Equation (8) that with the increase in temperature, the band gap of each sub cell decreases, which increases the absorbed wavelength range and changes the EQE at different temperatures. Here, we get the triple-junction cell EQE characteristic curve by measuring it at 25 \({}^{\circ}\)C. The EQE at other temperatures can be calculated by the linear interpolation method mentioned in the literature [29]. According to the optical model, the irradiance distribution incident on the battery surface can be calculated, which is the function of position of the cell surface \((x,y)\). If the density of the irradiance power on the cell is \(G(\lambda)\), it can be expressed as follows:  
\[G(\lambda)=f(x,y)\hskip 28.452756 pt(x_{1}<x<x_{1}+\Delta x,y_{1}<y<y_{1}+ \Delta y), \tag{10}\]  
Figure 2: (a) \(\text{GalnP}/\text{GalnAs}/\text{Ge}\) triple-junction solar cell; (b) Double diode circuit network model. Photon energy is calculated by \(hc/\lambda\) (where c is the speed of light and h is the Planck constant). The photogenerated current of a certain wavelength range [\(\lambda_{1},\lambda_{2}\)] at a given temperature can be expressed as follows:  
\[I_{L}=\int_{\lambda\Omega}^{\lambda 2}\frac{qEQE(\lambda)G(\lambda)\lambda}{hc}d\lambda \tag{11}\]
## 4 Free-Space and On-Body Link Budget
### Free-Space Communication  
As a reference, measurements of path loss (S\({}_{21}\)) were initially carried out in free-space in a parking space, as illustrated in Figure 13. Tx and Rx antennas were installed on two tripods at the height of 1 m above the ground, with a proper vertical alignment. Besides, in S\({}_{21}\) measurement, the distance (\(r\)) was set at 2 m, 5 m, 8 m, and 11 m to estimate the path loss in an open area. Due to large distances, it is not feasible to conduct a full-wave simulation; instead, we considered the open space parking lot measurement to estimate  
Figure 12: Measurement equipment setup for the path loss (LNA: low noise amplifier; BPF: band pass filter; PA: power amplifier).  
the path loss exponent. The log-distance path loss model was considered, which can be expressed as:  
\[PL_{dB}(r)=PL(r_{0})+10 nlog\left(\frac{r}{r_{0}}\right), \tag{2}\]  
where \(n\) is the path loss exponent, \(r_{0}\) is the close-in reference distance, and \(r\) is the separation between Tx and Rx [33]. Using \(r_{0}=2\) m and \(r=11\) m, we obtained an average \(n=2.95\), which is well-matched with the urban areas [33; 34].
## 3 Comparative Analysis of Existing Evaluation Methods
### Existing Evaluation Methods
#### 3.1.1 [PERSON]'s Liquefaction Probability Assessment Method  
[PERSON] et al. (2018) applied the Bayesian probability method to liquefaction probability assessment, exploring the SPT, CPT, and \(V_{s1}\). This discussion centers on the shear wave velocity method. Given the complexity of the original \(CRR\) (cyclic resistance ratio) empirical correction steps, [PERSON] et al. (2016) applied artificial neural networks to establish a new \(CRR\) equation, resulting in a new \(CRR\) formula based on the clean sand equivalence of stress-corrected shear wave velocity (\(V_{s1,cs}\)):  
\[CRR=0.013\left[\frac{V_{s1,CS}}{100}\right]^{4}+\frac{1}{225-V_{s1,CS}} \tag{1}\]  
\(V_{s1,cs}\) is calculated as follows:  
\[V_{s1,cs}=KV_{s1} \tag{2}\]  
\(K\) is the adjustment coefficient obtained by regression analysis of the \(V_{s1}\)-\(CRR\) data points, calculated as follows:  
\[K=1+(FC-5)\left[0.0090-0.0109\left(\frac{V_{s1}}{100}\right)+0.0038\left(\frac {V_{s1}}{100}\right)^{2}\right],5\%\leq FC\leq 35\% \tag{4}\]  
\[K=1+30\left[0.0090-0.0109\left(\frac{V_{s1}}{100}\right)+0.0038\left(\frac{V_ {s1}}{100}\right)^{2}\right],FC\geq 35\% \tag{5}\]  
\(V_{s1}\) is calculated as follows:  
\[V_{s1}=V_{s}\left(\frac{P_{a}}{\sigma_{\mathrm{v}^{\prime}}}\right)^{0.26} \tag{6}\]  
\(P_{a}\) is the standard atmospheric pressure and \(\sigma_{\mathrm{v}^{\prime}}\) is the vertical effective stress.  
Since many geotechnical engineers prefer to represent liquefaction potential using the factor of safety (\(F_{S}\)), [PERSON] et al. established the relationship between \(F_{S}\) and liquefaction probability through regression analysis of the Andrus sand and silt database (2016), resulting in the following liquefaction probability formula for sand and silt:  
\[P_{L}=\frac{1}{\left[1+\left(\frac{F_{S}}{072}\right)^{3.1}\right]} \tag{7}\]  
\(P_{L}\) is the liquefaction probability. [PERSON] conducted logistic regression analysis on the \(V_{s}\)-based liquefaction database and compared the probabilistic correlation curve derived from the Bayesian mapping function with those obtained from logistic regression. The results demonstrated a good consistency, verifying the validity of the proposed Bayesian probability method.
# In Situ and Partial In Situ Synthesis of Cellulose Magnetic/Maghemite Composites
## 3 Results  
### Structural Characterization  
#### 3.1.1 FTIR Analysis  
The structure of the pristine cellulose and composites was first assessed by infrared spectroscopy. Infrared spectra of pristine cellulose (C) and CM0 and CM15 composite samples are shown in Figure 1.  
For pristine cellulose, peaks are observed at 3346 and 3385 cm\({}^{-1}\) (OH stretching vibration), 2899 cm\({}^{-1}\) (CH symmetrical stretching), 1647 cm\({}^{-1}\) (OH bending of absorbed water), 1433 cm\({}^{-1}\) (HCH in-plane bending vibration and/or CH\({}_{2}\) scissoring motion and/or CH\({}_{2}\) symmetric bending at C6), 1371 cm\({}^{-1}\) (CH in-plane bending), 1317 cm\({}^{-1}\) (CH\({}_{2}\) rocking vibration at C6), 1163, 1111 cm\({}^{-1}\) (COC stretching), 1059 cm\({}^{-1}\) (CC stretching), 1030 cm\({}^{-1}\) (CO at C6), 897 cm\({}^{-1}\) (COC stretching at \(\beta\)-(1\(\rightarrow\)4)-glycoside linkages), 667, and 613 cm\({}^{-1}\) (8 COH out-of-plane bending) [8; 25; 26; 27]. Some peaks in ferromagnetic samples are practically unchanged, while ultrasonic irradiation also causes certain variations. The Fe-O stretching band of the magnetite located at 560 cm\({}^{-1}\) and maghemite at 580 cm\({}^{-1}\) in the spectrum of CM0 and CM15 composites is superposed on cellulose absorptions in the 400-700 cm\({}^{-1}\) region [5; 7; 8].  
Table 2 shows the results of the TCI, LOI, and HBI calculated using Equations (1)-(3). The TCI is proportional to the crystallinity degree of cellulose, while the LOI is about the overall degree of order in cellulose and the hydrogen bond intensity. Considering the chain mobility and bond distance, the amount of bound water HBI refers to the crystal system and the degree of intermolecular regularity.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Sample** & **LOI** & **TCI** & **HBI** \\ \hline C & 1.95 & 1.33 & 2.16 \\ CM0 & 1.12 & 1.08 & 2.52 \\ CM15 & 1.16 & 1.14 & 2.27 \\ \hline \end{tabular}
\end{table}
Table 2: Lateral order index (LOI), total crystallinity index (TCI), and hydrogen bond intensity (HBI) for pristine cellulose C and sonicated composites.  
Figure 1: Infrared spectra for cellulose (C) and ferromagnetic composites (CM0 and CM15).  
From Table 2, a decrease can be observed for the composites in the crystallinity parameters, as well as an increase in the intensity of the hydrogen bond. Low values of crystallinity parameters (LOI and TCI) indicate degradation of cellulose in the presence of iron oxides in the crystalline region due to ultrasonication. The HBI index increases slightly, indicating the potential presence of new hydrogen bonds.
## 3 Results
### Comprehensive Performance Evaluation
#### 3.2.6 Semantic Quality Assessment with BERTScore
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Direction**} & \multirow{2}{*}{**Lang. Pair**} & \multicolumn{4}{c}{**GPT-4o**} & \multicolumn{4}{c}{**Llama-3-70B**} \\ \cline{3-10}  & & \multicolumn{2}{c}{**Random**} & \multicolumn{2}{c}{**MuRXLS**} & \multicolumn{2}{c}{**Random**} & \multicolumn{2}{c}{**MuRXLS**} \\  & & **1-Shot** & **2-Shot** & **1-Shot** & **2-Shot** & **1-Shot** & **2-Shot** \\ \hline \multirow{6}{*}{\(X\rightarrow\)\(EN\)} & Th-En & 0.75 & 0.77 & 0.79 (+0.04) & **0.81** (+0.04) & 0.73 & 0.72 & 0.77 (+0.04) & **0.79** (+0.07) \\  & Gu-En & 0.74 & 0.76 & 0.78 (+0.04) & **0.80** (+0.04) & 0.74 & 0.75 & 0.79 (+0.05) & **0.80** (+0.05) \\  & Ma-En & 0.74 & 0.77 & 0.79 (+0.05) & **0.80** (+0.03) & 0.72 & 0.72 & 0.77 (+0.05) & **0.78** (+0.06) \\  & Pa-En & 0.76 & 0.78 & 0.79 (+0.03) & **0.81** (+0.03) & 0.72 & 0.72 & **0.78** (+0.06) & 0.78 (+0.06) \\  & Bu-En & 0.77 & 0.78 & 0.81 (+0.04) & **0.82** (+0.04) & 0.72 & 0.72 & 0.80 (+0.08) & **0.80** (+0.08) \\  & Si-En & 0.76 & 0.78 & 0.80 (+0.04) & **0.83** (+0.05) & 0.72 & 0.73 & 0.79 (+0.07) & **0.82** (+0.09) \\ \cline{2-10}  & **Average** & 0.75 & 0.77 & 0.79 (+0.04) & **0.81** (+0.04) & 0.73 & 0.73 & 0.78 (+0.06) & **0.80** (+0.07) \\ \hline \multirow{6}{*}{\(EN\rightarrow\)\(X\)} & En-Th & 0.75 & 0.76 & 0.77 (+0.02) & **0.79** (+0.03) & 0.73 & 0.74 & 0.74 (+0.01) & **0.76** (+0.02) \\  & En-Gu & 0.71 & 0.71 & 0.75 (+0.04) & **0.76** (+0.05) & 0.67 & 0.72 & 0.74 (+0.07) & **0.76** (+0.04) \\ \cline{1-1}  & En-Ma & 0.74 & 0.73 & 0.77 (+0.03) & **0.79** (+0.06) & 0.71 & 0.70 & 0.73 (+0.02) & **0.77** (+0.07) \\ \cline{1-1}  & En-Pa & 0.70 & 0.60 & 0.71 (+0.01) & **0.73** (+0.13) & 0.66 & 0.68 & 0.70 (+0.04) & **0.75** (+0.07) \\ \cline{1-1}  & En-Bu & 0.71 & 0.71 & 0.74 (+0.03) & **0.78** (+0.07) & 0.67 & 0.68 & 0.72 (+0.05) & **0.75** (+0.07) \\ \cline{1-1}  & En-Si & 0.74 & 0.75 & 0.76 (+0.02) & **0.81** (+0.06) & 0.72 & 0.71 & 0.75 (+0.03) & **0.76** (+0.05) \\ \cline{1-1} \cline{2-10}  & **Average** & 0.75 & 0.76 & 0.77 (+0.03) & **0.79** (+0.07) & 0.69 & 0.70 & 0.73 (+0.04) & **0.76** (+0.05) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: BERTScore F1 comparison between MuRXLS and Random approaches across language pairs. Values in red indicate improvements of MuRXLS over the Random (Random) baseline. **Bold** values indicate the highest BERTScore for each language pair and model.
## 4 Results
### Generation of Landslide Susceptibility Maps  
The final model results produce a landslide susceptibility map, which can be used as an effective tool for managing future landslide occurrences. These maps were constructed following these steps. Firstly, the whole study area was converted to pixels using ArcGIS (Esri, CA, USA) software. Then, the training dataset was trained in a training phase, then all pixels were predicted based on the learned trend and assigned in unique indexes. Next, all indexes were classified based on the natural breaks (Jenks) classification scheme [62; 63]. Finally, these indexes for the three models were classified into four classes: Low, moderate, high, and very high susceptibility (Figures 5-7). The percentage of area covered by low, moderate, high, and very high susceptibility for the three models is shown in Figure 8. For the BLRK model, these areas are 28.94%, 26.86%, 23.58%, and 20.63%, respectively. For the KLR model, they are 17.68%, 32.03%, 32.53%, and 17.77%, respectively, and for SVM, the area percentages are 24.59%, 28.29%, 27.50%, and 19.62%, respectively.  
Figure 4: Importance of conditioning factors.  
Figure 5: Landslide susceptibility map using the bagging-based two-class kernel logistic regression (BKLR) model.  
Figure 6: Landslide susceptibility map using the kernel logistic regression (KLR) model.
# Development of Energy Storage Systems for High Penetration of Renewable Energy Grids
## 2 Battery Energy Storage System (BESS) Frequency Regulation Technology  
The existing application technologies for frequency regulation on the Taiwan power system include such as static regulation (sReg) and dynamic regulation (dReg), among others. The novel virtual inertia control strategy has not yet been implemented, but it has been proposed by many scholars. The three strategies have different response times, deadband ranges, frequency response functions, and frequency ranges. Next, the strategies are introduced.