esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### Definitions and Application of Digital Twins  
Since 2016, the adoption of DT technology has steadily increased within the construction industry. In this context, numerous scholars have proposed varying definitions for DTs within the built environment; despite this, there is a lack of consensus regarding this definition [28]. For example, some scholars have interpreted this concept as a virtual representation of a physically built asset, e.g., [PERSON] et al. [5], achieved through digital-twin-enabling technologies, including sensors, communication networks, and 3D models [29]. An additional study conducted by [PERSON] et al. [28] compares ten various definitions of the term \"Digital Twins\" found in the literature. The findings indicate that the DTs approach facilitates real-time updates and enables bidirectional coordination, ensuring that the virtual model accurately reflects a replica of the physical asset.  
This is attributed to the transformative abilities of Industry 4.0, which is regarded as the convergence of nine digital technologies, including cybersecurity, big data and analytics, simulation technologies, augmented reality, 3D printing, cloud computing,advanced robotics, the Internet of Things (IoT), and integrated horizontal and vertical systems [30]. Consequently, applying DTs across various industries has demonstrated significant progress and advantages. Conversely, the adoption of DTs in the construction sector remains relatively limited compared to the manufacturing industry [20]. In addition, the applications of DTs can be divided into three purposes: simulation, monitoring, and control [31].  
The study employed a structured literature review analyzing recent academic studies to identify benefits/challenges. This combined desk research and stakeholder insights to contextualize findings within Saudi Arabia's construction sector.  
Moreover, several studies focused on exploring the concept of DTs in various contexts. This allowed us to develop a comprehensive understanding of the current state of the digital transformation, including the prevailing challenges, associated solutions, and emerging needs to enhance its physical realism. DT technology is applied across various sectors, including healthcare, meteorology, manufacturing and process technology, education, urban transportation, and the energy industry [4]. Notably, the literature identifies the development of advanced prognostics and health management (PHM) systems as the most prevalent application of DT technology [32].  
In the construction industry, DTs are recognized as an up-and-coming advanced digital platform that generates virtual representations of tangible assets [33] This platform operates on the foundation of the IoT [34]. This implies that the successful implementation of DTs depends on the degree of IoT integration employed and the volume of information necessary to create and refine the model [35].
# Time Series Clustering Model based on DTW for Classifying Car Parks
## 2 Data and methodology  
### Dataset  
The data collected from 27 car parks operated by the Birmingham City Council NCP ([[https://archive.ics.uci.edu/ml/datasets/Parking+Birmingham](https://archive.ics.uci.edu/ml/datasets/Parking+Birmingham)]([https://archive.ics.uci.edu/ml/datasets/Parking+Birmingham](https://archive.ics.uci.edu/ml/datasets/Parking+Birmingham))) is chosen as the dataset, which contains 4 features, including car park's name, capacity, occupancy, the date, and time of the data collected. The dataset indicates the change of the occupancy rate of these 27 car parks during 2016.10.4 to 2016.12.19, which means that it is obviously time series data. Then the data of the car parks in November, 2016 (see Figure 1) is chosen as samples for classifying the car parks, the reasons can be described in two aspects, one is that the data of car parks is time series, so it is essential to select a continuous time data; another is that car park data in different seasons are clearly different, so the sample data cannot cover a long period.  
In Figure 1, the horizontal coordinate is the hour of the date from 2016.11.01 to 2016.11.30, and the vertical coordinate is the occupancy rate of individual car park. The top right corner of each curve indicates the maximum capacity of this car park. Figure 1 shows that the change of occupancy rate of these car parks evidently has two periodic characteristics, namely daily periodicity and weekly periodicity. Subsequently, we can classify the car parks by their occupancy rate or their weekly occupancy rate. However, the occupancy rate of car parks on weekend and workday are significantly different. In order to ensure the validity of clustering results, it is very critical to classify car parks by weekly occupancy rate. In order to test whether holidays will affect the results of car park clustering,  
Figure 1: The occupancy rate of 27 car parks in Nov., 2016.  
two consecutive time series data, data1 from Nov. 13 (Sunday) to Nov.19 (Saturday) and data2 from Nov. 20 (Sunday) to Nov. 26 (Saturday), are selected as training data.
# Lp-Norm Inversion of Gravity Data Using Adaptive Differential Evolution
## 4 Simulation Tests
### The Effect of \(p\) Value  
For the Lp-norm based gravity inversion, the value of \(p\) actually plays a key role in determining the density distribution and shape of density source. To get a proper \(p\) value, the U prism model is selected to conduct the inversion with different \(p\) values. In this  
Figure 4: The inversion results of the model shown in Figure 2b with different \(s_{f}\). (**a**) \(s_{f}=0\), (**b**) \(s_{f}=1\), (**c**) \(s_{f}=2\), (**d**) \(s_{f}=3\).  
Figure 5: The iterative process corresponding to each \(s_{f}\). (**a**) the fitting process for the rectangular prism, (**b**) the fitting process for the dipping prism.  
paper, the candidate values of \(p\) are 1, 1.1, 1.3, 1.5, 1.7, and 2, respectively. The inverted results are shown in Figure 6. We can observe that the density obtained by a larger \(p\) value is lower than the one with a smaller \(p\). Besides, by comparing with the designed model, the large \(p\) values, especially when the \(p\) is close to 2, will increase the range of the predicted density model, and make the boundaries of model blur. On the contrary, a smaller \(p\) is helpful in describing the shape of the density model accurately, but it may weaken the continuity of the obtained model. Overall, in our opinion, it is appropriate to set \(p\) less than 1.5.
# Energy-Efficient Subcarrier Pairing and Power Allocation for DF Relay Networks with an Eavesdropper
## 7 Conclusions  
In this work, we resort to the joint design of subcarrier pairing and power allocation to improve the EE of the DF relay network in the presence of an active eavesdropper. The original problem is highly non-convex. We transform this problem into a convex optimization problem. A dual decomposition-based, iterative algorithm for joint subcarrier pairing and power allocation is investigated for maximizing the EE. A suboptimal algorithm is also investigated to solve the problem with relatively lower complexity. The simulation results demonstrate that the EE performance of the proposed algorithms can be enhanced by increasing \(K\) in the joint design.  
This work was supported by the Ministry of Science and Technology of Taiwan under Grant MOST 106-2221-E-008-014.  
[PERSON] and [PERSON] performed the simulation, analyzed the results and wrote the paper; [PERSON] and [PERSON] proposed the problem and helped in designing the system model and the problem formulation.  
The authors declare no conflict of interest.
## 3 Case Study--The ANN Models for Load Forecasting  
Electricity infrastructure is a crucial component of modern society and ranks among the most vital of the Critical Infrastructures (CIs). In Norway, electric grids play a significant role in supplying electricity to millions of households and businesses through an intricate and susceptible network comprising power plants, transmission lines, and distribution networks. Electricity is the primary energy source in Norway, with a consumption of 104,438 Terawatt-hours (TWh) in 2021, heavily influenced by prevailing weather conditions. In this study, the electricity consumption in Norway serves as input data for predicting Artificial Neural Network (ANN) models. Using ANN models requires some specific procedures, regardless of the problem and the task involved, and can be divided into six steps, as shown in Figure 8. It starts by collecting the data around the problem at state in the required format to build, train, and use the ANN models for any given task.  
Shallow ANN models explained in Section 2 with almost the same number of layers and the same number of trainable parameters are constructed, trained, and tested following the steps in Figure 8 and will be discussed in detail in the following sections.
# Sensorless Air Flow Control in an HVAC System through Deep Learning
## 3 Predicting Static Pressure Using Deep Learning
### Proposed Deep Learning Model
#### 3.2.2 Hyperparameters  
For prediction using time-series data, the interval of the data used for learning has a strong influence on the prediction result. If we use an interval that is too long or too short for learning, it is difficult to make accurate predictions. As shown in Figure 12, it can be seen that the error rate becomes larger based on the case where the timestep is 10. Because determining the number of time steps depends on the prediction application, we derived the optimal interval length for our application through experimental measurements.  
Using metrics such as the Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE)of the static pressure prediction while changing the time step, we observed that the error was frequently minimized at a time step size of 10. Table 1 shows our experiment with the time steps used for prediction. For a sampling interval of 1 min, the minimum error rate was achieved with 10 time steps. Therefore, in our model, a total of 10 time steps from \(t-9\) to \(t\) min were used to predict the static pressure at time \(t\).  
Increases in the number of LSTM cells in the first hidden layer and the number of neurons in the second hidden layer increase only the learning time, and the accuracy of prediction does not improve. The early stopping method was used to prevent overfitting, and the batch size was selected to be 72,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Time Step** & **Units of LSTM** & **Batch Size** & **MAE** & **MAPE** & **RMSE** \\ \hline
10 & 50 & 36 & 0.197 & 1.715 & 0.451 \\  & & 72 & 0.194 & 1.698 & 0.433 \\  & & 128 & 0.218 & 1.962 & 0.487 \\  & 100 & 72 & 0.195 & 1.711 & 0.425 \\  & 50 \(\times\) 24 & 72 & 0.182 & 1.608 & 0.421 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Prediction performance with 1-min time steps.  
Figure 11: Loss of train and validation.  
Figure 12: Prediction performance by time step.  
which produced the best error rates (Table 1). The MAE and Adam optimizer were used for the loss function and the training method, respectively. Table 2 shows the final selected hyperparameters.
## 4 Discussion
### Limitations and Implications  
In this study, we found that the effects of the CI should be considered in GPP multi-linear estimation models of soybean crops using \(\text{SIF}_{\text{TOT\_FULL\_PSII}}\) and \(\Delta\text{PRI}\). However, the results are only applicable to soybean crops. In addition, there are large uncertainties in estimating NPQ using the PRI [27; 31]. The separation of sunlit/shaded areas and diffuse/direct beam radiation among multilayered canopy structures is also important for understanding how GPP relates to SIF and the PRI [18; 61]. To comprehend the complicated dynamics of SIF and the PRI, more research is required.  
In addition, we built a multiple linear regression model for GPP estimation with the assumption that the effects of PRI and SIF on GPP are linear. However, some studies have reported that SIF is non-linearly connected to GPP because of the saturation effects in dark reaction processes. This must be investigated in research on the performance of GPP estimation using other models. In this study, we also used an RF model to evaluate the effects of the CI on the modeling of GPP using \(\text{SIF}_{\text{TOT\_FULL\_PSII}}\) and \(\Delta\text{PRI}\). The different relationships between \(\text{GPP}_{\text{RF\_CI}}\) and the ratio of \(\text{GPP}_{\text{EC}}\) to \(\text{GPP}_{\text{RF}}\) will reveal the influence of the CI on GPP estimation (Figure 8).  
However, the results only show that the relationship of GPP to \(\text{SIF}_{\text{TOT\_FULL\_PSII}}\) and \(\Delta\text{PRI}\) are influenced by the CI. The influence of environmental stress on GPP estimation using \(\text{SIF}_{\text{TOT\_FULL\_PSII}}\) and \(\Delta\text{PRI}\) should be further investigated from the leaf to the canopy. More leaf and canopy observation experiments should be carried out in the future [34]. The Fluorescence Explorer (FLEX) is the eighth Earth observation satellite to be commissioned, and it will be used in the future [62]. It can help us improve the understanding of the interaction between carbon dioxide, vegetation, and the atmosphere by obtaining contemporaneous PRI and SIF data. In addition, FLEX data will be in conjunction with data from the Copernicus Sentinel series of satellites, which utilize thermal and light sensors, to better understand how photosynthetic processes affect the water and carbon cycles. Overall, our results demonstrated that GPP can be accurately estimated using \(\text{SIF}_{\text{TOT\_FULL\_PSII}}\) and \(\Delta\text{PRI}\) and significantly enhanced by taking the impact of the CI into account. These results will help increase the accuracy of GPP estimation under various weather conditions.
## 2 Study Area and Data Processing
### Satellite Precipitation Data
#### 2.3.1 TMPA Version 7 Products  
TMPA SPPs were designed to combine various MW and IR satellite-based measurements and ground-based gauge observations to provide 3-hourly quasi-global quantitative precipitation estimates [1]. The TMPA V7 3-hourly rainfall products were released in late 2013 and comprises two products, near-real-time version (3B42 RT) and post-real-time version (3B42V7), with a spatial resolution of 0.25\({}^{\circ}\). 3B42 RT is directly produced from the combination of calibrated PMW estimates and IR data calibrated by the PMW from satellites with a latency of 8 h. The 3B42V7 product is derived by bias-adjusting the near-real-time product with the GPCC monthly gauge-analysis precipitation data set, and it has a two-month latency. In this study, the 3B42 RT and 3B42V7 3-hourly SPPs from 1 April 2014 to 31 December 2016 were downloaded from the PMM website [63]. Using the method suggestedby [PERSON] et al. [47], the present study converted 3B42 RT and 3B42V7 SPPs into 3-hourly precipitation estimates in accordance with the local time that is six and a half hours ahead of UTC.
# A Linear Strong Constraint Joint Solution Method Based on Angle Information Enhancement
## 3 A Synchronization and Fine Ranging Method Based on the 5G Signal
### A Three-Stage Time-Frequency Synchronization Method Based on Group Peak Time Sequence Tracing
#### 3.1.2 The Frequency Offset Estimation Method Based on the CP  
During the down conversion process of the receiver, phase noise is generated due to the instability of the carrier signal generator, making it difficult for the receiver to generate the exact same carrier frequency as the signal source. If the receiver and the signal source are in relative motion, the relative velocity will produce a Doppler frequency shift. The frequency difference between the transmitter and receiver caused by these two reasons is called the carrier frequency offset (CFO). For the convenience of the following analysis, the CFO will be normalized, namely:  
\[\varepsilon=\frac{f_{offset}}{\Delta f}=\epsilon_{i}+\epsilon_{f} \tag{8}\]  
The impact it has on the receiving end is:  
\[y[n]=x[n]e^{2\pi n\epsilon/N_{FFT}} \tag{9}\]  
\[Y[k]=X[k-\varepsilon] \tag{10}\]  
Among them, \(\epsilon_{i}\) is the integer carrier frequency offset (IFO), and \(\varepsilon\)_f is the fractional carrier frequency offset (FFO). The IFO causes cyclic shift in the signal during reception, but the frequency components of subcarriers still have orthogonality and do not generate intersymbol interference; the FFO will cause distortion in the amplitude and phase of subcarrier components, and as the FFO increases, the amplitude and phase distortions become severe, resulting in inter symbol interference and disrupting the orthogonality between subcarriers.  
In complex scenarios, the movement of personnel wearing terminals is slow and does not cause significant Doppler frequency deviation. Fixed normalized frequency deviation is mostly in the form of decimals. Therefore, in this article, only the situation where decimals exist is considered.  
After completing the symbol synchronization of the signal, the CFO with a size of \(\varepsilon\) will cause a phase rotation of \(2\pi n\epsilon/N_{FFT}\), resulting in a phase difference of \(2\pi\varepsilon\) between the symbol tails of the CP and the \(N_{FFT}\) sampling points in between. If the CFO does not change in a short period of time, a set of data within a sliding window can be conjugate multiplied and averaged [22]:  
\[\hat{\varepsilon}=\frac{1}{2\pi}\text{arg}\Bigg{\{}\sum_{n=0}^{N_{\text{op}}-1 }y[n]y^{*}[n+N_{\text{FFT}}]\Bigg{\}}=\frac{1}{2\pi}\text{arg}\Bigg{\{}\sum_{ n=0}^{N_{\text{op}}-1}|x[n]\,|^{2}e^{2\pi x}\Bigg{\}} \tag{11}\]  
Among them, \(\text{arg}(\bullet)\) represents the complex number angle taking operation, and the FFO is estimated and compensated by the above method.
# Nutritional Care Process in Hospitalized Patients with Obesity-Related Multimorbidity
## 10 Recommendations on the Use of Parenteral Nutrition  
Parenteral Nutrition (PN) is defined as the set of procedures carried out to administer nutrients intravenously through a catheter.  
PN is indicated when tube-based EN is contraindicated due to [62]:  
* Gastrointestinal dysfunction
* Intolerance to EN for more than 3 to 7 days
* Intestinal obstruction
* Hollow viscous perforation
* Necrotizing pancreatitis with intolerance to EN
* Intractable vomiting and/or diarrhea
* Severe enteritis
* Intestinal ischemia
* High-output enteral fistula (greater than 500 mL per day)
* Need for bowel rest or fasting for more than 7 days
* Short bowel syndrome or type III intestinal failure.  
PN is more expensive and increases the risk of thrombophlebitis, infections, and metabolic complications compared to EN. However, early initiation of PN in hospitalized patients with ORM reduces the risk of infectious complications and in-hospital morbidity and mortality. Therefore, PN must be properly indicated and calculated, avoiding prolonged underfeeding or overfeeding, and the nutrient supply must be personalized for each clinical condition associated with obesity [34; 43] (Tables 2 and 3).  
During monitoring, assess the absence of metabolic complications associated with overfeeding, such as hepatic steatosis, cholestasis, hyperglycemia, hypertriglyceridemia, CO\({}_{2}\) retention, azotemia and hydroelectrolyte imbalances. The care team must also monitor for infections, which mainly derive from catheter care and PN admixture [62].
# Classification Performance of Thresholding Methods in the Mahalanobis-Taguchi System
## 2 The Concept of Mahalanobis Distance (MD)  
MD is a dimensionless distance measure based on the correlation between features and pattern differences that can be analysed with respect to a reference population [19], as shown in Figure 1. This reference population is called the normal space. The distance measure is termed the Mahalanobis Scale (MS) and aids the discriminant analysis approach by assessing the level of abnormality of datasets against the normal space.  
MD has an elliptical shape (see Figure 1) due to the correlation effect between the features. If there is no correlation, the MD is the same as the Euclidean Distance (ED) that has a circular shape. MD is different from Euclidean Distance since the latter does not consider the correlation among the features of the data points.
## 4 Overcurrent Protection Coordination Results, Analysis and Discussion
### Protection Coordination Results in Scenario 4
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**(a) Scenario 1** & & & & & & & & \\ \hline \multirow{4}{*}{Overcurrent relays} & \multirow{4}{*}{Desired tripping time} & Calculated tripping time (in seconds) of the related OCRs for the 3 ph-G fault at Bus 5 of Scenario 1 & & & & & \\ \cline{3-8}  & & GA & GSA & Hybrid PSO-GSA & GA & GSA & Hybrid PSO-GSA \\ \hline The REC Can-Thanh 163 OCR & 0.01 s & 0.01 s & 0.01 s & 0.01 s & & & & \\ \hline The REC Hao-Vo OCR & 0.31 s & 0.32 s & 0.31 s & 0.31 s & & & & \\ \hline The 471 Hao-Vo OCR & 0.61 s & 0.62 s & 0.61 s & 0.62 s & 5 s & 8 s & 14 s \\ \hline The highest difference in the tripping time between the & & & & & & & \\ desired one and the calculated one among the OCRs & & 0.01 s & 0 s & 0.01 s & & & \\ \hline \multicolumn{8}{p{113.8 pt}}{**(b) Scenario 2**} \\ \hline \multirow{4}{*}{Overcurrent relays} & \multirow{4}{*}{Desired tripping time} & Calculated tripping time (in seconds) of the related OCRs for the 3 ph-G fault at Bus 3 of Scenario 2 & & & & & \\ \cline{3-8}  & & GA & GSA & Hybrid PSO-GSA & GA & GSA & Hybrid PSO-GSA \\ \hline The REC Can-Thanh 163 OCR & 0.01 s & 0.01 s & 0.01 s & 0.01 s & & & \\ \hline The REC Can-Thanh Hao-Vo OCR & 0.31 s & 0.33 s & 0.32 s & 0.33 s & & & \\ \hline The REC Can-Thanh 36 OCR & 0.61 s & 0.65 s & 0.62 s & 0.65 s & 6 s & 7 s & 24 s \\ \hline The highest difference in the tripping time between the & & & & & & & \\ desired one and the calculated one among the OCRs & & 0.04 s & 0.01 s & 0.04 s & & & \\ \hline \multicolumn{8}{p{113.8 pt}}{**(c) Scenario 3**} \\ \hline \multirow{4}{*}{Overcurrent relays} & \multirow{4}{*}{Desired tripping time} & Calculated tripping time (in seconds) of the related OCRs for the 3 ph-G fault at Bus 3 of Scenario 3 & & & & & \\ desired one and the calculated one among the OCRs & & 0.04 s & 0.01 s & 0.04 s & & & \\ \hline \multicolumn{8}{p{113.8 pt}}{**(d) Scenario 4**} \\ \hline \multirow{4}{*}{Overcurrent relays} & \multirow{4}{*}{Desired tripping time} & Calculated tripping time (in seconds) of the related OCRs for the 3 ph-G fault at Bus 3 of Scenario 4 & & & & & \\ \cline{3-8}  & & GA & GSA & Hybrid PSO-GSA & GA & GSA & Hybrid PSO-GSA \\ \hline The REC Can-Thanh 163 OCR & 0.01 s & 0.01 s & 0.01 s & 0.01 s & & & \\ \hline The REC Can-Thanh Hao-Vo OCR & 0.31 s & 0.32 s & 0.31 s & 0.33 s & & & \\ \hline The CB GEN OCR & 0.31 s & 0.35 s & 0.32 s & 0.35 s & 5 s & 7.8 s & 16 s \\ \hline The REC Can-Thanh 36 OCR & 0.61 s & 0.64 s & 0.63 s & 0.65 s & & & \\ \hline The highest difference in the tripping time between the & & & & & & & \\ desired one and the calculated one among the OCRs & & 0.04 s & 0.02 s & 0.04 s & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison in the calculated tripping time of the related OCRs and the computation time of GA, GSA and hybrid PSO-GSA algorithms for Scenarios 1, 2, 3 and 4.