esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data and Methodology
### Methodology
#### 2.3.1 Sentinel-2 Time Series Curve Composition
The formulas for MSAVI, SAVI, EVI, and GNDVI for Sentinel-2 are provided below, respectively:  
\[\text{MSAVI}=\frac{1}{2}\bigg{[}2*\rho_{\text{NIR}}+1-\sqrt{(2*\rho_{\text{NIR }}+1)^{2}-8*(\rho_{\text{NIR}}-\rho_{\text{RED}})^{2}}\bigg{]} \tag{2}\]  
\[\text{SAVI}=\frac{\rho_{\text{NIR}}-\rho_{\text{RED}}}{\rho_{\text{NIR}}+ \rho_{\text{RED}}+\text{L}}(1+\text{L}) \tag{3}\]  
\[\text{EVI}=\frac{\rho_{\text{NIR}}-\rho_{\text{RED}}}{\rho_{\text{NIR}}+ \theta\rho_{\text{RED}}-7.5\rho_{\text{BLUE}}+1} \tag{4}\]  
\[\text{GNDVI}=\frac{\rho_{\text{NIR}}-\rho_{\text{CREEN}}}{\rho_{\text{NIR}}+ \rho_{\text{CREEN}}} \tag{5}\]  
where \(\rho_{\text{NIR}}\) is the reflectance in the near infrared band, \(\rho_{\text{RED}}\) is the reflectance in the red band, \(\rho_{\text{CREEN}}\) is the reflectance in the green band, L is the soil conditioning coefficient, and \(\rho_{\text{BLUE}}\) is the reflectance in the blue band.
## 4 CO2 Emission Calculation for a Tunnel Project
### CO\({}_{2}\) Emission Calculation of the Main Equipment
#### 4.4.2 Calculating the CO\({}_{2}\) Emission for Other Equipment  
Finding the volume needed from the materials quantity extracted by Revit, as shown in Table 5, and referring to the national standard estimating reference by KICT, and assuming the necessary data, it can calculate the fuel consumption for the dump truck and mixer-truck. The equipment CO\({}_{2}\) emission of mixer-truck and dump truck is calculated shown as follows:  
Calculation for CO\({}_{2}\) emission of mixer-truck:  
\[\begin{array}{l}\text{Productivity per hour (mixer-truck): Q = 60\text{-}\text{w-e/}[\text{t1 + 60\text{-}(\text{l/v1 + l/v2) + t3 + t4}}]}\\ \text{(m${}^{3}$/h)}\end{array} \tag{2}\]  
Applying variable data in Table 6 to Equation (2); Q(Mixer-truck) = 9.37 m\({}^{3}\)/h, Q(Mixer-truck for concrete lining) = 5.95 m\({}^{3}\)/h, Q(Mixer-truck for shot concrete) = 4.36 m\({}^{3}\)/h.  
\begin{table}
\begin{tabular}{c c} \hline
**Equipment Variable** & **Data** \\ \hline q (bucket capacity) & 1.72 m\({}^{3}\) \\ k (bucket coefficient) & 1.0 \\ f (soil conversion factor) & 77\% \\ e (production efficiency) & 60\% \\ m (reciprocal of the velocity) & 1.8(s/m) \\
1 (average distance) & 8.0 m \\ t1 (load time) & 6 s \\ t2 (idle time) & 14 s \\ \hline \end{tabular}
\end{table}
Table 4: Wheel loader variable data.  
\begin{table}
\begin{tabular}{c c c} \hline
**Work Type** & **Section** & **Volume(m\({}^{3}\))** \\ \hline Earthwork & Excavation scope & 748.4 \\ \hline Concrete work & Drainage way & 19.95 \\  & Concrete lining & 62.61 \\  & Shotcrete & 11.45 \\  & Concrete slab (30 cm) & 22.5 \\ \hline Cement work & Cement treated base & 11.25 \\ \hline \end{tabular}
\end{table}
Table 5: Quantity of work.  
So, the hours of the work are: (19.95 + 22.5 + 11.25)/9.37 + 62.61/5.95 + 11.45/4.36 = 18.88 h. The fuel consumption is: 13 L/h x 18.88 h = 245.44 L. Referring to the LCI DB, the CO2 emission factor is 2.6 kg/L, the CO2 emission of the mixer-truck is: 2.6 kg/L \(\times\) 245.44 L = 638.14 kg. Calculation for CO2 emission of dump truck:  
\[\begin{array}{l}\text{Productivity per hour (dump truck): Q = 60(T/rt-L) f:E/[Cms-T:L/(60-Es-q:k:rt) + (l/v1 + l/v2)-60 + t3 + t4 + t5)] (m\({}^{3}\)/h)} \\ \end{array} \tag{3}\]  
Applying variable data in Table 7 to Equation (3); Q (Dump truck) = 8.34 m\({}^{3}\)/h.  
So, the hours of the work are: 748.4/8.34 = 89.74 h. The fuel consumption is: 15.9 L/h x 89.74 h = 1426.87 L. Referring to the LCI DB, the CO2 emission factor is 2.6 kg/L, the CO2 emission of the dump truck is: 2.6 kg/L \(\times\) 1426.87 L = 3709.86 kg.
# Designing an Educational Metaverse: A Case Study of NTUniverse
## 3 Digitalization of NTU Campus
### Reconstruction of Laboratory  
Digitalization of the existing laboratories, classrooms, tutorial rooms etc., can also be performed via laser scanning. Point-cloud representations of the laser-scanned internal spaces can then be converted to BIM. BIM is widely used in digital-twin construction. Figure 8 shows the reconstruction of a laboratory in NTU. A Faro camera shown in Figure 8a is used to scan the laboratory, generating a point cloud as shown in Figure 8b, which is then converted to a BIM-based digital twin with reconstructed structure and mechanical or electrical appurtenances as shown in Figure 8c. The BIM model so reconstructed can be used for energy simulation, indoor climate management, and other sustainability studies [18].
# Secure Information Transmission with Self Jamming SWIPT
## 4 Conclusions  
In this paper, we implemented the SWIPT based self jamming physical layer security model, which relied on the channel precoding method to transmit information securely to the legitimate user. We considered different case studies by assuming that the eavesdropper had high channel correlation with the legitimate user. We also considered the possibility of channel estimate error (the channel link between jamming antennas to the legitimate user), which negatively affected the channel precoding. All the study cases were analyzed with the theoretical expressions supported by the simulation results.  
The BER performance of the legitimate user and eavesdropper was improved by using the IB-DFE receiver, and we concluded that the channel precoding error could be further reduced at the legitimate user by taking advantage of all the available knowledge (i.e., including the variance of channel precoding error) in the IB-DFE receiver. Thus, the legitimate user could perform better than the eavesdropper, and the performance of the legitimate user improved over the eavesdropper with the increase in the jamming signal power. The secrecy rate analysis of legitimate user demonstrated that the performance of the legitimate user with the channel precoding error did not improve beyond 20 dBm SINR and slightly degraded with the increase in jamming signal power. In the case of perfect channel precoding, the secrecy rate performance of the legitimate user improved proportionally with the increase in SINR and with the increase in the jamming signal power.  
Figure 7: The secrecy rate of \(R(a)\) and \(R(b)\) and in Scenario B, \(\epsilon_{E1R}=0.02\).  
**Author Contributions:** Conceptualization, A.R. and R.D. ; investigation, A.R., M.B., and R.D.; software, A.R.; supervision, R.D. and D.N.K.J.; validation, D.N.K.J. and R.D.; visualization, A.R.; writing, original draft, A.R., M.B., D.N.K.J., and R.D.; writing, review and editing, A.R and R.D. All authors read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by FCT/MCTESthrough national funds and when applicable co-funded EU funds under the project MASSIVE5G(PTDC/EEI-TEL/30588/2017), Copelabs (UIDB/04111/2020), and Instituto de Telecomunicacoes (UIDB/EEA/50008/2020). The research work was developed in part with Universidade Lusofona/ILINDfunding and supported, in part, by the Russian Foundation for Basic Research (RFBR) Grant No. 19-37-50083\(\backslash\)19.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results
### Reduction in Travel Distance  
The results demonstrate that redistributing workstations using the EA significantly reduced operational movement (Table 2). In the initial layout, the total distance travelled by operators was 868.95 m. After optimization with the EA, this was reduced to 400.56 m, representing a 53.90% improvement. In comparison, the CRAFT algorithm achieved a 38.60% reduction, while SLP resulted in only a 27.70% improvement (Table 3).  
A detailed analysis of individual operators revealed that Operator 2, who initially travelled 385.85 m, reduced their movement to 162.02 m with the EA (a 58.00% reduction), while Operator 3 decreased their travel distance from 359.22 m to 153.15 m (a 57.36% reduction). This confirms that the EA not only optimizes the overall workflow of the workshop but also enhances the individual efficiency of each operator.
# Rheumatoid Arthritis Diagnosis: Deep Learning vs. Humane
## 2 RA Diagnosis Methods: Modernism vs. Traditionalism
### The Physician Method
#### 2.3.2 Physical Examination  
A well-known tool when coming to the regular evaluation of the course of RA patients in clinical praxis is the Disease Activity Score (DAS28). DAS28 has also been widely used in clinical trials when RA patients have been recruited in order to compare RA activity throughout the study. The DAS28 is a measure of RA disease activity that combines information from tender joints, swollen joints and inflammatory markers (CRP or erythrocyte sedimentation rate--ESR) [33]. The physician needs to press the below-marked 28 joints and mark the number of joints that are painful. CRP is the so-called C-reactive protein and ESR of the erythrocytes' sedimentation rate. Both of them are measured in the blood and are high in the case of inflammation or infection. DAS28 can be calculated for both of them. Thus, there is the DAS28 CRP and the DAS28 ESR, respectively. Number 28 represents the 28 joints, which are typically being assessed in the clinical praxis by rheumatologists. These joints are presented on the mannequin below (Figure 7):  
DAS28 CRP or DAS28 ESR can be easily calculated online [35].  
In Table 1, RA activity in correlation with DAS28 score is presented.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**DAS28** & **RA Activity** \\ \hline DAS28 \(<\) 2.6 & remission \\
2.6 \(\leq\) DAS28 \(\leq\) 3.2 & low disease activity \\
3.2 \(<\) DAS28 \(\leq\) 5.1 & moderate disease activity \\ DAS28 \(>\) 5.1 & high disease activity \\ \hline \hline \end{tabular}
\end{table}
Table 1: Discrimination of works based on deep learning model data input [36; 37].  
Figure 7: Self-administered index (SAI) modified from [34].  
Another widely used score for the clinical course of RA patients is CDAI (clinical disease activity index) (Figure 8). CDAI is calculated similarly. The main difference to DAS28 is inflammatory markers are not included; therefore, this score is more subjective. In CDAI, physicians are also called to quantify the disease activity on a scale from 0 to 10. CDAI is also based on tenderness and swelling of the same 28 peripheral joints.  
Because of the possible extra-articular manifestations, it is always important that the physician examines the RA patient's whole body and not just the joints. The skin, the eyes, heart and lungs, palpation for possible lymph nodes and abdomen also need to be thoroughly examined.
# A Model-Free Online Learning Control for Attitude Tracking of Quadrotors
## 4 Validation  
### Comparative Simulation  
The efficacy of the proposed method is illustrated with numerical simulations. The parameters of the quadrotor are chosen according to a quadrotor developed in [23]. The key parameters for the quadrotor used for simulation are as follows:  
* Total mass of the quadrotor: \(m=4.14\) kg;
* The distance from the center of mass to each motor: \(l=0.315\) m;
* The inertia matrix:\[\mathbf{J}_{0}=\begin{bmatrix}0.082&0&0\\ 0&0.0845&0\\ 0&0&0.1377\end{bmatrix}\left(\mathrm{kg}\cdot\mathrm{m}^{2}\right) \tag{31}\]  
To test the robustness of the MFOLC algorithm, the uncertainty of inertia and external disturbances are also considered in the simulation.  
\[\Delta\mathbf{J}(t)=\mathrm{diag}\begin{pmatrix}5\cos 0.5t-1\sin 0.5t-3\\ 3\cos 0.5t+2\sin 0.5t-4\\ 4\cos 0.5t-1.5\sin 0.5t+5\end{pmatrix}e^{-0.1t}\times 10^{-2}\Big{(}\mathrm{kg} \cdot\mathrm{m}^{2}\Big{)} \tag{32}\]  
\[\mathbf{d}(t)=\begin{bmatrix}2\sin\phi t+5\cos 0.15t+3\\ -3\cos\phi t-4\sin 0.7t-4\\ 8\cos\phi t-4\sin 0.5t-1\end{bmatrix}\times 10^{-3}(\mathrm{N}\cdot\mathrm{m}) \tag{33}\]  
where \(\phi=0.5+\|\mathbf{\omega}\|\). The external disturbance is shown in Figure 1.  
The proposed MFOLC and the ordinary OLC developed in [19] are compared. The desired attitude quaternion is \(\mathbf{q}_{d}=[0.9353,0.2273,0.2708,0.01459]^{T}\), which is equivalent to a \(30^{\circ}\) roll angle, a \(30^{\circ}\) pitch angle, and a \(10^{\circ}\) yaw angle. The initial state of the quadrotor is \(\mathbf{q}(0)=[1,0,0,0]^{T}\), \(\mathbf{\omega}(0)=[0,0,0]^{T}\), and \(\mathbf{\omega}_{d}(0)=\dot{\mathbf{\omega}}_{d}(0)=[0,0,0]^{T}\). The simulation starts from \(t=0\) and \(\mathbf{u}(t\leq 0)=[0,0,0]^{T}\). The control parameters are listed in Table 1.  
Note that according to Assumption 3, \(\overline{J}\) can be selected by \(\overline{J}=ml^{2}=0.4108\ \mathrm{kg}\cdot\mathrm{m}^{2}\), such that \(\|\mathbf{J}_{0}\|=0.1377<\overline{J}\). According to Table 1, \(\underline{k}>\overline{J}(1.5k_{p}+3\overline{\omega}_{d})+1=1.6162\) is also satisfied. In addition, to maintain hover, attitude control torques must be limited, with the maximum roll, pitch, and yaw control torque of 1 Nm, 1 Nm and 0.1 Nm, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Controller** & **MFOLC** & **OLC** \\ \hline \multirow{4}{*}{Parameters} & \(k_{p}=1\) & \(k_{p}=2.15\) \\  & \(K_{L}=\mathrm{diag}(0.8,0.85,0.8)\) & \(k_{1}=0.8\) \\  & \(\mathbf{K}=\mathrm{diag}(1.625,1.7,1.725)\) & \(k_{2}=0.7\) \\  & \(\tau=0.01\) & \(\tau=0.01\) \\  & \(\kappa_{1}=3.5\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Control parameters chosen for simulation.  
Figure 1: The external disturbance used in the simulation.
# Monitoring Cropland Abandonment in Hilly Areas with Sentinel-1 and Sentinel-2 Timeseries
## 2 Materials and Methods
### Datasets
#### 2.2.3 Ground Data and GF-2 for Verification of the Reliability of the Method  
The ground-truth data contained samples of 752 irrigated cropland, 1237 rainfed cropland, 1576 wood vegetation, and 623 herbaceous vegetation from 2019 to 2021. In addition, cloudless GF-2 satellite imagery in July 2020 was obtained in the study area. These data were used to verify the accuracy of the classification and spatial detail.
# GPS-Based Indoor/Outdoor Detection Scheme Using Machine Learning Techniques
## 4 I/O Detection Scheme  
The proposed scheme is shown in Figure 4: firstly, signal received from GPS satellites was collected through a specialized Mobile application. After collected, the raw input was reprocessed to filter out the errors and missing data. Input data was then analyzed and classified by various machine learning techniques to find the best solution for the I/O detection problem. For special cases of semi-indoor environments, we proposed another preprocessing process to increase the accuracy of proposed scheme. By using different types of machine learning techniques, the proposed scheme becomes flexible to deal with various problems in real life. Furthermore, the proposed preprocessing process will increase the overall accuracy in the semi-indoor environment, which is hard to deal with.
## 2 Materials and Methods
### Proposed Model
#### 2.1.1 Data Augmentation  
Data augmentation is a method of intentionally increasing the volume and complexity of existing data. In recent years, data augmentation has been a popular research topic in the field of deep learning. Neural networks require a large number of training samples in deep learning; yet, existing datasets in the medical area have low resources. To expand the diversity of the original dataset, a data augmentation step is required [12].  
The most well-known methods:  
* Flipping: makes a mirror replica of the original image.
* Rotation: rotating an image around the centre pixel.
* Saturation: changing the separation between colours of an image.
* Value: changes the relative lightness or darkness of a colour.
* Hue: the process of changing the shade of the colours in an image.
* Adding Noise: addition of noise to the image.  
In the proposed model, at first, images were flipped, afterwards, randomly selected values were applied for the brightness in the range [\(-\)0.3,\(-\)0.1], saturation in the range [\(-\)0.4,0.2], and hue in the range [0.05,0.25]. Figure 2 shows a visualization of different applied augmentation techniques. The original image was flipped in Aug1 and Aug2, and random saturation and hue values were applied in Aug1. Brightness and hue values were chosen at random for Aug2. Aug3 and Aug4 applied randomly selected saturation and brightness values to the original image, whereas Aug4 additionally applied random selection of hue values.  
Figure 1: Proposed model diagram.  
Figure 2: Samples of different applied augmentation techniques (**a**) original image, (**b**) output of Aug1, (**c**) output of Aug2, (**d**) output of Aug3, (**e**) output of Aug4.  
In this study, each tomosynthesis slice produced four augmented images. Two images have been flipped, while the other two have not been flipped. Following that, contrast limited adaptive histogram equalisation (CLAHE) was applied to improve the image's local contrast along with random brightness, saturation, and hue values. In the training set, scans from 480 patients were included of which 400 normal patients (total of 96,862 slices), 50 patients with benign findings (total of 7034 slices), and 30 patients with malignant findings (total of 4242 slices). The augmentation was carried out as the first stage for slice preparation for the training set. The total number of training slices after augmentation is 387,448 normal slices, 28,136 benign slices, and 16,968 malignant slices.
## 2 Types of Instruments
### Global Positioning System and Activity Trackers  
Global positioning system (GPS) devices and activity trackers can monitor movement patterns, distances covered, and overall activity levels. Typically, smart watches have used GPS to quantify parameters such as distance and velocity. However, GPS is one system that falls under the umbrella of Global Navigation Satellite Systems (GNSS). A smart watch that is multi-GNSS indicates that several systems are accessed such as GPS, GLONASS (Russia), Galileo (EU), BeiDou (China), and QZSS (Japan).  
Wearable technology that incorporates GNSS (e.g., GPS watches) are useful for quantifying different measurable aspects of outdoor training, activities, and performances such as distance covered, pace, elevation gain, and so forth. These devices are user-friendly and often come with proprietary software for data analysis. However, accuracy is compromised in indoor settings or areas with poor satellite reception.  
An alternative tracking device for indoor activities is a Local Positioning System (LPS) [24; 25]. The LPS is a technology that quantifies human movement by tracking an individual's position and motion in a confined area and is operational indoors [25]. Thesesystems measure the distance between transmitters (on the person or object being tracked) and fixed receivers to calculate precise positions in real-time. LPS provides high-resolution data on position, speed, and acceleration, making it valuable for analyzing circus artist movements indoors.  
Finally, it is important to note that non-GNSS devices such as activity trackers are built using accelerometers and gyroscopes. They provide information such as step count, time spent moving at different speeds, as well as acceleration information. In general, activity trackers typically offer a generalized overview of physical activity.  
This is not an exhaustive list of wearable technology that is available. Furthermore, wearable technology devices are evolving due to miniaturization of instruments, increased computing power, and more sophisticated processing algorithms, for example. Overall, wearable technology has the capability to enhance circus biomechanics and physiology research by enabling real-time, field-based data collection. However, the validity, reliability, and practicality of each device must be carefully considered to ensure meaningful insights into performance optimization and injury prevention.
# Performance Analysis of the Supercritical Carbon Dioxide Re-compression Brayton Cycle
## 3 Results and Discussion
### Exergy Efficiency (\(\eta_{ex}\)) and Irreversibility Ratio (IR)  
Figure 5 presents the \(IR\) and the \(\eta_{ex}\) values for the \(T_{7}\) of 700 \({}^{\circ}\)C. The optimal value for \(x\) is the one for which \(\eta_{ex}\) shows the maximum value. The \(IR\) is the minimum, and at the same time, \(\eta_{ex}\) is the maximum at \(x=0.74\).
# Modulation Characteristics of High-Speed Transistor Lasers
## 3 Modulation Characteristics of TL
### Optimizations of QW  
The QWs can be optimized by changing the number, location, thickness, and main excitation level of QWs in the active region. The effect of QW thickness on the tuning bandwidth is discussed in the base optimization section.
## 1 Introduction
### Contributions  
Many studies have attempted to extract non-redundant association rules (NRARs) using frequent closed item sets (FCIs) [13; 14; 15; 16] and mining minimal NRARs as an extension of NRAR mining (NRARM) [17; 18; 19]. Nevertheless, to the best of our knowledge, the generated NRARs may still contain structural anomalies and methods for data-driven knowledge using data mining techniques to obtain non-anomalous rule bases are very limited. The aim of this study was to develop a method to extract a set of concise and anomalous-free PRs for a rule-based system and to improve the efficiency of mining non-anomalous rule bases and also find the most comprehensive rules from the dataset. Therefore, the main contributions of this study are as follows:  
1. A method, based on NRARs, to support the extraction of rule-based knowledge bases from cerebrovascular examination dataset.
2. An additional method to deal with structure anomalies (i.e., subsumption and circular rules) from the extracted NRARs to verify the knowledge through logical testing and finally, a concise non-anomalous knowledge base can be derived for a rule-based system for cerebrovascular examination datasets.
3. The proposed method takes a certain factor value [20] of each rule into consideration as it plays important roles in PRs.  
The remainder of this paper is organized as follows. The proposed method is presented in Section 2, followed by the Results and Discussion in Sections 3 and 4, respectively.
# Moving Vehicle Tracking with a Moving Drone Based on Track Association
## 3 Multiple Target Tracking  
A block diagram of multiple target-tracking with the new association scheme is shown in Figure 3. Each step of the block diagram is described in the following subsections.