esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment
### Metrics
#### 4.1.2 Structural Similarity Index Measure  
The \(SSIM\) is a metric used to quantify the similarity between two digital images [41]. It is commonly employed to evaluate the quality of a distorted image when compared to an undistorted reference image. Assuming that the original image is \(x\) and the reconstructed image is \(y\), the \(SSIM\) between the two images is evaluated from their brightness, contrast, and structure. The formula for the \(SSIM\) is as follows:  
\[\text{SSIM}(x,y)=[l(x,y)]^{\alpha}[c(x,y)]^{3}[s(x,y)]^{\gamma} \tag{44}\]  
In the formula, \(x\) represents the reconstructed image, while \(y\) represents the original image. The brightness is denoted as \(l(x,y)\); the contrast is \(c(x,y)\); the structure is \(s(x,y)\).  
\[l(x,y)=\frac{2\mu_{x}\mu_{y}+c_{1}}{{\mu_{x}}^{2}+{\mu_{y}}^{2}+c_{1}} \tag{45}\]  
\[c(x,y)=\frac{2\sigma_{x}\sigma_{y}+c_{2}}{{\sigma_{x}}^{2}+{\sigma_{y}}^{2}+c_ {2}} \tag{46}\]  
\[s(x,y)=\frac{2\sigma_{xy}+c_{3}}{{\sigma_{x}}^{2}+{\sigma_{y}}^{2}+c_{3}} \tag{47}\]  
where \(\mu_{x}\) and \(\mu_{y}\) are the mean values of \(x\) and \(y\) respectively, \({\sigma_{x}}^{2}\) is the variance of \(x\), \({\sigma_{y}}^{2}\) is the variance of \(y\), \({\sigma_{xy}}\) is the covariance of \(x\) and \(y\). \(c_{1}=(k_{1}\,\mathrm{L})^{2}\), \(c_{2}=(k_{2}\,\mathrm{L})^{2}\), \(c_{3}=c_{2}/2\) are constant values, and \(L\) is a range of pixel values. Let \(k_{1}=0.01\), \(k_{2}=0.03\), and \(L=255\). The values of \(\alpha\), \(\beta\), and \(\gamma\) are all greater than 0. Let \(\alpha=\beta=\gamma=1\), then the simplified SSIM equation is as follows:  
\[\text{SSIM}(x,y)=\frac{\big{(}2\mu_{x}\mu_{y}+c_{1}\big{)}\big{(}2\sigma_{xy} +c_{2}\big{)}}{\big{(}{\mu_{x}}^{2}+{\mu_{y}}^{2}+c_{1}\big{)}\big{(}{\sigma_ {x}}^{2}+{\sigma_{y}}^{2}+c_{2}\big{)}} \tag{48}\]
## 2 Materials and Methods  
### Patient Imaging and Manual Contouring  
In this study, 11 liver, 24 prostate, and 12 lung cancer patients (see Table 1 for patient characteristics) were scanned between 28 September 2011 and 15 June 2022 using a 3 tesla (T) MRI (Philips Achieva, Eindhoven, the Netherlands) with a balanced steady-state free precession (bSSFP) sequence (field of view = 30 \(\times\) 40 to 40 \(\times\) 40 cm\({}^{2}\), acquisition matrix size = 128 \(\times\) 128, reconstructed matrix size = 256 \(\times\) 256, voxel size = 2.3 \(\times\) 3.1 \(\times\) 10-3.1 \(\times\) 3.1 \(\times\) 15 mm\({}^{3}\), echo time = 1.1 ms, repetition time = 2.2 ms, imaging time per frame = 275 ms) to acquire > 300 dynamic images per patient (approximately 2 min imaging time). The patients did not receive motion management nor breathing guidance during the scan. For liver and lung patients, the sagittal plane was imaged as it includes the superior-inferior direction, which is the dominant direction of liver and lung motion [38; 39; 40]. Each prostate patient was imaged in one of the three imaging planes (sagittal, coronal, and axial) as the dominant prostate motion is often not in one direction [41; 42]. The same imaging plane was used throughout the training and testing of the algorithm for each patient. For these patients, our foremost clinical goal in nifteRT is to realize hypofractionated 3D conformal radiotherapy using linac-MR technology. Hypofractionation would be possible because treatment margin can be reduced via nifteRT without increasing the normal tissue complication probability. In nifteRT, we aim to remove or minimize the ITV margin, which is assigned to address intrafractional physiological movements and variations in size, shape, and position during irradiation. The planning target volume (PTV) margin accounting for the interfractional variations in anatomy and patient setup error will still be assigned as in the current treatment techniques.
# Clinical Applications of Optical Coherence Angiography Imaging in Ocular Vascular Diseases
## 2 Diabetic Retinopathy
### Role of OCTA in Diabetic Retinopathy
#### 2.2.2 OCTA for Proliferative Diabetic Retinopathy
A recent study using 12 mm \(\times\) 12 mm widefield OCTA imaging showed that extra-macula areas had higher rates of non-perfusion compared to the macula area. Macula non-perfusion was also higher in eyes with proliferative diabetic retinopathy than severe non- proliferative diabetic retinopathy; no such difference was found in the extra-macula areas. They proposed that large arterioles residing in the SCP and DCP may act as perfusion boundaries. In the macula, overlapping feeding arterioles joined by the DCP may act as collateral vessels, which may reduce the probability of non-perfusion but be more susceptible to damage in proliferative disease [41]. Diabetic macular ischemia at the level of the DCP has been shown to be associated with outer retinal disruption, which correlates to losses in visual function [31]. OCTA is useful for detecting and monitoring the progression of macular ischemia [14]. OCTA is especially helpful for explaining visual loss in patients with normal retina structure and for predicting long-term visual prognosis [38]. A pre-operative OCTA to assess a patient with diabetic retinopathy who is undergoing cataract surgery may be useful for pre-operative counselling and predicting post-operative visual prognosis. Figure 3: A case of bilateral severe non-proliferative diabetic retinopathy with intraretinal microvascular abnormalities. (**A**): Color fundus photos depicting scattered retinal hemorrhages and cotton wool spots; en face OCTA montage (**B**) and cross-sectional OCTA (**C**) reveal intraretinal microvascular abnormalities (yellow circles) in areas with slightly decreased capillary perfusion.
# Stateflow-Based Energy Management Strategy for Hybrid Energy System to Mitigate Load Shedding
## 5 Results and Discussions
### Resiliency in Summer
The load is assumed to be the same as before, but a load shedding of 4 h is considered (based on the schedule allocated by the utility). The irradiance and cloud profile for the selected day is shown in Figure 14.  
The HES operation for this scenario is presented in Figure 15. Since the Islanded_Mode is found to be the most critical one, it is prudent to observe the system resiliency in this mode. As can be seen in Figure 14, due to the cloud covers, the irradiance is low, and the PV alone is not able to fulfill the load demand, even during daytime. The ESU contributes the major portion of load at 6-7 h. During another load-shedding interval, i.e., at hours 8-10, the combined contribution of PV and ESU is still inadequate to fulfill the load demand. Thus, the additional power from the generator (Gen1_Mode) is needed. Since the power deficit (less than 10 kW) is lesser than the capacity of the smaller generator, only Gen1 is utilized. Accordingly, due to the absence of PV power at hour 18-19 (late evening), the ESU and Gen1 maintain the power supply. Also due to the lower irradiance, PV is not able to support grid operation on this day. The contribution from different sources under different operating modes can be seen in plot b. Clearly, the system is resilient throughout the day, as indicated by plot c.  
The results in Figures 13 and 15 suggest that the proposed system is 100% resilient during both normal and abnormal days of the summer season. Despite the outage from the grid, the stable and uninterrupted power supply operation is achieved by the combined operation of all three alternate energy sources. The number of PV modules (\(N_{pe}\)), ESU batteries (\(N_{bat}\)), and the power of a diesel generator (\(P_{Gen}\)) (for this simulation) are considered according to Table 4. Keeping in mind the economic feasibility, reliability, and the environmental effects, minimum sizes of HES components are selected to have peak power corresponding to the peak load power [43; 44]. Figure 14: The global tilted irradiance and the cloud opacity (on 29 August 2019).
# A Survey of Autonomous Vehicles: Enabling Communication Technologies and Challenges
## 3 System Architecture for Autonomous Vehicles
### Camens
GNSS utilises a set of satellites orbiting around the earth's surface to localise [80]. The system contains the information of AV's position, speed and the exact time [80]. It operates by calculating the ToF between the satellite emitted signal and the receiver [81]. The AV position is usually extracted from the Global Positioning System (GPS) coordinates. The extracted coordinates by GPS are not always accurate and they usually introduce an error in the position with a mean value of 3 m and a standard deviation of 1 m [82]. The performance is further degraded in urban environments and an error in position can increase up to 20 m [83] and in some extreme cases the GPS position error is around 100 m [84]. In addition to this, the RTK system can also be used in AVs to precisely calculate the position of the vehicle [85]. Furthermore, dead reckoning (DR) and the inertial position can also be used in AVs to determine the position and the direction of the vehicle [86]. A technique known as odometry can be used to measure the position of the vehicle by fixing the rotary sensors to the wheels of the vehicle [79]. To make the AV capable of detecting slippage or lateral movements, the inertial measurement unit (IMU) is used and it detects this using accelerometers, gyroscopes and the magnetometer sensor's data. The IMU combined with all units can rectify the errors and increases the sampling speed of the measuring system. Although the IMU cannot provide the position error unless it is not accompanied by the GNSS system, AVs can get information from different sources such as RADAR, LiDAR, IMU, GNSS, UWB and camera to minimise the possibilities of error and perform reliable position measurement [43]. GPS can be combined with IMU techniques such as DR and the inertial position to confirm and improve the position estimate of the AV [87].
## 6 Case Study on Metro M
### Cross-Section Passenger Flow Data  
Table 6 summarizes the passenger flow volume in each inter-station segment during morning peak hours (7:30-8:30). The maximum passenger flow volume of inter-station segments in the up direction is 24,407 persons/h, and the maximum passenger flow volume of inter-station segments in the down direction is 21,048 persons/h. This line applies 6B trains with 17 pairs/h departure frequency in the morning peak hours, and the rated capacity of each train is 240 passengers.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Inter-Station** & **Sectional Passenger Volume in** & **Sectional Passenger Volume in** \\
**Segment No.** & **the Up Direction (Person/h)** & **the Down Direction (Person/h)** \\ \hline
1 & 1637 & 1013 \\
2 & 2938 & 2049 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Passenger Flow Volume in Each Inter-station Segment of Metro Line M.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Symbol** & **Definition** & **Value** & **Unit** \\ \hline \(f_{0}\) & Minimum departure frequency & 10 & pairs/h \\ \(f_{m}\) & Maximum departure frequency & 36 & pairs/h \\ \(Z_{0}\) & Number of vehicles used in a single train & 180 & vehicles \\ \(S_{j}\) & Stop time of the train at station \(j\) & 40 & s \\ \(t_{Z}\) & Turn-back time of trains & 100 & s \\ \(n_{0}\) & Maximum number of marshalled vehicles & 10 & vehicles \\ \(\gamma_{\min}\) & Lower limit with load factor of the trains & 60 & \% \\ \(\gamma_{\max}\) & Upper limit with load factor of the trains & 120 & \% \\ \(C\) & Capacity of each train & 240 & persons \\ \(\alpha_{1}\) & Weight of total travel time & 0.686 & - \\ \(\alpha_{2}\) & Weight of total operating trains & 0.314 & - \\ \hline \hline \end{tabular}
\end{table}
Table 5: The Value of Parameters.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Inter-Station** & **Inter-Station** & **Inter-Station** & **Inter-Station** & **Inter-Station** & **Inter-Station** \\
**Segment No.** & **Distance/m** & **Running Time/s** & **Segment No.** & **Distance/m** & **Running Time/s** \\ \hline
1 & 1.15 & 89 & 11 & 1.48 & 113 \\
2 & 1.38 & 106 & 12 & 1.80 & 138 \\
3 & 1.76 & 135 & 13 & 1.63 & 125 \\
4 & 1.52 & 116 & 14 & 1.26 & 96 \\
5 & 1.30 & 100 & 15 & 1.76 & 135 \\
6 & 1.32 & 101 & 16 & 1.13 & 87 \\
7 & 1.43 & 110 & 17 & 1.29 & 96 \\
8 & 1.63 & 125 & 18 & 1.39 & 107 \\
9 & 1.24 & 95 & 19 & 2.11 & 150 \\
10 & 1.27 & 97 & 20 & 1.42 & 109 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Overview of Metro Line M.
# Conceptual Design Evaluation Considering Confidence Based on Z-AHP-TOPSIS Method
## 3 Materials and Methods
### The Method of Converting Z-Number to Regular Fuzzy Number  
Assume a Z-number Z = (\(A\), \(B\)). Let \(A=\{(x,\mu_{A}(x)|\forall\in X,\mu_{A}(x)\in[0,\ 1])\}\) and \(B=\{(x,\mu_{B}(x)|\forall\in X,\mu_{B}(x)\in[0,\ 1])\}\). The steps to convert Z to a regular fuzzy number are as follows [32]:  
Step 1. Turn the second part (reliability) of the Z-number into a crisp number.  
\[\alpha=\frac{\int x\mu_{B}(x)dx}{\int\mu_{B}(x)dx}, \tag{5}\]where \(\int\) denotes an algebraic integration.  
Step 2. Add the weight of the second part (reliability) to the first part (restriction).  
The weighted Z-number can be denoted as:  
\[\widetilde{Z}^{\alpha}=\left\{(x,\mu_{A^{\alpha}}(x))|\mu_{A^{\alpha}}(x)=\alpha \mu_{A}(x),x\epsilon[0,\ 1]\right\}, \tag{6}\]  
Equation (6) can be simplified as shown in Equation (7), representing the Z-number after multiplying the reliability:  
\[\widetilde{Z}^{\alpha}=(A;\alpha), \tag{7}\]  
Step 3. Convert the irregular fuzzy number (weighted restriction) to a regular fuzzy number.  
Since Equation (7) is still not in the form of the standard fuzzy number, a conversion operation is required.  
The regular fuzzy number can be denoted as:  
\[\widetilde{Z}^{\prime}=\left\{\langle x,\mu_{\widetilde{Z}^{\prime}}(x))|\mu_ {\widetilde{Z}^{\prime}}(x)=\mu_{A^{\alpha}}\left(\frac{x}{\sqrt{\alpha}} \right),x\epsilon[0,\ 1]\right\}, \tag{8}\]  
Equation (8) can be denoted by Figure 3.  
A numerical example is used to illustrate the procedure of this approach. Assume a DM gives his opinion as good (3, 5, 7) TFN and his confidence is absolutely (0.5, 0.75, 1, 1) TpFN, the DM's knowledge can be expressed with a Z-number as: Z = (good, absolutely) = ((3, 5, 7), (0.5, 0.75, 1, 1)). At first, we should convert the DM's reliability into a crisp number according to Step 1.  
\[\alpha=\frac{\int x\mu_{B}(x)dx}{\int\mu_{B}(x)dx}=0.8056\]  
Second, add the weight of reliability to the constraint:  
\[\widetilde{Z}^{\alpha}=((3,5,7);0.8056)\]  
Third, convert the weighted Z-number to a regular fuzzy number:  
\[\widetilde{Z}^{\prime}=\left(\left(\sqrt{0.8056}\times 3,\sqrt{0.8056}\times 5, \sqrt{0.8056}\times 7\right);1\right)=(2.6926,4.4876,6.2827)\]  
Then, calculate (good, weak), where \"weak\" is assigned (0, 0.25, 0.5) TpFN. By converting Z = (good, absolutely) and Z = (good, absolutely) into regular fuzzy numbers and drawing their corresponding membership function images, Figure 4 can be obtained. The linguistic term that means \"good\" is assigned (3, 5, 7) TFN, \"good, weak\" is assigned (1.3329, 2.2046, 3. 0867) TFN and \"good, absolutely\" is assigned (2.6926, 4.4876, 6.2826) TFN  
Figure 3: The regular fuzzy number transformed from Z-number.  
over the total range of [1; 9]. It can be seen from Figure 4 that even the same evaluation will lead to a deviation due to different confidence levels, which will have a specific impact on decision making.
# GNSS Spoofing Detection Based on Wavelets and Machine Learning
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AUC & Area Under Curve \\ BOC & Binary Offset Carrier \\ BoF & Bag-of-Features \\ CNN & Convolutional Neural Networks \\ DoA & Direction of Arrival \\ DWT & Discrete Wavelet Transform \\ FFT & Fast Fourier Transform \\ FPR & False Positive Rate \\ GLCM & Gray Level Co-Occurrence Matrix \\ GNSS & Global Navigation Satellite System \\ GPS & Global Positioning System \\ I/Q & In-phase and Quadrature \\ KNN & k-Nearest Neighbors \\ ML & Machine Learning \\ NMEA & National Marine Electronics Association \\ NN & Neural Networks \\ OAKBAT & Oak Ridge Spoofing and Interference Test Battery \\ OOB & Out-of-Bag \\ PR & Precision Recall \\ RBF & Radial Basis Function \\ RF & Random Forest \\ RFF & Radio Frequency Fingerprinting \\ RGB & Red, Green, and Blue \\ ROC & Receiver Operating Characteristic \\ SDR & Software-Defined Radio \\ SQM & Signal Quality Monitoring \\ SURF & Speeded Up Robust Features) \\ SVM & Support Vector Machine \\ TEXBAT & Texas Spoofing Test Battery \\ ToA & Time of Arrival \\ TPR & True Positive Rate \\ \end{tabular}
## 3 Experiments
### Data Preprocessing
#### 3.2.1. Offset Elimination  
During vibration testing, sensors or acquisition devices are likely to offset due to their performance problems or environmental disturbances (e.g., temperature, power supply, etc.). The offset will directly affect the accuracy of signal analysis and should be eliminated. In this paper, the elimination of offset was achieved by subtracting the mean value from the samples. For the time series \(X=\{x_{1},x_{2},\ldots,x_{n}\}\), the formula for eliminating the offset is as follows:  
\[\hat{X}=X-\frac{1}{n}\sum_{i=1}^{n}x_{i} \tag{11}\]
# Farm Monitoring System with Drones and Optical Camera Communication
## 4 Computer Simulation
### Simulation Conditions  
Table 2 summarizes the parameters used in the simulation. To evaluate the performance of the proposed algorithm under realistic conditions, the parameters are the same as those used in the preliminary experiments in Section 5. These values are based on the specifications of the drone and the sensor nodes. The altitude of the receiver camera was set to 5 m. The speed of the drone was set to 3 m/s. The drone stops to film the sensor node for a certain amount of time depending on the size of the transmitted data. The filming time ranged from 0 to 3 s. The horizontal and vertical angles of view were \(77^{\circ}\) and \(40^{\circ}\), respectively. The elevation angle of the camera was set to \(20^{\circ}\).  
The sensor nodes were distributed randomly in a square area. The density of sensor nodes was set to 4 per \(10\times 10\) meters. The size of the study area ranged from \(50\times 50\) to \(200\times 200\) m. The simulation was iterated 1000 times for each condition with different seeds. The performance of the proposed trajectory control algorithm was compared with that of the shortest path algorithm. We employed several clustering algorithms such as k-means, group average method, and Ward's method.
## 3 Demand-Side Management Strategy and Resource Forecasting
### Wind and Solar Forecasting
#### 3.2.1 Random Forest Regression  
Random Forest (RF) is a bagging ensembled-based machine learning algorithm combining several single models of classification and regression trees. The classical decision tree has a shortcoming  
Figure 3: Proposed schematic forecasting framework.  
of over-fitting the training data and variance tending to be high. Thus, the superior advantage of RF over other machine learning algorithms is adopted. The randomization methodology in the RF algorithm reduces the high model's variance considerably [26]. The training of each decision tree is based on randomly selected attributes and randomly selected bootstrap samples from the training dataset [27; 28].
## 3 Results
### Average Exposure  
At each position, the time-averaged electric-field strength \(E_{avg}\) was measured both without an active UE (i.e., the actual exposure case) and when maximizing the downlink stream (in theory up to 100%) to the UE (i.e., the maximum exposure). As the signals were relatively stable, an averaging time of 30 s per electric-field component was chosen. The resulting ranges of \(E_{avg}\) are shown in Figure 7. Without inducing traffic at the measurement location, \(E_{avg}\) was very low; the maximum was only 0.05 V/m. Furthermore, in NLOS, the electric-field level did not increase much when inducing 100% downlink traffic, whereas in LOS, the field levels increased by 13 to 43 dB (on average 28 dB), and a maximum \(E_{avg}\) of 0.5 V/m was found, while the average \(E_{avg}\) was 0.3 V/m.  
Figure 6: Scatterplot of the maximum electric-field strength \(E_{max}\), using the experimental extrapolation—based on the direct measurement of \(E_{RE,PDSCH}\) (Equation (2))—versus the theoretical extrapolation—based on the difference in antenna gain, derived from the antenna patterns (Equation (1)). The locations were grouped by their relative position to the NR base station: within (blue dots) or outside its scanning range (red dots). Markers with a white X depict positions in non-line-of-sight.  
In Figure 8, the \(E_{avg}\) values were normalized to an antenna input power \(P_{in}\) of 1 W and shown as a function of the horizontal distance to the NR base station. The normalized maximum was observed at a distance of approximately 190 m--outside of the scanning range--although large variations were observed at this distance.  
Finally, the \(E_{avg}\) values measured by the FSV during 100% downlink were compared to the (experimentally) extrapolated \(E_{max}\) values based on \(E_{RE,PDSCH}\) and Equation (2) (Figure 9). The extrapolated values were higher overall, but the deviation was limited to a maximum of 3.5 dB in LOS, whereas the differences at the two positions in NLOS were much higher (up to 9 dB), which may be due to the uncertainty of the \(E_{RE}\) measurements close to the measurement setup's noise level.  
Figure 8: Average electric-field strength \(E_{avg}\), measured during 100% downlink traffic directed towards the user equipment (UE), normalized to an antenna input power \(P_{in}\) of 1 W, as a function of the horizontal distance to the NR base station. The locations were grouped by their relative position to the NR base station: within (blue dots) or outside its scanning range (red dots). Markers with a white X depict positions in non-line-of-sight.  
Figure 7: Range of average electric-field strengths \(E_{avg}\) measured at each measurement position. The antenna input power \(P_{in}\) is also given. Two positions were in non-line-of-sight: the 3 rd at the first site (red), and the 6 th at the third site (green).
## 4 Impacts of HTST and UHT on Food Quality
### Basic Physicochemical Characteristics
#### 4.2.2 pH  
Many researchers have found no significant changes in pH after UHT [35; 38; 40; 41; 53], as well as during storage [27; 39; 41; 53]. Only a few researchers found a drop in the pH after the preservation process [17; 54; 63] or during storage [45].  
Typically, researchers indicate that there is no significant effect of HTST heating on pH immediately after preservation [5; 13; 30; 32; 40; 46; 48; 50; 64; 65; 66; 67; 68; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 86; 87; 88;  
significant reduction of 3% in peach juice pH, while the pH dropped by 9.4% over 28 days of storage. However, the dynamics of the changes in pH were less than those in fresh juice, for which the pH decreased by 16.4% [31]. A decrease in pH after the process was also observed by other researchers [69]. There was a decrease in pH of 38.6% during a 50-day storage period, as stated by [PERSON] et al. [66]. [PERSON] et al. [5] indicated that there was no effect of HTST heating on pH over 9 weeks of storage.