esa-sceva/satcom-chunk-collection huggingface dataset:
# EmuCD: An Emulator for Content Dissemination Protocols in Vehicular Networks
## 1 Introduction
To the best of our knowledge, there is no solution that: (1) offers easy integration with a custom protocol/software, especially if the code is unavailable, only the binaries; (2) is as resilient and invisible as possible to a developer when debugging, so the developer can focus only on the protocol/software at hand; and (3) provides results as close to reality as possible while offering customizable network metrics and tunable parameters. In this article we propose a solution to address the previous issues. We built an emulator, denoted as EmuCD, that contains the following characteristics:  
* a versatile emulator designed to host new routing and dissemination protocols for VANETs;
* a VANET emulator that allows the development of new content dissemination protocols which can be migrated to real communication box/interfaces with little to no adaptations required;
* a set of tools to facilitate the integration of mobility simulators with the VANET emulator;
* an emulation platform which allows the use of real or simulated data traces of vehicle mobility and radio connectivity between vehicles, and between vehicles and the infrastructure. We performed tests in real vehicular interfaces with existent content dissemination protocols (without any changes required in the code/implementation versus running them with EmuCD),in a VANET with five simultaneous nodes, and results have shown that under similar conditions, EmuCD performs closely to the real VANET, only lacking in the finer details that are extremely hard to emulate, such as varying loads in the hardware. EmuCD is also used to run the content distribution protocols in a 200-node VANET, being able to comparatively assess them with low CPU and memory usage. The remainder of this article is organized as follows. In the following section we present a review of concepts and related works. Section 3 describes the proposed architecture. Section 4 discusses the integration facilities of our emulator to operate in conjunction with other simulators/emulators/tools. In Section 5 we discuss the results of the emulator and examples of testing protocols and scenarios. Finally, the conclusions and future work are presented in Section 6.
## 5 Conclusions  
We have demonstrated an all-fiber low-intensity noise Yb-doped amplifier seeded by a narrow-linewidth low-noise NPRO laser. The intensity noise is greatly suppressed by a well-designed feedback loop and temperature control of the pump LD, fiber splitter and photodetector. By using a theoretical noise model, we recognize the most important noise sources of the amplifier output power, i.e., fluctuations in the pump LD power, fiber splitter temperature and photodetector responsivity. Accordingly, we show that good temperature control of the pump LD, fiber splitter and photodetector is crucial for low-intensity noise operation of the fiber amplifier. With fine temperature control and a carefully designed intensity noise suppressing feedback loop, the output RIN can be down to \(-72.5\) dBc/Hz @ 1 mHz and \(-90\) dBc/Hz @ 0.1 Hz. Based on measurement of the additional linewidth, no spectral broadening could be measured for the fiber amplifier. We believe the guidelines and experimental results in this work will be valuable for ultralow-intensity noise fiber amplifier design, especially for the amplifiers oriented to space-borne gravitational wave detection.  
Conceptualization, Z.W., Y.L. and Q.L.; methodology, Z.W. and Y.L.; software, J.W.; investigation, J.W.; resources, F.L. and L.T.; data curation, J.W.; writing--original draft preparation, Z.W.; writing--review and editing, Y.L. and Q.L.; project administration, Q.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China, grant number 2020 YFC2200403; National Natural Science Foundation of China, grant number U22A6003, 62175122.  
Figure 12: (**a**) Voltage fluctuations of the fiber splitter, pump LD, in-loop and out-of-loop photodetectors; (**b**) RIN results measured by G12180-130A and DET01 CFC.
## 2 Materials and Methods
### Evaluation Metrics  
For the evaluation of the classifiers, five different performance measures are applied. The measures are the accuracy, sensitivity, specificity, F1 score and AUC of the results. The measures are known by finding the number of true/false positive and true/false negative outputs:  
* True positive (TP) denotes the malignancy correctly predicted as malignancy.  
Figure 8: ANFIS system.  
* True negative (TN) denotes the benignity correctly predicted as benignity.
* False positive (FP) denotes the benignity mistakenly predicted as malignancy.
* False negative (FN) denotes the malignancy mistakenly predicted as benignity.  
The measures are defined as:  
1. \(\text{Sensitivity}=\frac{\text{TP}}{\text{TP}+\text{FN}}\times 100\%\)
2. \(\text{Specific}=\frac{\text{TN}}{\text{TN}+\text{FP}}\times 100\%\)
3. \(\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}}\times 100\%\)
4. \(\text{F1 score}=\frac{2\times 7}{\text{TP}+\text{FP}+\text{FN}}\times 100\%\)  
Sensitivity is the ability of the classifier to detect that malignancy is present, given that malignancy is present.  
Specificity is the ability of the classifier to detect that benignity exists, given that benignity exists.  
Accuracy is the ability of the classifier to correctly detect benignity and malignancy.  
The criterion to find the best combination of the features is to find the highest accuracy of the system. Once the highest accuracy is found, then the sensitivity and specificity are calculated [41; 59].  
For the classifiers FCM and ANFIS, ROC curves were extracted, helping to extract the area under the ROC curve (AUC) that is an objective index [33]. AUC has values between 0 and 1. The closer to 1, the better the classifier. ROC curve can enhance the optimum performance of a classifier only when the classifier has as an output a range of numbers. ROC curve tests different thresholds for classifying the range of the outputs and suggests the best threshold value. The K-means method has as an output only two values if the clusters are two, and the ROC curve cannot be applied to it.  
The F1 score also provides an objective evaluation of a classifier [30; 33].
# NTARC: A Data Model for the Systematic Review of Network Traffic Analysis Research
## 7 Analysis of NTA Research (NTARC Database)
### Research Goals
#### 7.1.2 Anomaly Detection  
In anomaly detection research, we found two main trends: (a) analysis of multi-dimensional data points, in which the studied objects are commonly communication flows; and (b) analysis of time series, in which time series usually represent aggregated network data or network properties.  
A peculiarity often observed in anomaly detection research is that the definition of \"anomaly\" is usually preconceived, therefore resulting in a blurred construct that mixes notions of novel attack, disruption, abnormality, and outlierness. Given that real traffic contains massive volumes of known, irrelevant traffic (i.e., novel attacks are negligible in comparison), real applications require the strong prevention of false positives, which might become as relevant as minimizing false negatives. This issue was already mentioned by [PERSON] [121] in the past century, who claimed that IDS must face the base-rate fallacy challenge. Therefore, anomaly detection proposals must sooner or later cope with some evidence, namely:  
* [noitemsep,topsep=0 pt]
* Harmless, legitimate traffic is often also anomalous and a source of deviations as well.  
Figure 4: NTA top-level research goals and main research foci (NTARC database).  
* Neither attacks, nor anomalies have to appear isolated, but can occur as bursty events or small clusters.
* The feature space and the underlying distributions of real traffic have a strong, decisive impact on the performance of unsupervised analysis methods.  
Neglecting these aspects leads to solutions that might be deemed as irrelevant and unpractical. For this reason, some suggestions for best practices are:  
* Clear, unambiguous definitions of the type of anomalies.
* The study of the selected NTA features and the problem spaces drawn by feature sets.
* The use of datasets with distributions that are representative of real-life scenarios. For instance, testing unsupervised detection frameworks with synthetically crafted IDS datasets is not recommended.
## 2 Materials and Methods
### Experimental Design
The research thoroughly evaluated the device's capability to provide accurate lactate measurements by comparing its results with the laboratory standard YSI 2300 STAT Plus. The findings indicated a high correlation between the Lactate Plus analyser and the reference system (r > 0.90), demonstrating its validity. Additionally, the device showed high internal consistency (ICC > 0.85) in repeated measures, supporting its reliability. These results suggest that the Lactate Plus analyser is a valid and reliable tool for measuring lactate levels in clinical and sports science applications. Measurements were taken at rest and at 1, 3, 5, 7, and 10 min post-running and cycling tests. The peak blood lactate level was identified as the highest concentration recorded during the post-test period. Heart rate (HR) was continuously monitored at 1 s intervals using Garmin HR monitors. RPE was assessed immediately following each test to gauge subjective effort and fatigue levels. Power output for the running exercises was estimated using a validated equation that considers the athlete's body mass, the distance covered, and the time taken to complete the sprint. The sequence of the tests was planned such that players performed the running test first, followed by the cycling test 48 h later. Participants were asked to refrain from intense physical activities for 24 h before each testing session to ensure no residual fatigue could affect performance. Additionally, they were advised to limit caffeine and alcohol intake to no more than one standard drink or one cup of coffee per day, respectively, within the same 24 h period. These measures were implemented to guarantee that the effects observed on performance were more likely to result from the experimental conditions themselves, rather than from outside lifestyle influences. Figure 1 presents the experimental design, outlining the sequence from the familiarisation session through the execution of both anaerobic sprint tests.
# Bearing Fault Identification Using Machine Learning and Adaptive Cascade Fault Observer
## 4 Adaptive Cascade Observer with Machine Learning for Fault Identification
### Fault Detection and Classification Using SVM
\right].\text{ Next, the other parameters }(\varnothing,\ c)\text{ are represented as:}\]  
\[\varnothing=\sum_{i}\partial_{i}w_{i}K(x_{i},x) \tag{22}\]  
\[c=\frac{1}{|SV|}\sum_{i\in SV}(w_{i}-\sum_{j}\partial_{j}w_{j}K\big{(}x_{i},x_{ j}\big{)}) \tag{23}\]  
Here \(K\big{(}x_{i},x_{j}\big{)}\) and \(SV\) are kernel function which is defined as a nonlinear function and support vector, respectively. In Equation (23), the support vector is represented as:  
\[SV=\{i\big{|}0\leq v_{i}\leq\rho\} \tag{24}\]  
Finally, the SVM with nonlinear function is represented as:  
\[w_{i}=sgn(\sum_{i}\partial_{i}w_{i}K\big{(}x_{i},x_{j}\big{)}+c) \tag{25}\]  
The FOR-adaptive cascade observer and SVM technique for fault classification are detailed in Algorithm 1. ```
1: Approximate the normal signal using FOR technique (4)
2: Estimate the signal based on the FOR-PI observer (FOR-PIO) (6,7)
3: Improve the power of uncertain and unknown condition rejection using FOR-SPI observer (FOR-SPIO) (8,9)
4: Increase the accuracy using FOR-cascade observer (10-12)
5: The reliability and stability are improved using FOR-adaptive cascade observer (FOR-ACO) (13-15)
6: Residual signal is generated using Equation (16)
7: The energy feature is extracted from residual signal using Equation (17)
8: The signal and fault are classified using SVM (25)
```  
**Algorithm 1**. The adaptive cascade observation technique for fault diagnosis in a bearing
# Revolutionizing Palm Dates Harvesting with Multirotor Flying Vehicles
## 2 Proposed Harvesting System
### Quadrotor Flying Vehicle
To extend the hover time, it is advisable to upgrade to higher-capacity batteries while also working on reducing the overall weight of the multirotor flying vehicle (MRFV). An alternative method for powering flying vehicles, particularly drones or other electric-powered aircraft, is to connect them to batteries housed on a small ground vehicle via wired connections. This approach allows the flying vehicle to draw energy from a ground-based power source, significantly extending flight duration by eliminating the need for heavy onboard batteries. By relocating the batteries to the ground vehicle, the quadrotor benefits from reduced weight, which not only optimizes performance but also improves stability and maneuverability. This configuration enhances operational efficiency, especially in applications like date harvesting, where longer flight times are crucial for productivity. Additionally, the ground vehicles can easily navigate fixed locations, ensuring a continuous power supply without restricting the quadrotor's mobility. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No** & **Component** & **Quantity** & **Weight/Item (Kg)** & **Weight (Kg)** \\ \hline \multirow{2}{*}{1} & Quadrotor (without & \multirow{2}{*}{1} & \multirow{2}{*}{3.750} & \multirow{2}{*}{3.750} \\  & batteries) & & & \\ \hline
2 & Robotic arm & 1 & 2.464 & 2.464 \\ \hline
3 & Winch & 1 & 0.750 & 0.750 \\ \hline
4 & 4 S Lipo batteries & 3 & 0.797 & 2.391 \\ \hline
5 & 3S Lipo battery & 1 & 0.349 & 0.349 \\ \hline \multicolumn{5}{c}{Total weight} & \multicolumn{2}{c}{9.704} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Weight of the quadrotor main components. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No. ** & **Throttle** & **Thrust (Kg)** & \ \begin{tabular}{c} **Motor Speed** \\ **(RPM)** \\ \end{tabular}  & \begin{tabular}{c} **Ampereage** \\ **(A)** \\ \end{tabular}  & **Power (W)** \\ \hline
1 & 25\% & 0.42 & 2120 & 1.5 & 19 \\ \hline
2 & 37.5\% & 0.89 & 2920 & 4 & 52 \\ \hline
3 & 50\% & 1.44 & 3640 & 8 & 104 \\ \hline
4 & 62\% & 1.89 & 4240 & 13.6 & 178 \\ \hline
5 & 75\% & 2.43 & 4780 & 20.2 & 264 \\ \hline
6 &The system's control is achieved through two Raspberry Pi companion computers, accessible via an Android app. The first Raspberry Pi is securely mounted on the arm and serves to govern the operation of the saw, linear actuator, and end effector. Meanwhile, the second Raspberry Pi is affixed to the quadrotor and handles tasks such as winch control and video stream capture from the depth camera. Each Raspberry Pi is capable of capturing video streams from two cameras, including depth and USB cameras. This dual Raspberry Pi setup enhances the system's versatility and control capabilities.
## Appendix A
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Num.** & **Conditional Entropy (2)** & **Cases** & **Method B** & **Num.** & **Conditional Entropy (3)** & **Cases** & **Method E** \\ \hline
1 & 2.431 & 11 & 7 & 1 & 2.760 & 11 & 4 \\
2 & 1.508 & 10 & 7,(11) & 2 & 1.878 & 10 & 4,7(1) \\
3 & 0.821 & 9 & 7,11,(1) & 3 & 0.951 & 9 & 4,7(1) \\
4 & 0.488 & 8 & 7,11,15,4 & 0, & 0.439 & 8 & 4,7(1,10) \\
5 & 0.216 & 7 & 7,11,15,4,8 & (5) & 0.317 & 7 & 4,7(1,10,8) \\
6 & 0.173 & 6 & 7,11,15,4,8,6 & (10) & 8 & 0.106 & 4 & 4,7(1,10,8,5,11,9) \\
8 & 0.106 & 4 & 7,11,15,4,8,6 & (10) & 8 & 0.106 & 4 & 4,7(1,10,8,5,11,9) \\
9 & 0.088 & 3 & 7,11,15,4,8,6 & (10,3,2) & 9 & 0.067 & 3 & 4,7(1,10,8,5,11,9,6,2) \\
10 & 0.072 & 2 & 7,11,15,4,8,6 & (10,3,2) & 10 & 0.045 & 2 & 4,7(1,10,8,5,11,9,6,2) \\
11 & 0.044 & 1 & 7,11,15,4,8,6 & (10,3,2),9 & 11 & 0.022 & 1 & 4,7(1,10,8,5,11,9,6,2) \\  & & & & & & & & \\ \multicolumn{1}{c}{Computing time: 10.6 s} & & & & & & & \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c c c} \hline
**Num.** & **Joint Entropy (2)** & **Cases** & **Method C** & **Num.** & **Joint Entropy (3)** & **Cases** & **Method F** \\ \hline
1 & 2.429 & 11 & 7 & 1 & 2.754 & 11 & 4 \\
2 & 1.961 & 10 & 7,(3) & 2 & 2.021 & 10 & 4,5() \\
3 & 2.498 & 165 & 2,6,7 & 3 & 2.812 & 165 & 4,10,11 \\
4 & 4.229 & 330 & 2,56,7 & 4 & 4,953 & 300 & 4,7(1,10,1) \\
5 & 6.015 & 462 & 2,79,10,11 & 5 & 7.283 & 462 & 1,47(1,10,11) \\
6 & 7.919 & 462 & 2,6,7,9,10,11 & 6 & 9.565 & 462 & 1,24,7(1,10,11) \\
7 & 9.812 & 330 & 2,56,7,9,10,11 & 7 & 11.786 & 300 & 1,24,6,7,10,11 \\
8 & 11.706 & 165 & 2,45,8,6,7,9,10,11 & 8 & 13.995 & 165 & 1,24,6,79,10,11 \\
9 & 13.343 & 55 & 2,45,8,6,7,9,10,11 & 9 & 16.201 & 5 & 1,24,6,79,10,11 \\
10 & 15.067 & 11 & 1,24,5,6,7,8,9,10,11 & 10 & 18.362 & 11 & 1,24,5,6,7,8,9,10,11 \\
11 & 16.598 & 1 & 1,23,4,5,6,7,8,9,10,11 & 11 & 20.845 & 1 & 1,23,4,5,6,7,8,9,10,11 \\ \multicolumn{1}{c}{Computing time: 21.42 s} & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of a select optimal rain gauge network of methods (shrink).
# Student's t Kernel-Based Maximum Correntropy Criterion Extended Kalman Filter for GPS Navigation
## 2 Review on Extended Kalman Filter with Maximum Correntropy Criterion
### MCCEKF Algorithm 2: MCCEKF-2  
The second type of MCCEKF is reviewed in this section. Based on the correntropy properties proposed by [PERSON] et al. [28], the MCCEKF can be derived involving the regression approach. By constructing the system in a nonlinear recursive form, the formulated equations for the state and measurement [28] are as follows:  
\[ \begin{bmatrix}\mathbf{\kappa}_{k|k-1}\\ \mathbf{z}_{k}\end{bmatrix} = \begin{bmatrix}\mathbf{x}_{k}\\ h(\mathbf{x}_{k})\end{bmatrix} +\mathbf{\delta}_{k} \tag{11}\]  
where \(\mathbf{\delta}_{k}= \begin{bmatrix}\mathbf{\kappa}_{k|k-1}-\mathbf{x}_{k}\end{bmatrix} \) \(\mathbf{v}_{k}\end{bmatrix}^{T}\)  
\[ \begin{split} E\Big{[}\mathbf{\delta}_{k}\mathbf{\delta}_{k}^{T}\Big{]}& =\begin{bmatrix}\text{E}\Bigg{[}\Bigg{(}\mathbf{x}_{k}-\mathbf{\kappa}_{k|k-1})(\mathbf{x }_{k}-\mathbf{\kappa}_{k|k-1}\Bigg{)}^{T}\Bigg{]}&0\\ 0&\text{E}\big{[}\mathbf{v}_{k}\mathbf{v}_{k}^{T}\big{]}\Bigg{]}\\ &=\begin{bmatrix}\textbf{P}_{k|k-1}&0\\ 0&\textbf{R}_{k}\end{bmatrix}=\begin{bmatrix}\textbf{B}_{\textbf{P}}\textbf{B}_ {\textbf{P}}^{T}&0\\ 0&\textbf{B}_{\textbf{R}}\textbf{B}_{\textbf{R}}^{T}\end{bmatrix}=\textbf{B}_{ k}\textbf{B}_{k}^{T}\end{split}  \tag{12}\]  
where \(\textbf{B}_{k}\) is obtained through Cholesky decomposition, combining \(\textbf{P}_{k|k-1}\) and \(\textbf{R}_{k}\), which are the prediction error and measurement noise covariance matrices, respectively. In this context, \(\textbf{B}_{k,\textbf{P}}\) and \(\textbf{B}_{k,\textbf{R}}\) can be obtained from Cholesky decomposition of \(\textbf{P}_{k|k-1}\) and \(\textbf{R}_{k}\), respectively. Multiplying \(\textbf{B}_{k}^{-1}\) by both sides of Equation (11) yields the following model:  
\[\textbf{B}_{k}^{-1} \begin{bmatrix}\mathbf{\kappa}_{k|k-1}\\ \mathbf{z}_{k}\end{bmatrix} =\textbf{B}_{k}^{-1} \begin{bmatrix}\mathbf{x}_{k}\\ h(\mathbf{x}_{k})\end{bmatrix} +\textbf{B}_{k}^{-1}\mathbf{\delta}_{k} \tag{13}\]  
\[\textbf{D}_{k}=\textbf{B}_{k}^{-1} \begin{bmatrix}\mathbf{\kappa}_{k|k-1}\\ \mathbf{z}_{k}\end{bmatrix} ,\textbf{g}_{k}=\textbf{B}_{k}^{-1} \begin{bmatrix}\bm {x}_{k}\\ h(\mathbf{x}_{k})\end{bmatrix} ,\textbf{e}_{k}=\textbf{B}_{k}^{-1}\mathbf{\delta}_{k} \tag{14}\]  
\[\textbf{D}_{k}=\textbf{g}_{k}+\textbf{e}_{k} \tag{15}\]  
and \(\textbf{e}_{k,i}=\textbf{d}_{k,i}-\textbf{ g}_{k,i}\). According to the definition of correntropy, the cost function can be written as  
\[J_{\text{MCC-2}}(\textbf{x}_{k})=\frac{1}{L}\sum_{i=1}^{L}G_{\sigma}\big{(} \textbf{d}_{k,i}-\textbf{g}_{k,i}\big{)} \tag{16}\]  
where \(L=n+m\). Taking the derivative of \(J_{\text{MCC-2}}\) with respect to \(\textbf{x}_{k}\) to find the optimality condition, we obtain \(\frac{\partial J_{\text{MCC-2}}}{\partial\textbf{x}_{k}}=0\)  
\[(\frac{\partial\textbf{g}_{k}}{\partial\textbf{x}_{k}})^{T}\mathbf{\Lambda}_{k}( \textbf{D}_{k}-\textbf{g}_{k})=0 \tag{17}\]  
\[\mathbf{\Lambda}_{k}= \begin{bmatrix}\mathbf{\Lambda}_{\textbf{x}}&\textbf{0}\\ \textbf{0}&\mathbf{\Lambda}_{z}\end{bmatrix}
## 4 Results and Discussion
According to Figure 7, regardless of the value of solar irradiance in an assumed steady-state, for the cases where the PV sections consist of one and two PV modules, the air flow (over the upper surfaces of the PV section and the acrylic plate) is laminar; and for the cases where the PV sections consist of five and six PV modules, the air flow is turbulent. Furthermore, for the cases where the PV sections consist of three and four PV modules, the air flow changes from laminar to turbulent at assumed steady-state solar irradiances of 600 W/m\({}^{2}\) and 250 W/m\({}^{2}\), respectively. Therefore, free convection heat transfer from the upper surfaces of the PV section and the acrylic plate is significantly affected under steady-state conditions by the solar irradiance \(q_{\rm E,S}\) and the number of PV modules per section (i.e., the PV section length \(L_{\rm PV}\)). Finally, it is necessary to compare the powers of different roof- and facade-integrated PV sections cooled by a solar chimney. The power of the PV sections consisting of one, two, three, four, five, and six PV modules (with the lengths \(L_{\rm PV}=0.52\) m, \(L_{\rm PV}=1.04\) m, \(L_{\rm PV}=1.56\) m, \(L_{\rm PV}=2.08\) m, \(L_{\rm PV}=2.6\) m, and \(L_{\rm PV}=3.12\) m, respectively) depending on the solar irradiance \(q_{\rm E,S}\) is shown in \[T_{\text{PV}}=\frac{(T_{\text{PVb}}+T_{\text{PVu}})}{2}, \tag{23}\]7 For the solar chimney in these simulations, a solar chimney with a height of 0.5 m is chosen. Furthermore, for the purpose of comparing the results, the length of the bottom section is assumed to be \(L_{\rm BS}=0\) m. Figure 8a corresponds with the case of roof-integrated PV modules, while Figure 8b corresponds with facade-integrated PV modules. \[T_{\text{PV}}=\frac{(T_{\text{PVb}}+T_{\text{PVu}})}{2}, \tag{23}\]8 Fundamental dimensionless number for free convection depending on solar irradiance in assumed steady-state conditions for six different numbers of PV modules per section. According to Figure 8a,b, linear dependences of the PV section power \(Q_{\rm el}\) on the solar irradiance \(q_{\rm E,S}\) are obtained for all the cases of the roof- and facade-integrated PV sections cooled by the solar chimney. As could be expected, the PV section power \(Q_{\rm el}\) increases with an increase in the solar irradiance \(q_{\rm E,S}\), as well as with an increase in the number of PV modules per section. Since the solar irradiance decreases from \(q_{\rm E,S}=1000\) W/m\({}^{2}\) to \(q_{\rm E,S}\)sin(\(37^{\circ}\)) \(=601.815\) W/m\({}^{2}\) with an increase in the inclination angle \(\theta\) from 37\({}^{\circ}\) to 90\({}^{\circ}\), this means that the design with facade-integrated PV modules will have a lower power \(Q_{\rm el}\) compared to the corresponding design with roof-integrated PV modules. For the considered angles of inclination, this reduction amounts to 39.82% and corresponds to the reduction in the solar irradiance \(q_{\rm E,S}\).