esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Optimal Tilt Angle Dependencies  
Downstream of the results collected and analyzed so far, the following is presented: an analysis aimed at assessing the influences of some parameters on the annual optimal tilt angle for one applicative scenario. This analysis used some of the tools and modeling techniques reviewed. In particular, the Python libraries Bifacial_radiance [48] and PVIib [79] were used to calculate the influence of the PV system design parameters in defining the optimal tilt angle \(\gamma_{\text{opt}}\) to maximize the annual equivalent incident irradiance. While this issue has been extensively analyzed in the literature for mo-PVs, this is not the case for bi-PVs. Specifically, the analysis aimed to investigate the \(\gamma_{\text{opt}}\) dependencies on the ground albedo, module height, and weather conditions. The scenario considered was a residential bi-PV application mounted in a garden or on a rooftop, reflecting the current prevalence of residential PV systems. The scenario consisted of a single module and single-row bi-PV with a module receiving surface of 2 m \(\times\) 1 m (height \(\times\) width), \(\varphi_{\text{mod}}\) equal to 0.85, facing south, and located in Bologna, Italy (coordinates: 44.5\({}^{\circ}\) lat., 11.3\({}^{\circ}\) lon.). To evaluate the effect of all listed parameters, the yearly equivalent incident bi-PV radiation \(G_{\text{eq},y}\), obtained as the sum of hourly \(G_{\text{eq}}\), was used. Then, \(\gamma_{\text{opt}}\) was the \(\gamma\) that provided the maximum \(G_{\text{eq},y}\). Since the scenario consisted of a single module and the ground shading did not have a significant impact, it was assumed that \(N\)\(U\) had a negligible effect on the annual energy yield. In addition, this analysis was limited to evaluating the incident irradiance without considering the electrical energy produced. This approach was also adopted to facilitate the generalization of the results, regardless of the module construction technology considered. Finally, the latitude of the scenario was considered constant throughout the entire section.  
Figure 10: \(G_{\text{rear}}\) and \(N\)\(U\) obtained from the multiple-module configuration of the PV system. Image elaborated from [75].
# Optimization Processes in Automotive Logistic Flow
## 3 Results
### Optimization of the Production Process in WITNESS Horizon
Figure 38: The degree of operation of the work points after optimization. After optimizing the manufacturing architecture, we can draw several important conclusions based on the data and reports obtained, which are as follows:  
1. Increase in production capacity: * Production capacity increased significantly, from 2618 decks per week to 3980 decks. This represents an improvement of approximately 52%, demonstrating the effectiveness of the implemented optimizations. 2. Reduction in cycle times: * The cycle time for work points was decreased. This allowed for a more efficient use of resources and an increase in the productivity of work points. The cycle time  
Figure 41: The graphic report of the degree of operation of the conveyors after optimization. Figure 39: The graphic report of the degree of operation of the work points. was reduced by increasing the welding speed of the robotic cells and reducing the distances between the work points so that the operators could feed the work points faster. 3. Elimination of logistical blockages: * Replacing forklifts with conveyors eliminated bottlenecks and improved the material flow. Five of the six conveyors had a movement percentage of over 90%, indicating an optimized logistic flow. 4. Improving the degree of operation of robotic cells: * The optimizations reduced the waiting time and increased the operating percentage of the robotic cells. For example, the robotic welding cell OP2 reduced its waiting time from 44.822% to 35.77% and increased its uptime from 55.178% to 64.23%. 5. Streamlining the input flow of parts: * Decreasing the time of entry of parts into the flow allowed for an increase in the number of processed components. For example, for the bushing part, the input time was reduced from one minute to 0.7 min, increasing the number of parts processed from 6961 to 9943. 6. Reduction in waste: * Technological and functional optimizations also contributed to reducing the number of scraps. This aspect, although not detailed in the reports, is implicit in increasing production efficiency and quality. 7. Efficiency of workstations: * Reports indicate more efficient use of workstations, with increased uptime and reduced waiting time, thus contributing to higher productivity. 8. Reducing blockages in conveyors: * The only conveyor with significant bottlenecks was C6, suggesting that future optimizations may focus on this conveyor to completely eliminate bottlenecks. The optimizations performed in the manufacturing architecture resulted in a significant increase in production capacity and operational efficiency. These improvements demonstrate the effectiveness of the optimization approach, both functionally and technologically. Detailed reports provide a solid basis for continuous monitoring and improvement in manufacturing processes in the future. Mathematical Calculation for Maximizing the Profit by Optimizing the Logistic Flow Using the Simplex Method Mathematical
###### Abstract  
In recent years, Energy Efficiency (EE) has become a critical design metric for cellular systems. In order to achieve EE, a fine balance between throughput and fairness must also be ensured. To this end, in this paper we have presented various resource block (RB) allocation schemes in relay-assisted Long Term Evolution-Advanced (LTE-A) networks. Driven by equal power and Bisection-based Power Allocation (BOPA) algorithm, the Maximum Throughput (MT) and an alternating MT and proportional fairness (PF)-based SAMM (abbreviated with Authors' names) RB allocation scheme is presented for a single relay. In the case of multiple relays, the dependency of RB and power allocation on relay deployment and users' association is first addressed through a \(k\)-mean clustering approach. Secondly, to reduce the computational cost of RB and power allocation, a two-step neural network (NN) process (SAMM NN) is presented that uses SAMM-based unsupervised learning for RB allocation and BOPA-based supervised learning for power allocation. The results for all the schemes are compared in terms of EE and user throughput. For a single relay, SAMM BOPA offers the best EE, whereas SAMM equal power provides the best fairness. In the case of multiple relays, the results indicate SAMM NN achieves better EE compared to SAMM equal power and BOPA, and it also achieves better throughput fairness compared to MT equal power and MT BOPA.  
machine learning; LTE-A; energy efficiency; resource block allocation; bisection based optimal power allocation; water filling algorithm; proportional rate constraint +
Footnote †: This paper is an extended version of our paper published in 16 th Biennial Baltic Conference on Electronics and Embedded Systems, Tallinn, Estonia, 8–10 October 2018.
# Enhancing Knowledge on Energy Refurbishment of Buildings and Green Procurement through Living Labs
## 3 Results
### Level of General Knowledge Gain through Participation in Living Labs  
In our research, we explored the level of general knowledge, before and after participating in LL. As seen from Figure 2, eight general topics were addressed regarding the knowledge on green policies, mentoring, funding, procurement process, best practices, LL concept, pilot actions, and the eGPP tool related to green procurement and the energy refurbishment of buildings. Level of knowledge has been examined in all LLs, regardless of their specialization. The survey results indicate a significant enhancement in knowledge across diverse areas of green policies and practices. This improvement is characterized by a substantial decrease in the percentage of stakeholders having lower knowledge levels ('Poor' and 'Fair') and an increase in higher levels ('Good', 'Very good', and 'Excellent') after participating in LLs as seen in the left part of Figure 2.  
As illustrated in Figure 1 (the right part indicating average values (\(\overline{x}_{B},\overline{x}_{A}\)) and differences (\(\Delta\overline{x}_{A-B}\)), the lowest level of knowledge before participating in LLs was observed for the topic of the Living Lab concept (\(\overline{x}_{B}=2.03\)) and eGPP tools (\(\overline{x}_{B}=2.08\)), while the highest level of knowledge was observed for the topic of green policies (\(\overline{x}_{B}=2.62\)). This indicated that stakeholders had, on average, fair knowledge of LLs and eGPP tools and quite good knowledge of green policies. After participating in LLs, the highest level of knowledge was observed in the field of pilot actions (\(\overline{x}_{A}=3.75\)). In relative terms, knowledge level improved in all areas, with the highest relative improvement being observed in the field of eGPP tools (\(\Delta\overline{x}_{A-B}=1.45\)), the Living Lab concept (\(\Delta\overline{x}_{A-B}=1.44\)), and knowledge related to pilot actions (\(\Delta\overline{x}_{A-B}=1.42\)). Besides assessing the improvement in general knowledge from participating in Living Labs (LLs), the aim of this research was also to evaluate the enhancement of in-depth knowledge specific to the LLs' areas of specialization. Thus, the subsequent section provides insights into the level of expertise developed in specialized fields such as green policies, funding, mentoring, and green public procurement among LLs focused on these topics.
## 3 Research Framework and Methodology
### Patent Geographic Information Mining Based on Applicants and API Map  
In Phase 1, the patents search string is formulated based on the customers' requirements and industrial characteristics. Those keywords include patent application regions, keywords, classification numbers, and the starting and ending date of the patent application. After collecting patents, we employed a process of consolidating duplicate or repeatedly submitted patents to ensure that they are not counted multiple times. For example, there will be a new patent filed for both the invention and its utility model in China. Meanwhile, there will also be authorized patents for inventions and open patents for inventions. In this case, there would be separate patents in examination and publication, even though both of these patents refer to the same technological solution. Herein, we then proceed to acquire the patent in detail: information regarding the applicant, address, abstract, claims, and other relevant textual data.  
The patent contains both the name and address of the applicant. Due to name similarity and multi-location application issues of patent applicants, as well as the applicant's address showing only the approximate location, it is impossible to determine the geographic location solely on patent application information accurately. With the existing open onlineAPI map system, the geographic distribution of applicants can be accurately estimated by combining both applicants' names and addresses. By adopting this approach, we can overcome the limitations of the existing system and accurately obtain the longitude and latitude information of the applicant.  
To further improve the accuracy of obtained information of subsequent technological subject extraction, we conducted a manual review of all the patents' abstracts and claims. This allowed us to create a stop word list and dictionary by eliminating common or insignificant words, symbols, and numbers while replacing certain synonyms.
# Single-Pixel Color Imaging Method with a Compressive Sensing Measurement Matrix
## 3 Design of Pseudo-Random Circulant Measurement Matrix Based on Chaotic Sequence  
### Design of Measurement Matrix Based on Chaotic Sequence  
Common chaotic sequences include Chua, Lorenz, logistic and Rosser chaotic sequences [25]. The measurement matrix generated by using logistic chaotic sequence can best fit CS reconstruction [26] and can be produced with hardware.  
The logistic chaos system is a deterministic system, with pseudo-random features. The form of 1D logistic chaos system is expressed as follows.  
\[x_{k+1}=1-ux_{k}^{2},\ -1<x_{k}<1 \tag{5}\]  
The logistic map enters chaos state when the original values are 0.23, 0.37, and 0.70 under system parameter \(u\in[1.872,\,2.0]\). The chaotic sequence is distributed on interval \((-1,\,1)\), the logistic map is the full map, and the chaotic performance is the best when \(u=2.0\). The simulation results show that the logistic map has period doubling bifurcation, ergodicity, and averaging properties.  
The probability density function when \(u=2.0\) is shown in Formula (6).  
\[\rho(x)=\frac{1}{\pi}\Big{(}1-x^{2}\Big{)}^{-\frac{1}{2}} \tag{6}\]  
The probability of chaotic sequence between \((-1,\,0)\) and \((0,\,1)\) equals each other, which is \(1/2\), by using the properties of random various that conform to uniform distribution. Then, binarization is conducted on the chaotic sequence generated by Formula (5), as indicated in Formula (7).  
\[b_{n}=sgn(x_{n})=\left\{\begin{array}{l}+1\ x_{n}\geq 0\ P=\frac{1}{2}\\ -1\ x_{n}<0\ P=\frac{1}{2}\end{array}\right. \tag{7}\]  
Thus, the sequence probability of using \(+1\) and \(-1\) is \(1/2\), and the sequence generated by logistic chaos system can satisfy Bernoulli distribution. The uniformity and stability of the logistic chaotic sequence are better than that of the random Bernoulli sequence.  
Ref. [27] demonstrated that an \(M\times N\) Bernoulli matrix can meet the RIP with high probability when \(k\leq c_{1}M/\log(N/k)\), with probability at least  
\[1-2e^{-c_{2}M}, \tag{8}\]  
where constants \(c_{1},\ c_{2}>0\) exist that only depend on \(\delta\in(0,1)\).  
The sequence has statistical independence, satisfies Bernoulli sequence, and meets the RIP. Thus, the measurement matrix in CS can be constructed by chaotic sequence. The form of the constructed measurement matrix \(A\) is indicated follows:  
\[A=\frac{1}{\sqrt{M}}\left[\begin{array}{ccc}b_{0}&\cdots&b_{M(N-1)}\\ \vdots&\ddots&\vdots\\ b_{M-1}&\cdots&b_{MN-1}\end{array}\right], \tag{9}\]  
where \(1/\sqrt{M}\) is the normalization coefficient, and the matrix element arranges the chaotic sequence based on column. The measurement matrix based on logistic chaotic sequence (LCS-MM) requires the hardware to generate at least \(M\ \times\ N-1\) elements of chaotic sequence, and the required measurement matrix is formed by ranking based on column. The measurement matrix has image restoration capability, which is the same as that of random Bernoulli sequence. However, the measurement matrix requires considerable calculated amount, and causes immense burden on the hardware system.
# A Deep Belief Network Classification Approach for Automatic Diacritization of Arabic Text
## 5 Methodology
### Training with DBN
#### 5.4.2 Rectified Linear Unit (ReLU) Activation Function  
Activation functions are the core of deep learning. These functions usually facilitate deep neural networks by introducing non-linearity to the learning process. The non-linearity feature gives the neural network the ability to succeed in training and learning complex patterns [77]. Training a deep network with saturated activation functions has been experimentally proved to be hard. Nowadays, non-saturated activation functions are one of the key factors contributing to the success of the modern deep learning models.  
Formally, ReLU function is defined as in Equation (9). Where \(x_{i}\) is the input and \(y_{i}\) is the output from the activation function. The function returns 0 if it receives a negative input, and for a positive value \(x_{i}\) it returns that value back.  
\[y_{i}=\begin{cases}x_{i}&\text{if }x_{i}\geq 0\\ 0&\text{if }x_{i}<0.\end{cases} \tag{9}\]  
ReLU was first used in RBMs to improve their performance [78]. In [79] it was shown that adding ReLU to the hidden layers of the network could improve the learning speed of the deep network. Using ReLU, the vanishing gradient problem was avoided, this is due to its ability to make the gradients at the positive values become constants and do not vanish any more.  
ReLU is known for its simplicity and light computation as there is no complicated math. The advantage of these characteristics allow the model to train and run in a relatively short time. Another advantageous characteristic of ReLU is its sparsity. Since ReLU gives output zero for all negative inputs, it is likely for any given unit to not activate at all which causes the network to be sparse. In a sparse network it is more likely that neurons are actually processing valuable parts of a problem [80]. For example, a model that is processing images of the universe may contain a neuron that is specialized to identity stars. That specific neuron would not be activated if the model was processing images of satellites instead. It is worth mentioning that sparsity results in concise models that often have better predictive power and less noise [81].
# A Fast Calibration Method for Sensors of Atmospheric Detection System
## 2 System Physics Modeling
### Dynamic Heat Transfer Model of System  
To control the temperature calibration system quickly and accurately, we established a dynamic heat transfer model of the system. In the modeling process, some basic assumptions need to be put forward to ensure the simplicity and feasibility of the model and to ensure that the model can accurately restore the dynamic performance of the existing system.
###### Abstract  
High penetration of wind power plants may have an adverse impact on power systems' stability by reducing the inertia, and problems like frequency stability could appear due to total inertia in the system being reduced, making the power system more vulnerable to disturbances. However, most recent grid codes include an emulation inertia requirement for wind power plants, because modern wind turbines are capable of providing virtual inertia through power electronic converter controls to improve frequency stability issues. Because of this, it is necessary that the inertia estimation analyze and quantify the impact of the inertia reduction in power systems. In this paper, an implementation of a methodology for the inertia estimation of wind power plants is presented. It is evaluated through synchrophasor measurements obtained from a Real-Time Digital Simulator (RTDS) implementation, using industrial Phasor Measurement Units (PMUs). This methodology is based on the swing equation. Furthermore, a comparison of the results obtained between two professional tools RSCAD and DIgSILENT PowerFactory is performed, in order to evaluate the accuracy and the robustness of the methodology. This methodology is applied for the inertia estimation of an equivalent of the southeast zone of the Mexican power system, where there is a large-scale penetration of wind power plants. The results demonstrate that this methodology can be applied in real power systems using PMUs.  
inertia constant; swing equation; phasor measurement units; RTDS; wind power plants 2018 8 2413; doi:10.3390/app8122413
## 5 Benchmark Testing and Performance Analysis of MRSO
### Experimental Configuration and Evaluation Methods  
The implementation was carried out using MATLAB R2020a on a Windows 11 system. To achieve accurate and reliable results, the initial population was selected randomly. The experiment used a population size of 30, with 500 iterations, and each algorithm was run 30 times.  
To evaluate the performance of the MRSO, we utilized the MATLAB code of eight recent and well-known algorithms, including the Sine Cosine Algorithm (SCA) [26], the Mud Ring Algorithm (MRA) [27], the Liver Cancer Algorithm (LCA) [28], the Circle Search Algorithm (CSA) [29], the Tunicate Swarm Algorithm (TSA) [30], the Dingo Optimizer Algorithm (DOA) [31], the Elk Herd Optimizer (EHO) [32], and the White Shark Optimizer (WSO) [33], for comparison. For our evaluations, we utilized two sets of benchmark functions: 23 classical benchmark functions and 10 CEC-2019 benchmark functions. The evaluation of the MRSO involved several key criteria:  
* Average and Standard Deviation: The average and standard deviations were calculated to compare the performance of the standard RSO and several recent metaheuristic algorithms against the proposed MRSO approach.
* Box and Whisker Plot: A box and whisker plot was used to visually compare the performance of the RSO, recent metaheuristic algorithms, and the proposed MRSO approach.
## 4 Machine Performance Comparison
### Loss and Efficiency  
Under the same current density, the core loss and efficiency of the eight PM Vernier machines are presented in Figures 12 and 13, respectively, with the speed range from 0 to 2500 rpm. As can be seen from Figure 12, the core loss of Structures II, III, and IV are significantly larger than the traditional Structure I (spoke type) because they have an inner yoke in the rotor. In addition, if it is the same structure, the core loss of the FP winding method is greater than that of the SP winding method. Due to the long magnetic circuit of the armature winding, it is more saturated at the magnetic bridge and stator teeth. At the same time, the copper loss of the FP winding configurations is higher because the end length of the winding increases. Therefore, under the same structure and speed, the efficiency of the SP winding method is slightly higher than that of FP winding.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Winding Configurations** & **Structure** & **Average Torque** & **Value** \\ \hline \multirow{4}{*}{FP} & I & 30.92 Nm & 0.59\% \\  & II & 39.78 Nm & 0.61\% \\  & III & 47.45 Nm & 0.88\% \\  & IV & 43.98 Nm & 0.98\% \\ \hline \multirow{4}{*}{SP} & I & 23.58 Nm & 0.49\% \\  & II & 36.21 Nm & 0.95\% \\ \cline{1-1}  & III & 40.42 Nm & 0.86\% \\ \cline{1-1}  & IV & 41.17 Nm & 0.96\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Torque Performance.  
Figure 11: Back EMF with the harmonic distribution. (**a**) PM Vernier machines with FP winding configurations, (**b**) PM Vernier machines with SP winding configurations.
# Evaluation of Yogurt Quality during Storage by Fluorescence Spectroscopy
###### Abstract  
The physico-chemical parameters including pH and viscosity, and the fluorescence signal induced by fluorescent compounds presenting in yogurt such as riboflavin and porphyrin were measured during one week's storage at room temperature when five brands of yogurt samples were exposed to ambient air. The fluorescence spectra of yogurt showed four evident emission peaks, 525 nm, 633 nm, 661 nm, and 672 nm. To quantitatively investigate the quality of yogurt during deteriorating, a calculating method of the average rate of change (ARC) was proposed to study the relative change of fluorescence intensity in the spectral range of 600 to 750 nm associated with porphyrin and chlorin compounds. During the storage, the time evolution of two ARC, pH value, and viscosity were regular. Moreover, the ARC showed a good linear relationship with pH value and viscosity of yogurt. Further, multiple linear regression (MLR) models using two ARC as independent variables were developed to verify the dependence of fluorescence signal with pH value and viscosity, which showed a good linear relationship with an R-square of more than 85% for each class of yogurt. The results demonstrate that fluorescence spectra have a great potential to predict the quality of yogurt.  
fluorescence spectroscopy; yogurt quality; deteriorating; pH; viscosity +
Footnote †: journal: Applied Physics Letters