esa-sceva/satcom-chunk-collection huggingface dataset:
# Reduction of Phase Shifters in Planar Phased Arrays Using Novel Random Subarray Techniques
## 2 Random Subarray Design Methodology
### Phase Distribution and Operating Frequency  
Phase distribution refers to the phase shift applied to the signal in each element of the array. It plays an essential role in determining the direction of the main beam and side lobes of the antenna pattern. In the case of subarrays, the overall phase distribution of the antenna array is determined by the combination of the phase shifters within each subarray and the phase shifters controlling the entire array.  
Although the physical fabrication of the resultant array is outside of the scope of this work, it is intended to be a microstrip patch antenna composed of a substrate material and planned to be used as part of a cognitive radio system XCVR for satellite communications operating in the Ku-band with a downlink signal in the 10.7-12.7 GHz radio spectrum [15]. In addition, the radio system is intended to integrate a cognitive motor that employs machine learning algorithms for proper allocation of spectrum channels and accurate angle  
Figure 1: Representation of a \(8\times 8\) planar array as a matrix of integers with \(PS_{m}\times M\) subarrays (\(6\times 8\)). Each row or sequence in the array is denoted by \(\{S_{m}\mid m\in[1,2,\ldots,M-1,M]\}\).  
of arrival (AoA) estimation based on closed-loop adjustments of the adaptive complex weights of the digital beamforming network.  
The cognitive radio approach to channel allocation allows keeping track of the physical location of the users in the network and canceling interference from other sources present, while sensing the spectrum for user assignment in a desired real-time scenario [16]. Thus, there is a need for adequate artifact corrections, in order to reduce the number of phase errors caused by unwanted time-delays inserted into the RF circuit by means of mutual coupling, or fabrication errors in the length of the signal paths [17].
## 4 Sensitivity Analysis
### Effect of the Skew Angle  
The results of the current parametric studies revealed that the skewness of the bridge is indeed a key parameter that has a significant effect on the _LDF_s for both the bending moment and shear force [33; 34]. Figure 8b shows a plot of the effect of skewness on the shear and moment _LDF_s for the external and internal girders of a 45 m, four-lane loads, bridge with a girder spacing of 2.5 m. Here, the bending moment _LDF_s for the external and internal girders decreased by up to 45% and 34%, respectively, as the skew angle rose from 0\({}^{\circ}\) (non-skewed bridge) to 75\({}^{\circ}\). Unlike the moment _LDF_s, the _LDF_s for the shear force rose by approximately 19% and 23%, respectively, for the external and internal girders, as shown in Figure 8b. This indicates that skewness must be considered in the proposed equations for the _LDF_s for bending moment and shear force.
# A New Parameter-Free Slope Unit Division Method That Integrates Terrain Factors
## 3 Experimental Results
### Slope Units Extracted via a Hydrological Method  
Currently, a widely used method for slope unit extraction in the field of geological hazard assessments is the hydrological analysis method proposed by [PERSON] et al. [13]. We will validate the effectiveness of our method by comparing it with the hydrological analysis method.  
Surface hydrological analysis was used to divide slope units on the basis of the DEM, as shown in Figure 4. The steps mainly include DEM inversion, depression filling, flow direction extraction, confluence accumulation calculation, river network generation, catchment basin generation, and raster-to-vector conversion. The core of this process involves identifying valley lines and ridge lines via positive and negative DEM datasets, respectively. By integrating the watershed areas from both datasets and making necessary manual adjustments to ensure reasonable division, the resulting regions separated by watershed divide lines are classified as slope units.  
In this study, the flow accumulation threshold for both the positive and negative DEM datasets was set to 100 for slope unit division. When determining the flow accumulation threshold, we referenced the method proposed by [PERSON] et al. [23], which identifies the appropriate threshold based on the relationship between flow accumulation and stream density. As the flow accumulation threshold increases, stream density initially decreases  
Figure 4: Flowchart of the hydrological analysis method for dividing slope units.  
rapidly and then stabilizes. Generally, the inflection point is chosen as the appropriate threshold for slope unit extraction. However, due to errors in the hydrological method, such as the erroneous classification of parallel pseudovalleys, standard slope units could not be formed, thus limiting further quantitative assessments. Therefore, manual adjustments were made. Following the adjustments, the study area was divided into 1184 slope units.
## 3 Security Analysis
### Parameter Estimation with the Finite-Key Size
For \((\alpha,\beta)\in\{(v,v),(v,a),(a,v),(v,b),(b,v)\}\), \(n_{a\beta}\) can be expressed as:  
\[n_{vv}=2p_{d}(1-p_{d})N_{vv}, \tag{17}\]  
\[n_{va}=n_{av}=2\Big{[}(1-p_{d})e^{-\eta\mu_{a}/2}-(1-p_{d})^{2}e^{-\eta\mu_{a}} \Big{]}N_{va}, \tag{18}\]  
\[n_{vb}=n_{bv}=2\Big{[}(1-p_{d})e^{-\eta\mu_{b}/2}-(1-p_{d})^{2}e^{-\eta\mu_{b}} \Big{]}N_{vb}. \tag{19}\]  
To close the gap between the expected values and observed values, we apply Improved-Chernoff bound [41; 42; 43] to obtain the upper and lower bound of the expected value of \(n_{a\beta}\) considering independent event conditions:  
\[\left\langle n_{a\beta}^{U}\right\rangle=\frac{n_{a\beta}}{1-\delta_{U}}\left \langle n_{a\beta}^{L}\right\rangle=\frac{n_{a\beta}}{1+\delta_{L}}. \tag{20}\]  
where we can obtain the values of \(\delta_{U}\) and \(\delta_{L}\) by solving the following equations:  
\[\frac{1}{2}p_{abort}\|\rho_{S}-\rho_{U}\otimes\rho_{E}\|\leq\varepsilon_{\text {sec}},\]0  
Obviously, from Equations (20) and (23), we can easily obtain the upper and lower bound of the expected value of \(S_{\alpha\beta}\):  
(24)  
Similarly, in the case of the finite-key size, the probability of signal loss of the coherent states from the random set can be calculated as:  
\[\frac{1}{2}p_{abort}\|\rho_{S}-\rho_{U}\otimes\rho_{E}\|\leq\varepsilon_{\text {sec}},\]1  
from the Equation (25), we can conclude the proportion of quantum states reaching [PERSON] with non-randomness:  
\[\frac{1}{2}p_{abort}\|\rho_{S}-\rho_{U}\otimes\rho_{E}\|\leq\varepsilon_{\text {sec}},\]2  
the proportion of quantum states reaching [PERSON] with randomness is:  
\[\frac{1}{2}p_{abort}\|\rho_{S}-\rho_{U}\otimes\rho_{E}\|\leq\varepsilon_{\text {sec}},\]3  
Due to the quantum states in the random part being attenuated, the number of the effective counting rate \(n_{1}\) and the phase-flip error rate \(e_{1}^{ph}\) in the \(Z\) window may change. The quantum states in the non-random set cannot generate the security key, and only the quantum states in the random set may generate the security key. The number of the effective events caused by single-photon states from the non-random set in \(Z\) windows that cannot generate the secret key is:  
\[\frac{1}{2}p_{abort}\|\rho_{S}-\rho_{U}\otimes\rho_{E}\|\leq\varepsilon_{\text {sec}},\]4  
The number of the effective events caused by single-photon states from the random set that can generate the secret key is:  
\[\frac{1}{2}p_{abort}\|\rho_{S}-\rho_{U}\otimes\rho_{E}\|\leq\varepsilon_{\text {sec}},\]5  
where the lower bound of the effective counting rate \(s_{1}^{Z}\) of the finite-key size satisfies [16; 17]:  
\[\frac{1}{2}p_{abort}\|\rho_{S}-\rho_{U}\otimes\rho_{E}\|\leq\varepsilon_{\text {sec}},\]6  
Moreover, in order to estimate the upper bound of \(e_{1}^{ph}\), we need to define two new subsets \(C_{\Delta}^{+}\) and \(C_{\Delta}^{-}\) of \(X_{1}\) windows when \(|\delta_{A}-\delta_{B}|\leq\frac{\Delta}{2}\) and \(|\delta_{A}-\delta_{B}-\pi|\leq\frac{\Delta}{2}\), where we have supposed that \(\psi_{AB}=0\).
# A Matheuristic for Joint Optimal Power and Scheduling Assignment in DVB-T2 Networks +
###### Abstract
More in detail, the contributions of our work are the following:  
* As first step, we show how a mathematical optimization model for DVB-T design can be derived. We address the two crucial decisions of establishing which DVB broadcasting station must serve a specific portion of territory, through binary decision variables, and the power setting of the stations, expressed as continuous decision variables. Furthermore, we include the design of the horizontal antenna diagram of the transmitters. The resulting optimization model is a Mixed Integer Linear Programming (MILP) problem, which includes canonical signal-to-interference ratios to assess service coverage of the territory. * We provide a discussion about the mathematical weakness of the MILP model, due to a typically ill-conditioned coefficient matrix and the presence of the notorious big-M coefficients, and we discuss how to strengthen it through the discretization of the power emissions of the stations, deriving a purely binary power-indexed model, and how to include further strengthening valid inequalities expressing conflicts between station power configurations and territory coverage. * In order to solve the mathematically stronger yet still complex power-indexed model, we propose a new matheuristic, i.e., a heuristic algorithm based on combining mathematical programming techniques and metaheuristics (see e.g., [8; 9; 10]). Specifically, our new matheuristic is based on combining a Genetic Algorithm (GA) with variable fixing heuristics exploiting a suitable (tight) linear relaxation of the problem and an Integer Linear Programming (ILP) improvement heuristic. The purpose of the GA is providing an efficient exploration of the solution space associated with the discrete power emissions of the stations. The relaxation-based fixing heuristic and the ILP heuristic have the task to pursue the improvement of the best solution found by the GA, in particular, by operating a so-called _exact_ large neighborhood search: In an exact search, the search is modeled as an ILP problem which is tackled by exploiting the computational power of a modern state-of-the-art solver [11]. * We report results that we obtained from computational tests considering realistic DVB-T data. The results clearly indicated that our new matheuristic offers a superior performance with respect to a state-of-the-art ILP solver, returning solutions associated with much better values of the objective function expressing service coverage of the users. We remark that this paper is an extended journal version of [12]. The remainder of the paper is organized as follows: in Section 2, we introduce an optimization model for DVB-T2 network design, while in Section 3, we discuss how to mathematically strengthen it; in Sections 4 and 5, we introduce the new matheuristic and report the computational tests, respectively. At last, we discuss conclusions and future directions of research in Section 6.
# Smart Manufacturing Application in Precision Manufacturing
## 6 Outlook on Future Work
### Analytics Stage  
As more data are generated from multiple manufacturing systems at the enterprise level and at the factory floor level, the BI tool (e.g., MS Power BI) hosted on the cloud will need to process and analyze the data to be translated into actionable KPIs to provide insights into deviation diagnoses, problem identification, and the identification of improvement opportunities, which will eventually help companies adapt to changing needs.  
However, smart manufacturing is no longer just about finding ways to operate faster while reducing expenses; it is also about achieving this in a data-driven and intelligent way. Future work should consider utilizing big data with advanced algorithms and models (e.g., machine learning, deep learning, neural networks) to enable predictive analytics to transform manufacturing from being reactive to proactive. Instead of analytics that focuses on descriptive metrics and diagnostic analytics that explain why things have happened, the focus shifts to advanced analytics, \"predictive and perspective intelligence-based analytics\", which predict and find suggested patterns to enable actions to be taken. This will help to discover and reveal new answers to complex problems and can provide decision-based options, which will have a positive impact on operational performance and economic benefits.  
The application of advanced analytics will also shift the gear to the fifth stage, \"AI\", of the smart factory roadmap discussed earlier in Figure 1. This can be achieved by empowering AI applications with advanced intelligent-based analytics that enhance capabilities such as perception, reasoning, continuous learning, and even autonomous decision making, transitioning the industry from automation to autonomy.
## 4 Design Specification
### Foundation Manager Algorithm  
The Foundation Manager component configures the personalized foundational content for the topics to learn by consulting the GPT server and presents the content on the screen. The system first determines the unit and its topic to learn for each student by referring to the 'Table of Learning Progress' as shown in Figure 9.  
Figure 8: Process of personalizing learning content.  
Figure 9: Table of ‘Learning Progress’.  
This table keeps track of the learning progress, and it is created for each registered offering. The system enters the date of completion when a student completes a learning activity. For example, the whole of unit 1 is completed, including exercise submission and its grading. A student finished studying all four topics in unit 2 but did not start the rest of the activities. The student started learning the first two topics in unit 3, even before completely finishing unit 2.  
The control flow of 'Foundation Manager' is specified in Algorithm 1.  
```
Input: Table of Progress Output: Content of Foundation for the Learning Scope
1:genereate_Foundation() {
2://Step 1. Generate the scope to learn.
3:LANG:= get_PL(); //Retrieve the target language to learn
4:If (Auto-Resume) then SCOPE := get_scope() //From Progress Table
5:else
6:SCOPE := get_scope_from_Student(); //A student enters the scope.
7:compound_level:= compute_compound_level();
8://Step 2. Generate a personalized GPT prompt
9:PROMPT:= \"A student wants to learn about <SCOPE> of a programming language <LANG>.\";
10:PROMPT += \"Generate an instructional material for the given topics in the following order.\";
11:PROMPT += \"(1) Introduction to the given topics,\";
12:PROMPT += \"(2) syntax of the relevant language constructs,\"
13:PROMPT += \"(3) examples of using the syntax, and\";
14:PROMPT += \"(4) additional guidelines for utilizing the language constructs.\"
15:PROMPT += \"Personalize the learning contents for this student who has the following features;\"
16:PROMPT += \"The student's age is \" +toString(current_Student.getAge());
17:PROMPT += \"Applications of interest are in the order of \" +toString(domain_list);
18:PROMPT += \"The student's language proficiency level is \" +toString(compound_level);
19://Step 3. Submit the prompt to and receive the response from GPT server.
20:RESPONSE:= GPT_Server.chat(PROMPT);
21:RESPONSE:= verify_Foundation(RESPONSE);
22://Step 4. Display the learning foundation
23:foundation_content:= format(RESPONSE);
24:display(foundation_content);
25://Step 5. Store the current session on database.
26:store_session(LANG, SCOPE, PROMPT, RESPONSE);
27:END
```  
**Algorithm 1**Control Flow of 'Foundation Manager'  
Note that the GPT prompt for generating the foundation to learn is personalized with student-specific features such as age, domain, and proficiency level, as shown in step 2.
# Distance Estimation with a Stereo Camera and Accuracy Determination
###### Abstract  
Distance measurement plays a key role in many fields of science and technology, including robotics, civil engineering, and navigation systems. This paper focuses on analyzing the precision of a measurement system using stereo camera distance measurement technology in the context of measuring two objects of different sizes. The first part of the paper presents key information about stereoscopy, followed by a discussion of the process of building a measuring station. The Mask R-CNN algorithm, which is a deep learning model that combines object detection and instance segmentation, was used to identify objects in the images. In the following section, the calibration process of the system and the distance calculation method are presented. The purpose of the study was to determine the precision of the measurement system and to identify the distance ranges where the measurements are most precise. Measurements were made in the range of 20 to 70 cm. The system demonstrated a relative error of 0.95% for larger objects and 1.46% for smaller objects at optimal distances. A detailed analysis showed that for larger objects, the system exhibited higher precision over a wider range of distances, while for smaller objects, the highest accuracy was achieved over a more limited range. These results provide valuable information on the capabilities and limitations of the measurement system used, while pointing out directions for its further optimization.  
distance measurement; stereo vision; image processing; accuracy determination Article
###### Abstract  
This paper aimed to assess the effects of feeding fresh forage on volatile organic compounds (VOCs) of buffalo milk and mozzarella cheese. Sixteen lactating buffaloes were equally allotted into two groups fed diets containing (experimental (Exp) group) or not (control (Ctl) group) 20 kg/d of fresh sorghum. Milk from the groups was separately collected and transformed in the traditional 'Mozzarella di Bufala Campana' Protected Denomination of Origin (PDO). Three batches of mozzarella were produced for each diet and they were analyzed, along with the two bulks of milk, for VOC composition, by using solid phase micro-extraction (SPME) coupled with gas-chromatography/mass spectrometry (GC/MS). The use of fresh forage increased the levels of long chain fatty acids along with the contents of aldehydes, and this could be responsible for an increase in green notes of milk. The use of the Ctl diet, containing a higher proportion of silage, increased the ketones, acids, and esters, which are compounds that could raise the cheese and fruity notes of milk. The mozzarella was less affected by the dietary treatment than milk. The use of fresh forage (sorghum) enhanced the green notes of milk and induced a few changes in the VOC profile of the typical PDO Mozzarella di Bufala Campana cheese, that were nonetheless detectable by sensory analysis. The low level found for butanoic acid, 2,3-pentanedione, and propyl acetate in mozzarella cheese obtained with fresh forage diet can lead to perceive less the olfactory notes of cheese, cream, and fruit.  
buffalo milk; buffalo Mozzarella; fresh forage; silage; volatile organic compounds; SPME-GC/MS +
Footnote †: journal: molecules
# Morphological Reconstruction for Variable Wing Leading Edge Based on the Node Curvature Vectors
## 4 Experimental Validations
### Results and Discussion  
#### 4.2.1 Comparison of Curvature and Strain Reconstruction Errors between LI-CPM and NCV-CPM  
Taking the leading-edge deflection angle of 12\({}^{\circ}\) as an example, based on the method presented in Section 2.2, the reconstructed skin curvature field and strain field after unfolding the variable wing leading edge were obtained. These distributions are depicted in Figure 15a,b. The extremum of the reconstructed curvature distribution was located at an arc length of 500 mm, while the reconstructed strain field exhibited a sudden change in the position of the main spar. Both phenomena were consistent with the results obtained from numerical simulations.  
Compared to the conventional curvature recursion method (LI-CPM), the average relative error of the curvature reconstruction for the validation points on Path B of the wing leading edge decreased from 25.09% to 6.32%, and the root mean square error decreased from 2.15 um\({}^{-1}\) to 0.81 um\({}^{-1}\), as shown in Figure 15a.  
The experimental results demonstrated that the conventional curvature recursion method, due to neglecting the initial curvature and sole reliance on linear interpolation to obtain curvature information for unattached sensor positions, resulted in accumulated errors between the calculated curvature field distribution and the actual curvature distribution. In contrast, the proposed method in this study, by sequentially constructing a strain-curvature function that could characterize the dynamic variation in the curvature of the variable leading edge and solving the curvature node vectors of the arc segments, effectively mitigated the impact of the aforementioned cumulative errors on the accuracy of the curvature field reconstruction.  
As depicted in Figure 15b, this method, based on the curvature-strain mapping function and the characteristics of the reconstructed curvature field distribution, could also achieve the inverse reconstruction of the strain field of the wing leading edge. This  
Figure 14: Layout scheme of strain-sensing measuring points in staggered complementary mode.  
provided data support for the structural health monitoring and fatigue prediction of the wing leading edge.
## 3 Optimal Design of the FCPM Model
### LHS (Step II)  
To design the optimal shape of the rotor structure, the design variables were selected, and the sampling range of the selected design variables was determined using the experimental design method [24]. In the optimal design process, the LHS technique was used to determine the experimental points. In the LHS technique, the experimental points are represented as a matrix of \(n\) rows and \(k\) columns, and each row is arranged as an experimental point, where \(n\) is the number of levels and \(k\) is the number of design variables. The characteristic of the LHS technique is that it is easy to implement without causing any overlap between the experimental points depending on the design level at the time of model construction. Two-dimensional FEM is used to calculate the characteristics of the model based on the design variables for each experimental point. Table 5 shows the analysis results corresponding to each DOE case.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Design Variable**} & \multirow{2}{*}{**Unit**} & \multicolumn{2}{c}{**Design Limits for x1**} \\  & & **Minimum** & **Maximum** \\ \hline R1 (inner radius) & mm & 3.5 & 6.5 \\ R2 (outer radius) & mm & 6.5 & 9.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Inner and outer radius for x1 setup.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Design Variable**} & \multirow{2}{*}{**Unit**} & \multicolumn{2}{c}{**Design Limits**} \\  & & **Minimum** & **Maximum** \\ \hline x1 (magnet angle, \(\copyright\)) & deg. & 55 & 95 \\ x2 (polar angle, \(\copyright\)) & deg. & 25 & 45 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ranges of the design variables for LHS.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Number of**} & \multirow{2}{*}{**x1**} & \multirow{2}{*}{**x2**} & \multirow{2}{*}{**Back-EMF**} & \multirow{2}{*}{**THD**} & \multirow{2}{*}{**Cogging Torque**} \\  & [Deg.] & [Deg.] & [Vrms] & [\%] & [Nm] \\ \hline
1 & 61.5 & 38.4 & 28.77 & 43.6 & 0.134 \\
2 & 78.4 & 37.5 & 31.46 & 9.1 & 0.101 \\
3 & 82.7 & 40.2 & 31.20 & 9.8 & 0.08  
The analysis result values shown in Table 5 represent the cogging torque, back-EMF, and THD according to the DOE model. The kriging model was used to obtain a more accurate approximation model based on the nonlinear analysis results. The GA is applied to find the optimal solution. Optimal design and optimal point convergence using GA are covered in the next section.