esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization Techniques for Design Problems in Selected Areas in WSNs: A Tutorial
## 5 Fundamentals of Nonlinear Optimization
An **improving feasible vector** A feasible direction vector \(\mathbf{d}\
eq\mathbf{0}\) at \(\bar{\mathbf{x}}\) is called an improving feasible direction vector if: \[f\left(\bar{\mathbf{x}}+\lambda\mathbf{d}\right)<f\left(\bar{\mathbf{x}} \right)\quad\forall\quad 0\leq\lambda\leq\frac{\epsilon}{\|\mathbf{d}\|}\] An important remark is that for \(\mathbf{d}\
eq\mathbf{0}\) and \(\mathbf{d}^{T}\
abla f\left(\bar{\mathbf{x}}\right)<0\), \(\mathbf{d}\) is an improving feasible direction vector at \(\bar{\mathbf{x}}\). 7. **Convex function** is a function \(f\left(\mathbf{x}\right)\) that satisfies: \[\mathbf{h}\left(\mathbf{x}\right) =\mathbf{0} \tag{6b}\]0 where \(0\leq\lambda\leq 1\), this is illustrated in \[\mathbf{h}\left(\mathbf{x}\right) =\mathbf{0} \tag{6b}\]1 A **concave function** is a function \(f\left(\mathbf{x}\right)\) that satisfies: \[\mathbf{h}\left(\mathbf{x}\right) =\mathbf{0} \tag{6b}\]2 Strictly convex or concave functions are ones that _strictly_ satisfy the inequalities (7) and (7) respectively. \[\mathbf{h}\left(\mathbf{x}\right) =\mathbf{0} \tag{6b}\]3 Taxonomy of nonlinear programming (NLP) optimization methods. 8. **Convex set**: A set \(\mathbb{S}\) is convex, if and only if a line segment connecting any two points \(\mathbf{x}^{i},\mathbf{x}^{i}\in\mathbb{S}\) falls entirely in the set \(\mathbb{S}\). This is illustrated in \[\mathbf{h}\left(\mathbf{x}\right) =\mathbf{0} \tag{6b}\]4
9. A **convex NLP** is a minimization problem that consists of a convex objective function and a convex feasible set of solutions (feasible regions). A **concave NLP**, is a maximization problem in which the objective function is concave, and the feasible region is a convex set of feasible solutions. It is worth saying that for a convex (or concave) NLP, every local optimum solution is also globally optimum. Also, another point that is worth mentioning about convex NLPs, is that a sufficient condition for having a convex region is that all the constraint functions \(g_{i}\left(\mathbf{x}\right)\) be convex functions. One important aspect related to the convexity of multivariate functions is its _Hessian Matrix_\(\
abla^{2}f\left(\mathbf{x}\right)\) of the function. A convex function must yield a _positive semi-definite_ Hessian matrix, i.e., \(\mathbf{u}^{T}\
abla^{2}f\left(\mathbf{x}\right)\mathbf{u}\geq 0\;\;\forall \mathbf{u}\in\mathbb{R}^{n}\). One final word before discussing the Lagrangian function in the following subsection, is that for any local critical point of a function, be it a minimum, maximum or a saddle point, the gradient of the function is necessarily a zero vector, i.e., \(\
abla f\left(\mathbf{x}\right)=\mathbf{0}\).
# Power Profiling of Smart Grid Users Using Dynamic Time Warping +
## 6 Potential Privacy Issues with Power Profiling
### Risks of Malicious Exploitation  
Unauthorized access to detailed power consumption data presents a significant cyber-security risk. If utility providers or third parties were to release user consumption profiles, individuals could face threats such as energy fraud [79; 80; 81], phishing attacks, or targeted scams [82]. Furthermore, power profiling can reveal when a home is unoccupied, increasing the risk of burglary or other physical security threats [83]. Attackers could also leverage energy patterns to tailor ransomware or phishing campaigns based on user-specific vulnerabilities. To counter these risks, smart grid systems should enforce strong encryption,access control policies, and real-time anomaly detection to detect and prevent unauthorized data access.
## 1 Introduction  
Given the prediction of a global population increase to 9.8 billion by 2050 by the UN Population Division [1], and also considering the Lancet study that indicates a population peak of 9.7 billion in 2064 [2], the food production and agriculture sector faces significant challenges. These challenges are intensified by urbanization, reduction of arable land, and weather extremes due to climate change, making agricultural greenhouses vital solutions not only for food production under harsh conditions but also as platforms for the implementation of advanced technologies that seek to optimize agricultural production [3; 4; 5; 6].  
At the forefront of protected agriculture, recent scientific research has focused on optimizing the design and functionality of greenhouses, with the aim of achieving superior operational and energy efficiency. Researchers such as [7] highlight the crucial importance of innovation in greenhouse design, highlighting how the choice of materials can significantly influence energy efficiency. In addition, the integration of advanced technologies, as illustrated in the work of [4; 8], demonstrates an ongoing commitment to increasing energy efficiency, contributing to the reduction in resource consumption and greenhouse gas emissions. These innovations not only increase the economic viability of agricultural operations but also respond to the urgent need for sustainable food production, aligning with the goals of environmental sustainability and long-term climate resilience.  
Figure 1 highlights the complexity of the factors that affect energy efficiency in agricultural greenhouses, including the interconnection of construction and thermal elements. This comprehensive review study explores site selection to the implementation of material innovations and thermal management techniques, such as the use of air/ground exchangers, from a holistic perspective focused on sustainability. This article addresses the main  
factors that influence energy performance. However, it only briefly mentions Automation and Control Strategies and the use of Water Resources without providing in-depth details on these details.  
Technologies such as passive solar heating, discussed by [9], and the use of geothermal energy combined with Earth-Air Heat Exchanger (EAHE) systems for thermal optimization, explored by [10; 11; 12], are highlighted in this study. In addition to showcasing these emerging technologies, the study aims to identify gaps in current knowledge and suggest future directions for research and development. The goal is to provide engineers, researchers, and farmers with information on efficient solutions, promoting sustainable and energy-efficient agriculture, and addressing the growing global challenges.
# Wavenumber-Frequency Analysis of Internal Aerodynamic Noise in Constriction-Expansion Pipe
## 3 Governing Equations and Numerical Methods  
### Governing Equations  
As the governing equations for the fluid flow in a pipe, the compressible Navier-Stokes equations are used in the following form:  
\[\frac{\partial\rho}{\partial t}+\
abla\cdot\left(\rho\overset{\rightarrow}{v} \right)=0 \tag{15}\]  
\[\frac{\partial}{\partial t}\left(\rho\overset{\rightarrow}{v}\right)+\
abla \cdot\left(\rho\overset{\rightarrow}{v}\right)=-\
abla p+\
abla\cdot\overset{ \rightarrow}{\tau}+\overset{\rightarrow}{F} \tag{16}\]  
where \(\rho\) is the fluid density, \(\overset{\rightarrow}{v}\) is a velocity vector, \(p\) is the static pressure, \(\overset{\rightarrow}{\tau}\) is the viscous stress tensor, and \(\overset{\rightarrow}{F}\) is the external body force. The stress tensor, \(\overset{\rightarrow}{\tau}\), is found as follows:  
\[\overset{\rightarrow}{\tau}=\mu\left[\left(\
abla\overset{\rightarrow}{v}+ \
abla\overset{\rightarrow}{v}^{T}\right)-\frac{2}{3}\
abla\cdot\overset{ \rightarrow}{v}I\right] \tag{17}\]  
where \(\mu\) is the dynamic viscosity, and \(I\) is the unit tensor. The energy conservation is described by the following equation:  
\[\frac{\partial}{\partial t}(\rho E)+\
abla\cdot\left(\overset{\rightarrow}{v }(\rho E+p)\right)=-\
abla\cdot\left(\sum h_{j}J_{j}\right)+S_{h} \tag{18}\]  
where \(E\) is the energy per unit mass, \(h_{j}\) is the heat transfer coefficient, and \(J_{j}\) and \(S_{h}\) are the diffusion flux and heat source, respectively.
###### Abstract  
In this article, we present an analysis of the accuracy level of methods for modeling the multi-service overflow systems that service Erlang, Engel, and Pascal traffic. In systems with traffic overflow, new calls that cannot be serviced by the primary resources are overflow (directed) to other available resources that can service a given call, that is, to the secondary resources (alternative resources). In the article, we focus on studying the influence of methods for determining the parameters of traffic that overflows to the secondary resources on the accuracy of determining the traffic characteristics of overflow systems. Our analysis revealed that the main source of the inaccuracy of the existing methods is their approach to determining both the average value and the variance of multi-service Pascal traffic streams offered to the secondary resources. Therefore, we proposed a new method for determining the parameters of Pascal overflow traffic. The method is based on the decomposition of multi-service primary resources into single-service resources and the subsequent conversion of Engel and Pascal streams into equivalents of Erlang traffic. The results of the analytical calculations obtained on the basis of the new method are then compared with the results of simulation experiments for a number of selected structures of overflow systems that service Erlang, Engel, and Pascal traffic. The results of the study indicate that the proposed theoretical model has a significantly higher accuracy than the models proposed in the literature. The method can be used in the analysis, dimensioning, and optimization of multi-service telecommunication systems composed of separated resources, for example, mobile cellular systems.  
telecommunications networks; performance evaluation; analytical modeling; traffic overflow +
Footnote †: journal: Electronics  
0
Footnote 0: Copyright**: © 2021 by the authors. Licenses MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 5 Experiments and Evaluation  
In order to make an experimental sensing field, we deployed ten sensor motes. One mote is connected to TinyONet as the base station of the sensing field and the remained nine motes are deployed in three rooms. We have located three motes in each room's corner in order to evaluate the effectiveness of TinyONet. We conducted two experiments to examine the response time and the accumulated number of generated packets on WSN.
## 2 Materials and Methods
### Statistical Analysis  
The statistical analysis was based on raw scores. A single operator who was blinded to patient allocation (with treatment allocations therefore masked in the dataset) performed the statistical analysis. Data were analyzed using conventional descriptive statistics and inferential statistics. Means and standard deviations are presented for all variables, both cephalometric and dental measurements.  
Figure 6: Cephalometric analysis and landmarks. Landmarks: A, most posterior point of the frontal concavity of the maxilla between the anterior nasal spine and the alveolar process; B, innermost point of the lower jaw region, between the alveolar process and pogonion; N (nasion), most anterior point of the junction of the nasal and frontal bone (frontonasal suture); S (sella), center of the hypophyseal fossa; Go (gonion), midpoint of the curvature at the angle of the mandible; Gn (gathion), point of the mandibular symphysis on the facial axis; Me (menton), most inferior point of the mandibular symphysis; ANS (anterior nasal spine), the tip of anterior nasal spine; and PNS (posterior nasal spine), most posterior point of the bone palate. Reference: NA (nasion A line)—line through N and A; NB (nasion B line)—line through N and B; SN (sella-nasion line)—line through S and N; MP (mandibular plane)—line connecting the Go point to the Gn point; posterior facial height—line connecting the S point to the Go point; anterior facial height—line connecting the N point to the Me point; PP (palatal plane)—line connecting ANS and PNS. Linear distances/skeletal landmarks: the angular measurements are represented by: SNA (angle formed by the intersection of lines SN and NA); SNB (angle formed by the intersection of lines SN and NB); ANB (difference between angles SNA and SNB); SN—GoGn (craniomathular angle)—indicates the degree of divergence of the mandible from the cranial base; SN’ANS-PNS—indicates the degree of divergence of the maxilla from the cranial base; MM (bimaxillary angle); ANS/PNS’GoGn—indicates the degree of divergence of the mandible from the maxilla; P-A face height (S-Go/N-Me).  
Statistical significance was set at \(p<0.01\), and significant results at \(p<0.001\) and \(p<0.05\) are alternatively reported when necessary. Analyses were performed using SPSS version 20.0 (SPSS IBM, New York, NY, USA). In order to accept or reject the null hypothesis H0: u1 = u2, where u1 is the mean of the population of patients with the two-banded expander and u2 the mean of the population with the splint resin expander, the \(t\)-test was applied to compare variations between initial cephalometric measurements and dental (maximly and mandibular) measurements at baseline (T0). Subsequently, the paired \(t\)-test was applied to compare significant differences between the two groups related to cephalometric and dental measurements, both at T1. The \(t\)-test was also applied to compare the differences in cephalometric and dental measurements at T1 and T0 between the two groups.
## 4 Conclusions  
In this paper, we propose a method that combines social network analysis and machine learning techniques to help decision makers estimate the managerial potential of the members in an organization. First, we describe the five types of interactions commonly found in organizations and staff attributes for training. In addition, we propose to adopt PageRank to represent the importance of nodes with different types of interactions. We also propose to use word embedding to extract the textual features with phrases. These newly introduced features are shown to be effective for predictions in traditional machine learning models. We also introduce the use of graph convolutional networks (GCN) to analyze the intensity graph. It is found that the intensity graph provides complementary information in addition to the five types of interactions using the graph neural network.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**(In Percentage)** & **SVM-Linear** & **SVM-Gaussian** & **SVM-Sigmoid** & **k-NN** & **MLP** \\ \hline Features * & 65.07 & 70.48 & 65.52 & 67.07 & 72.30 \\ Features * + PageRank & 69.48 & 71.33 & 67.57 & 68.02 & 72.51 \\ Features * + word2 vec * & 77.38 & 74.38 & 73.98 & 74.93 & 76.90 \\ Features * + PageRank + word2 vec * & 78.33 & 76.93 & 75.29 & 75.40 & 78.55 \\ \hline \hline \end{tabular}
* Node feature,
* Word embedding using word2 vec.  
\end{table}
Table 8: Model accuracy by traditional learning approach.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Size of Hidden Neurons in** & & & & \\
**Graph Convolutional** & **Features** & **Features + PageRank** & **Features + word2 vec** & **Features + PageRank + word2 vec** \\ \hline
256 & 77.67 & 77.48 & 77.9 & 76.95 \\ \hline
1024 & 79.52 & 80.02 & 79.38 & 79.64 \\ \hline \hline \end{tabular} (in percentage).  
\end{table}
Table 9: Model accuracy by graph convolutional network.
## Results
#### Comparison of the Results
This analysis is crucial for understanding how each approach utilises the available resources and aligns with the maintenance objectives, particularly highlighting the trade-offs between resource consumption and maintenance outcomes across different strategies. The analysis of resource utilisation reveals significant trade-offs between different maintenance strategies. The PH strategy uses nearly 100% of available person-hours and achieves the highest corrosion reduction, but it does not optimise for regulatory demands or system criticality. In contrast, the target strategy demonstrates high efficiency by achieving the desired corrosion levels while consuming significantly fewer PH, though it also does not account for regulatory demands or criticality factors. The criticality and regulatory demand strategies utilise almost all the available person-hours while meeting the corrosion target. Still, each prioritises different objectives--the former focuses on critical systems while the latter addresses regulatory compliance requirements. These results demonstrate that APM can effectively adjust the resource allocation according to the strategic priorities. This adaptability allows maintenance managers to choose the strategy that best aligns with their particular requirements: whether to prioritiise maximum corrosion reduction (the PH strategy), resource efficiency (the target strategy), critical system maintenance (the criticality strategy), or regulatory compliance (the regulatory demand strategy). The choice ultimately depends on the platform's current priorities and constraints.
# Combining Dilution of Precision and Kalman Filtering for UWB Positioning in a Narrow Space
## 3 Experiments and Results
### Experimental Results of Positioning
#### 3.3.2 Dynamic Positioning  
In the experiment, the handheld tag moved at a constant speed according to the preset trajectory. During this period, the system performed positioning every 0.1 s and stopped data acquisition when it reached the end point. In Scene A, the tag started from (\(-0.5\), 0) and moved around a rectangle with a length of 24 m and a width of 1 m. In Scene B, the tag started at (0.5, 0) and moved around a rectangle with a length of 20 m and a width of 1.5 m. Since the LS method has a poor positioning effect in this space, its positioning was not presented in the report. The positioning results of WLS and the proposed algorithm are shown in Figures 11 and 12. The solid line represents the real trajectory of the moving object.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Tag Number**} & \multicolumn{3}{c}{**Scene A**} & \multicolumn{3}{c}{**Scene B**} \\ \cline{2-7}  & **LS (m)** & **WLS (m)** & **Fusion (m)** & **LS (m)** & **WLS (m)** & **Fusion (m)** \\ \hline T1 & 1.648 & 0.328 & **0.311** & 0.412 & **0.157** & **0.157** \\ T2 & 0.807 & 0.256 & **0.199** & 0.268 & 0.124 & **0.123** \\ T3 & 1.420 & 1.125 & **0.171** & 0.260 & **0.177** & **0.177** \\ T4 & 0.211 & **0.210** & 0.212 & 0.030 & 0.030 & **0.028** \\ T5 & 1.347 & 0.759 & **0.110** & 0.090 & 0.090 & **0.088** \\ T6 & 0.189 & 0.118 & **0.095** & 0.082 & **0.046** & 0.047 \\ T7 & 1.139 & 0.221 & **0.210** & 0.055 & 0.095 & **0.095** \\ T8 & null & null & null & 0.314 & **0.101** & **0.101** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Positioning error of each point in static experiment.  
Figure 10: RMS error of different points in Scene B.  
According to Figure 11, in Scene A, when the tag position was solved by WLS, the positioning result had a large oscillation, and the positioning trajectory was quite different from the real trajectory. However, when the fusion positioning was used to solve the tag position, the positioning result is smoother and the whole positioning trajectory is roughly consistent with the real trajectory. The calculation showed that the RMS errors of the WLS positioning algorithm and the fusion positioning algorithm were 34.91 and 21.17 cm, respectively. The accuracy of the fusion positioning algorithm was 39.36% higher than that of the WLS positioning algorithm.  
According to Figure 12, in Scene B, whether WLS or the fusion positioning algorithm were used to solve the tag position, the positioning results of the two were not much different, and both trajectories fit well with the real trajectory. The RMS errors of the WLS positioning algorithm and the fusion positioning algorithm were 11.25 and 10.34 cm, respectively. In this scene, the average accuracy of the fusion positioning algorithm was 8.09% higher than that of the WLS positioning algorithm.  
Figure 11: Dynamic trajectory diagram in Scene A.  
Figure 12: Dynamic trajectory diagram in Scene B.
## 4 Results and Analysis
### Acceleration Response of the Foundation  
Figure 11 is the acceleration change curve of measuring line 1 (see Figure 7) on the natural foundation and composite foundation crossing the ground fissure at three angles. Under the three intersection angles, the acceleration response of the composite foundation was significantly smaller than that of the natural foundation, and the fluctuation at the ground fissure was reduced, which indicated that the composite foundation can effectively control the problem of weak stratum caused by the ground fissure. In both the composite foundation and natural foundation, the change in acceleration exhibits a jump phenomenon in the formation on both sides of the ground fissure, specifically, the acceleration reaches its maximum at the position of the hanging wall close to the ground fissure, especially obvious when \(\theta=30^{\circ}\). Therefore, crossing the ground fissure zone at a small angle poses a serious threat to the safe operation of the train. It is recommended to cross the ground fissure at a large angle.  
Figure 12 is the horizontal variation curve of the acceleration amplitude inside the foundation (measuring lines 4 and 5; see Figure 7). The acceleration inside the composite foundation is symmetrically distributed along the center line of the subgrade, and the acceleration amplitude near the center line of the high-speed railway is large but small on both sides. The acceleration on the hanging wall is significantly greater than that of the football. The response difference of acceleration first decreases from the center of the line to both sides and increases suddenly at the edge of the subgrade, which is caused by the propagation of vibration waves in different media.  
Figure 13 shows the acceleration attenuation curve of natural and composite foundations (measuring lines 2 and 3) with buried depth at the hanging wall and footwall of the ground fissure site. In the two types of sites, the difference of acceleration response of the embankment in the hanging wall and footwall is small, and the difference inside the foundation is large. The acceleration attenuation amplitude of the composite foundation is greater than that of the natural foundation. The acceleration attenuation of the natural foundation hanging wall is about 92.7%, and that of the footwall is about 96.7%, while that of the composite foundation hanging wall is about 95.5% and that of the footwall is about  
Figure 11: Variation curves of stress amplitudes across ground fissures at different angles in two kinds of foundation forms (measuring line 1): (**a**) \(\theta=90^{\circ}\); (**b**) \(\theta=60^{\circ}\); (**c**) \(\theta=30^{\circ}\); (**d**) schematic diagram of the location of the measuring line and the cross-ground fissures at different angles.  
96.9%. The critical influence depth is reduced from 25 m for the natural foundation to 15 m for the composite foundation. This is because the CFG pile bears part of the train load, the soil around the pile is less affected by the vibration, and the composite foundation has a good restraining effect.