esa-sceva/satcom-chunk-collection huggingface dataset:
# Polynomial Expressions for the Thermal Efficiency of the Parabolic Trough Solar Collector
## 3 Results  
### Initial Analysis  
The first part of this analysis is to investigate the relationship between thermal efficiency and the usually used parameters. Generally, the correlation of the thermal efficiency and the parameters \"\(\Delta T\)\"/\(G_{b}\)\" (m = 1, 2, 3, 4) is suggested by the literature. Figure 2 shows these correlations, as well as the correlation between thermal efficiency and \"\(\Delta T\)\". The results are given for different solar beam irradiation levels and more specifically for 400, 600, 800 and 1000 W/m\({}^{2}\). Figure 2a shows that there is not an important correlation between the thermal efficiency and the parameter \"\(\Delta T\)/\(G_{b}\)\" because the curves are not all together but they have different trends for different solar irradiation levels. Similar results are found for Figure 2e with the parameter \"\(\Delta T\)\" in the horizontal axis. Figure 2b shows that the curves are closer to each other, especially in low values of the parameter \"\(\Delta T\)/\(G_{b}\)\", but it is not enough to describe the thermal efficiency with only the parameter \"\(\Delta T^{2}\)/\(G_{b}\)\". However, the situation is different in Figure 2c,d for the parameters \"\(\Delta T^{3}\)/\(G_{b}\)\" and \"\(\Delta T^{4}\)/\(G_{b}\)\" respectively. More specifically, the curves are very close to each other when there is a plotting with the parameters \"\(\Delta T^{3}\)/\(G_{b}\)\" and \"\(\Delta T^{4}\)/\(G_{b}\)\" and especially in the case with \"\(\Delta T^{3}\)/\(G_{b}\)\", which seems to be the best one. However, a deeper analysis is needed in order for the final conclusions to be extracted. In any case, it has been found that there is a need for using terms of high power and also there is a need for using more than one parameter in order to predict the thermal efficiency in an accurate way.  
The next step is the detailed analysis of the correlation of the thermal efficiency with simple expressions of the format \"\(a_{0}+a_{\rm m}\cdot\Delta T^{m}\)/\(G_{b}\)\" by using 120 operating points. Figure 3 depicts the obtained results. The valuation indexes (R\({}^{2}\) and MAPE) are also included in these figures. The vertical axis is the value of the predicted efficiency while the horizontal axis shows the real value of the efficiency which has been calculated with EES. The results show that the use of the third power term leads to the  
To conclude, the use of the third power term leads to high accuracy, compared to the other cases, and especially when it is combined with the \"\(\Delta T\)\" it leads to a very reliable model. In any case, the use of the extra term \"\(\Delta T\)\" helps in all the cases and the expressions \"\(a_{0}+a_{\text{m}}\cdot\Delta T^{m}/G_{b}+b\cdot\Delta T\)\" are more accurate than the respective \"\(a_{0}+a_{\text{m}}\cdot\Delta T^{m}/G_{b}\)\", which is a reasonable result.
## 1 Introduction
As a summary, the main contributions of this paper are as follows:  
Figure 1: Contention-based random access procedure and physical random access channel: (a) Contention based random access procedure; (b) Physical random access channel. * The framework of the proposed HSTMAC protocol is detailed in this paper. The reserved uplink RBs are divided into TDMA part for H2H traffic with large size packets and S-ALOHA part for M2M traffic with small size packets. The foundation of HSTMAC protocol is proved through system capacity analysis. * Considering the impact of ACB factor and backoff scheme, this paper derives the analytical performance of HSTMAC iteratively. Then the system equilibrium under arbitrary retransmission limit is analyzed rigorously, which can provide an insight on QoS provision. * Based on the equilibrium analysis, a dynamic pre-backoff algorithm is designed for QoS guarantee by adaptively scattering new M2M arrivals over the transmission interval according to the highly synchronized traffic load. * The superiority of HSTMAC in terms of success possibility, resource utilization ratio, delay and average access request number, is validated through numerical and simulation results. Compared to uniform pre-backoff (UPBO) algorithm, the proposed DPBO algorithm can achieve a higher success probability with a much reduced delay. The remainder of this paper is organized as follows: Section 2 gives a brief review of the related works. Section 3 presents the frame structure of HSTMAC and its fundamental motivation. We exploit an iterative method to analyze the performance of HSTMAC in Section 4. Then we derive a more rigorous equilibrium analysis in Section 5, in which we also design a dynamic pre-backoff scheme. In Section 6, the performance of HSTMAC is analyzed through numerical and simulation results. We conclude this paper in Section 7.
## 3 Design Approach
### Open Source Network Stack
#### 3.2.1 Localization Algorithm  
For the position calculation, a particle filter implementation is available by default, based on the work of Reference [24]. The particle filter uses a set of N particles that represents different possible states. The states of each particle are updated with each incoming range, resulting in a position update for every reporting message. The particle server requires incoming ranges from at least three different anchors for a good 2D position estimate and at least four different anchors on different heights for a 3D estimate.  
Every incoming position estimate is published as an MQTT [25] resource, which is available either on the mobile tag or transmitted through one of the radio interfaces to a back-end server. By subscribing to the relevant MQTT topic, new localization algorithms can easily be implemented. Similarly, each position estimate is published as an MQTT topic, allowing easy integration with existing systems.
# UnifiedFace: A Uniform Margin Loss Function for Face Recognition
## 3 Our Method
### Margin-Based Loss Functions  
The traditional softmax loss function, which does not explicitly encourage enhanced intra-class compactness and inter-class separation, is expressed as follows:  
Figure 3: Structural design of GF-Bottleneck. Ghost module consists of normal convolution and linear operations (LO). DWConv represents depthwise separable convolutions. (**a**) Stride = 1 and (**b**) Stride = 2.  
\[L_{softmax}=-\frac{1}{N}\sum_{i}^{N}log(\frac{e^{W_{y}^{T}x_{i}+b_{y}}}{\sum_{j}^{n }e^{W_{j}^{T}x_{i}+b_{j}}}) \tag{5}\]  
where \(N\) is batch size and \(n\) is the number of classes. \(b_{y}\) indicates the bias term for the \(y\)-th class.  
To effectively alleviate this problem, margin-based softmax loss functions are proposed with great success. Commonly, the margin-based softmax constraint redefines logit as the following equation:  
\[W_{j}^{T}x_{i}=\parallel W_{j}\parallel\parallel x_{i}\parallel cos\theta_{j} \tag{6}\]  
where \(\theta_{j}\) denotes the angle between the feature and the \(j\)-th weight vector. The feature is normalized by \(L_{2}\) normalization and then re-specified as \(s\). The weight is fixed to 1 by \(L_{2}\) normalization.  
In [7], \(cos\theta\) is replaced by the target angle activation function (non-target angle activation function). The margin-based softmax is uniformly defined as follows  
\[L=log(1+\sum_{i\
eq y}e^{Q(\theta_{y}\theta_{i},s,m)}) \tag{7}\]  
\[Q(\theta_{y},\theta_{i},s,m)=s\cdot(\eta(\theta_{i})-\eta(\theta_{y})+\Delta( \theta_{y}) \tag{8}\]  
The \(m\) indicates the margin, including additive margin or multiplicative margin. \(\eta(\theta_{i})-\eta(\theta_{y})\) denotes the confidence difference of various classes. \(\Delta(\theta)\) is the characteristic function to describe angular margins, including the size of the margin and the training stability. The characteristic function is defined as follows:  
\[\Delta(\theta)=\eta(\theta)-\psi(\theta)>0 \tag{9}\]  
where \(\eta(\theta)\) and \(\psi(\theta)\) are defined as the target activation function and non-target activation function, respectively. In particular, different \(\eta(\theta)\) and \(\psi(\theta)\) constitute various margin-based loss functions, as shown in Table 2. A comparison of the different margin-based loss functions for the characteristic functions is given in Figure 4a. Despite the great progress of these margin-based loss functions, the problem of how to introduce margins more efficiently remains a tricky one. That is, designing a simple and excellent performance angular margin is still a problem. It is natural to think of unifying additive and multiplicative margins to solve this problem.  
Figure 4: (a) Comparison between UnifiedFace and existing methods (b) Comparison of UnifiedFace at different \(m\).
## 3 Research Case Study
### Experimental Tests
#### 3.1.2 Exposure Analysis  
After data gathering and pre-posterior analysis, it is necessary to analyze surface damage to assess exposed defects [44]. The critical zone was selected based on initial data gathering and the abutment of the bridge. The durability feature has a direct link with the surface defect and drainage location is a potential point for surface damage. The traditional  
Figure 4: Case study subject–RC element.  
method relies on subjective interpretation based on visual inspection. Meanwhile, image processing software monitors the rate of defect expansion. Figure 5A,B illustrates the result of this analysis.  
The measurement of the damaged area has been carried out using automated software in this research. Although the surface of the concrete is intact at the first step of data gathering, this zone is exposed to wastewater due to poor drainage design and damage over time.  
Therefore, the first row in Figure 5A,B (number 1-4) depicts intact concrete before surface damage at the first step of gathering data. The second row in both pictures (number 5-8) demonstrates the damaged surface over time. Therefore, based on Figure 5A, the damages reveal minor damage over a short time. Figure 5B shows significant defects over a longer time when compared to the first image. The third row (number 9-12) is a result of a comparison between intact concrete and damaged concrete over time. As the first data gathering session, based on Figure 5A, revealed a minor defect in a short time, the damaged area is smaller than in the second data gathering session seen in Figure 5B after a longer time. Additionally, the first column in Figure 5A,B (number 1, 5 and 9) demonstrates the greyscale and pure image of the object without noise. The second column (number 2, 6 and 10) shows the result of image processing through fuzzy logic. The third column (4, 8 and 12) illustrates the image processing analysis and comparison using the Sobel algorithm, and finally, the fourth column (number 4, 8 and 12) is the output of the comparison by the Prewitt operator. The software output comprises a comparison between damaged and intact surfaces and is verified by double-checking using manual measurement tools.  
Figure 5: Cont.
## 2 Materials and Methods  
### USV Modernisation  
The first stage of research work was the technical modernisation of a hydrographic USV. The modernised USV was a small vessel controlled directly using Radio Control (RC) equipment, with  
Figure 4: Graphic (navigation) interpretation of the automatic course adjustment system using an autopilot.  
no automatic mode (understood as the ability to independently accomplish a planned mission). It had a length of 1.1 m and a width of 0.7 m, with a total weight of approximately 18 kg. The USV was classified as X class, in accordance with the adopted global American nomenclature [31]. The hydrographic vessel was a commercial HyDrone vehicle manufactured by the Seafloor Systems Inc. (United States of America--USA). Its design included two hulls made from High-Density Polyethylene (HDPE) resistant to environmental conditions and mechanical factors. They were connected by an H-shaped frame made from aluminium (Figure 5a). It was normally equipped with a hydrographic system comprising a geodetic GNSS receiver (e.g., Trimble R10) and a Single Beam Echo Sounder (SBES) (e.g., SonarMite BTX). The main aims of the USV modernisation were to:  
* Enable the performance of hydrographic measurement campaigns in automatic mode involving independent (with no operator's participation) sailing along the planned sounding profiles;
* Improve the operating parameters and functionality through increasing the operating range, extending the operation time, and enhancing the reliability characteristics of both particular components and the entire system.  
The USV modernisation concerned the following components: a PixHawk Cube autopilot was installed; an RC microwave transmission was modernised to increase the operation range by three times; for the autopilot control, a low-cost multi-GNSS receiver (u-blox NEO-M8N) with a built-in Fluxgate compass was used; the drive system was replaced and its power was increased (\(2\times 50\) N); the previously used Absorbent Glass Mat (AGM) batteries (\(2\times 108\) Wh) were replaced by LiPo batteries (\(2\times 326\) Wh) to increase the time of operation by four times while reducing the weight by 28% (Figure 5b). Table 1 lists the basic technical characteristics and operational properties which were subject to modernisation. They were divided into two categories, related to automation (A) and operation (O).  
The scope of the conducted modernisation of the hydrographic USV included all its main systems: drive, control, telemetric, and positioning. The only component that was not modified was the hydrographic system, comprising a geodetic GNSS receiver and an SBES, which met the International Hydrographic Organization (IHO) requirements. Figure 6 shows the basic components subjected to modernisation and a schematic diagram of electrical connections.  
Figure 5: USV before (**a**) and after (**b**) the modernisation.
## 2 Description of the Forward Model
### The Linearized Aerosol Scattering Model  
A linearized aerosol model is an independent tool that can derive both the aerosol optical properties and their Jacobians with respect to aerosol microphysical properties [44]. These optical properties are aerosol extinction and scattering optical depth, the phase-function-normalized scattering matrices, as well as the corresponding coefficients expanded using generalized spherical functions.  
The aerosol extinction or loading profile is specified at a reference wavelength. Instead of specifying aerosol loading at each level (which implies the retrieval of the entire aerosol profile), we use a parameterized aerosol profile described by only one or two parameters. In this study, we focus on the tropospheric aerosol, which is assumed to be distributed in the lower atmosphere (0-3 km). The total aerosol loading can be described either by the column number density or by the AOD. A distribution function \(h(z_{k})\) is used to parameterize the aerosol profile [39], so that AOD (or number density) in layer \(k\) at height \(z_{k}\) is expressed in terms of the total column AOD (or number density) and distribution function as follows:  
\[\tau_{\text{aer},k}=\tau_{\text{aer}}h(z_{k})\Delta z_{k}, \tag{1}\]  
where \(\tau_{\text{aer}}\) is total AOD and \(\Delta z_{k}\) is the depth of layer \(k\). The distribution function \(h(z_{k})\) can be selected as an exponential, linear or Gaussian function.  
The extinction and scattering cross-section and normalized scattering matrix expansion coefficients are obtained from the linearized Mie code [44]. For simplicity, we use a mono-modal particle size distribution (PSD) like what the researchers have used in [PERSON] et al. [38]. Although aerosol particles in the actual atmosphere always exist as a mixture of several components [45], here we focus on the sensitivity of CAPI measurements to each aerosol component. Four typical types of aerosol (dust, soot, sea salt and sulfate) are used in our experiment. The Mie code will also calculate analytical partial derivatives of the optical properties with respect to the aerosol refractive index components and PSD parameters [44].
## 3 Results
### Trees' Thermal and LAI Patterns
Games-Howell post hoc analysis indicated significant disparities in both the T\({}_{\text{IQR}}\) and the T\({}_{\text{OPR}}\) among (a) trees with cavities (T\({}_{\text{IQR}}\) = 0.46 \(\pm\) 1.01; T\({}_{\text{OPR}}\) = 0.35 \(\pm\) 0.98) and trees with other defects (T\({}_{\text{IQR}}\) = -0.64 \(\pm\) 0.52; T\({}_{\text{OPR}}\) = -0.39 \(\pm\) 0.94) (T\({}_{\text{IQR}}\): \(p\) < 0.05, 95% C.I. = 0.93, 1.27; T\({}_{\text{OPR}}\): \(p\) < 0.05, 95% C.I. = 0.52, 0.99), and (b) trees with cavities and trees with no signs of defects (T\({}_{\text{IQR}}\) = -0.69 \(\pm\) 0.35; T\({}_{\text{OPR}}\) = -0.68 \(\pm\) 0.31) (T\({}_{\text{IQR}}\): \(p\) < 0.05, 95% C.I. = 0.99, 1.31; T\({}_{\text{OPR}}\): \(p\) < 0.05, 95% C.I. = 0.99, 1.19) (Figure 4a,b). However, no significant differences were observed regarding the T\({}_{\text{IQR}}\) between trees with other defects and trees with no signs of defects (_p_ > 0.05, 95% C.I. = -0.08, 0.19). In contrast, significant differences were found concerning the T\({}_{\text{OPR}}\) between these two groups (_p_ < 0.05, 95% C.I. = 0.07, 0.49) (Figure 4b). In alignment with the observed disparities in thermal patterns across the three categories, we noted corresponding patterns in LAI\({}_{\text{mean}}\) and LAI\({}_{\text{range}}\). LAI\({}_{\text{mean}}\) exhibited similar variability to T\({}_{\text{OPR}}\) (Figure 4c), while LAI\({}_{\text{range}}\) exhibited similar variability to T\({}_{\text{IQR}}\) (Figure 4d). Intraspecies thermal and LAI patterns exhibited significant variations when analyzing all recorded trees. Specifically, the mean z-scores of the thermal and LAI indices demonstrated significant differences [T\({}_{\text{IQR}}\): t (456.41) = -12.58, \(p\) < 0.05; T\({}_{\text{OPR}}\): t (241.70) = -5.73, \(p\) < 0.05; LAI\({}_{\text{mean}}\): t (239.20) = -4.52, \(p\) < 0.05; LAI\({}_{\text{range}}\): t (415.28) = -7.54, \(p\) < 0.05] between trees with structural defects (T\({}_{\text{IQR}}\): 0.21 \(\pm\) 1.02; T\({}_{\text{OPR}}\): 0.13 \(\pm\) 0.98; LAI\({}_{\text{mean}}\): -0.10 \(\pm\) 0.99; LAI\({}_{\text{range}}\): 0.13 \(\pm\) 1.06) and healthy trees (T\({}_{\text{IQR}}\): -0.64 \(\pm\) 0.52; T\({}_{\text{OPR}}\): -0.40 \(\pm\) 0.93; LAI\({}_{\text{mean}}\): 0.32 \(\pm\) 0.95; LAI\({}_{\text{range}}\): -0.41 \(\pm\) 0.60). At the species level, this distinction between trees with defects and healthy trees persisted across almost all the examined cases (Table 2). Figure 4: Box plots showing the (a) T\({}_{\text{IQR}}\), (b) T\({}_{\text{OPR}}\), (c) LAI\({}_{\text{mean}}\), and (d) LAI\({}_{\text{range}}\) z-score values regarding trees with cavities, other defects, and with no signs of defects. Horizontal lines: medians; boxes: interquartile ranges (25–75%); whiskers: data ranges.
## 5 Case Study
### Comparison Results and Analysis

Table 7: Comparison of solution efficiency across different algorithm combinations (\(\uparrow\) indicates that a larger value within the normal range corresponds to better algorithm performance, while \(\downarrow\) indicates that a smaller value within the normal range corresponds to better algorithm performance). Figure 9: Comparison of Pareto fronts among different algorithmic combinations. After conducting a comparative analysis of each algorithm across three dimensions--algorithm performance, solution quality, and computational efficiency--a comprehensive ranking was synthesized, yielding the integrated ranking results presented in Table 8 (with equal weights assigned to each dimension). The results clearly demonstrate that the proposed NSGA-II-MOEA/D-CIS algorithm achieves the highest overall ranking, thereby conclusively validating its applicability in solving bi-level optimization problems.
## 3 Analysis Process
### Data Analysis
#### 3.1.5 Search 5: Carbon Footprint and Energy Efficiency in Manufacturing
\begin{table}
\begin{tabular}{c p{142.3 pt}} \hline \hline
**Title** & **Description** & **Sources** \\ \hline Number of & Although the summaries do not provide an exact number of articles on “Carbon & \\ Published Articles & Footprint and Energy Efficiency in Manufacturing” over the past 10 years, they & [73; 74; 75; 76] \\ Key Findings & Energy Efficiency: There is great potential for improving energy efficiency in manufacturing, driven by organizational and economic factors. & [74] \\  & Carbon Footprint: Many studies have been conducted to reduce carbon emissions by improving energy efficiency and adopting renewable energy sources. & [76; 77] \\ Evolution of Research & Increased Focus: Over the past decade, research on improving energy efficiency and reducing the carbon footprint in manufacturing has increased. & [73; 74; 75] \\  & Regulatory Impact: International standards such as ISO 50001 [78] have played a role in shaping research and practices. & [79] \\ Common Methodologies & Systematic Literature Reviews (SLR): Used to synthesize existing research and identify key drivers and barriers. & [74; 75] \\  & Data envelopment Analysis (DEA): Applied to assess energy productivity and efficiency. & [80] \\  & Multiple Linear Regression: Used to analyze the relationships between production, energy usage, and carbon emissions. & [81] \\  & Implementation Barriers: Organizational, technological, and economic obstacles hinder the adoption of energy efficiency practices. & \\ Major Challenges and Opportunities & Data Availability: There is a lack of published data on actual energy consumption and potential improvements. & [74; 76; 79; 80] \\  & Technological Innovations: The adoption of advanced technologies and renewable energy sources can significantly reduce the carbon footprint. & [74; 76; 79] \\  & Policy Support: Effective energy policies and economic incentives can contribute to improvements in energy efficiency. & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of Search 5. Under the Energy Usage heading, significant findings are provided on reducing energy consumption and optimizing industrial processes. The Efficiency Studies section explores strategies to improve energy efficiency and how these strategies are implemented, particularly in developing regions. Figure 5 demonstrates that academic studies on energy usage and efficiency are approached from a wide range of perspectives, highlighting the scientific diversity and richness of this field. The graph presents complex data in an understandable format.
## 3 Results and Discussion
### Antioxidant Potential of Sorbets  
The sorbets were analyzed for the content of total phenolic compounds, monomeric anthocyanins, and in vitro antioxidant activity in relation to the ABTS**+** cation radical (Table 5). The total phenolic compound content of sorbets ranged from 561.04 to 665.03 mg GAE kg\({}^{-1}\) of sorbet, the value of monomeric anthocyanins ranged from 196.20 to 259.29 mg cy3 glcE kg\({}^{-1}\) of sorbet, and the antioxidant activity against the ABTS**+** cation radical between 3.84 and 4.39 mmol TE kg\({}^{-1}\) of sorbet. The sorbets that contain elderberry pomace in their composition show higher values of the bioactive compounds analyzed as well as higher antioxidant activity compared to sorbet C (without the addition of pomace). The highest values of bioactive compounds and antioxidant activity belong to sorbet E-50 that was prepared with the most concentrated elderberry pomace extract (665.03 mg GAE kg\({}^{-1}\), 259.29 mg cy3 glcE kg\({}^{-1}\), and 4.39 mmol TE kg\({}^{-1}\), respectively, for total phenolic compounds, monomeric anthocyanins, and ABTS**+** antioxidant activity).