esa-sceva/satcom-chunk-collection huggingface dataset:
# Power System Reliability Evaluation Based on Chronological Booth-Balieriaux Method
## 4 Case Study
### IEEE RTS 2020
#### 4.1.2 Proposed Method Verification  
This section assumes the same environment as the previous study that evaluated the reliability of RTS 2020 using the COPT method to verify the proposed method [32]. Preventive maintenance and load uncertainty were not considered, and rounding was performed to represent 1 MW units. The definitions of reliability indices used for verification, LOLH and EUE, are as follows:  
\[\text{LOLH}=\sum_{t=1}^{T}EL_{t,N}(0), \tag{17}\]  
\[\text{EUE}=\sum_{t=1}^{T}\biggl{(}\int_{0}^{\infty}EL_{t,N}(x)dx\biggr{)}, \tag{18}\]  
where LOLH is the expected loss of load hour, the sum of the hourly LOLP, and EUE is the expected unserved energy, the sum of the hourly unserved energy. Three methods were  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Group** & **Type** & **Pmax** & **Number of** & **FOR** \\  & **[MW]** & **Generators** & **[\%]** \\ \hline U12 & Oil/Steam & 12 & 7 & 2.0 \\ U20 & Oil/CT & 20 & 12 & 10.0 \\ U55 & Gas/CT & 55 & 27 & 3.1 \\ U76 & Coal/Steam & 76 & 7 & 2.0 \\ U155 & Coal/Steam & 155 & 7 & 4.0 \\ U350 & Coal/Steam & 350 & 2 & 8.0 \\ U355 & Gas/CC & 355 & 10 & 3.1 \\ U400 & Nuclear & 400 & 1 & 12.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: IEEE RTS 2020 generation mix.  
simulated for verification: (1) the COPT method, which is used in [23]; (2) the proposed method; and (3) the traditional method, which updates the ELDCs, increasing their length. Table 2 lists the reliability indices determined by each method.  
The simulations were performed in an environment with an AMD Ryzen 7 3700X 8-Core Processor CPU @ 3.59 GHz with 32 GB RAM. MATLAB source code on GitHub was used to measure the runtime of the COPT method, and the remaining methods were implemented using MATLAB 2021a [33]. Three methods were mathematically equivalent, representing the same reliability indices. The proposed method was approximately five times faster than the traditional method. Although the proposed method takes slightly less computation time than the COPT method, it is competitive because it can apply chronological characteristics.
## 3 Results
### Hourly \(R_{e}\) at the Regional Scale  
Figure 5 presents hourly \(R_{e}\) results on the same summer day as in Figures 4 and 6, to give examples of sub-daily variations of biogenic CO\({}_{2}\) emission. At night, in evergreen forests in the southeast, northeast and west of the study area, hourly \(R_{e}\) was strong (about 0.18 \(\pm\) 0.06 to 0.22 \(\pm\) 0.10 gC m\({}^{-2}\) h\({}^{-1}\)), probably because of active microbial and chemical decomposition of _SOC_ under favorable soil temperatures and moisture conditions, where litter carbon was abundant. In contrast, hourly \(R_{e}\) was lower in DBF near the eastern coastline and in most urban grid cells with partial vegetated cover. The latter hourly \(R_{e}\) was roughly 0.04 \(\pm\) 0.01 gC m\({}^{-2}\) h\({}^{-1}\) according to Figure 6. In the daytime, the total of autotrophic and heterotrophic respiration in all types of ecosystem increased, up to 0.35 \(\pm\) 0.13 gC m\({}^{-2}\) h\({}^{-1}\) in forests, 0.35 \(\pm\) 0.27 gC m\({}^{-2}\) h\({}^{-1}\) in grassland, and 0.19 \(\pm\) 0.06 gC m\({}^{-2}\) h\({}^{-1}\) in partially vegetated urban area. Diurnal temperature difference and additional growth respiration of plants to use photosynthesis-captured carbon most likely caused such a day-night difference of hourly \(R_{e}\).  
Figure 5: Diurnal variation of (**a**–**l**) hourly modeled \(R_{e}\) (gC m\({}^{-2}\) h\({}^{-1}\)) in the study area on 3 July 2018.
# Lithological Discrimination of Khyber Range Using Remote Sensing and Machine Learning Algorithms
## 5 Discussion
### MLA Classification Accuracy  
The classification accuracy of the lithological map produced from each classifier and dataset stated above was assessed using the following metrics: overall accuracy, producer accuracy, and kappa coefficient, which were derived from the confusion matrix. The overall accuracy may be calculated by dividing the total number of correctly identified pixels by the total number of test pixels. The likelihood that a classifier has accurately classified an image pixel is known as the producer's accuracy, and the likelihood that a pixel has been properly categorized into its pre-specified class is known as the user's accuracy. The kappa coefficient quantifies the level of agreement between the classified map and the reference data. The whole accuracy is not the same as the kappa coefficient, which assesses the consistency of the results and accounts for the entire contingency matrix [103]. The purpose of the evaluation was to compare the results of different methodologies and datasets in order to determine how successfully classification techniques may be utilized to map the lithology of the study region.  
Even though the supervised technique MLC is counted among the best for many purposes, its utilization for lithology categorization in the Khyber area shows somewhat lower consistency as compared to SVM. Different rock units could differ in weathering susceptibilities due to differences in mineralogical composition, age, texture, and erosion rate [104]. This leads to a variety of topographic expressions in the field. For example, alluvium deposits frequently form flatlands, whereas igneous or metamorphic rocks, due to their low erosion, produce towering hills. The topographic behavior of lithologies could also be responsible for comparatively higher user and producer accuracy. For a better understanding of lithological discrimination, DEM data can be used for understanding the differences in analysis. The data from satellite-derived DEMs might be used to quantify the topographical manifestations of rock units (Figure 16).  
Figure 16: Elevation profile of study area (from DEM) with extracted lineaments as black lines.
## 2 Materials and Methods
### Measurement of Compressive Strength and Linear Expansion of GPC Samples  
Cube samples of GPC with dimensions of 50 mm \(\times\) 50 mm \(\times\) 50 mm for each mix formulation were casted and cured in a water bath at 60 \({}^{\circ}\)C and atmospheric pressure, and a curing chamber at 90 \({}^{\circ}\)C and 3000 psi, which is approximately 20.68 MPa. Measurements of compressive strength and linear expansion were performed on the samples, which were cured for 1 day, 14 days, 30 days and 60 days. Measurement of compressive strength was conducted in accordance with API SPEC 10A [5], using a digital compressive strength tester of Model 4207D manufactured by Ametek Chandler Engineering (Tulsa, OK, USA). The compressive strength (\(F\)) of the samples was determined based on Equation (5).  
\[F=\frac{P}{A} \tag{5}\]  
where \(F\) is the compressive strength measured in psi, \(P\) is the compressive load at the point of failure measured in lbf and \(A\) is the cross-sectional surface area of the sample measured in inch\({}^{2}\).  
Measurement of linear expansion was performed based on the procedure described in API RP10B-5 [55], using an expansion cell based on Equation (6). The slurry was subjected to conditioning for 30 min and then poured into the expansion cell. The distance between two steel balls of the cell before expansion was recorded as the initial length (\(L_{i}\)). Subsequently, the cell was placed into the water bath that was preheated to 60 \({}^{\circ}\)C and cured for a predetermined curing time. After curing, the cell was removed from the water bath, and the distance between the two steel balls after expansion was recorded as the final length (\(L_{f}\)) [17].  
\[\frac{\Delta l}{l_{0}}=\frac{\left(L_{f}-L_{i}\right)\times 0.358}{L_{i}} \times 100\;\% \tag{6}\]where \(\Delta\)/\(l_{0}\) is the linear expansion measured by percentage, \(L_{f}\) is the final length measured in mm and \(L_{i}\) is the initial length measured in mm.  
The process flow of the research methodology employed in the present study is illustrated in Figure 3.  
Figure 3: Process flow of the research methodology employed in the present study.
###### Abstract  
Lifetime optimization is one of the key issues among the many challenges of wireless sensor networks. The introduction of a small number of high-performance relay nodes can effectively improve the quality of the network services. However, how to deploy these nodes reasonably to fully enhance the network lifetime becomes a very difficult problem. In this study, a modified and enhanced Artificial Bee Colony is proposed to maximize the lifetime of a two-tiered wireless sensor network by optimal deployment of relay nodes. First, the dimension of the problem is introduced into the candidate search equation and the local search is adjusted according to the fitness of the problem and number of iterations, which helps to balance the exploration and exploitation ability of the algorithm. Second, in order to prevent the algorithm from falling into local convergence, a dynamic search balance strategy is proposed instead of the scout bee phase in the original Artificial Bee Colony. Then, a feasible solution formation method is proposed to ensure that the relay nodes can form the upper-layer backbone of the network. Finally, we employ this algorithm on a test dataset obtained from the literature. The simulation results show that the proposed algorithm for two-tiered wireless sensor network lifetime optimization can obtain higher and stable average network lifetime and more reasonable relay node deployment compared to other classical and state-of-the-art algorithms, verifying the competitive performance of the proposed algorithm.  
two-tiered wireless sensor networks; relay node deployment; Artificial Bee Colony; lifetime optimization; Swarm Intelligence +
Footnote †: journal: Sensors
# Assessment of Several Approaches to Biofortified Products: A Literature Review
## 2 Materials and Methods
### The Complexity of Dimensions of the Biofortified Products
#### 3.2.1 Biofortification, Human Health and Reducing Hunger  
For the first group of papers, the most important contribution starts with a reflection on how micronutrient malnutrition, known as hidden hunger [5], is a serious problem for more than half of the world's population, especially for women and pre-school children [4].  
Biofortification is considered a new method to support public health to combat major nutritional deficiencies (vitamin A, iron, zinc), especially in developing countries. Food crops such as rice, wheat, maize, and potatoes are enriched with micronutrients through cultivation techniques [24; 25; 26]. A distinction is then made between poor and rich countries. This is because the huge majority of the world's population consumes foods composed of cereals (rice, corn, wheat, soya, cassava), the so-called \"staple crops.\" These crops basically meet the caloric requirements, but have a low bioavailability of essential trace elements. In underdeveloped countries, this limitation is particularly widespread, as a varied and balanced diet, with a daily intake of fruit, vegetables, meat and fish, sufficient to provide the necessary doses of vitamins and minerals, is not always possible [5; 27; 28]. In industrialized countries, the situation is different, as inadequate nutrition is not linked to a lack of food, but rather to its misuse. In addition to vitamins and minerals, which are necessary for good health, there are compounds such as \"phytonutrients,\" which help our bodies to increase cell metabolism [29; 30]. The carotenoids and polyphenols are often linked together in an antioxidant diet [31; 32].
# Two-Way Multi-Antenna Relaying with Simultaneous Wireless Information and Power Transfer
###### Abstract  
In this paper, we propose various kinds of two-way multi-antenna relaying with simultaneous wireless information and power transfer (SWIPT) and investigate their performance. Specifically, we first consider a two-way relay network where two single-antenna end nodes communicate with each other through a multi-antenna relay node that is energy constrained. This relay node harvests energy from the two end nodes and use the harvested energy for forwarding their information. Six relaying schemes that support the considered network then build on the power splitting-based relaying and time switching-based relaying protocols. The average bit error rates of these schemes are evaluated and compared by computer simulations considering several network parameters, including the number of relay antennas, power splitting ratio, and energy harvesting time. Such evaluation and comparison provide useful insights into the performance of SWIPT-based two-way multi-antenna relaying.  
Keywords:amplify-and-forward; bit error rate; decode-and-forward; network coding; simultaneous wireless information and power transfer (SWIPT); space-time coding; two-way multi-antenna relaying +
Footnote †: journal:  
_Article_
## 3 Results and Validation
### Parameterization Results
#### 3.3.4 Effect of the Type of Adhesive on the Transmission Force and Deformation  
A summary in terms of the \(k\) factor, % of transmission, and % of error with respect to the case without an adhesive is reported in Table 10.  
The % deviation of the \(k\) factor with respect to the ideal bonding is plotted against the shear modulus of the adhesive in Figure 23. Finally, in Figure 24 the beam axial strain against the shear modulus of the adhesive.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multirow{2}{*}{\(\Gamma\)} & **Shear** & **Tip Displacement—Eq.** & \multirow{2}{*}{**Tip Displacement—Eq.**} & \multirow{2}{*}{\(k\)} & \multirow{2}{*}{\(\Delta k\)**} \\
**Adhesive** & & **Modulus** & & **Thermal Exp** & & **(m)** & \\  & & **(GPa)** & & & **(m)** & \\ \hline Loctite-Hysol-9395 & 28.5 & 1.543 & \(-1.50\times 10^{-8}\) & \(-6.23\times 10^{-8}\) & 0.241 & \(-7.18\) \\
3M Scotch-Weld & 28.1 & 1.5 & \(-1.47\times 10^{-8}\) & \(-7.30\times 10^{-8}\) & 0.202 & \(-22.07\) \\
2 Cottle-Hysol-9394 & 27.7 & 1.461 & \(-1.46\times 10^{-8}\) & \(-8.01\times 10^{-8}\) & 0.182 & \(-29.80\) \\
3M Scotch-Weld & & & & & & \\ Structural AF & 23.3 & 1.03 & \(-1.35\times 10^{-8}\) & \(-1.40\times 10^{-7}\) & 0.097 & \(-62.72\) \\
3109-2 & & & & & & \\
3M 2216 & 13.4 & 0.342 & \(-1.18\times 10^{-8}\) & \(-3.67\times 10^{-7}\) & 0.032 & \(-87.61\) \\ No adhesive & h & - & \(-1.61\times 10^{-8}\) & \(-6.23\times 10^{-8}\) & 0.259 & - \\ \hline \hline \end{tabular}
\end{table}
Table 10: Adhesives and properties considered for the parameterization.  
Figure 22: Beam axial strain over piezo free strain vs. angular extension of the piezoelectric device.
## 3 Obstacle Vertex Extraction Algorithm Based on Equivalent Path
### Obstacle Vertex Extraction Algorithm (OVEA)
#### 3.3.1 Axial-Oblique Movement  
It is assumed that in an SGP, from grid B to grid M, there are k consecutive axial moves \(\{\mathrm{m}_{1},\mathrm{m}_{2},\ldots,\mathrm{m}_{k}\}\) and l consecutive oblique moves \(\{\mathrm{n}_{1},\mathrm{n}_{2},\ldots,\mathrm{n}_{i}\}\), where the starting point of \(\mathrm{n}_{i}(\mathrm{i}\in[1,\mathrm{n}])\) is \(\left(x_{1}^{i},y_{1}^{i}\right)\), and the endpoint is \(\left(x_{2}^{i},y_{2}^{i}\right)\). For each oblique move, we attempt to translate in the opposite direction of the axial move to construct an equivalent path. We continue until we encounter an obstacle vertex or reach the extension of the previous equivalent path, as illustrated in Figure 7.  
Assuming the starting point of \(n_{i}\) after translation is at the grid \(\left(x_{1}^{i\prime},y_{1}^{i\prime}\right)\) and the endpoint is at the grid \(\left(x_{2}^{i\prime},y_{2}^{i\prime}\right)\), we will sequentially check the four grids: \(\left(x_{1}^{i\prime},y_{1}^{i\prime}\right)\), \(\left(x_{1}^{i\prime},y_{2}^{i\prime}\right)\), \(\left(x_{2}^{i\prime},y_{1}^{i\prime}\right)\), and \(\left(x_{2}^{i\prime},y_{2}^{i\prime}\right)\). If only one of these four grids is an obstacle, we conclude that \(n_{i}\) has stopped due to touching an obstacle vertex. If \(\left(x_{1}^{i\prime},y_{1}^{i\prime}\right)\) equals \(\left(x_{2}^{i-1\prime},y_{2}^{i-1\prime}\right)\), we conclude that \(n_{i}\) has stopped because it has moved to the extension of the previous equivalent path.  
Figure 7: Axial–oblique movement to construct equivalent paths and search obstacle vertices.  
After the equivalent path is constructed, for the equivalent path that stops due to touching the obstacle vertex, we add the obstacle vertices it passes in the order of sequence into the TSP candidate point set.
# Multi-Objective Edge Node Deployment Method Based on Improved Heuristic Algorithms for Smart Mines
## 7 Conclusions  
This paper investigates the challenge of edge node deployment in mining environments, categorizing the problem into two primary scenarios: edge node deployment for fixed devices and for mobile devices. These categorizations are based on the specific characteristics of application scenarios and production environments. For the fixed equipment scenarios, we conducted a thorough analysis that took into account response delays and deployment costs. A weighted multi-objective optimization model was developed for node deployment, and the IBGA method was introduced to achieve near-optimal solutions. A testing platform integrated with the existing production line system was established to comprehensively evaluate the performance of the algorithm. The experimental results enabled a detailed analysis of the relationship between latency, deployment costs, and the number of devices and edge nodes. Ultimately, the findings demonstrate that the proposed method consistently outperforms alternative approaches in both efficiency and cost-effectiveness. In the mobile device scenario, we formulated an edge node deployment model focused on minimizing task completion delay and applied the ISCSO algorithm to derive the optimal solution. This study represents the first application of the SCSO algorithm to address node placement challenges, particularly in a mobile device simulation environment using a laboratory-developed inspection vehicle. The performance of the proposed algorithm was rigorously evaluated within this context. Empirical findings demonstrate that ISCSO consistently outperforms other algorithms in minimizing total delay. Therefore, the method proposed in this paper can ensure the minimal delay and deployment cost in the mining environment. In the future, we will further optimize the node deployment plan based on the service distribution of edge nodes.  
Conceptualization, F.Z. and Z.Y.; methodology, Z.B.; software, Z.B.; validation, Z.B.; investigation, F.Z.; resources, Z.Y.; data curation, Z.B.; writing--original draft preparation, Z.B.; writing--review and editing, Z.B., F.Z. and Z.Y.; visualization, Z.B. and F.Z.; supervision, Z.Y. and F.Z.; project administration, Z.Y.; funding acquisition, Z.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Special Project for Industrial Foundation Reconstruction and High Quality Development of Manufacturing Industry under the approval number TC230A076-13.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. They are restricted to experimental results.  
The authors declare no conflicts of interest.
# Quantum Circuit Implementation of Multi-Dimensional Non-Linear Lattice Models
## 3 Quantum Computing Principles  
Key concepts and principles are briefly reviewed here. A more detailed introduction is beyond the scope of the present work. Quantum algorithms are represented in the present work as quantum circuits. In the quantum-circuit diagrams used here, the qubits in the quantum register are represented in a vertical arrangement, where the solid horizontal lines represent the time progression. Traversing the quantum circuit from left to right, the quantum state of the qubit registers gets modified by a series of unitary gate operations acting on one or more qubits. For a quantum circuit, its width is defined by the number of qubits, while the horizontal extent, directly related to the number of successive gate operations, represents the circuit depth. The circuit depth that can be executed on a quantum computer is limited by the time the quantum register can stay in a coherent quantum state. The combination of limited feasible circuit depth and width, along with the limited connectivity of qubits in current and near-future quantum computers, formed the basis of the concept of quantum volume [17] to specify quantum hardware capability. For machines with limited quantum volume, a hybrid quantum-classical computing approach termed the Variational Quantum approach [18; 19] has been widely used. Shallow, parameterized quantum circuits are used that are tightly coupled in terms of data exchange with classical hardware. The problem considered is typically an optimization problem where the classical computer is tasked with performing a parameter optimization problem in each of a number of iterations. These approaches have their origin in quantum chemistry, but a wider range of practically relevant problems can be tackled with optimization-type approaches [20]. Figure 1 shows an example of the Variational Quantum approach, where the required solution is constructed from a layered network. As shown, multiple layers are used (four in the illustration), each taking multiple qubits as input (six in the example shown). Using depth five in the example, the quantum circuits defined by \(U(\lambda)\) involve 13 two-qubit gates. Each of these gates has a parameter \(\lambda_{i}\in[1,13]\) associated with it. A classical computer creates optimized parameters \(\lambda_{i}\) employing an iterative approach that takes the measured state of the ancilla qubit as input. A further key part of the approach is the problem-specific Quantum Non-linear Processing Unit (QNPU), shown on the right-hand side of Figure 1.  
In the context of the present work, it is particularly interesting that [PERSON] et al. [PERSON] _et al._ (2019) published an example of the QNPU for the non-linear Burgers equation. It is important to emphasize at this point the contrasting approach used in the present investigation, where quantum circuits with larger circuit depth are developed based on the assumption that more fault-tolerant quantum computers will be available in the near future.
# Blind Quality Evaluation for Screen Content Images Based on Regionalized Structural Features
## 3 Experimental Results  
### Experimental Protocol  
To validate the advantages of the proposed BSRSF method, comparison experiments are made on the two SCI databases SIQAD [18], and SCID [21]. The SIQAD includes 20 original SCIs and 980 impaired SCIs caused by seven degradation types and seven degradation levels. These seven degradation types comprise GN, GB, MB, CC, JPEG, JP2K and LSC. The SCID consists of 40 raw SCIs and 1800 degraded SCIs. For each raw SCI in the SCID, nine degradation types and five degradation levels are applied and these degradation types include GN, GB, MB, CC, JPEG, JP2K, color saturation change (CSC), color quantization with dithering (CQD) and high-efficiency video coding (HEVC).  
Here, three generally employed criteria are used to evaluate the predictive ability of IQA models: the Pearson linear correlation coefficient (PLCC), Spearman rank-order correlation coefficient (SROCC) and root mean squared error (RMSE). PLCC and SROCC are used to test the predictive accuracy and monotonicity, respectively. RMSE is used to test the predictive consistency. If an IQA model can simultaneously derive larger PLCC and SROCC values and smaller RMSE values, this model achieves better predictive performance. Since the predictive values generated from different IQA models have diverse dynamic scopes, in this paper, a mapping function is used to map predictive values into a uniform scope:  
\[f(v)=\alpha_{1}\bigg{(}0.5-\frac{1}{1+e^{(\alpha_{2}(v-\alpha_{3}))}}\bigg{)} +\alpha_{4}v+\alpha_{5} \tag{41}\]  
where \(v\) denotes a predictive value, \(f(v)\) represents the mapped predictive value and \((\alpha_{1},\alpha_{2},\ \ldots,\ \alpha_{5})\) stand for the parameters to be fitted.