esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Economic Evaluation  
Economic viability is of great importance when investing in a project. Net present value (NPV) is introduced as an economic indicator to evaluate the SAGD and bottom-up assisted pressure drive processes. NPV is defined below [39].  
\[\text{NPV}=\sum_{t=0}^{n}\frac{C_{t}}{(1+r)^{t}},\]  
where \(r\) is the discount rate, \(t\) is the number of time periods (years in this study), and \(n\) is the total number of time periods (years in this study). \(C_{t}\) is the project cash flow at a given year of \(t\), which equals the total income minus total cost. It can be estimated using the equation below.  
\[C_{t}=(\text{bitumen volume }\times\text{bitumen price})-(\text{steam volume }\times\text{steam cost})-(\text{drilling}\]  
and completion costs).  
The steam generation cost is USD 6/bbl, and the bitumen price is assumed to be USD 36/bbl at a discount rate of 10% per year. The bitumen price was set at 60% of the discount on the West Texas Intermediate (WTI), which is assumed to be USD 60/bbl in the economic analysis. The drilling and completion cost is approximately USD 1.5 MM [19; 40]. It should be noted that the NPV calculation in this study considers the major revenue and costs associated with one injector and one producer in operation. The facilities investment is not considered because only one well pair is considered in this work [19]. The purpose of NPV calculation is to compare the economic feasibility of the two processes. The relevant parameters to calculate the NPV are summarized in Table 4. The project cash flow and variations of NPV as a function of time are presented in Figure 9. The cash flow in the first year of the bottom-up assisted pressure drive process is the greatest because of high oil production. In the first four years, the cash flow calculated from bottom-up assisted pressure drive is higher than that obtained from SAGD. The cash flows of SAGD exceed from the fifth year when the oil production rate peaks. By the end of the projects, the NPV values calculated from bottom-up assisted pressure drive are higher than the NPV values calculated from the SAGD process. Although cumulative oil production in the bottom-up assisted pressure drive is 20% higher than that in the SAGD process, the NPV of bottom-up assisted pressure drive is 27% higher than that in the SAGD process. This is mainly attributed to high oil production in the early stage of the process.  
Figure 9: Comparison of cash flow and NPV results between SAGD and bottom-up assisted pressure drive processes indicating the bottom-up assisted pressure improves the economics.
### NMR Metabolites Fingerprinting
#### 2.4.1 Fatty Acids  
A key feature of the assignment of unsaturated fatty acids (M1, M2 and M3) was based on the signals (-CH\({}_{3}\)) \(\delta\)H 0.89 ppm, a long chain of methylene groups (-CH\({}_{2}\))\({}_{n}\) at 8H 1.2 ppm, and olefinic bond (s) at 8H 5.30-5.37 ppm, and HSQC cross-peak correlation with \({}^{13}\)C showed signals at \(\delta\) 15.7, 31.9, 128.8-132.1 ppm, respectively. Additionally, confirmation of unsaturation in fatty acids was based on allyic methylenes resonating at \(\delta\) 2.05-2.10 ppm and correlated with \({}^{13}\)C at \(\delta\) 29.4 ppm. Bis allyic (-CH\({}_{2}\)) with two triplets at \(\delta\) 2.76 and 2.78 with \({}^{13}\)C at \(\delta\) 26.2 and 27.8 ppm were correlated to \(\omega\)-6 (M2) and \(\omega\)-3 (M3) fatty acids, respectively. Furthermore, confirmation of these data was done using other 2D NMR experiments, as HSQC and HMBC displayed in Figures S2-S4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metabolite** & **Assignment** & **\(\delta\) \({}^{1}\)H in ppm** & **\(\delta\) \({}^{1}\)H COSEY (ppm)** & \(\delta\) \({}^{13}\)C in ppm** & **HMDC Correlations \(\delta\)** \\ \hline Flavanoids & & & & \\ kaempeferol dev. & C (3′, 5′) & \(\delta\) 6.98
## 4 Conclusions  
In this study, historical orthophoto images of a single region from different years were used and an attempt was made to create a dataset based on these images. The fact that the data were grayscale increased the similarity between the different photographs we produced. Objects which are normally in separate categories in other optical datasets used in the literature were grouped under four main classes due to the restriction caused by the lack of color information. Images in each class were converted from single-band to three-band to run the SR model. The different features offered by each band in a multispectral image were obtained with images of different years and with different spatial resolutions containing the same region. We tried to achieve the contribution of color information, which allows the model to learn different features during training, using intensity values distributed over a wide range. These approaches were applied to minimize the restrictions and reduce the smoothing effect on the improved images obtained with the SR model. Image quality metrics validated the proposed approach for improving grayscale historical aerial photographs. In the future, the diversity of data categories containing historical photographs can be increased by evaluating different datasets obtained from different sources to achieve better results in both metric values and visual evaluation. As another type of optical data, satellite images may be included in studies of historical photographs in grayscale form by applying extra distorting effects. Other datasets may contain images created separately for airports, railways, and industrial zones. Such structures may not have existed at the time when historical aerial photographs were obtained. In addition to enriching the dataset in terms of content, DL-based coloring algorithms can be applied on historical aerial photographs for better learning.  
Conceptualization, A.H.I; methodology, A.H.I. and U.A.; writing--original draft preparation, A.H.I; writing--review and editing, U.A. and O.A.; supervision, D.Z.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The historical orthophoto images presented in this study is confidential and may be provided only with restrictions by the responsible data provider institution (General Directorate of Mapping). The other models and codes generated in the study are available from the corresponding author upon reasonable request.  
The authors are grateful to the General Directorate of Mapping for sharing the orthophoto images used within the scope of the study.  
The authors declare no conflicts of interest.
# A Historical Survey of Advances in Transformer Architectures
## 3 Survey Results
### Text-Based Applications  
Transformers have revolutionized the realm of text-based applications and natural language processing (NLP) through providing solutions to a variety of problems such as text classification, question answering, text summarization, machine translation, and text generation [66]. The first prevalent model in text-based applications is one already analyzed previously--the BERT model proposed in 2018 by [PERSON] et al. [17]. Despite being a number of years old, this architecture is still relevant to this day due to how groundbreaking it was when it was proposed. Indeed, the BERT model's NLP transformer has been the base for various other prevalent models such as the RoBERTa [26] in 2019, which achieved excellent results by proposing a variation of the BERT model, ETC [67], in 2020, which reported high performance when building upon the BERT model and using the weights provided by the RoBERTa as well as Big Bird [68] in 2021, which was proposed as a variation of the BERT model for longer sequences. Another notable implementation of transformers for the text domain is that of TENER [69], proposed in 2019 as a solution to using transformers for the named entity recognition task--which is the task of finding the start and end of an entity in a sentence and assigning a class for this entity. This is especially useful in applications such as question generation [70], relation extraction [71], and coreference resolution [72]. This model adapts the transformer encoder to model character-level features and word-level features.
# Communication Architecture for Grid Integration of Cyber Physical Wind Energy Systems
## 2 Cyber Physical Wind Energy System
### Application Layer  
The SCADA systems are used for data acquisition, remote monitoring, real-time control, and data recording. There are multiple applications covered by the SCADA systems in a WF. The three main applications are as follows: the turbine SCADA system provides the connectivity among wind turbines and enables remote monitoring and control for each wind turbine and their associated sub-systems, the WF SCADA system connects all devices from all wind turbines, as well as the electric substation together, and the security SCADA system provides the IP telephony services and video surveillance. The SCADA system remotely collects the process information from WF components and stores it in historical databases and servers (historical servers, metering server, meteorological server, etc.). Based on the collected information, the control center executes appropriate actions.
# A Low Complexity Persistent Reconnaissance Algorithm for FANET
## 4 Results  
This article considers a 3D scene with a randomly generated threat PDF, in which multiple monitoring UAVs perform reconnaissance tasks at different locations. Various numbers of relay UAVs are provided to forward the detected targets' relevant data and the variation in threat PDF at the scene. The high-altitude UAV, known as ACP, oversees the entire FANET in the whole process.  
For the sake of effective comparison, four algorithms, RWP [36], PSO, TARFC, and TA&CRFO, are respectively implemented under the condition that the values of all parameters are consistent. The simulation process is executed on MATLAB. In the simulation, we assume that there are no projectiles in the environment, such as birds or obstacles, which may block the flight path of the UAV, and it is assumed that the battery capacity of the UAVs meets the requirement of continuous reconnaissance.  
The alteration of each index in the process of algorithm execution is compared, and the reasons for different results obtained by different algorithms are analyzed. Simulation results demonstrate that the FANET net constructed by the TA&CRFO algorithm is 6.06-7.23% lower than TARFC in connectivity and 0.46-1.21% higher in UAV's average threat value, but the time consumption of the algorithm is only 19.86-20.31% of TARFC. At the cost of other performances' slight impairment, the TA&CRFO achieves UAV's limited distributed control and a significant reduction in computing overhead.  
Table 3 describes the simulation scenario and lists the parameters during the simulation. The horizontal dimension of the simulated scenario is set as 1 km \(\times\) 1 km, and the flight height of the UAV is set to 100 m and 200 m. In valid experiments, the number of RUs ranged from 7 to 19. Given that too few RUs cannot establish communication links at multiple reconnaissance sites simultaneously, too many RUs may not offset the outlay despite the increased performances. We assume that the relay drones can either hover or travel at a maximum speed of 15 m per second. The shortest path routing algorithm, whose link usage is calculated as the cube of its length, is the default routing protocol. In terms  
of distance, the low-altitude UAVs' safety distance, maximum communication distance, and the diameter of the perceived threat area are set to 20 m, 200 m, and 50 m, respectively. Our work not only uses the original PSO algorithm as the comparison algorithm but also constructs a new one based on the PSO algorithm. Therefore, the PSO-related parameters are listed explicitly for ease of reference. Finally, the parameters related to the KKT method and our algorithms are also listed in Table 3.
## 4 Experimental Analysis and Verification
### Anomaly Reasons and Analysis  
As seen in the results in Figures 8 and 9d, the abnormal cells identified all reached the charging voltage limit (CVL) earlier than the normal cells. Given that the trends in these abnormal curves are similar and no single cell differs significantly from the others, it can be assumed that these cells are affected by the same cause, leading to the anomaly rather than necessarily being faulty.  
For the inconsistencies among the battery cells caused by manufacturing, the State of Charge (SOC) among the battery cells is inconsistent. In the case of lithium-ion batteries that are connected in series to form a battery pack, the series connection ensures that the current passing through each cell is identical while the SOC among the cells varies, which causes certain cells to reach the CVL too early during the charging process or to reach the discharging voltage limit (DVL) too early during discharge. If any cell reaches the CVL during charging, the entire battery pack stops charging; conversely, if a cell reaches the DVL during discharge, the entire pack stops discharging. This phenomenon is akin to the \"barrel effect,\" as illustrated in Figure 11. The \"barrel effect\" significantly reduces the usable capacity of the battery pack. The inconsistency within a lithium-ion battery pack adversely affects its normal usage and the system operation. Therefore, after detecting abnormal cells using the method proposed in this article, it is feasible to employ the battery balancing techniques, which can enhance the consistency, capacity utilization, and lifespan of the series-connected lithium-ion battery pack.  
The battery consistency deteriorates over prolonged periods of charging and discharging, and the maximum usable capacity of the battery gradually decreases with the increase in the number of charge and discharge cycles. When the maximum usable capacity of a battery degrades to 70% to 80%, it needs to be retired. Because over-charging or over-discharging behaviors of battery cells significantly increase the likelihood of dangerous incidents, such as explosions and fires, the charging or discharging of the battery pack should be halted immediately when any cell in the battery pack reaches the CVL or DVL. Therefore, the accurate detection of abnormal voltages of battery cells within battery packs is crucial during the routine operation of an energy storage station.
## 3 Numerical Studies
### Two-Degree-of-Freedom System  
A damped two-degree-of-freedom model, as shown in Figure 3a, was selected to verify the accuracy of the proposed method. The mass, stiffness, and damping of each floor are 5 kg, 10 N/m, and 1 N\(\cdot\)s/m, respectively. An external load \(P_{1}\) is applied at the \(m_{1}\) position on the first floor, as shown in Figure 3b. It is assumed that \(m_{1}\) and \(m_{2}\) only experience horizontal displacement without vertical movement. The motion state of the system can be represented by the displacement coordinates \(u_{1}\) and \(u_{2}\), which are the distances of masses \(m_{1}\) and \(m_{2}\) from their respective origin positions. The proposed method is used to compute the displacement response within 1 s. By analyzing the forces based on the principle of virtual work, the motion equation of the structure can be expressed as follows:  
\[\begin{pmatrix}m_{1}&0\\ 0&m_{2}\end{pmatrix}\begin{matrix}\ddot{u}_{1}\\ \ddot{u}_{2}\end{matrix}\right\}+\begin{pmatrix}c_{1}+c_{2}&-c_{1}\\ -c_{2}&c_{2}\end{pmatrix}\begin{matrix}\dot{u}_{1}\\ \dot{u}_{2}\end{matrix}\right\}+\begin{pmatrix}k_{1}+k_{2}&-k_{1}\\ -k_{2}&k_{2}\end{pmatrix}\begin{matrix}u_{1}\\ u_{2}\end{matrix}=\begin{cases}P_{1}\\ 0\end{matrix} \tag{17}\]  
Initial conditions:  
\[u(t=0)=0,\ \dot{u}(t=0)=0,\ i=1,2\]  
where \(\dot{u}\) and \(\ddot{u}\) are the velocity and displacement, which are the first and second derivatives of displacement \(u\), respectively. \(m\) denotes the mass of the mass point, \(c\) represents the damping of the system, and \(k\) represents the stiffness. The system parameters are set as \(m_{1}=m_{2}=5\) kg, \(c_{1}=c_{2}=1\) N\(\cdot\)s/m, and \(k_{1}=k_{2}=10\) N/m. The external load and time \(t\) satisfy \(P_{1}=-\sin(5\times t\times\pi)\times 1000\) N. Let \(f\) represent the left-hand side of the motion equation.  
The loss function of this model is expressed as follows:  
\[Loss=Loss_{1}+\lambda_{1}Loss_{u}+\lambda_{2}Loss_{v} \tag{18}\]  
\[Loss_{1}=MSE(f(\mathbf{u}_{1}),0)+MSE(f(\mathbf{u}_{2}),P_{1}) \tag{19}\]  
\[Loss_{u}=\sum_{i}^{2}MSE(\mathbf{u}_{i}(0),0),\ Loss_{v}=\sum_{i}^{2}MSE\big{(} \dot{\mathbf{u}}_{i}(0),0\big{)} \tag{20}\]  
where \(Loss_{u}\) and \(Loss_{v}\) are the loss terms of the initial displacement and initial velocity, respectively, and \(\lambda_{1}\) and \(\lambda_{2}\) are the weighting coefficients for the initial conditions.
## 2 Case Studies
### Case C Study  
The final case study (case C) examines a two-dimensional truss structure composed of 15 rods, organized into eight groups according to their cross-sectional properties (see Figure 3). Although the configuration may appear impractical from an engineering design standpoint, it is widely adopted in the literature as a benchmark model for evaluating the performance and robustness of structural optimization algorithms.  
In this case, three state variables are defined:  
* \(SV1\): Minimum allowable cross-sectional area \(A_{min}\) = 64.5 mm\({}^{2}\);
* \(SV2\): Stress limits of all the members \(SL\) = \(\pm\)172.45 MPa;
* \(SV3\): Displacement limits of each node \(DL\) = \(\pm\)20 mm.  
This configuration exhibits increased complexity, with a total of 22 design variables. Specifically, 16 of these correspond to sizing parameters: 8 outer diameters (ranging from \(OD_{min}\) = 80 mm to \(OD_{max}\) = 300 mm) and 8 thicknesses (ranging from \(TK_{min}\) = 10 mm to  
Figure 3: The 15-bar planar truss (case C).  
Figure 2: The 25-bar truss structure (case B). (**a**) Coordinates of all nodes; (**b**) cross-sectional areas arranged in eight groups.  
\(TK_{max}\) = 39 mm). Five additional variables determine the positions of nodes 3, 4, 6, 7, and 8 (see Table 2, where \(L\) = 10,160 mm, and Figure 3).  
Furthermore, five distinct configurations are evaluated to explore various internal topologies (Figure 4).  
In particular, in each iteration of the algorithm, the discrete topological variable DT (see Table 3) is determined, yielding a specific rod configuration, as shown in Figure 4.  
This comprehensive parameterization not only presents a challenging optimization problem but also facilitates the investigation of topology optimization via a discrete topology variable method.
# VFLD: Voxelized Fractal Local Descriptor
## 2 Related Works
### Fractal Dimension as Descriptor  
The fractal dimension has previously been used as a descriptor with promising results. Regarding the field of plant recognition, in [23], the authors researched a range of algorithms for calculating the fractal dimension. They segment the veins of the plants and their borders, and calculate the fractal dimension using this information, classifying them into different species of Passiflora, obtaining an average accuracy of 97.77%.  
Another example of the use of the fractal dimension as a descriptor is [24]. In this work, which is more theoretical, the authors propose the box-counting algorithm to calculate the fractal dimension. They aim to demonstrate how fractal geometry can describe properties of the urban development that cannot be captured with Euclidean geometry. They use 2D binary images of maps of 20 different American cities to carry out the analysis, concluding that there is a correlation between the fractal dimension and the level of urban development of each city. However, they find no evidence of a correlation between this dimension and population density.  
A final example is [25], which uses the fractal dimension to analyze signals from a magnetoencephalogram. In this case, the fractal dimension is considered an indicator of the complexity of a signal, which is typically calculated following the Higuchi algorithm [26]. The authors seek to verify the reliability of the fractal dimension in detecting Alzheimer. Using this technique, they achieve 87.8% accuracy and 95.24% specificity, with five channels, obtaining good results for disease diagnosis.  
A specific feature appears in each of the methods described above, which is that they all work with the fractal dimension in two dimensions, namely, in images or signals. The first work to use this concept to perform 3D object classification was [6]. In this case, it is used to generate a global descriptor for the whole object in order to perform classification. To do this, a 3D discretization of the 3D object is performed. For each voxel, the fractal dimension is calculated. A descriptor is then created concatenating the values of each voxel.  
Although the proposed method and [6] share the fractal dimension calculus, in the current work, we propose to calculate the fractal dimension in a given 3D point, using different radii. Thus, we obtain a descriptor composed of the fractal dimensions at different radius.  
Consequently, the present work proposes a method to capitalize on the properties of fractals to construct a robust descriptor for three-dimensional points that can cope well with the problems of noise and point density where other descriptors have more difficulties.
## 3 Methodology
### On-Grid Arrays Design Based on GEVOA  
For on-grid arrays, the positions are discrete on half wavelength grid points. Therefore, the V-shaped transfer function is used to discretize the output of GEVOA. The V-shaped transfer function maps the whale position to the [0,1] interval. When the mapped values fall in different intervals, discrete values are selected. The expression of the V-shaped transfer function is:\[V\big{(}x_{ij}(t)\big{)}=\big{|}\frac{e^{x_{ij}(t)}-1}{e^{x_{ij}(t)}+1}\mid \tag{20}\] \[x^{\prime}_{ij}=\begin{cases}G&0\leq V\big{(}x_{ij}(t)\big{)}<r_{1 }\\ G-1&r_{1}\leq V\big{(}x_{ij}(t)\big{)}<r_{2}\\ \ldots&\\ 0&r_{M}\leq V\big{(}x_{ij}(t)\big{)}<1\end{cases}\]  
where \(x^{\prime}\) is the converted discrete value. \(r_{1},r_{2},\ldots,r_{k-1}\) are random numbers between \((0,1)\), which satisfy \(0<r_{1}<r_{2}<\ldots<r_{k-1}<1\). When \(0\leq V\big{(}x_{ij}(t)\big{)}<r_{1}\), the whale position is mapped to \(G\). \(G=\textbf{floor}(2*L/\lambda)\) is the largest grid point (integer multiple of half wavelength), where \(\textbf{floor}()\) is a function to find the nearest integer less than or equal to the parameter. The algorithmic details of the proposed method for on-grid array design based on GEWOA are summarized in Algorithm 2.  
```
Input: Initial chaotic sequences \(\widetilde{\textbf{S}}\) and maximum iterations \(t_{Max}\) Output: Optimal individual whale position  Initialize \(\widetilde{\textbf{X}}\) using chaotic sequence \(\textbf{S}\);  Mapping \(\widetilde{\textbf{X}}\) to discrete grid points \(\textbf{G}\) using V-shaped transfer function;  Find the individual position corresponding to the optimal fitness value \(\textbf{X}^{*}\); while\(t<t_{Max}\) or \(\textbf{PSLL}\) does not meet demand do  Set the parameters \(\textbf{a},\textbf{r},l,p\), and calculate \(\textbf{A},\textbf{C}\).;  Update \(\widetilde{\textbf{X}}(t)\). if\(p<0.5\)then if\(|\textbf{A}|<1\)then Enforcing shrinking encircling mechanism and updating  position according to (4) and (5) ; else Random search using (8) and (9); else  Archimedes spiral strategy using (11);  end if  Set \(k\) and \(n\) and use refraction learning to find the inverse solution \(\hat{\textbf{X}}^{*}(t)\);  Mapping \(\widetilde{\textbf{X}}\) and \(\hat{\textbf{X}}^{*}(t)\) to discrete positions \(\textbf{X}^{\prime}\) and \(\hat{\textbf{X}}^{\prime}\) using V-shaped transfer function;  If \(\textbf{PSLL}(\textbf{X}^{\prime})<\textbf{PSLL}(\hat{\textbf{X}}^{\prime})\), choose \(\textbf{X}^{\prime}\) as a new population; Otherwise, choose \(\hat{\textbf{X}}^{\prime}\) as a new population; \(t=t+1\)  end while
```  
**Algorithm 2**On-grid array design based on GEWOA.