esa-sceva/satcom-chunk-collection huggingface dataset:
# Earthquake Prediction for the Duzce Province in the Marmara Region Using Artificial Intelligence
###### Abstract  
By definition, an earthquake is a naturally occurring event. This natural event may be a disaster that causes significant damage, loss of life, and other economic effects. The possibility of predicting a natural event such as an earthquake will minimize the negative effects mentioned above. In this study, data collection, processing, and data evaluation regarding earthquakes were carried out. Earthquake forecasting was performed using the RNN (recurrent neural network) method. This study was carried out using seismic data with a magnitude of 3.0 and above of the Duzce Province between 1990 and 2022. In order to increase the learning potential of the method, the b and d values of earthquakes were calculated. The detection of earthquakes within a specific time interval in the Marmara region of Turkey, the classification of earthquake-related seismic data using artificial neural networks, and the generation of predictions for the future highlight the importance of this study. Our results demonstrated that the prediction performance could be significantly improved by incorporating the b and d coefficients of earthquakes, as well as the data regarding the distance between the Moon and the Earth, along with the use of recurrent neural networks (RNNs).  
earthquake; recurrent neural network; prediction; artificial neural network Article
## 2 System Model and Problem Formulation
### Transmission Model  
Let \(\alpha_{u,j,k},\forall u\in\mathcal{U},i\in\mathcal{I},k\in\mathcal{K}\) denote the association indicator of the buoy-UAV and UAV-OBS. \(\alpha_{u,m,k}=1,\forall m\in\mathcal{M}\) if the buoy \(m\) is associated with UAV \(u\) at time slot \(k\). Otherwise, \(\alpha_{u,m,k}=0,\forall m\in\mathcal{M}\). Similarly, \(\alpha_{u,0,k}=1\) if UAV \(u\) is associated with the OBS at time slot \(k\); otherwise, \(\alpha_{u,0,k}=0\). At time slot \(k\), each UAV can only collect the data from one buoy, and each buoy can only be associated with one UAV at most.  
\[\alpha_{u,i,k}\in\{0,1\},\forall u\in\mathcal{U},i\in\mathcal{I},k\in\mathcal{K} \tag{9}\]  
\[\sum_{i=0}^{M}\alpha_{u,i,k}\leq 1,\forall u\in\mathcal{U},k\in\mathcal{K} \tag{10}\]  
\[\sum_{u=1}^{U}\alpha_{u,m,k}\leq 1.\forall m\in\mathcal{M},k\in\mathcal{K} \tag{11}\]  
The system total spectrum bandwidth is \(B\) and allocated to each buoy-UAV link and UAV-OBS link equally. Each buoy-UAV link and UAV-OBS link uses mutually independent and non-overlapping spectrum resources. Then, the spectrum bandwidth of each buoy-UAV link and UAV-OBS link at time slot \(k\) can be given by  
\[B_{k}=\frac{B}{U_{k}{}^{*}}, \tag{12}\]  
where \(U_{k}{}^{*}\leq U\) is the number of UAVs successfully associated with the buoys and OBS at time slot \(k\).  
The transmission rate of the buoy-UAV and UAV-OBS at time slot \(k\) is expressed as  
\[R_{u,j,k}=B_{k}\alpha_{u,j,k}\text{log}_{2}(1+g_{u,j,k}).\forall u\in\mathcal{ U},i\in\mathcal{I},k\in\mathcal{K} \tag{13}\]  
In order to ensure that the data collection requirements of the buoys are satisfied, the following condition is considered:  
\[\sum_{u=1}^{U}\sum_{k=1}^{K}R_{u,m,k}\geq C_{m},\forall m\in\mathcal{M} \tag{14}\]  
where \(C_{m}\) denotes the data volume that needs to be collected from buoy \(m\). It is necessary to guarantee that all data collected by the UAVs are offload to the OBS. Moreover, the UAV that collects enough data is allowed to offload the data at time slot \(k\), even though the data in the buoys are not completely collected. The total data volume collected by UAV \(u\) before time slot \(k\) is expressed as \(C_{u,k-1}^{\infty}=\sum_{k^{\prime}=1}^{k-1}\sum_{m=1}^{\mathcal{M}}R_{u,m,k^ {\prime}}\delta\). The total data volume offload from UAV \(u\) to the OBS before time slot \(k\) is expressed as \(C_{u,k-1}^{\infty}=\sum_{k^{\prime}=1}^{k-1}R_{u,0,k^{\prime}}\delta\), where \(k^{\prime}\) denotes the time slot in which the data in all buoys are collected completely, and \(k^{\prime}<K\). Hence, each UAV should satisfy the following constraints:  
\[C_{u,k-1}^{\infty}-C_{u,k-1}^{\text{of}}\geq R_{u,0,k}\delta,\forall u\in \mathcal{U},k\in\left\{1,2,\ldots,k^{\prime}\right\} \tag{15}\]  
\[\sum_{u=1}^{U}\sum_{k=1}^{K}R_{u,0,k}\delta\geq\sum_{m=1}^{M}C_{m}. \tag{16}\]  
In particular, even if UAV \(u\) does not have enough data, it can only offload the data to the OBS rather than at time slot \(k^{\prime}\).
## 5 Overview of 5G-Enabling Technologies
### Key 5G-Enabling Technologies
#### 5.1.8 Small Cells or Heterogenous Networks  
Small cells are low-power base stations that may be positioned every 100 m to cover small geographic areas, and they are critical to the implementation of 5G wireless networks [83]. Modern data transmission methods utilized by small cells in the 5G networks include MIMO, beamforming, and mmWave. Small cells, or low-power base stations, are used to serve limited geographic areas.  
The deployment of small cells is much easier and quicker due to the hardware's simplicity. Compact cells can be placed anywhere because they are light and small [9; 100; 99]. Femtocells, picocells, and microcells are possible components of the small cell, as shown in Figure 8.  
Small cells are employed as a relay since millimeter waves cannot travel over longer distances [102]. Small cells help with quick switching and call loss reduction, but they also need security because data relaying compromises data security. To send signals around the obstructions, tens of thousands of compact, low-power base stations are deployed. This enables consumers to continually receive signals without experiencing route loss. Thus, the retransmission process will use less energy at a minimal cost. Additionally, a device near a small cell base station will broadcast data at a low power level, thus reducing the power output of cell phones and extending battery life [20].  
The small cell can compress even further and fit in small spaces when millimeter waves are employed, rather than the conventional sub-6 GHz spectrum. Small cells are further categorized into microcells, femtocells, and picocells on the basis of their user capacity and coverage area. Small cells are essential for the delivery of ultra-low latency, high-speed mobile broadband in 5G. Network power consumption, another major challenge  
Figure 8: Heterogenous Architecture Consisting of Macrocells, Microcells, Picocells, and Femtocells [101].  
confronting the implementation of 5G networks, will also be significantly reduced when small base stations (SBC) are used [101]. In Figure 9, authors [101] presented the result of the power consumption of a transmitter site with a three-sector macro base station (MBS). Their outcome showed that the deployment of small cells significantly decreased power consumption from 4300 to 166 W.
## 2 Manufacturing and Measurement Techniques  
### Manufacturing Method and Materials  
In this investigation, components manufactured from four different materials and four different AM techniques were selected to characterize their surface textures. Different materials and AM processes were selected to validate the feasibility of the investigative measurement framework. The details of the AM techniques, their process parameters, and the post-processing done on the as-built components are given in Table 1. The components' build orientation was 90 degrees, and their side surfaces were considered for all the measurements.
# Energy Trading with Electric Vehicles in Smart Campus Parking Lots
## 3 Proposed Market Mechanism
SEV: seller electric vehicle; BEV: buyer electric vehicle; DSO: distribution system operator; PLCC: parking lot control center. given in Equation (1). The PLCC aims to maximize energy selling to charging vehicles and minimize purchasing/buying energy from discharging vehicles while respecting vehicles' requirements. \[\text{Maximize }PROFIT_{PLCC}\ \sum_{t=1}^{T}\sum_{i=1}^{N}\sum_{j=1}^{K}\left(BA_{j}^{t }\times BP_{j}^{t}\times x_{j}^{t}-SA_{i}^{t}\times SP_{i}^{t}\times x_{i}^{t}\right) \tag{1}\]  
where  
\(BA_{j}^{t}\)Buying amount of energy by \(\text{BEV}_{i}\) at time t  
\(BP_{j}^{t}\)Buying price by \(\text{BEV}_{j}\) at time t  
\(x_{j}^{t}\)Allocation index for \(\text{BEV}_{j}\) at time t  
\(SA_{i}^{t}\)Selling amount of energy by \(\text{SEV}_{i}\) at time t  
\(SP_{i}^{t}\)Selling price by \(\text{SEV}_{i}\) at time t  
\(x_{i}^{t}\)Allocation index for \(\text{SEV}_{i}\) at time t  
\(T\)Number of auction time interval  
\(N\)Number of SEVs  
\(K\)Number of BEVs  
The main objective of PLCC is to maximize its profits (\(PROFIT_{PLCC}\)) by selling/buying energy to/from electric vehicles as well as the power grid, as given in Equation (2). \[PROFIT_{PLCC}=\sum_{t=0}^{T}\left(\text{Revenue}_{PLCC,t}-COST_{PLCC,t}\right) \tag{2}\]  
where  
\(\text{Revenue}_{PLCC,t}\)Total revenue earned by PLCC  
\(COST_{PLCC,t}\)Total cost paid by the PLCC  
\(T\)Number of scheduling intervals per day  
The total revenue of PLCC (\(Revenue_{PLCC,t}\)) includes selling energy to the grid as well as selling energy for charging EVs, as given in Equation (3). \[\text{Revenue}_{PLCC,t}=\text{Revenue}_{STG,t}+\text{Revenue}_{STV,t} \tag{3}\]  
Figure 3: System model for energy trading in a smart parking lot. \[\text{Revenue}_{STG,t}=P_{STG,t}\times p_{STG,t}\times\Delta t \tag{4}\] \[\text{Revenue}_{STV,t}=\Delta t\times\sum_{i=1}^{K}P_{STV,i,t}\times p _{STV,i,t} \tag{5}\]  
where  
\(\text{Revenue}_{STG,t}\)Revenue earned by providing power from SEVs to the grid (selling to grid)  
\(\text{Revenue}_{STV,t}\)Revenue earned by supporting power to charging BEVs (selling to BEVs)  
\(P_{STG,t}\)Power sold to the grid in kW  
\(p_{STG,t}\)Electricity price in money unit per kWh  
\(\Delta t\)Length of scheduling interval  
\(K\)Number of BEVs  
\(P_{STV,i,t}\)Power used to charge BEV\({}_{i}\) in kW  
\(p_{STV,i,t}\)Electricity price in money unit per kWh  
The total cost of PLCC (\(COST_{PLCC,t}\)) includes buying energy from the grid as well as buying energy from SEVs, as given in Equation (6). \[COST_{PLCC,t}=COST_{BFG,t}+COST_{BFV,t} \tag{6}\]  
\[COST_{BFG,t}=P_{BFG,t}\times p_{BFG,t}\times\Delta t \tag{7}\]  
Figure 2:0  
where  
\(COST_{BFG,t}\)Cost of energy purchased from the grid  
\(COST_{BFV,t}\)Cost of energy purchased from SEVs  
\(P_{BFG,t}\)Power bought from the grid in kW  
\(p_{BFG,t}\)Electricity price in money units per kWh  
\(\Delta t\)Length of scheduling interval  
\(N\)Number of SEVs  
\(P_{BFV,i}\)Discharge power from SEV\({}_{i}\) in kW  
\(p_{BFV,i}\)Electricity price in money unit per kWh
## 4 Experimental Results
### Comparison of YOLO Models
#### 4.3.2 Qualitative Comparisons  
For the qualitative analysis, three samples presenting complex detections from the test set of the adapted dataset were considered, as illustrated in Figure 10. The first sample, presented in the first row, contains partially occluded stamps that overlap with other components of the document. The second sample, shown in the second row, includes the presence of black stamps that may be confused with textual elements. In the third sample, the stamp is partially occluded and mixed with other document components, as evidenced in the last row of the figure. To visualize the models' detections, a threshold of 0.4 was  
Figure 9: Confusion matrix for YOLO models: (a) YOLOv8s. (b) YOLOv9s. (c) YOLOv10s. (d) YOLOv11s.  
used. The number above each bounding box represents the detection confidence level of the stamp in the document, with values ranging from 0 to 1. Each model determines the exact location of the stamp, and the highest confidence level of detection represents the best model for detecting that type of stamp. Additionally, each column illustrates the detection results of the following analyzed models: YOLOv8s, YOLOv9s, YOLOv10s, and YOLOv11s, respectively.  
Figure 10: Comparison of stamp detection results: (**a**) Using YOLOv8s. (**b**) Using YOLOv9s. (**c**) Using YOLOv10s. (**d**) Using YOLOv11s. The confidence score for each stamp detection is depicted above the bounding boxes.  
In the first sample, the YOLOv8s, YOLOv9s, and YOLOv10s models were able to detect all the stamps present in the document. However, the YOLOv11s model failed to detect the overlapped stamp in the image. Considering detection reliability, YOLOv9s demonstrates better generalization capability when analyzing the detection of all three stamps. In the second sample, it is observed that the detection performed by the YOLOv8s model resulted in a false positive, incorrectly detecting a stamp. In contrast, the YOLOv9s model reliably detected the stamp present in the document. However, the YOLOv10s and YOLOv11s models failed to detect the stamp. Finally, when analyzing the results for the third sample, the only model that successfully detected the stamp was YOLOv9s.
## 4 Discussion
### Yp Derived from Ya and Yp\({}_{0}\)  
Ya derived from remotely sensed data is also useful for quantifying Yp and Yg [1; 28]. Pixel-level Yp could be estimated as a high percentile (95 th or 99 th) of pixel-level Ya values within a small region around the pixel under investigation. However, as previously mentioned, this method assumes optimum field managements are achieved in some croplands (or pixels) within the domain under investigation. A novel approach avoiding the assumption in estimating Yp based on satellite data was proposed in this study, and the new method only assumes optimum SDT is achieved in on-farm managements. The \"Potential yield\" derived from Ya is equivalent to \"potential farmers' yield (Yp\({}_{\text{f}}\)),\" as defined in Liu, Yang, Lin, Hubbard, Lv and Wang [6]. In the maize belt of the US, where farmers' management skills were maintained at a high level, Yp\({}_{\text{f}}\) was close to Yp; however, in other regions, where crop growth was strongly stressed, Yp was poorly represented by Yp\({}_{\text{f}}\)[1]. We investigated the differences between Yp\({}_{\text{f}}\) and Yp derived using the new method proposed in this study (Supplementary Figure S4). Modeled Yp\({}_{\text{f}}\) values were significantly smaller than modeled Yp. The regional-scale mean annual Yp\({}_{\text{f}}\) is 8.7 t hm\({}^{-2}\), which is significantly less than the Yp estimated in this study as well as previous studies [11; 67; 68], whereas our method produced a result closer to previous estimates. This implies that large gaps exist between farmers' potential yield and the Yp of summer maize over the NCP. The newly developed method in this study provides a more reliable approach to estimating Yp and can improve the understanding of the Yg of summer maize in the study region.
# Adaptive Neural-Network-Based Lossless Image Coder with Preprocessed Input Data
## 2 Scientific Background
### AdNNs vs. Deep Learning Approach  
Deep-learning algorithms for lossless image coding may have data compaction properties close to those for the best \"classic\" approaches, and better than for the codecs presented in this paper; however, this is not obtained without a cost. Firstly, efficient deep-learning codecs are extremely computationally complex [41]. On the other hand, they can be easily parallelized, and as a result their coding and decoding times heavily depend on the experimental equipment used. Let us consider a comparison of codec L3C [42] to codec [3]. It should be highlighted that the latter \"classic\" coder is implemented sequentially. In [43], the coding time of 512 \(\times\) 512 pixel images for L3C is 1.5 to 3.33 times longer than for WebP, while the decoding time is 110 to 1614 times longer. In [42], the proportions are 1.5:1 and 5.25:1, respectively. In [44], the coding time for L3C is 12.8 times shorter than for WebP. Finally, ref. [45] provides full data for L3C: 0.49 s and 0.54 s for a 512 \(\times\) 512 pixel image on NVIDIA GeForce RTX 2080 plus Intel i7-9700. Unfortunately, there are no data for WebP. Of course, such equipment cannot be found in laptops or smartphones.  
In the paper, times for AdNNs are provided for a sequential processor, and there is no hidden time for off-line computations.  
Secondly, deep-learning algorithms are trained on very small images, usually 32 \(\times\) 32 or 64 \(\times\) 64 pixel ones. As a result, deep-learning algorithms work the best for the smallest practical image sizes, i.e., 512 \(\times\) 512 pixels; for larger images, their performance deteriorates [11]. Imperfect training sets may cause network overfitting, and this effect poses a question regarding the validity of the experimental results [11]. AdNNs are taught on the set of pixels from the neighborhood of the coded one, so such problems do not appear.  
Thirdly, AdNNs have much smaller memory complexity than deep-learning algorithms. They contain only one hidden layer of size 12 (Concept 1) or size 7 (Concept 2); the coder needs only minimal \"cache\" to remember data for them; see Section 5. The deep-learning networks are much larger and may need memory for large sets of parameters.
# Seismic Effect of Marine Corrosion and CFRP Reinforcement on Wind Turbine Tower
## 2 Establishment and Verification of FE Model
### FE Model with Marine Corrosion  
The corrosion area of steel plate and steel pipe pile exposed to the marine environment is usually divided into the marine atmospheric zone, spray splash zone, ocean tidal zone, seawater immersion zone and submarine mud zone [27]. [PERSON] et al. further calibrated the parameters of the Weibull corrosion model with the measured data from a ship with a 10-year service period [28]. [PERSON] et al verified the applicability of the model in offshore platforms and used the model to analyze the corrosion effects of different areas on jacket platforms [29]. In this study, according to the corrosion probability model of the Weibull function proposed by [PERSON] [30] and [PERSON] [31], the corrosion thickness of the tower structure in the droplet zone is calculated. The expression of the model is as follows:  
\[d_{n}(t)=\begin{cases}0;&0\;<\;t\;<\;T_{st}\\ d_{\infty}\Big{\{}1-\exp\Big{[}-((t-T_{st})/\eta)^{\beta}\Big{]}\Big{\}},&T_{st} \;<\;t\;<\;T_{L}\end{cases} \tag{3}\]  
Here: \(d_{n}(t)\) is corrosion depth corresponding to time; \(d_{\infty}\) is Corrosion limit depth; \(t\) is corrosion duration; \(T_{st}\) is the beginning of corrosion; \(T_{L}\) is Structure life or structure maintenance time; \(\eta\) and \(\beta\) is Environmental parameters of marine corrosion.  
Write the Python program according to the execution logic shown in Figure 4. After inputting the initial modeling parameters, the program determines the diameter distribution of corrosion pits according to the normal distribution law (Formula (3)). Calculate the number of corrosion pits and the loss rate of corrosion volume. When the number of generated corrosion pits reaches the target corrosion rate, the generation of corrosion pits is stopped. Otherwise, the program adjusts the number of corrosion pits until the target corrosion rate is met. After the number of corrosion pits is determined, the random function is used to generate the location of each corrosion pit, when two corrosion pits overlap, Z and \(\theta\) Value are computed until all corrosion pits are randomly distributed to the whole structure without coincidence. Figure 5 shows the FE model with random corrosion pits established by Python and ABAQUS.  
\[f(h)\;=\;\frac{1}{\sqrt{2\pi\sigma}}e^{-\frac{(h-y)^{2}}{2\sigma^{2}}} \tag{4}\]  
Here: \(\sigma\) is Standard deviation; \(\mu\) is average value; \(h\) is corrosion pit depth.  
Figure 3: FE model of wind turbine tower structure.  
The finite element model with corrosion for 10 years, 20 years and 30 years is established according to the above method. The wall thickness of the tower varies with time, as shown in Figure 6.  
The CFRP model is created using the S4R unit in the ABAQUS software. Use Create Composite Layup to set the number of fiber layers, the thickness of each fiber layer, and the ply angle of each fiber layer. The material properties of CFRP are expressed by the \"ENGINEERING CONSTANTS\" model and the \"HASHIN DAMAGE\" model.  
Figure 4: Python program execution process.
## 3 Background
### The Methods of the ELECTRE Family  
The methods of the ELECTRE family are based on non-compensatory logic to establish outranking relationships between pairs of alternatives [48]. An alternative to outranks an alternative b (aSb) if it is at least as good as b in the criterion under consideration; this is the central idea expressed by the concept of outranking [49].  
The peer-to-peer comparisons allow for evaluating the importance of different alternatives to obtain a final classification using an appropriate [50]. Such a process requires the DM to declare the performance of each alternative about the established criteria and allows the expression of its knowledge about the problem to be solved [51].  
According to [PERSON] et al. [52], the modeling of preference relationships considers indifference, strict preference, and incomparability relationships. Cutting plans are established by minimum levels of agreement and maximum disagreement [53].  
The ELECTRE methods cover two main procedures: the construction of one or more overcoming relationships and, later, an exploration procedure [52])  
Since the first ELECTRE, described fifty years ago, other authors developed other methods, constituting the ELECTRE family: ELECTRE I [54]; ELECTRE II [55]; ELECTRE III [56]; ELECTRE IV [57]; ELECTRE IS [58]; ELECTRE TRI [59]; ELECTRE TRI-B [60]; ELECTRE TRI-C [60]; ELECTRE-TRI-nC [49]; ELECTRE TRI-rC [61]; and ELECTRE TRI-nB [62].  
Several extensions, variations, and proposals related to ELECTRE methods have been found in the literature and identified as cardinal methods. In cardinal methods, DMs must express their preferences for each alternative or another in each criterion. In contrast, in the ordinal methods, it is sufficient to order the alternatives for each [63].  
According to [64], there were no ELECTRE methods with the input of ordinal weights. The ELECTRE-MOr method covered this gap in the literature, allowing the entry of ordinal weights and evaluation of quantitative and qualitative criteria.