esa-sceva/satcom-chunk-collection huggingface dataset:
# An Open Data Approach for Estimating Vegetation Gross Primary Production at Fine Spatial Resolution
## 2 Materials and Data  
### Revised EC-LUE Model  
The EC-LUE model framework has the following form [12]:  
\[\text{GPP}=\text{FPAR}\times\text{PAR}\times\epsilon_{\text{max}}\times \text{C}_{\text{s}}\times\text{min}(\text{T}_{g^{\prime}}\text{W}_{\text{s}}) \tag{1}\]  
where FPAR is the fraction of absorbed photosynthetically active radiation calculated by fine-spatial-resolution normalized difference vegetation index (NDVI), calculated as:  
\[\text{FPAR}=1.24\times\text{NDVI}+0.168 \tag{2}\]  
The NDVI in Equation (2) is calculated from red and near-infrared band reflectance from different-spatial-resolution remote sensing data (e.g., Landsat, MODIS, AVHRR). The PAR in Equation (1) is the photosynthetically active radiation (MJ m\({}^{-2}\)); \(\epsilon_{\text{max}}\) (LUEmax, g C MJ\({}^{-1}\)) is the maximal light use efficiency, which is biome-specific; Cs, Ts, and Ws represent the downward-regulation scalars for the respective effects of atmospheric CO\({}_{2}\) concentration (ppm), temperature (\({}^{\circ}\)C), and atmospheric water demand (vapor pressure deficit, VPD, in kPa) on LUE, respectively; min denotes the minimum value of Ts and Ws. The Ts is calculated as:  
\[\text{Ts}=\frac{(\text{Ta}-\text{Tmin})\times(\text{Ta}-\text{Tmax})}{(\text {Ta}-\text{Tmin})\times(\text{Ta}-\text{Tmax})-(\text{Ta}-\text{Topt})^{2}} \tag{3}\]where Ta is the air temperature. The Tmin, Tmax, and Topt are set as 0, 40, and 20.33 \({}^{\circ}\)C in the model, respectively. The Ws is calculated as:  
\[\text{Ws}=\frac{\text{VPD0}}{\text{VPD0}+\text{VPD}} \tag{4}\]  
where VPD0 is biome-specific half-saturation coefficient of the VPD (in k Pa). The Cs is calculated as:  
\[\text{Cs}=\frac{\text{Ci}-\vartheta}{\text{Ci}+2\vartheta} \tag{5}\]  
with Ci as leaf internal CO2 concentration [12]. The \(\vartheta\) in Equation (5) is the CO2 compensation point in the absence of dark respiration, which is also biome-specific [3]. Since the NDVI data sources are different from those used by [PERSON] et al. (2019) [12], we needed to optimize the model coefficients based on the time series of continuous Landsat NDVI. The fine-spatial-resolution GPP model framework is shown in Figure 1.  
To evaluate the merit of fine-spatial-resolution GPP based on reconstructed Landsat data (GPPL), we compared GPPL with the GPP measured at FLUXNET towers (GPPFLUX). Next, we compared GPPL with two other coarse-spatial-resolution GPP products based on the revised EC-LUE model with MODIS and AVHRR data as input, named GPPM and GPPA [3], respectively. The GPPM is driven by MODIS-based 500-m LAI product [13]. The GPPA is based on AVHRR data at 0.05 degrees (\(\sim\)8 km) of spatial resolution.
# Joint AP Association and Bandwidth Allocation Optimization Algorithm in High-Dense WLANs
## 3 Network Model and Design Requirements
### Design Requirements
#### 3.3.2 Proportional Fairness  
Proportional fairness is an efficient solution to performance anomaly in multi-rate WLANs. Unlike the legacy DCF mechanism with all clients having the same probability to access the channel, the proportional fairness manner allocates the bandwidth to the clients that are proportional to their transmission rates [21]. We assume that there are m clients in the multi-rate WLANs. The set {b1, b2,..., bm} is the effective bandwidth of client \(i\), which is proportionally fair if it is feasible (that is \(b_{i}\geq 0\) and \(b_{i}=\frac{\tau t_{i}}{T_{i}}\)) and for any other feasible vector \(b_{i}^{*}\), the aggregate of proportional changes is either zero or negative, i.e.,  
\[\sum_{i=1}^{m}\frac{b_{i}^{*}-b_{i}}{b_{i}}\leq 0 \tag{8}\]  
The network utilization \(U(b_{i})\) with the bandwidth coefficient is given. It is assumed that a small feasible perturbation \(b_{i}\to b_{i}+\delta b_{i}\) could exist, which increases \(U(b_{i})\), provided by the following equation.  
\[\sum_{i=1}^{m}\mathrm{U}(b_{i})^{\prime}\delta b_{i}>0 \tag{9}\]  
According to proportional fairness, Equation (9) can be transformed as follows.  
\[\sum_{i=1}^{m}\frac{\delta b_{i}}{b_{i}}>0 \tag{10}\]  
And then the equivalent Equation (11) can be achieved from Equation (10).  
\[\sum_{i=1}^{m}(\log b_{i})^{\prime}\delta b_{i}>0 \tag{11}\]  
Using the constraints above, the objective function of proportional fairness can be formulated as follows.  
\[\mathrm{max}\sum_{i=1}^{m}\log b_{i} \tag{12}\]  
Ultimately, Equation (12) can be transformed into Equation (13).  
\[\left\{\begin{array}{rl}\mathrm{max}&\sum_{i=1}^{m}\log t_{i}\\ s.t.&\sum_{i=1}^{n}t_{i}=T_{i},\\ &0\leq t_{i}\leq T_{j},\\ &1\leq i\leq n.\end{array}\right. \tag{13}\]  
According to Equation (13), the proportional fairness can be achieved by maximizing the allocated transmission time, which can prevent one client from dominating the channel, thus obtaining the airtime fairness and improving the system throughput.
# Development of a Generic Numerical Transtibial Model for Limb-Prosthesis System Evaluation
## 2 Materials & Methods
### Numerical Simulation and Validation of the Numerical Model  
A multistep static FEM simulation was performed using the ANSYS Workbench 2021 R2 (ANSYS, Inc. Canonsburg, PA 15317 USA) on the HPC at the Faculty of Mechanical Engineering, University of Maribor. The duration of the simulations varied, depending on the load case and was approximately 2 h for the donning and single-leg stance load cases combined and 6 h for the simulation of all steps on an Intel Xeon CPU E5-2670 (2.60 GHz) processor with 16 cores. The contact pressure at the limb-liner interface and the deformation of the socket for all load cases and both rectification methods were simulated and analysed in the following sections.  
Figure 5: Load cases in the numerical simulation. Donning of the socket—the adapter platform and femur cavity are fixed via remote points. Single-leg stance—vertical force is applied to the femoral cavity. P5 —– heel strike load case according to ISO 10328. P5 —push-off load case according to ISO 10328.  
In order to obtain mesh-independent numerical results, a mesh analysis was carried out for a single-leg stance load case following the donning of the socket. The complex geometry of the model, the nonlinear materials and the frictional contacts make it difficult for the model to converge. To overcome this problem, the size and resolution of the mesh were changed until the results did not improve with a finer mesh. The FE model was developed and analysed in ANSYS Mechanical, where the global mesh resolution is defined on a scale of 1 to 7 (the higher the number, the finer the mesh). In addition to the resolution, a size was set, starting with 7 mm and decreasing stepwise to 3 mm. Due to the complex geometry of the model, second-order 3D 10-node elements were used (SOLID187). The mesh was evaluated based on the average contact pressure at the limb-liner interface.
# A Remote Sensing Image Target Detection Algorithm Based on Improved YOLOv8
## 3 Improvement of YOLOv8
### C2f-E Module  
In computer vision detection tasks, the effectiveness of feature extraction can have an impact on the final detection accuracy. The introduction of a spatial attention mechanism can generate more recognizable features, which can significantly improve the accuracy of detection. The C2f module is one of the basic modules in the structure of the YOLOv8 network and can provide richer gradient flow information while keeping the network lightweight. The algorithm proposed in this paper is based on the good characteristics of the C2f module and embeds an efficient multi-scale attention module (EMA) based  
Figure 2: The structure of our network.  
on cross-spatial learning, which constitutes the C2f-E module and further enhances the network's ability to extract the target features and ensure localization accuracy with a complex background.  
The structure of the C2f-E module is shown in Figure 3. After the feature map is convolved and input to the split function, it enters the EMA attention module, which divides the given feature map into N sub-feature maps along the direction of the channel dimension in order to learn different feature information. EMA extracts the attention weights of the sub-feature maps by three parallel routes, where two parallel paths are on the 1 \(\times\) 1 Conv branch and the third path is on the 3 \(\times\) 3 Conv branch. In order to capture the dependencies between all the channels and to reduce the computational cost, after encoding the channels, two averaging pooling operations were performed along the X and Y spatial directions, respectively, in the 1 \(\times\) 1 branch, while only one 3 \(\times\) 3 convolutional kernel was stacked in the 3 \(\times\) 3 branch. The module connects the feature information extracted in the two spatial directions of the 1 \(\times\) 1 branch and inputs it to a 1 \(\times\) 1 convolution layer, then splits the output into two vectors and utilizes two sigmoid functions to fit the two-dimensional binomial distributions; finally, the obtained results are input into the cross-spatial learning module together with the feature information extracted from the 3 \(\times\) 3 branch. The cross-spatial learning module establishes the interdependence between channels and spatial locations; its structure is shown in Figure 4. The outputs of the 3 \(\times\) 3 branch and 1 \(\times\) 1 branch are first globally average-pooled, and the softmax function is used to fit at the output of the average pooling. The respective spatial attention feature maps on the two branches are obtained by multiplying the output of each branch by the matrix dot product operation. Finally, the output feature information of the two branches is mapped to the set of two spatial attention weight values, which are fed into the subsequent modules by the sigmoid function.  
Figure 4: The structure of the cross-spatial learning module.  
Figure 3: The structure of the C2f-E module.
## 1 Introduction
### Allan Variance Approaches towards Noise Source Modelling  
Allan variance was developed by [PERSON] in 1966 [63] to specify an idea that an invariant measure for a quality factor in terms of a frequency standard can be determined by driving the canonical transformation of the power for the source of white noise [64]. The high similarities in analogies between the inertial sensors and Allan variance account for the application that the method has been adopted to analyze the characterization of random drift error in various devices [65]. It is also frequently applied to come up with the instability characteristics of precision oscillators [66]. It is worth noting that the generalized method of wavelets moments (GMWM) has recently been used for the modelling of inertial sensor errors [67] and GNSS positioning errors [68] as well. However, the Allan variance tool is adequate to be capable of identifying the error distribution in this research. The way to use GMWM quantifying the local clock error model of the GNSS receiver may be achieved in the future. In this work, the outcomes of Allan variance are corresponded to four basic distributions of the noise terms existing in local receiver clock and vector tracking loop of the GNSS SDR, i.e., white frequency, random walk frequency, flicker noise and exponentially correlated or Markov noise.
# An Empirical Approach for Extreme Behavior Identification through Tweets Using Machine Learning
## 4 Experiments
### Data Collection and Preprocessing
#### 4.1.1 Twitter Data Pruning  
Data pruning is an essential step, after data crawling, to improve accuracy by making data samples unique and with less noise. Many tweets occurred multiple times in the dataset due to the retweet feature of Twitter. All such tweets are removed by using unique tweet ID assigned to each newly generated tweet. Next, few tweets that are not in English language were also removed. Furthermore, on tweet level all the terms that cannot derive any sentiment are discarded. The task of preprocessing includes removal of duplicate tweets, character case standardization, removal of user mentions and URLs, stop words removal and stemming.
## 3 Results and Discussion
### Calibration Model Using Important Wavelengths  
A total of nine different important wavelengths were chosen (405, 445, 425, 455, 585, 630, 1000, 1075, and 1095 nm) using regression coefficients. The predictive performance of the newly developed models utilizing the chosen important wavelengths is presented in Table 3.  
It can be found that the \(\text{R}_{\text{c}}^{2}\) value (0.3143) had a slight improvement in the reduced model as compared to the model that utilized full wavelengths (0.2602) (Table 3). This could be attributed to the removal of noise and pixel outliers. The prediction accuracy using the selected wavelengths is equivalent to that using the entire range of wavelengths, and approximately 93% of the wavelengths were eliminated from the entire range of wavelengths, which simplifies the models.
# Visual Description Augmented Integration Network for Multimodal Entity and Relation Extraction
## 3 Results and Discussion
### Experimental Results of MNER and MRE Tasks
We find that models using visual features combined with image descriptions to generate semantic features perform significantly better than HVPNet and MEGA, which only utilize visual features. VDAIN attains state-of-the-art results on all three datasets, showcasing the efficacy of our approach. Specifically, compared to the second-ranked HVPNet model, VDAIN shows a 0.48% increase in overall F1 scores on the Twitter-2015 dataset, a 0.91% increase on the Twitter-2017 dataset, and a 0.69% increase on the MNRE dataset. The experimental findings suggest that incorporating image description generation allows the model to capture essential image information and reduce modality gaps. 5. Figure 5 illustrates the performance of various models in terms of F1 score when solely utilizing datasets related to food safety. For this study, we selected data on the theme of food safety from three publicly available datasets and constructed a new dataset named FOOD datasets. We compared our model, VDAIN, with four other models: UMT, MEGA, and HVPNet. The results demonstrated that VDAIN outperforms the other multimodal models. This further validates the efficacy of our proposed method in entity recognition and relation extraction from food safety-related data on social media. Notably, despite the smaller volume of the FOOD datasets, our model still manages to enhance its performance under low-resource conditions. Despite the semantic features generated from image descriptions and those from text information residing within the same modality and effectively narrowing modality gaps, there are instances where the interpretations between the two do not perfectly align. This disparity can be attributed to the inherent complexities associated with transposing visual context onto textual descriptors. Furthermore, although our visual description augmented integration mechanism proficiently mitigates image noise, it sometimes fails to fully filter out irrelevant or misleading visual cues. This can potentially introduce inaccuracies in entity recognition and relation extraction. As a result, refining the transformation and fusion mechanisms between these two modalities could be a constructive direction for optimizing our model's overall performance.
# Software Design and Experimental Evaluation of a Reduced AES for IoT Applications
## 3 Materials and Methods
### Affine Transformation  
The affine transformation can be achieved in the finite field GF(2\({}^{4}\)) modulo of the irreducible polynomial \(x^{4}\) + x + 1 using Equation (1), for 0 \(\leq i\) < 4, where \(bi\) is the \(i\)th bit of the 4-bit input (b), and \(Ci\) is the \(i\)th bit of a 4-bit constant (C = 0110 b). For example, the affine transformation of 0 is computed by first finding the inverse of 0 from Table 4, then the affine transformation is computed using the constant \(C\).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**Input (x)** & **0** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **A** & **B** & **C** & **D** & **E** & **F** \\ \hline Output (\(x^{-1}\)) & 0 & 1 & 9 & E & D & B & 7 & 6 & F & 2 & C & 5 & A & 4 & 3 & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Inversion table values in hexadecimal.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Primitive Polynomial** & \multicolumn{2}{c}{**Inverse in Primitive**} & **x** & **x\({}^{-1}\)** \\ \hline
1 & x\({}^{0}\) & 0 0 0 1 & 0 0 0 1 \\ x & x & 0 0 1 0 1 0 0 1 \\ x\({}^{2}\) & x\({}^{2}\) & 0 1 0 0 & 1 1 0 1 \\ x\({}^{3}\) & x\({}^{3}\  
\[\text{b}_{i}\text{'}=\text{b}_{i}\oplus\text{b}_{(i+2)}\bmod 4\oplus\text{b}_{(i+3)} \bmod 4\oplus\text{C}_{i} \tag{1}\]  
Similarly, this transformation can be achieved in a matrix notation in GF(2\({}^{4}\)) modulo with the irreducible polynomial f(x) = x\({}^{4}\) + x + 1 using Equation (2), where matrix \(A\) is invertible over GF(2), and \(C\) could be a compatible constant number in GF(2\({}^{4}\)). In this design, we set the constant \(C\) to 616, which is equal to the vector 0110. Matrix A used in the current implementation is represented as follows:  
\[A=\left[\begin{array}{cccc}1&0&1&1\\ 1&1&0&1\\ 1&1&1&0\\ 0&1&1&1\end{array}\right]\]  
where \(A\) has an inverse over GF(2), given by:  
\[A^{-1}=\left[\begin{array}{cccc}1&1&1&0\\ 0&1&1&1\\ 1&0&1&1\\ 1&1&0&1\end{array}\right]\]  
\[xt=A\cdot\left(x^{-1}\oplus\text{ }C\right) \tag{2}\]  
The S-box table is constructed using Table 4 and Equation (1). The entry of the S-box that corresponds to an input x is computed first by finding the inverse of x from Table 4, then applying the affine transformation in Equation (1) to the output of Table 4. This produces the S-box lookup shown in Table 5.  
Table 6 shows the Inv S-box lookup table used in the decrypting function. The entries of the Inv S-box table are computed after the multiplicative inverse is applied to the output of Equation (3). Table 6 illustrates the entries for the Inv S-box over GF(2\({}^{4}\)).
## 3 Results
### SOS and POS Trend Analysis (2001-2021)
The change in the time duration of the SOS-POS interval was also analysed in the light of the individual changes in SOS dates. Figure 8 depicts the change in the SOS-POS interval, showing an increase in the number of days as the magnitude of the SOS advance grows and highlighting the influence of an advanced SOS on the lengthening of SOS-POS duration. The analysis was also conducted considering three different levels of altitudinal and latitudinal ranges, but no clear trends emerged. Figure 8: Relationships between changes in SOS-POS duration and changes in SOS during the period 2001–2021. Figures represent the same equation, highlighting sites graphically according to altitude (left) or latitude (right). Sites are divided into 3 different classes of altitude (low, 0–650 m; medium, 650–1300 m; high +1300 m) and latitude (low, 40–45\({}^{\circ}\); medium, 45–50\({}^{\circ}\); high +50\({}^{\circ}\)). Figure 7: Changes in SOS-POS (number of days) interval during the period 2001–2021 for all test sites. Figure 9 shows an example of the trend in average temperatures for the 30 days following the SOS at the DEN site, highlighting how an earlier SOS date results in a generally colder initial growing season, which can lead to a delay in the POS date and a lengthening of the SOS-POS duration (e.g., DEN). Although colder average temperatures are generally present when the start of the season occurs early, the lengthening of the SOS-POS duration, visible for example at the DEN site, was not always visible at all test sites (e.g., Figure 7), likely due to different grass species.
# Applying Game Theory to Teach the PERT Scheduling Method
## 3 Use of the PERT Method in Construction Planning and Scheduling
### PERT Method (Program Evaluation and Review Technique)
#### 3.3.2 Simple PERT Example Using Game Theory  
The elaboration of a PERT diagram will be based on the data shown in Table 2, where the times are measured in days.  
Activities A, C, and E in Table 2 will be solved using the PERT method with known times, and Equation (4) will be used to find the expected time for each activity. However, activities B and D in Table 2 will be solved using Game Theory applied to the PERT method. For Activity B (debris removal), the following extensive game will be played, as shown in Figure 1: the best combination of equipment must be found to perform the work at the lowest cost and in the shortest time.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Activity** & **Description** & **Dependence** & \(T_{o}\) & \(T_{m}\) & \(T_{p}\) \\ \hline A & Mobilization & - & 2 & 3 & 4 \\ B & Debris removal & A & & & \\ C & Facilities & A & 4 & 5 & 6 \\ D & Soil improvement & B & & & \\ E & Pavement & C, D & 3 & 5 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Precedence matrix.  
Using the pairs of numbers \((X,Y)\), where \"\(X\)\" represents the cost of the activity and \"\(Y\)\" represents the time in days, the best option, also called the equilibrium of the game in (6, 3.5), can be found. The expected time is 3.5 days, the optimal time is 2 days because it is the lowest \"\(Y\)\", the pessimistic time is 7 days--the longest time that the activity can take--and the average time is 3 days.  
As in the previous activity, for activity \(D\) (soil improvement), different options in the game in matrix form are found, where the optimum between cost and time is found in the pair of numbers (5, 6). Table 3 is filled in with the times obtained using both methods, and the PERT network is then constructed.  
Thus, Table 4 shows the precedence matrix with the final estimated times, and Figure 2 shows the final network with the times calculated using Game Theory.  
Given this data, the process's duration can be determined, as shown in Figure 2. For this specific example, the critical path will be A-B-D-E, indicating that the process will take at least 18 days.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Activity** & **Description** & **Dependence** & \(T_{o}\) & \(T_{m}\) & \(T_{p}\) & \(T_{e}\) \\ \hline A & Mobilization & - & 2 & 3 & 4 & 3 \\ B & Debris removal & A & 2 & 3 & 7 & 3.5 \\ C & Facilities & A & 4 & 5 & 6 & 5 \\ D & Soil improvement & B & 0 & 7 & 8 & 6 \\ E & Pavement & C, D & 3 & 5 & 10 & 5.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Precedence matrix with estimated times.  
Figure 1: Activity B: debris removal 1400 m\({}^{3}\).