esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data and Preprocessing of the Data  
#### 2.2.1 Satellite Imagery  
This study utilized multi-temporal archived Level-2A orthorectified atmospherically corrected surface reflectance Sentinel-2 satellite images obtained from GEE via the Earth Engine Python API. The Sentinel-2 mission encompasses two satellites, namely Sentinel-2A and Sentinel-2B, launched in 2015 and 2017, respectively. Both satellites offer global coverage of the Earth's surface every 10 days, thereby enabling a combined revisit cycle of 5 days for the satellite constellation. The multi-temporal characteristics of Sentinel satellite imagery allow it to effectively capture the unique phenological and temporal patterns of different crops, drawing upon insights emphasized by [PERSON] et al. [44] regarding the accuracy of crop classification using multi-temporal satellite data.  
In this study, Sentinel-2 products underwent preprocessing on the GEE platform, involving acquiring images at 10-day intervals with cloud coverage below 20% from April to October 2021. A median function was applied to each 10-day interval image collection to select the median pixel reflectance values, resulting in 10-day interval composites. The median function enabled the production of cloud-free images across the study area. Additionally, this function eliminated erroneous values caused by the presence of extremely bright, dark, or noisy pixels [45].
# Spatial Evolution Analysis of Tailings Flow from Tailings Dam Failure Based on MacCormack-TVD
## 2 Tailings Pond Breach Modeling
### Assumptions and Limitations of the Model  
The Massflow software is based on the MacCormack-TVD finite element model, and a regular square network element system is used in the analyses, where each grid node can be given a fixed elevation and flow velocity, and data such as flow depth and flow rate can be obtained through post-processing. A small time step between the network cells is used to calculate the average tailing sand flow process.  
Assumptions and limitations are made in the software analysis: (1) the tailing flow is a steady state flow in differential time, and turbulent states are not considered; (2) the roughness of the terrain is only divided according to zones, with a uniform distribution of roughness in all zones; (3) there is no limitation on the distribution of hydrostatic pressure; (4) tailing flow elevation values and Cullen and Manning basal friction coefficients are available at each node on the grid; and (5) there is no consideration of sediment carryover and substrate erosion.
# An Analytical Framework in OFDM Wireless Networks Servicing Random or Quasi-Random Traffic
## 1 Introduction
To overcome this problem of complexity that appears in [23], a recursive formula is proposed in [26] for the calculation of CBP and resource utilization. In this paper, firstly, we extend the model of [23] by incorporating restricted accessibility. We name this model P-S/res. In such a system, each state is related to a pre-specified blocking probability and therefore a call may be blocked and lost even if subcarriers do exist at the time of the call's arrival [38]. For the proposed model, we show that the calculation of CBP and resource utilization can be based on recursive formulas. In addition, we show the relationship of the P-S/res model with the P-S model under the bandwidth reservation (BR) policy (P-S/BR model). This policy permits the subcarriers' reservation so as to favor those calls that have high subcarrier requirements. In that sense, the BR policy provides QoS to certain service-classes. Secondly, we propose the quasi-random P-S model with restricted accessibility (qr-P-S/res model) and provide recursive formulas for the determination of time and call congestion (TC and CC, respectively) probabilities as well as resource utilization which are the main performance measures. TC probabilities, for a particular service-class, can be calculated via the proportion of time the system has no available resources for this service-class. On the other hand, CC probabilities can be determined via the proportion of arriving calls that find no available resources in the system. Note that in the P-S model, TC and CC probabilities coincide (and named CBP) due to the Poisson process. Thirdly, we show the relationship between the qr-P-S/res model and the qr-P-S/BR, qr-P-S models, where the arrival process remains quasi-random but the adopted policy is the BR and the CS policy, respectively. The calculation of all performance measures in the qr-P-S/BR and the qr-P-S models can also be based on recursive formulas. Based on simulation results, the accuracy of all proposed formulas is quite satisfactory. This paper is organized as follows: In Section 2, we review the P-S model and present the formulas for the CBP determination and resource utilization. In Section 3, we propose the P-S/res model and present recursive formulas for the determination of the performance measures. In addition, we show the relationship between the P-S/res model and the P-S/BR model. In Section 4, we study the case of quasi-random traffic, propose the qr-P-S/res model, and present recursive formulas for the determination of the performance measures. In addition, we show how the qr-P-S/res model and the qr-P-S/BR, qr-P-S models are related. In Section 5, we compare the simulation with analytical results for the P-S, the P-S/res and the qr-P-S models. The comparison verifies the accuracy of all formulas. We conclude in Section 6.
# Off-Grid Power Plant Load Management System Applied in a Rural Area of Africa
## 5 Pattern Matching-Based Load Anomaly Detector
### Pattern Matching Scheme  
To reduce the influence of random events on predictions and further improve the detection accuracy, the pattern matching-based anomaly detector is composed of three parts: (1) result pattern matching, (2) the classification of pattern matching results, and (3) the evaluation of pattern matching results.
# Lightning Surge Analysis for Overhead Lines Considering Corona Effect
## 3 Modeling of Corona \(q\)-\(u\) Curves  
The traditional approach is only suitable for modeling of the \(q\)-\(u\) curves under double exponential surges [20]. It introduced the corona charge \(q_{\rm C}\) by subtracting the induced charge \(C_{0}u\) from the total charge \(q\)[20, 22, 23], as illustrated in Figure 5. The corona current is described by:  
\[i_{\rm C}=\left\{\begin{array}{cl}0&q_{\rm C}>(C_{2}-C_{0})(u-U_{2})\\ f(u,q_{\rm C})&(C_{1}-C_{0})(u-U_{1})<q_{\rm C}<(C_{2}-C_{0})(u-U_{2})\\ h(u,q_{\rm C})&0<q_{\rm C}<(C_{1}-C_{0})(u-U_{1})\end{array}\right. \tag{1}\]  
where,  
\[\begin{array}{c}f(u,q_{\rm C})=\alpha[(C_{2}-C_{0})(u-U_{2})-q_{\rm C}]\\ h(u,q_{\rm C})=f(u,q_{\rm C})+\beta[(C_{1}-C_{0})(u-U_{1})-q_{\rm C}]\end{array} \tag{2}\]  
where \(C_{1}\) and \(C_{2}\) are the dynamic capacitances (\(C_{2}>C_{1}>C_{0}\)); \(U_{1}\) and \(U_{2}\) are the critical voltages (\(U_{1}>U_{2}\)); \(\alpha\) and \(\beta\) are the coefficients (\(\alpha>0\), \(\beta>0\)), as shown in Figure 6.  
Figure 4: Formulation of minor loops.  
Figure 5: Diagram of corona charge.  
Description of the subsequent curve can be made by analogy with that of either the curve segment BB\({}^{\prime}\) or the curve segment B\({}^{\prime}\)B\({}^{\prime\prime}\). In Equations (1)-(6), the parametric values of \(\alpha\)-\(\alpha^{\prime\prime}\), \(\beta\)-\(\beta^{\prime\prime}\), \(C_{1}\)-\(C_{2}\)\({}^{\prime\prime}\), \(U_{1}\)-\(U_{1}\)\({}^{\prime\prime}\) and \(U_{2}\)-\(U_{2}\)\({}^{\prime\prime}\) can be determined by fitting the given \(q\)-\(u\) curve. The integral of the corona current \(i_{C}\) from \(t-\Delta t\) to \(t\) gives the corona charge  
\[q_{C}(t)=\frac{\Delta t}{2}i_{C}(t)+q_{CP}(t-\Delta t) \tag{7}\]  
where \(\Delta t\) is the time step and \(q_{\rm CP}(t-\Delta t)\) is corona charge at the preceding time step:  
\[q_{CP}(t-\Delta t)=\frac{\Delta t}{2}i_{C}(t-\Delta t)+q_{C}(t-\Delta t) \tag{8}\]  
Figure 8: Modeling of the third curve segment.  
Figure 7: Modeling of the second curve segment.  
According to Figure 3, the total charge \(q\) on the \(q\)-\(u\) curve is evaluated by:  
\[q(t)=q_{C}(t)+C_{0}u(t) \tag{9}\]
## 9 Language Network Units
### Language Network Unit Analysis  
In Table 8 column four we have listed one standard error values for these tests. The phoneme units are the most robust. In Figure 8 we have plotted word correctness (\(x\)-axis) for each speaker along the \(y\)-axis over three figures, one figure per language network unit. The viseme network is top, phoneme network middle, and word network at the bottom. The viseme network is the lowest performing score (\(0.02\pm 0.0063\)). On the face of it, the idea of visemes classifiers is a good one because they take visual co-articulation into account to some extent. However, as seen here, a language model of visemes is too complex because of homophenes. This leaves us with a choice of either phoneme or word units for our language model in step five of our new hierarchical training method.  
In Figure 8(middle) we have our phoneme language network performance with both viseme and phoneme trained classifiers. This is more exciting because for all speakers we see a statistically significant increase in \(C_{w}\) compared to the viseme network scores in Figure 8(top). Looking more closely between speakers we see that for four speakers (2, 9, 10 and 12), the viseme classifiers outperform the phonemes, yet for all other speakers there is no significant difference between the two. On average they are identical with an all-speaker mean \(C_{w}\) of \(0.19\pm 0.0036\) compared to the viseme classifiers (Table 8, column 3).  
In Figure 8(bottom) we show our \(C_{w}\) for all speakers with a word network paired with classifiers built on viseme, phoneme, and word units. Our first observation is that word classifiers perform very poorly. We attribute this to a low number of training samples per class due to the extra number of classes in the word space compared to the number of classes in the phoneme space, so we do not continue our work with word-based classifiers. Also shown in Figure 8(bottom) are the phoneme and viseme classifiers (in green and red respectively) with a word network. This time we see that for five of our 12 speakers (3, 5, 7, 8, and 11), the phoneme classifiers outperform the visemes and for our remaining speakers there is no significant difference once a work network is applied.  
Figure 8: Effects of support network unit choice with each type of labeled HMM classifier units. Along the \(x\)-axis is each speaker, \(y\)-axis values are correctness, \(C\). Viseme network is at the (**top**), phoneme network plotted in the (**middle**), and word networks at the (**bottom**).  
These results tell us that for some speakers viseme classifiers with phoneme networks are a better choice whereas others are easier to lipread with phoneme classifiers with a word network. Thus, we continue our work using both phoneme and word-based language networks.
###### Abstract  
In 5G scenarios, the dynamic resource allocation of network slicing is crucial for quality-of-service (QoS) guaranteed under fluctuating traffic demands in rapidly changing communication environments. In this paper, we propose a novel QoS guaranteed joint resource allocation framework for NR with supplementary uplink (SUL) called QGJRA-SUL, where three parameters of SUL admission, TDD pattern, and band slicing scheme are jointly optimized. The framework is driven by a well-designed deep reinforcement learning agent. By combining the activation functions tanh and softmax, the agent can jointly optimize three parameters at the same time. Under the original problem of QoS satisfaction rate maximization, we introduce the load unbalance degree of slices into the reward function as a penalty term. The simulation results show that the framework can guarantee the QoS satisfaction rate well and balance the load of slices. QGJRA-SUL can accommodate 15% more user equipments (UEs) with the same QoS satisfaction rate than that of a traditional single-band solution without SUL, and achieve a 73% increase in the performance of load balancing than that without a load balancing mechanism near the full load.  
network slicing; supplementary uplink; deep reinforcement learning 2023
## 3 Proposed Methodology
### Segmentation  
The Figure 1 shows the segmentation flow. CNN is used for tumor segmentation because the tumor size is so small that other approaches do not work correctly. CNN does not use the entire image for training. Instead, it was trained using patches extracted at random from images. Images are divided into patches and target categories of center points during training. The method is commonly used in biomedical image processing such as skin cancer images and mammography images [18]. The training strategy based on patches required more storage and time than the training strategy based on full-sized images. The former is preferable for tumor segmentation because small tumor areas and using full-sized images would result in false positives. CNN outperforms other methods, but it has some flaws. This paper circumvents that weakness. The limitation is that CNN segmentation is completed perfectly but with rough edges and low contrast, which sometimes misses  
Figure 3: Three-Classes Detection Phases: Segmentation, Attention Mechanism and Activation.  
the tumor boundary and increases false positives. The current segmentation techniques disregard overlapping patch boundary overlaps. The overlap of patches increases the detection and localization error of tumors. The proposed CNN segmentation method maps patches, but condition-based conditional random field extracts improve the overlap. This improves the accuracy of detection by enhancing segmentation, feature mapping, and classification.  
\[\in(x)=\phi(xi\,)+\phi(xi\,+xj) \tag{1}\]  
\(\phi(x)\) Its gradient, which computes every pixel,  
\[\phi_{u}(x)=-logp(xi\,) \tag{2}\]  
\(p(xi)\) is labeled assignment of probabilistic parameters.  
To improve segmentation, combine Conditional Random Field (CRF) with CNN. CRF is a framework for probabilistic modeling. CRF always used iterative parameter estimation techniques. We optimized the probabilistic model on his framework, using each category's contribution network properties. It is our energy function. Following the receipt, the CRF output segmentation results were modified using a variety of morphological approaches. The most connected slice was chosen as the candidate area then sections larger than 300 pixels were identified as malignant. After obtaining segmentation results for each slice, the tumor was 3D smoothed using a box filter with a 3-dimensional convolution kernel to make the segmentation findings more natural. In Segmentation, these are the following steps:  
Step-1: A convolutional neural network should be used. CNN employs a four-layer CFAR-10 network.  
Step-2: We used the RELU nonlinear activation function in the CNN network. This function removes unnecessary noise and provides patches as regions of interest.  
Step-3: Patches are obtained by CNN, but the edges are overlapping or not clear. It is reasoned that the intensity of that region is less.  
Step-4: Conditional Random Forest (CRF) improves these regions by increasing the intensity threshold and smooth edge area.
## 2 Description of the Problem  
The combined use of WT-PV accounts for the convoluted nature of the OPF as the WT and PV output power is intermittent and time-varying. To consider such uncertain behavior, the OPF problem is expressed in the present study by taking into account some assumptions:  
* The active power output of WT-PV is uncertain and time-varying [50],
* The OPF is executed ten times in a period of 10 min. So, irradiance and wind speed are sampled periodically at each 1 min.
* Noting that WT/PV units can also generate reactive power, the voltage magnitudes of WT/PV buses have been assumed to be control parameters [51].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Year** & **Contribution** & **Area of the Application** \\ \hline \multirow{4}{*}{[47]} & \multirow{4}{*}{2022} & A combined of multi-fidelity meta-optimization (MFM) and TFWO (MFM-TFWO) & Unit commitment (UC) in the power systems \\  & & (MFM-TFWO) & Short term planning of hydrothermal power systems with PVs and \\ \hline \multirow{4}{*}{[48]} & \multirow{4}{*}{2022} & Quasi-oppositional TFWO & \multirow{2}{*}{
\begin{tabular}{c} Short term planning of hydrothermal power systems with PVs and \\ pumped-storage plants \\ \end{tabular} } \\  & & & \\ \hline \multirow{4}{*}{[49]} & \multirow{4}{*}{2022} & Selecting the parameters of a proportional-integral-derivative (PID) controller \\ \cline{1-1} \cline{3-4}  & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._Equation (1) describes the mathematical expression of the OPF problem [52].  
\[\min F(x,y) \tag{1}\]  
Constrained by:  
\[g(x,y)=0 \tag{2}\]  
\[h(x,y)\leq 0 \tag{3}\]  
\[xeX \tag{4}\]  
\(F\) shows the objective function; \(x\) is a vector with decision variable elements, active energy of units (\(P_{G}\)) except for the slack bus (Bus 1), output voltages of generating units (\(V_{G}\)), transformer taps (\(T\)), and (\(Q_{C}\)) denotes the shunt VAR compensations [53]:  
\[x=\left[\begin{array}{c}P_{G2},\ldots,P_{GNG},V_{G1},\ldots,V_{GNG},V_{WT}, V_{PV},\\ T_{1},\ldots,T_{NT},Q_{C1},\ldots,Q_{CNC}\end{array}\right] \tag{5}\]  
\(NG\), \(NT\) and \(NC\) indicate the number of thermal generators, transformers, and VAR compensators, respectively.  
In addition, \(y\) is the vector of dependent variables, such as power at the slack bus (\(P_{G1}\)), the voltage at the load bus (\(V_{L}\)), the reactive output power of a generator (\(Q_{G}\)), and apparent power flow through the transmission line (\(S_{l}\)) [54]:  
\[y=\left[\begin{array}{c}P_{G1},V_{L1},\ldots,V_{LNL},Q_{G1},\ldots,Q_{GNG}, \\ Q_{WT},V_{PV},S_{11},\ldots,S_{INTL}\end{array}\right] \tag{6}\]  
\(NTL\) and \(NL\) show the size of network lines and load buses.
# A Survey of Detection and Mitigation for Fake Images on Social Media Platforms
## 7 Limitations and Challenges
### Interpretability  
Interpretability is a significant challenge in detecting fake images using deep learning methods. In artificial intelligence, achieving explainability or interpretability is a common and critical issue. This need for interpretability becomes even more pressing as deep learning models, with their inherent complexity, excel in classifying images as fake withimpressive accuracy but fall short in providing clear explanations for their decisions. While tools like Grad-Cam and LIME offer insights into image differences, their interpretation still requires domain expertise. This challenge remains a significant obstacle if we intend to release reliable solutions to the public. From the author's perspective, addressing the issue of interpretability in fake image detection is vital for building trust in AI systems and ensuring that users can make informed judgments about the authenticity of visual content.
## 3 Results and Discussion
### CO Flux Inversion Results  
The mean posterior CO fluxes inverted by the DA experiments are listed in Table 11. Their distributions and their differences relative to the prior CO fluxes are shown in Figure 14. The mean posterior CO fluxes of the four consecutive winter seasons in western Europe from 2017 to 2020 were \(28.45\) mol km\({}^{-2}\) h\({}^{-1}\), \(21.79\) mol km\({}^{-2}\) h\({}^{-1}\), \(20.19\) mol km\({}^{-2}\) h\({}^{-1}\), and \(19.86\) mol km\({}^
## 3 Optimizing In-Orbit SAR Operational Parameters for Altitude Variation
### PRF Optimization
#### 3.2.3 PRF Optimization Using Travelling Return Time  
PRF is related to the echo sampling time that is defined as the time interval between the sampling window start time (SWST) and sampling window end time (SWET). The travelling return time to the horizontal area is defined as \(Rank\). SWST, SWET, and \(Rank\) are expressed, respectively, as follows [9].  
\[SWST=\frac{2R_{\text{s,near}}}{c}-\frac{Rank}{PRF} \tag{7}\]  
\[SWET=\frac{2R_{\text{s,far}}}{c}-\frac{Rank}{PRF}+\tau_{\text{p}} \tag{8}\]  
\[Rank=fix\left|\frac{2R_{\text{s,near}}}{c}\cdot PRF\right| \tag{9}\]  
Here, the function \(fix(\cdot)\) means the decimal cut. The echo sampling time is a function of the slant ranges, \(R_{\text{s,near}}\) and \(R_{\text{s,far}}\), that are defined as the return times, respectively, from near incidence angle and far incidence angle of the swath. Using the guard times, \(\tau_{\text{TxRoGuard}}\) and \(\tau_{\text{RxToGuard}}\) in Figure 7, inequalities for SWST and SWET are expressed as follows.  
\[SWST\geq\tau_{\text{p}}+\tau_{\text{TxRoGuard}} \tag{10}\]  
\[SWET\leq\frac{1}{PRF}-\tau_{\text{RxToGuard}} \tag{11}\]  
Figure 14: Available PRF range in PRF diamond diagram: (a) SW#3; (b) SW#6; (c) SW#8.  
The minimum PRF is calculated using Equations (7) and (10) as follows.  
\[PRF\geq\frac{Tx_{\text{duty}}+Rank}{\frac{2R_{\text{s,near}}}{c}-\tau_{\text{Tx RxGuard}}} \tag{12}\]  
Also, the maximum PRF is calculated using Equations (8) and (11) as follows.  
\[PRF\leq\frac{Rank+1-Tx_{\text{duty}}}{\frac{2R_{\text{s,near}}}{c}+\tau_{\text{ RxTxGuard}}} \tag{13}\]  
The allowable upper and lower limits of PRF are derived from the above the SWST and SWET inequalities.  
\[\frac{Tx_{\text{duty}}+Rank}{\frac{2R_{\text{s,near}}}{c}-\tau_{\text{TxRxGuard }}}\leq PRF\leq\frac{Rank+1-Tx_{\text{duty}}}{\frac{2R_{\text{s,far}}}{c}+\tau_ {\text{RxTxGuard}}} \tag{14}\]  
The optimized PRF is given as an average of the PRF upper and lower limits in Equation (15).  
\[PRF_{\text{opt}}=\frac{1}{2}\bigg{\{}\frac{Tx_{\text{duty}}+Rank}{(\tau_{ \text{near}}-\tau_{\text{TxRxGuard}})}+\frac{Rank+1-Tx_{\text{duty}}}{(\tau_{ \text{far}}+\tau_{\text{RxTxGuard}})}\bigg{\}} \tag{15}\]