esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion and Conclusions
Therefore, the results of this research enable strategic decision-making aimed at: (a) environmental preservation, perfectly delimiting the areas where it is viable to place a particular activity; (b) economic viability, classifying the territory according to its suitability for economic development; and (c) social equity, making it possible to plan between needs and available resources throughout the territory. Finally, some limitations of the present work must be recognized. First, it is necessary to consider that certain territorial variables of resources, capabilities, and outcomes have not been included in this work, so future research could contribute to improving the proposed model. Because the territory rankings that appear by applying the proposed methodology are for a specific research context, it would be interesting to replicate the model within other territories and sectors. In this sense, further studies could be carried out on other types of renewable energy industries such as photovoltaic, biomass, bio-fuel, or biogas power. To this end, indicators should be re-designed so that they get closer to the selected context configuration. Additionally, this methodology is useful to the study of synergies between different, potentially complementary sectors (e.g., tourism, agro-food, commerce, transport, and so on). Taking advantage of the possibilities that the integration of multi-criteria analysis with geographic information systems offers, other applications are possible to evaluate a territory and reveal which areas can be suitable for the development of synergies between different activities. Conceptualization, F.S.S., F.R.A.V., and P.D.S.P. ; Methodology, F.S.S., F.R.A.V., and P.D.S.P. ; Validation, F.R.A.V. and P.D.S.P. ; Formal Analysis, F.S.S. ; Investigation, F.S.S., F.R.A.V., and P.D.S.P. ; Resources, F.S. ; Data Curation, F.S.S. ; Writing--Original Draft Preparation, F.S.S. ; Writing--Review & Editing, F.R.A.V. and P.D.S.P. ; Visualization, F.S.S. ; Supervision, F.R.A.V. and P.D.S.P. This research received no external funding. The authors declare no conflict of interest.
## 7 Current Challenges and Future Research Directions
### OpenFlow Switch Overload  
In OpenFlow switches, the flow table rules are stored in TCAM. This type of memory is restricted to a limited number of flows and is expensive, which attracts the attention of LDDoS attackers. LDDoS use less resources to load the flow table than it takes to exhaust the network controller. The attacker 1 attempts to load and run through its restricted capacity, causing the switch to stop working and potentially terminating network services. Although applying a detection method in the OpenFlow switches can reduce the controller's computing burden and communication between the data plane and the control plane, it increases the complexity of the hardware and incurs an additional expense. Therefore, effective deployment of security modules in switches while reducing the complexity of communication between devices is an open problem.
# An Accurate Platform for Investigating TCP Performance in Wi-Fi Networks
###### Abstract  
An increasing number of devices are connecting to the Internet via Wi-Fi networks, ranging from mobile phones to Internet of Things (IoT) devices. Moreover, Wi-Fi technology has undergone gradual development, with various standards and implementations. In a Wi-Fi network, a Wi-Fi client typically uses the Transmission Control Protocol (TCP) for its applications. Hence, it is essential to understand and quantify the TCP performance in such an environment. This work presents an emulator-based approach for investigating the TCP performance in Wi-Fi networks in a time- and cost-efficient manner. We introduce a new platform, which leverages the Mininet-WiFi emulator to construct various Wi-Fi networks for investigation while considering actual TCP implementations. The platform uniquely includes tools and scripts to assess TCP performance in the Wi-Fi networks quickly. First, to confirm the accuracy of our platform, we compare the emulated results to the results in a real Wi-Fi network, where the bufferbloat problem may occur. The two results are not only similar but also usable for finding the bufferbloat condition under different methods of TCP congestion control. Second, we conduct a similar evaluation in scenarios with the Wi-Fi link as a bottleneck and those with varying signal strengths. Third, we use the platform to compare the fairness performance of TCP congestion control algorithms in a Wi-Fi network with multiple clients. The results show the efficiency and convenience of our platform in recognizing TCP behaviors.  
Wi-Fi emulator; TCP; congestion control; evaluation +
Footnote †: journal:
## 2 Methodology
### QSA Method  
This paper proposes a methodology that combines the QSA method and a classifier to classify SCT levels in transformers automatically. The algorithm presents three stages as shown in Figure 1: (1) QSA, (2) Statistical calculus, and (3) Training and evaluation.  
In the QSA stage, the quaternion signals are formed, rotated and the modulus of each rotation is obtained. After that, some samples are selected by a data window to calculate the statistical values.  
The quaternion signal \(q(t)\) is formed by coefficients of the transformer current signal \(I(t)\) and three accelerations, which represent vibrations \(V_{x}(t)\), \(V_{y}(t)\), and \(V_{z}(t)\), as is shown in Equation (6). The current and vibration signals have the advantage of being able to simultaneously be obtained while the transformer is in operation, allowing for real-time online monitoring. The QSA method requires four values, and since the magnetostriction phenomenon correlates the current and vibration signals, the application of this method using these signals becomes feasible. The current signal is measured in amperes and the vibration signals in g force. All signals are discrete with \(N\) samples in time.  
\[q(t)=I(t)+V_{x}(t)i+V_{y}(t)j+V_{z}(t)k \tag{6}\]  
Similarly, the delayed quaternion signal \(q_{d}(t)\) is formed by the corresponding current signal and vibration signals coefficients in the displaced time \(\Delta t\), called \(I_{d}\), \(V_{xd}\), \(V_{yd}\), and \(V_{zd}\) as is shown in Equation (7).  
\[q_{d}(t)=I_{d}(t)+V_{xd}(t)i+V_{yd}(t)j+V_{zd}(t)k \tag{7}\]  
One sample of the signals \(q(t)\) and \(q_{d}(t)\) are selected from the same time. As is presented in Figure 2, \(q_{r}\) is calculated through the present and delayed sample to obtain one quaternion model.  
Figure 1: Flowchart of the proposed methodology.  
A set of models is obtained through the quaternion rotation of the signals \(q(t)\) and \(q_{d}(t)\). The natural quaternion of rotation is obtained from (8) which is originally (4). \(N-1\) behavior models are obtained in the full signal.  
\[q_{r}=\left[\begin{array}{ccc}1-2V_{yd}^{2}-2V_{zd}^{2}&2(V_{xd}V_{yd}+I_{d}V_ {zd})&2(V_{xd}V_{zd}-I_{d}V_{yd})\\ 2(V_{xd}V_{yd}-I_{d}V_{zd})&1-2V_{xd}^{2}-2V_{zd}^{2}&2(V_{yd}V_{zd}+I_{d}V_{yd })\\ 2(V_{xd}V_{zd}+I_{d}V_{yd})&2(V_{yd}V_{zd}-I_{d}V_{xd})&1-2V_{xd}^{2}-2V_{yd}^ {2}\\ \end{array}\right]\left[\begin{array}{c}V_{x}\\ V_{y}\\ V_{z}\\ \end{array}\right] \tag{8}\]  
The modulus of each quaternion in all the signal \(q_{r}(t)\) is calculated. After that, \(M\) consecutive modulus are selected to create the window vector \(w[m]\), as shown in (9).  
\[w[m]=[|q_{r}(t)|,|q_{r}(t+\Delta t)|,|q_{r}(t+2\Delta t)|\ldots|q_{r}(t+(M-1) \Delta t)|] \tag{9}\]
# Asymptotic Performance of GNSS Positioning Approaches under Cross-Correlation Effects
###### Abstract  
Conventional global navigation satellite system receivers typically employ a two-step positioning procedure (2 SP) by first independently estimating the synchronization parameters and then using these parameters to solve a system of superdeterministic equations derived from multi-lateration to accomplish positioning. Direct position estimation (DPE) has emerged as a promising alternative that utilizes a single-step procedure to obtain the maximum likelihood estimate of a position. This approach has been shown to effectively mitigate biases incurred by the second estimation step in 2 SP. However, for code-division multiple-access systems, the pseudo-orthogonality of the spreading codes causes the estimation problem not to be mapped to a perfectly orthogonal space. Additionally, the cross-correlation interference between satellites renders the maximum likelihood invariant theory untenable in the first estimation step of the 2 SP. This study presents the derivation of the Cramer-Rao bound constraint for both the 2 SP and DPE, evaluating the performance degradation of the 2 SP compared to that of the DPE with the consideration of cross-correlation. Furthermore, a more stringent result is proven, indicating that the 2 SP is not as asymptotically efficient as the DPE in all scenarios. The derived bounds are validated using realistic scenarios, and the root-mean-square error performance of the respective maximum likelihood estimators is compared.  
direct position estimation (DPE); Cramer-Rao bound (CRB); maximum likelihood estimation; cross-correlation; satellite navigation systems +
Footnote †: journal:  
Article  
Asymptotic Performance of GNSS Positioning Approaches under Cross-Correlation Effects. _Rancet Sens._
## 4 Discussion
Indeed, the S\({}_{0}\) and S\({}_{3}\) shifts, returning the best separation values, are not able to detect some of the cells, which, through the left and down shifts (S\({}_{1}\) and S\({}_{2}\)), become categorized as the urban ones due to a change in their class coverage. The ability of the algorithms to recognize some tiles as urbanized is hence conditioned by the grid position, due to its translations involving a change in the single class coverage inside each cell, leading to a possible different tile classification. The dissolution of the resulting urbanized cells picked from the best clustering methods for the single grid shift allow for the consideration of all the possible external boundaries of the urbanized area. The analyses performed for the province of L'Aquila also show a prevalence of agricultural areas. As can be seen in Figure 4, the UA dataset for this territorial scope is characterized by a core artificial area, surrounded by large buffers of agricultural areas. This correspondence is shown clearly in the final elaborations of the current study, returning a prevalence of this latter category for the urbanized cells. It is also important to consider that the emerged threshold values regarding the urbanized cells only belong to the province of L'Aquila. A different distribution of the artificial areas (subtended by different planning policies and different urban expansion dynamics) can likely lead to variations in the UA class frequencies inside the cells, resulting in variations in the relative threshold values. Regardless, the latter are a direct consequence of the proposed methodology. By using this, the recurring problem of the definition of threshold values is outdated, leaving the operator with the definition of the quantitative aspects [40]. Another important consequence is the simplicity and uniformity of this approach. Indeed, by using the shifts and the clustering algorithms, the wide variety of data and the task to reorganize them with specific rules are entrusted to the software rather than to the operators themselves, who do not need to understand the heterogeneity of methodologies present in literature, an aspect that can be confusing for those who are approaching these analyses [39].
## 1 Introduction
Instead of using [PERSON]'s board as the evaluation metrics, which are used by another paper [15] with the same dataset, we use mIoU (mean intersection-over-union) as the evaluation metric because the scoring mechanism of [PERSON]'s board is not public, making it difficult to analyze the experimental results reasonably. Compared with the performance of the baseline MaskRCNN model, the mIoU of the CloudRCNN (auxiliary loss) model improves by 15.22%, and that of the CloudRCNN (feature fusion) model improves by 13.57%. The main contributions of this research are listed as follows:  
* We propose two designs to improve MaskRCNN according to the characteristics in the satellite cloud images (the clouds and the background can be easily distinguished while different types of clouds have little difference);
* We explore two branch designs of MaskRCNN, including using auxiliary loss to supervise the foreground and designing a feature fusion module to restore the feature map, so as to judge the feature extraction of the first-stage convolution;
* We design comprehensive experiments on the Kaggle competition dataset called \"Understanding Clouds from Satellite Images\" to evaluate the performance of our model, which achieves substantial improvements over MaskRCNN. The rest of this paper is organized as follows. Section 2 reviews related research work. Section 3 explains our proposed model and dataset. In Section 4, comprehensive experiments are conducted to evaluate the effectiveness of the proposed model. Finally, in Section 5, we conclude the paper and provide future work.
###### Abstract  
The South China Sea has abundant reserves of natural gas hydrates, and if developed effectively, it can greatly alleviate the pressure on the energy supply in China. But the hydrate reservoirs in the sea area are loose, shallow, porous, and have poor mechanical properties. During the drilling process, the invasion of drilling fluid into this kind of reservoir is likely to induce mass decomposition of gas hydrate and, in turn, a significant reduction in mechanical strength around the wellbore as well as instability of the wellbore. In this study, in light of the engineering background of exploratory wells at the South China Sea, a temperature and pressure field model in a gas hydrate reservoir at sea during open circuit drilling was established, and then, based on this model, a comprehensive model for the stability analysis of the well drilled in the hydrate reservoir at sea was constructed, both of them with errors of less than 10%. With these two models, the effects of different drilling parameters on wellbore stability were investigated. The gas and liquid produced by the decomposition of hydrates in the formation will increase the pore pressure in the formation, thereby reducing the effective stress in the formation. The closer the formation is to the wellbore, the more thorough the decomposition of hydrates in the formation and the greater the effective plastic strain. Keeping all other conditions constant, the increase in drilling fluid invasion pressure and temperature, as well as reservoir permeability, will lead to a decrease in the mechanical strength of the formation around the wellbore and an expansion of the wellbore yield zone. The results can provide a theoretical reference for the stability analysis at sea.
# Rapid Estimation of TVWS: A Probabilistic Approach Based on Sensed Signal Parameters
## 5 Results and Discussion
### Confusion Matrix  
With all the collected information, we create the two-dimensional Confusion Matrix (CM) observed in Figure 17, where we can visualize the relationship of the sensed signals [23]; here, we include \(P_{d}\), \(P_{fa}\), \(P_{m}\), and \(P_{n}\). Rows represent the predicted condition and columns represent the observed (real) condition. This matrix helps us to identify characteristics and behaviour of the analyzed signals. Derived values from CM are presented in Table 6, where the Positive Predictive Values (PPV) [24] are calculated by using  
\[PPV=\frac{P_{d}}{P_{d}+P_{fa}} \tag{14}\]  
and it represents the proportion of positive results obtained statistically that correspond to the \(P_{d}\). It describes the performance of the statistical method used. A high PPV value (\(\simeq\)100%) can be interpreted as an accuracy result. In this case, the average obtained is \(PPV_{(P1\to P10)}=93.81\%\), and it means that the \(P_{d}\) calculated for each case is accurate enough to be used as a reference parameter to find E-TVWS.  
Figure 16: Extreme cases analyzed: (**a**) ROC curves for Scenarios 1 and 2 in full scale; (**b**) extreme ROC curves for Scenarios 1 and 2 vs. the \(P_{TVWS}\) curves for points P1 to P10. Green arrows indicates the upper and lower limits for \(P_{d}\) corresponding to 3% to find E-TVWS.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \(\boldsymbol{Point}\) & \(\boldsymbol{P}\boldsymbol{U}\) & \(\boldsymbol{P}_{d}\) & \(\boldsymbol{\overline{W}}\) & \(\boldsymbol{\sigma}_{W}\) & \(\boldsymbol{\overline{X}}\) & \(\boldsymbol{\sigma}_{X}\) & \(\boldsymbol{Z}_{X}\) & \(\boldsymbol{I}\) & \(\boldsymbol{T}\) & \(\boldsymbol{Z_{1}X}\) \\ \hline Scenario 1 & 32 & \(83.89\) & \(-109.22\) & \(2.1952\) & \(-94.66\) & \(10.901\) & \(-0.9966\) & \(-104.79\) & \(-105.09\) & \(-69.261\) \\ Scenario 2 & 0 & \(3.70\) & \(-109.36\) & \(1.7327\) & \(-108.80\) & \(1.5323\) & \(1.7879\) & \(-105.97\) & \(-106.06\) & \(-105.23\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Tabulated results obtained from simulated Scenarios 1 and 2.
# Formation Control of Nonlinear Multi-Agent Systems with Nested Input Saturation
## 6 Conclusions  
In this work, a decentralized robust adaptive control scheme for formation control of MAS with constraints regarding the magnitude and slew-rate of the control signal was designed. The proposed control algorithm tackles the inherent conflict between user-defined output specifications and the system's input constraints while ensuring adaptive output performance boundedness of the control signals without the need for extensive gain-tuning procedures. The theoretical results are validated by comprehensive experimental results on a swarm of 5 quadcopters. Employing the adaptive performance technique ensures the stability of the closed-loop system while enabling precise tracking of the leader with predefined characteristics when operating outside the saturation area.  
Regarding future directions, our aim is to tackle the challenge of feedback delays in order to enhance the efficiency and resilience of adaptive performance controllers. Additionally, while the relaxation of performance specifications is critical for the boundedness of the closed-loop signals, it is essential to emphasize that provably ensuring inter-agent collision avoidance remains an open problem requiring further investigation.  
Conceptualization, C.P.B.; methodology, P.S.T. and C.P.B.; software, A.T. and P.S.T.; validation, P.S.T.; formal analysis, P.S.T.; investigation, P.S.T. and A.T.; writing--original draft preparation, P.S.T.; writing--review and editing, C.P.B.; visualization, P.S.T.; supervision, C.P.B.; project administration, C.P.B. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Hellenic Foundation for Research and Innovation (H.F.R.I) under the second call for research projects to support post-doctoral researchers (HFRI-PD19-370).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy restrictions.  
The authors declare no conflict of interest.  
Figure 9: The evolution of control inputs \(u_{l,j},\ i=1,\ldots,N,l=x,y,z\) for each agent. The grey dashed lines denote the magnitude saturation limits \(m_{l},\ l=x,y,z\).
## 5 Results and Analysis
### Acceleration Mode  
The flywheel starts to accelerate when a positive torque of 90 N\(\cdot\)m is applied and is fully charged in approximately 2.17 min. Then, at \(t=130.2\) s, the flywheel transfers to standby mode in 0.06 s when the mechanical torque and stator currents drop to zero--Allowing the flywheel to run idle with a stored energy of 5 kWh. Figure 6 presents the results of energy storage and percentage state of charge (% SoC) of the flywheel. During the transition from charging to standby mode, three-phase currents reduce to zero smoothly with no major current spikes or waveform distortions as shown in Figure 7. The system is left on standby mode for a long period of time (\(t=130.2\)-1200 s) to show the flywheel fast charging and idling losses.  
Figure 5: MATLAB/Simulink model of SCIM-FESS.  
Figure 6: %SoC and energy stored by flywheel.  
During the standby mode, the flywheel speed and SoC are decreased due to the system losses including the aerodynamic and bearing friction losses. The copper losses are zero because currents stop flowing in the stator of the IM during the standby mode due to the use of electromagnets in the stator windings. During the standby mode, the flywheel state of charge drops to 96.5% due to self-discharge giving about 215 Wh of energy losses which is 4.3% of the total stored energy. Total power loss (p.u.) in standby mode is calculated by ratio between change in kinetic energy of the flywheel (\(\Delta KE_{flywheel}\)) in Joules over the period of (\(\Delta t\)) in second, with respect to the rated kinetic energy of the flywheel (\(KE_{rated\ FESS}\)) in Joules, as expressed by Equation (37) [78]. Calculation of power loss gives 9.5% of total power lost over the period of 20 min.  
\[\%P_{loss}=\frac{(\Delta KE_{FESS})}{\Delta t\times KE_{rated\ FESS}}\times 100 \tag{37}\]
# Backtracking-Based Iterative Regularization Method for Image Compressive Sensing Recovery
## 3 Image CS Recovery via Backtracking-Based Adaptive IST  
### New Objective Function  
There exist abundant similar image patches in natural images, which can be utilized through nonlocal regularizations for high-order sparsity in image CS recovery. In the paper, the image self-similarity is characterized by means of the last item in the following equation:  
\[\hat{x}=\operatorname*{argmin}_{x}\frac{1}{2}\|y-\mathbf{A}x\|_{2}^{2}+\mu \sum_{i=1}^{G}\|\mathbf{\Gamma}_{\text{3D}}x_{i}\|_{0} \tag{3}\]  
where the last item is derived from BM3D [10], which is used to seek patch correlation in image denoising. To obtain \(\mathbf{\Gamma}_{\text{3D}}x_{i}\), we first stack similar image patches to a 3D group, where \(x_{i}\) is the \(i\)-th group and \(G\) is the total number of groups; then 3D wavelet transform (WT) \(\mathbf{\Gamma}_{\text{3D}}\) is conducted on \(x_{i}\), which is achieved by conducting 2D WT on each patch and then conducting 1D WT along the third axis.  
Problem (3) can be solved by IST; however, as the iteration increases, the recovered image tends to be over-smoothed. As a result, the algorithm runs into prematurity. To avoid this problem, we add a backtracking constraint to add back-filtered details and force a noisier image:  
\[x^{(k)}=\underset{x}{\text{argmin}}\frac{1}{2}\|y-\mathbf{A}x\|_{2}^{2}+\mu \sum\limits_{i=1}^{G}\left\|\mathbf{\Gamma_{3D}}x_{i}\right\|_{0}+\sum\limits_ {j=1}^{B}\frac{b_{j}}{2}\left\|x-x^{(k-j)}\right\|_{2}^{2} \tag{4}\]  
The last item in Equation (4) enforces the current solution which is similar to the previous noisier ones, and the similarity degrees between them are controlled by parameters \(b_{1}\), \(b_{2}\),..., \(b_{B}\). Intuitively, a larger \(b_{j}\) means more noise added back. We have observed, in a large number of experiments, that the algorithm always achieves improvements and converges for a wide range of choices of parameters \(b_{1}\), \(b_{2}\),..., \(b_{B}\).