esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulations and Results
### Results
#### 4.2.1 Single Criterion  
The single criterion used to implement the first approach is renewable energy capacity of prosumers. The greater the capacity, the higher the priority and vice versa. The user who has a  
Figure 4: Price of selling surplus energy in Pakistani Rupees (PKR).  
Figure 3: Shortage/surplus energy before optimization.  
shortage and the highest installed renewable capacity will be served first. The capacity of 6 users is shown in Figure 5.  
The objective is to reduce the shortage of energy by selling surplus energy to users with a shortage in an optimized manner. At the same time, using this criterion encourages prosumers to increase their renewable capacity. Note that an upper limit on the capacity may be imposed according to the market drivers. In that case, the criterion would incentivize the users to increase the renewable capacity up to the allowed limit. The current implementation assumes that renewable energy has to be produced as much as possible. This is realistic particularly for the developing countries. Shortage and surplus energy after implementation of the optimization algorithm is shown in Figure 6. As the figure indicates, much of the energy shortage has been addressed by the other users having surplus energy. Price of surplus energy offered to users having a shortage is shown in Figure 7. First criterion simulations show a 30.16% reduction in overall shortage of six users over 24 h. The total price of surplus energy available in this case is 668 PKR.  
Figure 5: Renewable capacity.  
Figure 6: Shortage and surplus energy after the optimization.  
Note that the shortages still appearing in Figure 6 can be addressed using a storing mechanism and/or from other communities. Similarly the surplus can be stored (for future use) and/or sold to some other community. We will have more to say on this in the conclusion section.
## 3 System Overview
### Operation Flow  
Figure 1 shows a simplified operation flow of the system. The system contains an efficient ROI detection module, an ROI management module, an identification module, and an XROI-DCT/encoding module.  
Figure 1: Operation flow diagram of the proposed system.  
The video is first loaded onto the system, and then each frame is analyzed to determine its frame type. When video frames are loaded for the prediction, the proposed system also performs UAV detection and calculates optical flow. If the frame is predicted to be an I-frame, the system performs UAV detection to locate the UAV in the video. For frames predicted as non-I frames, optical flow is calculated based on the current non-I frame and the previous frame. The UAV detection and optical flow calculation operation saves the detected UAV information and the optical flow information for each frame, which are used to in ROI management operations. The ROI management operation is after the UAV detection and optical flow calculation operation. The ROI management operation manages the location of ROI using the detected UAV information and optical flow information of each frame. Before ROI management operations are conducted, the system analyzes the frame and performs frame prediction [26]. Such prediction based on predictive coding exploits spatial and temporal redundancy between video frames for file size compression. The reason for conducting such a process is to find key frames called I-frames. For I-frames, the system sets ROI according to the area where the UAV is predicted to be by the UAV detector based on YOLO. For other frames, the system manages the ROI by moving ROI (from the previous frame) according to the motion vector derived from the optical flow information. Then, frames are ready for XROI-DCT. The frames are divided into smaller macroblocks, which are discrete cosine transformed for compression. XROI-DCT is based on discrete cosine transform (DCT) [27]. During XROI-DCT, the high frequencies are deleted to provide extra compression on non-ROI macroblocks. This forces the encoder to give a high compression rate to the non-ROI. Transmission efficiency can be dramatically improved by giving a different compression rate on ROI and non-ROI areas. Afterwards, the proposed system follows the standard MPEG4 encoding process.  
As shown in Figure 1, from acquiring video to ROI management is executed in a single flow. However, precisely speaking, 'XROI-DCT/Encoding' is included in the single flow. Inference by our UAV identifier does not guarantee real-time execution. So, UAV identification--querying for inference from our CNN--is conducted in parallel. This decision is based on the fact that the model of an already-detected UAV is highly unlikely to change as long as it is constantly tracked. Therefore, UAV identification does not have to be executed for every frame: this saves execution time and computing resources.
# A Protocol Layer Trust-Based Intrusion Detection Scheme for Wireless Sensor Networks
## 2 Related Work
[PERSON] et al. [33] put forward a lightweight and robust trust establishment scheme using the weight of misbehavior. In their scheme, a new trust component, misbehavior frequency is introduced to improve the resiliency of the trust mechanism. [PERSON] et al. [34; 35] utilizes weighting method to build the trust evaluation model and statistical method to analyze the false alarm probability. In [34], they presented a trust-based intrusion detection scheme using a highly scalable cluster-based hierarchical trust management protocol. It considers both quality of service trust and social trust as trust metrics and uses an analytical model based on stochastic Petri nets to evaluate the performance of the scheme, as well as a statistical method to calculate the false alarm probability. They adopt honesty to measure social trust and energy and cooperativeness to measure quality of service trust. In [35], intimacy, honesty, energy, and unselfishness are considered as four different trust components. In [36; 37], some new models are used to evaluate the trustworthiness. [PERSON] et al. [36] put forward a trust evaluation method for clustered wireless sensor networks based on cloud model, which implements the conversion between qualitative and quantitative of trust metrics and produces different types of trust cloud to evaluate trust values of cluster heads and cluster members. [PERSON] et al. [37] presented a trust based adaptive acknowledgment intrusion detection system for WSNs based on number of active successful deliveries, and Kalman filter to predict node trust. It is important for a trust management scheme to select proper trust factors to evaluate the trustworthiness of a SN. From the literature on this topic, we can find that the trust factors of a SN is mainly based on the nodal communication behavior, energy level, or recommendation from the third party and there is no unified standard in the selection of the trust factors. The attacks initiated at each protocol layer and their influence on the parameters of the corresponding protocol layers lack comprehensive analysis. To the best of our knowledge, there is still no trust management scheme which elaborately describes the trustworthiness of a SN from the standpoint of protocol layer. Thus, it is interesting to build the trust evaluation model based on the protocol layer trust. In view of the reality of intrusion detection scheme, we mainly consider the direct trust of a node in our scheme and the trustworthiness of a node is evaluated according to its behaviors at different protocol layers. The consideration of trust worthiness from the viewpoint of multiple protocol layers distinguishes this paper from the previous related works [23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37]. Since the deviations of the key parameters of multiple layers are used to evaluate the trustworthiness of a node, it is helpful for our scheme to detect nodal malicious behaviors initiated from different protocol layers, which is effective for detecting cross-layer attacks.
## 4 Proposed Low Complexity DL TS Design Based on a Waterfilling Algorithm
Auxiliary variable \(\mathbf{\Sigma}_{\text{tot}}\in\mathbb{R}^{N\times N}\) is a non-negative diagonal matrix \(\mathbf{\Sigma}_{\text{tot}}=\text{diag}\{\sigma_{1,\text{tot}},...\sigma_{T_ {\text{tot}}},...,0,...,0\}\) with an optimized power allocation based on a waterfilling algorithm as given by (19). The power allocation should satisfy the transmit power constraint and be arranged in decreasing order with \(t\) and at the same order as the eigenvalues of \(\mathbf{R}_{\text{tot}}\), i.e., \(\lambda_{t,\text{tot}}\) where the training matrix satisfies \(tr\big{(}\mathbf{S}_{\text{tr}}^{\text{I}}\mathbf{S}_{\text{tr}}\big{)}=\rho_{ \text{tr}}T_{\text{tr}}\). The waterfilling/optimum power assignment can be obtained using a Lagrange multiplier method as_  
\[\sigma_{t,\text{tot}}=max\bigg{\{}\frac{1}{\sqrt{\mu}}-\frac{1}{\lambda_{t, \text{tot}}},0\bigg{\}}, \tag{19}\]  
_where \(\mu>0\) is the Lagrange multiplier, which is chosen to satisfy the transmit power constraint, i.e., \(\sum_{t}^{T_{\text{tr}}}\sigma_{t,\text{tot}}=\rho_{\text{tr}}\)._  
The waterfilling-based approach introduced in Proposition 1 identifies which eigenmodes are essential for DL training and estimating the CSI. The intuition behind the waterfilling-based approach is to consider eigendirections of \(\mathbf{R}_{\text{tot}}\) to allocate more power in these few effective directions while assigning zero power to the weakest directions. In particular, the power in the diagonal matrix \(\mathbf{\Sigma}_{\text{tot}}\) corresponds to only \(T_{\text{tr}}\) largest eigenvalues of \(\mathbf{R}_{\text{tot}}\), which are deemed as essential by the waterfilling criteria. The minimum number of training length is obtained by the number of active training powers \(\sigma_{t,\text{tot}}\) that satisfies the transmit power constraint \(\rho_{\text{tr}}\). The rank of \(\mathbf{\Sigma}_{\text{tot}}\) is equal to the rank of \(\mathbf{S}_{\text{tr}}\), which is equal to the maximal number of active \(\sigma_{t,\text{tot}}\). To the best of our knowledge, this DL training framework design has not been proposed for FDD m-MIMO with distinct spatial correlation matrices.
# Flickering-Free Distance-Independent Modulation Scheme for OCC
## 3 Theoretical Description of the Proposed Modulation
### Statistical Measures in Digital Image Processing  
The working principles of the proposed system are based on some traditional statistical measures. Concretely, the 2D mean, the 2D standard deviation, and the correlation coefficient are the fundamental operations that conform the procedure. The 2D mean (\(\mu_{A}(x,y)\)) is a basic statistical measurement and it represents the average value of the pixels intensity or the expected value of a pixel in a given region. In the same way, the 2D standard deviation (\(\sigma_{A}(x,y)\)) is a variability measure or diversity of the pixels' intensities. It shows how much dispersion exists in the image from the average value. A low standard deviation indicates that the pixels tend to be very similar. Finally, the correlation coefficient assesses the degree of linear correlation between two image matrices \(A\) and \(B\). In this work, Pearson Correlation Coefficient (PCC) is employed (although other similarity measures could be used, extending this procedure), and it is calculated using Equation (1), which is bounded in the interval \([-1,1]\).  
\[PCC(A,B)=\frac{\sum_{x}\sum_{y}(A_{xy}-\overline{A})(B_{xy}-\overline{B})}{ \sqrt{\Big{(}\sum_{x}\sum_{y}(A_{xy}-\overline{A})^{2}\Big{)}\Big{(}\sum_{x} \sum_{y}(B_{xy}-\overline{B})^{2}\Big{)}}} \tag{1}\]  
\(\bar{A}\) and \(\bar{B}\) are the expected values of the input matrices \(A\) and \(B\), respectively. If the two images are equal, they are perfectly correlated and therefore the coefficient would be 1. In the case of similar images, as the ones shown in Figure 3, the value of the coefficient is close to one.  
On the contrary, if the two images are exactly opposite, as Figure 4 illustrates, they are perfectly anti-correlated so the coefficient would be \(-1\).  
Figure 3: Similar images with \(PCC=0.7306\) (close to 1).  
Figure 2: Transmission diagram.  
If there is no linear correlation between the images, the coefficient would be close to 0. For example, the images in Figure 5 are not similar nor opposite, therefore resulting in a coefficient close to zero. It must be noted that a low linear correlation does not mean necessarily low correlation in general terms. This could happen in nonlinear systems, which would require other types of correlation measures, but this work assumes that the signals are linearly distorted.  
These tools are the building blocks of the proposed system, which consume relatively low computational resources. Hence, this reception process could be implemented in real-time applications suitable for IoT systems. For real-time operation, the PCC calculation should be carried out before the arrival of the next frame. In conventional cameras, this time ranges between 33.33 ms and 16.66 ms, which is enough time for a simple PCC calculation that requires less than 10.5 ms using an interpreted programming language such as MATLAB. Nevertheless, this calculation time can be reduced significantly by using adequate software and hardware.
## 3 Design Assessment Methods  
The proposed 3U STM design was critically assessed against the design goals. Since a 3U satellite has not been developed by Kyutech in the past and an actual satellite was not available to evaluate with, for assessment purposes, a demo 3U CubeSat model based on the conventional type was modeled with a backplane board electrical interface. The structure utilizes several long rods to stack up the PCBs, as shown in Figure 9. To avoid any design merits due to the design difference between the two concepts during the assessment, the quantity and shape of the basic structural parts, such as the top, bottom, and side plates, are kept similar. This helps the assessment to focus on the interface method used between the main structure and internal subassembly instead of the change in design. However, often the conventional design may only have four rails along the Z axis instead of plates and connecting rods in actual case.  
As previously described, important evaluation parameters, such as part and assembly count analysis and complexity analysis, were used to assess the design goals to measure the impact of the change in design on the integration efficiency of mass production missions. First, the count analysis was conducted by comparing the total quantity of only structural parts, such as plates, frames, spacers, and fasteners. The internal subsystem components were not included in this analysis because that may vary depending on the mission type. Second, complexity analysis was carried out to measure the level of difficulty encountered  
Figure 8: Structural and full satellite assembly of STM 3U.  
Figure 9: Conventional demo 3U model.  
during the integration phase. Then, the scalability of the design concept to other CubeSat form factors was demonstrated using a 1U platform. Since this study focuses on 3U CubeSat, due to the satellite mission requirement, the new design concept was developed for that purpose. However, it is important to assess its scalability. Due to the popularity of the 1U CubeSat platform in the CubeSat community, checking the scalability of the 3U slot-based design is particularly important. A similar assessment of count and complexity analysis was conducted by comparing this platform with the previous 1U conventional-type BIRDS-3 satellite platform, which was developed by Kyutech. The BIRDS-3 satellite has already finished its two-year mission in ISS orbit. Finally, the assembly step count was used to evaluate the speed at which each assembly action was carried out. Here, the steps are for the complete satellite assembly only, without the solar panels.
###### Abstract  
In this work, diatomaceous earth (DE) or diatoms are modified with Mg-Al-layered double hydroxide (DE-LDH) using the facile co-precipitation method to demonstrate their application for the removal of toxic dyes such as Congo Red (CR), which was used as a model. Field emission scanning electron microscopy (FE-SEM) characterization confirms the successful modification of diatom microcapsules structures, showing their surface decorated with LDH nano patches with sheet-like morphologies. The surface area of the DE was enhanced from 28 to 51 m\({}^{2}\)/g after modification with LDH. The adsorption studies showed that the maximum CR removal efficiency of DE and DE-LDH was ~15% and ~98%, respectively at pH 7, which is a significant improvement compared with unmodified DE. The maximum adsorption capacities of DE-LDH were improved ten times (305.8 mg/g) compared with the bare DE (23.2 mg/g), showing very high adsorption performances. The recyclability study of DE-LDH up to five cycles, after desorbing CR either by methanol or by NaOH, showed the efficient removal of the CR by up to three cycles via adsorption. The presented study suggests the promising application of DE-LDH as an effective material for application in the removal of CR from aqueous solutions for industrial wastewater treatment.  
Keywords:diatom; layered double hydroxides; zeta potential; Congo Red; dye removal +
Footnote †: journal: Applied Sciences
## 5 Experimental Results
### Evaluation of Classifiers per Dataset
#### 5.3.2 Optimized Classifiers  
Each classifier supports multiple configurations, which are set by the value of specific parameters. The selection of these parameters which potentially optimizes the classifier's performance is explored here for the k-NN and the ANN classifiers. The parameters under evaluation are:  
* The number of neighbors is the core deciding factor for the k-NN classifier. The parameter **n_neighbors** determines the number of nearest neighbors.
* The parameter **hidden_layer_sizes** used by the ANN classifier corresponds to the number of neurons in a layer.  
Figure 4 illustrates the optimization process for each configuration. The search for the optimal configuration is performed by empirically exploring the set of parameters giving the smallest mean error. For instance, Figure 4(a) depicts how the smallest error is obtained when K is equal to 1 for the k-NN classifier. This configuration leads to the highest performance for the classifier k-NN using the dataset BDLib. Similar methodology is applied for the ANN classifier. Figure 4(b) also depicts the exploration of the size of the hidden layer parameter of the ANN classifier for the dataset BDLib. This exploration enables the selection of the optimal configuration for the classifiers based on the dataset. As a result, the empirical values of the classifiers' parameters which lead to higher accuracy are summarized in Table 5. Notice the dependency between the parameters and the dataset. Not only the audio recordings but also the number of categories determine the optimal configuration of the classifiers.  
Figure 6 shows the achieved accuracy of the optimized classifiers. The features used as input vector for the classifiers are the same as the non-optimized case. As expected, the optimized classifiers significantly increase their accuracy, reaching higher accuracy than detailed in Table 3 for similar comparisons. The evolution of the execution time of the classifiers when optimized is depicted in Figure 7. The ANN classifier demonstrates being more sensitive to optimizations, since it not only increases its accuracy but also significantly increments its execution time for the ESC-50 and the UrbanSound datasets. Nonetheless, this classifier remains significantly faster than the k-NN classifier, as shown in the sections below.
# Stability and Failure of the Edge-Closed Honeycomb Sandwich Panels with Face/Core Debonding
## 4 Progressive Damage Model
### Failure Theory of Honeycomb Sandwich Panels
#### 4.1.2 Debonding  
The cohesive zone model (CZM) technique based on damage mechanics and fracture mechanics has an irreplaceable advantage in predicting the initiation and expansion of cracks. This technique was originally proposed by [PERSON] [32] and [PERSON] [33] to deal with the fracture process area at crack tips. Heretofore, CZM technique has been widely used to simulate the inter-lamina of composite laminates [34] and the adhesive of sandwich composites [5]. The conclusions in literature [5] show that the CZM approach is robust, and is able to predict debonding phenomenon of sandwich structure accurately.  
Several cohesive laws exist charactering the relationship between displacement and traction of the crack, among them, the bilinear law is widely used, and it can take into account both the precision and efficiency of calculation [35]. Therefore, bilinear constitutive model is adopted in this paper to model the adhesive.  
Damage onset occurs when the following quadratic stress failure criterion is satisfied [36]  
\[\left(\frac{\left(\sigma_{n}\right)}{N}\right)^{2}+\left(\frac{\tau_{s}}{S} \right)^{2}+\left(\frac{\tau_{t}}{T}\right)^{2}=1 \tag{1}\]  
where \(\langle\ \rangle\) is the Macaulay bracket, \(\sigma_{n}\), \(\tau_{S}\) and \(\tau_{t}\) represent the normal and two shear tractions, \(N\), \(S\) and \(T\) are the normal and two shear strengths.  
A fracture energy-based linear softing law is adopted to address the damage evolution of the adhesive, which gives  
\[\int d\delta=G_{C} \tag{2}\]  
where \(\sigma\) and \(\delta\) are the effective traction and displacements at interfaces. \(G_{c}\) is the mixed-mode fracture energy release rate which determined by Benzeggagh and Kenane criterion [37]. As shown by Equation (3).  
\[G_{C}=G_{IC}+\left(G_{IIC}-G_{IC}\right)\left(\frac{G_{II}}{G_{I}+G_{II}} \right)^{\eta} \tag{3}\]  
where \(G_{IC}\) and \(G_{IIC}\) are the critical fracture energy release rate of mode I and mode II. \(\eta\) is an interaction parameter that describe the effect of mixed-mode ratio \(G_{II}/(G_{I}+G_{II})\) of total fracture toughness \(Gc\).