esa-sceva/satcom-chunk-collection huggingface dataset:
# IEEE 802.11 ah: A Technology to Face the IoT Challenge
## 3 Comparative Analysis of IEEE 802.11 ah with Previous IEEE 802.11 Amendments
### Comparison of IEEE 802.11 Amendments Based on MAC Features
#### 3.1.4 Hybrid Coordination Function (HCF)  
HCF, which combines the aspects of both the contention based DCF and controlled channel access based PCF, is a Quality of Service (QoS) aware MAC protocol that includes appropriate service differentiation mechanism. HCF defines two methods of channel access.  
* _HCF Controlled Channel Access (HCCA)_ It is similar to PCF and uses the same polling mechanism to assign transmission opportunity to QoS enabled stations.
* _Enhanced Distributed Channel Access (EDCA)_ EDCA is an extension of the DCF mechanism that tries to implement service differentiation by classifying the traffic into different categories with different priorities. In EDCA mode, a traffic class can make itself a higher prioritized traffic class by statistically reducing its transmission delay by declaring an Access Category (AC) that has higher priority for contending shared channel.
# Biomass and Cellulose Dissolution--The Important Issue in Renewable Materials Treatment
## 3 Dissolution of Biomass and Its Components
### Deep Eutectic Solvents (DESs) in Biomass Processing
#### 3.7.1 Delignification of Biomass with DESs  
The big advantage of DESs in biomass treatment is their higher activity in lignin dissolution compared to ionic liquids or alkaline solutions [132; 133]. The main goal of biomass delignification is to increase the access of catalysts/enzymes to cellulose in further transformations into valuable chemicals. It was shown that efficient delignification can occur at elevated temperatures (above 100 \({}^{\circ}\)C). Lower temperatures are not sufficient for the breakage of the ether bonds in lignin. Poor mass transfer at lower temperatures is also a problem [45; 134]. However, it must be noted that higher temperatures (above 160 \({}^{\circ}\)C) may, in turn, decrease the delignification level, because of phenolic side-product formations and their polymerization [135]. The effective delignification depends mostly on the properties of DESs. Generally, DESs based on acids are more effective (e.g., oxalic acid, lactic acid) than those based on polyols. It can be explained by the mechanism of lignin dissolution--ether bonds tend to break easier under acidic conditions [136; 137]. Despite the large number of recent reports on biomass processing in DESs, the selection of the most effective solvent and process parameters still needs to be examined due to biomass variety and differences in its structure and chemical composition. Several examples of biomass processing with DESs are presented in Table 4.  
Figure 11: Hydrogen bond acceptors and donors most frequently applied in DESs, used in biomass processing [129; 130; 131].
## 3 Data Analysis
### Relaxation Voltage Drop  
In the same way as internal resistance, relaxation voltage drop has been estimated at certain moments of the batteries' life, usually at the same time that internal resistance measurements were taken (Figure 4). To achieve this, the battery voltage values were taken 2 h after the end of the charging process. Then, relaxation voltage drop was determined:  
\[\mathrm{V_{SD}=V(t=0)-V(t=2h)} \tag{1}\]  
As in the internal resistance, the increase in relaxation voltage drop is one of the indicators of the aging of batteries [14]. As was performed with the internal resistance, to study the relaxation voltage drop evolution, its percentage increase, in reference to its first estimated value of approximately 0.005 V, which represents 100%, is taken into account.  
Results show that for batteries 1 to 6, the rate of growth of relaxation voltage drop is accelerating throughout the life, so that the evolution turns out to be exponential, to a greater or lesser extent. An exception is found in battery 7 whose evolution of relaxation voltage drop, similar to its evolution of internal resistance, presents quite a different profile from the rest, with a sharp increase from the beginning of life. It was observed that the evolution of relaxation voltage drop turns out to be linked to that of internal resistance, with which the growth rates of both parameters increase at the same time.  
Figure 5 underlines that the relationship of both parameters is practically linear, which supports the trend explained above that presupposes a direct relationship between the increase in internal resistance and an increase in relaxation voltage drop. Consequently, in the study of aging, only the increase in resistance will be used as a value to be contrasted with the rest of the parameters, although the same conclusions obtained from the analysis  
Figure 3: Internal resistance evolution.
## 3 Improved Gangue Recognition Model Based on YOLOv5
### Improvement of YOLOv5 Algorithm
#### 3.2.4 Modification of the Loss Function  
The bounding box regression loss for YOLOv5 was calculated using CIOU_Loss, which is calculated as shown below [28]:  
\[v=\frac{4}{\pi^{2}}\left(\arctan\frac{w^{st}}{h^{st}}-\arctan\frac{w}{h}\right)^{2} \tag{4}\]  
\[L_{CIOU}=1-IOU+\frac{\rho^{2}(b,b^{st})}{c^{2}}+\alpha v \tag{5}\]  
\[\alpha=\frac{v}{(1-IOU)+v} \tag{6}\]  
where \(v\) is the consistency parameter that measures the aspect ratio of the predicted frame to the real frame; \(w\), \(w^{st}\), h and \(h^{st}\) represent the width and height of the real and predicted boxes; \(\rho(b,b^{st})\) denotes the Euclidean distance between the predicted frame and the center of the real frame; \(b\) is the center of the predicted frame; \(b^{st}\) is the center of the real frame; c refers to the diagonal length of the smallest closure region that can incorporate the predicted and real frames; and \(\alpha\) is a parameter for balancing ratios.  
In terms of CIOU_Loss, while considering the bounding box regression in terms of overlap area, centroid distance, and aspect ratio, in the formula, v reflects the aspect ratio difference instead of the real difference between w, h, and their confidence levels, respectively. In addition, the gradients of w and h concerning v are in opposite directions, which means that both cannot simultaneously be increased or decreased, hindering the effective optimization of the model to some extent. To address this problem, we chose to adopt the EIOU_Loss proposed by [PERSON] et al. [29], which on the basis of the CIOU penalty term separately calculates the predicted box and true box's height and width using the influence factors of their aspect ratios, in order to address the issues with CIOU.  
\[L_{EIOU}=L_{IOU}+L_{dis}+L_{asp}=1-IOU+\frac{\rho^{2}(b,b^{st})}{\left(w^{c} \right)^{2}+\left(h^{c}\right)^{2}}+\frac{\rho^{2}(w,w^{st})}{\left(w^{c} \right)^{2}}+\frac{\rho^{2}(h,h^{st})}{\left(h^{c}\right)^{2}} \tag{7}\]  
where \(w^{c}\) and \(h^{c}\) represent the width and height, respectively, of the smallest outer bounding box that covers the predicted box and the true box, and \(\rho^{2}\) denoting the squared difference. It can be seen that EIOU mainly includes IOU loss (\(L_{IOU}\)), distance loss (\(L_{dis}\)), and height-width loss (\(L_{asp}\)). By further optimizing the definition of the height-width loss, the model pays more attention to the consistency of the width-height ratio between the predicted frame and the actual target frame during the training process, which leads to accelerated model convergence and enhanced positioning effects.
## 6 Security Considerations within 5G
The designer must consider not only remote software intrusion, but also local intrusion at the device level focusing on avoiding the following security links [75]:  
* Identity
* Authentication
* Assurance
* Key management
* Crypto algorithm
* Mobility
* Storage
* Backward compatibility
* Assurance \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Technologies Prone to Attacks**} \\ \cline{3-8}
**Security Threat Description** & **Targeted Network Element** & **Software-Defined Networking (SDN)** & **Network Functions (NFV)** & **Channels Cloud** & **Is Privacy Compro-mized?** \\ \hline Configuration attacks & \begin{tabular}{c} SDN (virtual) \\ switches, routers \\ \end{tabular} & ✓ & ✓ & & & \\ \hline Denial of service (DoS) & Centralized control & ✓ & ✓ & ✓ & & \\ attack & elements & ✓ & ✓ & & \\ \hline Hijacking attacks & \begin{tabular}{c} SDN controller, \\ hypervisor \\ \end{tabular} & ✓ & ✓ & & & & \\ \hline \begin{tabular}{c} International mobile \\ subscriber identity (IMSI) catching attacks \\ \end{tabular} & Subscriber identity & & & ✓ & & ✓ \\ \hline Penetration attacks & \begin{tabular}{c} Virtual resources, \\ clouds \\ \end{tabular} & ✓ & & & & \\ \hline Saturation attacks & \begin{tabular}{c} SDN controller and \\ switches \\ \end{tabular} & ✓ & & & & \\ \hline Signaling storms & \begin{tabular}{c} 5G core network \\ elements \\ \end{tabular} & & & ✓ & ✓ & \\ \hline \begin{tabular}{c} Man-in-the-middle \\ attack \\ \end{tabular} & SDN & ✓ & ✓ & & & \\ \hline \begin{tabular}{c} Transmission control \\ protocol (TCP) level \\ attack \\ \end{tabular} & \begin{tabular}{c} controller-switch \\ communication \\ \end{tabular} & ✓ & ✓ & & & \\ \hline Reset and IP spoofing & Control channels & & ✓ & & ✓ \\ \hline Scanning attacks & Open air interfaces & & ✓ & ✓ \\ \hline Security keys exposure & \begin{tabular}{c} Unencrypted \\ channels \\ \end{tabular} & ✓ & & ✓ \\ \hline Boundary attacks & & & ✓ & & ✓ \\ \hline \
\begin{tabular}{c} Semantic information \\ attacks \\ \end{tabular} & Subscriber location & & ✓ & ✓ \\ \hline Timing attacks & & & & & ✓ \\ \hline User identity theft &
\begin{tabular}{c} User information \\ data bases \\ \end{tabular} & & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Security challenges in 5G technologies (adopted from [74]).
## 4 Experiments and Performance Analysis
### Datasets  
In this study, we took two widely-used datasets acquired separately by different hyperspectral sensors to test the performance of fusion methods. The first dataset was collected over the area of Pavia University, northern Italy [49], by the reflective optics system imaging spectrometer (ROSIS) sensor, with a total of 103 spectral bands from 430 to 860 nm (corresponding to Landsat TM bands 1-4). The second dataset was captured by an airborne visible/infrared imaging spectrometer (AVIRIS) sensor over Moffett Field [50], CA, with which a total of 183 bands (ranging from 400 to 2500 nm) approximate to the Landsat TM bands 1-5 and 7. Therefore, we can separately construct the spectral response transform matrices \(\mathbf{F}_{1}\in\mathbb{R}^{4\times 103}\) and \(\mathbf{F}_{2}\in\mathbb{R}^{6\times 183}\), which spectrally degrade the reference data \(\mathbf{Z}\) to generate their respective observed MSI data \(\mathbf{Y}_{m}\)[16, 25, 32, 36]. Furthermore, we selected the sub-scene of size \(L=160\times 160\) as the target region for each dataset [16, 32, 36], as shown in the top row of Figure 5. With a Gaussian kernel of size \(r=5\) and variance 2 [25, 51], we were able to establish the matrix \(\mathbf{G}\in\mathbb{R}^{25600\times 1024}\), which spatially degrades the reference data \(\mathbf{Z}\) to obtain the observed HSI data \(\mathbf{Y}_{h}\) with the size of \(L_{h}=32\times 32\) pixels [38, 52, 53], which are depicted in the bottom row of Figure 5.
# Technology and Data Fusion Methods to Enhance Site-Specific Crop Monitoring
## 5 Data Fusion for Site-Specific Crop Parameter Monitoring
### Agb  
AGB is the total quantity of plant-based living and dead organic substance. Remote sensing systems are widely adopted to estimate AGB in a rapid manner. The fusion of images acquired from a UAV using different sensors have shown efficient performance because the data fusion develops the data acquisition process by merging benefits of different spatiotemporal, spectral, and thermal information of the sensing systems (Table 1).  
Spectral and LiDAR data fusion were tested for AGB estimation [192]; spectral indices using the RGB images were acquired for AGB estimation [223; 224; 225; 226; 227]. These studies showed that data fusion significantly improved the estimation [228; 229; 230; 231; 232] and addressed saturation and disaggregation problems in acquiring the AGB, particularly for higher density biomass [233]. [PERSON] and [PERSON] [234; 235] used fusion of data from several vegetation indices for dynamic monitoring and classification of crop traits. Studies showed that the fusion of SAR with optical sensor data improves AGB estimation, as optical data offset the saturation impact. Therefore, AGB monitoring has improved as a result of the latest developments in satellite systems [236; 237; 238; 239]. However, the SAR works for a particular range of AGB, and beyond this range, the system provides coarse data [240]. To address this problem, the fusion of multisource data (optical images) and SAR is suggested to improve the accuracy of AGB estimation (Table 2) [241; 242]. This process is based on two methods: (i) the fusion of SAR with optical image data to generate a new dataset (PCA) [243], and (ii) the fusion of all the imagery data together [242]. However, this fusion method is still undergoing various developmental phases. [PERSON] et al. [244], [PERSON] et al. [245], [PERSON] et al. [246], [PERSON] et al. [247], [PERSON] et al. [248], [PERSON] et al. [249], and [PERSON] et al. [250] tested the fusion of hyperspectral and multispectral data, which are collected by different sensors, to further achieve high-spatial- and spectral-resolution images. Thus, the best solution for the highly accurate estimation of AGB is the fusion of SAR Sentinel-2A and Sentinel-1 imagery, and it was reported that this fusion generated a significant trend for the estimation of the AGB [245; 246]. However, studies on the fusion of ALOS-2, PALSAR-2, and Sentinel-2A data for estimating AGB are not yet fully established. Therefore, further research on the fusion between SAR products is needed to collect more data and reach logical conclusions.
_Applied_  
_sciences_  
Analysis of MPPT Failure and Development of an Augmented Nonlinear Controller for MPPT of Photovoltaic Systems under Partial Shading Conditions  
[PERSON] *,+, [PERSON] +, [PERSON] and [PERSON]  
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China; [EMAIL_ADDRESS] (S.M.); [EMAIL_ADDRESS] (J.W.); [EMAIL_ADDRESS] (L.H.)  
Footnote †: These authors contributed equally to this work.  
Academic Editor: [PERSON]  
Received: 14 December 2016; Accepted: 14 January 2017; Published: 19 January 2017  
The output-voltage-power curves of photovoltaic (PV) arrays exhibit complex multi-peak shapes when local shading occurs. The existing maximum power point tracking (MPPT) algorithms to solve this multi-peak problem do not consider the possibility of tracking failures due to the time of the irradiance change. In this study, first, the reason for the failure of the global MPPT (GMPPT) algorithm is analyzed based on the PV array mathematical model and its output characteristics under partial shading conditions; then, in order to estimate the MPP voltage, an artificial neural network (ANN) is trained using environmental information such as irradiance. A hybrid MPPT method using an augmented state feedback precise linearization (AFL) controller combined with an ANN is proposed to solve problems such as the shift of the static operating point of the DC/DC boost converter. Finally, numerical simulations are conducted to validate the proposed method and eliminate the possibility of MPPT failure. The proposed hybrid MPPT method is compared with the conventional perturb and observe (P & O) method and the improved P & O method through simulations. Using the proposed neural network and nonlinear control strategy, the MPP can be tracked rapidly, accurately, and statically, proving that the method is feasible and effective.  
photovoltaic (PV) systems; partial shading condition (PSC); maximum power point tracking (MPPT); artificial neural network (ANN); nonlinearity controller; augmentation system
# Cooperative Multi-Objective Optimization of DC Multi-Microgrid Systems in Distribution Networks
## 4 Cooperative Multi-Objective Optimization Model
### Constraints of the Inner-Loop Model
#### 4.5.4 System Power Balance Constraint  
For MMGS, the active power output should meet the power balance constraint.  
\[P_{m,t}^{EV}+P_{m,t}^{G}+P_{m,t}^{WT}+P_{m,t}^{PV}=P_{m,t}^{L} \tag{31}\]  
where \(P_{m,t}^{L}\) is the total load of the \(m\)-th MG at time \(t\).
# Quality of Experience (QoE)-Aware Fast Coding Unit Size Selection for HEVC Intra-Prediction
## 2 Background and Related Work
### Background  
The block-based structure employed in HEVC initially partitions a given frame into CTUs, which have the size \(64\times 64\). Each CTU can then be recursively subdivided into CUs, ranging from \(8\times 8\) to \(64\times 64\). For example, Figure 1 illustrates a typical partitioning structure for an encoded frame. Figure 2a shows a CTU which is recursively partitioned into multiple CUs, while Figure 2b depicts the corresponding hierarchical quadtree structure. In addition, a CU can be further partitioned into multiple Prediction Units (PU) and Transform Units (TU) to hold prediction and transform information, respectively [6]. The increased number of partitioning combinations available for a CTU in the HEVC standard requires the HEVC compatible encoders to determine the optimal partitioning structures for a given content. A typical encoder in this case follows a brute-force approach of evaluating all possible coding parameter combinations using RD optimisation to determine the optimum set of coding parameter combination for a given content.  
RD optimisation utilises a Lagrangian cost function to evaluate the RD cost of each coding mode; and a partitioning structure to select the coding parameter combination that results in the minimum cost. This cost function can be expressed as  
\[\underset{p}{\text{minimise}}\Big{\{}D(p)+\lambda R(p)\Big{\}},\quad p\in P, \tag{1}\]  
where \(\lambda\geq 0\) is the Lagrange multiplier, \(p\) is a coding parameter combination from the set of all the possible coding options \(P\), and \(D(p)\), \(R(p)\) are the distortion and rate associated with the selected set of coding parameters, respectively [4]. Although this process enables identifying the highest attainable coding efficiency, it is one of the main sources of encoding complexity in the encoder. Thus, recent literature has focused on algorithms to minimise the computational complexity involved in the brute-force RD computation cost while keeping the coding performance intact. As part of this, more attention is given to the fast selection of CU structure since a large portion of the encoding time is devoted to the computations that take place at CU level [5].  
Figure 1: Coding Unit (CU) partitioning structure for a typical intra-predicted frame in “[PERSON] and Sara” sequence.
## 3 Results and Discussion
### Factors Screening in a Full 2\({}^{5}\) Factorial Design for the Initial Regression Model
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Response** & **Model Terms** & **Effect** & **Coefficient** & \(p\)**-Value** \\ \hline \hline  & Model Equation (7) & - & 31.3439 & **0** \\  & \(A\) & 8.806 & \(-\)0.00816 & **0.009** \\  & \(B\) & \(-\)6.519 & \(-\)37.7222 & **0.027** \\  & \(C\) & 4.431 & \(-\)43.3667 & **0.041** \\  & \(D\) & \(-\)7.644 & \(-\)10.8661 & **0.018** \\  & \(E\) & 19.644 & 1.29028 & **0.006** \\  & \(AB\) & \(-\)2.494 & 0.032778 & 0.268 \\  & \(AC\) & \(-\)0.669 & 0.042667 & 0.657 \\  & \(AD\) & \(-\)0.669 & 0.014172 & 0.657 \\  & \(AE\) & 5.019 & 0.003003 & **0.039** \\  & \(BC\) & 1.581 & 128.333 & 0.392 \\  & \(BD\) & \(-\)0.294 & 30.1528 & 0.837 \\  & \(BE\) & \(-\)3.581 & \(-\)11.5278 & **0.048** \\  & \(CD\) & \(-\)2.444 & 95.75 & 0.273 \\  & \(CE\) & 2.644 & \(-\)10.6167 & 0.255 \\  & \(DE\) & \(-\)5.506 & \(-\)5.40194 & **0.031** \\ Hydrogen yield (\%) & \(ABC\) & \(-\)0.919 & \(-\)0.1 & 0.562 \\  & \(ABD\) & \(-\)0.519 & \(-\)0.03181 & 0.724 \\  & \(ABE\) & \(-\)2.606 & 0.012222 & 0.258 \\  & \(ACD\) & 1.006 & \(-\)0.1295 & 0.534 \\  & \(ACE\) & \(-\)0.456 & 0.022333 & 0.753 \\  & \(ADE\) & \(-\)0.431 & 0.007131 & 0.766 \\  & \(BCD\) & \(-\)0.994 & \(-\)272.917 & 0.538 \\  & \(BCE\) & 0.269 & 74.1667 & 0.85 \\  & \(BDE\) & \(-\)0.181 & 23.0069 & 0.898 \\  & \(CDE\) & \(-\)1.406 & 12.7861 & 0.428 \\  & \(ABCD\) & 2.456 & 0.345833 & 0.272 \\  & \(ABCE\) & \(-\)0.831 & \(-\)0.10833 & 0.593 \\  & \(ABDE\) & \(-\)0.906 & \(-\)0.03493 & 0.567 \\  & \(ACDE\) & 0.894 & \(-\)0.02364 & 0.571 \\  & \(BCDE\) & \(-\)0.181 & \(-\)83.8194 & 0.898 \\  & \(ABCDE\) & 1.119 & 0.124306 & 0.497 \\ \hline \hline \end{tabular} \(R^{2}\) = 0.998, \(R^{2}\)(adj) = 0.944.  
\end{table} 
Table 4: Analysis of the regression coefficient of the initial model for hydrogen yield (\(Y_{2}\)). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Response** & **Model Terms** & **Effect** & **Coefficient** & \(p\)**-Value** \\ \hline \hline  & \(ABD\) & 0.635 & \(-\)0.08751 & 0.74 \\  & \(ABE\) & \(-\)0.731 & 0.03016 & 0.706 \\  & \(ACD\) & 1.525 & \(-\)0.20745 & 0.488 \\  & \(ACE\) & 0.499 & 0.046393 & 0.792 \\  & \(ADE\) & 0.042 & 0.005443 & 0.982 \\  & \(BCD\) & \(-\)4.111 & \(-\)402.183 & 0.219 \\  & \(BCE\) & 0.96 & 139.889 & 0.632 \\  & \(BDE\) & \(-\)0.528 & 27.7172 & 0.78 \\  & \(CDE\) & \(-\)1.716 & 25.9847 & 0.451 \\  & \(ABCD\) & 3.46 & 0.557974 & 0.256 \\  & \(ABCE\) & \(-\)1.642 & \(-\)0.17284 & 0.465 \\  & \(ABDE\) & 0.062 & \(-\)0.03197 & 0.973 \\