esa-sceva/satcom-chunk-collection huggingface dataset:
# Valorization of Date Fruit (_Phoenix dactylifera_ L.) Processing Waste and By-Products: A Review
## 3 Nutritional and Biochemical Composition of Date Fruit and Date By-Products
### Bioactive Compounds--Phenolic Compounds and Antioxidant Activity
\begin{table}
\begin{tabular}{p{108.4 pt} p{108.4 pt} p{108.4 pt} p{108.4 pt}} \hline \hline
**Date Varieties** & **Method(s)** & **Findings** & **Reference** \\ \hline \hline [PERSON], [PERSON], and [PERSON] (Saudi Arabia) & Antioxidant activity by DPPH, hydroxyl radical scavenging and total antioxidant capacity assay. The qualitative hemoglobin composition was determined using UPLC-QTOF-MS. & The extracts showed DPPH radical scavenging (IC\({}_{0}\) 103-127 \(\mu\)m) and hydroxyl radical scavenging (IC\({}_{0}\) 11-155 mg/mL) activity and total antioxidant capacity assay. The UPLC-QTOF-MS revealed a total of 22 compounds in the date cultures classified into common phenolics, flavonoids, sterols, and phytorestores. & [61] \\ \hline
13 varieties from UAE; 10 varieties from Pakistan; 1 variety from Tunisia & Total phenolic content and antioxidant activity using the Folin-Ciocalleu, ABITS\({}^{*}\), FRAP, and DPPH assays. & The tenfold 100 \(\mu\)m of the I/O 100 g with aging FRAF, and 0.0-1.8 mg/mL using DPPH. & [63] \\ \hline Medjool (Mexico) & Total phenolic content by the Folin-Ciocalleu method, antioxidant activity by DPPH, ABTS, and 5-carotene assay. & The nine of the total phenolic contents ranged from 142.52 \(\pm\) 0.64 to 298.02 \(\pm\) 0.95 mg GAE/100 g on fresh fruit weight basis. Seven phenols of chlorogenic acid, caffeine acid, vanilla acid, and quantified in four varieties of date seeds. & [34] \\ \hline Kabkab, Rabbi, Zahedi, and Mazzat (Iran) & Total phenolic content by the Folin-Ciocalleu method and phenolic compound identification and quantification by HPLC analysis. & The phenolic contents for the date pit extracts ranged 1483-3377 mg GAE/100 g dw. & [63] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Studies on the phenolic compounds and antioxidant properties of date fruits. Compared with date fruit, date seed has a relatively higher content of antioxidant compounds, especially phenolic compounds. In addition, date seeds have a high level of total polyphenol content compared to other fruits such as grapes and other types of seeds like nut seeds. The high polyphenol content in date seed has been proven to possess a variety of pharmacological effects, including anti-inflammatory, anticancer, and antimutagenic activities [26; 64]. Current studies show that date seeds contain phenolic acids such as caffe, chlorogenic, \(p\)-coumaric, ferulic, gallic, syringic, and vanillic acid [65]. Some of the important phenolic compounds that can be isolated from date pits are listed in Table 3.  
Iranian date seeds were previously reported to have high antioxidant activity, with 37.42 mmol Fe\({}^{\text{II}}\)/100 g, showing a potential to be applied in medicinal and commercial areas. Also, the phenolic content of Iranian date seeds reached a high content of 3541 mg gallic acid/100 g dry plant [69]. The Mabseeli variety of date seeds has a substantial concentration
## 2 Materials and Methods
### Online Network Topology Inference  
Additional challenges arise with real-time network data collection, where analytics must often be performed \"on-the-fly\" and without the opportunity to revisit past graph signal observations due to e.g., staleness or storage constraints [2]. Consider now the online estimation of \(\mathbf{S}\) (or even tracking \(\mathbf{S}_{t}\) in a dynamic setting) from streaming data \(\{\mathbf{y}_{1},\ldots,\mathbf{y}_{t},\mathbf{y}_{t+1},\ldots\}\). To this end, a viable approach is to solve at each time instant \(t=1,2,\ldots\), the composite, time-varying optimization problem (cf. (8))\[\mathbf{S}_{t}^{*}\in\underset{\mathbf{S}\in\mathcal{S}}{\operatorname{argmin}}\;F_{t }(\mathbf{S}):=\|\mathbf{S}\|_{1}+\underbrace{\frac{\mu}{2}\|\mathbf{S}\hat{ \mathbf{C}}_{\mathbf{y},t}-\hat{\mathbf{C}}_{\mathbf{y},t}\mathbf{S}\|_{F}^{2} }_{g_{t}(\mathbf{S})}. \tag{13}\]  
In writing \(\hat{\mathbf{C}}_{\mathbf{y},t}\) we make explicit that the covariance matrix is estimated with all signals acquired by time \(t\). As data come in, the covariance estimate will fluctuate and hence the time dependence of the objective function \(F_{t}(\mathbf{S})\) through its smooth component \(g_{t}\). Notice that even as \(t\) becomes large, the squared residuals in \(g_{t}\) remain roughly of the same order due to data averaging (rather than accumulation) in \(\hat{\mathbf{C}}_{\mathbf{y},t}\). Thus a constant regularization parameter \(\mu>0\) tends to suffice because \(g_{t}(\mathbf{S})\) will not dwarf the \(\ell_{1}\)-norm term in (13).  
The solution \(\mathbf{S}_{t}^{*}\) of (13) is the batch network estimate at time \(t\). Accordingly, a naive sequential estimation approach consists of solving (13) using Algorithm 1 repeatedly. However online operation in delay-sensitive applications may not tolerate running multiple inner PG iterations per time interval, so that convergence to \(\mathbf{S}_{t}^{*}\) is attained for each \(t\) as required by Algorithm 1. If \(\mathcal{G}\) is dynamic it may not be even prudent to obtain \(\mathbf{S}_{t}^{*}\) with high precision (hence incurring high delay and unnecessary computational cost), since at time \(t+1\) a new datum arrives and the solution \(\mathbf{S}_{t+1}^{*}\) may deviate significantly from the prior estimate. These reasons motivate devising an efficient online and recursive algorithm to solve the time-varying optimization problem (13). We are faced with an interesting trade-off that emerges with time-constrained data-intensive problems, where a high-quality answer that is obtained slowly can be less useful than a medium-quality answer that is obtained quickly.
# Robust Backstepping Control of Wing Rock Using Disturbance Observer
## 2 Problem Formulation and Preliminaries
### Problem Statement
Thus, it is very important to design a robust controller to suppress the wing-rock motion, and render the roll angle and sideslip angle converge to zero quickly. Considering that the wing rock motion is characterized by uncommanded roll-dominated oscillatory coupled with yaw oscillation, the aileron deflection angle \(\delta_{a}\) and the lateral thrust vectoring angle \(\delta_{y}\) are chosen as the practical control input. However, with the increase of AOA, the aileron control efficiency is reduced drastically [40]. Hence, the input saturation must be taken into consideration in the wing rock control. And the input saturation model can be written as  
\[u_{i}\left(v_{i}\right)=sat\left(v_{i}\right)=\left\{ \begin{array}{ll}sign \left(v_{i}\right)u_{M_{i}},&\left|v_{i}\right|\geq u_{M_{i}}\\ v_{i},&\left|v_{i}\right|<u_{M_{i}}\end{array} \right. \tag{3}\]  
where \(u_{M_{i}}\) is the bounds of \(u_{i}(t)\), \(i=1,2\). Figure 3: Roll angle oscillation with disturbances. Figure 2: Sideslip angle oscillation without disturbances.
# Deep Learning-Based ECG Arrhythmia Classification: A Systematic Review
## 3 Results
### Preprocessing  
Before inputting ECG signals into DL models, a preprocessing step is often applied to those signals, which could improve the learning efficiency and reduce the computational complexity of DL models [27]. In this review, the preprocessing step is reviewed from two aspects, i.e., denoising [28] and data augmentation [29]. The two deal with noisy ECG signals and imbalanced datasets, respectively, which are common cases in real clinical scenarios.
## 1 Introduction
derived the weighting factors from the subscriber's preference for different resolutions [32; 33]. In theory, the subband distortions presented in the reconstructed frame through inverse wavelet transform can be accurately derived from the filter-based weighting factors. However, all the temporal subbands in each temporal decomposition level have diverse features from each other. As a consequence, it is necessary to consider both the diverse content characteristics of the temporal subbands and the impact of subband coupling in the Lagrange multiplier selection process. Inspired by the methods mentioned above, we propose a content adaptive Lagrange multiplier selection algorithm for RDO in 3-D wavelet-based SVC. During the RDO process, the wavelet filter types, subband coupling phenomenon, and temporal subband content information are all taken into account to adaptively compute the Lagrange multiplier. Our strategy aims to not only accurately select the Lagrange multiplier of each temporal subband in the MCTF decomposition level, but also yield better video quality. By performing the proposed algorithm, our codec clearly outperforms the existing well-known 3-D wavelet-based SVC coding schemes with higher PSNR gains and much lower video quality variations through the whole reconstructed video sequence. The rest of this paper is organized as follows: Section 2 presents a brief overview of related work. The proposed content-adaptive Lagrange multiplier selection is introduced in detail in Section 3 with its performance evaluation provided in Section 4. Conclusions and discussions are drawn in Section 5.
## 2 Materials and Methods
### Entropy Measures  
According to [PERSON]'s definition, which is based on the concepts of information theory (IT), spectral entropy is a measure of uncertainty (or variability) associated with random variables [25]. It describes the information carried by a signal and allows quantifying its complexity. The introduction of this quantity for Structural Health Monitoring purposes arises from the assumption that the complexity of a structural system increases with the development of damage [26]. In the case of perturbations with specific, non-random behavior, there are low values of spectral entropy. On the contrary, a more complex and stochastic behavior leads to higher values of spectral entropy.  
Shannon Spectral Entropy (SSE) evaluates the spectral power distribution from the Fourier transform of the signal to represent the dataset behavior in the whole observation  
Figure 1: Map showing the T3 section of the Line C subway and the numerosity of points obtained by satellite interferometry over the analyzed urban area of Rome (Italy).  
period. Yet, it could be possible to use a spectrogram to estimate the instantaneous entropy and obtain its time series vectors. This study adopts the non-instantaneous spectral entropy, which is evaluated for each point of a system to describe the whole signal. In addition, the values are normalized with respect to the spectral entropy of the corresponding white noise to fit the regression inequalities rules. Given the signal \(x(t)\), and the probability distribution \(P_{f}\) in the frequency domain, the normalized Shannon Spectral Entropy is given by  
\[H_{n}=-\frac{\sum_{f=1}^{N}P_{f}log_{2}\left[P_{f}\right]}{log_{2}N} \tag{1}\]  
where \(N\) is the total number of frequencies, and \(log_{2}N\) is the maximal spectral entropy of white noise. Consequently, this indirect measure can be used to realize maps or diagrams. In the following section, the spectral entropy is plotted on a dispersion diagram as a function of the signal energy. A regression model is adopted to highlight the trend and define the related probability distribution, which is used to impose a threshold for identifying the outliers.  
The entropy-energy dispersion and the LOS information are evaluated together, employing scatter plots to assess how the points subjected to subsidence are located on the dispersion. In addition, the representations are evaluated for two subsequent periods, before and after the excavation, comparing their regression parameters to highlight the differences in the results.
# DK-SLAM: Monocular Visual SLAM with Deep Keypoint Learning, Tracking, and Loop Closing
## 4 Experiments
### Pose Evaluation in the UAV Scenario
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **MH01** & **MH02** & **MH03** & **MH04** & **MH05** & **Avg** \\ \hline DSO & 0.046 & 0.046 & 0.172 & 3.810 & 0.110 & 0.8368 \\ SVO & 0.100 & 0.120 & 0.410 & 0.430 & 0.300 & 0.2720 \\ DSM & 0.039 & 0.036 & 0.055 & **0.057** & 0.067 & 0.0508 \\ ORB-SLAM3 & 0.016 & 0.027 & 0.028 & 0.138 & 0.072 & 0.0562 \\ LIFT-SLAM & 0.044 & 0.053 & 0.049 & - & - & - \\ \hline DK-SLAM & **0.013** & **0.013** & **0.027** & 0.077 & **0.055** & **0.0370** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of absolute translation errors (in meters) between the proposed DK-SLAM and other baselines on the EuRoC dataset. Scaling with the ground truth is necessary for evaluation due to the absence of absolute scale in monocular visual SLAM methods, as noted in the table. ”-” indicates performance failure. Figure 6: The generated trajectories of our proposed DK-SLAM on Sequences MH01, MH02, MH03, MH04, and MH05 of the EuRoC dataset, compared with DSO and ORB-SLAM3.
## 5 Roles of SDN for Cloud-Fog Enabled 5G and Beyond
### SDN for Resource Allocation in Cloud-Fog
Data that are time-sensitive and require real-time processing are executed in the FN sector. Data that are less time-sensitive are processed by AFN and that are time-insensitive are processed by cloud. In this architecture, SDN is utilized as a backbone network for the things- fog-cloud architecture. It is utilized to orchestrate and operate the overall functionalities of the proposed architecture. The FNs and AFNs keep the SDN controller constantly aware of capacities and the location of the things devices to be served. The SDN controller helps with the routing and allocation of resources on the grounds of QoS parameters. The cloud layer is the highest layer of this proposed architecture. It provides a powerful computing and storage capabilities. Ref. [54] was evaluated using iFogsim. In [55], [PERSON] et al. provided a framework for integrating heterogeneous IoT networks with 5G networks. The suggested framework deploys three main communication paradigms: MEC, device-to-device communications (D2D), and SDN. The proposed SDN-based architecture includes two main partitions where the heterogeneous IoT networks, D2D-based networks, and RAN are located in the first partition and the second partition consists of the core network (CN). Cloud computing with powerful computing capabilities is available in the CN partition and connected to the control plane of SDN networks. The authors utilized a controller placement algorithm (already proposed in their previous work) to dynamically define the optimum number of SDN controllers required for the networks, according to the current situation of network traffic. The idea was simulated in the CloudSimSDN simulator [56]. The authors claimed that the proposed framework can reduce the percentage of blocked tasks by an average of 30%, compared to other, traditional IoT networks. In addition, it can reduce the overall energy consumption by an average of 20%, compared to existing IoT networks.
# A Multipath Data-Scheduling Strategy Based on Path Correlation for Information-Centric Networking
## 5 Evaluation
### Basic Performance Evaluation
#### 5.1.3 The Effect of Bandwidth on Performance  
In this subsection, we set the default packet loss rate and delay for the path E1-R3-R4-E2 to 0% and 5 ms, respectively. In addition, we set the default bandwidth of E1-R3-R4-E2 to 90 Mbps, 80 Mbps, 60 Mbps, 40 Mbps and 20 Mbps, respectively, to analyze the impact of bandwidth differences on transmission performance. Similarly, we have source node A establish a multipath connection with destination node B and transfer 100 chunks.  
Figure 9 shows the relationship between the average throughput and bandwidth differences of the four scheduling strategies. As the available bandwidth of the path E1-R3-R4-E2 decreases, the average throughput of the four methods gradually decreases, and the throughput of RRDS is always lower than that of the other three methods. In addition, compared with the LRFDS and FDPS, the proposed PSDS can maintain the highest average throughput, which indicates that our method performs well in the scenario in which the path bandwidths differ greatly.  
Figure 10 shows the proportion of the out-of-order chunks of the these four data-scheduling strategies in different link bandwidths. As the available bandwidth of path E1-R3-R4-E2 decreases, the proportion of out-of-order chunks of the methods increases gradually. The proportion of RRDS is always kept at a high level. We can also ascertain that the proposed PSDS can maintain a relatively minimum out-of-order proportion when the path bandwidths differ greatly.  
Figure 8: Proportion of out-of-order chunks under different packet loss rates.  
Figure 9: Average throughput under different bandwidths.
## 4 Numerical Simulation
### Initial and Boundary Conditions  
Mathematically, the boundary conditions are defined for the experiment measurements campaigns as follows:  
* At the inlet, uniform velocities and temperature profiles are considered.
* At the outlet, pressure is equal to the atmospheric pressure and the axial variation of temperature is equal to zero.
* The no-slip condition and zero normal pressure gradient temperature are used on all solid-fluid interfaces.
* The square section box in which the SC module is placed is a perfectly insulated surface.
* The initial conditions are defined over domain as follows:
* The inlet air temperature is the initial temperature condition.
* An automatic initial guess is used for the velocity field.
## 2 Definitions  
Some important terms used in this paper are defined as follows:  
_Refuse-operation:_ A communication delay or bit error causes the relay protection to lock, and as a result, the relay protection doesn't start when a given fault occurs within the protection range.  
_False trip:_ A communication delay or bit error causes the relay protection device activation when no fault has occured within the protection range.  
The communication delay is commonly measured by the \"poll and answer\" method which is based on the IEEE1588 network time synchronization technology [15, 16, 17, 18]. This technique measures the time delay by transmitting \"poll and answer\" signals across the communication medium. The limitation of this technique is that it can provide only accurate delay measurements if the outgoing and return channel delay times are the same. If these times differ, then an error is introduced.  
Figure 1 shows the process of the signal transmission in current differential protection. Assuming the slave sends a frame packet to the host to measure the channel delay. \(T_{d1}\) is the channel delay when the packet is sent from the slave to host, \(T_{d2}\) is the channel delay when the packet is sent from host to slave. Then the _inconsistent delay_ is defined as the difference between \(T_{d1}\) and \(T_{d2}\):  
\[T_{cd}=\left|T_{d1}-T_{d2}\right| \tag{1}\]  
and the _consistent delay_ is defined as the average of \(T_{d1}\) and \(T_{d2}\):  
\[T_{d}=\frac{T_{d1}+T_{d2}}{2} \tag{2}\]  
Figure 1: Signal transmission in current differential protection.
# Identify Road Clusters with High-Frequency Crashes Using Spatial Data Mining Approach
## 2 Methodology  
### Process Map  
This paper presents a three-stepped spatial data mining approach to directly identifying RCHC by using: (1) preprocessing, (2) conceptualization, and (3) spatial data mining. The process map for the approach is shown in Figure 1.  
Preprocessing  
The first step (preprocessing) is storage of the road network, crashes, and region boundary in spatial database. In this paper, we use PostGreSQL database [29] and PostGIS spatial data engine [30] to store and query massive spatial roads and crashes. The attribute and geometry info of road and crash table inherited from the input road and crash shapefile.  
Conceptualization  
The second step (conceptualization) is to build the conceptualization of spatial relationships. There are two types of conceptualization of spatial relationships: (1) the conceptualization of linear road relationships and (2) the conceptualization of road-crash relationships. Spatial weight matrix of roads (SWMR) is constructed to describe the conceptualization of road spatial relationships. The road network topology is created using pgRouting [31], which extends the PostGIS/PostgreSQL geospatial database to provide road network geospatial routing ability functions (e.g., pgr_createTopology, pgr_createVertices, pgr_analyzegraph, pgr_nodeNetwork). The road network DBF table with count of crashes is calculated using crash spatial aggregation algorithm to describe the conceptualization of spatial relationships between roads and crashes.  
Spatial data mining  
The third step (data mining) is to identify RCHC using the cluster and outlier analysis (local Moran's I). According to studies of the point-based spatial clustering analysis method, this paper proposes a method of directly identifying RCHC by using line-based Moran's I index. We can  
Figure 1: Process map for the approach of directly identifying road clusters with high-frequency crashes (RCHC).  
quantitatively identify RCHC (positive autocorrelation) and road outliers (negative autocorrelation) via map visualization of road clusters and outliers shapefile.
# A Comparative Study on Violent Sloshing with Complex Baffles Using the ISPH Method
## 5 Analyses and Discussion
### Height of Simple Vertical Baffle  
In this section the liquid sloshing in a tank with a vertical baffle of various heights and under different excitation frequencies are computed and compared with the sloshing in a tank without any baffle. Using the baffle configuration in Figure 5b, a series of ISPH computations are made based on the parameters provided in Table 2. Through monitoring the impact pressures at P2 at every time step, picking up the maximum value of every sloshing period, and averaging across the frequencies, Figure 7 shows the maximum impact pressure at this location under the non-dimensional excitation frequencies of 0.3-1.2 for the normalized baffle height of 0.083-0.217. It can be seen that the pressure values decrease obviously when the sloshing tank is positioned with a vertical baffle, and also, they decrease accordingly with an increase of the baffle height. Through quantitative analysis, it is found that the maximum pressure is reduced by around 20% comparing the systems with and without a vertical baffle, and this difference reaches up to 38.2% when the highest baffle is installed. The findings demonstrate that installing a vertical baffle on the bottom of a sloshing tank is an effective way to reduce the impact load of sloshing liquids, especially when a higher baffle is installed.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multicolumn{1}{c}{**Baffle Type**} & **Length** & **Distance from Bottom** & **Number of Particle** & **Excitation Frequency (rad/s)** & **\Omega/D\({}_{0}\)** \\ \hline No baffle & — & — & 22,500 & 1.5–5.0 & 0.3–1.2 \\ \hline \multirow{4}{*}{One vertical only} & 0.083 & — & 22,275 & \multirow{4}{*}{1.5–5.0} & \multirow{4}{*}{0.3–1.2} \\  & 0.117 & — & 22,165 & & \\  & 0.167 & — & 22,000 & & \\  & 0.217 & — & 21,835 & & \\ \hline \multirow{4}{*}{Two horizontal} & & 0.117 & \multirow{4}{*}{1.7–4.5} & \multirow{4}{*}{0.4–1.1} \\  & 0.183 & & 21,825 & & \\ \cline{1-1}  & 0.167 & & 0.250 & & \\ \cline{1-1}  & & 0.250 & & & \\ \cline{1-1}   One vertical and one horizontal & & 0.250 & & & \\ \cline{1-1}   One vertical and two horizontal & & 0.250 & & & \\ \hline \multirow{4}{*}{One T-shaped only} & 0.083 & — & 21,710 & \multirow{4}{*}{1.7–4.5} & \multirow{4}{*}{0.4–1.1} \\ \cline{1-1}  & 0.117 & — & 21,600 & & \\ \cline{1-1}  & 0.167 & — & 21,435 & & \\ \cline{1-1}  & 0.217 & — & 21,270 & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of different baffle configurations and external excitation conditions.  
Figure 7: Comparisons of impact pressure under different heights of the vertical baffle.