esa-sceva/satcom-chunk-collection huggingface dataset:
# Self-Organizing Networks for 5G and Beyond: A View from the Top
## 5 Future SON Research Directions for B5G
### SON for 6G Networks  
In the 6G era, energy efficiency, ultra-low latency, and high reliability shall be key issues to handle in standard mobile communication networks. Apart from this fact, in special cases, such as vehicular communications, the nodes must be able to perform self-organizing functions; thus, SON functions and network automation shall keep evolving [120; 121]. As far as the next move towards 6G networks is concerned, and we are at the very early stages of research and standardization, SON systems must embed self-coordination capabilities to manage the complex relations between the different applications that are running, so that slow reactions in real-time situations are avoided. According to the authors of [122], hybrid SONs (H-SONs) combined with feedback from multiple feedback loops might be the ideal case. Virtualized, containerized, multi-tenant architectures as well as federated transfer learning and collective intelligence based on conventional AI techniques can improve the landscape as we move towards 6G architectures.  
Another key technique as we move towards 6G regards O-RAN. O-RAN (Open RAN) is another key technique based on the rationale of splitting the centralized unit (CU), distributed unit (DU), and remote unit (RU). Intelligence in O-RAN architectures relies on the separation of the RAN Intelligent Controller (RIC) and allows one to gather radio resource management (RRM) and self-organizing network (SON) applications, controlling the radio resources and network. In addition to this fact, RIC is separated from the processing units and allows us to gather radio resource management (RRM) and self-organizing network (SON) functions, which control the radio resources and network. In the O-RAN concept, intelligence resides in the RIC, employing AI and ML models for radio network automation [123].  
Finally, the prominent benefits of network slicing techniques are expected to be blended with SON functions as we move towards 6G infrastructures. As an example, a SON-based network management architecture spread across RAN and core network shall be necessary for controlling networks in a holistic manner with SON functions operating at the user and control plane and cooperating with others running at the core level [124].
## 2 Materials and Methods
### Materials  
Waste material: biomass ultrafines (BA)  
Supplier: Spanish power plant  
Origin: herbaceous + woody (eucalyptus, fruit, pine tree, etc.)  
Chemical composition: (Table 1) CaO + MgO + SiO\({}_{2}\) -70 wt%; alkali content \(\sim\)12 wt%; Al\({}_{2}\)O\({}_{3}\) + Fe\({}_{2}\)O\({}_{3}\) content \(\sim\)7 wt%  
XRD-determined mineralogical composition: amorphous hump at 20 = 20\({}^{\circ}\)-35\({}^{\circ}\) indicative of amorphous silica; diffraction lines characteristic of cristobalite and quartz; other crystalline phases: sylvite (KCI), calcite (CaCO\({}_{3}\)), mulhite (Al\({}_{6}\)Si\({}_{2}\)O\({}_{13}\)), hematite (Fe\({}_{2}\)O\({}_{3}\)), and alkaline orthoclase-like alkaline feldspars (KAlSiO\({}_{3}\)); all reported earlier as components in similar biomass waste [32].  
Specific surface: 2800 cm\({}^{2}\)/g; density: 2.75 g/cm\({}^{3}\).  
Waste material: mixed C&DW ultrafines (U\({}_{\text{C\&DW}}\))  
Origin: C&DW management plant in the Spanish province of Caceres  
Chemical composition: (Table 1) SiO\({}_{2}\) + Al\({}_{2}\)O\({}_{3}\) + Fe\({}_{2}\)O\({}_{3}\) -66 wt%; CaO \(\sim\)13 wt%; alkalis \(\sim\)5 wt%; trace elements  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Composition (wt\%)** & **U\({}_{\text{C\&DW}}\)** & **BA** & **SF** \\ \hline SiO\({}_{2}\) & 50.10 & 52.75 & 97.94 \\ \hline Al\({}_{2}\)O\({}_{3}\) & 11.82 & 3.19 & - \\ \hline Fe\({}_{2}\)O\({}_{3}\) & 4.06 & 3.72 & 0.10 \\ \hline MgO & 2.12 & 1.93 & 0.30 \\ \hline CaO & 13.21 & 14.91 & 0.23 \\ \hline Na\({}_{2}\)O & 1.41 & 1.85 & 0.21 \\ \hline SO\({}_{3}\) & 0.76 & 2.26 & - \\ \hline K\({}_{2}\)O & 3.58 & 10.13 & 0.50 \\ \hline P\({}_{2}\)O\({}_{5}\) & 0.19 & 1.64 & - \\ \hline Cl- & 0.04 & 1.00 & 0.04 \\ \hline Other oxides & 0.72 & 0.52 & 0.01 \\ \hline LOI & 12.00 & 6.10 & 0.67 \\ \hline \end{tabular} Note—U\({}_{\text{C\&DW}}\): mixed ultrafines; BA: bottom ash ultrafines; SF: silica fume.  
\end{table}
Table 1: SCM chemical composition.  
Mineralogical composition (Figure 1): majority phases (as in other mixed C&DW recycled aggregates [38]) = quartz, feldspars (albite and orthoclase), phyllosilicates (chamosite and biotite) and hematite; traces of calcite.  
Specific surface: 5700 cm\({}^{2}\)/g; density: 2.88 g/cm\({}^{3}\).  
Standardized supplementary cementitious materials: silica fume  
Origin: commercial (Elkem Microwhite(r))  
Characteristics: white; SiO\({}_{2}\) content -98%, specific surface area \(\geq\)15 m\({}^{2}\)/g  
Material: cement (OPC)  
Class/strength: EN 197-1 [8]-compliant CEM I 42.5 R  
Manufacturer: Lafarge plant, Villalueng de la Sagra, Spanish province of Toledo  
Specific surface: 3560 cm\({}^{2}\)/g; density: 3.1 g/cm\({}^{3}\).
## 2 Proposed Methods for Speaker Recognition  
In previous sound-processing methods, numerous signals imposed a considerable burden on computers. It is challenging to find characteristics in many signals because they contain too much noise and irrelevant signals; thus, extensive audio processing has been required. Many feature extraction techniques have been developed that have made significant contributions in audio signal processing, such as MFCC [2]. In this paper, we study text-independent [2] speaker recognition, which does not need to retain the original signal of the sound. Therefore, MFCC technology will be used to extract the features of the sound sequence. This study utilizes neural network [15] techniques to construct a speaker identification model. Firstly, the results of MFCC feature extraction are used as inputs to the model. After passing through the neural network model, a set of feature vectors representing the speaker's identity is obtained. Subsequently, similarity distance calculations are used to determine the accuracy of the identification results. This paper presents three methods for speaker recognition in total. In terms of deep learning models, the LSTM-MFCC-TL method and BLSTM-MFCC-TL method employ the LSTM model and BLSTM model for speaker recognition, respectively. The BLSTM-MFCCAE-TL method also utilizes the BLSTM model and employs a pre-trained autoencoder to generate AE features. We encode the speakers' speech and then concatenate these AE features with the original MFCC features as input for training the BLSTM model. These features encoded by the autoencoder can enhance learning effectiveness and provide better accuracy in speaker recognition.  
The main challenge focuses on the proposed BLSTM-MFCCAE-TL method in this paper. In this proposed method, we employ an autoencoder to encode the speaker information in the speech signal and concatenate this set of encoded AE features with MFCC features as inputs to the deep learning model. Since the AE features are the result of speaker encoding, the incorporation of these AE features can effectively enhance the learning performance of the model. In the following subsections, we discuss the three proposed methods in detail.  
Long Short-Term Memory with Mel-Frequency Cepstral Coefficients for Triplet Loss (LSTM-MFCC-TL) Method  
The first method in this paper is the LSTM-MFCC-TL method. The speaker recognition process of the proposed LSTM-MFCC-TL method is illustrated in Figure 1. We divide the LSTM-MFCC-TL method into five steps. The first step is to perform MFCC feature extraction on the input audio signal. Feature preprocessing is performed on sound feature sequences of different lengths in the second step. The third step is to group all input features for subsequent training using the cluster training method. We construct the model using LSTM [21] in the fourth step. In the fifth step, the loss function used during training is the triplet loss [31].
## 3 Results and Discussion
### Molecular Docking Studies  
All of the produced CT complexes were docked against serotonin and dopamine receptors. The donor molecule (Bar) was used as control. The potential binding energy of the four CT complexes was larger than that of Bar drug alone in both receptors (Table 2).  
When (Bar-CLA) was molecularly docked with serotonin and dopamine, the theoretical binding energies were found to be \(-8.2\) and \(-7.8\) kcal/mol, respectively (Table 2). Serotonin and (Bar-CLA) have a greater interaction than dopamine does with (Bar-CLA) due to their higher binding energies. The best (Bar-CLA) docking data are shown in Figure 3 and Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Docking Score (kcal/mol)**} \\ \cline{2-3}  & **PDB:6 BQH** & **PDB:6 CM4** \\ \hline (Bar–CHL) & \(-7.7\) & \(-6.9\) \\ (Bar–TCNQ) & \(-8.1\) & \(-7.1\) \\ (Bar–DBQ) & \(-7.5\) & \(-7.2\) \\ (Bar–CLA) & \(-8.2\) & \(-7.8\) \\ Bar & \(-5.3\) & \(-5.1\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Docking score of Bar and four synthesized CT complexes against serotonin (6 BQH) and dopamine (6 CM4).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Receptor**} & **Binding Free Energy** & \multicolumn{2}{c}{**Interactions**} \\  & **(kcal/mol)** & **HBond** & **Others** \\ \hline (Bar–CLA) & \(-8.2\) & Thr386, Lys385, Lys320, & Ala321, Val324, Leu105, \\  & & Asn110, and Asn107 & and Lys104 (\(\pi\)–alkyl) \\ Bar & \(-5.3\) & Arg173, Asn187, and & Ala176 and Ile177 \\  & & His182 & (\(\pi\)–alkyl) \\ \hline \hline \end{tabular}
\end{table}
Table 3: The interaction data between (Bar–CLA) or Bar and serotonin (6 BQH).  
Thr386, Lys385, Lys320, Asn110, and Asn107, among other amino acid residues, were found to form hydrogen bond interactions with O atoms in the BarCS complex (Figure 4a). Lys104, Val324, and Leu105 were also present as \(\pi\)-alkyl interactions with C, Cl, and C atom in BarCS [48; 49]; whereas the amino acid residues Arg173, Asn187, and His182 formed hydrogen bonds with O atom in the BarS complex (Figure 4b). Ala176 and Ile177 were also found as \(\pi\)-alkyl interactions with C atom in BarS (Table S2).  
Two-dimensional illustrations of interactions between receptors and ligands are represented in Figure 5. All the other data are presented in detail in Supplementary Tables S1 and S2.  
Figure 4: The 3D illustration of a serotonin receptor docked with (**a**) (Bar–CLA) complex and (**b**) Bar alone.  
Figure 3: Serotonin (PDB ID: 6 BQH) helical model in the best docking position with (**a**) (Bar–CLA) complex or (**b**) Bar alone.
## 2 System Model and Analysis
### Control Method  
The specific switching-control process is shown in Figure 2. The PD controller is an automatic regulation device composed of two control units: proportional and differential. Its core principle is to respond to system deviations in real time through proportional action and predict the trend of deviation changes in combination with differential action, thereby correcting the dynamic characteristics of the system in advance.  
P: Control signals are directly generated based on the current system deviation, which has a linear relationship with the deviation amount, enhancing the system response speed.  
D: Predict future trends by calculating the rate of deviation change, generate advanced correction signals, and effectively suppress system oscillations.  
The first control phase designs the PD controller for the prismatic actuated link, moves the actuated link from the starting position to the desired state, and ensures that the prismatic actuated link can reach the target state. Meanwhile, the third rotating underactuated link will be in a drift state and rotate freely.  
In the second control phase, the first prism-actuated link is kept in the target state at all times, and the planar PPR underactuated robot model is reduced to a PR underactuated robot model. According to the nilpotent approximation characteristic of PR underactuated robot and the iterative contraction theory, the second prism-actuated link is made to move under the open-loop iterative command. The underactuated link is controlled indirectly and quickly leaves the drift state.  
In each iteration period, the starting state and the ending state of the prismatic actuated link are consistent with the state before the start of the iteration, the angle of the rotating underactuated link gradually approaches the desired angle, and the angular speed gradually slows down and stabilizes at zero speed to achieve stability control.
## 1 Introduction
The database contained data collected regarding patients' initial presentation at the emergency department (ED), from May 2020 until October 2020, and comprises more than 50,000 cases. Despite their growing use in the field of medicine, machine learning models remain extremely difficult to interpret, as they often lack explainability. In this study, we introduce a combined model that integrates a LIME-based explainability model with machine learning model [22]. The combined model can explain the predictions of the MLP classifiers in an interpretable and accurate manner. More importantly, our explainability model may help physicians to identify the factors that most influence a patient's severity of illness. These properties would help medical staff to take preventive measures (when possible) to reduce a patient's risk of deterioration. We summarize the contribution of this study as follows:  
1. We introduce MLP and decision tree machine learning models to predict the level of severity of illness in patients diagnosed with COVID-19. Our experimental results indicate that our models can achieve up to 80% prediction accuracy. 2. Applying the proposed machine learning models can assist medical staff in performing fast, early, and effective triage, thereby taking the necessary measures to prevent patient deterioration and conserve strained medical resources. 3. We integrate a LIME-based explainability model with the machine learning model and demonstrate how such integrated model can be used to provide further insights into an individual patient's medical condition and can identify the factors that most contribute to the patient's illness severity. 4. Finally, integration into a mobile application makes the proposed models accessible and easy-to-use by medical staff everywhere. The rest of this paper is organized as follows: Section 2 reviews prior related works, Section 3 describes the dataset of patients diagnosed with COVID-19 that we used for our study, Section 3 presents our machine learning model, Section 4 introduces the explainability method that has been combined with our MLP model, Section 5 presents our mobile application design, and Section 6 summarizes our conclusions and suggestions for future studies.
## 1 Introduction
[47] proposed a fast, low-overhead image flow monitoring and scheduling system called FlowSeer for data flow mining. The model can accurately and quickly predict the speed and duration of any initial data flow and meet the needs of the dynamic adjustment of data flow-routing strategies. Many research methods on network performance prediction and network-intensive task scheduling are discussed above, but most of these methods aim to improve the internal execution efficiency of the network, avoid data collisions by scheduling time slots, and achieve the purpose of reducing the network data delay. In order to improve the resource utilization of the collection nodes of remote sensing data collection users and effectively improve the collection speed of a large amount of remote sensing data, this paper uses the backpropagation (BP) neural network algorithm to predict the collection speed of the remote sensing data collection node. The dataset used in this work was obtained from Sentinel2, and the selected area is the whole China Region in 2019 [10]. According to the difference in the speed of the collection task performed by each node at different times, a two-stage combined dynamic task scheduling algorithm (TSCD-TSA) is proposed to match the task set and the resource node set, making dynamic adjustments to achieve the aim of improving the utilization of resource node sets. Comparative experiments were designed to evaluate the performance of applying different improved dynamic task scheduling algorithms, including FCFS-PSO, SJF-PSO, MAX-MAX-PSO, and PSO. In order to evaluate the effectiveness of the proposed optimized task scheduling algorithms, the CloudSim platform was used to evaluate the algorithms. The execution results of different task sets were analyzed. The rest of this paper is organized as follows: In Section 2, methods for task scheduling and our proposed optimized task scheduling algorithms are introduced. In Section 3, the experiment design to compare the performance of the different proposed optimized task scheduling algorithms is described, and the experimental results are presented and discussed. Finally, the conclusion is drawn in Section 4.
## 2 ACS-SSI Algorithm and Its Verification
### Numerical Simulation in Multi-DOF System
#### 2.2.2 Effect of Noise on Identification Results  
The signal-to-noise ratio (SNR) can substantially affect the identification accuracy. Due to the complexity of actual testing and monitoring conditions, noise generation is inevitable and it is difficult to remove from effective signals, which requires the identification algorithm to have good robustness to noise suppression. Therefore, it is necessary to verify the identification correctness of the ACS-SSI algorithm under different SNRs.  
The robustness of the ACS-SSI algorithm is checked under six different SNRs, i.e., 9, 4, 2.3, 1.5, 1, and 0.67 respectively. Each case of the experiment was repeated 10 times, and noise occupying 10%, 20%, 30%, 40%, 50% and 60% of the total signal power was added to the system response. The average correlation calculation is still carried out 50 times, and the sampling time of each segment is 20 s. The comparison among the SNRs shows that the third order mode presents obvious divergent trends, so the third mode is selected for the statistical verification.  
As Table 1 shows, although the identification of frequency and mode shapes is still valid when the SNR in the simulation reached 0.67, the damping ratio shows a divergent trend. It shows that the SNR has a great influence on the identification accuracy. To improve the identification accuracy, tolerance values of frequency, mode shape, and damping ratio must be properly adjusted to achieve better results.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**SNR** & **9** & **4** & **2.3** & **1.5** & **1** & **0.67** \\ \hline Frequency error (\%) & 0.14 & 0.55 & 0.64 & 0.57 & 1.21 & 1.02 \\ Damping ratio error (\%) & 5.03 & 42.89 & 25.52 & 40.57 & 28.01 & 30.2 \\ Modal shape error (\%) & 0.34 & 0.46 & 1.21 & 1.41 & 2.43 & 3.81 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Identification error of ACS-SSI under different signal-to-noise ratios (SNRs).  
Figure 2: Input signal of stochastic subspace identification (SSI) and average correlation signal based stochastic subspace identification (ACS-SSI). m\({}_{1-3}\) = mass blocks. Acc: Acceleration Correlation.
## 4 Discussion
This is influenced by the environment that surrounds the mobile antenna: the 900 MHz frequency band is used more often in rural areas and rarely in populated areas, whereas the 1800 and 2100 MHz frequency bands are used more often in densely populated cities. [PERSON] investigated mobile antennas [42] in the densely populated city of Riyadh in Saudi Arabia (where the distance between the antenna and residential buildings was 50 m, while in the present study the distance between the antenna and residential buildings was 200 m). Usually, in the monitoring of field measurements, each antenna generating RF EMR is investigated separately, but the total RF EMR created by two mobile antennas has not been evaluated. Furthermore, no investigations of each antenna's contribution to the total RF EMR have been carried out. The methodology to conduct the field measurements, described in the presented study case research can be further used for evaluation of the epidemiological state of a certain area such as: metropolitan area, urban agglomeration area, etc.
# Automatic Era Identification in Classical Arabic Poetry
## 3 Dataset and Methods
### Methods
#### 3.2.4 Topic Analysis  
We now describe the process and results of the topical analysis of the poem diwans in more detail. While Arabic uses superscript vowels (_tasktl_), we found it necessary to remove these vowels as well as punctuation marks from the text prior to topic analysis; specifically, this step allows us to collect more comprehensive statistics for related word variants. We performed several forms of topic analysis: (a) using the original Arabic text; (b) converting the original word forms first into their Arabic stems to account for possible sparsity due to morphological inflections; and (c) using the glosses in English, to be displayed for broader interpretability of the models.  
As described before, we applied an LDA model, experimenting with two pre-defined numbers of topics: 20 and 50. Based on manual inspection and the classification results using the two possibilities, we favored the analysis of 20 topics over 50 topics.  
Figure 2 details the words that are most associated with each of the 20 topics discovered. For wider readability, we present the topics learned using the English glosses. A manual inspection of these automatically inferred word distributions reveals associations to topics that are typical of classical Arabic poetry (as discussed before), including: praise--of [PERSON], a tribe, or a person; war and bravery; love of a woman; love of wine; and elegies.  
As illustrated in Figure 3, each discovered topic appears in multiple poems within our collection, indicating that these topics are general. Overall, every topic appears to some extent in 93 poems on average, with a maximum of 265 and a minimum of 75 poems per topic. Thus, the topics are distinctive in that no topic appears in a very large number of poems and only in a limited set of poems. Given this result and the fact that we modeled hundreds of poems, it is clear that there is both topical similarity and diversity among them.  
For each poet, we analyzed the distribution of the different topics in their poems. Figure 3 showcases the distribution of the topics in the poems of [PERSON]. As shown, the distribution of the topics is not uniform, with a small number of topics being more dominant than the others. For example, we observe that Topic 14 appears in 25 of the 173 poems (14%) by [PERSON].
# Timing Performance Testing and Regularity Analysis of eLoran System
## 2 eLoran Timing Error  
### Main Error  
The main sources of error in eLoran timing include control errors at the transmitter and broadcaster, propagation path correction errors, receiver errors, and other miscellaneous errors [19; 20; 21; 22; 23].