esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Motivations of Integrating SDN and Inofrmation Centric Networking
### Software Defined Networking enabled Named Data IoTs (SDN-NDN-IoT)
Energy consumption is a huge problem for companies around the world because of the need for continuous availability and the enormous architecture of IoT devices. Companies spend a huge amount of revenue to solve this problem of energy consumption. One solution is SDN, which can reduce the consumption of overall energy. The programmability, openness, and feasibility of SDN reduces the implementation complexity for software and hardware. It is a candidate by which we can limit our resources, and reduction in resource usage in turn reduces overall energy consumption. Energy-saving techniques and power-saving schemes can be applied to reduce energy consumption. Energy consumption is not consistent at all times. For example, during the day, Internet traffic is high while at night, traffic is significantly reduced. In scenarios with low Internet traffic, most of the nodes are unutilized. Flexible programming in SDN can solve this problem and can make intelligent decisions according to the situation of the traffic. Unutilized nodes can be sent to sleep mode while the activation period in network for the nodes can be defined through SDN. A content-based method can also be a solution for SDN resource management. This method ensures reduced power consumption with proper resource utilization because content length can be known to the forwarder in NDN and ICN, so the required resources can be delivered to the most fitted nodes. Named data networking can also be beneficial to the SDN-IoT combination. Load balancing and flow management techniques in the SDN can reduce overall energy consumption. Traffic management based on information from the nodes and switches can reduce collisions of interest and data packets. Constant and timely updates to the SDN controller about energy consumption and resource allocation can enhance network performance against energy consumption, interest satisfaction rate and network life time. Finally, the combination of SDN and IoT with NDN can be a great addition to research advancements and the development of science because of their fruitful results.
## 3 Bearing Fault Recognition Based on SSL-VCST Method
### Principle of SSL-VCST Algorithm  
To address the issue of insufficient labeled samples, SSL [37] is introduced into the VCST classification network utilizing unlabeled data for training. The core idea of SSL is to utilize unlabeled data by automatically generating labels to improve the generalization  
Figure 5: Structure of the continuous Swin-T Block.  
performance of the model. The cyclic training process of SSL-VCST is shown in Figure 6, and the specific training process is as follows:  
(1) Supervised learning of labeled data. In the initial stage, supervised learning is performed on labeled data using a classification network. The maximum probability values after model prediction are used to optimize the network parameters by measuring the distance between prediction results and real labels through a cross-entropy loss function Equation (16). This ensures that the model has good performance on limited labeled data.  
\[H(p,q)=\sum_{i=1}^{L}p_{i}\log(q_{i}) \tag{16}\]  
where \(p_{i}\) is the true value, \(q_{i}\) is the predicted value, and \(L\) is the category.  
(2) Pseudo-label generation for unlabeled data. The trained model is applied to the unlabeled data, and if the probability value is greater than \(\alpha\) (confidence level), its corresponding sample category is used as the pseudo-label value of the sample. Otherwise, the sample is considered temporarily unavailable. The confidence distribution is smoothed using Exponential Moving Average (EMA), as shown in Equation (17). For each new training round, an exponential moving average of the current confidence distribution is calculated and used as the dynamic threshold. This helps to reduce the fluctuation of the threshold and makes it more stable.  
\[\alpha=\beta X_{e}+(1-\beta)\alpha_{e-1} \tag{17}\]  
where \(\beta\) is the smoothing coefficient, and \(X_{e}\) is the current training round value.  
The interval of \(\beta\) is (0, 1), which determines the contribution of new observations to the average value. In general, the closer to 1, the greater the impact of the new observations on the mean.  
(3) Pseudo-labeled data fusion. The generated pseudo-labeled data are merged with labeled data to form an expanded dataset containing both of them.  
(4) Recurrent training. The model is further trained on the merged dataset using consistent regularized loss, calculated as in Equation (18). It improves the model's generalization ability for unknown data by predicting the same output on unlabeled data under different perturbations.  
\[Loss=-\frac{1}{N}\sum_{i=1}^{N}KL\Big{(}\hat{y}_{i}^{L}\|\hat{y}_{i}\Big{)} \tag{18}\]  
where \(N\) is the number of consistent samples, \(\hat{y}_{i}^{L}\) is the pseudo-label, \(\hat{y}_{i}\) is the predicted probability of the model, and \(KL\) denotes the K-L divergence calculation.  
(5) Until all data have been labeled, the expanded dataset completes the network training, and the diagnostic model is output to complete fault identification of the test set.  
Figure 6: Training process of SSL-VCST dataset.
# DPMS: Data-Driven Promotional Management System of Universities Using Deep Learning on Social Media
## 5 DPMS Implementation and Analysis
### Machine vs. Human Analysis  
Obtaining the correct prediction from the BiLSTM network is a mandatory requirement for the effectiveness of the proposed DPMS. To evaluate the prediction from the BiLSTM network, the classifications from the network and from human inspection have been compared in this section, side by side. In this analysis, 100 random instances have been used to classify them into one of the four classes, ten times. Concurrently, the feedback has been analyzed by humans to check if the classification from the BiLSTM network is accurate or not. Table 4 lists the comparison between the class identification of the BiLSTM network and human inspection.  
Table 4 compares human inspection and BiLSTM network classification across the 'Good', 'Average', 'Neutral', and 'Poor' categories, revealing a high degree of alignment between the two methods. Across ten attempts, the deviations are remarkably minimal, often within a one- to two-unit range, underscoring the model's reliability. For instance, in categories like 'Good' and 'Average', the BiLSTM closely mirrors human assessments, diverging by, at most, one unit. The 'Neutral' and 'Poor' categories show similar minor  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline  & \multicolumn{3}{c}{**BiLSTM Classification**} & \multicolumn{5}{c}{**Human Inspection**} \\ \hline
**Attempt** & **Good** & **Average** & **Neutral** & **Poor** & **Good** & **Average** & **Neutral** & **Poor** \\ \hline
1 & 28 & 35 & 22 & 15 & 29 & 34 & 21 & 16 \\ \hline
2 & 30 & 27 & 25 & 18 & 30 & 28 & 25 & 17 \\ \hline
3 & 34 & 30 & 15 & 21 & 35 & 29 & 15 & 21 \\ \hline
4 & 36 & 30 & 1  
variances. However, a noticeable discrepancy exists in the eighth attempt for the 'Poor' category, where the BiLSTM model categorizes one more than the human inspection. Despite this, the overarching consistency between the human and machine evaluations affirms the BiLSTM model's robustness and high reliability. Its ability to mimic human-level categorization for various sentiments makes it an invaluable tool in sentiment analysis tasks. The comparison has been illustrated in Figure 7. The visual representation also supports the similarity between BiLSTM classification and human inspection.
## 1 Introduction
### Branch Voltage Drop Model  
The maximum voltage drop in each branch in the topology needs to be controlled within a certain range. The impedance, maximum current carrying capacity, etc., of each cable segment is determined after the cross-sectional area thereof is determined. A subset \(\mathbf{SF}_{i}\) is created for each branch \(F_{i}\) to hold the number of all cable segments subordinate to this branch, and then the maximum voltage drop \(\Delta U_{F,i}\) for this branch is calculated using Equation (4) [36].  
\[\Delta U_{F,i}=\sum_{j=1}^{N_{F,j}}\sqrt{3}J_{j}I_{E_{j},j}\left(R_{j}\cos \varphi+X_{j}\sin\varphi\right)\ \forall j\in\mathbf{SF}_{i} \tag{4}\]  
where \(N_{F,j}\) is the number of cable segments in \(\mathbf{SF}_{i}\); \(R_{j}\) is conductor resistance of the cable segment \(j\); \(X_{j}\) is inductive reactance of the cable segment \(j\)[37]; and \(l_{j}\) and \(l_{E,j}\) are the length and rated current of the cable segment \(j\), respectively. The maximum voltage drop of the branch is less than 1%.
# Decentralization of Virtual Linkage in Formation Control of Multi-Agents via Consensus Strategies
## 2 Problem Statement
### Preliminaries of Digraphs and Consensus  
To implement the decentralized formation control, the communication topology among a group of agents is represented as a diagram \(G=(R,\varepsilon)\). In detail, \(R=\left\{R_{i}|i=\left\{1,2,\ldots n\ \right\}\right\}\) is a set of agents and are called as _nodes._\(\varepsilon\in R\times R=\left\{\left(R_{i},R_{j}\right)\right\}\) is a set of unordered pair of nodes and called as _edge._ If \((R_{i},R_{j})\) is an edge of the diagram, then information flow from \(R_{i}\) to \(R_{j}\) is allowed and \(R_{i}\), \(R_{j}\) are neighbors. Especially, self-loop edges in the form \((R_{i},R_{i})\) are not allowed. Another way to represent \(G\) is called the adjacency matrix \(A=\left[a_{ij}\right]\in R^{n\times n}\). The elements \(a_{ij}\) equals \(1\) if there exists an edge \((R_{i},R_{j})\in\varepsilon\), otherwise \(a_{ij}=0\).  
**Theorem 1** (Consensus algorithm [22]).: _Let \(A=\left[a_{ij}\right]\in R^{n+1\times n+1}\) be the adjacency matrix, where \(a_{ij}=1,\forall i,j\in\left\{1,\ldots,n\right\}\) once the agent \(j\)'s formation state estimate is available to agent \(i\) and 0 otherwise. \(a_{i(n+1)}=1\) if agent \(i\) has knowledge of reference value \(\xi_{contr}\) and 0 otherwise, and \(a_{(n+1)k}=0,\forall k\in\left\{1\ldots n+1\right\}\) 0._ Then the consensus algorithm  
\[\dot{\xi}_{i}=\frac{1}{\eta_{i}}\sum_{j=1}^{n}a_{ij}\left[\xi_{j}-\kappa\left( \xi_{i}-\xi_{j}\right)\right]+\frac{1}{\eta_{i}}a_{i(n+1)}\left[\dot{\xi}_{ conttr}^{r}-\kappa\left(\xi_{i}-\xi_{contr}\right)\right] \tag{1}\]  
_guarantees that \(\xi_{i}\rightarrow\xi_{d},\forall i\), asymptotically if and only if the graph of \(A\)_ has a directed spanning tree.
# Robust Burst Detection Algorithm for Distributed Unique Word TDMA Signal
## 6 Conclusions  
In this paper, a robust burst detection algorithm for distributed unique word (DUW) TDMA signal is proposed. First of all, we analyze the principle of traditional dual correlation (TDC) and the influence of DUW structure on the delay conjugate multiplication. We combine DUW with TDC and obtain a dual correlation detection algorithm, which is suitable for DUW. The experimental results show that the proposed algorithm has high detection accuracy and good stability. Under the same SNR, detection probability of the algorithm is at the same level for DUWs with different distribution structures. In addition, when the normalized frequency offset is 10% and SNR is greater than 4 dB, detection probability of the algorithm is greater than 98%, which is better than the existing DUW detection algorithms.  
Conceptualization, K.Z. and J.D.; methodology, K.Z., K.G. and Z.L.; software, K.Z.; validation, K.Z., K.G. and J.D.; writing--original draft preparation, K.Z.; writing--review and editing, K.Z., P.S. and J.D.; project administration, J.D., K.G. and P.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 61901417), the Science and Technology Key Project of Henan Province (No. 212102210173 and No. 21210221056), the Cutting-edge Science and Technology Innovation Project of the Key Research and Development Program of China (No. 2019 QY0302).  
Not applicable.
# Power Consumption Modeling of Discontinuous Reception for Cellular Machine Type Communications
## 2 Overview of DRX Mechanism  
In an LTE-A network, when a device is powered on, it usually has two radio resource control (RRC) states, which include RRC_Connected and RRC_Idle. The transition from RRC_Connected to RRC_Idle and vice versa depends on the packet activity. An Inactivity timer is triggered to push the device from the RRC_Connected to the RRC_Idle state. When there is no traffic activity until the expiry of the inactivity timer, the device transitions from the RRC_Connected to the RRC_Idle state. Moreover, the RRC inactivity timer is reset with any uplink or downlink data activity [9].  
In an LTE-A network, a device may be configured with a DRX mechanism by RRC in both connected and idle states. However, the functionality and configuration parameters in different states also vary. Since data activity mainly occurs in a connected state, the main target modes are in the active and DRX states. In addition to the RRC Inactivity timer, the network also triggers a DRX inactivity timer to move the device from active mode to DRX mode. When there is no packet activity, the device stays connected but transitions to the DRX mode. Furthermore, the DRX inactivity timer is reset when there is a packet activity [11]. In an LTE-A network, the RRC inactivity timer is set to be greater than the DRX Inactivity timer. An overview of the DRX mechanism in an LTE-A network is illustrated in Figure 1.  
In an LTE-A network, the DRX mode is configured by several parameters and timers, such as the on duration timer \((T_{on})\), DRX inactivity Timer \((T_{I})\), short cycle timer \((T_{SC})\), and long cycle timer \((T_{LC})\), and optionally, the number of short cycles \((N)\). \(T_{I}\) is restarted on any downlink packet indication in PDCCH (physical downlink control channel) or uplink packet transmission. Upon the expiry of \(T_{I}\), the device enters the short DRX cycle [12]. After \(N\) consecutive short DRX cycles, the device enters the long DRX cycle. If the short DRX cycle is not configured, the device enters the long DRX cycle directly. However, the short DRX cycle is important to be configured for delay-sensitive traffic as it reduces the overall latency and the wake-up delay. A brief description of various DRX parameters is provided in Table 1.
# DEM Simulation of the Load Transfer Mechanism of a GRPS Embankment with a Fixed Geogrid Technique
## 2 Establishment and Calibration of DEM Model
### Embankment Backfill Modeling  
In the laboratory models, three types of metal rods with different diameters were uniformly mixed according to the same volume ratio (1:1:1) as the embankment fill material. The length of the round steel rods was 60 mm and the diameters were 3, 4, and 5 mm, respectively. The biaxial rods were widely adopted to study the mechanical behavior of geotechnical materials in two-dimensional physical models ([PERSON] et al., 2017). Biaxial simulation tests, holding the same size (width \(\times\) height = 200 mm \(\times\) 220 mm) with the laboratory biaxial tests, were used to invert the micromechanical parameters based on the macromechanical response in this study. The embankment fill was simulated using unbonded two-dimensional disk particles. In the biaxial simulation tests, the numerical specimen was established with a porosity of 0.145. The confining pressures of 20, 30, and 40 kPa were applied through the wall servo mechanism. After the unbalanced force in the model dissipated to a stable state (<1 \(\times\) 10\({}^{-5}\)), biaxial loading at a constant velocity (\(v_{\mathrm{w}}\)) of 1 \(\times\) 10\({}^{-6}\) m/s was applied to the upper and lower walls.  
Figure 2 shows the comparison of the results between the simulation and the laboratory tests. The curve of deviatoric stress versus axial strain of the numerical simulation was in good agreement with the experimental results. The friction angle \(\phi\) of the assembly was calculated as follows:  
\[\sin\varphi=\frac{\sigma_{1}-\sigma_{3}}{\sigma_{1}+\sigma_{3}} \tag{1}\]  
where \(\sigma_{1}\) and \(\sigma_{3}\) represent the maximum and minimum principal stresses, respectively. According to the laboratory model tests ([PERSON], 2019), the friction angle of the assembly in the numerical model was 24\({}^{\circ}\). It is noteworthy that the friction angle of particles under the two-dimensional arrangement obtained in this way was generally lower than that of natural granular materials, and this phenomenon was reported in many laboratory tests ([PERSON], 2019; [PERSON], 2020).
## 1 Introduction  
The directed shaping of materials is a field of high interest for different applications as well as for fundamental questions regarding anisotropic crystallization. Zinc oxide (ZnO) specifically is of great interest in the scientific community due to its wide variety of properties and the applications derived from them, including semiconductor properties, piezoelectricity and UV absorbance [1]. The unique optical properties of ZnO are determined by the size and the morphology of its particles [2; 3; 4; 5]. This was the main motivation for researchers to prepare ZnO particles with different structures and shapes. ZnO particles with various structures were synthesized by different methods, such as chemical vapor deposition [4, 6], electrodeposition [7], and hydrothermal methods [2, 5, 8; 9; 10; 11]. Another approach enabling the preparation of \"tailor made\" particles is the use of organic polymer template. This approach involves the insertion of ZnO precursor into polymeric template particles, decomposition of the precursor within the template particles to obtain ZnO particles with controlled structure, predetermined by the template structure, and then removal of the template by dissolution or by thermal decomposition. Some examples for ZnO particle synthesis using this approach are the preparation of hollow ZnO microparticles by calcination of hydrozincite coated poly(styrene) beads [12], or poly(methyl methacrylate) impregnated by Zn(NO\({}_{3}\))\({}_{2}\)[13], and the synthesis of hexagonal shaped ZnO microparticles, nanowires and nanocubes by employing different \"molecular building blocks\" to direct the assembly of ordered metal-organic coordination-polymer templates [3, 14, 15].  
Our previous study described the formation of porous spherical shaped micrometer-sized ZnO and C-ZnO particles of narrow sized distribution by entrapping zinc acetate within the pores of uniform polydivinyl benzene (PDVB) porous microspheres. ZnO and C-ZnO microparticles were then prepared by decomposition of these zinc acetate entrapped PDVB particles in an air or inert atmosphere, respectively [16]. In the present study, we demonstrate the preparation of \"cauliflower-like\" ZnO microparticles, using the same PDVB porous template microspheres. ZnCl\({}_{2}\) was used as a precursor, and was entrapped within the PDVB template microspheres pores. The \"cauliflower-like\" ZnO microparticles were then prepared by thermal decomposition of the PDVB template microspheres, in the same manner as the ZnO porous spherical shaped microparticles. However, the different Zn precursors dictated different micro-environments that led to porous sphere ZnO microparticles in one case and \"cauliflower-like\" ZnO microparticles in the other, as will be discussed.
## 5 Case Study and Simulation Results  
To verify the performance of the proposed control scheme, at first, the frequency characteristic of the closed loop system is shown in Figure 7. In comparison with the frequency characteristic of an open loop system in Figure 3b, it is evident that the resonance mode of the controlled system has been damped completely. Furthermore, different simulation case studies are provided in this section. The switching frequency and the sampling rate both are 10 kHz, and the computational time in each sample is considered 100 us in a simulated system. The parameter values of the system and controllers are presented in Table 3.  
In this section, three cases are studied: (i) the converter is connected to an ideal grid with zero impedance, without any uncertainty in the system parameters, (ii) the converter is connected to the grid through non-zero impedance, and (iii) the grid impedance is non-zero, and there is uncertainty in the parameters of the system.  
In the following, the description of the operating condition in the scenarios are provided. At first, \(P_{dc}\) is 30 kW, \(Q^{ref}\) is 10 kVAr, and the desired DC-link voltage is 1000 V. To show the dynamic results, the following changes are applied to the operating point.  
1. at \(t=0.1\) s, \(Q^{ref}\) steps up to 20 kVar;
2. at \(t=0.2\) s, \(P_{dc}\) steps up to 25 kW;
3. at \(t=0.3\) s, \(V^{ref}_{dc}\) steps up to 1050 V;
4. at \(t=0.4\) s, \(V^{ref}_{dc}\) steps down to 1000 V.
# Multi-Person Action Recognition Based on Millimeter-Wave Radar Point Cloud
## 2 System Framework Design
### Data Preprocessing
The pseudocode for this filtering method is as shown in Algorithm 1:  
```
Input: frames--a list containing multiple frames Output: filtered_frames--a list of frames after filtering Calculate the range difference of x, y, z coordinates between adjacent frames FUNCTION calculate_range_difference (frame1, frame2): range1 = calculate_range (frame1) range2 = calculate_range (frame2) difference = absolute_value (range2-range1) RETURN difference Calculate the range of a frame FUNCTION calculate_range (frame): min_coords = min (frame, axis=0) max_coords = max (frame, axis=0) range = max_coords - min_coords RETURN range Calculate the differences between all adjacent frames FUNCTION frame_differences (frames): differences = [] for i from 1 to length (frames) - 1: difference = calculate_range_difference (frames[i - 1], frames[i]) differences.append (difference) RETURN differences Get the median of all adjacent frame range differences as the threshold FUNCTION get_median_threshold (differences): sorted_differences = sort (differences) median_diff = median (sorted_differences) RETURN median_diff Filter noise in point cloud data FUNCTION filter_noise (frames, threshold): filtered_frames = [] for i from 1 to length (frames) - 1: difference = calculate_range_difference (frames[i - 1], frames[i]) IF all_elements_less_or_equal (difference, threshold): filtered_frames.append (frames[i]) RETURN filtered_frames
```  
**Algorithm 1** Filtering Method Based on Frame Differences  
In this algorithm, for each pair of neighboring frames, it is necessary to compute the range of each coordinate axis and find the maximum and minimum values. Each operation requires traversing all the points, and the time complexity is \(O(m)\), assuming there are \(m\) points in each frame. Assuming a total of \(n\) frames, there are a total of \(n-1\) pairs of neighboring frames, so the time complexity of calculating the range difference between the neighboring frames is as follows:  
\[O((n-1)*3m)=O(nm)\]In point cloud data processing, calculating the range of points (maximum and minimum) in each frame is the primary operation, so the final time complexity of this algorithm is O(nm). As shown in Figure 2a, 2b and 2c, respectively, represents the original point cloud, the point cloud after pass-through and statistical filtering, and the final point cloud after inter-frame difference filtering. The inter-frame difference filtering method effectively eliminates the noise caused by multipath interference and noise that exceeds the normal range of human action.