esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Setup  
### Corrosion Resistance of the Resin-Protected Piezoceramic Transducers  
In the steel bar corrosion detection experiments, the piezoceramic transducers and the steel bar were simultaneously immersed in the sodium chloride solution. To ensure that the piezoceramic transducers worked properly without corrosion, it was necessary to experimentally study the property of the piezoceramic transducers in the solution. For this experimental study, two pieces of piezoceramic transducers were bonded on both sides of a piece of organic glass disk by using an ultrasonic vibrator adhesive, as shown in Figure 5a. Then, an epoxy resin was used to wrap them completely, in order to protect the piezoceramic transducers and the connection wires, as shown in Figure 5b.  
Figure 4: Chemical process of accelerated steel bar corrosion.  
Figure 5: The piezoceramic disk transducers.  
The piezoceramic transducers were immersed into a container filled with a solution of 20% NaCl. A Labview program was used to emit Gaussian pulse signals, whose frequencies were set as 100 KHz, 70 KHz, 60 KHz, 50 KHz, and 30 KHz, and the corresponding amplitudes were 10 V, 9 V, 7 V, 8 V, and 5 V, respectively. The focused signals were recorded after 55 h using the Labview program.  
Figure 6 shows the relationship between the focused signals and the corrosion time. Obviously, the amplitude of the voltage did not produce any obvious changes in the signal, and the transducer was stable in the NaCl solution. Therefore, the piezoceramic transducers that were protected with epoxy resin were not affected by corrosion in the NaCl solution, and remained working properly.
## 6 Numerical Simulation
### Tracking of Simple-Maneuvering Target
#### 6.1.1 Scenario 1: Constant Acceleration Target  
In this scenario, the true motion of a target is set as  
\[x_{\text{t}k}=16(kT)^{2}. \tag{52}\]  
Figure 3a shows the simulation results for the low gain case (\(\delta=0.1\)). The steady-state errors of the \(\alpha\)-\(\beta\), \(\alpha\)-\(\beta\)-\(\gamma\), and \(\alpha\)-\(\beta\)-\(\gamma\)-\(\delta\) filters were 320 m, 5.48 m, and 8.27 m, respectively. Because the target motion includes relatively high acceleration, the \(\alpha\)-\(\beta\) filter cannot track the target with sufficient accuracy. In contrast, the other two filters track this target with a higher accuracy. The best performance is achieved with the \(\alpha\)-\(\beta\)-\(\gamma\) filter because a constant acceleration model is assumed for the prediction process. Although the \(\alpha\)-\(\beta\)-\(\gamma\)-\(\delta\) filter can also track accelerating motion, its larger smoothing performance index compared with that of the \(\alpha\)-\(\beta\)-\(\gamma\) filter demonstrated in Figure 2 leads to a larger error. In addition, the variance of the errors in the \(\alpha\)-\(\beta\) filter is small compared with that of other filters. This is because the \(\alpha\)-\(\beta\) filter largely suppresses random errors compared with other filters, as indicated in the analysis results.  
Figure 3b shows the simulation results for the high gain case (\(\delta=1.5\)). The steady-state errors of the \(\alpha\)-\(\beta\), \(\alpha\)-\(\beta\)-\(\gamma\), and \(\alpha\)-\(\beta\)-\(\gamma\)-\(\delta\) filters were 24.4 m, 17.2 m, and 24.5 m, respectively. The performance of the \(\alpha\)-\(\beta\)-\(\gamma\)-\(\delta\) is worse in this case. This is because of the larger deterioration in the smoothing performance, as demonstrated in Figure 2. In addition, although the accuracy of the \(\alpha\)-\(\beta\) filter decreases due to the high gain setting, it is still quite high. Thus, in this case, the \(\alpha\)-\(\beta\)-\(\gamma\) filter achieves a better accuracy.
## 2 Materials and Methods
### Experimental Testing  
Movement kinematics were measured using the MVN BIOMECH inertial sensor-based motion capture system (Xsens Technologies B.V., Enskode, The Netherlands), which is equipped with 11 inertial sensors, set at a sampling frequency of 120 Hz. Sensors were placed on the head, sternum, sacral bone, and symmetrically on both upper limbs, to include the shoulder blades, arms, forearms and palms. Initial experiments involved the collection of anthropometric measurements and calibration of the system. Following this, kinematic measurements were recorded as participants drank from a mug. Each participant was assessed whilst sitting on a standard chair, with a seat height of 45 cm, at a table, which was 80 cm high. The starting position of the mug was clearly marked on the table surface and participants' were tasked to drink from the mug naturally, without prior instruction. The only requirement was that the mug had to be returned to its marked starting position. The whole movement was divided into 3 phases, the lifting phase, the drinking phase, and the lowering phase, when the mug was returned to its starting position. Each participant performed the task of drinking from the mug three times. The subjects performed the activity with both hands. First with the dominant hand, then with the non-dominant hand.  
This experiment allowed for the determination of the course of the kinematics of motion in the upper limb joints and the arrangement of particular segments of the spine. The kinematic motion waveforms of each repetition performed with the dominant and non-dominant hand were the basis for determining 30 input parameters (30 temporal and kinematic motion parameters identified in consultation with clinicians--see Section 2.4 for details). The 30 defined parameters (as single numerical values) were determined separately for each repetition of each subject, for both the dominant and non-dominant upper limb. The parameter results obtained for 3 repetitions (for each limb of the test subject) were not averaged. All values obtained for the tested subjects were included in the results database. The results database collected therefore included the values of the30 parameters obtained for all repetitions performed with the dominant and non-dominant limb, for all healthy subjects (reference group) and patients. The results database therefore included 216 results for each defined parameter from P1 to P30. In total, the results database included 6480 values (36 subjects \(\times\) 2 upper limbs \(\times\) 3 repetitions \(\times\) 30 parameters). This database was then used in calculations to select the parameters of the 3 defined criteria (M1, M2, M3). The UBI value was then determined for all 3 repetitions performed with the subject's dominant and non-dominant limb. The evaluated value of the UBI index for the tested person is the average value of these 3 values obtained for subsequent repetitions.
# Phased Antenna-Array Synthesis Using Taylor-Series Expansion and Neural Networks
## 3 The Taylor Method
### Synthesizing Antenna Arrays Using Taylor One-Parameter Distribution
#### 3.1.1 Define Radiation Pattern Requirements  
In the first step of antenna synthesis, it is essential to define how the antenna should radiate. This includes specifying the width of the main beam (which affects angular coverage), the acceptable levels of sidelobes (to reduce interference), and the placement of nulls (to suppress unwanted directions). These criteria guide the selection of parameters in the Taylor distribution, ensuring that the final design meets application-specific performance needs, such as high resolution in radar or reduced interference in wireless systems [45; 46]. The main lobe width defines the angular spread of the primary radiation direction, influencing the antenna's coverage area and directionality. Meanwhile, the side lobe level indicates the level of radiation in directions other than the main lobe, with lower levels indicating better rejection of unwanted radiation. Additionally, the placement of nulls in the radiation pattern can be strategically determined to minimize interference or steer the beam away from sources of noise or interference. These requirements serve as guiding principles for selecting design parameters, such as \(\alpha\) in the Taylor one-parameter distribution, shaping the antenna array's behavior to meet specific performance criteria essential for its intended application.  
* Determine the desired characteristics of the radiation pattern, such as the side lobe level, the main lobe width, and null placement.
* These requirements will guide the selection of the design parameter \(\alpha\).
# GPON PLOAMd Message Analysis Using Supervised Neural Networks
## 4 PLOAMd Analysis Using Various Recurrent Neural Networks and Results Discussion  
Considering the shape of the data samples, the suitable neural network for protocol analysis is based on recurrent neural networks (RNNs). They pass their internal state across the analyzing sequence [51]. RNNs are also able to analyze variably long sequence samples. RNN layers used in following experiments are: SimpleRNN, LSTM, and GRU. These are the most common RNNs, which have different tools for sequence analysis, and all of them are implemented by Keras application  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Message ID** & **1** & **3** & **4** & **5** & **6** & **8** & **10** & **11** & **14** & **18** & **20** & **21** & **24** \\ \hline Count & 82 & 88 & 114 & 94 & 54 & 286 & 86 & 545,934 & 93 & 103 & 63 & 54 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Filtered PLOAMd message counts from captured data traffic.  
programming interface (API). A simple RNN might be insufficient for analysis of long sequences, and, therefore, this paper verifies various types of RNNs (e.g., LSTM and GRU). LSTM enhances an RNN with an internal memory capable of holding information in longer sequences and a gating mechanism suitable for input/output data manipulation [52]. GRU is similar to an LSTM cell, but less complex and with fewer gates resulting in fewer learning parameters [53].  
All experimental models classify input message sequences into two classes. Thus, all models are binary classifiers. The first class represents a message sequence that keeps the rules of GPON protocol. The second class represents an abnormal or non-standard sequence. The final learned model is used for abnormal or faulty traffic identification in a real GPON network.
# Mining the Frequent Patterns of Named Entities for Long Document Classification
## 4 Experiments
### Baseline Methods
#### 4.2.2 Deep Learning-Based Methods  
* Word2 Vec-based classification methods [42] utilize distributed representations of natural words, Word2 Vec. Each word in the vocabulary is represented as a vector in a high-dimensional space. Words with similar meanings are close in vector space.
* Doc2 Vec-based classification methods [43] employ Doc2 Vec to represent the document, which is an unsupervised algorithm that learns vector representations of documents.  
Figure 5: The text length statistics of datasets. The c-axis in the figure represents the length of text, and the y-axis represents the quantity of text. Obviously, the average lengths of texts in CCF-BDCI 2020 and THUNews are longer than that of CCKS 2021. Specifically, the average lengths in the three datasets are 592.2, 969.6, and 941.4; and the medians are 289, 711, and 698, respectively. **(a)** The text length statistics of CCKS 2021; **(b)** The text length statistics of CCF-BDCI 2020; **(c)** The text length statistics of THUNews.  
TextCNN [44] applies convolutional neural networks (CNN) with different kernel sizes to extract different features for text classification.
* A recurrent neural network (RNN) [49] is a powerful method for text, string, and sequential data classification by assigning more weights to the previous data points of a sequence.
* Long short-term memory (LSTM) [50] is a special type of RNN that addresses text classification by preserving long term dependency in a more effective way in comparison to the basic RNN.
* BiLSTM [63] is built on LSTM, adding a backward LSTM, to encode information from back to front to obtain the semantics of the full text.
* HAN [59] is a hierarchical attention network for document classification. The HAN constructs word-level and sentence-level features, respectively. The attention mechanism is applied to assign weights to different words and sentences.
* BERT [57] is a pre-trained language model proposed by Google that extracts the semantics of texts. When dealing with long documents, we use truncation methods to get tokens for BERT.  
For the Word2 Vec-based and Doc2 Vec-based classification methods, we employed Word2 Vec and Doc2 Vec, respectively, to obtain the vector representations of the documents, and utilized the Naive Bayesian classifier to obtain the classes of the documents. Finally, the classification performance was measured by accuracy rate (ACC) and marco-F1 score.
# Using Interval Analysis to Compute the Invariant Set of a Nonlinear Closed-Loop Control System
## 6 Application Scenarios
### Pendulum Motion on a Circle in the Phase Plane
#### 6.1.3 Computation of the Invariant Set  
The invariant set \(S_{1}\) of the closed-loop system, where (33) is true, can be calculated via interval computation (see Figure 4 in which the same color code as in Figure 3 is employed).  
The vector field in Figure 5 supports this result. As is shown in these figures, the pendulum converges to the circle or the equilibrium point at the origin, that is inside the circle due to the Poincare fixed point theorem.  
Figure 4: Invariant set of the pendulum, where the sliding surface is a circle.
## 3 Global Equations and Parameters
### Electromagnetic Equation  
The governing equations of the numerical model used in this paper are obtained with the consideration of assumptions that the conduction current is dominated, to describe electromagnetic eddy-current problems extracted from [PERSON]'s equations, which describe the basics of electromagnetic theory given as follows [33; 34]:  
\[\overset{\rightarrow}{\
abla}\times\overset{\rightarrow}{H}=\overset{ \rightarrow}{J} \tag{2}\]  
\[\overset{\rightarrow}{\
abla}\times\overset{\rightarrow}{E}=-\frac{ \partial\overset{\rightarrow}{B}}{\partial t} \tag{3}\]  
\[\overset{\rightarrow}{J}=\overset{\rightarrow}{J}_{s}-\sigma\overset{ \rightarrow}{\partial\overset{\rightarrow}{A}} \tag{4}\]  
Using the relation \(\overset{\rightarrow}{B}=\overset{\rightarrow}{\mu}\overset{\rightarrow} {H}\) and the magnetic flux density potential \(\overset{\rightarrow}{B}=\overset{\rightarrow}{\
abla}\wedge\overset{ \rightarrow}{A}\). After replacing we obtain the 2D electromagnetic harmonic equation in terms of the Magnetic Vector Potential (MVP) with only the z direction component \(\overset{\rightarrow}{A}(0,0,A_{Z})\):  
\[\overset{\rightarrow}{\
abla}\times\left(\frac{1}{\mu}\bigg{(}\overset{ \rightarrow}{\
abla}\times\overset{\rightarrow}{A}_{Z}\bigg{)}\right)-j \omega\sigma\overset{\rightarrow}{A}_{Z}=-\overset{\rightarrow}{J}_{SZ} \tag{5}\]  
where: \(\overset{\rightarrow}{J}\)--the total current density, \(\overset{\rightarrow}{J}_{SZ}\)--the source current density, \(\mu\)--is magnetic permeability in the specimen, HAZ and in the weld zone respectively, \(\sigma\)--is the electrical conductivity, \(\omega\)--the angular frequency.  
In the Heat affected zone and the weld zone, the magnetic permeability is noted with indices (1), (2) and given as, \(\mu_{1}=[\mu\prime_{a}\ldots\ldots\ldots\ldots\mu_{THAZ}]\) and \(\mu_{2}=[\mu_{a}\ldots\ldots\ldots\ldots\ldots\mu_{T_{W}}]\). \(THAZ\) and \(T_{W}\) denote the total number of triangular elements obtained from the finite element meshing in the HAZ zone and weld area, respectively.  
\[[M]+j\omega[N][A]=[F] \tag{6}\]  
with: \([M]\)--stiffness matrix, \([N]\)--dynamic matrix, \([A]\)--unknowns vector and \([F]\)--source vector.
## 1 Introduction: Recombination Processes in Perovskites
The \"equilibrium state\" also leads to unavoidable radiative charge recombination of electron-hole pairs causing the recombination current density to be non-zero. Since the absorptive transition is allowed, also the reverse radiative transition is allowed. In fact, for the ideal solar cell material, only the unavoidable radiative charge recombination should occur, and no non-radiative charge recombination whatsoever: it should be eliminated and is avoidable. The Shockley-Queisser limit is useful to compare existing and novel photovoltaic technologies and their performances. Figure 4a shows the record-efficiency cells for various materials plotted against their band gap, together with their regions, expresses as a percentage of the S-Q limit (in red [below 50%], in green [between 50% and 75%] and in blue [\(>\)75%]). Most photovoltaic materials are currently below 75% of their theoretical maximum efficiency while perovskite technology, with a record efficiency of 25.2%, is starting to compete with commercialized technologies like \(c\)-Si (26.7%) [10; 11; 1]. GaAs clearly still is highest in the graph. Figure 4b shows that light management (which influences \(J_{\text{SC}}/J_{\text{SQ}}\)) is relatively good in perovskite, meaning that current losses are low (93% of the maximum current for that band gap). The carrier management, the ratio of the FF \(\times\)\(V_{\text{OC}}\) to their theoretical maximum (FF\({}_{\text{SQ}}\times\)\(V_{\text{SQ}}\)), is lower (0.80) for perovskites. Thus, in order to engineer perovskite towards the ultimate power conversion efficiency, optimization of the voltage (which is influenced by charge carrier management) is of great importance. Figure 4: Record-efficiency cell parameters compared to the detailed-balance limit. **(a)** Blue dots show materials that are closer to the Shockley–Queisser (SQ) detailed-balance limit (black line). Green and red dots (area) show the materials that are performing less good. **(b)** SQ-normalized current vs. normalized voltage (\(\times\) fill factor) indicates in which aspects materials could be improved: CdTe is close to the current limit, but loses out on voltage (which can be optimized by charge carrier management). GaInP gives an excellent voltage, but needs better light management (converting photons into electrons/current) to be improved. Reproduced with permission from _Science_**2016**, 352, aad4424. Updated graphs, taken from the website related to Reference [10].
# Saturation Based Nonlinear FOPD Motion Control Algorithm Design for Autonomous Underwater Vehicle
###### Abstract  
The application of Autonomous Underwater Vehicle (AUV) is expanding rapidly, which drives the urgent need of its autonomy improvement. Motion control system is one of the keys to improve the control and decision-making ability of AUVs. In this paper, a saturation based nonlinear fractional-order PD (FOPD) controller is proposed for AUV motion control. The proposed controller is can achieve better dynamic performance as well as robustness compared with traditional PID type controller. It also has the advantages of simple structure, easy adjustment and easy implementation. The stability of the AUV motion control system with the proposed controller is analyzed through Lyapunov method. Moreover, the controlled performance can also be adjusted to satisfy different control requirements. The outperformed dynamic control performance of AUV yaw and depth systems with the proposed controller is shown by the set-point regulation and trajectory tracking simulation examples.  
fractional calculus; FOPD controller; underwater vehicle; motion control
###### Abstract  
Opportunistic ad hoc networks are characterized by intermittent and infrastructure-less connectivity among mobile nodes. Because of the lack of up-to-date network topology information and frequent link failures, geographic routing utilizes location information and adopts the store-carry-forward data delivery model to relay messages in a delay-tolerant manner. This paper proposes a message-forwarding policy based on movement patterns (MPMF). First, one- and two-hop location information in a geographic neighborhood is exploited to select relay nodes moving closer to a destination node. Message-forwarding decisions are made by referring to selected relay nodes' weight values obtained by calculating the contact frequency of each node with the destination node. Second, when relays in the vicinity of a message-carrying node are not qualified due to the sparse node density and nodal motion status, the destination's movement and the location information of a one-hop relay are jointly utilized to improve the message-forwarding decision. If the one-hop relay is not closer to the destination node or moving away from it, its centrality value in the network is used instead. Based on both synthetic and real mobility scenarios, the simulation results show that the proposed policy performs incomparable efforts to some typical routing policies, such as Epidemic, PRoPHEtv2, temporal closeness and centrality-based (TCCB), transient community-based (TC), and geographic-based spray-and-relay (GSaR) routing policies.  
Message forwarding; geographic routing; relay selection; data dissemination; delay-tolerant networks; mobile opportunistic networks +
Footnote †: journal:  
00 000-0001-6697-2233][PERSON]  
1 Department of Communication Engineering, National Central University, Taoyuan City 32001, Taiwan;  
[EMAIL_ADDRESS] (H.Z.); [EMAIL_ADDRESS] (C.-L.H.)  
1 Correspondence: [EMAIL_ADDRESS]
## 3 Results
### Results of the Meta-Analysis  
#### 3.4.1 Meta-Analysis Results on Jump Performance  
Of the 26 studies included in the final analysis, 17 studies, encompassing a total of 252 subjects, compared the acute effects of different CAs on the CMJ height, as shown in Supplementary Files S6-S8. The heterogeneity test results for Supplementary Files S6 and S7 indicated \(l^{2}=0\%\), suggesting no heterogeneity among the studies; hence, a fixed-effects model was utilized for the analysis. On the other hand, Supplementary File S8 showed \(l^{2}=58.08\%\), indicating the presence of heterogeneity, which led to the employment of a random-effects model for the analysis. The three forest plots signify that the PAPE from the exercise interventions effectively enhanced CMJ height for the athletes [Hedges's \(g=0.2\), 0.2, and 0.23; 95%CI: (0.05, 0.34), (0.02, 0.39), and (0.05, 0.41); \(p=0.01\), 0.03, and 0.01 (<0.05)].  
Subgroup analyses of the CAs were executed, differentiating among resistance exercises, pylemetric exercises, and mixed exercises. The results of the subgroup analyses for Table 1A-C revealed that resistance exercises as a CA can effectively increase CMJ height in athletes [\(p=0.003\), 0.032, and <0.001 (<0.05)]. The pylemetric exercises' \(p\)-values = 0.154 and 0.805 (>0.05), whereas Table 1C showed a significant difference with a \(p\)-value = 0.027 (<0.05); however, the negative effect size ([PERSON]'s \(g=-0.177\)) and 95%CI (\(-0.335\), \(-0.020\)) indicated a decrease in CMJ height post-intervention. The mixed exercises' \(p\)-values = 0.409 and 0.429 (>0.05), indicating that neither pylemetric nor mixed exercise interventions acutely enhanced CMJ height in athletes effectively.  
The funnel plot results were not perfectly symmetrical, as illustrated in Figure 1A-C. The [PERSON]'s test for Figure 1A found no significant publication bias (\(p=0.622>0.1\)). In Figure 1B, the [PERSON]'s test indicated the presence of publication bias (\(p=0.03<0.1\)); however, after applying the trim-and-fill method, this change was not significant, and the results were considered acceptable [[PERSON]'s \(g=0.204\); 95%CI: (0.198, 0.516)]. Conversely, in Figure 1C, the [PERSON]'s test revealed a significant publication bias (\(p=0.0006<0.1\)); after the trim-and-fill method was employed, which was a significant alteration, the results should be interpreted with caution [[PERSON]'s \(g=0.232\); 95%CI: (0.059, 0.405) to [PERSON]'s \(g=0.035\); 95%CI: (\(-0.035\), 0.234)].