esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Network Performance Analysis: NB-IoT vs. LoRaWAN
### Throughput
#### 7.2.2 Security  
NB-IoT security capabilities are based on the radio access, network core, and user data encryption of an LTE system. As detailed in our previous study on the security of IoT networks [7], LTE-based technologies use encryption, authentication, and integrity protection mechanisms to provide data, and access and non-access stratum signalling confidentiality, integrity and availability between the UE and core network, the UE and radio network (RN), respectively. In our network, as shown in Figure 15, the MME uses NAS signalling security commands to secure UE and MME messages.  
Security is also an important part of LoRaWAN, primarily in terms of ensuring an authenticated connection, integrity protection, and confidentiality are present in the  
Figure 15: UE security capabilities.  
communication process. Three layers of security are common in the LoRaWAN: device; network; and application security. Device security ensures that only authenticated IoT device connection attempts are accepted by the network. For NB-IoT, the network and application layer security ensures the authenticity of nodes and the confidentiality of data, respectively. To deliver confidentiality in LoRaWAN, in this scenario, LoRa messages should be encrypted using AES, an encryption algorithm supported in IoT modules such as the Fipy. Due to the limitations of release 13 UEs in carrying out successful transmissions at 50 bytes as shown in Figure 13, we will present the evaluation of these security mechanisms using higher standard UEs in future work. Security techniques contribute a substantial data overhead which will be better processed using IoT devices with higher processing power. The security of IoT networks could in many cases be improved by implementing security measures such as locally performing server tasks where possible, to reduce exposure of data to external servers and the internet; ensuring messages are content length-padded to prevent the disclosure of side information through the length of messages; ensuring that suitably strong keys are used, and generated from high-quality random numbers; security keys loaded on the end device in a protected area of memory; along with other good security practices recommended for LPWANs [36].
# Using Transfer Learning to Realize Low Resource Dungan Language Speech Synthesis
## 5 Conclusions  
This study extends our prior research by implementing a transfer learning-based Mandarin speech synthesis and a low-resource Dungan speech synthesis under the Tacotron2+WaveRNN framework. We also developed a comprehensive Dungan text analyzer. Objective and subjective experiments revealed that the transfer learning-based Dungan speech synthesis under the Tacotron2+WaveRNN framework outperformed alternative methods and the monolingual Dungan speech synthesis framework. Furthermore, transfer learning did not compromise the speech quality and readability of the synthesized low-resource Dungan speech. Therefore, our approach holds significant potential for developing speech synthesis systems for low-resource minority languages.  
Numerous breakthroughs have been achieved in TTS based on deep neural networks. We have noticed that some new speech synthesis methods [50, 51, 52] have been proposed recently. Motivated by recent advancements in auto-regressive (AR) models employing decoder-only architectures for text generation, several studies, such as VALL-E [53] and BASE TTS [54], apply similar architectures to TTS tasks. These studies demonstrate the remarkable capacity of decoder-only architectures to produce natural-sounding speech. These studies demonstrate the remarkable capacity of decoder-only architectures to produce natural-sounding speech. Future research will focus on using these new methods to improve the quality of Dungan language speech synthesis, reduce the Dungan corpus size, and achieve speech synthesis for Dungan languages using a larger corpus. Additionally, multitask learning will be explored to realize speaker-independent scenarios and enhance the emotion of synthesized Dungan speech.  
Conceptualization, M.L. and H.Y.; formal analysis, H.Y. and R.J.; data curation, M.L. and R.J.; writing--original draft preparation, M.L. and R.J.; writing--review and editing, H.Y. and M.L.; supervision, H.Y.; funding acquisition, H.Y. All authors have read and agreed to the published version of the manuscript.  
The research is supported by the research fund from the National Natural Science Foundation of China (Grant No. 62067008).  
Not applicable for studies not involving humans or animals.  
Not applicable.  
We used two training datasets in the manuscript. One is a publicly available Mandarin dataset (THCHS-30), and the other is a Donggan dataset, including speech and text. The former has been public and can be accessed from [[http://www.openslr.org/18/](http://www.openslr.org/18/)]([http://www.openslr.org/18/](http://www.openslr.org/18/)) (accessed on 16 June 2024). The latter is a self-constructed dataset and is not publicly available. However, the data will be made available on request.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 4 Conclusions  
We have shown that 1,2-diol protection, an indispensable synthetic protocol during the construction of molecular targets containing reactive functional groups, can be easily achieved under heterogeneous conditions using biomass-derived carbocatalysts. An extensive benchmarking of different modified catalysts unveiled the superior performance of a sulfonated hydrothermal carbon obtained from saccharose (**HT-S**). All catalysts and products have been thoroughly characterized by analytical and spectroscopic methods. It is worth mentioning that the catalyst **HT-S** featured a higher oxygen content compared to the other catalysts, and sulfur doping did not alter its surface morphology significantly. Remarkably, the resulting acetonides could be readily isolated without requiring chromatographic purification. Two enabling technologies were also employed to improve mass transfer, thus accelerating the heterogeneous reaction. Firstly, ultrasonic activation, using cavitation intensifying bags, led to completion within one hour at 25 \({}^{\circ}\)C, while conventional reactions took longer at higher temperatures. Then, the protocol was performed under continuous flow conditions by loading the **HT-S** catalyst into a packed-bed reactor. In the flow reactor, optimal reaction conditions were set at 60 \({}^{\circ}\)C with a residence time of only 30 s, \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Entry** & **T (\({}^{\circ}\)C)** & \
\begin{tabular}{c} **Flow Rate** \\ **(mL/min)** \\ \end{tabular} &
\begin{tabular}{c} **Residence** \\ **Time (min)** \\ \end{tabular} & **Yield (\%) a** \\ \hline
1 & 25 & 0.125 & 5 & 16 \\
2 & 40 & 0.125 & 5 & 98 \\
3 & 60 & 0.125 & 5 & 98 \\
4 & 80 & 0.125 & 5 & 96 b  \\
5 & 40 & 0.208 & 3 & 94 \\
6 & 40 & 0.312 & 2 & 97 \\
7 & 40 & 0.623 & 1 & 88 \\
8 & 60 & 0.208 & 3 & 98 \\
9 & 60 & 0.312 & 2 & 98 \\
10 & 60 & 0.623 & 1 & 98 \\
11 & 60 & 1.246 & 0.5 & 99 \\ \hline \hline \end{tabular}  
* Determined by \({}^{1}\)H NMR. b Small amounts of unknown impurities were observed at 80 \({}^{\circ}\)C by \({}^{1}\)H NMR monitoring.  
\end{table} 
Table 10: Optimization of the packed bed temperature and residence time for the continuous flow protection of diol **10** with **HT-S**. Figure 14: Evaluation of the catalyst stability under continuous flow operation. affording the acetonide a nearly quantitative yield. This result holds promise for scaling up and industrial applicability of diol protection under heterogeneous acid-catalysis. In addition, the catalyst could be recycled without losing activity and had excellent stability in flow mode as well.
# Study on the Steepness of Landslide-Induced Wave and Nonlinear Motion of Container Ships
## 4 The Propagation Law of Landslide-Induced Surges
### The Wave Steepness Characteristics of Landslide-Induced Surge Waves  
Based on the basic definition of landslide-induced surge wave steepness, theoretical derivation was carried out in this experiment. By measuring the period and wave height of the landslide surge wave, the basic expression of the landslide-induced surge wave steepness and its variation rule were derived. During the test, the instrument was calibrated by a zero crossings method, which takes the average value of the wave water level as the zero line to make a graph of the wave shape and the average water level. When the wave curve rises and intersects with the zero line as the first point, the next wave curve rises and intersects with the zero line as the second point. If the horizontal axis is the time axis, the interval between the first and second points is the period, and the difference between the maximum and minimum values on the vertical axis is the wave value of the cycle. The research has shown that wave steepness is defined as the ratio of wave height to wavelength. Because the wave length is difficult to measure directly in practical engineering, it is difficult to determine the magnitude of wave steepness. In practical engineering, the wavelength is always estimated by the period with the help of the dispersion relation of the linear water wave theory. The dispersion relation is for each component wave, and it is reasonable to use it to convert the period and wavelength. The estimated value of wave steepness that has been proposed is mainly based on [PERSON] (2016) or [PERSON] et al. (2016), which is estimated by period in literature.  
\[S_{t}=\frac{2\pi H}{gT^{2}}, \tag{5}\]  
where, g is the gravitational acceleration, H is the wave height, T is the period, and the subscript \(S_{t}\) represents the wave steepness estimated by the period.
###### Abstract  
Active noise control can be used to reduce the scattered sound of a reflecting object to make it invisible to incident acoustic waves. For the multi-zone active noise control of scattered sound from an infinite rigid cylinder, an active control strategy is proposed that combines the least absolute shrinkage and selection operator (LASSO) algorithm with constraint points and regularized least squares (RLS) algorithm. The proposed control strategy is used to promote control performance through optimizing the secondary loudspeaker placement of an active noise control system. Compared with the RLS algorithm employing the uniformly placed loudspeakers and the traditional LASSO algorithm, the proposed strategy has better reduction performance both in the forward-scattered and backward-scattered sound target areas, and there is less sound amplification in the far field. From 400 Hz-1100 Hz, the proposed strategy provides a 5 dB-16 dB reduction performance advantage in the target area compared to the RLS algorithm employing uniformly placed loudspeakers.  
active noise control; scattered sound control; optimized loudspeaker placement; LS algorithm; LASSO algorithm +
Footnote †: journal: Applied Sciences
# Gyro Error Compensation in Optoelectronic Platform Based on a Hybrid ARIMA-Elman Model
## 4 Hybrid ARIMA-Elman Model
### Generalization Test  
In order to test the generalization ability of the method, another type of gyro data has been collected. The gyro signal is shown in Figure 11. According to the above-mentioned method, similar steps have been carried out concerning the new gyro error. The results, after compensation, are shown in Figure 12, and the relative comparison is provided in Table 2. From Figure 12, we can see that the red and green lines are basically coincident.  
Figure 11: The original gyro data.  
Figure 10: Allan variance after compensation.  
The change of error coefficients, before and after compensation, with different methods are shown in Table 2. In the table, all of the coefficient changes of the main types of noise contained in the gyro signal (quantization noise, angle random walk noise, zero offset instability noise, rate random walk noise, and rate ramp noise) have been listed. We can see that all of the item values have reduced after compensation with different methods. Among the three methods (single ARIMA model, single Elman model, and hybrid ARIMA-Elman model), the change with the hybrid model is the most significant one. The former two algorithms achieve similar results, which are close to the original values. For example, for the quantization noise, the coefficient of the original signal is 28.4648. After compensation, the value changes to be 22.0082, 20.1009, and \(-0.1692\), respectively.  
From Figure 12 and Table 2, it can be easily seen that, compared with a single linear or nonlinear approximation model, the Allan variance coefficients are the smallest when using the hybrid model. Therefore, it can be concluded that the hybrid method has good generalization ability and can be easily applied in similar fields, which is very important particularly in engineering fields.
# An Optimal Fuzzy PID Controller Design Based on Conventional PID Control and Nonlinear Factors
## 2 The Proposed Optimal Fuzzy PID Controller Design
### The Equivalent FLC from a Conventional PID Controller  
For a conventional PID controller in Equation (1), the output \(u(t)\) and the three inputs \(e(t)\), \(\int e(t)\), and \(\dot{e}(t)\) can be thought of as fuzzy variables in the FLC design. It is assumed that the operating ranges for \(u(t)\), \(e(t)\), \(\int e(t)\), and \(\dot{e}(t)\) are \(OR_{u}=[-a_{u},\ a_{u}]\), \(OR_{e}=[-a_{e},\ a_{e}]\), \(OR_{i}=[-a_{i},\ a_{i}]\), and \(OR_{d}=[-a_{d},\ a_{d}]\), respectively. In this subsection, some important results from our previous work [16] on the equivalence between fuzzy PID controllers and conventional PID controllers are excerpted to clarify the parameter definitions and notations.  
For input fuzzy variables \(e(t)\), \(\int e(t)\), and \(\dot{e}(t)\), each variable is represented by equally-spaced and triangular-shaped \(m\) fuzzy sets, as shown in Figure 1. As for the output fuzzy variable \(u(t)\), it is fuzzified by \(3m-2\) singleton MFs.  
With the overall fuzzy rules represented by the sliced cube fuzzy associative memory (FAM), as shown in Figure 2, and each fuzzy rule defined as:  
\[IF\ e(t)\ \text{is}\ E_{i}\ \text{and}\ \int e(t)\ \text{is}\ I_{j}\ \text{and}\ e(t)\ \text{is}\ D_{k}\ \ \ \ THEN\ u(t)\ \text{is}\ U_{l},\ l=i+j+k-2, \tag{2}\]  
and the crisp output \(u(t)\) of the corresponding equivalent fuzzy logic controller is given by:  
\[u(t)=\frac{a_{u}}{3a_{e}}e(t)+\frac{a_{u}}{3a_{i}}\int e(t)dt+\frac{a_{u}}{3a_ {d}}\frac{de(t)}{dt}, \tag{3}\]  
which implies a linear PID controller with:  
\[K_{P}=\frac{a_{u}}{3a_{e}},\quad K_{l}=\frac{a_{u}}{3a_{i}},\quad\text{and} \quad K_{D}=\frac{a_{u}}{3a_{d}}. \tag{4}\]  
We can find that Equation (4) has no relation with \(m\), and it is worth mentioning that the fuzzy inference and defuzzification processes should be limited to some specific operations [16]. Moreover, for the equivalent fuzzy PID Controller corresponding to a conventional PID controller, there are four parameters \([a_{e},\ a_{i},\ a_{d},\ a_{u}]\) in all for tuning.  
Figure 1: Graphical definition of MFs for fuzzy variables, \(e(t)\), \(\int e(t)\), \(\dot{e}(t)\), and \(u(t)\).
## 3 Methodology and Theoretical Background
### FE Simulation Considerations  
The 2D FE simulations were used to evaluate the performance of the selected LSHT machines, utilizing the commercial package ANSYS Electronics Desktop 2024R1 to directly evaluate electromagnetic variables and the commercial package ANSYS Workbench 2024R1 to include multi-objective optimization. A quad-layer mesh in the airgap was considered, along with a dense mesh in the tooth tip and slot areas, as illustrated in the following sections. No periodicity condition was applied in order to account for asymmetric faults, and an outer boundary of zero magnetic potential was considered near the outer diameter, which allows for the establishment of stray flux lines and confines the active solving area. Additionally, materials were assigned based on manufacturing data retrieved from ANSYS databases (Granta Producers Materials Data for Simulation). Electromagnetic efficiency computations in these FE simulations include stator winding losses, stator iron losses, magnet losses (if applicable), rotor solid conductor losses (if applicable), and rotor iron losses. Stator winding losses are calculated from the phase stator current and stator resistance. Stator and rotor iron losses, when laminated, are computed using the coefficients of hysteresis loss, excess loss, direct current loss, and eddy current loss, based on an electrical steel loss model. Solid losses are directly computed by the FE software, and end-winding effects are addressed by including pre-computed end-winding leakage inductance values. In turn, mechanical losses were neglected in order to maintain a focused scope on the electromagnetic performance. In Figure 5, a flowchart of the treatment of FE results is provided.
# Optical Fiber Sensor with Stable Operating Point for AC Magnetic Field Measurement
###### Abstract
Because of its small size, simple fabrication, convenient and stable operation, the proposed sensor has great potential for practical AC magnetic field sensing applications, such as health monitoring of transformers and fault diagnosis of induction motors. It is instructive to compare the sensor presented here with other representative magnetic field sensors based on FBG. Comparisons of sensing structure, effective sensing length, sensitivity, and temperature compensation method are listed in Table 1. For magnetic fluid-based FBG magnetic sensors, the fiber cladding should be etched to enhance the sensitivity, which makes the fiber fragile and increases the transmission loss [12]. The FBG coated with magnetostrictive thin film can realize compact size but has low sensitivity [14]. For magnetostrictive material-based FBG magnetic sensors, FBGs always bond to the magnetostrictive material surface or are embedded in the magnetostrictive material. In 2000, a standard FBG was bonded onto a Terfenol-D alloy surface, achieving a sensitivity of about 8.6 pm/mT for an effective sensing length of 25 mm [33]. FBGs embedded in a magnetostrictive polymer composite have the advantage of flexible sensor shape but at the expense of weak thermostability and low sensitivity [20]. In 2022, a new FBG encapsulation structure was designed to enhance sensor sensitivity based on a magnetostrictive polymer composite. However, the total encapsulation size increased accordingly, and the fabrication process is relatively complex [30]. For more conventional temperature compensation approaches, an extra temperature sensor head is required, which greatly increases the size of the sensor [14,20,33]. In contrast, the sensor proposed in this work can realize relatively high sensitivity with a short effective sensing, while it also eliminates temperature crosstalk through the photo-thermal effect of HAF instead of a separate temperature sensor.
# Multi-Hypergraph Neural Networks for Emotion Recognition in Multi-Party Conversations
## 2 Related Work
### Hypergraph Neural Networks  
Unlike conventional graph neural networks, hypergraph neural networks no longer focus on only pairwise interactions between nodes. As shown in Figure 1, the relationship between hyperedges and nodes is a many-to-many relationship.  
[PERSON] et al. [37] were the first to introduce hypergraphs in order to represent complicated relationships, and they proved that hypergraph-based learning outperformed graph-based learning on several clustering, embedding, and classification tasks. HGNN [21] was the proposal of a hypergraph neural network framework, and its ability to model complex high-order data dependencies through hypergraph structures was demonstrated. HyperGAT [38] used subject words to construct hypergraphs for text classification. HGC-RNN [22] adopted a recurrent neural network structure to learn temporal dependencies from data sequences and performed hypergraph convolution operations to extract hidden representations of data. HWNN [20] was the proposal of a graph-neural-network-based representation learning framework for heterogeneous hypergraphs, an extension of conventional graphs, which could characterize multiple non-pairwise relations well. SHARE [39] constructed different hyperedges through sliding windows of different sizes and extracted user intent through hypergraph attention for session-based recommender systems. MHGNN [40] used multi-hypergraph neural networks to explore the latent correlations among multiple physiological signals and the relationships among different subjects. HOTL [41] was the proposal of a new online cross-topic ECG emotion recognition method that used online transfer learning based on hypergraphs and effectively handled the online cross-subject scenario in which unknown target ECG data arrived one by one with varying overtime. [PERSON] et al. [42] used hypergraphs of each modality to represent the complex relationships among subjects and used multimodal physiological signals for emotion recognition through an edge-weighted hypergraph neural network.  
To the best of our knowledge, there is currently no work on building graph networks by using non-specific speaker contexts alone or on dealing with inter-dependency among speakers by using hypergraphs. In order to better capture the high-order relationships among utterances and model the two speaker dependencies, we treat dialogue as a hypergraph and solve the ERC task by using a hypergraph neural network.
## 3 Results
### Amplification during Tube Voltage (kVp) Variation  
The linearity coefficient of the signal to kVp variation was 0.9219 when the active area was 756 mm\({}^{2}\) (60 chips). The signal that was obtained per unit tube voltage was 0.0321 mV/kVp. Increasing the active area to 1512 mm\({}^{2}\) (120 chips) led to a 0.04% increment in the linearity coefficient of the signal to kVp variation; hence, the linearity coefficient slightly increased to 0.92225. Likewise, the signal that was obtained per unit tube voltage increased from 0.0321 to 0.05488 mV/kVp, which implied a 70.97% increment in mVs obtained per kVp. Therefore, during kVp alteration, there was a 0.09% increase in the mV/kVp for each mm\({}^{2}\) of active area increment, as observed in Figure 6a.  
Amplifications using the amplifier board produced a 2.75% increase in the signal's linearity during kVp alteration; thus, the linearity coefficient increased from 0.92225 to 0.9476. The signal detected per unit kVp increased from 0.05488 to 0.07000 mV/kVp, hence a 27.55% mV/kVp increase. Therefore, for kVp alterations, there was a 2.755 \(\times\) 10\({}^{-8}\)% increase in the mV/kVp for each voltage gain by the amplifier board, as observed in Figure 6b.