esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
When the response of the vibration isolation system is small enough, it is reliable to adopt the Taylor series expansion with low order to simplify the stiffness of the system. On the contrary, this kind of approximation is difficult to satisfy the accuracy requirement under large vibrations. The errors will lead to the deviation against actual isolation performance, the mismatch of structure parameters and isolated mass, and the limitation on working displacement of isolators. The changes of the predicted vibration responses caused by stiffness error have been presented in [21; 22]. Increasing the order of Taylor series expansion, the accuracy can be improved, while the difficulty and cost of calculation will be raised. In order to avoid the above approximation error problem in dynamic analysis of nonlinear vibration isolators, a novel cam-roller type negative stiffness corrector is proposed in this paper. In manufacturing and automobile engineering, the cam and its profile theory have been widely used [28; 29]. Vibration isolators with cam-roller mechanisms (CRMs) present superior isolation capabilities [16; 23; 24], but the cams in the existing CRMs all have the semicircular trajectory, which results in a complicated stiffness and fails to exploit the advantage that cam contours are easy to design. Therefore, for the desired stiffness characteristics, the NSC constructed in this paper employs the cam-roller mechanism with a quadratic polynomial trajectory (QCRM), and the nonlinear vibration isolator with this QCRM-NSC is established. Then, according to the static model of the vibration isolator, the simple quadratic polynomial stiffness of the NSC and the QZS condition of the isolator are given without stiffness approximation. Both the dynamic response and the vibration isolation performance under different excitations show that the nonlinear QZS vibration isolator with the special QCRM-NSC can effectively suppress the transmission of vibrations.
## 5 Service Life Predictions  
### Reinforced Concrete in the Marine Section  
Chloride penetration into concrete is much more severe than carbonation-induced corrosion of reinforced bars in concrete structures in marine environments. Therefore, only chloride-induced corrosion of reinforced bars was considered, and the revised DuraCrete model (Equation (8)) was used to predict the service life of a concrete structure. Since it is difficult to measure the concrete cover thicknesses of the box girder and platform, the designed cover thicknesses and the corresponding standard deviations with assumptions were used in the service life prediction calculations. On the other hand, the measured concrete cover thickness of the pier concrete was used in the service life prediction calculations. Figure 14 shows a photograph of the Jiaozhou Bay sea-crossing railway bridge in its completed state.
# The Optimal Deployment Strategy of Mega-Constellation Based on Markov Decision Process
## 2 Space Orbital Debris Flux and Collision Probability
### Collision Probability Analysis  
During the operation time of on-orbit satellites, the likelihood of a collision between satellite and debris can be described as the collision probability. Let \(A\) be a cross-sectional area, assuming that the number \(N\) is proportional to the flux \(F\), then the relationship equation is as follows:  
\[N=F\cdot A\cdot T \tag{8}\]  
Let \(n\) be the collision times during the flying-through time, \(n=0,1,\cdots,N\). The collision probability obeys the Poisson distribution, that is:  
\[P_{n}=\frac{N^{n}e^{-N}}{n!} \tag{9}\]No collision occurred when \(n=0\), \(P_{0}=\exp(-N)\) at this time, so the collision probability is shown as follows:  
\[P=1-P_{0}=1-\exp(-N) \tag{10}\]  
The relationship between the collision probability and different sizes of debris varying by years can be seen in Figure 5, and the relationship between the collision probability and different orbital heights varying by years can be seen in Figure 6.  
As seen in Figures 5 and 6, the trends of space orbital flux and collision probability mainly depend on the solar radiant activities. With orbital height increased and debris size enlarged, the probability would grow larger.  
Figure 4: The influence of orbital height for space debris flux as years.  
Figure 3: The influence of debris size for space debris flux as years.
## 2 Challenges in Under Water Sensors Localization and Time Synchronization  
Various issues and challenges are associated with the underwater environment as far as localization and time synchronization is concerned, some of them are as follows:  
The most common challenge in UWSNs is node deployment which is not only difficult but also very costly in the deep-sea environment. Secondly, drifting of modes due to the different activities and current of the underwater environment. If a node displaces from its position, then it is not possible to estimate its accurate location. In third place, it's a signal strength that is easily affected by many factors such as external noise, multipath propagation attenuation, and Doppler shift. Fourthly, it is a sound speed variation, which has taken to be constant in most of the localization techniques, but in actual it varies due to temperature, pressure, and salinity. Variation in any of these above-mentioned factors can cause a change in sound speed. Due to the high propagation delay, the speed of sound is five-time lower than radio waves. UWSNs face constraints like limited bandwidth, high propagation delay, 3D topology, and power constraints. Radio and optical waves are not feasible for communication at each point of the ocean. Also, the transmission range [7], frequency, and the data rate are less, and the bit error rate [8] is high. All the above-mentioned factors heavily affect the localization process, which means, to achieve accurate localization, these factors should be handled.  
Time Synchronization is another main challenge that needs to be calculated simultaneously with localization. synchronization is mostly ignored in many localization schemes, most of the researchers assumed that nodes are already synchronized. Since radio signals cannot travel underwater therefore it is difficult to achieve synchronization due to long propagation delay and variable sound speed, also GPS service is not available underwater. Challenges related to synchronization are many, but the most common is the cost of underwater devices which are not easily available in the market, as all these expensive devices need protection and high-power electricity to transfer the underwater data. Another main challenge in UWSNs is that the standard underwater transducer does not receive and send messages at the same time, which is why it is important to have the information about the sending and receiving time of messages during the collection of data in UWSNs.
# Sensor Technologies for Transmission and Distribution Systems: A Review of the Latest Developments
## 4 Outlook
### Advances in Transmission and Distribution Systems with Modern Day Sensor Technology
The current monitoring of the distribution system is mostly restricted to the substation. However, with the implementation of modern sensor technology, on-site, remote, and real-time distribution system asset monitoring can be realized [152]. This will strongly isolate the transmission system faults from dissipating into the distribution system, improving the reach of protection and the distribution system capacity. Furthermore, as discussed above for transmission lines, modern sensor technology could also improve the resiliency of the distribution systems by facilitating real time, time-synchronized condition monitoring and predictive maintenance [153] to cope with vulnerabilities of distribution systems to such things as faults, destabilizing issues, and cybersecurity threats. Hence, the development of transmission and distribution systems and the development of modern-day sensor technology go hand in hand. Thus, the study of the latest developments in sensor technology is a precursor to realize the necessary developments in the power grid.
## 4 System Methodology
### Preprocessing, Feature Extraction and Feature Ranking  
The preprocessing stage involves the removal of the artifacts and noise in the focal and non-focal EEG signals that are acquired during the recording of the signal. It also involves digital filtering, baseline correction, and signal re-referencing.  
For the successful implementation of the CAD system for the detection and classification of focal and non-focal EEG signals, efficient and relevant features with low dimension but which still retain useful information are extracted. In this paper, wavelet transform has been deployed to extract time-frequency features. Afterwards, statistical parameters and nonlinear parameters were calculated to extract more meaningful features and reduce the feature dimension so that the classifier's computational complexity would be minimized. The computational complexity is one of the factors to be considered when developing smart IoMT devices. The raw EEG signals and wavelet features contain a lot of redundancy information that can render the classification process complex and difficult. Therefore, feature ranking and selection were carried out using a \(t\)-test in SPSS software to determine the probability (\(p\)) values. Parameters with a value higher than the critical value of 0.05 are considered non-statistically significant and can be dropped from the features. Figure 8 depicts the block diagram of feature vector formation from various domains and feature ranking.  
Figure 6: EEG signal acquisition set up using 10–20 international system with seizure hotspot shown in red colour [50].  
Figure 7: Example of Focal (**top**) and Non-Focal (**bottom**) EEG signals.
# Review of Automatic Estimation of Emotions in Speech
## 4 Models to Use Measures of Analysis for Speech Emotion Classification
### Hidden Markov Models (HMMs)  
For ASR, a related application to SER, the most common modeling technique until recently has been HMMs [57; 58]. With a maximum likelihood approach, one seeks to maximize \(P(T\,|\,S)\), i.e., the most likely candidate text \(T\) for an input speech utterance \(S\), where \(T\) is a word sequence and \(S\) a set of spectral vectors from speech analysis. Using [PERSON] theorem, one instead maximizes \(P(S\,|\,T)\,P(T)\), where \(P(T)\) is a language model and \(P(S\,|\,T)\) an acoustic model (as \(P(T\,|\,S)\) is far too complex to use, given the immense number of possible speech signals \(S\)). HMMs can be models of linguistic units of various size (e.g., phonemes or words).  
Each HMM consists of a sequence of states, representing spectral patterns for shapes of the VT used to generate the sounds. To handle temporal variability, HMMs use timing transitions between states for successive frames. These transitions have trained probabilities (modeling speech durations), and a probability density function (often a weighted set of Gaussians, called a Gaussian Mixture Model (GMM)) is estimated for each state's spectrum. Phoneme HMMs often depend on the acoustic/articulatory context of immediate neighboring phonemes, to model VT coarticulation. For SER, which does not need individual decisions per frame, a small number of emotion classifications suffices; hence, single-state GMMs may be used [33].
## 1 Introduction
On the other hand, [PERSON] et al. [11] focused on near-field environments and established a relation between the array size and the Fresnel zones. The punctual approximation of the scattering characterization presented dependence on the second and third-order moments of the distance. On the contrary, for far-field, the dependence is given for the fourth power. [PERSON] et al. [12] employed some stochastic geometry tools to analyze the effect of the large-scale deployment of LIS on the performance of cellular networks in the presence of blockages surfaces. They established a relation between the density of LIS panels and blockages. [PERSON] [13] explores the idea of integrating LIS with mobile edge computing (MEC) technology that intends to leave computing involved in processing the received signal to a cloud server and describes how these technologies can mutually benefit and create a framework competitive for 6G. Finally, [PERSON] et al. [14] analyze the possible benefits of using intelligent and reflective surfaces to increase the privacy and security of mobile communications through secrecy rate, considering that passive eavesdroppers are involved in the system, in addition to legitimate users. In addition to the works related to optimal estimation and power control in transmission systems aided by LIS, it has become a trend to compute the channel's capacity in the face of eavesdroppers. The question to answer is: \"Does such a system offer the physical layer security that prevents an intruder from receiving a signal not intended for him?\" The secrecy outage probability metric can answer this question since it means the probability that the instantaneous secrecy capacity is less than or equal to a given capacity threshold. Below are some works that, like ours, are concerned with information security in systems assisted by LIS.
# UAV-Based Privacy-Preserved Trustworthy Seamless Service Agility for NextG Cellular Networks
## 3 Preliminaries
### Threat Model  
The considered inter-operator agility cooperative cellular environment is exposed to multiple security and privacy attacks. In this paper, we assume the following threat vectors:  
* Privacy Invasion: The inter-cooperation between different service providers can leak UEs' information to third parties and/or UE performance monitoring attackers. Additionally, the supporting UAVs as weak IoT devices can be a leaking point of UEs' information.
* Malicious UAV: A malicious UAV can capture the communication procedure to maliciously transmit repeated or delayed messages to either the UE or the guest network.
* IMSI-Catchers Attack: An attacker catches the gNB/eNB IDs and poses as a legitimate guest base station in the network.
* Compromised UE: An attacker poses a legitimate UE in need of the service aiming to gain access and collect sensitive data.
* Replay Attack: An attacker uses a sniffing attack on the UAV, UE, and the guest network communication to transmit repeated or delayed messages maliciously.
# Multi-Objective Edge Node Deployment Method Based on Improved Heuristic Algorithms for Smart Mines
## 3 System Model and Problem Description
### Problem Description
#### 3.2.2 Deployment Cost  
The objective of deploying edge nodes in a mining environment is to uphold service quality while concurrently minimizing costs. The cost can be expressed by Equation (9):  
\[C_{all}=\sum_{i=1}^{M}n_{i}pri_{i} \tag{9}\]  
\[pri_{i}=ax_{i}+A,x_{i}>0,i=1,2,...,M \tag{10}\]  
where \(n_{i}\) signifies the quantity of edge nodes i, a and A are constants, and the variable \(x_{i}\) corresponds to the computational capacity of the node, where the cost of the node is directly proportional to its computational capacity.
## 4 The System Model and Control System  
Sequential control systems are the most classic control systems which drive the plant with several discrete processes. In this paper, we construct the system model based on the sequential control system as shown in Figure 2. The plant is an example physical apparatus with several sensors and actuators. Actuators are extension devices such as valve, motor, mixer, and cam, which are controlled by the output values \(y_{1},y_{2},y_{3},\ldots,y_{n}\). A device is ON/OFF, while the corresponding value is set to \(\top\) (True)/\(\bot\) (False). Sensors can detect plant status such as temperature, water level, pressure, and rotating speed. The input values \(u_{2},u_{3},u_{4},\ldots,u_{n}\) are specific state switches of the plant if the current status of the plant is at \(u_{i}\) or includes it, then \(u_{i}=\top\). A start signal is sent to the PLC via \(u_{1}\), and all variables are OFF before \(u_{1}\) is ON (\(u_{1}=\top\)).  
When the entry process completes (\(u_{1}=\top\)), some actuators will be set to ON (\(y_{i}=\top\)), triggering new state switches and processes further. For example, we assume an example control system for a simplified temperature regulator consisting of a heater, a cooler, and a temperature sensor. The heater and cooler are controlled by the out variables \(y_{1}\) and \(y_{2}\), respectively. The temperature sensor can detect the temperature at low, high, or expected levels (corresponding to input variables \(u_{2},u_{3}\), and \(u_{4}\), respectively). A start  
Figure 2: The System Model Based on Sequential Control System.  
button corresponds to the input variable \(u_{1}\). The temperature regulator system follows a simple process: the heater is ON at the low-temperature level, and the cooler is ON at the high. The example system's process and control logic (abstraction level) is shown in Figure 3.  
Then, a unified description of the ransomware attack model is carried out: the PLC will operate normally until the delay time, which the adversary sets, leading to disruption. For example, in the temperature regulator system, disruption can cause actuators (heater and cooler) to go out of control (\(y_{i}=\bot\)), that is,  
\[y_{1}(t)=\left\{\begin{array}{l}(u_{1}\lor y_{1})\land\
eg u_{3},t\
ot\in \mathcal{T}_{\alpha},\\ \bot,t\in\mathcal{T}_{\alpha}\end{array}\right. \tag{1}\]  
\[y_{2}(t)=\left\{\begin{array}{l}(u_{1}\lor y_{2})\land u_{4},t\
ot\in \mathcal{T}_{\alpha},\\ \bot,t\in\mathcal{T}_{\alpha}\end{array}\right. \tag{2}\]  
\(\mathcal{T}_{\alpha}=[t_{s},t_{e}]\) is the duration time of ransomware attack, where \(t_{s}\) represents the start time, \(t_{e}\) represents the end time and \(y_{i}(t)\) presents the output signal at time \(t\).  
Not shown in this example, a _timer_ is a variable that presets delay time between the input and output variables, which may cause an inversion of the value. Sensors can be replaced with timers under certain conditions, such as the ransomware attack payload construction. The following section will discuss the sense of timer in attack implementation.
# Modeling E-Behaviour, Personality and Academic Performance with Machine Learning
## 5 Limitations and Future Work  
This research was a study on the methodology that guided the use of machine learning in an academic performance analysis rather than the efficiency and improvement of the algorithms themselves.  
Our results might likely differ across contexts, since different data and algorithm configurations can generate several model outputs. The results obtained serve only as proxies for the possible outputs in academic performance research.  
In the domain of an LMS system user engagement, there were no formal definitions and standards, analogues or equivalent metrics that proxied a student's e-behaviour and personality from LMS data. We modelled features as well-understood traits to further understand the relationships between behaviour, personality and academic performance.  
An unknown in all model outcomes was the presence of causality. For instance, whether e-behaviour had an _effect_ on performance was not known. Although the methodology followed aimed to set up conditions for inference, diction such as _tend to correspond with_, and _have relationships with_, instead of _causes_, showed sensitivity to all likelihood of effects from confounding variables.  
In encoding performance (student Grade), we used uniform importance across all modules. We did so despite some students' modules accounting for a higher proportion of points towards obtaining a qualification from the University. We did not have access to each student's relative weighting of each module and, therefore, did not account for the differences in module weightings.
# A Design of a Time Synchronization Protocol Based on Dynamic Route and Forwarding Certification
## 5 Analysis of the Simulation and Test
### Field Test on an Embedded Linux Platform
#### 5.2.2 Routing Influence on the Synchronization Precision  
In order to evaluate the influence of different routes on the synchronization precision, the four routes shown in Figure 9 were tested.  
The routing established by DRFC-TSP in the tests is shown in Figure 9(2), and the others are designed routes. As can be seen from Figure 10, route 0-1-5-8 shown in Figure 9(1) has the worst performance of synchronization, while route 0-2-7-8 shown in Figure 9(2) is the best, and the other two routes 0-4-6-8 and 0-3-6-8 perform better than route 0-1-5-8. The result of these field tests can be predicted from Table 3. Due to the poor performance of nodes 1 and 5 in route 0-1-5-8, node 8 has the worst synchronization performance. In comparison, route 0-2-7-8 established by DRFC-TSP provides the best synchronization, since each node in this route optimizes its reference nodes.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Node Num.** & **Hops** & **Certificated or Not** & **Reference** & **Offset (ms)** \\ \hline
1 & 1 & No & 0 & 8.24 \\ \hline
2 & 1 & Yes & 0 & 2.34 \\ \hline
3 & 1 & Yes & 0 & 6.24 \\ \hline
4 & 1 & No & 0 & 9.79 \\ \hline
5 & 2 & No & 2 & 7.55 \\ \hline
6 & 2 & Yes & 2 & 1.69 \\ \hline
7 & 2 & Yes & 2 & 0.26 \\ \hline
8 & 3 & - & 7 & 3.03 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of embedded Linux platforms after the test.  
Figure 9: **(1–4)** Comparison of routes in multi-hop experiments.
# JLMS25 and Jiao-Liao Mandarin Speech Recognition Based on Multi-Dialect Knowledge Transfer
###### Abstract  
Jiao-Liao Mandarin, a distinguished dialect in China, reflects the linguistic features and cultural heritage of the Jiao-Liao region. However, the labor-intensive and costly nature of manual transcription limits the scale of transcribed corpora, posing challenges for speech recognition. We present JLMS25, a transcribed corpus for Jiao-Liao Mandarin, alongside a novel multi-dialect knowledge transfer (MDKT) framework for low-resource speech recognition. By leveraging phonetic and linguistic knowledge from neighboring dialects, the MDKT framework improves recognition in resource-constrained settings. It comprises an acoustic feature extractor, a dialect feature extractor, and two modules--WFAdapter (weight decomposition adapter) and AttAdapter (attention-based adapter)--to enhance adaptability and mitigate overfitting. The training involves a three-phase strategy: multi-dialect AID-ASR multi-task learning in phase one, freezing the dialect feature extractor in phase two, and fine-tuning only the adapters in phase three. Experiments on the Jiao-Liao Mandarin subset of the KeSpeech dataset and JLMS25 dataset show that MDKT outperforms full-parameter fine-tuning, reducing Character Error Rate (CER) by 5.4% and 7.7% and Word Error Rate (WER) by 6.1% and 10.8%, respectively.  
Keywords:speech recognition; multi-dialect; Jiao-Liao Mandarin; multi-task learning; adaptation; low-resource; knowledge transfer +
Footnote †: journal: Applied Sciences