esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Central Nervous System Disorder  
The CNS alterations identified in the articles included in this study, which are clinically manifested by increased muscle tone, include multiple sclerosis, stroke, cerebral palsy, brain tumour, brain injury (traumatic or acquired), and spinal cord injury. The changes described have been evaluated in terms of tone in a clinical context by assessing changes in muscle resistance during passive stretching [2; 41]. These health conditions affect the upper motor neuron (UMN) and can manifest as increased muscle tone [8; 28]. The UMN connects the cortex to the anterior horn cells of the spinal cord, which, in turn, extend via lower motor neurons through the peripheral nerve to skeletal muscle [8; 9; 32].  
Movement disorders resulting from CNS injury have been described as a consequence of the presence of spasticity [42; 43]. This was the variable under study that was consistently identified by the authors in the articles included in this review [17; 28; 29; 30; 32; 33], followed by muscle tone, which was assessed in only one study [20]. According to the aforementioned definition of spasticity, this appears to be a consequence of altered tone modulation and a type of muscle hypertoronia [2]. However, in the studies included in this review, the assessment of resistance to passive movement of a segment at rest was proposed [17; 28; 29; 30; 32; 33], although it is described in the literature as a clinical assessment of muscle tone [2; 8].  
In fact, although this assessment is often mentioned as a way to assess changes in muscle tone, in reality it seems to refer only to the implicit non-neural component [6], without considering its neural component [2; 8]. For this reason, it seems essential to unequivocally distinguish between non-neural (tissue-related) and neural (CNS-related) contributions related to changes in muscle tone.
## 7 Seismic Performance of Composite Building Structures
### Contextual Variability in the Seismic Performance of Composite Structures  
The complexity of seismic performance in steel-concrete composite structures necessitates a critical examination of the inherent variability across different geological, geographical, and structural contexts [108]. While this review synthesizes comprehensive research findings, it is paramount to acknowledge that the behavioral characteristics of composite structures are profoundly influenced by a multitude of site-specific factors that cannot be universally generalized. Geological conditions, including ground motion characteristics, soil properties, and local seismic hazard profiles, play a crucial role in determining structural response that extends beyond standardized design parameters [125]. Regional variations in construction practices, material quality, and local building codes introduce additional layers of complexity that significantly affect the seismic performance of composite systems [126]. For instance, the same structural design may exhibit markedly different responses in regions with varying tectonic environments, such as subduction zones versus transform fault boundaries. Furthermore, micro-level variations in material composition, fabrication techniques, and construction quality can introduce substantial uncertainties that are challenging to predict through generalized analytical models [127]. The interaction between structural geometry, connection detailing, and local seismic loading conditions creates a dynamic system where small variations can lead to significantly divergent performance outcomes [128]. These contextual nuances underscore the importance of site-specific investigations and the limitations of broad, generalized conclusions. Researchers and practitioners must approach composite structural design with a nuanced understanding that recognizes the inherent complexity and site-specific nature of seismic performance. This approach demands a more sophisticated methodology that integrates local geological data, advanced numerical modeling, and comprehensive experimental validation to develop truly robust and context-sensitive design strategies for steel-concrete composite structures in seismic regions [110].
# Statistical Characterization of Supraharmonics in Low-Voltage Distribution Networks
## 2 Materials and Methods
### Probability Distributions for the Statistical Characterization of Supraharmonics  
The supraharmonics are assessed in this paper through two approaches. The first approach is a comprehensive one in which the TSHD is characterized through a considered probability distribution. The second approach is instead developed to characterize a single component within the supraharmonic range in order to suit the needs of the operator that focus on a specific frequency band for filtering, compensation, or capacity assessment purposes. In both approaches, the random variable object of the characterization is indicated with \(x\), which may stand for the TSHD (first approach) or for the \(i\)th component \(Y_{i}\) (second approach).  
The probability distributions considered for the characterization are briefly recalled below, with details on the maximum likelihood estimation (MLE) procedure used to estimate their parameters [26]. MLE makes statistical inference on a given dataset of observed samples in order to determine the estimates of the parameters of a distribution that maximize the probability of the observed data. Assuming that the generic distribution has probability distribution function (PDF) \(f(x|\mathbf{\theta})\), where \(\mathbf{\theta}\) is the parameter vector, the likelihood function \(L(\mathbf{\theta}|\mathbf{x})\) is defined for a given dataset \(\mathbf{x}=\left\{x_{1},\ldots,x_{N}\right\}\) of observed samples as the product of probabilities for the sample data \(\mathbf{x}\):  
\[L(\mathbf{\theta}|\mathbf{x})=\prod_{n=1}^{N}f(x_{n}|\mathbf{\theta}). \tag{3}\]  
The likelihood function depends only on the parameters of the distribution and, under the MLE approach, it is maximized to find the estimates \(\hat{\mathbf{\theta}}\) of parameters \(\mathbf{\theta}\). The generic optimization problem to be solved is:  
\[\hat{\mathbf{\theta}}=\underset{\mathbf{\theta}}{\mathrm{argmax}}\prod_{n=1}^{N}f(x_{ n}|\mathbf{\theta}). \tag{4}\]  
Most of the distributions considered in this paper are unimodal, but due to the particularity of the supraharmonics (which can be time-varying and can have different source regimes), also a multimodal distribution, i.e., the mixture of normal distributions, is analyzed in this paper.
## 4 Experiments
### Characteristics of Simple Black-Box Jailbreak Prompts  
The jailbreak prompts created by the proposed method are written in natural language, as they are obtained by rewriting the original question texts using an LLM. However, the same can be said for the state-of-the-art method, PAIR. To examine the differences in the jailbreak prompts created by the proposed method compared to PAIR, we assessed the difference in word count between the jailbreak prompts and the original questions used to create them (\(\Delta w\)).  
For questions where jailbreaking was successful using both the proposed method and PAIR, we extracted these questions and their corresponding jailbreak prompts for GPT-3.5, GPT-4, and Gemini-Pro, and evaluated \(\Delta w\) for both methods (Figure 3). Overall, the jailbreak prompts created by the proposed method were considerably shorter (closer to the word count of the original questions) than those created by PAIR. Specifically, for GPT-3.5 (Figure 3A), the average \(\Delta w\) was 2.5 (median: 2.0) for the proposed method, compared to 20.1 (median: 20.0) for PAIR. For GPT-4 (Figure 3B), the averages were 7.9 (median: 5.0) for the proposed method and 41.5 (median: 52.0) for PAIR. For Gemini-Pro (Figure 3C), the averages were 19.8 (median: 13.0) for the proposed method and 38.1 (median: 34.5) for PAIR. The peak at \(\Delta w=0\) for PAIR suggests the presence of questions for which LLMs provide appropriate answers, even without attacks.  
The relatively larger \(\Delta w\) for PAIR is due to its complex scenario settings used to improve the original questions while creating jailbreak prompts. The prompts tend to be unnaturally long (in terms of word count) compared to the original questions to explain these complex scenarios. In contrast, the proposed method does not require such complex settings. Although it allows for the addition of explanatory text if necessary, it only requests a simple rewriting to \"reduce discomfort\", resulting in jailbreak prompts not significantly longer than the original questions.  
Long prompts, like those created by PAIR, which are unnaturally lengthy compared to the original questions, could potentially be identified as jailbreak prompts based on their unnatural length. However, shorter prompts, like those created by the proposed method, would be more challenging to detect as jailbreak prompts.
# A Systematic Literature Review on Distributed Machine Learning in Edge Computing
## 4 Answering the RQs
### RQ4--Edge Intelligence Application Domains
#### 4.4.1 Industry 4.0  
Thanks to the development of the Internet of Things (IoT), more precisely the Industrial Internet of Things (IIoT) incorporating Artificial Intelligence (AI) and big data technologies, a new revolution in industry is possible, giving birth to the concept of Industry 4.0, the smart industry. In this new concept, sensors are spread throughout the entire industrial plant, collecting a huge amount of data in real-time. Traditionally, the data are sent to the cloud, where they are processed and analyzed by AI algorithms. The output of the processing is intelligent solutions that can improve manufacturing efficiency and inspection [68], enhance product quality, reduce cost, pollution and risk in industrial production, reducing manual labor and time spent.  
IIoT becomes a powerful tool in a big data era, where industrial companies are confronted with market pressures in managing both product quality and manufacturing productivity [53]. According to GE Digital, IIoT is estimated to benefit 46 percent of the global economy [154]. However, with the rise of big data sent to the cloud platforms in IIoT, some problems such as high latency and data privacy are emerging. EI is closer to the user in terms of geographical position and network distance, bypassing the bottleneck of network bandwidth, latency and cost [8]. Moreover, restricted data may be pre-processed before being sent to the cloud or not be sent at all.
# A Low-Cost, Small-Size, and Bluetooth-Connected Module to Detect Faults in Rolling Bearings
## 3 Proposed Solution
### Signal Conditioning and Data Acquisition  
A Microelectromechanical System (MEMS) microphone was exploited as a transducer because of its low cost, small size, and bandwidth. Before being sampled, the analog signal is band-pass filtered. Such an operation is performed by means of an audio amplifier, in order to filter the high frequency components that would cause aliasing and to adapt the signal to the Analog-to-Digital Converter (ADC) input range. The frequency band is [37 Hz, 20 kHz] and the in band gain is approximately 200. Subsequently, a 12 bit ADC, which is inside the microcontroller and provided with an internal voltage reference, digitizes the filtered input signal at a sampling frequency \(f_{s}\).
# A Parallel-Phase Demodulation-Based Distance-Measurement Method Using Dual-Frequency Modulation
## 3 2\(\pi\) Ambiguity Problem
### Sequential Dual-Frequency Modulation Approach  
Another method that can be used to solve the 2\(\pi\) ambiguity problem is dual-frequency modulation. [PERSON] et al. [24] used two different modulation frequencies to solve the measurement ambiguities. In this method, two measured distances are obtained for each modulation frequency sequentially using the AMCW method. The actual distance is then calculated using these two measured distances [24] as follows. Figure 2 shows the overall scheme when using these two modulation frequencies sequentially.  
When using this method, the unambiguous range, which is increased, can be given by  
\[d_{\text{max}}=\frac{c}{2f_{E}}=\frac{c}{2\times\text{gcd}(f_{1},f_{2})}, \tag{11}\]  
where \(f_{E}\) is the effective frequency, which is identical to the greatest common division of the two frequencies. In addition, \(f_{1}\) and \(f_{2}\) represent the two modulation frequencies.  
The advantage of this method is that the calculations are simple and it uses a high frequency to measure the distance, which improves the accuracy of the measurement. However, the total time for one-point distance measurement is doubled due to the sequential dual-frequency modulation process [22].
# Achieving Low Latency Communications in Smart Industrial Networks with Programmable Data Planes
## 5 Implementation and Performance Evaluation
### Experiments and Measurements
#### 5.2.2 Experiment 2: Network Processing Delay  
To analyze the effectiveness of the solution with SmartNIC processing inside the network, this experiment is aimed at measuring the communication delay introduced by the P4 processing at the SmartNIC. For this goal, four different tests have been conducted, and the corresponding delays measured. In the first **test (2.1)**, the traffic from the MQTT publisher travels to the cloud broker through the SmartNIC, where the corresponding INC is performed. In the second **test (2.2)**, the SmartNIC is removed and the traffic travels directly to the cloud broker, without any additional processing. As the delay in the communication with the cloud broker is higher than with the edge broker, we have decided to perform the third and fourth tests (2.3 and 2.4) measuring the same delays as in the first and second tests, but in the communications with the edge broker, both with and without the SmartNIC processing. In fact, as end-to-end delays in the IIoT-edge broker  
Figure 8: Testbed network of the industrial architecture based on SN4I.  
communications will be smaller than in the corresponding communications with the cloud, it will be easier to appreciate little variations in these delays. The aim of this experiment is to verify that processing in the SmartNIC does not introduce a delay big enough to counter the benefits exposed in the previous experiment.
# Crude Oil Price Forecasting Model Based on Neural Networks and Error Correction
## 2 Methodology
### Forecasting Methods
#### 2.3.2 Gated Recurrent Unit  
A RNN considers the influence of the previous state on the current state during the prediction by adding a recurrent structure to the model. This structure is beneficial in predicting the time series data [32]. However, the traditional RNN is prone to the bottlenecks of gradient disappearance or gradient explosion when handling long time series problems. The LSTM introduces the gated structure for solving the gradient problem in RNNs [33]. Compared with LSTM, a GRU only needs an update gate and reset gate, which can save memory and speed up the computation.  
The equations of the update gate and reset gate of the GRU are as follows [34]:  
\[z_{t}=\sigma(W_{z}*[h_{t-1},x_{t}]) \tag{9}\]  
\[r_{t}=\sigma(W_{r}*[h_{t-1},x_{t}]) \tag{10}\]  
where \(x_{t}\) represents the current state, \(h_{t-1}\) shows the last hidden state, and \(W\) represents the weight matrix.
# Spatio-Temporal Heterogeneous Ensemble Learning Method for Predicting Land Subsidence
## 3 Experiments  
### Study Area  
The Cangzhou area is located in the southeast of Hebei Province, China. It is 165 km wide from north to south and 187 km long from east to west, with a total area of about 14,304.26 km\({}^{2}\). Since the 1960s, to meet the needs of economic development, the region has conducted large-scale groundwater resource exploitation, resulting in a sharp drop in groundwater levels, which in turn has caused the most severe land subsidence problem in North China. Therefore, Cangzhou was selected as the study area for this research.
## 2 Materials and Methods
### Hybrid Deep Learning Model  
This study introduces an innovative hybrid deep learning-based IoHT attack detection model that combines an ANN with BLSTM and GRU architectures. The hybrid model integrates the ANN's ability to process intricate patterns, BLSTM's capacity to capture sequential dependencies in both directions, and GRU's efficiency in handling long-term dependencies. This integrated architecture aims to improve cyber-attack detection in the IoHT environment by leveraging the complementary strengths of these neural network components. The proposed system is designed to be adaptable and efficient, providing a comprehensive solution to cyber threats' dynamic and evolving nature in IoHT, as presented in Figure 2. Trained on an extensive dataset of IoHT, this model is specifically designed to detect four distinct types of attacks. These include ARP spoofing, DoS, Nmap port scans, and Smurf attacks. The ANN component efficiently processes complex patterns inherent in IoHT data. The BLSTM layer captures bidirectional dependencies, while the  
Figure 1: Proposed ECU-IoHT target data distribution.  
GRU layer excels in handling long-term sequential features. This integrated architecture enables the model to discern and classify diverse cyber threats within IoHT. As a result, it contributes to the security and integrity of healthcare data and services.  
The hybrid deep learning model outlined in the provided architecture incorporates multiple layers to harness the strengths of different types of neural networks, creating a comprehensive approach to classification tasks. The model starts with an input layer to receive pre-processed data, explicitly shaped based on the dimensions of the training data. It then splits into three separate paths: an ANN, a GRU, and an LSTM architecture.  
The ANN layer consists of a dense layer with 256 neurons activated by ReLU, introducing non-linearity and allowing the network to capture complex patterns. The GRU layer includes a bidirectional GRU layer, which processes data in both forward and backward directions, improving the model's ability to learn long-term dependencies by considering past and future contexts. The LSTM layer begins with a reshaping layer to adjust the input dimensions, followed by a bidirectional LSTM layer to capture dependencies similar to GRUs, and includes a dropout layer to prevent overfitting by randomly omitting neurons during training.  
The outputs from these layers are then combined and passed through a final dense layer with a softmax activation function, which generates a probability distribution over five classes suitable for multiclass classification tasks. This architecture leverages the strengths of both ANN and RNN components, aiming to effectively capture spatial and sequential features within the data, potentially enhancing performance in complex classification scenarios.  
Table 1 summarizes the key parameters for setting up the hybrid deep learning model. Different parameter values are tested to find the best performance.  
Figure 2: Hybrid deep learning-based IoHT attack detection model architecture.
## 3 Results
### Prediction Error  
In order to further analyze the effectiveness of whole-network adjustment, the 2-h clock offset data are used for slip prediction in the next one hour. The slip prediction result is compared with the actual observation value to obtain the prediction error. In order to better analyze the effectiveness of different methods, we also use only the satellite-ground data for comparison. Taking satellites C28, C29 and C30 as example, the clock offset prediction errors of only satellite-ground method, \"one-hop\" reduction method and the whole-network adjustment method are shown in Figure 7.  
As shown in Figure 7, the prediction error of only satellite-ground is fluctuating between \(\pm\)6 ns, and the RMSs of clock offset prediction errors of satellites C28, C29 and C30 are 0.54 ns, 0.68 ns and 1.12 ns, respectively. The clock offset prediction error of \"one-hop\"  
\begin{table}
\begin{tabular}{c c c c} \hline
**Satellite** & **SGL+ISL Method** & **WNA Method** & **Improvement Rate** \\ \hline C35 & 0.25 & 0.10 & 35.71\% \\ C36 & 0.37 & 0.14 & 54.76\% \\ C37 & 0.38 & 0.12 & 61.90\% \\ C41 & 0.32 & 0.10 & 52.38\% \\ C42 & 0.22 & 0.10 & 28.57\% \\ C43 & 0.32 & 0.13 & 45.24\% \\ C44 & 0.41 & 0.17 & 57.14\% \\ C45 & 0.29 & 0.13 & 38.10\% \\ C46 & 0.29 & 0.10 & 45.24\% \\ Mean & 0.32 & 0.13 & 45.06\% \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 7: Prediction error.  
reduction method are fluctuating between \(\pm\)4 ns, and the RMSs of clock offset prediction errors of satellites C28, C29 and C30 are 0.46 ns, 0.50 ns, and 0.91 ns, respectively. The clock offset prediction error of the whole-network adjustment method are fluctuating between \(\pm\)1 ns, and the RMSs of clock offset prediction errors of satellites C28, C29 and C30 are 0.15 ns, 0.26 ns, 0.23 ns, respectively. The RMSs of clock offset prediction errors of all satellites are further calculated and the statistical results are shown in Table 2.  
As shown in Table 2, the RMSs of predicted only satellite-ground method range between 0.33 ns and 1.66 ns, with a mean of 0.72 ns. The RMSs of predicted clock offset of \"one-hop\" reduction method range between 0.32 ns and 0.98 ns, with a mean of 0.54 ns. The RMSs of predicted clock offset of the whole-network adjustment method range between 0.15 ns and 0.43 ns, with a mean of 0.25 ns. Compared with the only satellite-ground method and \"one-hop\" reduction method, the prediction accuracy of the whole-network adjustment method improves about 62.13% and 52.15%, respectively.
## 4 Conclusions  
In this research, nonlinear Lamb wave was used with frequency-mixing technique to analyze the scattering features of wave propagation at micro-cracks to characterize the directivity of these cracks. An analysis of the interaction of Lamb wave with micro-cracks was performed using finite element simulation. The pulse inversion method was used for effective extraction of the sideband signals at the sum or difference frequency components. The acoustic nonlinearity parameter \(\beta\) which is related to the sideband signal at the difference frequency and the acoustic relative nonlinearity parameter \(\beta^{\prime}\) which reflects intuitively the scattering features of micro-cracks were introduced and proposed. Both the through-thickness micro-crack and the buried micro-crack were modulated in plate. The results show that:  
(1) For the two kinds of micro-crack models, the value of \(\beta\) in forward scattering has the largest magnitude, whereas \(\beta\) shows its smallest value in the backward-scattering direction compared to other directions.  
(2) For the two kinds of cracks the magnitude of \(\beta^{\prime}\) increased in the initial stage and then decreased with increasing micro-crack length, the reason is that as the micro-crack became longer, the contact stiffness of the interface firstly increased and then decreased.  
(3) The variation tendency of \(\beta^{\prime}\) was decreased monotonically with increasing width of the through-thickness crack or with increasing burial depth of the buried crack, because in the both cases the contact stiffness of the crack interface decreased continuously.  
[PERSON]. conceived of and designed the simulations. [PERSON] analyzed the data and wrote the paper. [PERSON] investigated the simulations. [PERSON] audited the content and reviewed the content. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (grants 61571222 and 11774167), the Natural Science Foundation of Jiangsu Province, China (grant BK20161009), and the Six Talent Peaks Project of Jiangsu Province, China.  
The authors declare no conflict of interest.