esa-sceva/satcom-chunk-collection huggingface dataset:
# Traffic Monitoring System Based on Deep Learning and Seismometer Data
## 4 Results  
### Training and Validation  
We split our augmented dataset randomly into three portions, using the scikit-learn splitting function, dedicating 60% for training, 20% for validation, and 20% for testing. We used the same training set for each of the three networks and trained them over  
Figure 5: (**a**) Seismic signal of a bus before adding noise. (**b**) Seismic signal after adding noise to produce a signal to noise ratio of 6.  
150 iterations, then selected the model with the best validation accuracy. We also improved our training experience and prevented overfitting in two ways.  
First, we applied early stopping in which the networks monitored the validation accuracy and terminated the training when accuracy did not increase for 20 iterations. Second, we set a 30% dropout chance for all weights and biases. So, in each iteration, all weights and biases have a 30% chance to be ignored in the training process. The dropout technique improves the independence of the individual weights [40]. Training took a short computation time: DNN took 87 s, CNN took 112 s, and RNN took 56 s. Because of early stopping, DNN and RNN trained for less than 150 iterations. All models showed a great improvement during training, reaching accuracies close to 99% (Figure 6).  
In the validation process, we checked the models' performance with new data or data that were not used in the training process. The models did not display any overfitting, thanks to the early stopping that curtailed training before any degradation of the validation accuracy. The resulting validation curve represents the generality of the model. Both DNN and CNN reached accuracies of approximately 97%, whereas RNN validation accuracy was approximately 85% (Figure 7).  
We also monitored the improvements in loss function and mean square error (Figure S2 in the Supplementary Material). Table 2 summarizes the performance of the three models during training and validation.  
Figure 6: Plot showing the improvement in accuracy with increasing iterations during the training process for DNN (red), CNN (blue), and RNN (green). RNN was stopped early at 79 iterations and DNN was stopped at 97 iterations.  
Figure 7: Plot showing the improvement in accuracy during the validation process for DNN (red), CNN (blue), and RNN (green).
# Low-Complexity Synchronization Scheme with Low-Resolution ADCs
###### Abstract  
An important function of next-generation (5G) and beyond mobile communication systems is aim to provide thousand-fold capacity growth and to support high-speed data transmission up to several megabits per second. However, the research community and industries have to face a dilemma of power consumption and hardware design to satisfy the increasing communication requirements. For the purpose of improving the system cost, power consumption, and implementation complexity, a novel scheme of symbol timing and frequency offset estimation with low-resolution analog-to-digital converters (ADCs) based on an orthogonal frequency division multiplexing ultra-wideband (OFDM-UWB) system is proposed in this paper. In our work, we first verified the principle that the autocorrelation of the pseudo-noise (PN) sequences was not affected by low-resolution quantization. With the help of this property, the timing synchronization could be strongly implemented against the influence of low-resolution quantization. Then, the transmitted signal structure and low-resolution quantization scheme under the synchronization scheme were designed. Finally, a frequency offset estimation model with one-bit timing synchronization was established. Theoretical analysis and simulation results corroborate that the performance of the proposed scheme not only approximates to that of the full-resolution synchronization scheme, but also has lower power consumption and computational complexity.  
low-resolution analog-to-digital converter (ADC); synchronization; receiver; orthogonal frequency division multiplexing (OFDM) +
Footnote †: journal: Information Information
# Prediction of Process Quality Performance Using Statistical Analysis and Long Short-Term Memory
## 3 Materials and Methods
### Prediction of PQP Using LSTM  
In this section, the prediction stage of PQP is described with composite statistical analysis results for PPD by applying long-term short memory (LSTM) after preprocessing of the analysis data. As shown in Figure 6, after preprocessing four composite statistical analysis outcomes, MCtA, PTAD, SPCA, and PCCA, a training process using LSTM is described. Here, the preprocessing results will be used as inputs of the dense layer; the outputs of the dense layer will be passed through the reshape layer, the LSTM cells, the dense layer, and finally the Softmax layer to determine the PQP. Details will be provided in subsequent sections.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Division** & **Quality** & **Normal Production Rate** & **Failure Production Rate** \\ \hline Cp \(\geq\) 1.68 & Excellent & 99.999991\% & 0.000009\% \\
1.34 \(\leq\) Cp \(\leq\) 1.67 & Very good & 99.999943\% & 0.000057\% \\
1.01 \(\leq\) Cp \(\leq\) 1.33 & Good & 99.9937\% & 0.0063\% \\
0.68 \(\leq\) Cp \(\leq\) 1.00 & Fair & 99.73\% & 0.27\% \\
0.00 \(\leq\) Cp \(\leq\) 0.67 & Poor & 95.45\% & 4.65\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Criteria of process capability, Cp.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Division** & **Quality** & **Normal Production Rate** & **Failure Production Rate** \\ \hline Cpk \(<\) 0.33 & Terrible & 99.85\% & 1.15\% \\
0.33\(\sim\)0.67 & Very poor & 95.45\% & 4.65\% \\
0.67\(\sim\)1.00 & Poor & 99.73\% & 0.27\% \\
1.00\(\sim\)1.33 & Average & 99.9937\% & 0.0063\% \\
1.33\(\sim\)1.67 & Good & 99.999943\% & 0.000057\% \\
1.67\(\sim\)2.00 & Very good & 99.999991\% & 0.000009\% \\ Cpk \(>\) 2.00 & Excellent & 99.99998\% & 0.000002\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Criteria of process capability index, Cpk.  
Figure 6: Procedure for applying LSTM to decide PQP.
## 4 Experimental Setup and Results
### Comparison between GRM with Other Augmentation Methods  
To further validate the effectiveness of the GRM, we also compare it with cutout [16] and mixup [42]. As shown in Table 3, the GRM is superior to the cutout in improving the diagnosis accuracy. The most significant improvement happens in the Inception network for retinal diseases, which raises the accuracy from 94.3 to 96.7. For pneumonia and glaucoma diseases, different degrees of improvement can also be found in various networks. Experimental results in Table 3 also demonstrate the advantage of GRM over mixup. It can be observed that the GRM outperforms mixup in most tasks except the ViT of retinal diseases, where the accuracy of mixup is 92.8, marginally higher than the 92.6 of GRM. One significant advantage of the GRM and cutout lies in the adaptivity to the inputs. The mask size in the cutout is fixed, whereas the GRM can adaptively adjust the size and location of the mask according to the input. For the Mixup, it combines paired inputs and labels convexly to alleviate the overfitting problem, which can be used together with GRM to increase the capacity of the networks.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Task** & **Network** & **Vanilla** & **GRM** \\ \hline \multirow{4}{*}{Retinal diseases} & Inception-V3 & 94.9 & 96.7 \\ \cline{2-4}  & ResNet-50 & 93.7 & 96.3 \\ \cline{2-4}  & DenseNet-121 & 93.6 & 96.0 \\ \cline{2-4}  & ViT & 89.9 & 92.6 \\ \hline \multirow{4}{*}{Pneumonia} & Inception-V3 & 90.4 & 92.8 \\ \cline{2-4}  & ResNet-50 & 90.0 & 94.2 \\ \cline{2-4}  & DenseNet-121 & 88.8 & 92.1 \\ \cline{2-4}  & ViT & 90.2 & 91.8 \\ \hline \multirow{4}{*}{Glaucoma} & Inception-V3 & 89.2 & 91.6 \\ \cline{2-4}  & ResNet-50 & 87.5 & 90.6 \\ \cline{1-1} \cline{2-4}  & DenseNet-121 & 88.9 & 90.0 \\ \cline{1-1} \cline{2-4}  & ViT & 87.5 & 88.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of accuracy (%) between the vanilla networks and the one applied with GRM on the three medical image analysis tasks.
## 2 Materials and Methods
### Electromyography  
The Noraxon TeleMyo 2400 recording system, with eight channels, was employed to capture biopotentials from muscles during each repetition of the jogging and running on the anti-gravity treadmill. Five muscles--the gastrocnemius (GC), tibialis anterior (T), quadriceps femoris (Q), biceps femoris (B), and gluteus (G)--were monitored. Preceding electrode placement, skin preparation involved shaving, abrasion, and alcohol cleansing. Electrodes (Dri-Stick Silver circular sEMG Electrodes AE-131, NeuroDyne Medical, Cambridge, MA, USA) were then positioned in accordance with SENIAM procedures [27], spaced 2 cm apart with an 11 mm contact diameter. sEMG signals, sampled at 1000 Hz, underwent bandpass filtering (8 Hz to 450 Hz) followed by root-mean-square (RMS) calculation. Beforeand after the experimental session, MVIC tests were conducted, with the highest value chosen for analysis, expressed as a percentage of maximum voluntary isometric contraction (%MVIC). Tests utilized with two 5 s maximum isometric contractions separated by a 10 s rest interval, and a 2 min rest interval between MVC evaluations per muscle, adhering to SENIAM procedures. The sEMG data were based on the average of the peak sEMG amplitude recorded across the trials for each muscle [28].
## 3 Results
### Controls on the EOS Turning Points  
The changes of the annual EOS turning points are consistent with the turning points of the climate variables in most subregions (Table 2). In subregions I and II, the year of the EOS turning point coincides with the year of the insolation and precipitation turning points, respectively. Furthermore, the years of the EOS and temperature turning points are consistent in subregions III-IX. The major determining climatic variable for the EOS turning points is precipitation, followed by temperature and insolation. The relationship with the EOS turning point and insolation is generally weak (R\({}^{2}\) < 0.05).  
The relationship between the EOS and human activities was studied at the province level owing to limited statistical data in certain counties and subregions. The economic data show a consistent turning point with the EOS. Before the turning point year (~1996 for Qinghai and ~1995 for Tibet), Qinghai maintained a large amount of sheep, which reflected high grazing activity, and the economic development was slow with low production in the primary, secondary, and tertiary sectors. However, after the turning point year, the grazing intensity decreased and reached a stable change rate, whereas the economy developed rapidly, especially in the secondary sector. For Tibet, the grazing intensity was small before the turning point year but showed a rapid growth rate after the turning point. Similar to Qinghai, Tibet experienced fast economic growth after the turning point, especially in the tertiary sector.  
At the province level, the annual EOS was closely related to climate, human activities, and their intersections (Table 3). For Qinghai, a combination of the turning points of climate and human activities can explain 78.86% of the EOS turning points changes, with climate  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**The EOS Turning Points versus Climate Turning Points** & **R\({}^{2}\)** & \(p\) Value** \\ \hline EOS–temperature & 0.331 & \textless{}0.01 \\ EOS–precipitation & 0.378 & \textless{}0.01 \\ EOS–insolation & 0.038 & 0.76 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Correlation coefficients and P values between the turning points of the EOS and the turning points of climate variables.  
Figure 5: Relative influence of different climate variables (temperature, precipitation, and insolation) on EOS changes before (**a**) and after (**b**) the turning point, and over the entire study period (**c**) with the EOS means values of HANTS and Polyfit methods. (**d**) Area proportions controlled by the different dominant climate variables.  
independently accounting for 40.22% and human activities accounting for 10.45%. The intersections of climate and human activities can explain 28.19% of the EOS variation in Qinghai. In Tibet, the EOS change due to climate (66.17%) was larger than that in Qinghai and the effect of human activities (6.8%) was weaker. The climate and human activities intersections in Tibet (9.98%) were also smaller than in Qinghai.
## 1 Introduction
A benchmark simulation model is developed in Matlab/Simulink for control design purposes [6], and it is validated against the OpenFAST v3.5.4 non-linear model. OpenFAST software is recognized in the research community for its high-fidelity simulation of wind turbines. First, a passive TMD is installed in the WT nacelle to reduce vibrations, typically measured as the top tower displacement (TTD) [7]. The effectiveness achieved in damping the vibrations' amplitude depends on the tuning of the TMD parameters, making this task challenging [8]. Therefore, alternative heuristic methods, including evolutionary algorithms like GA, have been explored [9; 10]. For the case of the 5 MW FOWT here presented, the authors compare the cases where a reference TMD as described in [11] is used, the case of a wind turbine without a TMD, and finally, the case of a wind turbine containing a passive TMD for which the parameters are tuned by GA in terms of vibration reduction, highlighting the improvements provided by the optimized structural control. It is important to note that the objective of this work is not the design of a specific TMD but rather to evaluate different methodologies for optimizing the generation of appropriate control signals for adaptive TMDs. The proposed methodologies can be applied to any adaptive TMD available in the existing literature. Next, a semi-active TMD control is designed, where the spring stiffness and damping parameters can be adapted in real time according to the turbine's operating conditions. Two cases are considered for the semi-active structural device: (i) control actions based on the amplitude of tower oscillations (TTD) and (ii) parameter optimization according to the input frequency. During simulations, these parameters are computed online using a gain scheduling approach to reduce tower oscillations. The simulation results obtained demonstrate that the semi-active structural control improves the dynamic response of the system and mitigates tower vibrations compared to the cases of passive control or no control at all. As far as the authors know, the few existing studies on semi-active structural control for floating wind turbines do not use the two variables considered in the gain scheduling control approach here explored. Additionally, the real-time identification of input frequency enhances the control's ability to handle dynamic changes caused by wave disturbances. The rest of the paper is organized as follows: Section 2 discusses related works on structural control for wind turbines. Section 3 validates the simplified model used in this study, incorporating periodic external forces to simulate wave impacts on the FOWT barge. Section 4 presents the metaheuristic optimization of the passive TMD's spring stiffness and damping coefficients. Two semi-active control strategies based on (i) tower oscillation amplitude and (ii) input frequency are introduced in Section 5, followed by the analysis of the results. Finally, Section 6 concludes the paper with a summary of findings and suggestions for future work.
## 3 Proposed Method
### Improved Transfer Component Analysis
#### 3.1.2 Improved Transfer Component Analysis  
TCA achieves domain adaptation by aligning the global distribution between the target domain and the source domain but lacks consideration for the same category between different domains, resulting in suboptimal transfer learning performance. Subdomain adaptation can align data distributions of the same category between different domains. Therefore, for the fault diagnosis of bearings with different types of variable working conditions, LMMD is used to reduce the data distribution of the same fault type between different working conditions and bearing types and improve classification accuracy. When TCA maps the feature vectors of two domains to a shared feature space, the original distribution of the data mixed with noise will cause the mapped samples to diverge. So, it is necessary to reduce the intra-class divergence and improve class recognition. Therefore, this article proposes an improved transfer component analysis (ITCA) using LMMD instead of MMD, and the dispersion factor is considered in the objective function. The objective function of ITCA combining Equations (6), (7), and (12) is shown in Equation (13):  
\[\text{min}_{\text{ITCA}}=LD_{H}(X_{s},X_{t})+u\times\text{tr}(W^{T}W)+\lambda S \tag{13}\]  
where \(u\) and \(\lambda\) are the trade-off hyper-parameters.
## 3 Feature Extraction  
Feature extraction is a process of defining a set of features that aims to reduce the signal size, but this size reduction should preserve the properties of the signal and make it  
Figure 4: Signal recorded by the transmission and scattering detectors for all three simulated regimes at 2 cm scale thickness and 55% void fraction.  
Figure 3: simulated flow regimes (**a**) stratified (**b**) homogeneous (**c**) annular.  
even more suitable for interpretation. There are several methods for extracting the features of signals. For instance, extraction of characteristics in the frequency domain, extraction of characteristics in the time domain, combined mode of extraction of characteristics in the time-frequency domain, and even other creative methods. [PERSON] et al. Examined time characteristics that used a creative method to select the characteristics of skewness, kurtosis, and 4 th order moment as efficient characteristics [21]. The same creative method was used in the research of [PERSON] et al. to select the appropriate frequency characteristics [22]. There are several methods to determine the effective and appropriate properties of the signal, for example, in [2], researchers used the GMDH neural network to determine the effective signal properties. They used the network's self-organizing feature as a tool for feature selection. Feature extraction method using wavelet transform has also been considered by many researchers [23,24]. By extracting the approximate and the details of the received signals, they extract the characteristic features of the signal. Correlation analysis methods have also been studied to determine suitable characteristics in other studies [25]. Nevertheless, the gap in their research was that they did not consider the scale thickness of the inner pipe. In this study, inspired by previous research [21], three time features of skewness, kurtosis, and 4 th order moment were extracted as follows:  
* 4 th order moment: \[m_{4}=\frac{1}{N}\sum_{n=1}^{N}[x(n)-m]^{4},\ \ \ m=\frac{1}{N}\sum_{n=1}^{N}x(n)\] (1)
* skewness: \[g_{1}=\frac{m_{3}}{\sigma^{3}}\,\ \ \ \ \ m_{3}=\frac{1}{N}\sum_{n=1}^{N}[x_{n}-m]^{3},\ \ \sigma^{2}=\frac{1}{N}\sum_{n=1}^{N}(x_{n}-m)^{2}\] (2)
* kurtosis: \[g_{2}=\frac{m_{4}}{\sigma^{4}}\] (3)
## 4 Security Analysis of the MAPP Protocol
### Resistance to Attacks
#### 4.2.3 System Key Compromising Attacks  
In our protocol, we use a certificataless public key authentication algorithm that supports the high-security level, using a pair of secret keys and public keys. In contrast to protocols based on a single secret key that sends the shared secret for each message, to allow message verification at the receiver side. In our methods, we send the public key to allow the receiver to authenticate the signature that generated the message, using the corresponding secret key. For the RCA method, we generated a new secret key from the original secrets and for the communicated zones and a new public key using the zone generators of the communicated overlapped zones. Hiding the original secret keys, enhances the security level of the RCA method over the TCA and SCA method. Only authorized vehicles that registered with the BSs have access to the security zone parameters (\(g_{2 ZID}\)). All vehicles at the initialization step register with the BS to receive the zone security parameters, after this, all parameters are stored securely at vehicles. Our protocol did not transmit the individual security parameters that make the system security high and difficult to break.
## 3 Related Work  
The integration of IoT devices in the field of telemedicine has garnered significant attention in recent years. This section provides an overview of key research and development in this domain.  
One of the fundamental applications of IoT in telemedicine is remote patient monitoring. In [7], IoT devices such as wearable sensors and continuous glucose monitors have enabled real-time data collection and transmission, allowing healthcare providers to monitor patients' vital signs and chronic conditions remotely. In particular, IoT-generated data in telemedicine is voluminous, and machine-learning techniques have been employed to extract valuable insights. In [8], machine-learning models have been used for early disease detection, treatment optimization, and predicting patient outcomes. In [9], the authors proposed a remote healthcare system enabling multiple medical sensors to connect to a server using multiple communication technologies such as Wi-Fi, Bluetooth, or GSM technologies.  
Concerning telemedicine platforms and ecosystems, refs. [10; 11] describe various telemedicine platforms and ecosystems that have been developed to facilitate the seamless integration of IoT devices. These platforms enable secure data transmission, video consultations, and remote diagnostics. Notable examples include the use of Microsoft Azure IoT for healthcare applications. In [12], the authors proposed WhatsApp (the famous mobile messaging application) as a telemedicine platform, but it is used only to share photos and lesion recordings to submit questionnaires to patients to monitor their health. Finally, researchers at the University of Basel (Switzerland) developed an Internet-based platform for telemedicine in 2001 named the Internet Pathology Suite (iPath) [13]. This platform has been implemented to allow the presentation and discussion of images provided by remote electron microscopes; no other device can be used on this platform. Finally, in [14], authors develop a prototype able to provide features for telemedicine like live video streaming, chat boxes, automatic prescription generation, and push notifications. The prototype is used for trial under the supervision of medical experts, and the data are compared with standard tests done in a pathological laboratory.  
Conversely to the above approaches, our system adopts a more open framework as it is IoT device agnostic: the modular design of the interacting components allows HealthTracker to be easily extended to include different IoT devices. To prove it, we demonstrate how our system can exploit an innovative device that provides, for the first time, two sensors able to record the frequency and intensity of the cough, the respiratory rate and depth, the nighttime breathing, including apnea and snoring, the acute fatigue, and lipothymia. All these parameters are essential for monitoring a patient's health condition and for reporting symptoms that must be detected as soon as possible, allowing for prompt action, if necessary, to halt the progression of the disease.