esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Research Design and Procedure
### Data Acquisition and Preprocessing  
The dataset from Data Hub, which included different features from the MIT-BIH Arrhythmia dataset (Physionet)--a two-lead ECG signal consisting of leads II and V--was the main focus of this investigation. 279 attributes total across sixteen classes make up this dataset. Table 1 shows the statistics for each of the specified attributes, whereas Table 2 illustrates the classifications. The MIT-BIH Arrhythmia Dataset provides a range of ECG recordings that provide meaningful information on cardiac abnormalities, hence the use of this dataset is justified. Although the dataset primarily focused on arrhythmias, the ECG patterns displayed in these recordings reflect the cardiac issues frequently observed in individuals with CKD, such as left ventricular hypertrophy and alterations in heart rate variability. Numerous studies have shown a connection between renal function and ECG findings, indicating that certain cardiac irregularities and arrhythmias may be indicators of the progression of chronic kidney disease [19; 20]. Furthermore, the MIT-BIH dataset facilitates cross-study and technique comparisons by acting as a recognized standard in the field of cardiovascular research. The study investigates the association between ECG features and cardiac anomalies using this dataset, therefore bolstering the hypothesis that ECG analysis might help predict and detect kidney disorders early. Since a large ECG dataset from CKD patients is difficult to acquire, we sought instead to use a publicly available MIT-BIH Arrhythmia dataset that contained normal and abnormal ECG signals with known cardiac anomalies. This dataset is useful to the research of  
Figure 1: Overview of the study’s methodology for abnormal ECG patterns using the LADTree model and ECG signal data, highlighting the comparison with various machine learning models and assessment measures. CKD because of the well-established associations between certain cardiac abnormalities and CKD, even though Table 1 lists distinct cardiac disorders rather than renal problems. Research has demonstrated that people with CKD often have unique cardiac problems, including left ventricular hypertrophy (LVH), reduced heart rate variability, and certain ECG abnormalities, such as longer QT intervals and peaked T waves as a result of hyperkalemia. These cardiac anomalies, which are included in the MIT-BIH dataset, offer important new information about the cardiovascular consequences of chronic kidney disease. The extensive ECG recordings in the dataset enable researchers to examine trends and characteristics that could point to the development of CKD.
## 5 Case Study
### Case Study for Residual Life Prediction  
The whole life process of the electrical connector is divided into 6 using states. Therefore, the 6 macro-state HSMM is introduced to build the model of residual life prediction (time unit: 10 min).  
A group of whole life intermittent failure data is collected firstly, the HSMM-based prediction model HSMM\({}_{\text{d}}\) (\(\lambda_{\text{d}}\)) is obtained using the parameter re-estimation algorithm. Then, by training the  
Figure 11: Evaluation results. (**a**) Evaluation results of the using state 1; (**b**) Evaluation results of the using state 2; (**c**) Evaluation results of the using state 3; (**d**) Evaluation results of the using state 4; (**e**) Evaluation results of the using state 5; (**f**) Evaluation results of the using state 6.  
model, the probability matrix of using state transition \(A\), mean and variance of duration probability of each using state is shown in Tables 1-3 separately.  
According to the training results, the duration of each using state can be obtained by Equation (5) as shown in Table 3  
Next, 20 samples were chosen from a life cycle test to verify the effectiveness of residual life prediction method proposed in this paper. The results of the prediction are shown in Table 4.  
According to Table 4, except for one of samples of using state 4, the other samples are predicted correctly within the interval of residual life. The proposed method can be used for the residual life prediction of electrical connectors.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Using State** & \(\mathbf{h_{1}}\) & \(\mathbf{h_{2}}\) & \(\mathbf{h_{3}}\) & \(\mathbf{h_{4}}\) & \(\mathbf{h_{5}}\) & \(\mathbf{h_{6}}\) \\ \hline \(h_{1}\) & 0.933 & 0.067 & 0 & 0 & 0 \\ \(h_{2}\) & 0 & 0.545 & 0.412 & 0.033 & 0 & 0 \\ \(h_{3}\) & 0 & 0 & 0.591 & 0.372 & 0.037 & 0 \\ \(h_{4}\) & 0 & 0 & 0 & 0.774 & 0.214 & 0.012 \\ \(h_{5}\) & 0 & 0 & 0 & 0 & 0.864 & 0.136 \\ \(h_{6}\) & 0 & 0 & 0 & 0 & 0 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The matrix of state transition probability.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Using State** & \(\mathbf{h_{1}}\) & \(\mathbf{h_{2}}\) & \(\mathbf{h_{3}}\) & \(\mathbf{h_{4}}\) & \(\mathbf{h_{5}}\) & \(\mathbf{h_{6}}\) \\ \hline \(D(h_{1})\) & 16.19 & 7.03 & 4.53 & 1.26 & 0.72 & 0.33 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The duration of each using state.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Test Groups** & \(\mathbf{h_{1}}\) & \(\mathbf{[\text{RUL}_{\text{mean}}-\text{RUL}_{\text{variance}},\text{RUL}_{\text{ mean}}+\text{RUL}_{\text{variance}}]}\) & **RUL** & **Results** \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 4: The result of residual life prediction.
# Financial Market Correlation Analysis and Stock Selection Application Based on TCN-Deep Clustering
## 3 Experimental Settings and Results
### Evaluation Indicators  
The evaluation methods for clustering results can generally be divided into internal and external evaluation methods. An external evaluation evaluates the goodness of the clustering results when the true labels are known, and since there were no true labels for the stock data, an internal evaluation was used. For a clustering method, a lower intra-cluster aggregation and higher inter-cluster coupling can indicate a better performance of the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No** & **Type** & **Layer** & **Output Shape** & **Parameters** \\ \hline
1 & — & Input\_1 (InputLayer) & (None, None, 1) & 0 \\
2 & Encoder (Layer 1) & residual\_block\_1 & (None, None, 40) & 5162 \\
3 & Encoder (Layer 2) & residual\_block\_2 & (None, None, 40) & 9762 \\
4 & Encoder (Layer 3) & residual\_block\_3 & (None, None, 40) & 9762 \\
5 & Encoder (Layer 4) & residual\_block\_4 & (None, None, 40) & 9762 \\
6 & Encoder (Layer 5) & residual\_block\_5 & (None, None, 40) & 9762 \\
7 & Encoder (Layer 6) & residual\_block\_6 & (None, None, 160) & 103,202 \\
8 & Encoder (Layer 7) & global\_max\_pooling1d & (None,160) & 0 \\
9 & Encoder (Layer 8) & dense & (None, 320) & 51,520 \\
10 & Encoder (Layer 9) & activation & (None, 320) & 0 \\
11 & Decoder (Layer 10) & dense & (None, 796) & 255,516 \\
12 & Decoder (Layer 11) & reshape & (None, 796, 1) & 0 \\
13 & Decoder (Layer 12) & decoder\_residual\_block\_1 & (None, 796, 40) & 5162 \\
14 & Decoder (layer 13) & decoder\_residual\_block\_2 & (None, 796, 40) & 9762 \\
15 & Decoder (Layer 14) & decoder\_residual\_block\_3 & (None, 796, 40) & 9762 \\
16 & Decoder (Layer 15) & decoder\_residual\_block\_4 & (None, 796, 40) & 9762 \\
17 & Decoder (Layer 16) & decoder\_residual\_block\_5 & (None, 796, 40) & 9762 \\
18 & Decoder (Layer 17) & decoder\_residual\_block\_6 & (None, 796, 1) & 170 \\ \hline \hline \end{tabular}
\end{table}
Table 2: TCN encoder-decoder network structure and parameter settings.  
Figure 6: The relationship between SSE and K.  
clustering method. In this paper, the SC (Silhouette Score) [24], CH (Calinski-Harabaz) [25], and DB (Davies-Bouldin) [26] were adopted to evaluate the clustering performance.
# Superimposed Perfect Binary Array-Aided Channel Estimation for OTFS Systems
## 4 Spectral Efficiency and Optimal Power Ratio  
Recall from Section 2 that the superimposed symbol \(x[k,l]\) is formed by superimposing the PBA symbol \(x_{p}[k,l]\) and data symbol \(x_{d}[k,l]\). Under the condition that the total power is constant, the power ratio \(\beta\in[0,1]\) between PBA symbol and superimposed symbol will have a significant effect on the channel estimation performance and SE of the proposed method. Similar to [17], to minimize BER and maximize SE, we derive the optimal power ratio for the proposed SPBA-OTFS systems.
Sensors_**2006**, \(6\), 164-192  
_Sensors_  
**ISSN 1424-8220**  
(c 2006 by MDPI  
[[http://www.mdpi.org/sensors](http://www.mdpi.org/sensors)]([http://www.mdpi.org/sensors](http://www.mdpi.org/sensors))  
_Special Issue on \"Satellite Altimetry: New Sensors and New Application\" Edited by [PERSON] and [PERSON]. [PERSON]_  
_Full Research Article_  
Performances Study of Interferometric Radar Altimeters: from the Instrument to the Global Mission Definition  
**[PERSON] 1,2,*, [PERSON] 3, [PERSON] 1, [PERSON] 4, [PERSON] 5 and [PERSON] 1,2**  
1 CNES, 18 av. [PERSON], 31055 Toulouse Cedex 4, France  
Tel : (33) 5 61 27 38 38, Fax : (33) 5 61 28 34 22, Email: [EMAIL_ADDRESS]  
2 LEGOS, 14 av. [PERSON], 31400 Toulouse, France  
Email: [EMAIL_ADDRESS]  
3 IFREMER, 155 rue [PERSON], 92138 Issy les Moulineaux, France  
Email: [EMAIL_ADDRESS]  
4 JPL, 4800 Oak Grove Drive, Pasadena, CA 91109, USA  
Email: [PERSON]5 ALCATEL SPACE, 28, av. [PERSON], 31000 Toulouse, France  
Email: [PERSON]*Author to whom correspondence should be addressed  
_Received: 4 August 2005 / Accepted: 2 February 2006 / Published: 7 March 2006_  
**Abstract:** The main limitations of standard nadir-looking radar altimeters have been known for long. They include the lack of coverage (intertrack distance of typically 150 km for the T/P / Jason tandem), and the spatial resolution (typically 2 km for T/P and Jason), expected to be a limiting factor for the determination of mesoscale phenomena in deep ocean. In this context, various solutions using off-nadir radar interferometry have been proposed by [PERSON] and al to give an answer to oceanographic mission objectives. This paper addresses the performances study of this new generation of instruments, and dedicated mission. A first approach is based on the Wide-Swath Ocean Altimeter (WSOA) intended to be implemented onboard Jason-2 in 2004 but now abandoned. Every error domain has been checked: the physics of the measurement, its geometry, the impact of the platform and external errors like the tropospheric and ionospheric delays. We have especially shown the strong need to move to a sun-synchronous orbit and the non-negligible impact of propagation media errors in the swath, reaching a few centimetres in the worst case. Some changes in the parameters of the instrument have also been discussed to improve the overall error budget. The outcomes have led to the definition and the optimization of such an instrument and its dedicated mission.  
**Keywords:** altimetry, interferometry, error budget, system analysis, ocean, mesoscale.
# Adaptive Packet Coding for Reliable Underwater Acoustic Communications
## 2 System Model
Then, the relationship is given as  
\[\left\{ \begin{array}{l}\sigma_{k,i}^{(3)}=\sigma_{k,i}^{(2)}+T_{g}\\ \sigma_{k,i}^{(4)}=\sigma_{k,i}^{(3)}+T_{\text{ACK}}\end{array} \right. \tag{4}\]  
where \(T_{\text{ACK}}\) is the time duration of ACK. The sender will receive the corresponding ACK after \(T_{d}\) time duration. Let \(\rho_{k,i}^{(3)}\) and \(\rho_{k,i}^{(4)}\) denote the beginning and ending time points for the received ACK at the sender. Then,  
\[\left\{ \begin{array}{l}\rho_{k,i}^{(3)}=\sigma_{k,i}^{(3)}+T_{d}\\ \rho_{k,i}^{(4)}=\rho_{k,i}^{(3)}+T_{\text{ACK}}\end{array} \right. \tag{5}\]  
The sender could estimate the CSI based on the pilot symbols in the ACK and decode the feedback messages \(s_{i}^{(k)}\). We use a low rate channel code for ACK messages to improve the reliability of feedback messages. To reduce the complexity of the problem, we assume the feedback messages are error-free. There is also a guard interval for the sender to process the ACK and change the mode. In this way, the \(i\)-th transmission for the \(k\)-th block is done. Hence, the total time duration for the \(i\)-th transmission is given as  
\[T_{i}^{(k)} =T_{p}N_{i}^{k}+2T_{d}+2T_{g}+T_{\text{ACK}} \tag{6}\] \[=T_{p}N_{i}^{k}+T_{\tau}. \tag{7}\]  
where \(T_{\tau}=2T_{d}+2T_{g}+T_{\text{ACK}}\) is fixed for each transmission. If the receiver has received enough coded packets to recover the original information from the \(k\)-th block, the sender will send the coded packets from another block in the next transmission. Otherwise, the sender determines the number of transmitted coded packets in the \((i+1)\)-th transmission based on the feedback ACK. Thus, the data rate of packet coding is not constant during the transmission. The sender dynamically adjusts the rate based on the current transmission result to adapt to the time variation of UWA channels.
###### Abstract
Moreover, monitoring cell growth by following the total suspended solids (TSSs) and volatile suspended solids (VSSs) by Raman spectroscopy would also provide important information on the system performance, such as, for example, the light availability per biomass concentration, a parameter that can affect photosynthesis efficiency [23]. Micro-Raman spectroscopy has been used for the simultaneous identification and quantification of the intracellular polymers poly-P, poly(3-hydroxybutyrate) (PHB) and glycogen in individual microbial cells from complex environmental samples, characterizing their distribution among conventional EBPR microbial populations [24; 25]. Recent studies have further developed Raman microscopy-based quantitative approaches to assess the structural dynamics and storage states of these relevant intracellular polymers, crucial for a fundamental understanding of the EBPR process [26; 27]. In addition, Raman microscopy was shown to identify and quantify poly-P in microalgal cells, specifically _Chorella vulgaris_[28]. However, Raman microscopy is not suitable for online measurements and although Raman spectroscopy has been suggested to be a fast and efficient tool for process control of PHB bioproduction through qualitative and quantitative in situ monitoring of intracellular PHB content in _Cupriavidus necator_ H16 cultures [29], its application to real-time monitoring in mixed microbial bioprocesses is limited and has never been demonstrated for a photo-BNR system. Unlike Raman microscopy, where specific Raman peaks can be used to follow the associated biomolecules, Raman spectra acquired through an immersion probe in a complex environmental ecosystem, such as a photo-BNR reactor, are very complex, including a large amount of data. Therefore, Raman spectroscopy needs to be combined with chemometric tools to extract the relevant information from the spectral data and develop quantitative mathematical models that will ultimately allow real-time predictions of the system properties and a concentration of various analytes based on new, in line, fast and non-destructive spectroscopic measurements. The present study aimed to develop a Raman-based monitoring strategy for the real-time prediction of several key parameters of a photo-BNR reactor for process control and optimization. Therefore, Raman spectra were acquired at-line, directly from mixed liquor samples harvested from a lab-scale photo-BNR reactor, and partial least squares (PLS) calibration models were developed to predict the concentration of TOC, VFAs, CO\({}_{2}\), NH\({}_{3}\), NO\({}_{3}\), PO\({}_{4}\), total P, PHAs, carbohydrates, TSSs and VSSs in the mixed liquid. The capacity of the calibration models to predict the reference data measured by standard analytical methods was evaluated by a full cross-validation procedure.
## 2 System Model
### The Proposed Flip-Filtered OFDM
#### 2.2.3 Optical Channel Model  
In the optical wireless communication systems of Figure 6, the two predominant sources of noise are photons and thermals in the receiving circuitry [40]. Photon noise is created from the discrete nature of photon arrivals, primarily caused by background light sources. Although optical filtering can minimize the received background light, shot noise is still generated even in a well-designed photodetector [41]. Since background light power is typically more substantial than the sent signal, the impact of the conveyed optical signal on this shot noise is insignificant.  
Consequently, it is typically assumed that shot noise is autonomous of the signal and can be quantified as white Gaussian noise. Similarly, in the absence of background light, the thermal (physical noise) is the dominant noise from the receiver preamplifier, which would also be signal-independent and follow a Gaussian distribution. In both instances, the whole noise source can be effectively constituted as Gaussian noise and signal-independent [40; 41].  
In this research, the transmitted signal, \(x(n)\), passes out of an optical impulse response channel represented by \(h(n)\), as shown in Figure 6. Then, an AWGN channel is employed,  
Figure 5: The receiver of the suggested Flip\(-\)Filter\(-\)OFDM.  
Figure 6: Optical communication model.  
and the received signal, \(y(n)\), can be given as follows, taking into account the addition of noise with a variance of \(\sigma_{\mu}^{2}\) and mean of zero [37]:  
\[y(n)=x(n)*h(n)+\mu(n) \tag{18}\]  
where \(*\) is the convolution, \(x(n)\) denotes the signal transmitted, and \(\mu(n)\) signifies the noise component. Then, the frequency symbol \(Y(\kappa)\) is obtained by convolving the frequency response \(H(\kappa)\) with the transmitted symbols \(X(\kappa)\) and adding the noise term \(M(\kappa)\), which in turn is extracted by:  
\[Y(\kappa)=X(\kappa)H(\kappa)+M(\kappa) \tag{19}\]
# Predicting Topology Propagation Messages in Mobile Ad Hoc Networks: The Value of History
## 5 Evaluation of OLSR-HBP in Mobile Scenarios
### Impact of OLSR-HBP on the Network Performance
#### 5.4.2 Energy Consumption  
Another potential benefit of decreasing the number of TP messages sent through the network is the energy savings. In order to compute the reduction in the amount of energy consumed for transmissions and receptions, we used the NS-3 energy model WiFiRadioEnergy. In this model, we configured the transmitter and receiver electric current to 0.38 and 0.313 Amperes respectively. The rest of the parameters were set to 0 Amperes. Thus, we focus the energy analysis on the power needed to just send and receive data through the wireless network.  
Table 9 shows the average normalized energy consumption for the four test cases. The values correspond to the average energy consumption (in Joules) per node and per hour, for the three ping periods considered. We performed 10 different executions for each of the three ping periods. The difference among the 10 executions was the initial location of the nodes and also the mobility paths, due to the impact of initial nodes location when the SLAW mobility pattern was used.  
\begin{table}
\begin{tabular}{c c c c} \multicolumn{4}{c}{a SLAW 10 nodes 1 h} \\ \hline
**Ping Period** & **10 s** & **1 s** & **0.1 s** \\ \hline OLSR & 2.01 & 6.34 & 51.4 \\ OLSR-HBP & 1.98 & 6.61 & 52.4 \\ Improvement & 1.5\% & \(-\)4.3\% & \(-\)2.0\% \\ \hline \multicolumn{4}{c}{b SLAW 10 nodes 4 h} \\ \hline
**Ping Period** & **10 s** & **1 s** & **0.1 s** \\ \hline OLSR & 2.25 & 7.85 & 63.1 \\ OLSR-HBP & 2.33 & 8.57 & 70.7 \\ Improvement & \(-\)3.6\% & \(-\)9.1\% & \(-\)12.1\% \\ \hline \multicolumn{4}{c}{c SLAW 20 nodes 1 h} \\ \hline
**Ping Period** & **10 s** & **1 s** & **0.1 s** \\ \hline OLSR & 3.9 & 12.0 & 95.3 \\ OLSR-HBP & 3.84 & 12.3 & 97.7 \\ Improvement & 1.8\% & \(-\)1.9\% & \(-\)2.5\% \\ \hline \multicolumn{4}{c}{d SLAW 20 nodes 4 h} \\ \hline
**Ping Period** & **10 s** & **1 s** & **0.1 s** \\ \hline OLSR & 5.30 & 17.5 & 142.6 \\ OLSR-HBP & 6.23 & 22.7 & 177.8 \\ Improvement & \(-\)17.5\% & \(-\)30.0\% & \(-\)24.7\% \\ \hline \end{tabular}
\end{table}
Table 9: Energy consumption (in Joules) per node and per hour.  
We can observe energy savings for the 10 s ping periods and 1 h execution times, specially in the 4 \(\times\) 4 grid case (11.14% energy decrease). For the rest of the 1 h values, the energy consumption of OLSR-HBP increases up to 4.59% (2.76% in average) with regard to the regular OLSR protocol. When the execution time lasts 4 h, the normalized energy consumption increases in both, OLSR and OLSR-HBP. This increment is in average 16.15%, and it grows up to 30.0%. The higher energy consumption of 4 h versus 1 h cases, reinforces the previous conclusion that indicates the use of shorter execution times provide better prediction results for OLSR-HBP.
## 2 An Overview of the Cellular Networks
### General Features of Cellular Networks
#### 2.1.2 Cells and Their Administrative Organization  
Cellular radio networks are based on the deployment of a large number of low-powered base stations for signal transmission, and each one has a limited transmission area called a cell. Each cell is characterized by a radio frequency and a geographical coverage area. Cells are grouped into clusters to avoid adjacent cells using the same frequency. Usually, a cell overlaps one or more others; a mobile device can distinguish among them by making use of their frequencies and scrambling codes (in the cases of UMTS and LTE).  
The coverage of base stations may vary considerably, according to a straightforward hierarchical division, depending on their usage scenarios. As an example, in a rural environment, tall towers with large coverage areas are needed, whereas in urban canyons or indoor environments, smaller low-power antennas are exploited for better propagation of the signal, resulting in smaller coverage areas. Deploying a large number of small cells is also a commonly used strategy for dealing with a large number of users, as is typical for urban areas.  
Cells in a mobile network are grouped together into administrative areas, known as location areas (LA) in 2G/3G voice services, routing areas (RA) in 2G/3G data services, and tracking areas (TA) in 4G networks. These administrative areas are used to determine in a rough way the current location of a mobile device in idle mode, that is, when it is switched on but is not using the network for any call or data exchanges. They play a fundamental role also in several other cases: when the mobile device switches to a different area and must be paged correctly; when it is switched on and a comparison with the previous identity is needed; and to perform periodic checks to ensure the network that the device is still reachable.
## 5 Optimization and Scheduling Model of the Complementary System
### Power Generation and Environmental Benefits
#### 5.2.2 System Benefit  
Consider the direct or indirect environmental benefits of building the complementary system, including electricity sales revenues \(S_{ele}\) and carbon emission reductions \(S_{CO_{2}}\):  
\[S_{ele}=C_{rlout}W_{out} \tag{16}\]  
\[S_{CO_{2}}=C_{CO_{2}}W_{out} \tag{17}\]  
where \(C_{rlout}\) is the electricity sales price, CNY/kWh; \(W_{out}\) is the electricity sold by the system, kWh; \(C_{CO_{2}}\) is the carbon emission factor of the power grid.