esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Soil Heavy Metal Source Apportionment  
#### 3.2.1 Correlation and Cluster Analysis  
A correlation analysis of soil heavy metals helps determine whether different metals share the same origin. A positive correlation among different heavy metals suggests similar sources, while a negative correlation indicates different sources or antagonistic effects [46]. The Kolmogorov-Smirnov test was applied to check the normality of the dataset. Since the data were not normally distributed, the Kruskal-Wallis and Mann-Whitney non-parametric tests were used to examine the heavy metal datasets. Spearman bivariate correlation analysis was conducted with two-tailed significance tests at 99% (\(p\leq 0.01\)**) and 95% (\(p\leq 0.05\)**) confidence levels (Figure 7). Variables showing significant correlations were subsequently highlighted [47]. The results of the correlation analysis and the correlation coefficients (\(r\)) of the various heavy metals in the study area's soil are presented in Figure 7. The \(r\) between heavy metals in the soil are as follows: Cd-Mn (\(r=0.74\)), Cu-Zn (\(r=0.62\)), Cd-Cu (\(r=0.61\)), Cu-V (\(r=0.59\)), Cr-Ni (\(r=0.59\)), Cd-V (\(r=0.59\)), Mn-Co (\(r=0.56\)), Cr-Cu  
Figure 5: Spatial distribution map of the \(RI\) and key enterprises.  
Figure 6: Map of changes in land use type for the study area in 1990, 2000, 2010 and 2020 [45] (In the figure, (**a**) represents the land use data for the year 1990, (**b**) for the year 2000, (**c**) for the year 2010, and (**d**) for the year 2020).  
(\(r=0.54\)), Cu-Co (\(r=0.53\)), Cd-Co (\(r=0.53\)), Zn-Co (\(r=0.52\)), Mn-V (\(r=0.51\)), and Cd-Zn (\(r=0.50\)). These significant positive correlations (\(p\leq 0.01\)**) suggest that these elements may share a common origin.  
Cluster analysis is a powerful tool for identifying sources of heavy metal pollution. It helps reveal similarities and differences between different soil heavy metals, thereby pinpointing potential pollution sources [1,48]. In the dendrogram of the cluster analysis, the distance represents the correlation between different heavy metals; the closer the distance, the more significant the correlation, implying a similar source. The cluster analysis results for various heavy metals in the study area's soil are shown in Figure 8. Cd, Mn, V, Co, Cu, and Zn are grouped into one category, while Cr, Pb, and Ni are grouped into another, further confirming the results of the correlation analysis.  
Figure 8: Correlation analysis of heavy metal elements in the study area’s soil.  
Figure 7: Correlation analysis of heavy metal elements in the study area’s soil.
# EEG Channel Selection for Stroke Patient Rehabilitation Using BAT Optimizer
## 5 Experiments and Results  
This section thoroughly explains the performance of the proposed method (BAT) of EEG channels for stroke patient rehabilitation. Since the proposed approach is non-deterministic, the mean accuracy rate over 20 runs is computed to avoid biased results. The experiments use a Ideapad 310 Lenovo PC, China. Intel Core i7(r) 2.59 Ghz processor, 16 GB of RAM, and official Windows 10. The performance of the proposed method is evaluated using two measures, namely, accuracy and the number of EEG channels selected, with light shed on the stability of the algorithms by computing the best, worst, and mean accuracy. Moreover, a statistical test is adopted based on the classification accuracy of the motor/imaging EEG dataset using the sum of ranks of the metaheuristic algorithms. The proposed method is tested using three types of EEG features: time domain features, frequency domain features, and entropy domain features. Furthermore, the performance results of the proposed method are evaluated using a statistical test to show the significance of the proposed method compared with other metaheuristic algorithms.  
Table 1 shows the parameters used for the selected metaheuristic algorithms that are used in this work.
## 7 Current Challenges and Problems
### Coils Used in the Wireless Telemetry System  
The wireless inductive coupling use the magnetic field to transfer data and power from the external part to the internal part. In general, RF short-range communication transmits a low power irradiated from the reader coil antenna to offer fixed sinusoidal carrier amplitude that provides a stable wireless transfer power. The bio-device system is composed by a primary coil integrated and isolated inside the human body and a secondary coil located outside the body. In most cases, the primary coil is tuned in series resonance to provide a low impedance load for driving the transmitter coil, whereas the secondary coil is almost invariably in parallel. Thus, to have better power transfer efficiency of inductive coupling link both sides of the link are tuned at the same resonant frequency and the stability of the RF signal need to provide a high readability at the implant device in terms of the distances from the reader coil.
# Contact-Area-Changeable CMP Conditioning for Enhancing Pad Lifetime
## 4 Conclusions  
In this study, we investigated contact-area-changeable swing-arm conditioning for enhancing pad lifetime. The difference between this study and existing studies is not the adjustment of process parameters but, rather, the improvement of the conditioning system, which provides a way to increase the life of the polishing pad. The proposed conditioning system was incorporated with a split conditioner designed to enable independent contact with the polishing pad during conditioning. First, we analyzed the pad cut rate profile using the numerical analysis method, and, among the four cases considered in this study, Case II exhibited a flat pad cut rate profile in the wafer-pad contact area.  
In the conditioning and CMP experiments, Case II, involving a contact-area-changeable conditioner, was compared with Case I, involving a conventional CMP conditioner. Experiments showed that, for Case I, locally excessive pad wear occurred in the pad-wafer contact area. Owing to the uneven pad wear in the wafer-pad contact area by conditioning, the MRR of the SiO\({}_{2}\) CMP in Case I began decreasing after 12 h of conditioning, whereas the WIWNU increased. In Case II, the wafer-pad contact area in Zones 2-5 was reduced, where excessive pad wear occurred in Case I. In Case II, the MRR and WIWNU remained the same until 20 h of conditioning. Experiments showed that maintaining uniform pad wear in the wafer-pad contact area using contact-area-changeable conditioning can improve the pad lifetime.  
The new conditioning system proposed herein can improve the CMP pad lifetime. Further studies regarding the effects of various process parameters should be conducted using the contact-area-changeable conditioning system.  
Conceptualization, H.L.; Investigation, J.S. and H.L.; Project administration, H.L.; Supervision, H.L.; Writing: original draft, J.S.; Writing: review and editing, H.L.; Funding Acquisition, H.L. All authors have read and agreed to the published version of the manuscript.  
Please add: This research was supported by the BB21+ Project in 2020 and Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (NRF-2018R1D1A1B07043169).  
Not applicable.  
Data Availability Statement: Data sharing is not applicable to this article.  
We thank G&P Technology Inc. for the support for the experiment.  
The authors declare no conflict of interest.
# The Effects of Train Passes on Dwell Time Delays in Sweden
## 4 Discussion  
Our first finding is that, in the data we studied, passes rarely happen as scheduled. As we see in Figure 3, the most common scenario at 76% was that train passes are cancelled, followed by unscheduled passes at 21%. Scheduled passes were the least common, at 3%, and 97% of the passes did thus not occur as scheduled. This indicates that the timetable is difficult to realise with a high level of accuracy. It also suggests a high degree of activity among dispatchers to cancel and reschedule train passes, shifting them from one station to another.  
Figure 3: Distribution of different types of train passes.  
Our second finding is that these three types of train passes are associated with very different delay probabilities. As indicated in Figure 4, the probability of delay was highest, 69%, when passes are unscheduled. A cancelled pass was only associated with an 8% probability of delays, while a scheduled pass was in between these two, with a 46% probability of delay. Cancelling a pass is thus a good way to reduce the risk of delays at one station, but it can bring with it a large increase in the probability of delay at another station. For context, when there is no pass, which is the most common situation in operations, there is a 22% probability of dwell time delays--better than when there is a scheduled pass, but worse than if there is a cancelled pass.
## 1 Introduction
The results of these studies have contributed to a better understanding of the effect of F-T action on AE characteristics. However, studies of crack development information that is contained in AE signals during loading are not well developed. Considering the relative importance of F-T damage on rock materials in cold regions, this work attempts to explore the acoustic, mechanical, and physical changes that are induced by F-T action on Urumqi sandstone. First, fresh sandstone was obtained from an open-pit mine slope and then processed into standard samples. Different amounts of F-T cycles were applied to the samples. Then uniaxial compressive tests were performed and the AE signals were recorded. Finally, the effect of F-T damage on the failure attributes of the Urumqi sandstone was investigated by AE signals in the loading process.
# Measurements and Modeling of the Nonlinear Behavior of a Guitar Pickup at Low Frequencies +
## 5 Nonlinear Parametric Model of the Pickup  
Observing the estimated filters of the Generalized Hammerstein model depicted in Figure 5, one can note that the dependence on frequency for all filters is approximately 6 dB/oct. Such a slope corresponds to \(j2\pi f\) in the frequency domain or to a simple derivative \(d/dt\) in the time domain.  
It is thus tempting to fit all the filter responses \(G_{n}(f)\) with a function \(\alpha_{n}j2\pi f\) in order to replace each branch of the Generalized Hammerstein model by a multiplicative coefficient \(\alpha_{n}\) in series with a derivative function \(d/dt\). A fit of the first two filters \(G_{1}(f)\) and \(G_{2}(f)\) (magnitude and phase), obtained using linear regression of absolute values within the frequency range 15-500 Hz, is depicted in Figures 6 and 7. It is interesting to note that the estimated phases of both filters are very close to \(\pi/2\) within the whole frequency range.  
Fixing the phase of all of the estimated filters \(G_{n}(f)\) to \(\pi/2\) and parameterizing the absolute value by \(\alpha_{n}\) (resulting coefficient of the linear regression), we can write the following relation  
\[u(t)=\sum_{n=1}^{N}\alpha_{n}\frac{d\ x(t)^{n}}{dt}=\frac{d}{dt}\left(\sum_{n =1}^{N}\alpha_{n}x(t)^{n}\right). \tag{8}\]  
This relation being a time derivative of a Taylor series, we can simplify the Generalized Hammerstein model to a Hammerstein model consisting of a static nonlinearity followed by a linear filter (Figure 1). In this particular case, the static nonlinearity is represented by a simple Taylor series with coefficients \(\alpha_{n}\), and the linear filter is represented by a time domain derivative, as shown in Figure 1. This result tends to confirm the model previously proposed by [12]. The fitted coefficients \(\alpha_{n}\) for the pickup under test are given in Table 1, and the corresponding input-output characteristic is depicted in Figure 8.  
Figure 6: Modulus (**a**) and phase (**b**) of the first filter \(G_{1}(f)\) of the identified Generalized Hammerstein model of the pickup under test (blue solid line) and the equivalent frequency response of the simplified Hammerstein model followed by a differentiator (green dashed line).  
Figure 8: Input–output graph of the static nonlinearity (power series development with coefficients \(\alpha_{n}\) given in Table 1).  
\begin{table}
\begin{tabular}{c c} \hline \(\alpha_{1}\) & \(7.50\times 10^{-2}\) \\ \(\alpha_{2}\) & \(6.75\times 10^{-3}\) \\ \(\alpha_{3}\) & \(2.11\times 10^{-3}\) \\ \(\alpha_{4}\) & \(4.75\times 10^{-4}\) \\ \(\alpha_{5}\) & \(8.31\times 10^{-4}\) \\ \hline \end{tabular}
\end{table}
Table 1: Coefficients \(\alpha_{n}\) of the estimated parametric Hammerstein model.  
Figure 7: Modulus **(a)** and phase **(b)** of the first filter \(G_{2}(f)\) of the identified Generalized Hammerstein model of the pickup under test (blue solid line) and the equivalent frequency response of the simplified Hammerstein model followed by a differentiator (green dashed line).
## 2 Synthesis of c-di-AMP
### DisA  
DisA, crystalized from _Thermotoga maritima_, was the first protein with a DAC domain to be characterized [29]. DisA is found in both Gram-negative and Gram-positive bacteria but is particularly prevalent in Gram-positive spore-forming bacteria such as _Bacillus_ and _Clostridium_ species. X-ray crystallography revealed that the DAC domains of two tetrameric DisA molecules interact to form a stable octameric structure. The direct contact of DAC domains is necessary for c-di-AMP production [29]. The carboxyl end of DisA enzyme contains a DNA binding domain, and the amino terminal end possesses a globular domain with catalytic activity. These two domains are connected by a helical domain [18]. RadA, a DNA repair protein in _B. subtilis_, is encoded together with DisA in a conserved operon, and their close genetic proximity suggests the involvement of DisA in maintaining DNA integrity [18; 32]. Indeed, DisA mutant strains are less able to repair damaged DNA than the wild type [32; 34]. It turned out that DisA is a checkpoint protein that directly regulates DNA repair mechanisms by responding to DNA damage [18]. During sporulation in _B. subtilis_, DisA scans DNA and detects damage of chromosomal breaks via its DNA-binding domain, thereby signaling a stop in the sporulation process [17; 18]. After repairing the DNA damage, c-di-AMP signals to restart the sporulation pathway [18]. Both high and low c-di-AMP levels impair the DNA binding activity of DisA [18].
# Towards the Internet of Smart Trains:
## 5 The Rise of the Internet of Trains
The same network that strengthens safety has enough capacity to deliver data that serves a variety of applications across the rail system to reduce costs and improve operations. The usage of IIoT is possible thanks to advances in the following underlying technologies:  
* Telecommunications networks are becoming dedicated to IIoT applications and, as it was described in Section 2, broadband communications are getting inexpensive, faster, and ubiquitous. Train companies run fiber along their tracks and have relationships with mobile operators to use their networks to maintain continuous mobile connectivity. M2M technology can boost efficiency by using sensors embedded into different objects and systems to automate tasks and deliver real-time monitoring and analysis. * Sensors for data acquisition are getting smaller, more affordable, and now consume less energy. In some cases, battery life can be extended to up to five years, which is important, because it is not always possible to be close to an electrical supply. * Cloud-based services have become more pervasive, fueled both by fast connectivity and ever-smarter devices. They can be used to store sensor data and to provide the computation required for big data analytics. * Big data and the Cyber-Physical System (CPS) enabled by Industrial IoT (IIoT) allow the different transportation modes to communicate with each other and with the surrounding environment, paving the way for truly integrated and intermodal solutions.
## 4 CNN-Based Signal Classification Model
### Model Training  
Sometimes the signal pattern in the time-frequency image is small and sparse, the features are not obvious. This often happens when the target signal has long broadcast cycle, weak power and small bandwidth (for example, Bluetooth). An example is shown in the Figure 21, the white dot in the area enclosed by the yellow box in the figure are Bluetooth signal pattern.  
This will cause the model to learn the noise features incorrectly during the training process and cause over-fitting. So, we introduce a negative learning (NL) training method to prevent CNN from overfitting noisy data which is proved by [PERSON] [41]. NL method does not require any prior knowledge of noise data such as type and quantity. Different from the positive label data used in positive learning (PL) which contains the target feature information that the model focuses on, the negative label data can tell the model about the feature information of noise and interference information, which helps to distinguish the useless features. By combining PL and NL, we can improve accuracy while ensuring training speed. PL can quickly reduce the loss, but it is easy to overfit in the end. The obtained model after PL is then subjected to NL to correct the over-fitting of the noise and improve the recognition accuracy. This article uses a combination of two NL and one PL for training. The training process is shown in the Figure 22.  
In order to obtain the best classification performance, we need to adjust three hyperparameters which are the initial learning rate, the mini-batch size, and the training iterations number. We set different hyperparameter values for the three training processes, and conduct a series of training to try different parameter combinations. The final parameter values are shown in Table 2.  
Figure 21: Bluetooth time-frequency image with unobvious characteristics.  
Figure 22: The training process combined NL and PL.
# Measuring Ocean Surface Current in the Kuroshio Region Using Gaofen-3 SAR Data
## 3 Gaofen-3 Data Experiment and Results
### Results Validation
Therefore, in terms of the factors that lead to the difference between the two models in retrieving the current field, the input data type and accuracy, the calculation and processing process, and the temporal and spatial characteristics of the current field products will make them different.
## 3 System Description  
### Overall Architecture  
The overall system architecture in which the proposed BAQALC solution should be embedded is depicted in Figure 2. The overall architecture includes a basic blockchain-based scheme where biomedical researchers transmit/request DNA data which is ensured by distributed ledgers. Also in this scheme, healthcare users may request to receive fast DNA analysis results or data in future medical informatics systems, which is an innovative field mentioned in prior research [39]. Note that the BAQALC solution is developed in C to be compatible with a wide range of platforms; this will also be discussed in Section 4.1.  
Figure 2: Overall System Architecture of BAQALC Modules.  
When a biomedical researcher (node) discovers a nucleotide/peptide sequences, the researcher may transmit DNA data using the BAQALC compression module, which is depicted in a red circle in Figure 2. Note that the BAQALC decompression module is depicted in green circles. The transmitted compressed DNA data is sent to a public DNA database server, for example the National Center for Biotechnology Information's (NCBI), which is the most widely used DNA database world-wide. This creates a transaction.  
Then, another biomedical researcher (miner) verifies whether the transaction is valid. As a miner, the researcher decodes the transaction sequence for verification. Verification includes filtering basic anomaly transactions such as uploading by researchers with unreliable histories, sequences that are too short or long compared to reference sequences, chimera detection, and many other errors. If the transaction is valid, the miner broadcasts the updated block chain into the blockchain network. Broadcasted updates are synced to all biomedical researcher nodes. This mechanism prevents any fraud or maliciously-fabricated updates by the nodes, creating a reliable database overall.  
The DNA database is stored and kept in the database in an encrypted (compressed) block chained form. Other biomedical researchers may access the database and request the DNA data. Note that simple requesting only calls the blockchain data by reference, so no transaction is triggered. Once the request is accepted, DNA data is accessed, and when it reaches the requesting biomedical researchers' servers, the data is decoded using a BAQALC decompression module. If other biomedical researchers wish to upload DNA discoveries of their own, they may also transmit DNA data using the BAQALC compression module, which will trigger more transactions for verification by miners.  
In addition to researchers, future healthcare users can subscribe to their own DNA data for medical informatics systems. Users can request DNA analysis results or even raw data of their health samples from medical researchers. Researchers analyze samples, and report results are transmitted to users. If the user has requested their own raw DNA data, the data is simply transmitted via BAQALC. After being decompressed by BAQALC, healthcare users can store and view their DNA through their mobile electronic devices.
# Analysis and Application of Lining Resistance to Water Pressure in Tunnel through Karst Cave
## 2 Failure Mode of Grouting in Tunnels Crossing High-Pressure Karst Caves
### Influence of Grouting Range on the Effect of Grouting
#### 3.1.1 Establishment of the Numerical Calculation Model  
In order to study the hydraulic resistance of the composite structure formed by the grouting circle and the remaining surrounding rock near the tunnel, the following model was established: it was assumed that, after the parallel heading expansion, 3 m thick surrounding rock remained between the tunnel and the side hidden cavity. At this point, the safety thickness of the surrounding rock may not have been sufficient, and the cavity filling needed to be reinforced with grout before the parallel heading expansion. The thickness of grouting reinforcement was 5 m; hence, the 3 m thick surrounding rock and 5 m thick grouted rock body could be regarded as a composite surrounding rock after the parallel heading expansion, and the cavity could be simplified as a water-bearing cavity to carry out a simulation study of the composite surrounding rock's resistance to water pressure. The model is schematically shown in Figure 12. The stratigraphic parameters, model excavation, and cavity water pressure application methods used in the calculations were as described above.  
Figure 11: Comparison of water pressure resistance of stone bodies under different grouting ranges.
## 4 Analysis and Discussion
### PUF-Based Authentication Challenges and Solutions
#### 4.4.2 Challenge 2: Bit Size of PUF  
PUFs are thought to be safer when the pattern of outputs (the number of different responses) is larger. The 192-bit response is more secure because it has more bits than one with 128 bits. However, the length of each bit is shortened because some bits of the response are different (random). Therefore, there is a need to utilize mismatch size to produce an additional stable bit so that PUFs become safer.