esa-sceva/satcom-chunk-collection huggingface dataset:
# A Literature Review of Adhesive Systems in Density: Key Components and Their Clinical Applications
## 5 Resin-Dentin Bonding
### Mechanism of Adhesion  
Adhesion occurs when two different molecules react upon being brought into contact as a result of a force of attraction between them [101]. The material used to cause adhesion is known as an \"adhesive\" and the substrate to which the adhesive is applied is named \"adherent\" [102; 103].  
Adhesion mechanisms that are normally used in dentistry are as follows:  
1. Physical adhesion includes: 1. Van der Waals interactions, which is the attraction among opposite charges on dipoles and ions. 2. Dispersion forces, which is the interaction of induced dipoles. 3. Hydrogen bond, an especially strong bond, can be involved in physical forces [101; 104].
2. Chemical adhesion includes: 1. Covalent bonding that includes sharing electrons between two molecules. It is a strong bond that liberates considerable amounts of energy. A covalent bond is existent in all organic compounds. 2. Ionic bonding that includes a transfer of electrons from an atom to another, like the ion exchange adhesion mechanism observed in glass ionomer cements. 3. Metallic bonding is the characteristics of the chemical bond of metals in which mobile electrons are exchanged among atoms in a typically stable crystalline assembly [101; 104; 105].
3. Mechanical adhesion includes: 1. A diffusion of a material into another at a microscopic level. For instance, in composite resins the bonding includes the diffusion of resin into the enamel and the dentin substrates and the establishment of RTs [101].  
After exploring the various adhesion mechanisms, suitable adhesion results are achieved by following these steps: dissolution of the smear layer from the enamel and dentin substrates; preservation of the collagen matrix; ensuring adequate wetting; effective monomer infiltration; photopolymerization within the tooth structure; and copolymerization with the resin composite matrix [102; 106; 107].
# Re-Keying Scheme Revisited: Security Model and Instantiations
## 7 Discussion
### Application to Sensor Network Devices  
Let us assume sensor devices that collect the sensor data and send them to a server via an edge device. The sensor devices and the edge device are supposed to be resource-constrained. Since these devices are located in the field, there is a fear of the SCA on these devices.  
Furthermore, assume that the sensor data are confidential. For example, the sensor devices are medical ones, and all the data include sensitive information of the patients. Alternatively, the sensor devices are located at an agricultural field, and all the data include key information, such as the temperature or the humidity, which is useful for optimal cultivation. Furthermore, let us assume that the data sent from the server to the devices are less sensitive; for example, the instructions for collecting and sending sensor data. Hence, let us consider that the sensor device encrypts its sensor data and sends them to the edge device; and then, the edge device decrypts the sensor data, re-encrypts them with a key shared between the edge device and the server, and sends the ciphertext to the server. Note that the decrypted sensor data are not revealed by the edge device.  
The first-order SCA-resistant encryption re-keying scheme is suitable for such a situation. Let us regard the sensor devices and the edge device as the encryption device and the decryption one in the scheme, respectively. As we discussed in Section 5.2.2, the SCA against the sensor device is difficult because of the fresh randomness \(r\). As for the edge device, it does not reveal the decrypted sensor data, but the re-encrypted ciphertext; therefore, the SCA against the edge device is also difficult, as we discussed in Section 5.2.2.  
Note that the previous schemes, [PERSON] et al.'s scheme and [PERSON] et al.'s first scheme, are unsuitable for this application. This is because, in these schemes, the input of \(\mathsf{BC}^{-1}\) in the edge device is unprotected and the message recovery attack with the SCA against the edge device is possible.
# Artificial Intelligence and Employment: A Delicate Balance Between Progress and Quality in China
## 5 Discussion
### Limitations  
Due to data availability, this study includes only 195 Chinese prefecture-level cities, primarily excluding certain western cities. Nevertheless, these cities constitute 70% of all prefecture-level cities in China, encompassing most of the population and diverse regional economies, thus adequately representing national economic conditions. Although the analysis focuses specifically on the initial AI integration phase (2011-2019), subsequent rapid advancements, such as generative AI and large language models post-2019, might significantly alter employment dynamics. However, this temporal limitation is valuable because it establishes a baseline for AI's early labor market impacts, providing a foundational reference for longitudinal analyses. Future research should integrate updated data to explore AI's evolving role in creative, managerial, and service sectors, contextualizing new developments such as gig economies and AI-driven platforms against previously identified trends. Additionally, this study measures employment quality through macro-level (urbanization and industrial structure) and micro-level (wages) indicators yet lacks qualitative worker-level data such as job satisfaction and skills acquisition. Incorporating individual-level survey data into future analyses could enrich our understanding of employment quality nuances.
## 2 Data Description and Network Partitioning
### Calculations of Network Traffic Variables  
The characteristics and disciplines of the regional traffic flow are investigated by network traffic variables, including average flow, average speed, and average density. The network traffic variables are calculated using the average values over all lanes in the study area. The flow is obtained directly through the original data from the cameras, while the speed and the density are obtained by calculation.  
Figure 2 is a schematic illustration of the position of the cameras. The data collected from two adjacent cameras along a link are used to calculate the traffic variables. In Figure 2, a pair of neighboring intersections \((i,j)\) is defined as a link with the length \(l_{(i,j)}\) and the number of lanes \(n_{(i,j)}\). There are three upstream cameras (straight-direction camera \(i_{c}\), left-direction camera \(i_{l}\), and right-direction camera \(i_{r}\)) and one downstream camera \(j_{c}\). Define \(\Omega\) be the set of all links, \((i,j)\in\Omega\). Link flows are counted at the downstream stop line of links and aggregated every 5 min as a period \(T\). Denote \(q_{(i,j)}((i,j)\in\Omega)\) as the cumulative  
Figure 1: Distribution of cemeteries and connecting roads in Tianjin.  
flow through the link \((i,j)\) in the period \(T\), and the network average flow \(q^{u}\) is the mean value of all lanes covered by cameras in period \(T\)[3], given as:  
\[q^{u}=\sum_{\Omega}q_{(i,j)}/\sum_{\Omega}n_{(i,j)} \tag{1}\]  
The travel time \(t^{s}_{(i,j)}\) for vehicle \(s\) passed link \((i,j)\) is the time difference between the two adjacent records on the link by license plate matching. Suppose there are \(m^{T}_{(i,j)}\) vehicles passed through the link \((i,j)\) in period \(T\). The network average speed \(v^{u}\) is calculated by the total distance \(L^{T}\) and the total travel time \(t^{T}\)[3], given as:  
\[v^{u}=L^{T}/t^{T}=\left(\sum_{\Omega}m^{T}_{(i,j)}*l_{(i,j)}\right)/\left( \sum_{\Omega}\sum_{s=1}^{m^{T}_{(i,j)}}t^{s}_{(i,j)}\right) \tag{2}\]  
The network average density is denoted as \(k^{u}\), which is the ratio of the average flow and the average density:  
\[k^{u}=q^{u}/v^{u} \tag{3}\]
# Framework for Behavioral Analysis of Mobile Networks
## 3 Proposed Framework  
This section presents the details of the proposed framework for the classification and analysis of pattern behaviors in a mobile network. The system is composed of 2 parts: a first block that analyses and identifies the types of cell behavior from a network based on information collected during a given period of time, and another block that monitors the network gathering updated information to detect changes in cellsbehavior over time. Figure 1 shows an overview of the proposed system, where the two main parts of the framework are distinguished as Training and Operation phases. The training phase is divided into two functional blocks: Cell Patterns Detection and Cell Patterns Classification. The former is responsible for identifying the behavior patterns in the studied network from an unlabeled dataset and it is based on SOM algorithm [8]. As a result of the SOM application, the original dataset can be labeled to be used in the subsequent supervised stage. The second block, Cell Patterns Classification, is focused on the training of a Random Forest algorithm [9] with the labeled dataset in order to build a classifier. Thus, the training phase ends with the framework prepared for monitoring the cells.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Algorithms** & **Context/Objective** & **Work** \\ \hline Classification Tree & Diagnosis & [24] \\ Unsupervised techniques (SOM as the & Diagnosis & [25] \\ center-piece) & Radio Frequencies (RF) conditions diagnosis & [26] \\ \hline Random Forest, Deep Learning, Ridge Regression & Transmission power prediction & [28] \\ (Separated tests) & Signal strength prediction & [29] \\ \hline SOM & Cell pattern detection based on context & [30] \\ SOM, K-Means & Cell pattern detection in 3G networks & [31] \\ SOM, K-Means & Radio access network analysis through behavioral & [32] \\  & patterns detection & [33] \\ SOM & Detection of daily traffic patterns & [33] \\ \hline Naive Bayes, Holt-Winters & Classification of cells in terms of traffic & [34] \\ Unsupervised (Hierarchical clustering) and & Classification of traffic patterns by apps (Facebook, & [35] \\ supervised (Random Forest) algorithms & Twitter, Gmail, etc) & [36] \\ Improved Random Forest & Traffic pattern classification & [36] \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 1: Block diagram of the proposed framework.  
Once information about cells behavior is extracted from the unlabeled dataset and the classifier is trained, the Operation Phase monitors the network to detect changes from new data collected.  
The training phase needs a dataset large enough to detect reliable behavior patterns. It is executed at the beginning of the framework application and might be executed again after long periods of time to find new patterns of behavior in the network. On the other hand, the Operation Phase can be executed with certain periodicity, such as daily, weekly or monthly according to the operator's preferences. Each element of the framework is described in detail in the following sections.
# New Trends in Separation Techniques of Lithium Isotopes: A Review of Chemical Separation Methods
## 1 Introduction
### Challenges in Development of Separation Methods for Lithium Isotopes Using Electrochemical
#### 2.4.3 Analysis Methods for Lithium Isotopes Using Thermal Ionisation Mass Spectrometry  
Historically, the thermal ionisation mass spectrometry has been the most-used method for determination of lithium isotope ratios using various ionization methodologies [63; 64; 65; 66; 67].  
[PERSON] et al. [68] employed ion exchange chromatography based on cation exchange resin followed by thermal Ionization Mass Spectrometry (TIMS) to determine the ratio of Li isotopes for both natural and reference samples (the solid NIST L-SVEC and BHVO-2 standards and seawater) by using two different types of thermal ionization mass spectrometers configured in static (the magnetic field remains static) and dynamic (the magnetic field varies) modes. The determined isotope composition of BHVO-2 was 3.43% vs. L-SVEC, while for the seawater it ranged from 30.6 to 33.7% vs. L-SVEC [68], values which correspond to the reported literature values. More recently, [PERSON] et al. [69] used a TIMS methodology called total evaporation and ion integration technique (TE and II). This technique involves complete evaporation of the lithium isotopes from the filament in the thermal ionisation source, and measuring of the ion beams of lithium isotopes until complete exhaustion of the sample [70]. Using this technique, the lithium isotope ratios were determined using the molecular ions \(m/z\) 71-73 (NaLiBO\({}_{2}\)+) and \(m/z\) 88-89 (Na\({}_{2}\)BO\({}_{2}\)+) emitted from the sodium lithium borate and a good precision of 0.05% was obtained [69]. Nevertheless, further studies are needed to elucidate the potential of total evaporation and ion integration techniques as alternative methodologies in lithium isotope determinations.
## 6 Conclusions  
The main contribution of this paper is to integrate the multivariate empirical mode decomposition (MEMD) for identifying modal parameters from the output-only multi-dimensional responses with a combination of fast Bayesian FFT (FBFFT) and stochastic subspace identification (SSI) methods. The MEMD is deployed as a preprocessing tool, aiming to reduce the effect of noise and extract the synchronous modal response. The multivariate signals are firstly decomposed into several aligned intrinsic mode functions, which only contain a dominant component in the frequency domain. Then, the modal parameters can be identified by advanced FFBFT and SSI directly.  
The illustrations of a numerical frame structure and a shaking table test were utilized to validate the MEMD-based methods. In the numerical illustration, by adopting the MEMD, the MARD of damping ratio by the FBFFT or SSI method significantly falls, implying that the combined method is superior to the traditional method. Compared with the original SSI method, it is easy to determine the associated system order of the MSSI method after employing the MEMD to decompose the original acceleration response. The combined method was also employed to determine the modal parameters of the Canton Tower using the full-scale measurement acceleration data. The same as before, the frequencies identified by various methods are identical, while damping ratios present high discreteness.  
Notably, the above study clearly demonstrates that although the identification accuracy of each modal frequency is reliable and accurate, associated damping ratios highly depend on the selected approach, whether the adopted tools are the probabilistic method such as the FBFFT or the traditional time-frequency method, such as the Hilbert-Huang transform, half-power bandwidth, and peak-pick. Therefore, the identification accuracy of damping ratios should be further improved in the future.  
M.H.: Conceptualization, Resources, Supervision. J.S.: Methodology, Software, Data curation, Formal analysis, Visualization, Writing--original draft. K.C.: Writing--review & editing. Q.L.: review & editing. All authors have read and agreed to the published version of the manuscript.  
The work described in this paper was partially supported by National Natural Science Foundation of China (Project No. 52178512), and Natural Science Foundation of Zhejiang Province (Project No. LZ22E080006).  
Not applicable.  
Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
## 4 Discussion
### Promising Acoustic Voice and Speech Features
#### 4.1.2 Maximum Phonation Time  
The effect size for MPT was moderate, at 0.49 [44]. This is a promising finding: MPT is related to phonatory ability and breath support, both of which we hypothesized would be affected by HF-related edema. It is important to note that the mean MPT even at discharge, when speakers were at their healthiest, was only approximately 10 s. In contrast, [PERSON] et al. [47] measured MPT in healthy adults aged 65 years or older and found that MPT did not decrease with age alone in the absence of comorbidities; i.e., healthy older adults had similar MPTs as younger adults. In healthy speakers aged 60-90 years, they found a mean MPT of 22 s, which is substantially longer than the mean 10 s MPT for our speakers at discharge. It is important to keep in mind that the admission versus discharge classification reported on is not the same as identifying whether someone is healthy or has HF. This study is only aimed at differentiating degrees of severity within already diagnosed individuals.  
Figure 4 shows box-and-whisker plots of the statistically significant difference between admission and discharge MPTs, as well as the admission-to-discharge change in MPT. Notably, one participant had very high MPT at both admission (31 s) and discharge (28 s). Those MPTs are even longer than the mean MPT for this participant's age group in [47]. The speaker's voice was severely dysphonic, with a CPP of 4.9 dB at admission and 7.4 dB at discharge. These CPP values are both substantially below the clinical threshold of 14.45 dB identified in [48] and are very likely to indicate a voice disorder. Perceptually, the speaker had substantial strain and low airflow, which likely allowed them to conserve breath and produce a long MPT. For this participant, and any other speakers with similar voices, long MPT likely does not indicate a low decompensation risk. This study attempted to exclude participants with a current or history of a voice disorder. However, many voice issues are undiagnosed, and elderly speakers may be particularly unlikely to seek diagnosis or treatment [49]. In future data collection, it may be desirable to actively screen participants for voice disorders using some acoustic or auditory-perceptual criteria. Data from speakers with non-HF-related dysphonia should be interpreted with caution.
# An Efficient Opportunistic Routing Based on Prediction for Nautical Wireless Ad Hoc Networks
## 1 Introduction  
The oceans require reliable and stable network services due to rapid marine economy development and continuously growing human activities. The ocean contains more than 71% of the natural resources on Earth's surface and includes an area of \(3.62\times 10^{8}\) km\({}^{2}\), with an average depth of 3682, 356,000 km of coastline and about 1.3 billion cubic kilometers of saltwater. Compared to terrestrial Internet, which can be conveniently accessed almost anytime and anywhere, these features mean that there are challenges in providing satisfactory services for the oceans. Nautical wireless ad hoc networks (NANETs) are self-organizing and self-healing networks with convenient and quick deployment, using any facilities to perform network functions on the water surface, such as vessels, buoys, ships and floating platforms [1]. They are expected to play an important role in oceans due to their cost-effectiveness.  
The NANET is a super-scale dynamic network, and its networking scenarios may alternate frequently between connected mobile ad hoc networks (MANETs) and partitioned opportunistic networks (OppNets), since sparse user distribution and mobility behaviors lead to continuous changes in network topology. The traditional routing protocols designed either for MANETs or OppNets alone cannot adapt well to NANETs. Therefore, in [2], an adaptive strategy switching scheme is designed to adapt to such networking environments. The scheme proposes a corresponding switching strategy based on intelligent network recognition. For MANETs, a utility function is used to find the shortest route as quickly as possible. For OppNets, a forwarding mechanism is introduced based on the broadcast nature of wireless networks. However, blind broadcasting in OppNets leads to unnecessarypacket requests and waste of network resources, and thus low delivery ratios and communication throughputs. Therefore, it is necessary to improve the message transmission efficiency in dynamic nautical wireless ad hoc networks.  
The contributions of this paper are summarized as follows:  
* Transmission quality prediction. According to the propagation model in oceans, the packet reception ratio (PRR) is evaluated between the sender node and its relay nodes to represent transmission quality.
* Relay advancement prediction. To guarantee the maximum transmission of information, we predict the probability of relay advancement to detect the unavailability of some apparently qualified relay nodes.
* Combining transmission quality and relay advancement prediction, we propose a novel weight-based routing scheme with prediction for OppNets called WRSP, which selects high-priority nodes to forward the packets.  
The rest of the paper is organized as follows. Section 2 introduces related work. Section 3 gives a detailed description of the proposed algorithm. The performance evaluation is given in Section 4. Finally, we conclude the paper briefly in Section 5.
## 3 Model and Methods
### Comparison with State-of-the-Art Methods  
To verify the performance, we selected 14 recently developed models to apply to the MARS, iLIDS-VID, and PRID-2011 datasets for the experimental comparison. The selected models are the attribute-driven feature disentangling (ADFD) [28] and appearance and motion enhancement (AMEM) [29], which uses person-attribute information; the multi-granular hypergraphs (MGH) [16], STGCN [18], and correlation and topology learning (CTL) [17], which use a GCN; the appearance-preserving 3D convolution (AP3D) [25], spatiotemporal representation factorisation (STRF) [26], and self-separated network to align parts for 3D convolution (SSN3D) [27] models, which use a three-dimensional convolution module; dense interaction learning (DenseIL) [21], spatiotemporal transformer (STT) [36], and SINet [23] models, which use an attention mechanism to enhance the extraction of time- or person-feature information; and the multi-granularity reference-aided attentive feature aggregation (MG-RAFA) [30], global-guided reciprocal learning (GRL) [31], and pyramid in transformer (PiT) [37] models, which consider feature information at different levels of granularity. The re-identification accuracy of each model is shown in Table 1. Our APMG network achieved the best performance in each of the three benchmark datasets. In the MARS dataset, its mAP scores are more than 1.4% higher than those of the next best model, and on the iLIDS-VID and PRID-2011 datasets, its Rank-5 scores are close to 100%. In Table 1, STT, PiT, and our APMG network are all based on a transformer structure. When compared with other CNN-based networks, the experimental results show that these three networks had the best performance, which proves the feasibility and effectiveness of a transformer structure for Re-ID tasks. When compared with the PiT network, our method is similar, as it attaches importance to the granularity of feature information; however, our method adds the aggregation of feature information according to 'importance', which enhances the feature representation at each level of granularity for a person video sequence. Moreover, we use the CAP module to generate feature embeddings instead of a fixed sliding window in the Pit network, which effectively improves the overall structure and semantic information consistency of feature embeddings and makes the network capture more detailed with comprehensive information between feature embeddings. The overall performance of the APMG network is therefore superior to that of PiT. Networks based on a GCN structure can effectively capture the dependencies of people in space and time, but these models adopt a CNN-based structure for feature extraction. Moreover, as the