esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions
Inaccuracies arising from emissivity retrieval and satellite calibration were not considered here, despite their relevance for the quality of a Meteosat LST CDR. Therefore, future work needs to investigate these error sources. Although it might be difficult to remove inter-Meteosat calibration errors completely, the present work demonstrates that, for the investigated ground stations, LST retrievals from well-calibrated MFG data can reach the accuracy of LSA SAF's operational GSW. This study was carried out as part of the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF) and on Land Surface Analysis (LSA SAF). The Swiss National Supercomputing Centre (CSCS) is acknowledged for providing computing and archiving resources for this project. We want to thank the Karlsruhe Institute of Technology (KIT) for maintaining the LST _in situ_ stations and for kindly providing the validation data. Research by [PERSON] was funded by the Portuguese Foundation for Science and Technology (SFRH/BD/52559/2014).
## 3 Results  
### Preparation and Characterization of the Films  
In previous works, films containing CTA as the polymer and different types of plasticizers were prepared by solvent casting and were tested for the extraction and preconcentration of organic pollutants under exhaustive extraction conditions [15; 16]. A mixture of 70% of CTA and 30% of plasticizer gave flexible and homogenous films, which were transparent and mechanically stable, as F1 in Figure 1a. Using the same solvent casting procedure, films using CTA and 10% MWCNTs were satisfactorily prepared (F3, Figure 1b). The slow evaporation of the solvent under mechanical agitation (in an orbital stirrer) was crucial to prevent the aggregation of MWCNTs and to obtain a quasi-homogenous film, but less flexible compared with F1. A film containing a mixture of plasticizer and MWCNTs was also prepared (F2 in Table 2) to evaluate the effect of the plasticizer on the homogeneity of the film and the extraction performance. No differences were observed in comparing F2 and F3 with regard to the preparation behaviour and the characteristics of the film  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Compounds** & **Rt** & \(m/z\) \\ \hline CPS & 13.39 & 197,341 \\ TCS & 14.80 & 218,288,290 \\ AHTN & 12.26 & 243,258 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Chromatographic data of the organic pollutants studied.  
as observed by the naked eye. The F2 film, containing a mixture of both plasticizer and MWCNTs, showed intermediate mechanical behaviour.  
As for the FT-IR spectra, the films with MWCNTs presented the characteristic bands of the polymer, as can be seen in Figure 2, particularly at 1734 and 1037 cm\({}^{-1}\), corresponding to the C=O and C-O-C stretching vibrations in the CTA polymer. The wavenumber of the transmission band did not shift due to the presence of the carbon nanomaterials (Figure 2b).  
SEM images of the F3 film are shown below (Figure 3). As can be observed, the surface of the film (Figure 3a) and the cross-section obtained after cryosection (Figure 3b) present a certain rugosity due to the presence of the nanotubes. Moreover, in the cross-section detail (Figure 3c-f), the presence of the nanotubes in the bulk of the film is even more evident. The distribution of the nanotubes in the form of layers at all depths of the membrane, rather than exclusively at the surface, can also be seen.  
The TEM images (Figure 4) confirm the \"layer like\" distribution of MWCNTs within the bulk polymer, as was seen by SEM. Detail of the MWCNTs can also be observed in Figure 4b.
## 6 Conclusions  
This paper constructs two spatiotemporal models based on the graph convolutional network for landslide displacement prediction, taking the Tangjiao 1# landslide as a case study. Firstly, the monitoring system is transformed into a fully connected graph (FCG) and expressed as an adjacency matrix. Secondly, the T-GCN and A3T-GCN spatiotemporal prediction models based on the graph convolutional network are established to predict the displacement of the Tangjiao 1# landslide. Thirdly, the prediction performance of the models is analyzed by using MAE, MAPE, and RMSE, and they are compared with the GRU model. Finally, the effectiveness of the proposed models is discussed. The prediction results of the Tangjiao 1# landslide displacement indicate the following:  
In terms of prediction accuracy, the spatiotemporal prediction models are superior to the single-point prediction model, which proves that considering the spatial correlation between monitoring points can enhance the accuracy of landslide displacement prediction.  
In most cases, the A3T-GCN model integrated with the attention mechanism has higher prediction accuracy than the T-GCN model. This shows that the attention mechanism is helpful in focusing on local information that is more crucial to the current output from the displacement sequence of the input, thereby achieving accurate prediction.  
Limited by the monitoring data of the Tangjiao 1# landslide, this paper only realizes prediction on a shorter time scale. In the future, more landslide examples will be combined to achieve longer-term prediction, and more influential factors will be considered for the spatiotemporal prediction of landslide displacement.  
Conceptualization, Y.S. and H.W.; methodology, Y.S., T.L., N.X., and H.W.; validation, Y.S., T.L., C.Z. and H.W.; formal analysis, Y.S. and T.L.; investigation, Y.S., T.L., C.Z. and H.W.; resources, Y.S. and H.W.; data curation, Y.S., T.L. and C.Z.; writing--original draft preparation, Y.S., T.L., C.Z., N.X. and H.W.; writing--review and editing, Y.S. and T.L.; funding acquisition, H.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China [Grant No.2023 YFC3007004].  
Not applicable.  
Data Availability StatementData are contained within the article.  
The authors would like to thank the editor and the reviewers for their helpful comments and suggestions.  
The authors declare no conflicts of interest.
# Adaptive Versioning in Transactional Memory Systems +
## 1 Introduction
### Contributions
When using _lazy_ versioning with commit time locking, Adaptive achieves up to 1.39\(\times\) better performance (i.e., 28% less execution time) than eager versioning and up to 1.5\(\times\) better performance (i.e., 33% less execution time) than lazy versioning. Moreover, Adaptive has up to 240\(\times\) and 17\(\times\) less number of aborts compared to that in _eager_ and _lazy_ versioning, respectively. In non-persistent TM systems, the results show that, when using lazy versioning with encounter time locking, Adaptive achieves up to 6.3\(\times\) better performance than lazy versioning and up to 5.5\(\times\) better performance than eager versioning. When using lazyversioning with commit time locking, Adaptive achieves up to 3.7\(\times\) better performance than lazy versioning and up to 5\(\times\) better performance than eager versioning. The minimum performance gain for Adaptive is 1.12. These results suggest that switching between eager and lazy versioning dynamically at runtime provides a way to exploit the positive aspects of both versioning methods for both persistent memory and non-persistent TM systems. In summary, we have the following three contributions. * (**Section** 4) We introduce a novel versioning approach, Adaptive, that switches between eager and lazy versioning dynamically at runtime, and provide two models of Adaptive that are suitable for persistent memory and non-persistent TM systems, respectively. * (**Section** 5) We discuss the limitations of basic design of Adaptive for non-persistent TM system and present two optimizations. * (**Section** 6) We evaluate experimentally the performance of Adaptive in both persistent and non-persistent TM systems using five micro-benchmarks and 8 complex benchmarks from STAMP and STAMPEDE suites, report the results, and provide observations.
# An Effective Adaptive Combination Strategy for Distributed Learning Network
## 2 The ATC Diffusion LMS Algorithm  
### Model Assumption  
Consider a network containing \(N\) nodes, which are used to estimate an \(M\)-dimensional unknown parameter \(\mathbf{w}_{o}\in\mathbb{C}^{M}\) collectively. \(\mathcal{N}_{k}\) denotes the set of neighbors of node \(k\), including \(k\) itself. The cardinality of \(\mathcal{N}_{k}\) is \(n_{k}\). For each node \(k\), at the time instant \(t\), the regressor \(\mathbf{u}_{k}(t)\in\mathbb{C}^{M}\) and the measurement signal \(d_{k}(t)\in\mathbb{C}\) are available. The signal model is given by  
\[d_{k}(t)\triangleq\mathbf{w}_{o}^{\mathrm{H}}\mathbf{u}_{k}(t)+v_{k}(t), \tag{1}\]  
where \(v_{k}(t)\) denotes the additive zero-mean white Gaussian measurement noise at node \(k\), with variance \(\sigma_{v,k}^{2}\). For any \(k\) and \(t\), \(v_{k}(t)\) is independent from \(\mathbf{u}_{k}(t)\), and for all \(k\
eq l\) or \(i\
eq j\), \(v_{k}(i)\) is independent from \(v_{l}(j)\).
# Post-Quantum Biometric Authentication Based on Homomorphic Encryption and Classic McEliece
## 2 Related Work
The limitation is that both servers cannot collude to avoid the leakage of biometric data, which may not be very realistic. Among the quantum-resistant solutions commented above, only the proposal in [10] employs CRYSTALS-Kyber, which is the public-key encryption and key-establishment algorithm standardized by the NIST post-quantum standardization process [24]. Classic McEliece, BIKE and HQC are the round 4 candidate algorithms of the contest [16] and can be standardized in the future, after further analysis. Classic McEliece provides, for each security level, the lowest ciphertext size and, thus, the lowest ratio (ciphertext size/plaintext size). This motivated the work presented in this paper because such an advantage is very interesting for biometric applications. The first McEliece cryptosystem was proposed in 1978 [25], specifically employing Goppa codes. Several proposals for biometric protection schemes are based on the application of the original McEliece cryptosystem to construct secure sketches by using the cryptosystem as the coding method [26; 27; 28]. The drawback of the original McEliece cryptosystem is that the size of the keys is large. In order to reduce the key sizes, several proposals employed variants [29]. However, structural attacks were successful for Reed Muller, Reed-Solomon, BCH, Low-Density Parity Check (LDPC) and Moderate-Density Parity Check (MDPC) codes. For variants that employed hyperelliptic curves, convolutional, quasi-cyclic and quasi-dyadic Goppa codes, algebraic attacks were reported. Another variant that applies Goppa codes based on structured error vectors with larger Hamming weights was attacked in [30]. The only variant that has remained stable together with the original McEliece cryptosystem is the Niederreiter cryptosystem proposed in 1994 [31] that also employed Goppa codes and is employed in Classic McEliece [32], as commented, recently proposed as a round 4 candidate of the NIST post-quantum standardization process.
## 3 Edge Computing and Related Paradigms
### Fog Computing  
Fog computing has emerged as a solution to address the challenges posed by traditional cloud computing models. It involves extending computing resources closer to the edge of the network, thereby reducing latency and improving efficiency [59]. Thisapproach enables offloading storage, networking, and processing tasks to the edge, catering to the intensive computational demands and stringent latency requirements of modern applications [59]. By bringing computational servers closer to users, fog computing aims to minimize latency and enhance the quality of service for delay-sensitive applications [60]. Moreover, fog computing is instrumental in improving the accessibility of IoT resources by extending the data management capabilities of the cloud [61].  
Like with any network-based paradigm, the security and privacy challenges associated with fog computing cannot be overlooked. As fog computing involves sharing data with the cloud for decision-making, there is an increased vulnerability regarding sensitive data sharing, necessitating robust security measures [62]. Researchers have highlighted the importance of addressing security and privacy concerns in fog computing to ensure the integrity and confidentiality of data [63]. Integrating fog computing with technologies like blockchain has been explored to enhance security and privacy-preserving mechanisms in fog-to-things environments [64]. Additionally, fog computing has been identified as a key technology in healthcare data aggregation and transmission in IoT, emphasizing the critical need for secure and efficient data handling in sensitive domains [30].
## 3 An Integral Sliding Mode Compensation Method with Weakened Integrals  
### Design of Sliding Mode Surface  
The general integral sliding mode surface is shown as (10) since the relative order of the system is one in the current loop [17]:  
\[S=e+K\int_{0}^{t}ed\tau \tag{10}\]  
where \(S\) denotes the sliding mode surface, \(e\) denotes the error, and \(K\) denotes an adjustable coefficient. As there is an integral role in the design of the ordinary integral sliding mode surface, it will generate the integral saturation phenomenon when the system error is large, and this will lead to excessive overshooting, which is not beneficial to the system control performance [20].  
Therefore, a boundary layer is introduced in the sliding mode surface in this article to weaken the integrals so as to improve the integral saturation issue. According to [21], the sliding mode surface is designed as follows:  
\[\left\{\begin{array}{c}\stackrel{{\bullet}}{{\rho}}=-m\rho+ \mu(1+\lambda)sat(\frac{S}{\mu})-\lambda S\\ S=e+m\rho\end{array}\right. \tag{11}\]  
where \(sat(\frac{S}{\mu})=\left\{\begin{array}{c}\frac{S}{\mu},|S|\leq\mu\\ \text{sgn}(S),|S|>\mu\end{array}\right.\) is the saturation function, \(\text{sgn}(S)\) denotes the sign function of the sliding mode surface, \(\mu>0\) indicates the range of the boundary layer thickness, and \(\lambda\geq-1\) is the regulatory factor of the degree of integral weakening. According to the relationship between the sliding mode surface and the boundary layer thickness, it is divided into the following three conditions:  
When \(|S|\leq\mu\),  
\[\stackrel{{\bullet}}{{\rho}}=-m\rho+S(1+\lambda)-\lambda S=e \tag{12}\]  
When \(S>\mu\),  
\[\stackrel{{\bullet}}{{\rho}}=-m\rho+\mu(1+\lambda)-\lambda S=e-( 1+\lambda)(S-\mu)=e-(1+\lambda)(|S|-\mu) \tag{13}\]  
When \(S<-\mu\),  
\[\stackrel{{\bullet}}{{\rho}}=-m\rho-\mu(1+\lambda)-\lambda S=e-( 1+\lambda)(S+\mu)=e+(1+\lambda)(|S|-\mu) \tag{14}\]  
From (11)-(14), it can be seen that \(\stackrel{{\cdot}}{{\rho}}=e\) plays the role of the conventional integral inside the boundary layer, and the integral is weakened outside the boundary layer. The degree of weakening hinges on \((1+\lambda)(|S|-\mu)\), which is associated with the error between the absolute value of the sliding mode surface and the boundary layer thickness, as well as the regulatory factor \(\lambda\)[21]. When the boundary layer thickness is certain, \(\lambda\) can also be adjusted to find the appropriate degree of weakening.
# A Cost-Efficient MCSA-Based Fault Diagnostic Framework for SCIM at Low-Load Conditions
## 4 Proposed System Model  
The proposed MCSA-based diagnostic framework for three-phase induction motors fundamentally features a Fourier-based peak detection module for discriminative feature extraction, interpreted by ML-based classifiers for diagnosis. Figure 1 shows the proposed diagnostic framework.  
As shown, the model receives current signals from the three phases of the induction motor, which are simultaneously processed via FFT, PSD, and ACF for peak-based feature extraction. The Fourier transform (and its variants) is named after [PERSON] (21 March 1768-16 May 1830), and it serves as the foundation for most frequency-domain signal processing techniques. As previously stated, FFT and PSD provide a spectral representation of the constituent periodic components in the current signatures and can be exploited for accurate condition monitoring. In addition, ACF provides the degree of similarity between a discrete signal and its delayed copy as a function of the delay \(\tau\) between them.  
Feature extraction from FFT, PSD, and ACF exploits peak coordinates \((f_{i},m_{i})\) from FFT and PSD spectra and \((\tau_{i},m_{i})\) from ACF such that for the first \(l\) tallest peaks in each of the three functions, their coordinates are concatenated to form the feature set. These labeled features are then received as input by the ML-based classifiers for discriminative modeling, validation, testing, and performance evaluation using standard classification performance evaluation metrics. The subsections below summarize the core modules of the proposed diagnostic framework.
# Aggregation Dispatch and Control Strategies for Multi-Type Loads in Industrial Parks
###### Abstract  
With the continuous expansion of renewable energy construction, the power system requires a larger-scale flexible dispatchable and controllable resource for power balance. Fully tapping into the power regulation capability of multi-type loads in industrial parks, making them a low-cost flexible dispatchable and controllable resource, is an effective approach to establish power regulation capability at scale in the new power system. However, the control characteristics of multi-type loads in industrial parks vary greatly, and their control delay characteristics, response speed, and sustainable response time are all different. Traditional dispatch and control methods cannot achieve precise control of the massive and multi-type loads in industrial parks. Therefore, this paper establishes unified models for the control characteristics of multi-type loads in industrial parks, quantitatively characterizes their control delay characteristics, start-stop characteristics, and control response speed. Based on this, the aggregated dispatch and control model and dispatch and control strategy for multi-type loads in industrial parks are developed, which provide a predictive control rate for individual loads considering the delay and segmented response characteristics to achieve precise aggregation control of multi-type loads in industrial parks. Simulation results show that the aggregated dispatch and control model and its aggregated dispatch and control strategy achieve precise control of multi-type loads in industrial parks. Flexible dispatchable and controllable loads can provide low-cost power regulation capability for the new power system.  
industrial parks; multi-type loads; control characteristics; aggregated control strategy; predictive control
# Wireless Internet, Multimedia, and Artificial Intelligence: New Applications and Infrastructures
## 1 Introduction  
The increasing power of the connected devices, as well as the continuous improvements in terms of reliability and bandwidth of the Internet, have given rise to the so-called _data age_ since an ever-increasing number of devices generate a continuous flow of information. This data flow can be exploited to our advantage, although it exposes us to risks in terms of security and privacy. For this reason, in recent years, we have witnessed an increase in investments in research, both in terms of money and human resources, in order to exploit the potential of this scenario and reduce the related risks.  
Therefore, massive data generation, which is mainly related to the capability of connecting heterogeneous devices, represents a distinctive mark of our age, but these stimulating technological opportunities must be properly managed, as the ever-increasing potentialities often do not go hand in hand with efficient implementations in terms of both functionality and security.  
For this reason, the idea behind our Special Issue was that of bringing together scientists from a large variety of research areas, as to face multiple aspects of the scenario taken into account, believing that the meeting between ideas from people that belong to different research areas can bring advantages to each of them.
## 6 Conclusions and Future Work  
SDP can assist software engineers in identifying defect-prone modules in a software system and consequently streamline the deployment of limited resources in Software Development Life Cycle (SDLC) during software development. However, the performance of SDP depends on the quality of software defect datasets which suffers from high dimensionality. Hence, the selection of relevant and irredundant features from software defect datasets is imperative to achieve a strong prediction model in SDP. This study conducted a comparative performance analysis via the investigation of eighteen FS methods on five software defect datasets from NASA repository with four classification algorithms. The FS methods were grouped into two Filter subset selections (FSS) (CFS and CNS) with seven different search methods (BFS, BAT, FS, AS, GSS, PSOS, and GS) and four Feature Filter Rank (FFR) (IG, RFA, GR, and CV) methods based on ranker search method. From the experimental results, IG recorded the best improvement on the prediction models over other FFR methods while CNS based on BFS had the best influence on the prediction models based on FSS methods. In addition, further analysis showed that prediction models based on FFR are more stable than other FS methods. It was conclusively discovered that the performance of FS methods varied across the dataset and that some classifiers behaved differently. This may be due to the class imbalance which is a primary data quality problem in data science. In the future, we intend to look at how other data quality problems, such as class imbalance and outliers, affect FS methods in SDP.  
Conceptualization, A.O.B.; Investigation, A.O.B. and A.S.H.; Supervision, S.B.; Validation, S.J.A.; Writing--original draft, A.O.B.; Writing--review & editing, S.B., S.J.A. and A.S.H.  
This research received no external funding.  
This research was partly supported by Ministry of Higher Education Malaysia, under the Fundamental Research Grant Scheme (FRGS) with Ref. No. FRGS/1/2018/ICT04/UTP/02/04.  
The authors declare no conflict of interest.
## 5 Performance Discussion
### Radiation Performance  
Figure 17(a) plots the measured S11 of four antennas. The impedance match of the planar feed antenna is slightly affected by the reflectarray and transmitarray. Both the GCPFTA and NCPFTA still maintain the broad band from 8.5 GHz to 10.6 GHz. Therefore, the broad band of the planar feed antenna and the well match of the transmitarray are proved.  
Furthermore, the measured realized gain is compared and analyzed, as shown in Figure 17(b). Compared with the planar feed antenna, the realized gain of the feed with reflectarray fluctuates slightly, which is caused by the coupling between the planar feed and the RA, as shown in Figure 10. Similarly, the realized gain of the GCPFTA and NCPFTA show slight fluctuations.  
Comparing the 3-dB AR of GCPFTA and NCPFTA, Figures 17(b) and 17(b) show that the bandwidth of NCPFTA is greatly widen.  
At present, there are little open literature focusing on the CPFTA. To further demonstrate the advantages of our work, some literature is selected for comparison, as listed in the Table 3 below. It can be seen that the proposed NCPFTA in this paper has appropriate radiation performance.