esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Auto-Regressive and Moving Average Model (ARMA)
#### 2.3.2 Parameter Estimation  
There are many methods for estimating the parameters of the ARMA model. The Least Squares Estimation method is used in this work. See Reference [43] for the specific parameters. After each parameter is calculated, it is substituted into ARMA \((p,q)\) to forecast each reconstructed component to obtain the predicted value \(\hat{X}\) and the fitted value \(\hat{c}_{k}\) of the modeling data. The residual \(\gamma=c_{k}-\hat{c}_{k}\) between the measured data and the fitted value of the model data is calculated. Then, \(\gamma\) is used to describe the modeling data and to obtain the residual forecast \(\hat{\gamma}\). After that, the revised forecast value is  
\[\widetilde{X}_{t}=\hat{\gamma}+\hat{X}_{t} \tag{17}\]  
In the formula, \(\gamma\) is the residual value of the observed value and the forecasted value; \(c_{k}(k=1,2,\cdots)\) is the observation value of the modeling data; \(\widetilde{X}_{t}\) is the prediction value after the prediction residual correction; and \(\hat{X}_{t}\) and \(\hat{\gamma}\) are the ARMA model prediction values and their corresponding residual values, respectively.
## 2 Context of the Experiments
### EVCI Users  
As at 1 February 2022, 376 RFID badges were issued for EV (including PHEV) owners. The number of badges is steadily increasing by around 100 badges per year. Two hundred and sixty cars are used by employees for their personal use. There are also 84 service cars. Cadarache site has also set up a taxi service provided by three Zoe cars. Besides, the CEA authorizes external companies to charge their 29 EVs.  
There are three involvement levels of the users in our experimentations:  
* Seventy-five EV owners (i.e.,about 78% of the volunteer experimenters) agree to give us control over the charging power of their charging sessions. They have also consented to send information about the State of Charge (SOC) of their battery and their forecast departure time. They also provide details on their car's features (capacity of the battery and maximum power of the onboard charger). The set of these cars is further called \"PControlled\".
* Twenty-one employees have not given us control over the charge of their car, but have agreed to send us information about their SOC and their forecast departure time. They also give us details on their car. The set of these cars is further called \"PU incontrolled\".
* The other users do not participate to the experiments. They are named \"Others\".  
The union of the first two categories of people is further called \"Participants\". The union of the last two categories is called \"Uncontrolled\".  
In terms of car models, there is a very clear predominance of Renault Zoe. Such a model represents 38% of all the EVs. There are also, among others, 10% of Peugeot e208, 7% of Renault Twingo and 8% of Tesla (Model 3 and Model S) and 5% of Nissan Leaf vehicles.
# Pulse-Shape Discrimination of SiPM Array-Coupled CLYC Detector Using Convolutional Neural Network
## 4 Results and Discussion  
We set the prompt and total window to [140 ns, 530 ns] and classified the unknown signal. The _PSD_ performance is shown in Figure 9. The charge comparison method exhibited a satisfactory _PSD_ performance for neutrons and \(\gamma\) rays in the thermal neutron energy region, as shown in the figure, but the _PSD_ performance gradually dropped with the decrease in energy until it could not be identified. Figure 10 is a histogram of the _PSD_ parameters of particles in all energy intervals in Figure 9. The _FoM_ value of the charge comparison method is 1.37, as calculated by Equation (2). Figure 11 shows the _PSD_ effect (_FoM_) diagram under different energy ranges. From the figure, it can be seen that the _FoM_ value range increases with the augmentation of energy. The tortuous part of the figure is due to the excessive number of signals in some energy segments.  
Figure 8: Changes in neutron and gamma signals after convolution layers: first layer (**a**); second layer (**b**).  
Figure 9: _PSD_ 2-D histogram of neutron and \(\gamma\)-rays.  
We converted the unknown signal into a two-dimensional matrix format and fed it into the trained CNN. Figure 12 shows the two-dimensional histogram of the CNN output parameter a. It can be seen from the figure that the _PSD_ performance of the CNN at different energy intervals has no obvious fluctuations. By projecting the count on the X-axis of Figure 12 to the Y-axis and performing Gaussian fitting, we obtained a one-dimensional projection of the output parameter a. Due to the characteristics of the activation function at the output layer, the Gaussian peaks were complemented (as shown in Figure 13) during Gaussian fitting, and the _FoM_ value of CNN is 37.20 based on Equation (2). To verify the reliability of the CNN output results, all the signals located in the thermal neutron energy region were extracted from the unknown concentration. After the unknown signal was given a clear label by the charge comparison method, the CNN was used to classify them, and the results of the classification using CNN were compared with the charge comparison method, as shown in Table 4. According to Table 4, the accuracy of the CNN in the thermal neutron energy zones discrimination result compared to the charge comparison method can be calculated as 99.95%, and the neutron F1 measure is 0.99988. The gamma ray events around 3 MeV are not shown in Figure 12, since the training set \(\gamma\)-ray selects the signal of the all-energy peak of \({}^{137}\)Cs, so the output of the high-energy \(\gamma\)-signal may be slightly larger, which is not shown in Figure 12. Even if the output of the high-energy \(\gamma\)-signal is greater than that of the low-energy \(\gamma\)-signal, by comparing the output of the CNN and charge comparison method classification results, it was found that the results of CNN have good accuracy and neutron F1 measure in the thermal neutron energy region.  
Figure 11: _FoM_ values at different energy ranges.  
Figure 10: _PSD_ one-dimensional projection.
# MBMQA: A Multicriteria-Aware Routing Approach for the IoT 5G Network Based on D2D Communication
## 3 System Model
### Device Selection Based on Packet Power Consumption Ratio with Back-Pressure Strategy  
The residual battery of a device refers to the amount of charge remaining on the battery attached to the device at an instant of time, which is calculated using the linear battery model [46]. It is a power consumption-aware metric and is embedded with the MARS metric to enhance the performance of the MBMQA approach by selecting the intermediate devices which possess higher energy level during the transmission, while intermediate devices with lower energy level are avoided. The \(DS_{RB}(i,j)\) metric enhances the packet power consumption ratio in the D2D communication-based IoT 5G network, which is calculated based on the devices' residual energy with back-pressure as follows:  
\[DS_{RB(i,j)}(t)=\max_{f\in\
eq i,j}\{Q_{i}^{f_{c}}(t)-\frac{RB_{j}(t)}{RB_{j}^ {\max}(t)}\times DR_{j}(t)\times Q_{j}^{f_{c}}(t)\} \tag{4}\]  
where \(RB_{j}(t)\) is the residual battery of the intermediate device \(j\), \(RB_{j}^{\max}(t)\) denotes the maximum battery level of device \(j\) in mAh, which is configured from the battery energy model, and \(DR_{j}(t)\) denotes the drain rate of device \(j\) in mAh per an instant of time \(t\), which is calculated as follows:  
\[DR_{j}(t)=\frac{Q\times E_{total}}{V\times T} \tag{5}\]  
where \(T\) refers to the simulation time in a second, \(E_{total}\) denotes the total energy consumption by devices in mWh, \(Q\) is the coulombs charge of devices in Ah and \(V\) is the voltage supply in volts.
## 6 Clustering, Virtual Backbone and Minimum Connected Dominating Set
### Multipoint Relaying (MPR) in CDS Construction  
Multipoint relaying is a technique that is widely used for flooding in wireless ad hoc networks. In multipoint relaying, each node \(u\) in the network selects a subset of its first-hop neighbors, named the multipoint relay set (MRS), which are responsible for forwarding packets from node \(u\) to \(u\)'s two hop neighbors. Only nodes in the MRS of \(u\) can forward packets sent by \(u\). Finding a minimum size MRS in a network is shown to be NP-Complete [104]. The MRS of a node \(u\) forms a local dominating set, which dominates nodes within two hops distance from \(u\). Although the MRSs do not directly generate a global CDS, they can be extended to do so.  
Note that in [104], node \(u\) and its MRS only form a local CDS. Some algorithms approximate MCDS for the whole network based on MRSs. One such example is MPR-CDS where a source-independent MPR algorithm is proposed to construct a global CDS based on the MPR approach [105]. The algorithm assumes that each node in a connected network has a unique ID. The global CDS is constructed based on the ID of nodes, according to the following criteria. A node \(u\) is added into the CDS if it has the smallest ID among all its first-hop neighbors, or it is a member of the MRS of its neighbor with the smallest ID. The time complexity is shown to be \(O(\Delta^{2})\), where \(\Delta\) is again the maximum node degree, while the message complexity is \(O(n)\).  
However, as pointed out by [PERSON] [106], the CDS generated by MPR-CDS can be inefficient as the nodes selected by the first criterion may not be necessary. This is because the first criterion only select CDS nodes based on their IDs. Hence for certain network topologies, nodes chosen by the first criterion are redundant. Therefore an _enhanced MPR_ (EMPR) was proposed by modifying the MPR-CDS in both the selection of the smallest ID nodes and the original MPR selection. Due to these two modifications, the EMPR has high computation complexity.  
Other MPR-based CDS construction heuristics include [PERSON] _et al._[107], [PERSON] _et al._[108, 109], and others. For a comprehensive survey about multipoint-relay-based broadcast schemes, see [PERSON] _et al._[110], which includes multipoint-relay-based CDS construction. Note that all the discussions of MCDS are based on the fact that the network is connected. When dealing with a disconnected network, we can only find CDSs of each connected part of the network.  
As the MCDS problem is NP-complete, future research will likely to focus on providing heuristic algorithms and approximations that are efficient and scalable. Another possible new research direction is to focus on networks with specific structures, as done by [PERSON] _et al._[88].
# An Internet of Things Access Control Scheme Based on Permissioned Blockchain and Edge Computing
## 4 System Model  
Figure 2 illustrates the system model of the scenario. As the resource requester, the user is the subject of access control, and the data perceived and stored by the IoT devices is the object resource. The proposed model mainly includes PEP, PDP, PAP, and PIP.  
Firstly, the system should be initialized and executed by the edge server to generate the public parameters required by the system to implement the corresponding encryption function. Secondly, users and various terminal devices can use their own ID to register on the network. Users then need to log in for authentication. Only after passing the authentication can they make access requests. The user first requests access to the device service from the object resource. After the access request is passed, the device can be operated. This stage is completed by calling the smart contract on the blockchain. The smart contract defines policies and constraints related to privileges and services.  
When different application devices make access requests to system devices (IoT devices), the challenge first locates the proxy device and makes the request, and the PEP is responsible for receiving and processing these requests, before it interacts with different practical modules to acquire useful resource access.  
The PDP and PAP features are described in detail in Table 1. The key statistics of access control in the main are in the attribute records managed by the means of PIP, and the attribute records are stored on the blockchain to facilitate the well-timed call of the policy contract (PC), while, at the identical time, using the traits of the blockchain to efficaciously forestall malicious nodes from tampering with the attribute statistics.  
Due to the susceptible storage and computing potential of most IoT units and the wide variety of IoT devices, the IoT gateway has the ability of both data caching and local computing. The IoT gateway is used as a proxy device to manage IoT devices. The ABAC model integrated into the blockchain retains the advantages of the traditional ABAC model, such as fine-grained access control, and adds some new advantages, such as the blockchain used to file the distribution of attributes, fending off the single factor of failure and also fact tampering.
## 2 Results
### Results Subsection
#### 2.1.2 Transcriptomic Study  
All the transcriptomic results are deposited in the NCBI database as a GenBank BioProject (Code number: PRJNA609262) and GEO series (Code number: GSE147316). The transcriptomic profile from the ST258-KPC3 CA isolate indicated a probable CHLX resistant profile. Indeed, this strain has a higher number of overexpressed genes (Log2 fold change > 1.5), especially for transporters and efflux pumps such as the methyl viologen resistance gene _smvA_ (Log2 fold change: 3.635), which is involved in the cationic biocide resistance. However, strain ST846-OXA48 CA showed what it was as a CHLX tolerant profile, with repressed genes (Log2 FoldChange < 1.5) for efflux pumps, TA systems, SOS response, and ppGpp mechanisms (Table 1). This strain also showed high levels of expression of genes, _pmrD_, and _pmrK_ (Log2 fold change 2.360 and 1.570, respectively), characteristics of the colistin resistance. Therefore, these transcriptomic results corroborated the results obtained by the time-killing curves, showing activation of molecular mechanisms of resistance and tolerance molecular mechanisms in response to CHLX in ST258-KPC3 CA and ST846-OXA48 CA strains, respectively. Figure 1: Time-killing curve in the presence of chlorhexidine (CHLX) (10 \(\times\) minimum inhibitory concentration (MIC)) in _K. pneumoniae_ chlorhexidine adaptation (CA) strains ST258-KPC3 CA (**A**) and ST846-OXA48 CA (**B**). The same strains without being exposed to biocide pressure are used as controls. The errors bar represents the standard deviation of the three replicates experiment.
# Voice Activity Detection Using Fuzzy Entropy and Support Vector Machine
## 3 Results and Discussions  
Speech signals for the proposed methodology were collected from TIMIT database [31] because it provides transcriptions down to word and phoneme levels. Each TIMIT sentence contains almost around 3.5 s, of which 90% is speech. To change the ratio of speech and non-speech regions by 40% to 60% [32] respectively, silence was added to the original speech of the TIMIT corpus. For experimental purposes, speech signals were selected randomly from the TIMIT database, contributing around 910 from training dataset and 320 from test dataset. Nonstationary noises for the experiment were collected from AURORA2 database [33] which was resampled to 16 kHz depending on the need. The speech signals were contaminated by various nonstationary noises of different SNR levels (\(-10\) dB to 10 dB). Four noises--namely airport, babble, car, and train noises--were selected for experimental purposes. Babble noise by name consisted of multiple speakers speaking in the background. Airport and train noises included some speech elements along with their noises. Car noise was the car interior noise with an impulse noise at a particular instance.
## 2 Methods  
In this paper, the point-wise acceleration approach was applied to recover the gravity field model from the 3D satellite positions, which were computed based on either the carrier phase differentiation method or orbit differentiation algorithm. The functional models for the satellite acceleration determination are given in Section 2.1, and the point-wise acceleration approach for recovering the gravity field model is briefly summarized in Section 2.2.
## 5 Discussion
### Directions for Further Studies  
Future research on AI-based digital DTs for Industry 4.0/5.0 should focus on increasing computational efficiency using advanced AI techniques such as federated learning and neuromorphic computing. Developing standardized interoperability frameworks and protocols will enable seamless integration across industrial systems and vendors [92; 93; 94]. Further research is needed on privacy-preserving AI methods such as differential privacy and secure multi-party computation to protect sensitive industrial data. Exploring hybrid edge-of-the-cloud computing solutions can optimize real-time processing while reducing the dependence on centralized cloud infrastructure. Developing self-learning DTs using continuous and reinforcement learning will enable adaptive optimization without the need for frequent retraining [95; 96; 97; 98]. Extending DT capabilities to support multi-agent collaboration will improve supply chain coordination and distributed decision making. Improved explainability and transparency in AI models will increase trust and usability for human operators, especially in Industry 5.0 applications. Research into integrating AI-driven DTs with digital ethics frameworks can help address issues related to bias, fairness, and accountability. Exploring the use of generative AI to generate synthetic data can improve the training and validation of DTs in cases where real-world data is limited. Developing robust validation and benchmarking methods will ensure the reliability and accuracy of AI-driven DTs across industries [99; 100]. Research into the application of quantum computing to DT simulations can accelerate complex optimizations and enhance problem-solving capabilities. Incorporating sustainability-focused AI models will allow DTs to optimize energy efficiency, waste reduction, and environmental impact. Improving human-AI collaboration in DTs through natural language processing and augmented-reality interfaces will improve decision making and human performance. Future research should explore blockchain integration to provide secure, transparent, and tamper-proof data management in DT ecosystems. Finally, improving the scalability of AI-based DTs through modular architectures and lightweight AI models will support broader adoption across industry sectors [101; 102; 103; 104].
# Joint Uplink and Downlink Resource Allocation for D2D Communications System
## 5 Conclusions  
In this paper, we propose the joint uplink and downlink resource allocation scheme for D2D communications system in the cellular networks. To maximize the total system capacity, we formulated the optimization function and found the optimal solution through two sub-problems: Power allocation and channel assignment, which can guarantee QoS requirements of both CUs and D2D pairs. It has been proved that the power allocation problem is a convex function, in which the optimization solution is derived by establishing an optimal transmission power set. Furthermore, the system capacity matrix was obtained, and the Hungarian algorithm was improved to achieve channel allocation for each CU-D2D pair. The proposed JUAD scheme not only can improve the total system capacity, but also satisfy QoS requirements. Simulation results demonstrate that the performance of the proposed scheme for jointly uplink and downlink is better than that of the other schemes.  
Conceptualization, X.H.; data curation, X.H.; formal analysis, X.H.; funding acquisition, X.S.; methodology, X.H.; project administration, X.S.; software, X.H.; supervision, X.S.; validation, X.H. and Y.N.; visualization, X.H.; writing--original draft, X.H.; writing--review and editing, X.S., X.H., L.D. and L.Q.  
This research was funded by the [National Nature Science Foundation of China] grant number [61473066] and [61601109], and the [Fundamental Research Funds for the Central Universities] grant number [N152305001].  
The authors declare no conflict of interest.
## 5 Conclusions  
In this research, a novel deep learning architecture for mapping wetlands was introduced by harnessing a combination of multi-source satellite data, including multispectral Sentinel-2 and Sentinel-1 SAR datasets. The proposed model, called Wet-ConViT, has been developed with the primary objective of achieving precise land cover classification while minimizing computational complexity.  
The proposed model leverages the capabilities of both convolutions for capturing local information and transformers for capturing the long-range dependencies of extracted features. Accordingly, a multi-head convolutional attention module (MHCA), a modified version of multi-head self-attention, and a local feed-forward network (LFFN) were used in the Wet-ConViT model within convolutional and transformer blocks. These integrated modules and blocks reduce the computational complexity associated with vanilla transformers and enhance the models' ability to incorporate local contextual information, thereby contributing to the overall efficiency and improved classification accuracy of the model.  
The performance of our proposed Wet-ConViT model was compared to several state-of-the-art CNNs, transformers, and hybrid models. The results demonstrated the superiority of the proposed model compared to other state-of-the-art models in terms of accuracy metrics and parameter efficiency. Although WMF exhibited the best efficiency in terms of inference time and memory usage, it fell short by approximately 5% in classification accuracy compared to the proposed model. Moreover, the second-best model in terms of accuracy, CoAtNet, required significantly larger memory and longer inference time, making the proposed model more balanced for practical applications.  
The Wet-ConViT model represents a notable achievement in maintaining a delicate balance between efficiency and accuracy in the context of complex land cover classification. Its superior performance, coupled with its efficiency in terms of parameters, memory, and processing time, underscores its value as an exemplary model for practical applications in wetland classification tasks.  
Conceptualization, A.R., F.M. and M.M.; methodology, A.R., F.M. and M.M.; investigation, A.R., F.M. and M.M.; writing--original draft preparation, A.R.; writing--review and editing, A.R., F.M. and M.M.; visualization, A.R.; supervision, F.M. and M.M. All authors have read and agreed to the published version of the manuscript.  
Figure 8: The effects of patch size on overall accuracy and processing time (measured in seconds per training epoch) of the proposed model.  
**Funding:** The financial support for this research was provided by the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grants program through grants awarded to [PERSON] (Grant No. RGPIN-2022-04766).  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.