esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Dual-Input Convolutional Neural Network  
In this section, the dual-input convolutional neural network (Dual-input CNN) is introduced and shown in Figure 1. The proposed dual-input CNN can arbitrarily construct its feature extraction network. Three well-known CNN architectures commonly used by users are LeNet, AlexNet and GoogleNet. AlexNet has two main characteristics: the first point is the use of a non-linear activation function-ReLU with faster convergence speed; and the second point is that using Dropout in the first and second fully connected layers can effectively reduce the overfitting problem. However, more complex problems still cannot be solved. Although GoogleNet can solve more complex problems, it has a very deep architecture and requires a long training time. Based on the above analysis, this study uses AlexNet with a moderate architecture length as the feature extraction network architecture. In the dual-input CNN, two feature extraction AlexNet results are used for data fusion and then passed to the subsequent fully connected layer.  
With regard to data fusion, this study proposes a weighting fusion method that assigns higher weights to strong feature inputs. The weighting fusion result is obtained more effectively than the concatenation method, sum method, product method and maximum method. Fusion function \(f:x_{t}^{a},x_{t}^{b}\to y_{t}\) is the fusion of two feature maps \(x_{t}^{a}\) and \(x_{t}^{b}\) at time \(t\). \(y_{t}\) is the fused feature value. The different fusion methods will be described as follows.
# Decentralized Storage with Access Control and Data Persistence for e-Book Stores
## 3 Existing Decentralized Storage Systems
### InterPlanetary File System (IPFS)  
The InterPlanetary File System (IPFS) is an open-source, peer-to-peer decentralized file system primarily developed by Protocol Labs in 2014 [7]. Since the architecture of the IPFS does not depend on a central server or super node, the IPFS has no single point of failure and is expected to operate with zero downtime. Each peer within the IPFS is recognized by a PeerID, obtained by hashing its public key. Peers can be located efficiently using a distributed hash table (DHT) based on S/Kademlia and Coral. Specifically, content uploaded to the IPFS is given a unique content identifier (CID), generated by hashing the content, and is split into numerous chunks and stored using a data structure known as a Merkle DAG. The vertices within this DAG are data types called IPFS objects, which comprise chunks and links to other IPFS objects. Each IPFS object possesses the CID of the original content as one of its attributes. A content-based addressing scheme leverages this CID, facilitating decentralized content storage with an expectation of high availability. Moreover, a DHT is used to create and index a mapping between the CID and its owning PeerID for rapid content retrieval.  
In a distributed application called IPFS Cluster, file persistence and availability are enhanced by combining the replication of stored files to multiple peers and pinning them  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Storage Systems** & **DLT** & \
\begin{tabular}{c} **Crypto** \\ **Incentives** \\ \end{tabular} & **Data Persistence** & **Access Control** &
\begin{tabular}{c} **Contract** \\ **Duration** \\ \end{tabular} \\ \hline IPFS & N/A & No & Depends & No & No \\ \hline ACL-IPFS & Ethereum (smart contract) & No & Depends & Yes & No \\ \hline Filecoin & Filecoin blockchain & Yes & Yes & No & Yes \\ \hline Arweave & Arweave (blockweave) & Yes & Yes & No & No \\ \hline Proposed System & Ethereum (smart contract) & Yes & Yes & Yes & No \\ \hline \hline \end{tabular}
\end{table}
Table 1: Feature-based comparison of decentralized storage systems.  
to the local storage of the peers. Specifically, a peer cluster formed by the IPFS Cluster manages the pins of a specific dataset to prevent important files from being lost from the network and evenly allocates files to peers in the cluster to distribute the load of uploaders and increase the tolerance to peer failures and departures.  
Despite its advantages, an IPFS lacks access control functions, allowing anyone to freely obtain uploaded content [18]. Furthermore, the absence of a mechanism for persisting uploaded content poses challenges, as nodes holding chunks can leave the network, and required chunks may become unavailable due to factors like priority deletion when a node nears its storage capacity [19]. Consequently, an IPFS may not be suitable as a repository for e-book services that require exclusive access to e-book data by the purchasing user on a permanent basis.
## 2 Materials and Methods
### Data Acquisition and Processing  
#### 2.2.1 Data Source  
The data for this experiment were selected from the South Pacific albacore catch data and marine environmental data from 2009 to 2019, both with a temporal resolution of months. Catch data were obtained from WCPFC ([[https://www.wcpfc.int/](https://www.wcpfc.int/)]([https://www.wcpfc.int/](https://www.wcpfc.int/)) (accessed on 7 June 2022)) and include the time of operation, location of operation (southwest corner of each 5\({}^{\circ}\)\(\times\) 5\({}^{\circ}\) latitude/longitude grid cell) and catch information (number of longline hooks and tons/tails). The marine environmental data were obtained from CMEMS ([[https://marine.copernicus.eu/](https://marine.copernicus.eu/)]([https://marine.copernicus.eu/](https://marine.copernicus.eu/)) (accessed on 20 October 2021)), including the sea surface temperature (SST), chlorophyll a concentration (Chl-a), sea surface salinity (SSS), sea surface height (SSH), ocean mixed-layer thickness (Molotsl) and sea water velocity (Vo), where the spatial resolution of Chl-a, Molotsl and Vo was 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\), while for SST, SSS and SSH, it was 0.083\({}^{\circ}\)\(\times\) 0.083\({}^{\circ}\). Tuna is sensitive to SST [36], and its survival has a higher requirement on SST, which can directly or indirectly affect the spawning and migration distribution of  
Figure 1: Study area and distribution albacore catch per unit of effort (CPUE) from 2009 to 2019.  
albaocore tuna [37]; Chl-a can characterize the abundance of marine plankton. Generally, fish and shrimp feed on plankton, while albacore mainly feed on fish, squid and plankton crustaceans. Therefore, Chl-a can directly or indirectly affect the distribution of albacore [38]; SSS affects seawater buoyancy and osmotic pressure in fish, thus indirectly affecting the feeding and spawning behavior of fish, and, therefore, the distribution of fishing grounds can be distinguished using the appropriate salinity range [31]; SSH can reflect the ocean mesoscale dynamic characteristics, such as surge currents, the seawater flow rate, and hot and cold water masses, which affect the formation of fishing grounds to some extent [39]. To ensure that the environmental and spatio-temporal factors are uniformly distributed in the input layer and thus improve the forecast accuracy, the catch and marine environmental data were gridded and matched with a uniform spatial resolution of \(5^{\circ}\times 5^{\circ}\) in this paper. In this paper, matched 2009-2017 catch and environment data were sorted by month to construct the model training set, and the 2018-2019 data were used as the validation set.
## 4 Numerical Simulation
### Model Parameter Calibration  
In order to establish a PFC model that can adequately characterize the mechanical properties of actual marble, it is necessary to verify the correctness and adequacy of the model based on the results of indoor tests. We performed a series of \"trial and error\" procedures [33,34] to determine a set of micro-mechanical parameters that can represent the marble, where the \"trial and error method\" involved adjusting the micro-mechanical parameters of the PFC model so that the stress-strain curve and the macroscopic damage form of the specimens in the numerical simulation and the indoor test are basically the same. The micro-mechanical parameters are delineated in Table 2.  
As demonstrated in Figure 6, the stress-strain curves of the conventional uniaxial compression test of intact marble are compared with the PFC simulation test. The trends of both changes are found to be essentially the same. It is evident that the presence of primary cracks and defects in the interior at the commencement of loading in the indoor test will result in a compression-density phase, consequently leading to a slight depression of the curve at the onset of the indoor test. However, given the rigid nature of the particles in the model and their uniform distribution, the compaction phase of the indoor test cannot be reflected, resulting in a smaller peak strain than the actual value of the indoor test. This discrepancy is attributable to the modeling characteristics of PFC and does not compromise the overall mechanical performance of the material. As illustrated in Table 3, the mechanical parameters of the numerical simulation and indoor test are found to be largely congruent, and the macroscopic morphology of the final failure of the two is found  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline Density (kg\(\cdot\)m\({}^{-3}\)) & 2700.00 & Porosity & 0.36 \\ Particle size ratio & 1.66 & Radius multiplier & 1.0 \\ Minimum particle size (mm) & 0.40 & Parallel bond modulus (GPa) & 33.2 \\ Particle contact modulus (GPa) & 33.2 & Parallel bond stiffness ratio & 3.5 \\ Particle contact stiffness ratio & 3.5 & Parallel bond normal strength (MPa) & 58 \\ Friction coefficient & 0.4 & Parallel bond tangential strength (MPa) & 62 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Numerical simulation microscopic parameters.  
Figure 5: Numerical simulation models: (**a**) filled specimen models; (**b**) contact model.  
to be largely analogous, thereby indicating that the model is capable of reflecting the macroscopic mechanical properties of marble.
# Study of 5G-NR-MIMO Links in the Presence of an Interferer
## 4 Numerical Results
### Partial Band Noise Jammer
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value/Description** \\ \hline Transmission antennas & 2 \\ Receiving antennas & 2 \\ Space-time code & Alamouti \\ Duplex mode & TDD \\ Fading model & Rayleigh \\ transmitted bits & \(10^{5}\) \\ Modulation & 2, 4, 8, 16–PSK \\ SNR & 15 dB \\ SIR & \(-10\) dB to 15 dB \\ \(\Delta\)SIR & 0.2 dB \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Simulations parameters. Figure 12: Comparison of BER as a function of the SIR for different types of interferers, 4–PSK modulation, and SNR = 15 dB.
## 3 Preliminaries
### HCR Protocol
#### 3.2.2 Cluster Head Selection  
After the gradient field is established, the gradient information \(k\) can be exchanged distributively among nodes for making the decisions about the cluster head selection and routing discovery. The cluster head selection algorithm in HCR is driven by the random backoff scheme [16]. The node decides whether to be the CH according to its back-off timer \(T_{b}\) and received ADV messages. The timer \(T_{b}\) is determined by the gradient \(k\) and the residual energy \(E_{r}\),  
\[T_{b}=(K-k)\times T_{slot}+(\frac{E_{max}-E_{r}}{E_{max}})\times T_{slot} \tag{5}\]  
where \(K\) is the maximum value of the gradient \(k\) and \(E_{max}\) is the initial energy of every node. According to Equation (5), the cluster head selection process is divided into \(K\) slots, whose length is \(T_{slot}\), and the set of CHs in \(\mathcal{R}(k)\) is selected in the \((K-k)\) slot. Besides, the nodes with more residual energy have smaller \(T_{b}\) in the same \(\mathcal{R}(k)\), which means a higher probability to become the CH.  
Another factor that impacts the cluster head selection is the ADV message broadcast by the CH. When a node is determined to be the CH, it should broadcast the ADV message for clustering (AC) in \(R_{c}\) and the ADV message for routing (AR) in \(R_{t}\). Both AC and AR carry its ID (identifier), gradient information (\(k\)), residual energy (\(E_{r}\)) and NEXT flag which denotes if it has found its next hop relay. According to the received messages, each node updates the data lists which are defined as follows:  
(1) JOIN list: record the cluster head candidates.  
(2) SERV list: record the requests for a relay from the cluster heads with larger gradient \(k\).  
When a node receives AC message, which means that there is a reasonable CH with more residual energy nearby, the node will record the sender information in the JOIN list. On the other hand, when a node receives AR message with \(k_{AR}>k\), it will check the NEXT flag in the AR message. If NEXT = 0, which means that the sender CH has not found a relay, the node will record the sender information in the SERV list and serve as a candidate relay for the sender CH.  
When \(T_{b}\) terminates, the node checks its JOIN and SERV lists. The node will turn out to be a CH if one of the following conditions is satisfied:  
(1) The JOIN list is empty, which means that the node has the smallest backoff timer in its one-hop neighbors.  
(2) The SERV list is not empty, which means that the node is required to be a relay for another CH and ensures the network is connected.  
Otherwise, the node will become a CM.
[[https://doi.org/10.3900/app1116/436](https://doi.org/10.3900/app1116/436)]([https://doi.org/10.3900/app1116/436](https://doi.org/10.3900/app1116/436)) 2021 21, 7436.  
literature reports that, compared to laminates with neat matrices, polymer composites containing cork powder have higher absorbed impact energy and glass transition temperature, promote less damage areas and have the best elastic recovery performance. At the same time, it became clear that cork powder increases the penetration threshold [17; 18; 19]. Therefore, high tolerance to damage from impact loads, wear and fire resistance, low thermal conductivity, and excellent damping are important characteristics for the use of cork in composites used in harsh environments [20; 21; 22; 23]. The open literature reports several studies on the effect of hostile environments on the mechanical properties of composite materials. For example, the effects of alkaline (NaOH) and acid (HCl) solutions in fibre-reinforced polymer composites were studied by [PERSON] et al. [24] and, according to the authors, alkaline solutions proved to be more harmful than acid solutions, resulting in lower flexural strength and modulus. Another study showed that flexural strength is insensitive up to 30 days after immersion in HCl, after which there is a 10% decrease [25]. [PERSON] and [PERSON] [26] studied the exposure of a composite laminate to a corrosive environment and observed that sharp cracks begin to develop and spread when the acid weakens the fibres, resulting in very low strength and highly flat fracture with low failure stress. The resins used as matrix in laminated composites, according to [PERSON] et al. [27], have a considerable influence on the final mechanical performance. When subjected to higher temperatures or for longer periods of time, authors conclude that polyester resin has a lower modulus than bisphenol A epoxy vinyl ester. Due to the importance of the matrix to the composite structural integrity, the epoxy resin's ability to deal with progressively adverse conditions will determine the composite's suitability for specific applications [27]. According to the literature, composite laminates are strong in the in-plane loading direction, but very weak in the out-of-plane loading direction [28]. Impact damage is believed to be the main source of composite delamination in service, which is quite dangerous because it significantly affects the performance of these materials [8; 9; 29] and, simultaneously, is difficult to identify visually [30]. However, there is a lack of research that incorporates impact loads and harsh environmental conditions, in particular involving composites reinforced with cork. Therefore, the main goal of this work is to investigate the benefit of cork powder on low velocity impact strength in carbon/epoxy composites after immersion into hydrochloric acid (HCl), sodium hydroxide (NaOH), sulphuric acid (H\({}_{2}\)SO\({}_{4}\)), diesel, distilled water, and seawater.
# An Evaluation Survey of Knowledge-Based Approaches in Telecommunication Applications
## 3 Evaluation of Different Knowledge-Based Approaches
### Assessment Results
#### 3.3.5 Tree-Based Formalisms  
Tree-based representations, typified by decision trees and random forests, organize information hierarchically, although they might be less expressive in capturing intricate relationships compared to graph-based approaches. They strive for high accuracy by creating straightforward yet effective models to precisely classify or predict outcomes based on given attributes. These models can represent domain-specific details, but might not explicitly capture nuanced relationships as graph-based structures, due to their hierarchical nature. While they can cover various attributes, their hierarchical organization may limit the coverage of complex relationships found in certain domains. In terms of generalizability, decision trees may perform well within specific domains, but might struggle to adapt to significantly different contexts without retraining or modification. However, they excel in explainability by providing transparent rules and paths, making it easy to interpret the decision-making process.  
With regards to their procedural qualities, tree-based structures might lack inherent mechanisms to explicitly represent uncertainty or probability. They ensure consistency by partitioning data based on specific rules, ensuring coherent classification or prediction within each node. However, they might not inherently provide confidence levels for predictions, often requiring additional techniques to estimate reliability. Regarding expandability, expanding decision trees might necessitate restructuring, and adding new attributes might not seamlessly integrate without altering the entire structure. Scalability challenges might arise when handling extensive and diverse knowledge, potentially impacting storage and efficiency.  
In summary, tree-based representation formalisms, like decision trees and random forests, effectively provide consistent and accurate results with high explainability. However, they may face limitations in handling uncertainty, capturing complex relationships, and scaling to diverse domains.
## 5 Experimental Results
### Experiments in Case 1: Both PMSMs Have the Same Speed and Load Torque  
In this experiment, both PMSMs are operated under the same conditions, with and without the phase shifting method. The speed of the PMSMs is 1600 rpm and the load torque is 9 Nm. The RMS value of the phase current is 70.86 A. The shape of the dc-link capacitor current is pulsating wave and the RMS value of the dc-link capacitor current is 90.9 A without the phase shifting method, which is shown in Figure 15a. The RMS value of the dc-link capacitor current decreases to 42.7 A with the phase shifting method, which is shown in Figure 15b. The dc-link capacitor current is decreased 53% by inserting phase difference between the carrier waves. The major harmonic component of the dc-link capacitor current is the second carrier harmonic (20 kHz), and its magnitude is 80 A for the system without the phase shifting method, which is shown in Figure 16a. The second carrier harmonic component is eliminated in the system with the phase shifting method and its magnitude is nearly zero, as shown in Figure 16b. Furthermore, the sixth and tenth harmonic components are cancelled by the phase shifting method.  
Moreover, both motors operate at 9 Nm load torque at varying speeds. The RMS of the dc-link capacitor current is observed on the oscilloscope, and the data are shown in Figure 17. The rate of reduction in the dc-link capacitor current ranges from 30% to 60%. The maximum dc-link capacitor current occurs at around the speed of 2000 rpm, which results in the highest stress on the dc-link capacitor. By introducing a phase difference between the carrier waves of the VSIs, the dc-link capacitor current is reduced by 50% at this speed range, and the stress on the dc-link capacitor is reduced considerably.  
Figure 16: FFT magnitude of the dc-link capacitor current for dual VSIs dual PMSMs system: (**a**) without phase difference; (**b**) with phase difference.  
Figure 15: The dc-link capacitor current (green), input voltage (purple), and input current (blue) of dual VSIs dual PMSMs system: (**a**) without phase difference; (**b**) with phase difference.
## Materials and Methods
### SnowSense Testsites in Canada
#### 2.2.2 The Foret Montmoreancy NEIGE Site near Quebec  
The Foret Montmorey is located 70 km north of the city of Quebec and is the largest teaching and research forest in the world with an area of 412 km\({}^{2}\). It is open to the public and the University Laval operates in this forest the NEIGE test site (673 m a.s.l.). The Foret Montmorey is drained by the Montmorey River and one of its tributaries, the Black River. The annual rainfall exceeds 1500 mm and in winter average snowfall is above 6 m.  
As we had no possibility to validate SWE and LWC in the Humber and Exploit catchments in Newfoundland, we additionally had the opportunity to install a SnowSense in situ station at the study site NEIGE at Foret Montmorey. At this location, numerous snow and meteorological instruments are tested and in operation. This encompasses, for example, the CS725 sensor, which was developed by Hydro Quebec (Canada) in collaboration with Campbell Scientific (Logan, UT, USA) and which was used for comparison with the GNSS in situ measurements in this study [6]. Moreover, manual snow pit measurements, taken on a weekly basis, were available as further validation information on SWE. The CS725/GMON sensor was located 25 m next to the GNSS sensors, and the manual measurement are performed within a distance of less than 150 m. The site is a flat, sand/gravel surface area without significant vegetation. The close-by meteorological station site is in a distance of less than 500 m with an elevation difference less than 25 m. Since 2014, the University Laval is managing the NEIGE site, which is following the World Meteorological Organisation's (WMO) Canadian Solid Precipitation Experiment's (C-SPICE) objectives for solid precipitation measurements and comparisons of automatic instruments. During the winter season 2017/2018, several sensors and manual measurements from nine partners altogether took place at this location, which was a profound basis for comparing the results of our GNSS in situ station with other sensors.