esa-sceva/satcom-chunk-collection huggingface dataset:
# Phonocardiography Signals Compression with Deep Convolutional Autoencoder for Telecare Applications
## 3 Method  
### Data Pre-Processing  
Before applying the measured data into the proposed deep convolutional autoencoder model, the data is pre-processed as follows:  
1. Normalization: the data are normalized, such that the values of all data sets are mapped into the ranges of [0, 1].
2. Segmentation: we edit the heart sound into several fixed-length segments. The time duration of each segment is 3 s, which corresponds to 6000 samples at the sampling rate of 2 kHz. Note that each segment contains roughly four or five cardiac cycles.
3. Overlapping: we apply a sliding window on the data segmentation to implement the data augmentation. Except for the first segment, each segment is overlapped with its previous segment by 93.33%, i.e., each segment has 400 new samples and keeps 5600 existing samples in the previous segment. It has been reported that such a time-shift-based data augmentation method is useful to prevent overfitting [36] when training the CNN networks [37].
# Hierarchical Visual Place Recognition Based on Semantic-Aggregation
## 3 Experiments and Results
### Ablation Study (Effects for Hierarchy and Candidates)
#### 3.3.2 Hierarchy or Single  
To compare the performance of hierarchical place recognition method, coarse matching only, and fine matching only, we conducted the Hierarchy or Single experiments. F1-Score was adopted as the performance indicator.  
Note that coarse matching only means that we get the final best match for the query just through a coarse matching. Fine matching only means that we get the final best match for the query just by fine matching.  
According to the result of ablation 3.3.1, we compared our hierarchical method with the _Candidates_\(n\) to be 10 and 15, respectively, coarse matching only and fine matching only on the North Campus Dataset and the Norland Dataset. The results are shown in Table 2.  
The results show that the performance of hierarchical strategy with _Candidates_ 10 and 15 is better than that of coarse matching only, and fine matching only on both two datasets. It reveals that our hierarchical place recognition is efficient. Comparing with using a single strategy, the hierarchical strategy behaves better.  
Figure 11: F1-score comparison of different number of _Candidates_.
## 4 Discussions
### Limitations and Future Works
#### 4.5.7 The Impact of Quantum Computing on Blockchain Storage Security  
The rapid advancements in quantum computing pose a significant threat to blockchain security. Traditional cryptographic schemes such as RSA and ECDSA, which rely on the difficulty of integer factorization and discrete logarithm problems, are vulnerable to [PERSON]'s algorithm, enabling efficient decryption of private keys. This vulnerability could compromise blockchain networks by allowing attackers to forge digital signatures and alter transaction histories.  
To mitigate this risk, Post-Quantum Cryptography (PQC) has been introduced, with cryptographic algorithms that remain secure against quantum adversaries. Among these, lattice-based cryptography, hash-based signatures (e.g., XMSS, SPHINCS+), and code-based cryptography (e.g., McEliece cryptosystem) are the most promising candidates for quantum-resistant blockchain systems.  
While PQC provides robust security, its adoption in blockchain networks presents challenges such as computational overhead, key size expansion, and verification speed reduction. For instance, lattice-based encryption schemes significantly increase key sizes, leading to higher storage demands and slower verification times.  
In response, blockchain projects such as Hyperledger Fabric, Ethereum's Ethereum 3.0 research, and NIST's Post-Quantum Cryptography Standardization project are actively investigating hybrid models that integrate PQC mechanisms while balancing security and performance.  
Future research should focus on the following:  
* Developing lightweight post-quantum cryptographic techniques that optimize performance while ensuring security.
* Implementing hybrid cryptographic models, integrating post-quantum and classical encryption methods for transition phases.
* Exploring quantum-resistant consensus mechanisms, ensuring blockchain integrity even in post-quantum environments.  
The adaptation of PQC in blockchain-based storage systems will be crucial to ensuring long-term security, particularly in sectors such as finance, healthcare, and IoT, where data immutability and authentication mechanisms are of paramount importance.
# Evaluation of the Barriers to Maintenance 4.0 for the Textile Industry via Pythagorean Fuzzy SWARA
## 3 Materials and Methods
### Barriers to Maintenance 4.0
#### 3.1.2 Technical Competence Main Criteria  
* Low maturity level of enabling technologies: New technologies, such as IoT, sensors, big data analytics, artificial intelligence and machine learning, cyber-physical systems, digital twins, and edge and cloud computing, are still-developing technologies with low maturity levels. Low maturity levels negatively affect reliability and stability. Early adoption of these technologies, which have high investment costs, can cause both material and moral losses to companies [1; 13; 23].
* Insufficient physical and digital \(\Pi\) infrastructure: For Maintenance 4.0 applications to be successful, continuous data collection, analysis, prediction, and automation systems must be established. This requires a strong physical and digital information technology infrastructure [1; 13; 51].
* Difficulty in system and software integration (interoperability issues): For the implementation of Maintenance 4.0 to be successful, many different systems must exchange data in real-time and work in collaboration. Each of these systems may have different data formats, protocols, API structures, and communication standards. This causes integration problems [1; 13; 51].
* Lack of sensors/low accuracy/high power consumption: These three factors are among the elements that affect the success of Maintenance 4.0. In the event of insufficient sensors, predictive maintenance cannot be performed since sufficient data cannot be collected. In addition, low-quality sensors result in incorrect or incomplete data. This causes artificial intelligence and analysis systems to make incorrect decisions, and maintenance planning is incorrect. The use of wireless sensors results in very frequent battery replacement, which leads to additional costs and operational burdens [19; 46].
* Integration challenges between \(\Pi\) and \(\Pi\) systems: Information technologies (IT) (software, data management, network infrastructure, data security, and digitalization) and operational technologies (OT) (physical machines, production devices, sensors, and industrial system management) are important to the success of Maintenance 4.0 policies. Weak or missing integration can lead to inefficient data collection, analysis, and maintenance processes [1; 52].
## 4 Results and Analyses of the Numerical Application of Global Cost and Carbon Impact of the PVCS
### Analyses of Action Levers to Reduce the Carbon Impact of the PVCS
3.1 Scenario 1: Reduction of the Emission Coefficient of the PV Panels from 0.04 kgCO2,eq/kWh to 0.025 kgCO2,eq/kWh  
By taking PV panels with a carbon emission coefficient of 0.025 kgCO2,eq/kWh, the carbon impact of the panels drops from 37,996 kgCO2,eq to 23,748 kgCO2,eq. As depicted in Figure 8, the carbon impact of the PVCS drops from 85,961 kgCO2,eq approximately to 71,713 kgCO2,eq, a decrease of 17.2% compared to the initially considered charging station. In addition, knowing that the carbon impact of a PGCS is around 77,436 kgCO2,eq, then over 30 years, the variation rate becomes \(-\)7.4%, which means that the carbon impact of the PVCS is lower than the PGCS' one. 3.2 Scenario 2: Reduction of the PV Panels' Emission Coefficient to 0.025 kgCO2,eq and the Infrastructure Is Based on Recycled Materials  
In addition to the PV panels, it is possible to reduce the carbon impact of the infrastructure by using recycled materials. The constituent materials of the infrastructure are steel and the concrete. For concrete, RC from wastes will be used, which will prevent some emissions from the use of new concrete. For steel, the metal structure will be made of recycled steel. Thus, the carbon impact of the immobilization drops from 15,439 kgCO2,eq to 8616 kgCO2,eq. For the emission coefficient of the PV panels, scenario 1 is considered. Thus, for these values, the carbon impact of the PVCS decreases from 85,961 kgCO2,eq to 64,890 kgCO2,eq, approximately 24.5% reduction compared to the initially considered infrastructure (Figure 9). Thus, over 30 years, the carbon impact of a PGCS is 16.2% higher than PVCS that contains PV panels with an emission coefficient of 0.025 kgCO2,eq/kWh and recycled infrastructure.
## 2 Background  
### Structured Grid-Based Sparse Matrix  
In the finite element method, finite difference method, and finite volume method, it is necessary to discretize the computational space by using grids. Several discrete formats of partial differential equations in structured meshes are listed in the following figures: Figure 1a shows the 5-point difference format for the 2D Poisson equation, (b) describes the 27-point difference format for the discretized 3D heat diffusion equation in HPCG [1], and (c) shows the 33-edge discretized format of the 3D Maxwell equation based on vector finite elements [16; 17; 18]. The sparse matrix generated by the structured grid is characterized by the fact that the matrix rows inside the grid have fixed nonzero elements, while the matrix rows on the boundaries have fewer nonzero elements. The number of nonzero elements per row can be expressed as follows:  
\[nnz\_row=n\_dof*n\_neib, \tag{1}\]  
where \(n\_neib\) denotes the number of points or edges associated with itself, and \(n\_dof\) denotes the number of degrees of freedom (DOF) possessed on one point or edge.  
For example, the simulation of 3D mantle convection is equivalent to solving a system of partial differential equations for the conservation of dimensionless mass, momentum, and energy. When using the finite element method to solve the partial differential equations, we can first derive the equivalent equation system by integrating the Galerkin weak form. Then, we use a structured mesh to discretize the solution domain, and Figure 2 shows the distribution of the field values on the mesh. The velocity field is attached to the nodes, with \(x,y,z\) components, and the pressure field is attached to the cell centers. By performing element integration, sparse matrix assembly, and using the arbitrariness of the weight function, we transform the problem into solving a linear system. When using the Uzawa algorithm and the multigrid solver to solve, it is necessary to use the Gauss-Seidel and SpMV functions; the detailed derivation and the solution can be referred to in the paper by [19].  
The coefficient matrix \(K\) of the velocity \(v\) conforms to the description of Equation (1): \(n\_dof=3\) entails the velocity components in three directions, \(n\_neib=27\) means that the matrix row corresponding to a point is associated with 27 nodes, and \(nnz\_row=3*27=81\) can be obtained.  
Figure 1: Discrete format of partial differential equations in a structured grid. (**a**) represents 5-point format, (**b**) represents 27-point format and (**c**) represents 33-point format.  
Figure 2: 3D mantle convection finite-element-structured grid partitioning (velocity field attached to nodes, and pressure field is attached to cell center).
# A Survey: Future Smart Cities Based on Advance Control of Unmanned Aerial Vehicles (UAVs)
## 2 Important Applications of UAVs in Smart Cities
### Real Estate  
Drones are used in the real estate industry to capture aerial views of properties. This provides potential buyers with a better perspective of the property, its surroundings, and features like the roof that is difficult to inspect from the ground [29].  
Understanding the types and characteristics of UAVs is essential for comprehending their capabilities and operation means that in order to fully grasp what UAVs are capable of doing and how they function, it is crucial to have knowledge about their specific qualities and physical makeup.
## 2 The Proposed Methodology
### Optimization Problem Formulation  
We consider both the single- and the multi-period optimization settings. In the single-period case, the energy manager aims at determining the optimal set of energy retrofit actions to be implemented all at once in the current period (\"one-off\" interventions)-denoted as \(k=1\) --to maximize the energy saving for a given initial investment. In the multi-period case, the implementation of retrofitting interventions is spread over time and leverages the financial refunding that results from recurrent energy cost savings resulting from the preceding periods. Consequently, the energy manager aims at identifying the optimal energy retrofit actions to be implemented over all the periods-denoted as ordered sequence \(\mathcal{K}=(1,\ldots,k,\ldots,K)\)-to maximize the total energy saving for a given initial investment.
# The YouGovern Secure Blockchain-Based Self-Sovereign Identity (SSI) Management and Access Control
## 2 Background Theory and Related Works
### Related Work
While these designs primarily target network efficiency and spectrum sharing, their integration into identity frameworks could pave the way for ultra-lightweight decentralized identity in edge and IoT scenarios. Existing SSI solutions such as uPort, Sovrin, and Microsoft ION offer varying degrees of decentralization, interoperability, and governance. However, these systems still exhibit significant limitations. uPort lacks native revocation mechanisms and fine-grained access control; Sovrin relies on a permissioned ledger, limiting its openness and scalability; and Microsoft ION, while decentralized, does not incorporate smart contract-based permission management or integrated data storage. Moreover, many existing systems do not offer seamless integration with decentralized file systems such as IPFS, limiting their flexibility in storing and sharing large verifiable credentials. These gaps highlight the need for a modular, low-cost, and access-controlled SSI framework--requirements addressed by YouGovern through its use of BSC, role-based smart contracts, and hybrid storage.
# Adaptive Virtual RSU Scheduling for Scalable Coverage under Bidirectional Vehicle Traffic Flow
## 6 Performance Evaluation  
Our proposed adaptive RSU scheduling solution will be evaluated with other RSU distribution strategies through numerical results. We will present the simulation environment with time-varying bidirectional traffic flows, as well as various request demands. Generally, we have two types of simulation environments as a region-level simulation for an urban area, and a vehicle-level simulation for a road segment.
# Transmit and Receive Diversity in MIMO Quantum Communication for High-Fidelity Video Transmission
## 3 Methodology  
This section outlines the proposed framework for video transmission over a quantum MIMO communication system, as illustrated in Figure 1. Initially, input videos are compressed using source-encoding techniques to extract their corresponding bitstreams. For simulation purposes, the Versatile Video Coding (VVC) standard [42] is used through the VVenC encoder [43]. The system is evaluated using videos in the YUV420 format, covering various spatial resolutions (320 \(\times\) 180, 1280 \(\times\) 720, 1920 \(\times\) 1080), frame rates (20, 30, and 50 fps), and different sizes of Group of Pictures (GOP) (8, 16, and 32) to thoroughly analyze performance under various conditions. Once the video bitstreams are generated, they undergo channel encoding using polar codes with a code rate of 1/2. These channel-encoded bitstreams are then transformed into quantum superposition states through a quantum encoder. Transmission is carried out over quantum MIMO channels using various diversity configurations, including MISO, SIMO, and SISO setups, as shown in Figure 2, all under Rayleigh fading conditions typical of quantum noisy environments. At the receiver, the quantum decoder first converts the received superposition states back into classical bitstreams. These classical bits are subsequently channel-decoded using the polar decoder and finally passed through the source decoder to reconstruct the original video frames. The performance of the proposed system is benchmarked against a classical MIMO communication system operating at the same bandwidth and under identical diversity scenarios.  
Figure 1: The proposed quantum MIMO communication system.  
Figure 2: MIMO-based diversity schemes employed in simulations: (**a**) No diversity (reference case), (**b**) Transmit diversity, (**c**) Receive diversity.  
The following subsections provide detailed descriptions of each functional block within the proposed methodology.
## 4 Case Study
### Deployment of the INPPCS
#### 4.2.1 Phase 1: Data Collection  
Regarding data collection, two types of data--historical cases and customer reviews--are collected. For the historical cases, the case company consolidates all of its previous NPD projects, which are managed in the case library. All cases are stored in the case library based on their index values for efficient case retrieval, while the case attributes are used to measure the similarity with other cases. From the case company, 30 cases were structured as above for retention in the case library for demonstration.  
Regarding the collection of the customer reviews of printing equipment, the customer reviews on Amazon up to 27 May 2022 were extracted using a web scraper based on the python library of Beautiful Soup, and thus the review data can be pulled from the HTML pages. To simply demonstrate the proposed mechanism, three printing devices were considered with 89 customer review titles and bodies containing 10,129 words. Moreover, text pre-processing was conducted to tokenize the collected textual data for the text classification using transfer learning, in which stemming, lemmatization and stop-word removal were applied. Subsequently, only meaningful tokens were extracted for each customer review, such as the document in the corpus. In addition to fine-tuning the pre-trained BERT model, 50 customer reviews related to the case company's products were then classified to obtain the weightings between different classes.
## 3 Results and Discussion
### Volatile Compounds in Cheese

Table 2: Sensory quality of cheeses (mean \(\pm\) SD). The volatile compounds in fresh cheese samples determined using an electronic nose and HS-SPME GC/MS are listed with their sensory descriptors in Table 3. The sensory descriptors are from the AroChemBase database (Alpha M.O.S., Toulouse, France) or The Good Scents Company Information System [23]. The obtained results enabled the indication of volatile compounds, the presence of which was related to the addition of wild garlic. These were allyl (E)-1-propenyl disulfide ((E)-1-propenyl 2-propenyl disulfide), diallyl disulfide (di-2-propenyl disulfide), and allyl methyl disulfide (methyl 2-propenyl disulfide). These sulfur compounds were previously determined in essential oils isolated from _Allium sursinum_ L. leaves [4]. Moreover, other compounds, such as 3-heptanol, 3-methyl butanal, isoamyl acetate, 3-methyl butanoic acid, propanoic acid, heptanoic acid, \(\alpha\)-pinene, and 2,3-dimethyl pyrazine were only determined in cheese samples with the addition of wild garlic. However, these volatiles are common in many types of cheeses not containing any herbs [24; 25; 26; 27; 28]. Figure 1: PCA of the cheese aromatic profile. Abbreviations: HF—cheese from Polish Holstein-Friesian breed Black-and-White type milk (gray); RP—cheese from Polish Red breed milk (black); 0—fresh samples; 2—samples stored for 2 weeks at 4 °C; N—cheese with no wild garlic addition; H—cheese with wild garlic. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Compounds**} & \multicolumn{2}{c}{**Determination**} & \multicolumn{2}{c}{**Energy**} & \multicolumn{2}{c}{**HF**} & \multicolumn{2}{c}{**HF**} & \multicolumn{2}{c}{**RP**} & \multicolumn{2}{c}{**RP**} \\  & \multicolumn{2}{c}{**Method**} & \multicolumn{2}{c}{**Sensory Descriptors 1**} & \multicolumn{2}{c}{**N**} & \multicolumn{2}{c}{**H**} & \multicolumn{2}{c}{**N**} & \multicolumn{2}{c}{**H**} \\ \hline \multirow{4}{*}{Alcohols} & 2-propanol & e-nose & alcoholic, ethereal & + & + & + & + \\  & 2-methyl-propanol & e-nose & alcoholic, bitter, chemical, & + & + & + & + \\  & 2,3-butanediol & HS-SPME GC/MS & & fruity, onion & & & + \\  & 3-heptanol & e-nose & green, herbaceous & & + & & + \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Volatile organic compounds in fresh cheese samples determined using an electronic nose with D \(>\) 0.9500 and via HS–SPME GC/MS.
## 1 Introduction
### Importance of the Two-Way Communication System in Smart Grids  
It is believed that DRES play a significant role in the reduction of greenhouse gas emissions [11; 12; 13; 14]. This improves the availability of the energy resource, increasing the efficiency and the quality of the supplied energy [15]. DRES is essential for the sustainability of the conventional electrical system and is part of the solution to the uncertainty of the demanded load. DRES are not easy to use, as they increase the complexity of the system [16; 17].  
The implementation of bidirectional communication technologies (low-cost and low-consumption) leads us to integrate the concept of the Smart Grid (SG) described in [18; 19; 20]. In an electrical network, an SG is conceived of as a network that can deliver electricity in a controlled manner, from the points of generation to the active consumers [21]. In addition, SG will adjust the amount of energy generated according to the real-time demand of consumers, thus avoiding the excess of generation and covering most of the required demand [22]. Therefore, changes in supply and demand require a more intelligent system that can handle the increasingly complex electrical network [15].  
As a result, an efficient design of SGs tackles three elements: communication, control and optimization [16; 19; 23; 24]. In this paper, we will give special attention to smart metering of electrical energy with the purpose of obtaining accurate information from electricity consumption and in this way run energy management processes at the lowest cost; enabling us to not only automate the distribution in energy, but in addition, allowing us to introduce the use of DRES to SG, granting enforcement and control of the system. The observance of the electrical system will allow us to know the instantaneous supply and demand. This is with the aim of predicting energy consumption [23].  
Advanced Metering Infrastructure (AMI) allows a two-way communication in which SMs must be able to send the information collected in the analysis tools and receive operating commands from the central office [25; 26; 27]. In order to avoid communication conflicts, it is very important to establish communication standards that allow the interoperability between different electronic equipment, as suggested in [12; 16].  
This paper proposes the implementation of a heuristic that provides a near optimal route map of SMs in a georeferenced area. The heuristic will be able to form clusters [28; 29; 30; 31], optimize resources and depict a route map observing parameters of capacity and coverage. In [23; 29; 32], some examples of the advantages of the groupings are described. These groupings are used for the following reasons: to optimize the use of bandwidth, to optimize the use of energy, to reduce overhead costs, to increase connectivity, to stabilize the network topology, to decrease delays and load balancing. This will reduce the Free Space Path Loss (FSPL) and decrease the end to end delay, ensuring real-time communications for optimal operation of the FiWi network in smart cities.