esa-sceva/satcom-chunk-collection huggingface dataset:
# Preliminary Propulsion and Power System Design of a Tandem-Wing Long-Range eVTOL Aircraft
## 3 Results and Discussion of the Propulsion System Design
### Sensitivity Analysis
#### 3.2.1 Sensitivity of the Design Procedure  
The first part of the sensitivity analysis concerns the blade design procedure explained in Section 2.1. For this procedure, the sensitivity analysis was divided into two parts: one concerning the inputs that are not a design choice (freestream velocity which comes from the flight performance analysis performed in the original project [3], and the propeller radius, which is bounded by the procedure laid down in Section 2.2); and one concerning the design variables that can be freely chosen while designing the propeller (propeller rpm and number of blades). Since the goal of the procedure is to maximise efficiency at a given thrust setting, the parameter of interest is the efficiency of the propeller. The results of this sensitivity analysis can be seen in Figures 10 and 11.  
From Figure 10, it can be seen that a higher radius increases efficiency, which is expected as it decreases disk loading. This leads to the design goal of maximising the radius of the propellers. The design point for the cruise speed \(V\), since it is selected based on requirements of the mission design in the original project [3], and is affected by other design disciplines, could not be chosen during the design process of the propellers, but it is still important to analyse the effect that it has on propeller performance. The graph includes the final design point for cruising conditions.  
From Figure 11, it can be seen that more blades mean more efficiency, which partly explains the reasoning behind the selected number of blades, as explained in Section 3. The increase in efficiency is due to an increase in the induced efficiency of the blades [6]. However, with the method shown in Section 2.1, the total solidity of the propeller remains nearly constant independently of the selected number of blades, which results in blades with a lower chord length for a higher number of blades, which results in a decrease in profile efficiency due to the lower Reynolds number of the blade [6]. Overall, the effect of the number of blades on the final result is not very high, especially close to the design point. For the rotational speed, the design value chosen was below the optimal one. The reason for this is that the thrust needed for cruising is very low compared to the maximum thrust required, and this last value needs to be achieved at a rotational speed of at most 4790 rpm, and thus this margin is needed.
# Student Behavior Prediction of Mental Health Based on Two-Stream Informer Network
## 3 Proposed Method
### Overview of the Framework  
As shown in Figure 1, the network structure based on the two-stream informer is divided into three layers: input layer, encoding layer and classification prediction layer. The input layer makes data embedding and location coding for the original data. In the encoding layer, a Time Encoder and Behavior Encoder based on multiple attention were designed to extract each embedded feature, and the features of the two encoders were fused using the Intermediate Fusion Module. The classification prediction layer uses a gating mechanism to classify and predict students' mental health status.
## 4 Simulation Verification  
To verify the identification ability of operational modal parameters of linear slow time-varying structures based on MWLPP, a mass slow-time-varying 3-DOF structure and a density slow-time-varying cantilever beam structure were designed. The moving window principal component analysis (MWPCA) method was used to identify the operational modal parameters of non-stationary vibration response simulation data for comparison.
## 3 Data Preparations for Fault Localization
### Hill Testing and Data Collection  
The memory-updated feature can be offered to debug faults during HiL testing of automotive software [14]. To generate memory-updated information, the following three types of data are necessary. First, the metadata of tests, such as I/O specifications and test results, are collected through test reports or scripts. Second, the executable file of the software is statically analyzed to collect the symbol name or section information of the memory data to be collected. Third, based on the section information obtained through the static analysis, the memory data is collected for every execution period of the main task.  
Figure 3: Process of data preparation for fault localization using memory-updated patterns.  
Collecting executed software memory at every execution period of the main task in an HiL test requires additional space. However, it is impossible to alter the hardware for testing. Therefore, we developed a high-volume data-collecting method considering the communicative load of ECU in HiL environments, as shown in Figure 4. In Figure 4, the test executor of the host PC processes the test script and manages the test result. The data collector requests the memory data from the SUT (test agent) and determines whether the collected memory data is updated. The test agent transmits the memory data of the observation range in the SUT during test time, in accordance to the request. The data collector and the test agent use an automotive communication network, such as a controller area network (CAN).  
With this method, the memory value of the variables section can be dumped without any loss, and periodically. Dumped memory at the period (T) within the main task is defined as a frame and is presented by Equation (1). The memory address is compared with the prior frame as Equation (2), and checks the updated with the change in the value saved in the address (memory updated: MU).  
\[\text{F}_{\text{k}}\equiv\{\forall\text{V}_{\text{A},\text{k}}\ |\text{V}_{\text{A},\text{k}}\text{ is the value of the address A at time T}\times\text{k};\text{k}\ \text{is an index number}\} \tag{1}\]  
\[\text{MU}_{\text{A},\text{k}}=\left\{\begin{array}{ll}1,\ \text{V}_{\text{A}, \text{k}-1}\
eq\text{V}_{\text{A},\text{k}}&;\ \text{k}\ \text{is a frame-index number}\\ 0,\ \text{V}_{\text{A},\text{k}-1}=\text{V}_{\text{A},\text{k}}&;\ \text{k}\ \text{is a frame-index number}\end{array}\right. \tag{2}\]
## 3 Related Work
### Blockchain-Based Federated Learning  
Conventional federated learning architectures predominantly rely on centralized servers, introducing vulnerabilities such as single points of failure and possible server misconduct. The integrity of federated learning ecosystems becomes jeopardized when the central servers experience security breaches [21]. Emerging blockchain technology has attracted significant interest, due to its decentralized nature and robust security features. Blockchain-enhanced federated learning (BFL) systems utilize distributed ledger technology to eliminate central server dependencies and associated risks. The BAFFLE framework [22] implements smart contracts (SCs) to orchestrate model storage and monitor participant statuses, executing both model updates and aggregation processes through automated contractual agreements. This approach enhances system resilience against centralized failures while ensuring equitable client participation. Parallel innovations such as BlockFL [23] leverage blockchain-based smart contracts for secure model update exchanges and validation processes, simultaneously resolving single-point vulnerability issues and incentivizing broader device participation through sample-size proportional rewards. Within medical applications, Swarm Learning integrates FL with blockchain technology to enable collaborative disease diagnosis models while preserving patient data confidentiality [10]. Existing implementations nevertheless face challenges, as smart contract-mediated model aggregation generates considerable computational demands and network congestion for blockchain nodes, while lacking mechanisms to detect adversarial gradient contributions. To mitigate these limitations, researchers have proposed committee-consensus BFL frameworks [24] that streamline computational requirements and enhance security against malicious actors, although the determination of optimal committee formation criteria remains an open research question.
## 2 Related Studies  
### Audio Information Hiding for Tampering Detection  
Tampering detection refers to methods and technologies used to identify unauthorized alterations or manipulations of data. Many tampering detection methods have been proposed so far for various media, such as video [33; 34], image [35], and audio [11; 36]. This work focuses on audio tampering detection using information hiding.  
Audio information hiding techniques for tamper detection have been explored in several studies. Reversible methods using discrete cosine transform (DCT) coefficients have been proposed, allowing for the perfect restoration of original data and embedded payloads [11; 37]. These approaches enable accurate tamper detection and localization while maintaining good sound quality. Chirp coding has also been utilized to embed watermarks without perceptual degradation, offering robust tamper detection against various attacks [38]. Additionally, fragile watermarking techniques using least significant  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Methods** & **Features** \\ \hline Spectrum & Employing the spectrogram as an input feature for extraction, DeepResNet is then used to learn the representations of distinguishing features, and the multiscale spectrograms enhance the diversity of input features to detect watermark; Fast Fourier Transform (FFT) is used to detect signal frequency changes. \\ \hline Steganalysis based on Goertzel algorithm [30] & Goertzel algorithm, as a steganalyzer in the frequency domain, is combined to allow the analyzed audio vectors to be compared; then, linear prediction is applied to the vectors to detect any modifications in the audio signal. \\ \hline Compress-steganalysis [31] & Correlation patterns between codewords in the compressed stream are considered indicators to extract features for steganalysis; global and local correlation mining in compressed speech data are used for steganalysis. \\ \hline MP3 stego & MP3 stego modifies the parity of the block length during embedding, which can be detected by observing disturbances in the bit reservoir after recompression; recompression calibration is applied to enhance the sensitivity of these features, making it easier to detect the watermark. \\ \hline The proposed method & Absolute discontinuity value of the spectrogram in the frequency bin indicates embedding. The peak of the log-likelihood value of the mean and variance of the discontinuity in the spectral domain is calculated to detect the watermark. \\ \hline \hline \end{tabular}
\end{table}
Table 1: A brief survey on audio steganalysis methods and the proposed method.  
bit (LSB) insertion in the spatial domain have been investigated, with DCT-LSB and DST-LSB methods showing higher sensitivity for tamper detection compared to traditional LSB [39]. These studies demonstrate the effectiveness of audio information hiding techniques in detecting malicious modifications and protecting the integrity of digital audio content, although some approaches are limited to detection without localization capabilities.
# Using Compressed JPEG and JPEG2000 Medical Images in Deep Learning: A Review
## 2 JPEG and JPEG2000 Compression Effects on Medical Images
### Compression Artifacts and Their Diagnostic Implications  
Due to the removal of high-frequency components, irreversible compression methods such as JPEG and JPEG2000 effectively reduce noise while maintaining minimal loss of diagnostic information. By discarding certain image data, these techniques enhance the visual appeal of the decompressed image [17]. However, this reduction in detail can also introduce artifacts, distortions, or anomalies that negatively impact overall image quality. While irreversible compression minimizes the amount of data required for storage or transmission, it results in a loss of finer details and textures, which may affect the accuracy and fidelity of the image. Table 2 provides an overview of the artifacts introduced by JPEG and JPEG2000 compression methods and their implications.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Artifacts** & **Implications** & **Compression Algorithms** \\ \hline Blocking Artifacts: Caused by the division of an image into & These artifacts can make it & \\ \(8\times 8\) pixel blocks during & difficult to distinguish fine & \\ compression. Discontinuities & details, such as small lesions or & JPEG \\ can appear at the boundaries of & microcalcifications in & \\ these blocks. & mammograms [21; 22]. & \\ \hline Ringing Artifacts: Appear as & Ringing artifacts can interfere & \\ oscillating patterns or halos near & with the interpretation of edges & \\ sharp transitions or edges in the & and boundaries, which is critical & JPEG \\ image, often resulting from & in identifying and measuring & \\ high-frequency loss. & abnormalities [23]. & \\ \hline \hline \end{tabular}
\end{table}
Table 2: JPEG and JPEG2000 compression artifacts and their implications for medical images.
# Real-Time Contrail Monitoring and Mitigation Using CubeSat Constellations
## 3 Mitigation Strategies Through Real-Time Monitoring
### Case Studies and Validation of the Technology  
To demonstrate the effectiveness of CubeSats in contrail monitoring, it is helpful to examine specific case studies where this technology could have been or has been applied. One such case study is the North Atlantic Corridor, which is one of the most heavily trafficked airspaces in the world. This region is known for its frequent contrail formation due to the high volume of long-haul flights that operate at altitudes where the atmospheric conditions are conducive to contrail development. In this case study, we simulate thedeployment of a CubeSat constellation over the region of high-air-traffic routes to enable real-time data from the CubeSats.  
Another relevant case study is the collaboration between Delta Airlines and the Massachusetts Institute of Technology (MIT), which has focused on predicting contrarail-prone areas and adjusting flight paths accordingly [45]. While this project has relied on predictive models based on historical data, it has not yet fully integrated real-time atmospheric data, which could limit the accuracy of its predictions. The introduction of CubeSat technology into this framework would enhance the precision of the models by providing up-to-the-minute data on atmospheric conditions, allowing for more accurate and responsive flight path adjustments [46].
## 3 Security and Privacy of Computing Paradigms
### Cloud-Related Aspects
#### 3.1.1 Cloud Data Security  
Data security is an essential aspect that plays a significant role in handling Cloud devices and keeps them running. This may involve protection and restoration guides for data and centers for Cloud services, and data involved in transmissions or transfers must always be protected.  
Generally, there is a need for simple yet robust mechanisms that offer a smooth method of learning about Cloud service capabilities before deployment and those that align with Cloud security features during the establishing stage. The presence of Cloudservice providers and Cloud customers also plays a role in the deployment plan since both parties must meet certain data security requirements [53]. Here, issues such as service level negotiation, information traffic, and especially data security will arise [54]. It is important for Cloud service suppliers to properly protect customers' data stored in the Cloud to reduce or eliminate security shortcomings. Techniques used in encrypting data must be very strong to guarantee better data security and implement authentication mechanisms that monitor other information access. Access control through data encryption should be established so that only the rightfully selected employees can reach the data.
## 2 Materials and Methods
### Flood Hazard Mapping and Analysis  
There are many available methodologies and computer software for flood hazard modeling [45; 46; 47] but what should be considered are the methods accepted and approved by the government agency mandated to disseminate disaster data. In the Philippines, this mandate falls under the Mines and Geosciences Bureau (MGB), an attached agency of the Department of Environment and Natural Resources (DENR). The flood hazard map developed in the study of [PERSON] et al. [47] was approved by the MGB and endorsed to the public for dissemination. Similarly, this study used a flood hazard map approved and endorsed by the MGB to ensure data reliability. In the study of Phil-Lidar 1 entitled flood hazard mapping of the Philippines led by the University of the Philippines, flood hazard modeling of all the principal river basins in the country was done using LiDAR data. LiDAR or Light Detection and Ranging uses light pulses (e.g., ultraviolet, visible, or near-infrared) to image objects at very high precision by measuring the time it takes for the reflections to be detected by the sensor [48]. Lidar data acquisition could be airborne or land-based. Phil-Lidar 1 program produced a flood hazard map for the Boac and Mogpog rivers with a lidar resolution of \(1\times 1\) m and vertical accuracy of \(\pm 15\) cm [49]. Flood hazard  
Figure 2: A map of the Philippines and Marinduque superimposed with surface water and floodplain soil sampling locations and location of Boac (red) and Mogpog (blue) rivers.  
maps were produced at different return periods regarding rainfall intensities that passed through the area historically.
## 4 Results and Discussions
### Spatial Pattern Analysis of the Coordination Degree
#### 4.3.2 Local Spatial Autocorrelation Analysis
As time went by, both hot spot aggregation regions and cold spot aggregation regions generally shrank, suggesting that the spatial aggregations and distributions of high-value and low-value provinces in regard to coordination degree tended to weaken, which is in line with previous analysis results based on the global Moran's I index. Hot spot aggregation regions were mainly distributed along the eastern coastal area, while cold spot aggregation regions were mainly distributed along the western inland areas, developing a transition path from hot spot aggregation regions in the east towards cold spot aggregation regions in the west. It can be seen that the level of China's coordination degree in regard to carbon emissions, economic development, and environmental protection is generally higher in the east and lower in the west, exhibiting a decreasing trend from the east coast towards the west inland, and demonstrating a contrasting east-west spatial distribution pattern.
## 4 Conclusions  
Combining MDM, WDM and OFDM at the same time can significantly increase the transmission capacity for optical interconnects. In this work, we proposed, fabricated and demonstrated a wideband and channel switchable MDM optical power divider on an SOI platform, supporting up to triple modes. The switchable MDM power divider consisted of two parts. The first part was a cascaded 3 \(\times\) 3 MZI switch for switching the data from their original TE\({}_{0}\), TE\({}_{1}\) and TE\({}_{2}\) modes to different modes among themselves. Then, mode up-conversion and Y-branch were utilized in the second part for the MDM power division. An average data rate of 57.02 Gbit/s was achieved in each wavelength. Here, a total capacity of 7.682 Tbit/s (i.e., 57.02 Gbit/s \(\times\) 3 modes \(\times\) 48 wavelengths) was achieved, satisfying the pre-HD-FEC threshold (BER = 3.8 \(\times\) 10\({}^{-3}\)). The average data rate of each OFDM channel was limited by our RTO. Furthermore, although up to three MDM modes were presented in this proof-of-concept demonstration here, the proposed scheme can be scaled to higher order modes operation.  
Data curation, T.-Y.H., G.-H.C. and Y.-Z.L.; Funding acquisition, C.-W.C.; Investigation, T.-Y.H., G.-H.C., Y.-Z.L., Y.-H.J., P.-C.K., C.-W.P., J.-F.T. and Y.L., Writing--original draft, T.-Y.H.; Writing--review and editing, T.-Y.H., C.-W.C., Y.L. and C.-H.Y. All authors have read and agreed to the published version of the manuscript.  
This paper was supported by the National Science and Technology Council, Taiwan, under Grant NSTC-110-2221-E-A49-057-MY3, NSTC-109-2221-E-009-155-MY3.  
Not applicable.  
The data presented in this study are available from the first author upon request.  
The authors declare no conflict of interest.
# Quantum Perspective on Digital Money: Towards a Quantum-Powered Financial System
## 4 Integration of Quantum Money with Quantum Networks
### Quantum Blockchain Integration  
To ensure the integrity and non-repudiation of quantum money transactions, a quantum blockchain can be employed as a decentralized ledger that securely records each transaction within a quantum-powered financial system. Unlike classical blockchains, which often rely on energy-intensive consensus protocols such as proof-of-work, quantum blockchains rely on quantum cryptographic protocols and quantum communication to achieve consensus more efficiently and with enhanced security [40].  
Quantum blockchains utilize quantum-secure consensus algorithms based on entanglement and quantum communication channels, which are inherently resistant to attacks by quantum computers [41]. To fully realize their potential, it is necessary not only to replace classical communication with quantum channels but also to develop new consensus protocols suited to quantum environments. In this context, the recently proposed Time-Bin Conference Key Agreement (TB-CKA) protocol offers a promising solution to the consensus problem in distributed systems [42]. This protocol is designed to operate effectively within large-scale QINs.  
Notably, quantum blockchain consensus mechanisms can incorporate quantum algorithms such as boson sampling or quantum amplitude amplification to significantly lower the computational complexity of transaction validation. Unlike classical mining processes, which are computationally intensive and energy-demanding, quantum protocols can achieve consensus with substantially reduced resource consumption. This advancement directly addresses one of the most critical limitations of classical blockchain systems--namely, their high energy inefficiency--by offering a more sustainable and scalable alternative [43].  
By replacing classical energy-intensive consensus with quantum-secure, efficient quantum protocols, quantum blockchains offer a potential for sustainable distributed ledger technology that complements and enhances the quantum money ecosystem.
# Land Cover Mapping in Southwestern China Using the HC-MMK Approach
## 3 Methodology
### Object-Oriented Multi-Resolution Segmentation  
Image segmentation is a crucial step in object-oriented classification, which groups homogenous neighboring pixels into meaningful objects. In this study, a wide-used multi-resolution segmentation algorithm [40; 41] embedded in the platform Cognition 8.7 was adopted to segment satellite images.  
Figure 4: The spatial distribution of field samples used for generating the CLC-SW2010 product. The pictures (**a**–**k**) represent respectively the deciduous broadleaf shrubs, meadows, herbaceous wetlands, evergreen needleleaf forests, tea garden (shrub plantations), paddy fields, karst vegetation (herbosa), bare soils, permanent snow and ices, rubber plantations (woody plantations) and eucalyptus plantations (woody plantations).  
The scale, shape and compactness are three critical parameters. The optimal values of these parameters were determined by the trial-and-error method and visual assessment of the segmentation results. For the highly heterogeneous landscape, the scale parameter was set as 25, the shape parameter was set as 0.1 and compactness as 0.7. All of the Landsat images and the HJ images were incorporated in the segmentation process, except the topographical data and available thematic maps.