esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Hierarchical Cache Management (HCM) Policy  
The key to hierarchical cache management lies in how to allocate cache levels for replicas based on the read and write characteristics of different storage media, ensuring the efficient utilization of cache resources at each level. At the same time, it is necessary to design tailored replica replacement policies for each cache layer based on its differentiated workload characteristics, in order to improve the hit rate of the caching system.  
The hierarchical cache management (HCM) policy designed in this paper is shown in Figure 2. DRAM serves as the upper-level cache, responsible for caching new replicas, aiming to fully utilize DRAM's high memory bandwidth to improve the overall I/O throughput of the cache system. A cascading connection is used between the upper-level DRAM cache and the lower-level SSD cache, meaning the write load to the SSD cache mainly originates from the replica replacements in the DRAM cache, ensuring the exclusivity of the layered cache and efficient use of cache resources. To further reduce the write load on the SSD cache, a cache admission filter is placed in front of the SSD cache. This filter uses information such as replica popularity and redundancy to filter the write load, thereby reducing the write/erase cycle frequency and wear-related operational costs of SSDs.  
Considering the filtering effect of the upper-level cache on request loads, differentiated replica replacement policies are adopted between each cache layer to accommodate their respective request arrival patterns. Specifically, considering the temporal locality [45] characteristics of the request sequence, each cache level adopts LRU as the base management policy to improve the hit rate of short-term popular data. For the heavy-tailed distribution of data popularity [46], an SSD filter is introduced to further filter out long-term popular data replicas. In addition, to address the diverse optimization objectives of cache management, the SSD cache employs a multi-queue replica management scheme based on  
Figure 2: Illustration of hierarchical cache management (HCM) policy.  
cache utility, optimizing the overall cache performance. In the following sections, we will provide a detailed description of the three key components of HCM: DRAM cache replica management, SSD cache admission filtering, and SSD cache replica management.
# Sensors for Sustainable Smart Cities: A Review
## 3 Results
### Smart Cities in the World
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**City** & **Health** & **Security** & **Mobility** & **Water** & **Waste** & **Energy** \\ \hline \hline Tampere & & Smart & & & \\
[27] & & transportation & & & \\ \hline Helsinki & & Car charging & & Automated waste & \\
[25] & & facilities & & collection & Smart grids \\ \hline Amsterdam & ICT in health, & & & & Clean energy \\
[4] & Health Lab & & & & generation \\ \hline Vienna [27] & & Smart parking, car & & & Energy efficiency \\ \hline Copenhagen & & Bike lane network & Water quality & Optimized waste & \\
[4] & & Bike lane network & monitoring & & \\ \hline Stockholm & & Water management & Waste management & \\
[4] & & & policies & system & \\ \hline Milton & & Smart parking, & & Sensors in recycling & Smart metering \\ Keynes & & MotionMap app & & centers & app \\ \hline London & & App for public & Smart Waste & \\
[27] & & transport & & collection & \\ \hline Malaga [4] & & Electric vehicles, & & & Smart grids, \\  & & charging stations & & clean energies, \\ \hline Barcelona [4] & Remote healthcare & Incident detectors at home & Traffic and public & & Smart Containers & Centralized heating/cooling \\ \hline Santander & & Smart Parking, GPS monitoring & Smart park & & Smart public \\
[26] & & Smart monitoring & irrigation & & lighting \\ \hline Paris [4] & eHealth, smart & & Bike sharing, & & \\ medical records & & charging stations & & \\ \hline Geneva & & Smart & & Fiber-optic, smart & grid networks \\
[27] & & transportation & & \\ \hline Singapore & & Siren alerts for natural disasters & Traffic maps, public & Apps for water & Apps for energy consumption \\
[33] & & disasters & transport apps & tracking & tracking \\ \hline Hong & Smart card IDs & Open, real-time & Smart waste & \\ Kong [4; 27] & for citizens & traffic data & management & \\ \hline Shanghai & & & Pedestrian & \\
[28; 37] & & movement analysis & & \\  & & (Big data) & & \\ \hline Beijing & & V2E solutions, & & \\
[28; 38] & & smart cards for & & \\ \hline Songdo & Remote medical & Self-charging & Underground & \\
[25; 27] & equipment and & electric vehicle & waste suction & Smart buildings \\ checkups & & technology & system & \\ \hline Seoul [27] & & Bus service based & & \\  & & on data analytics & & \\ \hline Taiwan & & Smart defense & & \\
[39] & & system for law & & \\  & enforcement & & & \\ \hline Indonesia & Flood & & & \\
[33] & monitoring and & & & \\  & report app & & & \\ \hline Thailand & & tsunami and & Water management & \\
[33] & flood & apP & & \\ \hline India [4] & & Smart transport & & Clean energy, \\  & & systems & & green buildings \\ \hline Toronto & & Smart urban zone & & \\
[28] & & growth & & \\ \hline \end{tabular}
\end{table} 
Table 1: Smart cities implementations around the world.
## 4 Evaluation Research on a Real Aircraft Structure  
In order to validate the capability of the designed WSN based AST systems, in this paper a multi-point system is developed for a real aircraft undercarriage for static testing in Xian Aircraft Strength & Research Institute of China. Figure 12 shows the real aircraft fore-undercarriage of a fighter plane and the strain gauge distribution. In the three axes of the fore-undercarriage wheel and two axes of the wheel fork, 43 deployed strain gauges were chosen for the static test and connected to the wireless sensor nodes. Figure 13 (a) shows the field setup of the wireless sensor network system which involves 14 sensor nodes and four cluster heads. Table 4 shows the correspondence of connection between 43 strain gauges and 14 wireless sensor nodes. Figure 13 (b) shows the monitoring interface of the network system which is controlled simultaneously with the static test loading facility.  
Before the setup of the WSN based AST system, the existing cable-based measurement system was been used to static test the real aircraft fore-undercarriage, and the testing data was saved for drawing a capability comparison between the existing measurement system and the WSN system. Figure 14 shows the test data comparison between the WSN based AST system and the existing cable-based measurement system. At the same testing point, the maximal difference between the cable-based measurement data and the WSN testing data is no more than 10 \(\upmu\)e. In figure 14, the two testing curves for the same testing point almost coincide. Therefore, the test results in Figure 14 show that the developed WSN system can be successfully used for structural strength test. Figure 15 shows their system setup comparison. The evaluation experiment shows the WSN based AST technology can obviously reduce the number of wires employed. The overall cost of the WSN based AST system in  
Figure 14: Test data comparison between WSN and the cable-based system.  
Figure 13: (a) Field setup of the wireless sensor network system. (b) Monitoring interface of the network system.  
this paper is nearly one-tenth of the existing wired system in Xian Aircraft Strength & Research Institute of China. Because of the processing capability of wireless sensor nodes, the efficiency of the WSN system deployment and debugging in this evaluation experiment is evidently higher than the wired system. For example, the deployment time for the WSN system in this paper is about two hours, whereas the wired system needs almost five hour to be deployed.
## 2 Background
### Actively Secure Evaluation Protocol  
An MPC-based protocol can ensure the privacy of devices against Byzantine devices by utilizing the concepts of verifiable secret sharing [20] and Lagrange interpolation. Each device \(i\) divides the secret \(\mathbf{g}_{i}\) into \(N\) committed shares locally and transmits each \(\left\|\mathbf{g}_{i}\right\|_{T}^{j}\) to all other devices \(j\) (\(j\in[1,N]\)). Collaboration among devices enables secure computation of addition, multiplication by a constant, and multiplication. Addition and multiplication by a constant can be performed easily by each device through direct local operations. Figure 3: Blockchain-based federated learning (BFL) for data sharing in IoMT. For the multiplication of two secrets, complex multiplication is transformed into local addition operations using multiplication triples, thereby reducing online overhead. Further elaboration on this protocol is provided below. 1. Secret Sharing: Each participant \(i\) possesses a secret \(\mathbf{g}_{i}\), allowing them to construct a polynomial \[f_{i}(\theta)=\mathbf{g}_{i}+\sum_{j=1}^{T}\mathbf{r}_{ij}\theta^{j},\] (3) where the coefficients \(\{\mathbf{r}_{ij}\}_{j\in[1,N]}\) are selected at random within the field \(\mathbb{F}_{p}\), with the constructed polynomial maintaining a maximum degree of \(T-1\). In particular, the polynomial's degree remains within \(T-1\) while its coefficients are randomly sampled from the finite field. Through the evaluation of \(f_{i}(\theta)\) across multiple distinct points, participating entities can obtain \[\llbracket\mathbf{g}_{i}\rrbracket_{T}=(f_{i}(1),\cdots,f_{i}(j),\cdots,f_{i}( N)).\] (4) Subsequently, participant \(i\) allocates the share \(\llbracket\mathbf{g}_{i}\rrbracket_{T}^{j}\) across all other devices \(j\). To maintain the integrity of these distributed shares, participant \(i\) also broadcasts verifiable commitments regarding the polynomial coefficients of \(f_{i}\), specifically defined as \[\mathbf{c}_{ij}= \begin{cases}\psi^{\mathbf{g}_{i}}\text{ }for\text{ }j=0\\ \psi^{\mathbf{r}_{ij}}\text{ }for\text{ }j=1,\cdots,T\end{cases} .\] (5) Here, \(\psi\) denotes a generator within the finite field \(\mathbb{F}_{p}\), with all arithmetic operations being executed under the modulus \(\lambda\). The prime \(\lambda\) must be appropriately chosen such that \(\lambda-1\) is divisible by \(p\), ensuring the required algebraic structure for implementation. Upon receiving the commitments in (2), each device \(j\in[1,N]\) can verify the secret share \(\llbracket\mathbf{g}_{i}\rrbracket_{T}^{j}=f_{i}(j)\) by checking the equation: \[\psi^{\llbracket\mathbf{g}_{i}\rrbracket_{T}^{j}}=\prod_{k=0}^{T}\mathbf{c}_{ ik}^{\theta^{k}_{j}},\] (6) where all mathematical operations within this framework are executed using modulo \(\lambda\) calculations. The cryptographic commitment protocol guarantees the accurate generation of confidential shares derived from Equation (2)'s polynomial expression, consequently establishing proof of their authenticity through verification processes. 2.
## 3 System Description
### Methodology
#### 3.1.5 Application and Validation of the Principle of Reciprocity  
Because it has been proposed in this study that the UL \(SNR\) uniformity can be assessed from the DL results by using POR, the last part focused on demonstrating this principle when applied to a single Tx and single Rx scenario. If the UL \(SNR\) uniformity matched the DL value, then the principle was considered to be valid and it could be applied to estimate the UL performance as proposed.
# A Dynamic Model Updating Method with Thermal Effects Based on Improved Support Vector Regression
## 6 Conclusions  
In this paper, a structural dynamic model updating method with thermal effects based on improved SVR is proposed. Assuming the thermophysical parameters are linear combinations of basis functions, which can be expressed by improved SVR, the numerical simulation results show the following conclusions:  
1. The functional updating method has a wider use range, higher precision and clearer physical meaning than the traditional matrix updating method and parametric updating method.
2. It obtains a reliable temperature distribution model according to the identification method with temperature dependence proposed in this paper.
3. The hierarchical correction method is introduced to modify the temperature distribution model and structural dynamic model and effectively solves the model updating problem in a temperature-dependent manner. Therefore, the temperature-dependent model updating method can be applied to practical complex heat transfer processes, which is beneficial to practical applications.  
Conceptualization, Z.C.; methodology, Z.C.; formal analysis, Z.C.; investigation, Z.C.; resources, Q.Z.; data curation, Z.C.; supervision, Q.Z. Both authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (12032012). In addition, this research is A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Low Temperature/20 \({}^{\circ}\)C** & **High Temperature/300 \({}^{\circ}\)C** \\ \hline Upper/GPa & 60 & 40 \\ Lower/GPa & 80 & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 11: The range of elastic modulus with temperature-dependent.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Order** & \begin{tabular}{c} **Real Natural** \\ **Frequency (Hz)** \\ \end{tabular} & \
\begin{tabular}{c} **Initial Natural** \\ **Frequency (Hz)** \\ \end{tabular} & **Deviation** &
\begin{tabular}{c} **Updated Natural** \\ **Frequency (Hz)** \\ \end{tabular} & **Deviation** \\ \hline First & 22.81 & 24.454 & 7.21\% & 22.98 & 0.75\% \\ Second & 110.17 & 117.32 & 6.49\% & 110.94 & 0.70\% \\ Third & 114.32 & 122.63 & 7.27\% & 115.59 & 1.11\% \\ Forth & 140.05 & 151.32 & 8.05\% & 141.53 & 1.06\% \\ \hline \hline \end{tabular}
\end{table}
Table 12: The comparison of real natural frequency, initial natural frequency and updated natural frequency.
# AI-Driven Innovations in Software Engineering: A Review of Current Practices and Future Directions
## 4 Case Studies
### McKinsey's Integration of Generative AI in Software Development  
McKinsey & Company, a global management consulting firm, has been at the forefront of integrating generative artificial intelligence (AI) into its software development processes. Recognizing the increasing demand for efficient and high-quality software solutions, McKinsey embarked on a comprehensive study to evaluate the impact of generative AI on software development productivity and quality [58].  
The initiative involved deploying generative AI tools across multiple development teams. These AI tools assisted in automated code generation, routine coding tasks, and intelligent suggestions for code improvements. Trained on extensive code repositories, the AI models could produce code snippets aligned with both industry best practices and project-specific requirements.  
The results demonstrated a notable increase in productivity--development teams using the generative AI tools completed coding tasks up to twice as fast compared to control groups. Code quality also improved modestly, evidenced by fewer bugs and enhanced maintainability. Crucially, McKinsey reported accelerated time to market for new software products.  
Despite these positive outcomes, McKinsey identified challenges, particularly the variability in AI tool effectiveness depending on task complexity, and the need for developers to maintain sufficient expertise to leverage AI assistance responsibly. This underscores the importance of striking a balance between automation and skilled human oversight.
# Modeling Terrestrial Net Ecosystem Exchange Based on Deep Learning in China
## 2 Methods
### Construction of Daily NEE Estimation Model  
#### 2.3.1 Data Processing Summary  
The main process of NEE data processing is depicted in Figure 2. The data processing procedure consists of three key steps: data preparation, data training, and data prediction. Firstly, satellite remote sensing data corresponding to the sites of interest were downloaded from the GEE platform and interpolated to fill in any missing data, ensuring daily scale completeness. Next, the satellite remote sensing data, along with the eddy covariance site data, were normalized and split into a training set (S) and a test set (X). Subsequently, an A-DMLP model was constructed for training, leveraging the training set. Secondly, the model training process involved evaluating the model results using metrics such as R-squared (R2), mean absolute error (MAE), and root mean squared error (RMSE). Through an iterative optimization process, the best training model was obtained. Finally, the optimized model was employed to make predictions on the test set, and its performance was assessed using the aforementioned evaluation metrics. To achieve the interpretability of the data-driven model and uncover the relationship between input variables and model output, an interpretability method for deep learning models was employed (detailed in the corresponding section).
## 5 Discussion  
The quantified security improvements validate the effectiveness of implementing a comprehensive, multilayered cybersecurity strategy encompassing access controls, monitoring, encryption, and system hardening to protect SCADA environments. The integrated framework successfully reduced vulnerabilities across people, policies, processes, and technologies.  
A key novelty of this approach is the multilayered integration of emerging capabilities like deep learning intrusion detection, granular access controls, and legacy system upgrading with traditional best practices like network segmentation and encrypted communications. This hybrid framework allows manufacturers to take advantage of new technologies while still leveraging established protections. The deep learning IDS, in particular, enables rapid automated threat detection and response, overcoming the limits of signature-based methods.  
Another innovative aspect is the edge computing integration for access management. Pushing identity and access controls to the network edge enhances security and reduces dependence on the cloud. The lightweight mutual authentication policy also demonstrates securing legacy devices through updates like encrypted protocols rather than wholesale replacement.  
This research highlights the importance of a proactive cybersecurity strategy as interconnectivity increases between OT and IT environments. As identified in the literature review, unprotected SCADA connectivity introduces significant risks that manufacturers must address. This framework provides a model to securely enable Industry 4.0 integration by mitigating vulnerabilities at multiple levels. Quantified metrics validated the threat detection, containment, and prevention capabilities achieved.  
The multilayered methodology can help industrial organizations maintain resilient operations even as attacks grow more sophisticated. By combining the latest technologies with robust policies and processes, manufacturers can confidently pursue digital transformation initiatives with the knowledge that risks are managed. As automation and connectivity expand through concepts like smart factories and the IIoT, cybersecurity will only increase in importance. This research offers both an effective blueprint for SCADA protection and a foundation to build upon as technology evolves.  
While this integrated framework demonstrated significant improvements in securing the SCADA environment, certain limitations should be noted. The costs associated with upgrading legacy hardware, implementing new access control systems, and developing customized deep learning algorithms may be prohibitive for some organizations. The complexity of managing multiple layered security controls could also pose implementation challenges. Further research is needed to streamline and simplify management across disparate tools. However, as demonstrated by the metrics, the risk-reduction benefits outweigh these limitations. Organizations must balance costs against the risks of cyber incidents in their unique environment.
# Electrospinning of Chitosan for Antibacterial Applications--Current Trends
## 7 Application Fields of Electrospun Chitosan Nanofibers and Chitosan Derivatives
### Air Filtration
Fibers were produced at a rate of 50 g/h, which was much greater than the conventional needle electrospinninging method's range of 0.02-1 g/h. Smooth and homogeneous nanofibers with diameters ranging between 25 and 60 nm were produced by adjusting the spinning parameters, and Ag nanoparticles were coated on the surfaces of the nanofibers. The chitosan hybrid nanofibers demonstrated a substantial improvement in filtering effectiveness (>99%). Thus, the nanoparticle aerosol removal was similar to commercial high-efficiency air filters for particles, but with less mess and a reduced pressure drop. They also demonstrated remarkable antibacterial activity while laying a thin coating of nanofibers, killing 99% of S. aureus and 97% of _E. coli_ within two hours. Under high-humidity and water immersion conditions, the crosslinked nanofibers showed good water resilience and maintained a stable microstructure [181]. In summary, in air filtration, electrospun fibers offer a novel and efficient technique for removing contaminants with excellent filtering efficiency in the submicron domain and low air resistance. Thus, problems with conventional non-woven macro fibers are overcome. It is feasible to regulate air permeability by varying the thickness of nanofibers so that they can remove aerosol particles as tiny as 0.5 um due to their high surface-area-to-volume ratio and associated strong surface cohesion [182]. The filtration efficiency may be improved by using smaller electrospun nanofibers, although their strength in this case as a filtering medium may be unfavorable due to their usage, which may be improved by crosslinking electrospun nanofibers.
## Abbreviations  
ADASAdvanced driver assistance system  
BSMBasic safety message  
CAMCooperative awareness message  
CANController area network  
CAVConnected and automated vehicle  
CTRVConstant turn rate and velocity  
C-V2 XCellular vehicle-to-everything  
DENMDecentralized environmental notification message  
DSRCDedicated short-range communications  
ECUElectronic control unit  
FMCWFrequency modulated continuous wave  
FOVField of view  
GNSSGlobal navigation satellite system  
ITSIntelligent transportation system  
NTRIPNetworked Transport of RTCM via Internet Protocol  
OBUOn-board unit  
ODDOperational design domain  
RSURoadside unit  
RTCMRadio Technical Commission for Maritime Services  
TTCTime-to-collision  
V2 IVehicle-to-infrastructure  
V2 NVehicle-to-network  
V2 Pvehicle-to-pedestrian  
V2 VVehicle-to-vehicle  
V2 XVehicle-to-everything  
WAVEWireless access in vehicular environments  
WGSWorld Geodetic System
# Radiation Hardened Read-Stability and Speed Enhanced SRAM for Space Applications
## 2 Proposed RHRSE-20T SRAM
### Single Node Upset Recovery Analysis
#### 2.5.3 Single Event Transient Occurs at Node S0  
S0 changes from '0' to '1'. This activates transistors N5 and N6, causing node Q to change from '1' to '0'. However, nodes S1, S2, and S3 remain unaffected by the SET at S0, preserving their original values. Consequently, transistor P4 remains deactivated due to node S3, while N1 is activated by S1, allowing S0 to recover to '0'. As the restored S0 deactivates transistors N5 and N6, Q recovers to '1'. Through this process, all nodes are restored to their original states.