esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analysis of Rotor Parameter Influence on Electromagnetic and Mechanical Stress Performance
### Pre-Treatment of Finite-Element Model
#### 3.1.2 Calculation Process of Electromagnetic Characteristics  
The electromagnetic calculation of the high-speed IPM motor is divided into no load and rated load, which are distinguished by the setting of the winding excitation. The pre-processing of electromagnetic calculation mainly includes establishing a geometric model, selecting the solution type, setting material, setting motion band, boundary condition, excitation source, and mesh.  
In this paper, in the process of analyzing the influence of rotor design parameters on motor performance, the geometric model is changed by changing the parameters. The solution type of the 2D maxwell model selects transient. The addition of materials is divided into stator and rotor electrical steel materials, PM materials, and winding conductor materials. For models with moving parts, motion boundaries need to be set. For the HSPMM analyzed in this paper, select the center of the air gap to draw the motion boundary and add the axis of rotation, direction of rotation, and speed of rotation.  
For the boundary conditions, vector potential is chosen because the whole model of the motor is used in this paper. That is, the outer vector magnetic potential of the boundary is a certain characteristic value (usually set to 0). In this paper, current sources are selected to excite the windings in the electromagnetic analysis process. The difference is that the magnitude of current during no-load electromagnetic analysis is set to 0, while the load simulation current is set to a three-phase rated current. Consistent with the rotor stress analysis process, the electromagnetic calculation process also requires the mesh of the motor model. The 2D-FEM of the electromagnetic calculation process is shown in Figure 5, including the mesh, magnetic flux density, and magnetic flux distribution. After finishing these pre-treatments, the electromagnetic model of the motor can be solved.  
Figure 4: The mesh of the rotor stress calculation model.  
Figure 5: The 2D-FEM of the electromagnetic calculation process.
# Audio-Visual Speech Recognition Based on Dual Cross-Modality Attentions with the Transformer Model
## 4 Experimental Results and Discussions
### Attention Visualization  
Figure 6 shows the attention weight maps between audio and video encoders using audio or video features as queries in our proposed model (TM-DCM) for clean and noisy reverberant data. The weights were computed by averaging over all the cross-modality attention heads. The weight map of the audio query cross-modality attention for noisy reverberant data was more noisy than that for clean data because noisy reverberant data could not provide clear clues than clean data, which might result in performance degradation by speech contamination. However, the video query cross-modality attention produced very clean weight maps even with noisy reverberant audio data as keys and values.  
In Figure 7, we display the encoder-decoder attention weight maps using previous decoder outputs after the self attention as queries and DCM attention outputs as keys and values for the cross-entropy and hybrid CTC/attention losses. The hybrid architecture made the maps, especially between audio encoders and decoders, more clean by forcing monotonic alignments even with noisy reverberant data.
## 3 Results
### Prediction of NPK Levels in Fruits Based on PLSR and PCR  
PLSR and PCR models described the relationship between electronic nose VOC-emission signals and the amounts of N, P, and K present in cucumber fruits. These models were evaluated for their ability to predict the content of the above macronutrients in terms of correlation of determination (R\({}^{2}\)) and root mean square error (RMSE). The root mean square error of validation (RMSEval) also was chosen as a numerical tool for selecting the optimal model, based on results shown in Table 3. The results show that the PLSR and PCR models were very close for the determination of N, P, and K contents. In addition, the model was able to predict the amount of nitrogen fertilizer with an R\({}^{2}\) around 0.90 and an RMSE of 0.047, indicating its accuracy; however, the values for potassium and phosphorus fertilizers were less accurate.  
Figure 5: Loading plot for PCA analysis for different levels of urea fertilizer.  
The PCR and PLSR models were found to be more accurate in predicting N than K or P, based on the results (Table 3). The e-nose predicted the N content determined from the VOC volatile gases in the headspace of cucumber fruit samples with higher accuracy than for K or P content. The coefficient of determination (R\({}^{2}\)) values of 0.90 and 0.91 (using PCR and PLSR models, respectively) indicated that nitrogen content levels were more highly correlated with changes in VOC emissions than were potassium levels (R\({}^{2}\) = 0.83) and phosphorus levels (R\({}^{2}\) = 0.81), based on VOC emissions detected by the electronic nose. Nevertheless, the relatively high correlation levels between e-nose VOC emissions and estimated levels of N, P, and K in cucumber fruits was sufficiently high to provide reasonably good estimates of the nutrient contents of these macronutrients in fruit tissues.  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline  & **Model** & **R\({}^{2}\)val** & **R\({}^{2}\)cal** & **RMSE-Val** & **RMSE-Cal** & **Offsetval** & **Offsetcal** & **Optimal Factor** \\ \hline Nitrogen & PCR & 0.899 & 0.905 & 0.047 & 0.046 & 0.328 & 0.323 & 1 \\  & PLSR & 0.905 & 0.906 & 0.047 & 0.046 & 0.334 & 0.322 & 1 \\ \hline \multirow{2}{*}{Potassium} & PCR & 0.832 & 0.850 & 0.142 & 0.132 & 0.780 & 0.706 & 4 \\  & PLSR & 0.831 & 0.848 & 0.144 & 0.133 & 0.789 & 0.713 & 2 \\ \hline \multirow{2}{*}{Phosphorus} & PCR & 0.808 & 0.814 & 0.216 & 0.211 & 0.117 & 0.115 & 1 \\  & PLSR & 0.806 & 0.814 & 0.217 & 0.210 & 0.119 & 0.115 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: PLSR and PCR model analysis results for chemical fertilizer residues in cucumber.  
Figure 6: Cluster analysis dendrogram responds to different levels of urea fertilizer applied to cucumber plants.
# SMS: A Secure Healthcare Model for Smart Cities
## 4 Proposed SMS Framework
### Three Stages of the Proposed Approach  
For better understanding of the process, the proposed approach has been divided into three phases.  
**Phase 1-Data collection**  
This is the initial step of the proposed SMS framework. In this phase, the \"real-time data\" of the patients are captured with the help of multiple sensor-based devices including wearable and other medical equipment to collect the physiological parameters of the patients and other health-related data.  
**Phase 2-Data Encryption and Standardization**  
In the second phase, the information captured by the data collection phase is encrypted and converted into a predefined standard format in order to maintain \"consistency,\" \"confidentiality,\" and \"privacy\" of the data. Data standardization is necessary because of the fact that different devices capture and store the data in their respective format and, in order to maintain the \"consistency,\" the data must be converted into a predefined standard format. The block diagram given below in Figure 2 presents the working of the data standardization unit.  
Figure 3 presents the architectural framework of the proposed SMS. The data captured by the different sensors are collected in the data collection phase. While capturing the required information, each sensor adds a sensor_id, model_no, data format, and size of the data in the data-header field (metadata). This step ensures the \"authenticity\" and \"legitimacy\" of the data at later stages (if required). After this, the essential portions among the data are identified and the outliers and noises are removed from the data. Then, the data are converted into the required standard format. Finally, the data are normalized in order to remove any duplicity or inconsistency. All this is governed by the central monitoring unit. The encryption unit is responsible for converting the data collected from the sensors  
Figure 2: Data standardization process.  
into an encrypted format to protect it from eavesdroppers (if any). The SMS uses the \"Twofish cryptographic technique\" to perform the encryption of the data. It is a \"symmetric key cryptographic\" technique that uses a \"Feistel network,\" and it is fast and has an intricate internal structure that is very hard to break but, at the same time, fairly simple to embed in the hardware devices because of the availability of its open-source APIs in several prominent languages. Further details about the Twofish algorithm can be found in [63; 64].
# Time Domain Room Acoustic Solver with Fourth-Order Explicit FEM Using Modified Time Integration
## 3 New Room Acoustic Solver Using Explicit TD-FEM with Modified Adams Method
### Designing a Higher-Order Time Integration Method
#### 3.2.2 Conventional Fourth-Order Adams-Bashforth Method  
In general, the weight coefficients of fourth-order Adams-Bashforth method are given as \(l=4\), \(b_{1}=55/24\), \(b_{2}=-59/24\), \(b_{3}=37/24\), and \(b_{4}=-9/24\)[32]. With the coefficients, Equation (24) is rewritten as  
\[\mathbf{p}^{n}=\mathbf{p}^{n-1}+\frac{\Delta t}{24}\mathbf{D}^{-1}(55\mathbf{M}\mathbf{v}^{n-1}-59 \mathbf{M}\mathbf{v}^{n-2}+37\mathbf{M}\mathbf{v}^{n-3}-9\mathbf{M}\mathbf{v}^{n-4}). \tag{26}\]  
In this case, the time marching scheme of explicit TD-FEM comprises Equations (26) and (8). However, a time-domain dispersion error analysis reveals that the simple use of general weight coefficients do not keep the fourth-order accuracy in time as below.  
\[c^{h}\approx c_{0}(1-i\frac{\omega\Delta t}{4}+\frac{5(\omega\Delta t)^{2}}{96 }-i\frac{(\omega\Delta t)^{3}}{128}-\frac{(kh)^{4}}{1440}(\frac{16143}{64} \tau^{4}+8-19\cos^{2}\!\theta\sin^{2}\!\theta)). \tag{27}\]  
Equation (27) shows that the resulting time marching scheme has fourth-order accuracy in space and first-order accuracy in time. In addition, odd-order terms of dispersion error include an imaginary number. These results suggest that the resulting scheme has low accuracy and that it is highly dissipative. They also suggest the necessity of designing appropriate weight coefficients to increase the accuracy in time of explicit TD-FEM.
# A Review-Unguided Optical Communications: Developments, Technology Evolution, and Challenges
## Abbreviations  
2D-PDA  
Two-dimensional photodetector array  
5G/6G  
Fifth generation/sixth generation  
ACO-OFDM  
Asymmetrically clipped optical OFDM  
ADC  
Analog to digital convertor  
ADO-OFDM  
Asymmetrically DC-biased optical OFDM  
AF  
Amplify forward  
AI  
Artificial intelligence  
AM  
Amplitude modulation  
ANN  
Artificial neural network  
AO  
Adaptive optics  
AoA  
Angle of arrival  
AP  
Access point  
APD  
Avalanche photodiode  
AR  
Amplify receive  
ARPANET  
Advanced research projects agency network  
ASCO-OFDM  
Asymmetrically and symmetrically clipping optical OFDM  
ASE  
Amplified spontaneous emission  
ASK  
Amplitude shift keying  
ATM  
Asynchronous transfer mode  
AUVs  
Autonomous underwater vehicles  
AWGR  
Arrayed waveguide grating router  
BCH  
Bose-Chaudhuri-Hocquenghem  
BER  
Bit error rate  
CATV  
Cable television  
CBS  
Cell-based bandwidth scheduling  
CCD  
Charge-coupled device  
CDM  
Color domain modulation  
CDMA  
Code division multiple access  
CIM  
Color intensity modulation HACO-OFDM Hybrid asymmetrically clipped optical OFDM  
HANs Home access networks  
HAP High altitude platform  
HD High definition  
HDTV High definition television  
HetNet Heterogeneous networks  
HICALI High speed communication with advanced laser instrument  
HLWNets Hybrid LiFi and WiFi network  
HOM Handover margin  
HSPSK Hybrid spatial phase-shift keying  
IC Integrated circuit  
IM/DD Intensity modulation/direct detection  
InGaAsP Indium gallium arsenide phosphide  
IoTs Internet of things  
IOUT Internet of underwater things  
IP Internet protocol  
IPS Indoor positioning system  
IQ In phase-quadrature phase  
IrDA Infrared aata associations  
ISDB Integrated service digital broadcasting  
ISC Image sensor communication  
ISRO Indian space research organization  
ITS Intelligent transportation systems  
JETTA  
Japan electronics and information technology industries association  
LACO-OFDM Layered ACO-OFDM  
LAN Local area network  
LAUV Light autonomous underwater vehicles  
LCF Limited content feedback  
LD Laser diode  
LDPC Low-density parity-check  
LED Light emitting diode  
LEO Low earth orbital  
LFF Limited frequency feedback  
Lidar Light detection and ranging  
Li-Fi Light fidelity  
LiGHTNETs Light networks  
LIP Location inventory problem  
LLCD Lunar laser communications demonstration  
LM Link margin  
LO Local oscillator  
LoS Line of sight  
LPF Low-pass filtering  
LTE Long term evolution  
MALB Mobility-aware load balancing  
MAN Metropolitan area network  
Mbps Mega bits per second  
MC-LEDs Multi-chip LEDs  
MCM Multi-carrier modulation  
MEMS Micro electromechanical systems  
MIMO Multi-input multi-output  
MISO Multiple-input single-output  
ML Machine learning  
M-LEDs Multicolour LEDs  
MLL Mode-locked laser  
MM Metameric modulation  
MMF Multi-mode fiber  
MMS Multimedia messaging service  
mm W Millimetre wave  
MPA Mono-pulse arithmetic
# A Cross-Chain Solution to Connect Multiple DNS Blockchains in Consensus Roots System
## 2 Related Work  
### Blockchain-Based DNS  
Blockchain technology is a distributed ledger technology that has features such as decentralization, immutability, distributed storage, anonymity, transparency, and smart contracts [8; 9]. Blockchain technology has many applications in fields like finance, electronic contracts, intellectual property, etc. The most popular application of blockchain is cryptocurrency, and Bitcoin and Ethereum are the most famous cryptocurrency applications. Bitcoin is a decentralized cryptocurrency that uses proof-of-work as its consensus mechanism, it has a limited supply, and it delivers anonymity and stability [10]. Ethereum is an open-source blockchain platform that also uses proof-of-work as its consensus mechanism, but it is changing it to a proof-of-stake mechanism instead. Ethereum introduces smart contract technology that allows developers to create and run distributed applications [11].  
Blockchain technology has features like decentralization, immutability, distributed storage, anonymity, transparency, and automation, and it has applications in fields like cryptocurrency, supply chain management, smart contracts, identity verification, healthcare, and energy trading. DNS blockchain technology, a notable application, stores domain name resolution records on the blockchain for decentralized management, improving domain name resolution's speed and security. Some projects in this domain are Namecoin [12] and Blockstack [13]. Namecoin, a blockchain-based cryptocurrency, uses distributed technology to manage domain name information, achieving DNS decentralization. Blockstack, a decentralized naming and storage system on the Bitcoin blockchain, enables global unique name registration for applications, data, and identities. It also offers decentralized identity authentication and file storage, aiming for a decentralized ecosystem to replace the Internet. Cross-chain technology solves interoperability challenges between different blockchains, enabling data exchange and asset transfer, including in the DNS blockchain sector, where it synchronizes and coordinates DNS domain names across various blockchains.  
Recent studies have further advanced the integration of blockchain into DNS. [PERSON] et al. proposed the DiSAuth framework, which combines DNS and blockchain to enforce access control in data-decoupled scenarios, offering verifiability, tamper resistance, and privacy protection [14]. Compared to traditional solutions, it improves verification efficiency by approximately three orders of magnitude. [PERSON] and [PERSON] developed the D-DNS system, introducing a domain index and a PoS consensus mechanism to enhance query efficiency and significantly reduce attack success rates [15]. Additionally, [PERSON] et al. explored the feasibility of integrating blockchain with AI and honeypot technologies to enhance DNS security, concluding that blockchain can significantly improve both the security and privacy of DNS systems [16].
## 2 Image Processing in Remote Sensing
### Image Fusion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Image Dataset**} & \multicolumn{4}{c}{**Performance Metrics**} \\ \cline{3-5}  & & **Mutual Information** & \(\mathbf{Q^{AB/F}}\) & **Visual Information** \\  & & **Information** & & **Fidelity** \\ \hline
1. Image cartoon texture decomposition and sparse coding [37] & NIR and visible IKONOS images & — & 0.357 & 0.3470 \\ \hline
2. Contourlet Transform [38] & SAR and panchromatic images & 9.342 & — & — \\ \hline
3. Expectation Maximization algorithm [39] & Landsat image set & — & 0.589 & — \\ \hline
4. Dictionary learning method [40] & Visible and IR image set & 5.778 & 0.660 & 0.578 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Image fusion methods and their performance metrics. finds the ratio between information and noise of fused image and Mutual Information which measures the similarity of image intensity between source and fused image.
# A Study on the Properties of Recycled Aggregate Concrete and Its Production Facilities
## 4 Development of the Technology to Improve the Quality of the Mixed Aggregates
### Principle of Technology  
In the Korea Standard, the material age is defined as the aggregate, with a density of 2.5 g/cm\({}^{3}\) or more, recycled coarse aggregate is defined as having a density of 2.2 g/cm\({}^{3}\) or more. A quality deviation is generated, when mixing aggregates of different densities at a content of 70% or more, in the ready-mixed concrete. Technology to appropriately mix such mixtures was developed. The purpose of this was to produce recycled aggregate concrete with a difference of 0.8% or less, in the unit capacity volume of the mortar, and 5% or less in the unit coarse aggregate volume, using the hand mixing capability index defined by KS F 2455 \"Test Method of Changes of Mortar and coarse aggregate volume from the Concrete Made with Mixer.\" The perspective drawing and mixing method of the aggregate mixing equipment are shown in Figure 7.  
Previous aggregate mixing equipment have primarily used gravity methods. For conical-shaped mixers, the aggregate is rotated, allowing the cone to fall simply due to gravity. In this process, the aggregates fall in lumps, while they are attached to the inner wall of the cone-shaped mixer by centrifugal force. As a result, no mixing occurs.  
This equipment uses gravity and forced mixing, with a mixing arm around the injection point, to mix the material as it is initially injected. A mixing plate is fixed inside the rotating drum, in a C-shape, for the secondary mixing, to prevent lumping when the material falls due to gravity. In addition, a counterclockwise rotation direction is used for a conical shape and a clockwise direction is used for the central axis. The mixing time is 30 s, and the mixing speed is 20 RPM.  
Figure 7: Perspective schematics of the aggregate mixing equipment and mixing method.  
Experiment and Results of the Mixed Aggregate Quality before and after the Penetration of the Aggregate Mixing Equipment  
The mixed aggregate concrete before and after entering the aggregate mixing equipment was examined. The recycled coarse and fine aggregates were displaced by 50% of the entire aggregate volume with ready-mixed concrete after mixing for 45 s. The differences in the unit capacity volume of mortar in the concrete and differences in the unit coarse aggregate volume from the concrete, and the compressive strength under the Korea Standard, were investigated.  
The experimental results are presented at Table 1.
# Automated Generation of Daily Evacuation Paths in 4D BIM
## 5 A Case Study and Implementation
### Benefits and Potentials  
The proposed approach, which uses the customized 4D BIM platform, has potential for any commercial 4D BIM tool such as Synchro or Vico. The pathfinding algorithm and process can be integrated into any 4D BIM application to generate egress paths and circulation paths. The key benefit of 4D BIM-based path planning is the automated generation of daily, weekly, or monthly updatable and changeable evacuation plans that reflect project progress, planned work schedules, and site conditions. Users can define work zones, schedules, work orders, and other required parameters on the 4D BIM platform to flexibly manage BIM models and analyze possible pathways. The accessible pathways, secured spaces, and installed exits shown in 4D project models that can be shared and trained in every day's tool-box meetings, will enhance emergency preparedness planning and help a rapid reaction in any emergency situations or construction accidents. In particular, in emergency situations, such as a collapse or fire on site, a dispatched rescue team can quickly recognize the site conditions using the mapped 4D models and determine the shortest and secured pathway to save labors from the site. From a project management perspective, the daily updated geometric representation of work zones and a construction site will improve logistics planning, task sequence management, and material stack/equipment movement planning by incorporating path planning and visualization capabilities into 4D BIM and daily pre-task planning. Since a construction project has to organize a series of tasks, numerous labors, diverse materials within a limited jobsite space, a site logistic, and work planning, using 4D BIM and its automated circulation path finding features will play a pivotal role in the project management of diverse construction projects. Furthermore, an explicit illustration of the existing conditions of project tasks and the available movement paths of equipment or material, will facilitate more effective construction project management and planning.