esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Merging Multi-Sensor Data and Isolating Fault Signals  
If we use multiple sensors to measure the same parameter of autonomous vehicle, we can merge the outputs of all sensors using the weight assignment method for data fusion purpose. The result of data fusion can be written as follows:  
\[O=WI=[w_{1},w_{2},\cdots,w_{n}][i_{1},i_{2},\cdots,i_{n}]^{T} \tag{21}\]  
where \(O\) is the result of data fusion; \(W=[w_{1},w_{2},\cdots,w_{n}]\) is the weight matrix; \(I=[i_{1},i_{2},\cdots,i_{n}]^{T}\) is outputs of sensors; \(n\) is the number of sensors.  
The principle of the method of weight assignment can be written as follows:  
\[\left\{\begin{array}{l}w_{j}=m_{j}/\left[\sigma_{j}^{2}\sum\limits_{i=1}^{n} \left(m_{i}/\sigma_{i}^{2}\right)\right]\\ \sum\limits_{i=1}^{n}w_{j}=1\end{array}\right. \tag{22}\]  
where \(\sigma_{i}\) and \(\sigma_{j}\) are the dispersions of the output errors of the \(i\)th and \(j\)th sensors, \(i=1,2,\cdots,n\); \(M=[m_{1},m_{2},\cdots,m_{n}]\) is the fault detection matrix.  
As the true values of vehicle motion states cannot be obtained, the work determines the average value of the different sensors outputs, which is usually used as the true value. However, this method is not suitable for our situation, since different types of sensors in different conditions can give a deliberately low accuracy or even failure; thus, the average value may have a large difference with the true value, which is extremely harmful for vehicle safety.  
Assume that at moment \(k\) sensor \(j\) gives the measured value \(T_{j}(k)\); then:  
\[\left\{\begin{array}{l}\Delta T_{j}(k)=T_{j}(k)-\hat{T}_{j}(k)\\ \Delta\overline{T}_{j}=\frac{1}{N}\sum\limits_{k=1}^{N}\Delta T_{j}(k)\\ \sigma_{j}(k)=\frac{1}{N}\sum\limits_{k=1}^{N}\left[\Delta T_{j}(k)-\Delta \overline{T}_{j}\right]^{2}\end{array}\right.\quad k=1,2,\cdots,N \tag{23}\]  
where \(\Delta T_{j}(k)\) is the measurement error of the \(j\)th sensor at moment \(k\); \(\Delta\overline{T}_{j}\) is the average value of the \(j\)th sensor; \(\sigma_{j}(k)\) is the variance of the output error of the \(j\)th sensor at moment \(k\); \(\hat{T}_{j}(k)\) is prognostic assessment of states information obtained using the Kalman filter; and \(N\) is the number measurements from each sensor.  
Since the outputs of federal Kalman filters are obtained after unreliable data detection and isolation, we can approximately consider the estimated information as true values of vehicle motion states.
# Moving Vehicle Tracking with a Moving Drone Based on Track Association
## 3 Multiple Target Tracking
### System Modeling  
The kinematic state of a target is assumed to follow a nearly constant velocity (NCV) motion. The uncertainty of the process noise, which follows the Gaussian distribution, controls the kinematic state of the target. The discrete state equation for multiple targets is as follows  
\[x_{t}(k+1)=F(\Delta)x_{t}(k)+q(\Delta)v(k),\ t=1,\ \ldots,\ N_{T}, \tag{5}\]  
\[F(\Delta)=\left[\begin{array}{c}1\Delta 00\\ 0100\\ 001\Delta\\ 0001\end{array}\right],\ q(\Delta)=\left[\begin{array}{c}\Delta^{2}/20\\ \Delta 0\\ 0\Delta^{2}/2\\ 0\Delta\end{array}\right] \tag{6}\]  
where \(x_{t}(k)=\left[x_{t}(k)v_{tx}(k)y_{t}(k)v_{ty}(k)\right]^{T}\) is the state vector of target \(t\) at frame \(k\), \(x_{t}(k)\) and \(y_{t}(k)\) are positions in the \(x\) and \(y\) directions, respectively; \(v_{tx}(k)\) and \(v_{ty}(k)\) are velocities in the \(x\) and \(y\) directions, respectively; \(N_{T}\) is the number of targets, \(\Delta\) is the sampling time, and \(\mathbf{v}(k)\) is a process noise vector, which is Gaussian white noise with the covariance matrix \(Q_{v}=diag\Big{(}\left[\sigma_{x}^{2}\sigma_{y}^{2}\right]\Big{)}\). The measurement vector for target \(t\) consists of the positions in the \(x\) and \(y\) directions. The measurement equation is as follows  
\[z_{t}(k)=\left[\begin{array}{c}z_{tx}(k)\\ z_{ty}(k)\end{array}\right]=H\mathbf{x}_{t}(k)+\mathbf{w}(k), \tag{7}\]  
Figure 3: Block diagram of multiple target tracking.  
\[H=\left[\begin{array}{c}1000\\ 0010\end{array}\right], \tag{8}\]  
where \(\mathbf{w}(k)\) is a measurement noise vector, which is Gaussian white noise with the covariance matrix \(R=diag\Big{(}\left[r_{x}^{2}r_{y}^{2}\right]\Big{)}\).
## 2 System and Channel Models  
In this article, satellite communication systems are applied to provide service for remote areas, where ground networks are unavailable due to economic challenges and physical barriers. We consider a three-hop hybrid FSO/RF system with parallel FSO/RF transmissions, which consists of the end-to-end links from the optical ground station (OGS) to different ground users in the remote areas. Specifically, as shown in Figure 1, the three-hop hybrid FSO/RF system consists of the high-throughput FSO feeder link between OGS and the LEO satellite, the FSO relay link between the LEO satellite and HAP, and the parallel FSO/RF links between HAP and ground users. The optical signal received at the LEO satellite is firstly amplified and then transmitted to the HAP through an FSO relay link during the downlink transmission. The optical signal received at the HAP is decoded and forwarded to ground users through the parallel FSO/RF links. Considering the large capacity of the FSO link, we assume that data transmission can be completed within the overhead time of a single LEO satellite, and the case of multiple LEOs is not within the scope of this work's discussion. The parallel FSO/RF links are adopted to achieve high speed and reliable transmission, where the limited power of HAP is allocated to FSO and RF links in a variable coefficient \(\rho\). The RF link provides extensive coverage and basic services for all users, while the FSO link provides enhanced services to the users in hotspot areas through ground access points (AP). The proposed system is quite practical as it can overcome the rate limitation caused by traditional hybrid FSO/RF design and reduce link outage caused by frequent switching.
# A Hybrid Encryption Scheme for Quantum Secure Video Conferencing Combined with Blockchain
## 6 Experimental Evaluation
### Execution Cost
From the experimental results, it can be seen that the query efficiency of using the cache layer B+ tree was 3.15-times higher than the original query efficiency of the blockchain, and the query efficiency has obvious advantages. Figure 12: Generate block time with different character lengths. Figure 13: Query time comparison.
## 2 Materials and Methods
### Quasi-Static Compression  
Quasi-static compression tests were performed with an electromechanical universal testing machine, Instron 3300 (Instron, Norwood, ME, USA) equipped with the static load cell (2530-10 KN, Instron, Norwood, ME, USA) (capacity: 10 kN, accuracy: 0.6%). Samples were compressed between two steel plates at a constant velocity of 0.2 mm/s (strain rate of 0.01 s\({}^{-1}\)) up to 80% compression. Force, time and displacement were recorded at a sampling rate of 1 Hz. The face of each sample with the speckle pattern was filmed using a MotionBLITZ EoSens(r) mini camera (MIKROTRON, Unterschleissheim, Germany) (4 Hz) to allow for a full-field strain measurement using digital image correlation (DIC), as per previous work [25; 26; 27]. Video footage was analyzed with the GOM Correlate (Gom, Braunschweig, Germany) DIC software to obtain lateral and axial local strains (supplementary material Figure S1 and Video 1). [PERSON]'s ratio was obtained by multiplying \(-1\) by the slope of the linear least square fitting of the curve lateral strain vs. axial strain between 0 and 2% compression.
## 6 Conclusions and Future Perspectives
### Challenges
#### 6.2.2 Insufficient Spatiotemporal Validation  
As discussed earlier, most existing studies primarily rely on the random split method to divide training and test data. While convenient, this experimental setup cannot fully evaluate the model's generalization capability across different spatial regions and temporal periods. Some datasets may contain consecutive samples in spatial and temporal dimensions, leading to data leakage between the training and test sets. Without proper validation in separate spatiotemporal contexts, this leakage may cause overfitting and overly optimistic assessments of model performance. For example, training and test samples in sea ice classification tasks often come from the same Arctic region and season, causing the DL models to perform well in the reported experiments but fail when applied to data from different areas or seasons with varying ice textures. In addition, some in situ observations, such as buoy data from NDBC, exhibit strong spatial clustering in coastal regions. This uneven distribution may cause DL models to overfit to densely observed areas and generalize poorly when applied to sparsely observed regions such as the open ocean. Similarly, temporal biases may arise when models are trained on relatively stable historical periods but are deployed in extreme conditions like hurricanes. Moreover, many studies report only aggregated metrics such as RMSE and R\({}^{2}\), without stratified performance analysis across different regions and times, making it difficult to assess the DL model's robustness and reliability in specific application scenarios.
## 4 Scenarios
### Targeted Application and Security Protocols
Then, a second, more basic, SCHC is carried out on the resulting DTLS packet, compressing away the DTLS header in order to further reduce the packet size. Table 3 summarizes the differences between the DTLS implementation for the cases with SCHC and without SCHC.
## 7 Discussion and Open Questions
### Risk Analysis in Digital Twin-Based Validation Frameworks
#### 7.4.3 Cybersecurity Risks in Connected Digital Twins  
The increasing connectivity of DT platforms exposes the validation environment to cybersecurity threats, as extensively presented in Section 8.3. Given the safety-critical nature of automotive lighting systems, such breaches could compromise validation integrity and vehicle operation; thus, embedding cybersecurity measures, such as encrypted communications, access control, and intrusion detection, is imperative [47].  
Examples include the following:  
* In 2023, a reported cybersecurity incident involved unauthorized access to a cloud-hosted DT environment used for vehicle software validation. Attackers manipulatedtest parameters, causing false positives in safety tests that could have masked latent system defects [48];
* Injection of false telemetry data into a digital twin during operational monitoring may lead to incorrect maintenance decisions;
* Denial-of-service attacks may occur that disable remote simulation capabilities during critical validation phases.
# Crushing Performance of Ultra-Lightweight Foam Concrete with Fine Particle Inclusions
## 3 Testing methods
### Measurement of Crushing Behavior Using a Penetration Test  
The crushing behavior of the samples was characterized using a penetration test at 7, 14, and 21 days, where one cylindrical specimen was tested per mixture at each age. This test method has been previously used for studying the crushing behavior of foam concrete [15]. For each test, a 47 mm diameter steel rod was vertically displaced into the cylinder specimen at a 75 mm/min rate from the top center, where the loading was controlled using an Instron-4502 testing frame. Due to the 10 kN loading capacity of the load cell used, the maximum indentation stress was 5.7 MPa.
# Modulus Waveform Design Based on Manifold ADMM Idea in Dual-Function Radar-Communication System
## 5 Conclusions  
In this paper, we studied the joint waveform design and beamforming for DFRC systems and proposed a manifold-ADMM framework to address both strict radar beampattern constraints and the trade-off between radar and communication performances. Simulations demonstrated that the radar waveform designed by the manifold-ADMM framework exhibits excellent matching performance, while the integrated waveform provides outstanding radar-communication integration. These results underscore the framework's effectiveness in balancing the dual requirements of radar and communication functionalities in DFRC systems. Future research could explore the use of fully polarimetric systems  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Average Amplitude** & **Consumption Time/s** \\ \hline DFRC-MA, \(w=0\) & 1.00 & 9.87 \\ DFRC-MA, \(w=0.25\) & 1.00 & 9.65 \\ DFRC-MA, \(w=0.5\) & 1.00 & 9.84 \\ BnB, \(w=0.25\) & 1.02 & 13.12 \\ BnB, \(w=0.5\) & 0.99 & 13.01 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of consumption time under different cases.  
Figure 7: (**a**) SER vs. transmit SNR. (**b**) Sum rate vs. transmit SNR.  
to enhance the system's capability in distinguishing different targets and improving overall performance. Additionally, further studies could focus on implementing the manifold-ADMM framework in real-world scenarios, investigating adaptive algorithms for dynamic waveform and beamforming adjustments, and integrating advancements from related fields such as machine learning and artificial intelligence to optimize DFRC system design.  
Conceptualization, Y.Z. and X.F.; data curation, Z.Z. and F.T.; formal analysis, X.F. and Y.F.; funding acquisition, X.F. and Y.Z.; investigation, Y.Z. and X.F.; methodology, X.F. and Y.F.; project administration, Y.Z., X.F. and Z.Z.; resources, Z.Z. and Y.F.; supervision, X.F.; validation, Y.Z. and F.T.; writing--original draft, X.F. and Z.Z.; writing--review and editing, X.F. and F.T. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by the major scientific and technological innovation projects of Shandong Province of China (grant no. 2021 ZLGX05 and 2022 ZLGX04).  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
# Current Status and Future Directions for Combined Tillage and Sowing Technology and Equipment
## 4 Research on Precision Sowing Technology
### Mechanical Precision Seed Metering Device
Numerous studies have carried out structural innovations on picker finger seed metering devices to optimize their operational parameters and improve certain operational performances. However, the current seeder still has poor adaptability to different flat-shaped seeds, a poor clamping effect when the  
Figure 11: Inclined spoon precision seed metering device. (**a**) Operating procedure. (**b**) Field operation. machine is operating with high amounts of vibration, and is unable to solve the problem of the reseeding leakage of round seeds. The seed-filling performance of the mechanical precision seed metering device is greatly affected by the external dimensions of seeds, and its adaptability is rather poor. Since it relies on the weight of seeds and mechanical components to carry out the seed-cleaning operation, it is prone to damaging the seeds. As the sowing quality will decline significantly under high-speed operation conditions, such as the phenomenon of uneven seed spacing, increased reseeding, and leakage becoming more serious, it is mostly applied under medium- and low-speed operation conditions. Therefore, it is necessary to further improve the performance of relevant equipment. The current research mainly focuses on the optimization and improvement of a single seeder and the analysis of related operation mechanisms, and there is less research under the conditions of multiple processes and multiple links. It is necessary to analyze the interactions between multiple components, such as the influence of the number of seed dischargers on the operational performance in different operational sequences, to explore the optimal matching of operational parameters and structural improvements, and to continuously improve the performance of the seeder and its use with a combined tillage and sowing machine to enhance their relevance.
## 1 Introduction  
In orthodontics, en masse retraction, in which the first premolars are extracted, and an orthodontic mini-screw (OMS) is used as an anchor to pull all the anterior teeth posteriorly at once to close the extraction space, is a very common treatment for those with forward protrusion or crowding. However, studies on tooth movement patterns according to the placement position of the OMS and the height of the anterior retraction hook (ARH) are limited [1; 2; 3; 4].  
In recent studies, tooth movement for multiple teeth connected with orthodontic wire was analyzed as a finite element, with most studies limited to the initial response [1; 2; 3]. In particular, finite element studies of continuous archwire make it difficult to accurately assess tooth movement because the initial response includes not only tooth movement but also displacement due to elastic deformation of the archwire, teeth, and alveolar bone. In addition, it does not include the changes in the force system that occur as the extraction space closes, making it difficult to accurately assess teeth movement.  
To minimize such problems, [PERSON] and [PERSON] [5] and [PERSON] et al. [6] have analyzed continuous tooth movement of maxillary teeth in a non-extraction model using finite element analysis (FEA). However, there are still few studies on continuous tooth movement along a continuous arch wire in the extraction model [7; 8; 9]. As an alternative to this, in a study of maxillary dentition by [PERSON] et al. [10], a model before the extraction space was closed (M1) and a model with the extraction space almost closed (M2) were produced to perform a finite element study on initial tooth movement at each stage. Their results showed that the pattern of tooth movement at the initial stage of extraction was different from the pattern of movement when the extraction space was closed. Therefore, a treatment plan based on the initial condition may not be appropriate.  
In recent FEA, a continuous FEA method has been actively applied to overcome these limitations [5; 6; 7; 8; 9]. In the present study, effects of OMS placement position and ARH length on tooth movement during en masse retraction in a model in which the maxillary first premolar was extracted were analyzed through continuous FEA to help us determine suitable OMS placement position and ARH length in clinical practice.
## 2 Materials and Methods
### Indoor Thermal-Hygrometric Conditions and Air Quality Monitoring  
We sampled the air in each OR 10 times during the study period (August 2016 to July 2017), for 4 consecutive hours (8:00 to 12:00 a.m.) each time, with no change in clinical ventilation frequency (30 ACH). The washout period of the ORs was at least 12 h before indoor air sampling. During the sampling period, the ORs were at rest (no patients or medical personnel were allowed inside) but the dehumidification air conditioning systems were still in operation as in active ORs. The sampling instruments were placed at least 1 m from the wall and 100 cm from the ground to simulate the position of the operation zone.  
The measurement of the indoor thermal-hygrometric conditions and air quality in the ORs included air temperature, RH, CO\({}_{2}\), PMs, and airborne bacteria. Air temperature, RH, and CO\({}_{2}\) were measured using a direct-reading thermo-hygrometer (TSI, Q-TRAKTM Indoor Air Quality Meter 7575, Shoreview, MN, USA). The accuracy of the thermo-hygrometer was \(\pm\)0.5 \({}^{\circ}\)C in the range of 0-60 \({}^{\circ}\)C for temperature; \(\pm\)3% in the range of 5%-95% for RH; and \(\pm\)3.0% in the range of 0-5000 ppm for CO\({}_{2}\). The sampling frequency was once per 1 min. Suspended particulate (PM\({}_{\geq 0.5}\), PM\({}_{10}\), PM\({}_{2.5}\), PM\({}_{1}\)) concentrations were measured using a portable particulate monitor (Model 1.109; Grimm Labortechnik Ltd., Ainring, Germany). The accuracy of the particulate monitor was \(\pm\)2% in the range of 1 to 2 \(\times\) 10\({}^{9}\) particles/m\({}^{3}\) for number concentration and \(\pm\)5% in the range of 0.1-10\({}^{5}\)\(\mu\)g/m\({}^{3}\) for mass concentration. The sampling frequency was once per 6 s. Airborne bacterial concentrations were monitored using a one-stage cascade impactor (N6; Andersen Samplers, Atlanta, GA, USA) using the parallel duplicate method. The flow rate for airborne bacterial sampling was 28.3 L per minute (LPM), with a sampling time of 5 min, using a 45 mL tryptic soy agar (TSA) culture medium (DIFCO, Franklin Lakes, NJ, USA). The sampled bacterial culture medium was placed in a constant-temperature incubator at 35 \(\pm\) 1 \({}^{\circ}\)C for 48 \(\pm\) 2 h, and we used a positive hole conversion table to calculate the number of airborne bacteria [24]. Dividing that number by the sampling flow rate and time allowed us to obtain the airborne bacterial concentration. A 10% blank test was conducted to ensure experimental quality. All instruments were properly calibrated to ensure accuracy.  
To evaluate the relationship between the ambient thermal-hygrometric conditions and energy consumption of different dehumidification air conditioning systems, this study collected the hourly data of ambient air temperature and relative humidity from the air quality monitoring station nearest the study hospital for a year. The air monitoring data quality was inspected regularly by Taiwan's Environmental Protection Agency to ensure data accuracy.
# Electrospinning of High-Performance Nanofibres: State of the Art and Insights into the Path Forward
## 3 Electrospinning of High-Performance Polymers
### Polyamide-Imide
Increasing the time of the atmospheric-pressure plasma treatment was found to decrease the water contact angle, but it increased again above 120 s. Therefore, it was concluded that 120 s of plasma treatment time was optimal to improve the hydrophilicity of the nanofibres. In summary, the electrospinninging of PAI has progressed into many specific applications. The ease of solubility of the polymer allows it to be readily electrospun from solution [54]. Furthermore, the cost-effective alternative to electrospinninging from a precursor has also been performed for PAI [16]. A major advantage of electrospun PAI nanofibres is their high strength relative to nanofibres spun from other polymers. This makes them an excellent candidate for applications requiring high strength, as well as for reinforcement in nanofibrous membranes made from other polymers. The scale-up of PAI polymers has also been studied, with effective parameters found to produce PAI nanofibrous membranes using needleless electrospinninging [20]. However, the performance characterization of membranes electrospun on the needleless setup has not been performed. Moving forward, it will be important to understand whether PAI nanofibrous membranes electrospun on a needleless setup can exhibit the same mechanical and thermal characteristics as those electrospun using a needle-based setup.