esa-sceva/satcom-chunk-collection huggingface dataset:
# Analyzing the Nonlinear Performance of Miniature Loudspeakers in Consideration of the Creep Effect
## 2 Nonlinear Model with Creep
### State-Space Method  
Based on the equivalent circuit in Figure 1, the NL-C model is represented by the following three loop equations:  
\[u=R_{e}i+L_{e}\frac{di}{dt}+Bl(x)v, \tag{1}\]  
\[Bl(x)i=M_{m}\frac{dv}{dt}+R_{m}(v)v+K_{m}(x_{1})\int v_{1}dt, \tag{2}\]  
\[K_{m}(x_{1})\int v_{1}dt=K_{2}\int\left(v-v_{1}\right)dt+R_{2}(v-v_{1}), \tag{3}\]  
where \(u\) denotes the voltage applied across the system, \(i\) represents the current flowing through the voice coil, \(v\) is the velocity of the voice coil, \(x\) is the displacement of the diaphragm, \(v_{1}\) corresponds to the equivalent velocity through the mechanical stiffness \(K_{m}\) (as seen in Figure 1), and \(x_{1}\) is the equivalent displacement associated with \(v_{1}\). The parameters \(Bl\), \(K_{m}\), and \(R_{m}\) are commonly considered as nonlinear parameters that depend on the displacement and velocity and are expressed by the following polynomials [6]:  
\[Bl(x)=b_{0}+b_{1}x+b_{2}x^{2}+b_{3}x^{3}+b_{4}x^{4}, \tag{4}\]  
\[K_{m}(x_{1})=k_{0}+k_{1}x_{1}+k_{2}{x_{1}}^{2}+k_{3}{x_{1}}^{3}+k_{4}{x_{1}}^ {4}, \tag{5}\]  
\[R_{m}(v)=r_{0}+r_{1}v+r_{2}v^{2}, \tag{6}\]  
where \(Bl\) and \(K_{m}\) are modeled as a fourth-order approximation, while \(R_{m}\) is modeled using a second-order approximation, consistent with the Klippel LSI (Large Signal Identification) module.  
Let \(x=\int vdt\), \(x_{1}=\int v_{1}dt\), then Equations (2) and (3) can be rewritten as follows:  
\[Bl(x)i=M_{m}\frac{dv}{dt}+R_{m}(v)v+K_{m}(x_{1})x_{1}, \tag{7}\]  
\[K_{m}(x_{1})x_{1}=K_{2}(x-x_{1})+R_{2}\bigg{(}v-\frac{dx_{1}}{dt}\bigg{)}, \tag{8}\]  
Choosing \(\mathbf{X}=\left[i,x,v,x_{1}\right]^{T}\) as the state vector allows for the reformulation of Equation (1), along with Equations (7) and (8), into the following state-space representation:  
\[\dot{\mathbf{X}}=\mathbf{F}\mathbf{X}+\mathbf{G}u, \tag{9}\]  
where \(\dot{\mathbf{X}}\) represents the derivative of the state vector \(\mathbf{X}\), \(\mathbf{G}=\left[1/L_{e},0,0,0\right]^{T}\), as well as the following:  
\[\mathbf{F}=\begin{bmatrix}-\frac{R_{e}}{L_{e}}&0&-\frac{Bl(x)}{L_{e}}&0\\ 0&0&1&0\\ \frac{Bl(x)}{M_{m}}&0&-\frac{R_{m}(v)}{M_{m}}&-\frac{K_{m}(x_{1})}{M_{m}}\\ 0&\frac{K_{2}}{R_{2}}&1&-\frac{K_{m}(x_{1})+K_{2}}{R_{2}}\end{bmatrix}, \tag{10}\]With the sampling period denoted as \(T_{s}\), discretization is conducted using the backward difference method according to Equation (9). Consequently, the state parameters at time instance \(n+1\) can be computed based on those at time instance \(n\) as follows:  
\[\mathbf{X}[n+1]=\mathbf{Y}[n]\mathbf{X}[n]+\mathbf{G}[n]u[n], \tag{11}\]  
where:  
\[\mathbf{Y}[n]=\begin{bmatrix}1-T_{s}\frac{R_{e}[n]}{L_{e}[n]}&0&-T_{s}\frac{Bl(x[n ])}{L_{e}[n]}&0\\ 0&1&T_{s}&0\\ T_{s}\frac{Bl(x[n])}{M_{m}}&0&1-T_{s}\frac{R_{m}(v[n])}{M_{m}}&-T_{s}\frac{K_{ m}(x_{1}[n])}{M_{m}}\\ 0&T_{s}\frac{K_{2}}{K_{2}}&T_{s}&1-T_{s}\frac{K_{m}(x_{1}[n])+K_{2}}{K_{2}}\\ \end{bmatrix}, \tag{12}\]
# Stability Analysis of Lane-Keeping Assistance System for Trucks under Crosswind Conditions
## 1 Introduction
[16] introduced a reinforcement learning-based approach to address the lateral control problem of dynamic nonlinear vehicle models for lane-keeping tasks. In summary, the current research on LKAS control strategies and crosswind issues mostly focuses on individual aspects or problems, without considering their interaction. Therefore, this study addresses the issue of low control accuracy when handling the lateral deviations of vehicles under crosswind conditions in the application of lane-keeping control systems. The LKAS control strategy based on the path-tracking preview algorithm and the LQR controller is proposed to improve the lateral stability of the vehicle.
## 4 Results and Evaluation
### Subjective Evaluation
The image provides an overview of the building’s roofs, with a zoomed-in view on the left side. The circles indicate areas that are difficult to reconstruct due to being hidden from street-level perspective images, which do not capture the roof. Figure created by the authors. Figure 7: Comparison between our Automatic 3D reconstruction (**left**) and Luma Labs’ Capture web app (**right**), GS and mesh extraction results, both using Blender 4.0 as a viewer. We can see that they share similar mesh reconstruction problems, namely incomplete areas where both our SuGaR and Luma Labs’ systems fail to accurately predict points. This leaves holes in the resulting meshes, which is unsatisfactory and points to a dataset problem, more so than a model issue. Image by the authors. Figure created by the authors. points, excess points) even when using Mip-splatting's anti-aliasing method. This points to a render quality issue, and it is not observed in Luma Lab's Capture web app results. Luma Labs solved the point cloud noise reduction problem through what seems to be post-processing noise reduction techniques. Since their codebase is closed, we are limited in speculating about their post-GS processing techniques. The Automatic 3D Reconstruction evaluation criteria can be summarized as follows (Table 4). The study focuses on 3D reconstruction from images using a combination of GS and SuGaR models, trained through stochastic gradient descent and k-nearest neighbors. It uses a dataset of 60 Romanesque-Mudejar temples, with over 32,000 synthetic images. Input data includes either 180 images or a 360\({}^{\circ}\) video per building. The output is a single textured 3D mesh. Techniques applied include Gaussian rasterization, anti-aliasing, and Poisson reconstruction. Training times vary, with GS requiring 22 min and SuGaR around 2 h. A key limitation is the absence of BIM class outputs, despite processing over one million points.
## 4 The Digital-Twin-Based Detection and Protection Framework
### Data Description in the Framework
The framework proposed in this paper is mainly used to detect and protect a satellite network during its routing update process. The framework does not interfere with the normal routing behavior and has no additional computational overhead except for sending data. In addition, during RIB and routing table transmission, the satellite network does not need to wait for the ground station detection results and continues to perform the next phase or other tasks. According to data from NOAA [1], the probability of high-energy protons or galactic cosmic rays causing an SEU is 16%. Under the experimental settings of this article, the experimental results of this article show that the probabilities of SH or GH occurrence caused by SEU are around 1% and 2%, respectively. All measurements and calculations in Sections 3.2 and 3.3 are specific to the experimental settings of this article. In most cases, the satellite network can complete routing updates without the help of the ground station. When SEU causes SH nodes or GH nodes, the ground station can also detect and repair the routing tables in time. Therefore, compared with the traffic-based detection method, the framework proposed in this paper can complete the detection before data forwarding, thus greatly improving the reliability and availability of satellite networks.
## 2 Experimental Test
### Experimental Results  
The results revealed a non-linear behavior of flexible adhesives, characterized by advantageous reduction of stress concentration and uniform stress distribution along the bond line. Based on the data obtained, several characteristic parameters of the specimens were identified. The results of bending tests on the laminated beams were analyzed. The impact of temperature and adhesive layer thickness on joint load capacity was described. Types of failure were described for each beam according to the EN ISO 10365:1992 standard [41], using the following designations: cohesive failure in wood (ct), cohesive failure in polymer (cp), adhesive failure at the wood-polymer interface (at-p), and mixed failure (md). The maximum force value (F\({}_{\text{max}}\)) and corresponding displacement (dL/F\({}_{\text{max}}\)) were recorded with LVDT sensor placed in the mid-span of the beam. Table 2 shows the average values of maximum force \(\upmu_{\text{max}}\) and corresponding displacement \(\upmu_{\text{8 max}}\) for the 6 specimens for each adhesive layer thickness and each temperature. The coefficients of variation were calculated (shown in brackets). The initial elastic stiffness \(K\) was determined for the range from 0.1 F\({}_{\text{max}}\) to 0.4 F\({}_{\text{max}}\) (the same procedure is used to determine the flexural modulus of wood according to EN 408:2012 [42]). A linear regression analysis of the measured deflections was performed; coefficients of determination (R\({}^{2}\)) greater than 0.99 were taken. Figure 1: The test set-up: view of the beam on the test bench in the temperature chamber (**a**), view of selected samples with PT100 temperature measurement sensor (**b**) and schematic view, where t—adhesive layer thickness (**c**). Bending test results for all specimens are presented as force-displacement diagrams for the chosen specimens (t = 1, 2 and 4 mm) at four temperature values. The results are presented in Figure 2.  
The average breaking force at 20 \({}^{\circ}\)C, depending on the adhesive layer thickness (t), was 9.39 kN for t = 1 mm, 9.34 kN for t = 2 mm and 9.41 kN for t = 4 mm, respectively. When the temperature increased to 80 \({}^{\circ}\)C, the force decreased to the following values: 5.76 kN for t = 1 mm, 5.25 kN for t = 2 mm and 5.50 kN for t = 4 mm, which were: 61%, 56% and 58% of the destructive force at 20 \({}^{\circ}\)C, respectively (Table 2). Similar relation was observed in the case of ultimate displacement, where the temperature increases to 80 \({}^{\circ}\)C caused a drop in values to: 7.9 mm for t = 1 mm, 7.7 mm for t = 2 mm and 9.1 mm for t = 4 mm, which were almost equal: 69%, 67% and 66% of the ultimate displacement at 20 \({}^{\circ}\)C, respectively
###### Abstract  
During the last decade, the research on Intelligent Transportation System (ITS) has improved exponentially in real-life scenarios to provide optimized transport network performance. It is a matter of importance that alert messages are delivered promptly to prevent vehicular traffic problems. The fact is an ITS system per se could be a part of a vehicular ad hoc network (VANET) which is an extension of a wireless network. In all sorts of wireless ad hoc networks, the network topology is subjected to change due to the mobility of network nodes; therefore, an existing explored route between two nodes could be demolished in a minor fraction of time. When it comes to the VANETs, the topology likely changes due to the high velocity of nodes. On the other hand, time is a crucial actor playing an important role in message handling between the network's nodes. In this paper, we propose Time delay-based Multipath Routing (TMR) protocol that effectively identifies an optimized path for packet delivery to the destination vehicle with a minimal time delay. Our algorithm gives a higher priority to alert messages compared to normal messages. It also selects the routes with the short round-trip time (RTT) within the RTT threshold. As a result, our algorithm would realize two goals. Firstly, it would speed up the data transmission rate and deliver data packets, particularly warning messages, to the destination vehicle promptly and therefore avoid vehicular problems such as car accidents. Secondly, the TMR algorithm reduces the data traffic load, particularly of the normal messages, to alleviate the pressure on the network and therefore avoids network congestion and data collisions. This, in turn, lessens the packets' retransmissions. To demonstrate the effectiveness of the proposed protocol, the TMR has been compared with the other protocols such as AOMDV, FF-AOMDV, EGSR, QMR, and ISR. Simulation results demonstrate that our proposed protocol proves its excellent performance compared to other protocols.  
vehicle-to-vehicle (V2V); vehicular ad hoc network; routing: Intelligent Transportation System; transport +
Footnote †: journal: Sensors
## 2 Materials and Methods
### Characterisation Analysis
#### 2.5.2 SEM and Raman Analysis  
The microstructure of the biomineral formed was analysed using scanning electron microscopy (SEM) with Thermo Scientific Pharos FEG-SEM (ThermoFisher Scientific, Waltham, MA, USA) with high vacuum mode, 15 KV acceleration voltage; elemental analysis was performed with an energy dispersive X-ray detector (EDS). Raman spectroscopy (LabRAM ARAMIS confocal Raman microscope [PERSON], Villeneuve d'Ascq, France) with 633 nm laser (1% power), 50X objective, 100 um pinhole, 600 L/mm grating was used.
# AI-Based Modeling and Monitoring Techniques for Future Intelligent Elastic Optical Networks
## 2 AI-Based QoT and Impairment Modeling
### Background and Challenges
Many previous works using a simple ANN or linear regression have already achieved good performances [5]. Finally, advanced ML algorithms like ensemble learning [39] and Theil-Sen regression [40] can address the drawbacks of the least squared algorithms and make models less sensitive to the outliers and fluctuations of data. Besides, training techniques like dataaugmentation [41; 42] can improve the model robustness to parameter uncertainty and avoid overfitting by adding interference manually. In this section, we reviewed various previous works for AI-based QoT and impairment modeling techniques.
# Investigation of THz Absorptive Signatures in Opioids
## 4 Experimental Results  
### Control Samples  
The two lactose control samples, L#1 and L#2, were measured first in order to check whether the spectrometer had enough dynamic range and also to unmask characteristic absorption lines of the \"filler\" material of the opioid samples, which was likely lactose monohydrate. The spectra of both SUTs (L#1, L#2) are plotted in Figure 6 (top), which displays the background (B), sample (S), and noise-floor (N) spectra. The dynamic range (B/N) is \(\sim\)60 dB at 0.2 THz and falls monotonically with frequency but remains adequate  
Figure 4: Oxycodone, the lowest 1.358 THz normal mode. A view of atomic motions (blue arrows) and dipole moment derivative (brown arrow).  
Figure 5: Hydrocodone, the lowest 1.40 THz normal mode. A view of atomic motions (blue arrows) and dipole moment derivative (brown arrow).  
Figure 3: Absorption lines for oxycodone and hydrocodone.  
at \(\sim\)20 dB up to 1.7 THz. Figure 6 (bottom) plots the extracted transmittance curves with (S-N)/(B-N). There are three attenuation \"dips\" of signatures exhibited at: 0.530, 1.205, and 1.370 THz, respectively. These signatures agree with previous studies by others, for example, Ref. [20]. The 0.530 THz line is sharp and well understood; the 1.205 THz line is the weakest; and the 1.370 THz line is the strongest and broad (\(\sim\)1.362 to 1.420 THz in Figure 6).
# Mdpi
## 3 Proposed IN Mitigation Scheme
### Computational Complexity  
The additional computational complexity incurred by the proposed IN mitigation algorithm mainly comes from the pre-IN mitigation block, the adaptive per-subchannel equalizer, and the iterative IN reconstruction block. We list these additional computational complexity as follows:  
1. For the pre-IN mitigation block, the computational complexity is dominated by calculating Equations (7) and (9). To calculate the Kurtosis value \(\rho[n]\), it requires 5 adders, 5 multipliers, and 4 dividers. To compute the adaptive threshold \(T_{1}[n]\), it needs one adder, one multiplier, and one divider.
2. To train the coefficients of the \(PSE_{k}\), the calculation of Equations (11)-(13) is necessary. The computation of the variable step-size needs 9 multiplier, one divider, 6 adders, and one square-root operation. The calculation of the error signal \(e_{k}[n]\) requires \(N_{E}\) multipliers and \(N_{E}\) adders. The weight updating recursion needs \((2N_{E}+1)\) multipliers, one divider, and \(2N_{E}\) adders. Thus, for \(M\) per-subchannel equalizers, it totally requires \(M\cdot(3N_{E}+10)\) multipliers, \(M\cdot(3N_{E}+6)\) adders, \(7M\) dividers, and \(M\) square-root operations.
3. In addition to one additional IDWT operation, the main computation burden in the iterative IN reconstruction block is resulted from the calculation of Equations (19)-(21). Assuming that the channel estimator is implemented by an FIR filter of length \(N_{h}\), then the calculation of the recursion (19) needs \(2\cdot N_{h}+1\) multipliers, one divider, and \(2\cdot N_{h}\) adders; the calculation  
Figure 4: Details of the iterative IN reconstruction block. CIR = channel impulse response.  
of Equation (20) needs \(N_{h}\) multipliers and \(N_{h}\) adders; the calculation of Equation (21) needs 9 multiplier, one divider, 6 adders, and one square-root operation. Thus, it requires about \((3N_{h}+10)\) multiplier, 7 dividers, \((3N_{h}+6)\) adders, and one square-root operation in total.  
With the fixed number of subchannel \(M\), the main computational cost is linear growth with respect to \(N_{E}\). Thus, the additional computation complexity associated with our IN mitigation scheme is affordable.
# Development of Robot-Assisted Untact Swab Sampling System for Upper Respiratory Disease
## 3 Experimental Results  
### 2d Workspace Test for Swab Positioning by the Slave Robot  
As shown in Figure 7a, the two-dimensional workspace for the NP swab is defined to cover a range of 60 mm in the horizontal (\(X\)-axis) and 80 mm in the vertical direction (\(Y\)-axis), centered on the lower facial area. For patients with differently sized faces, the center of the workspace can be changed by adjusting the height of the chin rest shown in Figure 1. In these experiments, we placed a piece of gridded paper in front of the slave robot to check whether the robotic end effector could translate the swab to the borders of the workspace. The results, as presented in Figure 7b, show that the robotic end effector can indeed move the swab from the center of the grid (which has a scale of 10 mm) to positions of \(\pm\)30 mm on the \(X\)-axis and \(\pm\)40 mm on the \(Y\)-axis.  
Figure 6: Screen capture of patient monitoring software viewed at the master site. A magnified facial view of the subject is displayed along with a slave site view, a master site view, and plotted force sensor data.  
The length of the swab holder was also chosen by considering the typical depth to the target area. Based on the life-size phantom model used in the experiments, the depth from the nostril to the nasopharynx wall is approximately 100 mm, as shown in Figure 7c; therefore, the swab insertion unit is designed to be able to extend the swab holder up to a depth of 150 mm, as shown in Figure 7d.
## 3 Methods
### Burned Area Detection Procedure
Parameters are used to detect core burned areas (thresholds on spectral index value V\({}_{\rm C}\) and spectral index maximum increment d\({}_{C}\)) and to control region-growing using a set of more relaxed rules (thresholds on spectral index value V\({}_{\rm RG}\) and on difference spectral index value d\({}_{\rm RG}\)). The third step (Step C) concerns the use of a region-growing algorithm to identify whether the pixels adjacent to previously identified burned area clumps (seed regions) exhibit similar spectral characteristics. During this iterative procedure, if both the spectral index difference value \(\mathrm{d}_{\mathrm{RG}}\) (difference index value for region growing) and the spectral index value \(\mathrm{V}_{\mathrm{RG}}\) (index value for region growing) (Figure 2) exceed the defined similarity threshold values, the pixel is classified as a burned area. The maximum number of iterations was set to 75, after performing a sensitivity analysis reported in Figure S1. Once the burned area identification is complete, a set of supplementary information is retrieved for each identified burned pixel. Table 1 provides the name, unit, and a short description of the retrieved supplementary information. Arable land use areas were an important source of commission error during the detection procedure of burned areas, mainly because they exhibit similar spectral characteristics. In order to remove arable land pixels misclassified as burned pixels, an OBIA approach based on the variability of the vegetation status before wildfire occurrence was conceived. For this purpose, the fourth step (Step D) of the procedure first involves clustering the single burned area clumps from the classified image on the base of their spatial and temporal features and then computing parameter \(\mathrm{CV}_{\mathrm{LAI\_pre}}\) (coefficient of variation of pre-fire LAI) for each burned area clump from the pre-fire date LAI. Burned area clumps smaller than the \(\mathrm{S}_{\mathrm{OBIA}}\) (size object-based image analysis) threshold and exhibiting a LAI coefficient of variation smaller than the \(\mathrm{CV}_{\mathrm{LAI\_pre}}\) threshold were discarded.
# An Efficient On-Chip Data Storage and Exchange Engine for Spaceborne SAR System
## 2 Analysis of Storage and Exchange Efficiency for SAR Data
### Analysis of Existing Data Exchange Solutions
#### 2.3.1 Matrix Block Three-Dimensional Mapping  
The matrix block three-dimensional mapping method fully utilizes the DDR3 chip's cross-bank priority data access feature. The two-dimensional data matrix is divided into many equal-sized sub-matrices, then sub-matrices are consecutively mapped to different DDR3 banks. The so-called \"three-dimensional mapping\" means that the sub-matrices with a consecutive range and azimuth direction will be mapped to different rows of different banks in DDR3, instead of mapping to consecutive rows of the same bank as in the conventional sequential mapping method. This means that the data points in the two-dimensional matrix will be mapped to different banks, different rows and different columns, i.e., to the three-dimensional space.
# SARDIM: High-Speed Near-Memory Processing Architecture for Synthetic Aperture Radar Imaging
## 4 Evaluation  
To evaluate the acceleration performance of the proposed system, the SARDIMM was modeled by modifying the cycle-level simulators gem5 [40] and DRAMsim3 [41], which are capable of measuring the execution time and energy according to memory requests. The baseline system used for performance evaluation comprised an Intel x86 CPU operating at a clock speed of 3 GHz. This processor was chosen due to its widespread use in standard computing environments, making it a suitable reference for comparison. For memory, the system employed a single-channel dual-rank x8 DDR4-2666 DIMM. This memory module was selected for its balance between speed and capacity, providing enough bandwidth and storage for intensive data processing tasks. The memory timing parameters used were those defined by the JEDEC [39] ensuring standardization and reliability in the memory performance. These parameters include crucial metrics such as column address strobe (CAS) latency, row address strobe (RAS) to CAS delay, and RAS precharge time, which together define the speed and efficiency of memory access and data retrieval processes. This configuration provides a standardized platform for evaluating the performance improvements achieved by the proposed SARDIMM architecture. The RDA, the application under evaluation, was modeled in C++ with both the normal version running on the baseline system and the NMP version running on the SARDIMM system. A performance comparison between the baseline system and the SARDIMM was performed by running the compiled executables in a simulator. Input images of various sizes were utilized for SAR image generation in the experiments. To address the need for a more robust evaluation, we used actual SAR data collected from the RADARSAT-1 satellite, specifically from 16 June 2002. This dataset includes high-resolution images of Vancouver, Canada, from RADARSAT-1's Fine Beam2 [42]. For our experiments, we upscaled this dataset to create larger SAR images, thereby simulating various sizes and types of SAR data.