esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Fault Detection
### Fault Detection Based on Residue Generation
: The system satisfies Lipschitz continuity, and there exists a real constant \(\mathbf{k}\) such that, for all \(\mathbf{a}\) and \(\mathbf{b}\), the property \(\|f(\mathbf{a})-f(\mathbf{b})\|\leq\kappa\|\mathbf{a}-\mathbf{b}\|\) holds. Proof. : Based on Assumptions 1 and 2, define the Lyapunov function as \(V=\mathbf{\overline{x}}^{T}\mathbf{\Xi}\mathbf{\overline{x}}\) and take the derivative of it, which gives:  
\[\begin{array}{rl}\dot{V}&=-\mathbf{\overline{x}}^{T}\mathbf{Q}\mathbf{ \overline{x}}+2\mathbf{\overline{x}}^{T}\mathbf{\Xi}\left[\mathbf{h}(\mathbf{x })-\mathbf{h}(\mathbf{\hat{x}})\right]-\mathbf{\overline{x}}^{T}\mathbf{\Xi} \mathbf{B}(\mathbf{x})\mathbf{\Lambda}\mathbf{\alpha}\mathbf{F}+2\mathbf{ \overline{x}}^{T}\mathbf{\Xi}\mathbf{\gamma}(t)\\ &\leq-\lambda_{\min}(\mathbf{Q})\left\|\mathbf{\overline{x}}\right\|^{2}+2 \lambda_{\max}(\mathbf{\Xi})\left\|\mathbf{\overline{x}}\right\|\left\| \mathbf{h}(\mathbf{x})-\mathbf{h}(\mathbf{\hat{x}})\right\|\\ &+2\lambda_{\max}(\mathbf{\Xi})\left\|\mathbf{\overline{x}}\right\|\left\| \mathbf{B}(\mathbf{x})\mathbf{\Lambda}\right\It can be seen that the estimation error converges as long as \(\overline{\mathbf{x}}\in\Omega(\overline{\mathbf{x}})\):  
\[\Omega(\overline{\mathbf{x}})=\left\{ \begin{array}{ll}\overline{\mathbf{x}}\in \mathbb{R}^{12\times 1}&\left\|\left\|\overline{\mathbf{x}}\right\|>\frac{2\lambda_{\max}( \overline{\mathbf{\Xi}})}{\lambda_{\min}(\mathbf{Q})-2\lambda_{\max}( \overline{\mathbf{\Xi}})\kappa_{h}}\Big{(}\big{\|}\mathbf{B}(\mathbf{x}) \boldsymbol{\Lambda}\big{\|}\big{\|}\mathbf{F}\big{\|}\lambda_{\max}(\boldsymbol {\alpha})+\big{\|}\gamma(\mathbf{x},t)\big{\|}\big{\|}\Big{)}\end{array} \right\} \tag{37}\]  
Equation (37) shows that both the fault signal and external disturbance restrain state estimation error from converging to zero, but to a bound. Hence, the idea of fault detection is to set a boundary of residues. If the residue exceeds the set described by Equation (37), it is regarded as fault happening. To this aim, the work [29] is adopted for boundary generation:  
\[r_{i}^{u}=\frac{K_{u1i}}{T}\int_{t-T}^{t}r_{i}(\tau)d\tau+\frac{K_{u2i}}{T} \int_{t-T}^{t}\left(r_{i}(\tau)-\frac{1}{T}\int_{\tau-T}^{t}r_{i}(\beta)d\beta  
The result of fault estimation is affected by the state estimation error. Thus, the observer gain should be selected carefully in case of making a fault estimation stop updating. On the other hand, the convergence of state estimation error is affected by the magnitude of the disturbance, which also has an impact on the fault estimation precision. The next step is to address the mechanism for the determination of the rotor fault.
## 2 Deterministic Model
### Numerical Procedure  
For the temperature analysis, the ANSYS Fluent 2022R1 software was used to solve the governing equations of steady-state molten salt flow and heat transfer. The inlet and outlet boundary conditions of the receiver tube were, respectively, the velocity inlet and pressure outlet. The standard \(k\)-\(\varepsilon\) model was adopted for turbulent flow simulation, and the SIMPLE algorithm was used for the pressure-velocity coupling [30].  
In the thermal stress analysis, the inner and outer tube wall temperature distributions were interpolated to the nodes of the meshes for the thermal stress analysis. The Abaqus  
Figure 1: Physical model of the solar receiver tube.  
software was used to analyze the tube wall heat transfer and thermoelasticity with the boundary conditions of free expansion. Based on the [PERSON] theory, the expression of the effective thermal stress equation in the cylindrical coordinates is shown as follows [31]:  
\[\sigma_{\text{eff}}=\sqrt{\sigma_{z}^{2}+\sigma_{r}^{2}+\sigma_{\theta}^{2}-( \sigma_{z}\sigma_{r}+\sigma_{r}\sigma_{\theta}+\sigma_{\theta}\sigma_{z})} \tag{3}\]  
where \(\sigma_{z}\), \(\sigma_{r}\), and \(\sigma_{\theta}\) are, respectively, the axial, radial, and circumferential stresses.  
The computation meshes for the temperature and thermal stress analyses are demonstrated in Figure 2. The grid independence was tested. It was found that the solid part with 45,296 grid elements and the fluid part with 322,734 grid elements were adequate for the temperature analysis. For the thermal stress analysis, a finer solid part with 209,196 grid elements was used to obtain more accurate results.  
Based on the deterministic, coupled thermal--structural model of the receiver tube, the model output can be obtained and expressed as:  
\[y=f(p_{1},p_{2},\cdots,p_{k}) \tag{4}\]  
where \(y\) is the maximum temperature or thermal stress inside the tube wall as the deterministic model's output, and \(p_{1}\), \(p_{2}\),... \(p_{k}\) are uncertain input parameters. The maximum temperature inside the tube wall as the model output is a function of seven parameters (\(k=7\)), whereas the maximum thermal stress inside the tube wall as the model output is a function of nine parameters (\(k=9\)). The uncertain input parameters remain unchanged in the deterministic model.
## 3 Results and Discussion
### Optimization of Subsequent Voltammetric Detection
#### 3.2.1 Effect of Detection Media  
In the literature, the highest peak currents were recorded at carbon-based electrodes in aqueous solutions of strong inorganic acids [1, 2, 33, 34]. Here, 0.005-mol L\({}^{-1}\) dibasic (H\({}_{2}\)SO\({}_{4}\)) and 0.01-mol L\({}^{-1}\) monobasic (HCl, HNO\({}_{3}\) and HClO\({}_{4}\)) strong mineral acids, always containing 0.1-mol L\({}^{-1}\) KCl to increase the electric conductivity, were tested as a potential detection medium. Slightly higher current responses of VK1 and \(\alpha\)-TOH were obtained for 0.01-mol L\({}^{-1}\) HNO\({}_{3}\) in the presence of 0.1-mol L\({}^{-1}\) KCl. As a result, this aqueous solution of HNO\({}_{3}\) was chosen as optimal.
# Transfer-Efficient Face Routing Using the Planar Graphs of Neighbors in High Density WSNs
## 4 Transfer-Efficient Face Routing
### Analysis
The first formula is the delay of legacy face routing, the second is that of face routing using 2-hop node information, and the last is that of TEFR:  
\[(n-1)\times(D_{ew}+D_{t}+k\times D_{pp}+D_{ps}+D_{q})\] \[D_{ew}+D_{t}+k^{2}\times D_{pp}+D_{ps}+D_{q}\] \[\geq D_{ew}+D_{t}+k\times D_{pp}+D_{ps}+D_{q}. \tag{2}\]End-to-end delay can be divided into propagation delay (\(D_{ew}\)), transmission delay (\(D_{t}\)), processing delay (\(D_{p}\)), and queueing delay (\(D_{q}\)) [28]; we divide \(D_{p}\) into the time (\(D_{pp}\)) required to execute planarization for individual neighboring nodes and the time (\(D_{ps}\)) for determining the node to which the message is to be sent. Furthermore, in the formulae, \(n\) is the number of nodes on the face boundary to be traveled within radio range (\(n>1\)) and \(k\) represents the average number of neighboring nodes. In Equation (2), since legacy face routing needs to sequentially travel the nodes on the face boundary, the end-to-end delay is highly influenced by the number of nodes constituting the face. However, TEFR and face routing using 2-hop node information are not affected by the number of nodes constituting the face because the message is forwarded directly to the most remote node within radio range. Since face routing using 2-hop node information performs planarization on all edges within 2-hop range of the message transfer node, the execution time of planarization is required for the square of the number of neighboring nodes. On the other hand, TEFR only requires the execution time for planarization on the local nodes because it performs planarization independently on each neighboring node. As the number of deployed nodes increases, the performance of face routing using 2-hop node information decreases rapidly due to the increase in planarization time caused by squaring the number of neighboring nodes. In contrast, TEFR can forward a message without an abrupt decrease in performance because the planarization time is gradually prolonged in proportion to the number of neighboring nodes. The time complexity of the computation time for message transmission can be inferred from Equation (2). Each node used for legacy face routing using local information, face routing using 2-hop node information, and TEFR can determine the next node for forwarding a message in \(\mathcal{O}(k)\), \(\mathcal{O}(k^{2})\), and \(\mathcal{O}(k)\) computation time respectively, where \(k\) is the number of deployed nodes.
# Seismic Vulnerability Assessment of Historical Masonry Buildings in Croatian Coastal Area
## 2 Seismic Vulnerability Assessment of the Test Site
### Seismicity of the Area  
The seismic hazard level for Croatia can be evaluated through two maps, expressed in terms of the peak horizontal ground acceleration during an earthquake, one map for a return period of 475 years, used in designing earthquake-resistant buildings, and the other one for a return period of 95 years, used to check the fundamental requirements of damage state limitations [44]. The maps were integrated into the National Annex in HRN EN 1998-1:2011 [45]. In the Kastela area, the seismic hazard, measured via the peak ground acceleration for the soil type A, is equal to \(\mathrm{a_{g}}=0.22\) g and \(\mathrm{a_{g}}=0.11\) g for the return periods of 475 and 95 years, respectively. In Croatia, the type 1 response spectrum for an earthquake magnitude higher than 5.5 was adopted.  
According to EN 1998-1:2011 [41] and HRN EN 1998-1:2011 [45], the soil factor S, for ground types different from A, increases the ordinate of the elastic response spectrum. The real hazard for a certain location can be obtained by combining the peak ground acceleration for ground type A with the soil factor S, describing the influence of local ground conditions on the seismic action.  
The test site was investigated to classify soils according to [46]. Three seismic lines were acquired in May 2019 in the test site [47] (Figure 6). A velocity analysis based on travel time tomography of P, SV, and SH arrivals, acquired on three seismic lines on the shore of the Kastela Bay, was performed. The \(\mathrm{V_{S,30}}\) map along each line was also computed by averaging the vertical \(\mathrm{V_{SH}}\) tomographic values from the surface to a depth of 30 m. Relatively high obtained values, between 1.2 and 1.7 \(\mathrm{km/s}\), indicate the presence of shallow hard rock [14], which can be classified as soil type A according EN 1998-1:2011 [46]. Considering the results obtained along the three investigated lines and given the size of the test area, the seismic hazard was assumed to be constant for all buildings in the area.
## 3 Validation of MANTiS-32 Sensing Performance
### Calibration and Accuracy Assessment of the MANTiS-6050 Sensor
#### 3.1.2 Verification of Output Accuracy for the MANTiS-6050 Sensor  
To verify the accuracy of the calibrated MANTiS-6050 sensor, a series of static tests were conducted under non-accelerated conditions. In the absence of external excitation, the sensor is expected to exhibit only intrinsic white noise, characterized by low-magnitude fluctuations. Under proper calibration, the axis aligned with the gravitational field should register a value close to 1 g with minimal white noise, while the orthogonal axes should remain centered around 0 g within similar noise bounds. As depicted in Figure 7a, the sensor was mounted on the shaker table of an electrodynamic vibration test system, with the x-, y-, and z-axes sequentially aligned along the vertical (gravitational) direction. Each configuration was maintained for 100 s to collect sufficient data for validation.  
As shown in Figure 7b, when each axis was oriented in the gravitational direction, the corresponding output consistently measured approximately 1 g, with only negligible white noise. The remaining two axes, perpendicular to gravity, exhibited near-zero outputs, also within expected noise levels. These results confirm the effectiveness of the three-point calibration process and validate the MANTiS-6050 sensor's capability to produce accurate and stable acceleration measurements under static conditions.
## 3 Results and Discussion
### Data Analysis by Response Surface Methodology
Optimization by Minitab@16 software was used to create the graph in Figure 14a, which shows that the maximum yield of the bio-adsorption process was higher than 100% and the desirability d = 1 for the following operating conditions: C = 10 mg/L; m = 1.5 g; pH = 2. Figure 14b represents the optimal graph of the experimental results applied to the quadratic model, giving the following results: C = 10 mg/L; m = 1 g; pH = 2. The yield is 96.41%, with a desirability d = 0.965. Figure 15 shows that the experimental values (R\({}^{2}\) = 99.58%) are close to the predicted value (93.25%), since the alignment of the scatterplots is very close to the straight line (R\({}^{2}\)adj = 98.82%), indicating that the model is relevant, valid, and usable. Figure 14: Process optimization for DR-81 removal (**a**) maximum yield of the bio-adsorption process by Minitab (**b**) optimal yield of the experimental results applied to the quadratic model. Figure 13: Three-dimensional (3D) response surface plots for DR-81 removal efficiency by CS: (**a**) effect of initial concentration/adsorbent dose; (**b**) effect of initial concentration/pH; (**c**) effect of adsorbent dose/pH.
## 2 Principal component aggregation in wireless sensor networks
### Principal component aggregation  
The computation of the principal component scores \(\mathbf{z[t]}\) can be performed within the network if each node \(i\) is initialized with the \(q\) scalar components \(\mathbf{w}_{i1},..,\mathbf{w}_{iq}\) of the principal component basis [14]. As proposed in [14], this initialization can done during a training stage where a set of measurements is first collected from the network and the related covariance matrix is estimated. This set of measurements is supposed to be large enough to provide an accurate estimate of the covariance matrix. Once the first \(q\) principal components are computed, they are sent back to the network and stored in the respective nodes (e.g., the \(i\)-th node stores only the elements \(\mathbf{w}_{i1},..,\mathbf{w}_{iq}\)). While our proposition for distributing the computation of the principal components will be introduced in Section 3, here we limit to review the aggregation mechanism to compute the PC scores in a distributed manner.  
The principal component scores can be computed by summing along the routing tree the vectors \((\mathbf{w}_{i1}x_{i}[t],..,\mathbf{w}_{iq}x_{i}[t])\) available at each node. The aggregation primitives take then the following form :  
\[init(x_{i}[t]) = \langle\mathbf{w}_{i1}x_{i}[t];..;\mathbf{w}_{iq}x_{i}[t]\rangle\] \[f(\langle x_{1};..;x_{q}\rangle,\langle y_{1};..;y_{q}\rangle) = \langle x_{1}+y_{1};..;x_{q}+y_{q}\rangle\]  
where partial state records are vectors of size \(q\).
# Investigation of Pre-Pulse Effects on Ultrashort-Pulse Laser Interaction with Structured Targets
## 4 Simulations
This field enhancement mechanism, which is shown in Figure 4b,c, is described in more detail in the work of [PERSON] et al. [41]. Figure 4: (a) Structured target and pre-plasma geometry shown on a 2D electron number density map normalized to critical density \(log_{10}\frac{n_{e}}{n_{c}}\). The red arrow indicates direction of the laser. (b) Electron  
energy spectrum of a flat target, structured target without pre-plasma, and structured target pre-plasma. (**c**) Time evolution of average accelerating field around the tips of the spikes with various targets. Time = 0 fs corresponds to the interaction of the peak of the pulse with target. (**d**) Proton energy cutoff in simulations with various pre-plasma expansion. In order to emulate the effects of different delay times and compare them with the experiment results, we considered various plasma expansions (from no pre-plasma up to 33 \(\upmu\)m, which corresponds to the delay of \(\approx\)13 ns between MP and APP). The resulting proton energy cutoffs are shown in Figure 4d. The energy qualitatively aligns with the observed experimental results, showing an optimum pre-plasma delay close to \(-\)4 ns, characterized by a sharp rise at shorter delay times and a gradual decline after the peak is reached.
# Silicon 3D Microdosimeters for Advanced Quality Assurance in Particle Therapy
## 4 Application of SOI Mushroom Microdosimeters
### Irradiation Facilities  
1.1 Passive Beam Irradiation at the HIMAC Facility with \({}^{16}\)O ion Pristine BP and \({}^{12}\)C Ion SOBP  
Measurements were performed with SOI microdosimeters on the passive biological beamline at the Heavy Ion Medical Accelerator in Chiba (HIMAC) facility in Japan with \({}^{16}\)O and \({}^{12}\)C ion beams at nominal beam energies of 400 MeV/u and 290 MeV/u, respectively. For \({}^{16}\)O irradiation, a pristine Bragg peak (BP) was delivered with a Tantalum (Ta) scatterer positioned in the beamline to passively widen the beam. Measurements were performed in a water phantom with an 86-mm-thick range shifter of PMMA and an additional 100-mm-water-equivalent thickness (WET) of PMMA positioned upstream. The passively scattered beam was collimated to a 10 \(\times\) 10 cm\({}^{2}\) field with a 50-mm-thick brass collimator.  
Figure 11: Response of the array of the 10-\(\upmu\)m-thick free-standing Mushroom microdosimeter embedded in polyimide to a 5.5 MeV He\({}^{2+}\) ion microbeam (**a**) Median energy map of a zoomed-in scan of a single SV and (**b**) a full array at 10 V bias [25].  
Figure 10: Response of the array of the trenched planar Mushroom microdosimeter to a He\({}^{2+}\) ion microbeam: (**a**) Median energy map of four SVs (5 \(\upmu\)m thick) in response to a 3 MeV He\({}^{2+}\) ion microbeam and (**b**) full array (10 \(\upmu\)m thick) in response to a 5.5 MeV He\({}^{2+}\) ion microbeam at a 10 V bias [22].  
The physical dose deposited by \({}^{16}\)O ions in water was measured using a pinpoint ionisation chamber (PTW 31066, Germany) at the same effective depth as measurements performed with microdosimeters on central axis of the beam.  
A 60-mm-spread-out Bragg Peak (SOBP) of 290 MeV/u \({}^{12}\)C beam collimated to a 10 \(\times\) 10 cm\({}^{2}\) field size was delivered using an Aluminum ridge filter positioned in the beamline. The range of 290 MeV/u of \({}^{12}\)C ions at the HIMAC beamline was 147.9 mm in water. For both radiation fields, the microdosimetric spectra were measured using an X-Y motion stage to control the location of the MicroPlus probe in a water phantom with sub-hundred-micron accuracy.
## 6 Discussion
### Queue Length  
Simulation results also revealed that under high traffic volume conditions, the queue length of vehicles during most signal cycles is slightly longer for yellow light durations of 4 s and 5 s compared to 3 s. This may be attributed to the lower perceived risk by drivers at the onset of a longer yellow phase duration (assuming equal vehicle speed and distance to the stop line). This perception reduces the gap between the actual perceived risk and the driver's desired risk threshold, meaning the perceived risk is only marginally greater than what drivers find acceptable. As a result, drivers exhibit lower deceleration rates, maintaining higher speeds for longer periods before deciding to stop or proceed. Consequently, vehicles arrive at higher speeds, leading to a cascading effect where subsequent vehicles in the queue adjust their behavior accordingly, maintaining a higher overall approach speed for the following queue. This increases the likelihood of more vehicles arriving during a subsequent short red phase [69], particularly under high departure rates where arrival patterns are less random. This phenomenon aligns with findings by [PERSON] et al. [58], which suggest that such cumulative effects are more pronounced under high traffic volumes.  
Another observation is that this phenomenon also occurs under higher speed limits for similar reasons. However, over the long term, yellow phase duration does not have a significant impact on traffic delays in approach lanes. This indicates that while queue lengths may vary across signal cycles due to risk perception and behavioral adjustments, the system tends to stabilize as drivers adapt to consistent traffic conditions, underscoring the importance of considering both short-term behavioral responses and long-term system-level impacts when optimizing signal timings.
# Medical Image Segmentation Network Based on Dual-Encoder Interactive Fusion
## 3 Methodology
### Transformer-CNN Dual-Encoder  
Motivated by existing medical segmentation networks such as TransFuse [22] and CTC-Net [23], which utilize parallel encoders, we advance a dual-encoder architecture that capitalizes on interactive learning and feature fusion. This design is intended to enhance the network's segmentation performance by exploiting the complementary strengths of different encoding systems. For the encoding phase, a dual-encoder network with a three-layer pyramid structure is established to effectively extract multi-scale features. In the Transformer encoder branch, for the input image \(X\in\mathbb{R}^{H\times W\times C^{\prime}}\), patch embedding layers \(PE_{i}\)with patch sizes of 4, 2, and 2 are sequentially applied between different Transformerblocks \(TF_{i}\). This arrangement produces feature maps of sizes \(\frac{H}{4}\times\frac{W}{4}\times C^{\prime}\), \(\frac{H}{8}\times\frac{W}{8}\times 2C^{\prime}\), and \(\frac{H}{16}\times\frac{W}{16}\times 4C^{\prime}\), while the Transformer layers focus on extracting global features \(F_{t}^{i}\).  
\[F_{t}^{1}=TF_{1}(PE_{1}(X))\in\mathbb{R}^{\frac{H}{4}\times\frac{W}{4}\times C ^{\prime}} \tag{1}\]  
\[F_{t}^{2}=TF_{2}(PE_{1}(F_{1}^{1}))\in\mathbb{R}^{\frac{H}{8}\times\frac{W}{8} \times 2C^{\prime}} \tag{2}\]  
\[F_{t}^{3}=TF_{3}(PE_{3}(F_{t}^{2}))\in\mathbb{R}^{\frac{H}{8}\times\frac{W}{8} \times 4C^{\prime}} \tag{3}\]  
In the CNN encoder branch, ResNet-34 is directly implemented to extract multi-scale local features from images. The 2 nd, 3 rd, and 4 th stages of ResNet align with the three blocks of the Transformer, maintaining consistent feature scales across both encoder branches.
# A Novel Equivariant Self-Supervised Vector Network for Three-Dimensional Point Clouds
## 3 The Equivariant Vector Network with Self-Supervised Learning
### Network Overview  
Our equivariant self-supervised vector network is an efficient autoencoder that reconstructs the original signal for arbitrarily rotated point clouds. Figure 1 illustrates the general framework of our approach. To address the challenges posed by the vast number of data and the inherent disorder in point clouds, an initial step involves partitioning the input point cloud into patches. This process aims to decrease the information density of the point cloud and alleviate the sensitivity of latent features to variations in point cloud density. This reduction in density helps facilitate subsequent processing of the point cloud data. At the same time, during patching, local vector features are also initialized in patches for the subsequent processing of our vector network. When the input data become point groups with a uniform scale, we mask them, and then only the visible point groups are processed, and a series of data processing steps such as vectorization, extraction of equivariant features, and feature embedding is carried out. Afterward, a Transformer-based autoencoder framework is employed to train and learn the embedding of the processed data. This learned embedding is then utilized to reconstruct the obscured point patches within the input point cloud. The above operations belong to the part of pre-training in the proposed scheme, and the decoder module is only used in the pre-training process. In downstream fine-tuning tasks, we combine the encoder module with different fine-tuning heads so that the network can be applied to different downstream tasks, such as rotation-invariant classification and segmentation or rotation-equivariant pose change estimation.