esa-sceva/satcom-chunk-collection huggingface dataset:
# A Low-Power Read-Decoupled Radiation-Hardened 16T SRAM for Space Applications
###### Abstract  
Advances in CMOS technology have significantly reduced both transistor dimensions and inter-device spacing, leading to a lower critical charge at sensitive nodes. As a result, SRAM cells used in space applications have become increasingly vulnerable to single-event upset (SEU) caused by the harsh radiation environment. To ensure reliable operation under such conditions, radiation-hardened SRAM designs are essential. In this paper, we propose a low-power read-decoupled radiation-hardened 16T (LDRH16T) SRAM cell to mitigate the effects of SEU. The proposed cell is evaluated against several state-of-the-art soft-error-tolerant SRAM designs, including QUCCE12T, WE-QUATRO, RHBD10T, SIS10T, EDP12T, SEA14T, and SAW16T. Simulations are conducted using a 90 nm CMOS process at a supply voltage of 1 V and a temperature of 27 \({}^{\circ}\)C. Simulation results show that LDRH16T successfully recovers its original state after injection at all sensitive nodes. Furthermore, since its storage nodes are decoupled from the bit lines during read operations, the proposed cell achieves the highest read stability among the compared designs. It also exhibits superior write ability, shorter write delay, and significantly lower hold power consumption. In addition, LDRH16T demonstrates excellent overall performance across key evaluation metrics and proves its capability for reliable operation in space environments.  
Article  
a 2025 15 6536
# Three-Dimensional Trajectory Optimization for UAV-Based Post-Disaster Data Collection
## 5 Simulation Results
### Placement of Hovering Points  
The placement of hovering points by sequenced K-means clustering is shown in Figure 11, where the blue and red dotted circles respectively indicate the coverage area before and after adopting the hill climbing algorithm. In this case, 12 is the smallest \(k\) that satisfies the flight altitude limitation. The cumulative distribution function (CDF) of the SNR before and after adopting the hill climbing algorithm is shown in Figure 12. From Figure 12, we can see that the hill climbing algorithm successfully optimized the UAV's flight altitude, achieving larger SNRs.  
Then, the placement of hovering points by the GA is shown in Figure 13. The solution of the LSCP with the maximum flight height of 150 m, which is shown by the red dotted circles, is first obtained. And then, the hill climbing algorithm is adopted to optimize the UAV's flight altitude below 150 m, which is shown by the blue dotted circles.  
The minimum number of hovering points obtained by sequenced K-means and GA is shown in Figure 14. From Figure 14, compared to traditional K-means clustering, the result of GA covers the same number of UEs with fewer hovering points, which indicates an improvement in efficiency.  
Figure 10: Sample of nearest neighbor search.  
Figure 11: Snapshot of hovering point placement by sequenced K-means clustering.  
Figure 12: Comparison of SNR before and after using hill climbing algorithm.
## 2 Problem Description and Model
### Notations and Variables
\right., \tag{7}\]  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Variable** & **Definition** \\ \hline \(op\) & Orbital parameters of the AOS \\ \(O=\{o_{l}|1\leq l\leq|O|\}\) & Set of orbits, a total of \(|O|\) orbits \\ \(OL(o_{l})\) & Period which meets the light condition in the orbit \(o_{l}\) \\ \(M\) & Maximum memory used for image storage \\ \(E\) & Maximum energy used for observation and attitude adjustment \\ \(rm\) & Memory consumption rate when the AOS observes \\ \(reo\) & Energy consumption rate when the AOS observes \\ \(rea\) & Energy consumption rate when the AOS adjusts its attitude \\ \(\left[r_{min},r_{max}\right]\) & Range of the observation roll angle, \(r_{min}=-r_{max}\) \\ \(\left[p_{min},p_{max}\right]\) & Range of the observation pitch angle, \(p_{min}=-p_{max}\) \\ \(T=\{t_{i}|i=1,\cdots,|T|\}\) & Set of tasks, a total of \(|T|\) tasks \\ \(loc_{i}=(lat_{i},lon_{i})\) & Location of the task \(t_{i}\), \(lat_{i}\) is its latitude and \(lon_{i}\) is its longitude \\ \(q_{i}\) & Minimum image quality requirement of the task \(t_{i}\), whose value is divided into ten levels from 1 to 10 \\ \(d_{i}\) & Required observation duration of the task \(t_{i}\) \\ \(p_{i}\) & Priority of the task \(t_{i}\), representing the observation profit \\ \(VTW=\{W_{i}|i=1,\cdots,|T|\}\) & VTW set of all tasks \\ \(W_{i}=\{w_{i}^{k}|k=1,\cdots,|W_{i}|\}\) & VTW set of the task \(t_{i}\), a total of \(|W_{i}|\) VTWs \\ \(w_{i}^{k}=[c_{i}^{k},st_{i}^{k},et_{i}^{k},r_{i}^{k}]\) & The \(k\)th VTW of the task \(t_{i}\) \\ \(c_{i}^{k}\) & Orbit index of the VTW \(w_{i}^{k}\) \\ \(st_{i}^{k}\) & Start time of the VTW \(w_{i}^{k}\) \\ \(et_{i}^{k}\) & End time of the VTW \(w_{i}^{k}\) \\ \(ra_{i}^{k}\) & Roll angle in the VTW \(w_{i}^{k}\) \\ \(A\) & Observation action sequence of the AOS \\ \(a_{i}^{k_{i}}\in A\) & Observation action when the AOS executes the task \(t_{i}\) in the VTW \(w_{i}^{k_{i}}\) \\ \(ost_{i}^{k_{i}}\) & Observation start time of the observation action \(a_{i}^{k_{i}}\) \\ \(oe_{i}^{k_{i}}\) & Observation end time of the observation action \(a_{i}^{k_{i}}\), \(oe_{i}^{k_{i}}=a_{i}^{k_{i}}+d_{i}\) \\ \(p_{d_{i}}^{k_{i}}\) & Observation pitch angle of the observation action \(a_{i}^{k_{i}}\) \\ \(mr_{i}\)\[y_{ij}=\left\{ \begin{array}{c}1,t_{i}\text{ is closely previous to }t_{j}\\ 0,\text{otherwise}\end{array} \right., \tag{8}\]  
\[z_{i}^{l}=\left\{ \begin{array}{c}1,t_{i}\text{ is executed in the orbit }o_{l}\\ 0,\text{otherwise}\end{array} \right.. \tag{9}\]
## 2 Sequential Modelling Strategy
### Net Dynamics
#### 2.2.2 Net Characteristics  
The MSD model discretises the net into \(N_{i}^{2}\) node masses interconnected by spring-damper systems, where \(N_{i}\) denotes the number of nodes in one side of the net. In this study, the net has the following characteristics:  
1. The net is square-shaped with a side length of \(L_{net}\).
2. The net mass is distributed among point masses, \(m_{i}\), referred to as nodes (for \(i\in 0,\ldots,N_{i}^{2}\)).
3. Four equal bullets with masses \(m_{b}\) are externally attached to the corners.
4. All threads have the same unstretched length, \(l_{0_{q}}\), and diameter, \(d_{q}\).
5. The threads can only stretch in the longitudinal direction.
6. The elastic behaviour of all threads is solely characterised by the axial stiffness, \(k_{q}\), and damping coefficient, \(c_{q}\).
# Band-Pass Sampling in High-Order BOC Signal Acquisition
## 4 Performance Analysis
### Computational Complexity  
We use the number of computations required for the acquisition process to assess the computational complexity.  
The samples of the received signals are cross-correlated against a stored reference PN-code. These cross-correlations are repeated with the lag between the received signal and the stored reference successively incremented, until the decision statistic exceeds a threshold, at which time a verification test is performed. If the latter test is passed, the initial acquisition process is complete.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & \(\mathbf{k_{2}}\) & \(\mathbf{k_{1}}\) & \(\mathbf{f_{IF}}\) (MHz) & \(\mathbf{f_{s}}\) (MHz) \\ \hline
1 & 2 & 1 & 38.3625 & 25.575 \\
2 & 3 & 1 & 25.575 & 12.7875 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sampling frequencies and intermediate frequencies.  
Figure 6: Detection probabilities of different methods.  
Figure 7: Detection probabilities with different sampling frequencies.  
If the sampling frequency is \(f_{s}\), the number of multiplies and adds required for each cross-correlation is \(T_{p}f_{s}\), where \(T_{p}\) is the code period. If the time searching step is \(\delta t\), then for a given initial time uncertainty, the number of the total computation is:  
\[N_{tot}=T_{p}f_{s}\cdot\frac{\Delta t}{\delta t} \tag{19}\]  
The time searching step is typically chosen to be one-half the distance between the correlation peak and its first zero value [6]. So for BOC(\(m\),\(n\)), the time searching step is \(\delta t_{1}=Tc/2\) for the proposed method and dual-side band method, and \(\delta t_{2}=nT_{c}/(4m)\) for match filer method, where \(Tc=1/f_{c}\) is the PN-code chip duration.  
We take BOC(15,2.5) as an example to show the computational complexity of the proposed method, dual-side band method, and match filter method. The results are list in Table 2.  
Table 2 shows that the computations required for match filter method is 67.2 times the proposed method. The result also indicates that if processors with the same speed grade are used, the proposed method and dual-side band method need much less time to acquire the signal.
###### Abstract  
This paper proposes a framework for predicting traversability probability in off-road environments by distilling knowledge from large language models (LLMs) such as GPT-4o into lightweight models. The GPT-4 off approach utilizes GPT-generated data to train a compact model capable of real-time operation on edge devices, such as the NVIDIA Orin board. Unlike traditional systems that focus on identifying traversable areas, this study emphasizes the prediction of traversability probability to facilitate faster decision-making in complex environments. This is particularly advantageous for unmanned ground vehicles, for which obstacles and terrain variability present significant challenges. The GPT-4 off framework improves real-time performance through knowledge distillation and domain-specific optimization, ensuring efficient resource use while maintaining LLM-level performance. Experimental results on the RUGD off-road dataset show that the lightweight model achieves GPT-level performance while being deployable on edge devices. This framework effectively reduces human annotation costs and RAM power consumption, improves the practicality of off-road autonomous driving systems, and demonstrates the potential of leveraging LLM capabilities for low-power real-time applications.  
GPT-4o; unmanned ground vehicle (UGV); self-driving; knowledge distillation; off-road driving; deep learning; large language model (LLM); edge board Article
# Research on an Energy Recovery Strategy for Fuel Cell Commercial Vehicles Based on Slope Estimation
## 3 The Adaptive Kalman Filter  
### Kalman Filtering  
As shown in Figure 3, the process of the Kalman filter can be categorized into two types: prediction and update. It is an optimal state estimation algorithm. The variables in the Kalman filter include state variables and observation variables. The Kalman filter algorithm aims to continuously approximate the true data by merging the state variables and observation variables, obtaining the value closest to the true data. This value is then used as the variable for the next state, and the process is iteratively repeated by merging it with the observation variables at the next moment.  
Firstly, we substitute the optimal estimate value \(x_{k-1}\) at time \(k-1\) into the prediction equation, which allows us to calculate the a priori estimate value \(x_{k|k-1}\) at time \(k\). Then, we compare the a priori estimate value at time \(k\) with the measurement value \(z_{k}\). By using the Kalman gain, we update both of these values to obtain the optimal estimate value \(x_{k}\) at time \(k\). This process is repeated iteratively until the estimate value becomes closer to the true value.  
The Kalman filtering algorithm is mainly expressed by five formulas:  
1. Prediction equation \[x_{k|k-1}=Fx_{k-1}+B_{k-1}+q\] (13) In the formula, \[F=\begin{bmatrix}1&g\Delta t\\ 0&1\end{bmatrix}\] (14) \[B_{k-1}=\begin{bmatrix}\frac{\Delta t}{m}\left(\frac{T_{tt}k^{t}k0 tT}{r}- gmf\right)\\ 0\end{bmatrix}\] (15) The covariance expression of the prior error is \[P_{k|k-1}=FP_{k-1}F^{T}+Q_{k}\] (16)  
In the formula, \(P\) is the prior error covariance, used to measure the uncertainty in the predicted state, is the error between the process error and the estimated value, and \(Q\) is the covariance matrix of the predicted noise.
2. Update equation The updated Kalman gain can be represented as:  
\[K_{g}=P_{k|k-1}H^{T}\Big{(}HP_{k|k-1}H^{T}+R_{k}\Big{)}^{-} \tag{17}\]  
In the formula, \(K_{g}\) is the Kalman gain, which is the difference between the predicted value and the true value. \(R\) is the covariance of the observed values, representing the uncertainty of the state.  
Best estimate value \(x_{k}\) update equation for k is:  
\[x_{k}=x_{k|k-1}+K_{g}\Big{(}Z_{k}-H_{k|k-1}\Big{)} \tag{18}\]  
In the formula, \(e_{k}=Z_{k}-H_{k|k-1}\).  
Posteriori error covariance \(P_{k}\) is:  
\[P_{k}=\big{(}I-K_{g}H\big{)}P_{k|k-1} \tag{19}\]  
In the formula, \(I\) is identity matrix, and the order is equal to the order of the state variable.
## 2 Materials and Methods
### Data Collection  
The injection molding machine used in this experiment was the FCS HT-150 SV, which was made in Taiwan. The part shape used for these experiments was a thin, rectangular part 125 mm in length, 40 mm in width, and 1.5 mm thick, as shown in Figure 3. This shape was chosen to expedite the process and reduce material consumption. The materials used were LCY PP 7533.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Level**} & \multicolumn{5}{c}{**Experiment 1**} \\ \cline{2-6}  & **Melt Temperature** & **Mold Temperature** & **Injection Speed** & **Packing Pressure** & **Screw Speed** \\  & (\({}^{\circ}\)**C**) & (\({}^{\circ}\)**C**) & **(mm/s)** & **(bar)** & **(rpm)** \\ \hline
**1** & 200 & 45 & 15 & 30 & 30 \\
**2** & 220 & 55 & 25 & 45 & 50 \\
**3** & 240 & 65 & 35 & 60 & 70 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Full factorial design for Experiment 1.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Level** & **Melt\_Temp** & **Mold\_Temp** & **Injection\_Press** & **Inj\_Speed** & **Packing\_Press** & **Screw\_Speed** \\ \hline
**1** & 57.73 & 59.79 & 58.98 & 59.78 & 59.09 & 58.93 \\
**2** & 59.62 & 60.53 & 60.16 & 61.27 & 60.59 & 61.88 \\
**3** & 62.06 & 59.09 & 60.27 & 58.36 & 59.73 & 58.60 \\
**Delta** & 4.34 & 1.44 & 1.29 & 2.91 & 1.49 & 3.29 \\
**Rank** & 1 & 5 & 6 & 3 & 4 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Response table for signal-to-noise ratios for weight.  
Figure 3: Schematic view of experiment product.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Level** & **Melt\_Temp** & **Mold\_Temp** & **Injection\_Press** & **Inj\_Speed** & **Packing\_Press** & **Screw\_Speed** \\ \hline
**1** & \(-28.85\) & \(-29.12\) & \(-29.16\) & \(-29.09\) & \(-29.07\) & \(-29.14\) \\
**2** & \(-28.99\) & \(-29.14\) & \(-29.04\) & \(-29.25\) & \(-29.25\) & \(-29.19\) \\
**3** & \(-29.53\) & \(-29.11\) & \(-2 Polypropylene (PP) was selected for its low cost, high heat distortion temperature, good mechanical properties, and ease of recycling [26]. PP is also the largest raw material used in the industrial sector, being utilized in packaging, automotive parts, toys, and a variety of industrial components [27].  
During the experimental process, energy data were collected by measuring the current at three different points: the mold temperature control device, the motor controller, and the melt temperature controller (Figure 4). After production, the gate sections of the produced parts were removed, and their weights were measured using an HZK FA-210 analytical balance. Each experimental run was performed at least three times, and the data used in the analysis were the average of these runs.
## 2 Fluxgate Technology
### Zero-Flux Principle Under a Closed-Loop  
Closed-loop fluxgate sensors are based on flux compensation technology, also known as the zero-flux principle, as shown in Figure 2. In the closed-loop sensor structure, both the excitation winding \(N_{ex}\) and the feedback winding \(N_{f}\) are wound around the core. Without the primary current \(I_{p}\), there is no current feedback, and the flux through the core is zero. When a primary current \(I_{p}\) is present, the sensor exits the zero-flux state. To obtain a null magnetic field in the magnetic circuit, the feedback winding \(N_{f}\) must be excited with an appropriate current [32]. Since the direction of the feedback magnetic field is opposite to that of the primary magnetic field generated by the primary current, the feedback current will gradually increase until the primary magnetic field is balanced by the feedback magnetic field [1; 2]. Finally, the sum of the magnetic fluxes \(\Phi_{p}\) and \(\Phi_{f}\) which are generated, respectively, by the primary and feedback currents is equal to zero. At that time, the whole system reaches a dynamic balance, namely the zero-flux state. Under zero-flux conditions, the relationship between the primary and feedback currents is as follows:  
\[I_{P}=N_{f}I_{f}. \tag{3}\]  
The zero-flux principle has the advantage of improving the linearity of current measurement and enabling larger dynamic ranges. Based on this principle, the zero-flux detection method is crucial. The standard method of detecting the zero-flux state in the sensor's magnetic circuit is through the average value of the excitation current \(I_{ex}\) in the excitation coil. The excitation current \(I_{ex}\) can be generated by a square wave voltage from a separate pulse generator or by the oscillation of a self-excitation circuit and the inductance of the magnetic core [33]. Assuming that the alternating excitation voltage is ideally symmetrical with a duty cycle of 50%, a symmetrical alternating excitation current is thus produced. When the primary current is non-zero, the voltage waveform across \(R_{s}\) becomes asymmetric, i.e., the average value of the excitation current \(\overline{I_{ex}}\) is non-zero. Subsequently, \(I_{f}\) must be adjusted to compensate for the magnetic flux density generated by \(I_{p}\), resulting in a symmetrical voltage waveform across \(R_{s}\), i.e., \(\overline{I_{ex}}\) is zero.  
Figure 1: A simple fluxgate sensor prototype.  
Figure 2: Zero-flux principle for the fluxgate current sensor.
# Performance Enhancement of Consumer-Grade MEMS Sensors through Geometrical Redundancy
## 5 Experimental Results
### Measurements Setup for On-Field Tests  
The proposed method has been assessed by means of the measurement setup shown in Figure 6. The current configuration was designed and optimized for a successive exploitation on drones with a useful payload of about 0.5 kg.  
The measurement setup comprised:  
* 7.4 V and 2000 mAh battery for general power supply;
* DC-DC (Direct Current) step down converter circuit, to provide the adequate supply voltage for the exploited electronics;
* STIM300, used as reference IMU;
* Prototype of redundant IMU;
* GNSS module and its antenna;
* Status indicator LED;
* Two SDCard interfaces.  
As for the realized prototype, a further microcontroller receives and saves on a dedicated SD-Card the reference measures provided by the STIM300. As for the initial attitude, both prototype and reference IMU have been oriented in such a way as to have the sensing axis \(z\) as more parallel as possible with the gravity acceleration. However, this is not a relevant constraint.
# Track-Bridge Interaction of CWR on Chinese Large-Span Bridge of High-Speed Railway
## 3 Track-Bridge Interaction Law of Different Bridge Types
### Arch Bridge  
#### 3.4.1 Track-Bridge Interaction Model  
The span of a long-span, multi-line, tied arch continuous beam bridge on the Harbin-Qiqihar passenger-dedicated line is (77 + 3 \(\times\) 156.8 + 77) m, with single-box, four-chamber box section, 4.2 m-high mid-span beam, and 1/37.33 ratio of height to span. The middle fulcrum beam is 8.6 m high, and the height span ratio is 1/18.23. The rise height of the side main span and side arch is 26 m, and the rise span ratio is 1/6, while the rise height of the middle arch is 31 m, and the rise span ratio is 1/5.03. The rise height of the side arch of the middle main span is 34 m, and the rise span ratio is 1/4.59, while the rise height of the middle arch is 39 m, and the rise span ratio is 1/4. The bridge deck is 29.4 m-wide, and four lines of ballasted track are laid on the bridge. The cross-section form is shown in Figure 7. The spatial finite element analysis model of the tied arch continuous beam composite structure bridge is established considering the concrete-filled steel tube arch rib, cross brace, suspender, beam body, pier, and track structure. The circular end CFST arch is simulated as a composite section beam composed of two kinds of materials, without considering the slip between the steel tube and concrete. The suspender is simulated by the rod element, and the suspender force is simulated by applying the initial strain. The equivalent stiffness matrix element is used to simulate the pile group foundation at the bottom of the brake pier, as shown in Figure 8.
## 3 Results and Discussion
### Comparison between Base Metal and Weld Metal Susceptibility  
The BM and WM behave differently in the hydrogen environment in which they were tested in this research. From the \(I\) values, it was observed that the WM is significantly less susceptible to HE in this environment than the BM, even though both were observed to fracture according to the same mechanisms in SEM fractography. The high resistance of an acicular ferrite microstructure to HE compared to a ferritic structure was previously noted by [PERSON] et al. who argued that the high toughness of the acicular ferrite phase impedes crack propagation [PERSON] et al. (2018). The difference might also be explained by the difference in grain size and structure between the two metals. The WM has a significantly more refined microstructure and as such a much higher grain boundary density. Grain boundaries have been characterised in the literature as having a range of trap densities [PERSON] et al. (2018); [PERSON] et al. (2018) which inhibit the diffusion of hydrogen towards crack tips. The crack will advance slowly at the beginning of fracture and increase in speed as the crack grows longer. A metal with a lower availability of diffusible hydrogen like the WM will therefore require slower crack growth to accumulate enough hydrogen at the crack tip to cause QC fracture. If the crack  
Figure 10: SEM Fractographic images of a notched weld metal samples tested in 70 barg H2. (**a**) Showing the macroscopic fracture morphology. (**b**) A magnified image of the fracture surface at area 2 as indicated in (**a**) in the transition zone between quasi-cleavage and microvoid coalescence fracture. (**c**) An overview of an area equivalent to 1 of the inside wall showing pure quasi-cleavage fracture. (**d**) An overview of an area equivalent to 3 of the outside wall showing microvoid coalescence fracture.  
outgrows this required speed, the fracture mechanism will revert back to ductile fracture as was observed in Figure 10a.