esa-sceva/satcom-chunk-collection huggingface dataset:
# The Impact of COVID-19 on Electricity Prices in Italy, the Czech Republic, and China
## 4 The Proposed Approach for EP Estimation
### The FLANN Model for EP Estimation
The polynomial expansion: \[\mathbf{\Phi}_{p}(x_{i})=\left\{x_{i},x_{i}^{2},...,x_{i}^{N}\right\}\] (3) where \(|x_{i}|\leq 1\), \(N\) is the degree of the polynomial, and \(M=N+1\) is the total number of terms after expansion. 3. The exponential expansion: \[\mathbf{\Phi}_{e}(x_{i})=\left\{x_{i},e^{x_{i}},e^{2x_{i}},...,e^{Nx_{i}}\right\}\] (4) where \(|x_{i}|\leq 1\), \(N\) is the number of exponential terms, and \(M=N+1\) is the total number of terms after expansion. 4. The Chebyshev expansion: \[T_{N+1}(x_{i})=2x_{i}\,T_{N}(x_{i})-T_{N-1}(x_{i})\] (5) where \(T_{0}(x_{i})=0,\ T_{1}(x_{i})=x_{i}\). If \(N=5\), then the expanded number of terms is \(M=N=5\). Those terms are as given in (6): \[\mathbf{\Phi}_{c}(x_{i})=\left\{x_{i},\ 2{x_{i}}^{2}-1,\ 4{x_{i}}^{3}-3x_{i},\ 8{x_{i}}^{4}-8{x_{i}}^{2}+1,\ 16{x_{i}}^{5}-20{x_{i}}^{3}+5x_{i}\right\}\] (6) where \(|x_{i}|\leq 1\). The FLANN model firstly expands the input \(x_{i}\) to \(M\) terms. Thereafter, each expanded value, \(\phi_{m}(i)\) is multiplied by a weight \(\omega_{m}(k)\). The value of \(k\) represents the index of the current iteration during the FLANN learning phase. The \(\omega_{m}(k)\) values are then modified by the standard least mean square (LMS) algorithm at the end of each repetition. The Sigmoid function is used as an activation function, as given in (7):  
\[\hat{y}(i,k)=\frac{1}{1+e^{-s(i,k)}} \tag{7}\]  
The \(s(i,k)\) value is calculated as in (8):  
\[s(i,k)=\sum_{m=1}^{M}\phi_{m}(i)\,\omega_{m}(k) \tag{8}\]  
The estimated error, \(e(i,k)\), is computed as:  
\[e(i,k)=y_{i}-\hat{y}(i,k) \tag{9}\]The value of \(y_{i}\) is the actual output corresponding \(i\)th input \(x_{i}\), and \(\hat{y}(i,k)\) is the estimated output at the \(k\)th iteration. The LMS updates the \(M\) weights for each \(i\)th input. The weights are modified as in (10):  
Figure 5:0  
The update of any \(m\)th weight in each run, \(\Delta\omega_{m}(k)\), is computed as:  
Figure 5:1  
where \(I\) is the sample set size used for the training. \(M\) is the number of expanded terms. \(\mu\) is the learning rate parameter, which should have a value between 0 and 1. The term \(\delta(i,k)\) is computed as:  
Figure 5:2
# CA-BSN: Mural Image Denoising Based on Cross-Attention Blind Spot Network
## 4 Experiments
### Comparison Experiment
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**SIDD**} & \multicolumn{2}{c}{**DND**} \\ \cline{3-6}  & & **PSNR (dB)** & **SSIM** & **PSNR (dB)** & **SSIM** \\ \hline Non-learning based & BM3D [32] & **26.65** & **0.685** & **34.51** & **0.851** \\ \hline \multirow{3}{*}{Supervised} & DrCNN [16] & 23.66 & 0.583 & 32.43 & 0.790 \\  & CBDNet [18] & 33.28 & 0.868 & 38.05 & 0.942 \\  & RIDNet [19] & **38.70** & **0.950** & **39.25** & **0.952** \\ \hline \multirow{6}{*}{Unsupervised} & GCBD [33] & - & - & 35.58 & 0.922 \\  & Noise2 Void R[22] & 27.98 & 0.668 & - & - \\ \cline{1-1}  & UIDNet [25] & 32.48 & 0.897 & - & - \\ \cline{1-1}  & CVF-SID [27] & 34.71 & 0.917 & 36.50 & 0.924 \\ \cline{1-1}  & AP-BSN [13] & 35.64 & 0.929 & - & - \\ \cline{1-1}  & AP-BSN [13] & 35.97 & 0.925 & 38.09 & 0.937 \\ \cline{1-1}  & Ours & **36.92** & **0.932** & **38.24** & **0.939** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Quantitative comparison of different denoising models on SIDD and DND. We use “AP-BSN” in the paper to represent “AP – BSN + R3\({}^{\prime\prime}\) in paper [13]. Therefore, the “AP-BSN” here is consistent with the value of “AP – BSN + R3\({}^{\prime\prime}\) in the paper. By default, we get the official evaluation results from SIDD and DND benchmark websites. R indicates that the result is reported by R2R [31]. \({}^{\dagger}\) indicates that we have retrained the model in the same way as our implementation details. The highest value is highlighted in bold for each type of denoising model. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **PSNR (dB)** & **SSIM** \\ \hline Noise2 Void [22] & 27.98 & 0.667 \\ CVF-SID [19] & 35.27 & 0.904 \\ AP-BSN [15] & 36.90 & 0.915 \\ Ours & 37.02 & 0.927 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quantitative comparison of different denoising models on mural images dataset. The highest value is highlighted in bold for denoising models. Figure 10: Comparison of visual quality of DND and SIDD images. The upper two rows are examples from the DND dataset, and the lower four rows are from the SIDD dataset. (**a**) Noisy (**b**) CBDNet (**c**) CVF-SID (**d**) AP-BSN (**e**) Ours. Best viewed zoomed in. For quantitative comparison, we mark per-sample PSNR/SSIM w.r.t. the ground-truth image at the upper left of each patch. Figure 11: Comparison of visual quality of mural images. (**a**) Noisy (**b**) Noise2 Void (**c**) CVF-SID (**d**) AP-BSN (**e**) Ours. Best viewed zoomed in. Figure 12: Mural images denoising effect display. (**a**) Noisy (**b**) Ours (**c**) Noisy (**d**) Ours. Best viewed zoomed in.
## 3 Stability Analysis of Cavity Interface
The material may only fail when the internal thermal stress exceeds the tensile strength at certain high-temperature moments. The formula for calculating thermal stress can be expressed as:  
\[p_{T}=\alpha E\frac{dT}{dx} \tag{1}\]  
This formula is used to calculate thermal stress caused by temperature gradients, where the symbols represent:  
\(p_{T}\)--thermal stress, MPa;  
\(\alpha\)--coefficient of thermal expansion;  
\(E\)--elastic modulus, GPa;  
\(\frac{dT}{dx}\)--temperature gradient of rock material, \({}^{\circ}\)C/m;  
Figure 2: Cross-sectional schematic diagram of the horizontal segment ring storage cavity. The horizontal segment of the ring storage cavity on the rock interface is mainly due to the pressure from the liquid alloy material's gravity in a direction perpendicular to the formation and pressure differences between the injection and production wells in the U-shaped well. The water density is 1000 kg/m\({}^{3}\), the water vapor density is 0.6 kg/m\({}^{3}\), and the density of low-melting-point liquid alloys is generally around 8500 kg/m\({}^{3}\). The force acting on the rock interface using low-melting-point liquid alloys as a heat exchange medium cannot be neglected due to the influence of gravity. The pressure on the rock interface by the heat exchange medium is expressed as:  
\[p_{a}=\rho_{a}g(h_{in}-h_{ex})+\Delta p \tag{2}\]  
where,  
\(p_{a}\)--represents the pressure of the thermal medium on the rock interface, Pa;  
\(\rho_{a}\)--the density of the low-melting-point liquid alloy, kg/m\({}^{3}\);  
\(h_{in}\)--the depth of the injection well's vertical section, m;  
\(h_{ex}\)--the depth of the production well's vertical section, m;  
\(\Delta p\)--pressure difference between inlet pressure and outlet pressure, Pa. In the horizontal section of a U-shaped well, within a high-temperature dry hot rock formation, heat conduction occurs from the inner lining layer to the lower-temperature heat transfer medium. The combined effect of thermal stress within the rock due to heat conduction and the pressure at the rock-heat transfer medium interface may exceed the tensile strength of the rock, leading to failure. Therefore, the criteria for determining the tensile failure of dry hot rocks can be expressed as:  
\[p_{T}-p_{a}\geq p_{\sigma i}\;\;\text{Rock Fragmentation} \tag{3}\]  
\[p_{T}-p_{a}<0\;\;\text{Intact Rock} \tag{4}\]  
where \(p_{\sigma i}\) is the critical tensile strength of rock under a certain temperature environment in MPa. Formula (3) indicates that rock fragmentation occurs when the difference between thermal stress and pressure from the heat transfer medium in the thermal channel on the rock interface, at a certain depth and temperature gradient, exceeds the critical tensile strength of dry hot rock. Formula (4) indicates that when the pressure from the heat transfer medium on the rock interface is higher than the thermal stress, the rock is under compression and remains stable.
# Towards Open-Set NLP-Based Multi-Level Planning for Robotic Tasks
## 2 Background
### High-Level Planning with LLMs  
The ability to parse arbitrary natural language commands is a crucial milestone to achieve before robots can be expected to safely and easily collaborate with people--especially people other than specially trained operators--in unstructured environments. LLMs have recently made great advances in natural language understanding, which has contributed to a large number of works attempting to leverage their use in robotics [13; 14]. While some work such as _DeepMind's RT2_[5] explores the use of transformer models to directly generate motion commands for the robot--in effect subsuming a large part of the traditional planning and control stack--this makes integration with discrete, structure environment representations and pre-existing industrial motion control stacks more complex, and necessitates training multi-modal models directly on robotics tasks. An alternative approach is that of a separate high-level planning (HLP) to produce a sequence of commands for a lower-level planning and control stack. When it comes to HLP, there are several ways an LLM can be integrated into the planning process.  
The first approach is end-to-end plan generation as in LLM-Planner [15]. Information about the scene is received during execution and replanning is performed to accommodate the current state of the environment as required. A somewhat similar approach is shown in Text2 Motion [16]--the plan is generated in full, then evaluated action by action to determine feasibility and replanned if necessary. Rather than generating the full plan, SayCan [4] uses an LLM to score a set of skills based on their likelihood to complete the given task, combined with a similar evaluation of the current visual scene to calculate the final decision, and does so action by action. On the other hand, LLM+P [17] is a method skeptical of LLMs' ability to perform true planning, rather putting focus on its natural language processing ability to create a PDDL description which then is passed onto a traditional planner.  
As highlighted in [13], many of these methods rely on large, service-based models, which, while showcasing superior results to the smaller LLMs [18], require much larger compute resources and, if provided as a service, a constant connection to the internet. Few works focus on using a smaller LLM as the main NLP and planning mechanism, but one such implementation can be seen with LLaRP [19], where the 7 billion parameter LLaMA [20] model is modified to also take visual observations and output actions, where reinforcement learning is used to train the action output module while the LLM remains frozen. The use of smaller LLMs can allow placing these systems closer to the actual robot performing the tasks and improve its practical autonomy.
# Influence of Structural Stiffness and Loss Factor on Railroad Vehicle Comfort
## 4 Application to Oversimplified Cases
### Rigid Beam on Stiff Supports. Geometric Filtering  
The very well known phenomenon of geometric filtering is simply a consequence of the fact that all points on a stiff beam are located at intermediate heights between the front and the rear. If the front rises while the rear sinks (or vice versa), all points in between have smaller amplitudes. In fact, when the wavelength of a harmonic input is such that the front is on a crest while the rear is on a trough, the center of the beam does not move vertically. In that case, the amplitude of the vertical motion grows from zero at the center to the maximum at both ends.  
While the previous paragraph is trivial, it is worth pointing out that many of these \"filtered frequencies\" for typical railway vehicle passenger car lengths and speeds lie within the range of interest for comfort evaluation. Input harmonics with frequencies \(f=(n+0.5)v/l\), \(n=0,1\dots\) are not felt at the center of a rigid beam on stiff supports. Higher speeds and shorter cars push some of these frequencies outside the 0-30 Hz range of interest, but even for the short cars built by the Spanish manufacturer \"Talgo\" (approximately 13 m), at 300 km/h, there are five filtered frequencies within said range.  
Figure 4 shows the modulus of transfer function \(H_{2}\) (Equation (13)) at four locations along a thirteen meter long rigid beam on infinitely stiff supports traveling at 300 km/h (see Table 1). Both ends of the beam are forced to replicate the input (stiff supports, \(|H_{2}(0,f)|=|H_{2}(l,f)|=1\)), whereas the amplitudes are attenuated elsewhere for most frequencies. Total canceling can only happen at the midpoint on the beam for the specific frequencies mentioned in the previous paragraph.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Fig.** & \(\mathbf{A_{v}}\) **(\(\mathbf{\mu}\)m rad)** & **1 (m)** & **m (kg)** & \(\mathbf{f_{b}}\) **(Hz)** & \(\mathbf{\xi_{b}}\) & \(\mathbf{E_{r}}\) **(GPa)** & \(\mathbf{\eta}\) & \(\mathbf{f_{3}}\) **(Hz)** & \(\mathbf{v}\) **(km/h)** \\ \hline Figure 3 & 0.6 & 13 & 13,370 & 1.3 & 0.05 & 70 & 0.01–0.1 & 8 & 300
## 2 The Ubiquitous Sensor Network Platform
### How the USN-Platform Works for Smart Devices  
Once connected to USN-Gateways, smart devices, together with any attached local sensors, are advertised in the USN-Enabler just like any other sensor system. More specifically, as depicted in Figure 2, the USN-Gateway establishes an HTTP connection to add a new basic SensorML description of the smart device with the _RegisterSensor_ operation defined using the SOS interface. In our electric car example, which will be described in Section 4, the sensor system is a particular electric car that provides battery level readings and precise charging information (mainly the time and the charging station chosen).  
In order for a smart device to become a Sensor Web producer it must implement two additional functions: observation _annotation_ and observation _publication_ (again, see Figure 2). The observation annotation function receives data from the smart application and generates O&M annotations, adding spatial and temporal information and specifying the type (e.g., temperature, time), the generator (ID of the sensor) and the entity of interest (e.g., the electric car). Then the observation publication function is used to publish it in the Observation Storage of the USN-Platform. This function uses the _InsertObservation_ operation provided by the USN-Enabler.  
Through these functionalities that the USN-Enabler provides, smart devices and other applications can look for and consume real-world information available in the Sensor Web.
# A Real-Time Chinese Traffic Sign Detection Algorithm Based on Modified YOLOv2
## 2 Related Work
Most of the traffic sign detection methods are focused on images. Another focus of study can be to analyze traffic videos for traffic sign detection by leveraging the semantic representations [20; 21]. Yet another focus could consider mining the correlations between the features of traffic signs by a semi-supervised feature selection framework [22] in traffic videos. Concerning the deep learning-based methods, the two-stage methods have advantages in recognition precision and localization accuracy. However, the efficiency of computation is low and the process requires large amounts of resources. The one-stage methods are much faster because of the unified network structures, although the process precision decreases. Moreover, the amount of data is a key factor for deep learning-based methods. Conclusively, the traditional detection methods have advantages in accuracy. The deep learning-based methods can achieve the robustness of an algorithm by self-learning when faced with the various problems. [PERSON] et al. [23] recently proposed a lightweight and accurate ConvNet with a sliding window detector to detect traffic signs on GTSDB. There is also a study [24; 25; 26] focused on Chinese traffic sign detection based on CNN directly. We present an end-to-end method to detect Chinese traffic sign inspired by YOLOv2. It can be faster and applied to a real-time system. To develop the precision, we take into consideration the characteristics of traffic signs and the CNN to improve the network structure.
# Research on Composite Control Strategy of Quasi-Z-Source DC-DC Converter for Fuel Cell Vehicles
## 5 Conclusions  
Considering the power conversion requirements in the power system of fuel cell vehicles, the quasi-Z-source network DC-DC converter is adopted. The theoretical voltage gain can reach 2/(1-2\(d\)). When the duty cycle varies from 0.4 to 0.45, the converter voltage gain varies from 10 to 20. Compared with other DC-DC converters, with this converter topology it is easier to achieve high gain, low component stresses and wide voltage input range. When high gain is achieved, the duty cycle is always less than 0.5 and the extreme duty cycle does not occur.  
Considering the \"soft\" output characteristic of the fuel cell, a composite controller including feedforward compensation and feedback control is designed for the quasi-Z-source network DC-DC converter. In the simulation and the experiment, the output voltage maximum excursion was less than 2% under the input voltage disturbance. The output voltage maximum excursion is less than 5% and the adjustment time is less than 30 ms under load 50% step change. Simulation and experiment results show that the proposed composite control strategy shows good dynamic response performance during load disturbance and has a good ability to resist input voltage disturbance.  
Figure 21: The output current waveform of the fuel cell under the WLTP.  
Figure 22: The converter output voltage waveform under the WLTP.  
Moreover, the converter was tested under the WLTP driving cycles. The results show that the converter can quickly track the load demand power variation and maintain the output voltage stability under the WLTP driving cycles. The effectiveness of the converter's actual operation performance has been verified. The composite control strategy improves the system's ability to resist voltage disturbance and dynamic response performance, which is useful for the fuel cell vehicle DC-DC converter system.  
[PERSON] and [PERSON] conceived the control method and revised the manuscript. [PERSON] performed the simulation and the experiment. [PERSON] wrote the full manuscript.  
This research was funded by the National Key Technologies R&D Program of MOST (2018 YFB0105403).  
The authors declare no conflict of interest.
## 2 CPW--Waveguide Transformer Design
### Simulated and Measured Results  
In order to measure the E plane probe transformer performance, it was necessary to design a back to back structure. Figure 3a shows the simulated configuration, and the fabricated photograph is shown in Figure 3b. The gold plated E plane probe was designed on the Al\({}_{2}\)O\({}_{3}\) substrate with thin film circuit technology, and the thickness of the gold was about 0.5 \(\upmu\)m. The copper plated with a gold waveguide was fabricated by a milling and discharging process, and a 127 \(\upmu\)m-deep slot was etched in the lower plane of waveguide. The E plane probe transformer was put in the slot with silver conductive glue. The S-parameters of the transition were measured by using a vector network analyzer (VNA) with WR10 frequency extenders. Figure 4 shows the simulated and measured S-parameters of the transition. By mainly optimizing the window position \(d\) and the fan-shaped radiation length \(r_{s}\), the transformer could obtain a good performance. The measured results verified that the transition had a wider impedance bandwidth of 38% for \(|\)S\({}_{11}\)\(|<-10\) dB, and the insertion loss was, on average, 1.2 dB. The discrepancy between the simulated and measured results was mainly due to the machining and assembly errors.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Parameter** & \(A\) & \(B\) & \(l_{s}\) & \(w_{s}\) & \(r_{s}\) & \(l_{a}\) \\ \hline value (mm) & 2.54 & 1.27 & 0.73 & 0.36 & 0.3 & 0.7 \\ parameter & \(d\) & \(w\) & \(g\) & \(h\) & \(r_{1}\) & \(r_{2}\) \\ value (mm) & 0.66 & 0.06 & 0.04 & 0.127 & 1.6 & 2.9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Geometrical parameters of the transformer.  
Figure 3: Back-to-back photograph of the transition. (**a**) Simulated configuration; (**b**) fabricated photograph (the upper layer is hidden).  
Figure 2: The configuration of the E plane probe.
## 2 Requirements from Autonomy and Remote Control in Smart Farms
### Requirements for Cyber and Network Security  
Security in smart farming and unmanned tractor scenarios is motivated by different cyber threats such as discontinued business due to ransomware, network unavailability due to botnet signaling, and human, animal, and food safety due to integrity violated machinery and vehicles or tampered situational awareness. Security challenges in the farm context vary. First, the heterogeneous device landscape indicates that security management and delivering security updates to devices are more challenging. Different devices are also dependent on each other. Business models are becoming more complex from a networking point of view. For instance, specialized service providers, such as tractor entrepreneurs, may need to connect to information sources on the farm. External input, such as satellite images or weather forecasts, may come from various sources. Moreover, maintenance and repair services are often outsourced and external users or devices may need to be granted access to assets, such as tractor systems, for a temporary period of time. Further, security cultures in farms are more varied. Farm personnel often have limited experience, skills, and resources for security configurations, making security error-prone. Finally, physical protection is often limited. The farm network can span large geographical areas without human guarding or monitoring.  
The cyber and network security architecture for private network bubbles must be able to address these challenges and support known applications and devices. At the same time, the architecture must be flexible to address emerging applications and security threats, and support restrictions in smart farms (e.g., demand of being easy to use and suitable for harsh conditions). Central objectives as well as functional and qualitative requirements for the security architecture are highlighted in Table 2.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Services** & **Packet Delay Budget** & **Packet Error Rate** \\ \hline V2X messages, remote driving [37] & 5 ms & \(10^{-5}\) \\ Information sharing for automated driving & 100 ms & \(10^{-4}\) \\ between vehicles or vehicle and road side & & \\ unit [38] & & \\ Platooning informative exchange [38] & 20 ms & \(10^{-1}\)–\(10^{-4}\) \\ Sensor sharing [38] & 50–100 ms & \(10^{-2}\) \\ Video sharing [38] & 10–50 ms & \(10^{-4}\) \\ Cooperative collision avoidance [38] & 10 ms & \(10^{-4}\) \\ Notification of dangerous situations [36] & 10 ms & \(10^{-3}\) \\ \hline Remote control of a tractor (15 km/h) & 34 ms & \(10^{-3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Network quality of service requirements for different unmanned vehicle-related services from 3 GPP [37; 38] and 5 GAA [36]. Packet delay requirements are additional delays due to a network for one directional transmission.