esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Contributions  
Over the past few years, VI has been explored in artificial intelligence, crowdsourcing and computer vision, etc. It has been proved an efficient way to compute hyper-parameters, and ground truth in various backgrounds that can be modeled as inference problems.  
We propose a new channel estimation algorithm for MIMO-OFDM (orthogonal frequency division multiplexing) systems based on the VI approach. The CEVTI algorithm first models the channel estimation problem into a probabilistic graphical model, and then uses a factorized distribution to approximate the marginal distributions of the desired variables. Finally, the CEVTI algorithm solvesthis optimization problem in an alternative manner, just like the EM algorithm. Simulations show that CEVTI performs better than the state-of-the-art channel estimation algorithms.  
This paper's main contribution is the development of a new algorithmic approach to channel estimation in WSNs, as discussed below. Based on a variational message passing algorithm, a kind of probabilistic graphical model, this approach solves the channel estimation problem. First, we make an assumption about the user's signal generative distribution, and then infer the channel coefficiency by using pilot feedback. Then we apply CEVTI in the MIMO-OFDM scenario and uplink massive MIMO scenario, and analyze its complexity and optimality. Analysis and simulations show that our algorithm can also be used in a higher dimension and results in better performance than the current channel estimation algorithms.  
The main contributions of the paper can be summarized as follows:  
1. The modeling of the OFDM channel estimation problem into a new variational message passing algorithm;
2. An evaluation of the performance error bound of our innovative variational tempering channel estimation algorithm; and
3. A numerical simulation of the performance of CEVTI to show that in general cases, the proposed CEVTI algorithm performs better than other algorithms.  
In the rest of the paper, we first review the background of channel estimation model and variational inference in Section 2, and then define the background assumptions of the inference model in Section 3. Next, we derive our algorithm stepwise in Section 4, and show the application of CEVTI in Section 5. Next, we show how we obtain the results in Section 6, where we also compare the results of our algorithm with the performance of other approaches. Our CEVTI algorithm requires fewer constraints on the prior distributions of users and channels, and it performs better than the existing channel estimation algorithms. Section 7 presents the conclusion of this study.
## 3 Analysis of Influence Factors on Deformation Allowance of Loess Tunnel
### Analysis of Influence of Cover Depth on Deformation Allowance  
Analyzing the correlation between the loess tunnel's cover depth and vault settlement allows for a reasonable prediction of the deformation allowance in loess areas, as  
Figure 9: Analysis of deformation allowance under the influence of moisture content.  
demonstrated in Figure 10. At a cover depth of approximately 50 m, within a concentrated measurement area, a 16 cm deformation allowance yields an 84.6% guarantee rate. Using the Tongluochuan tunnel as an example, at cover depths of 28 m, 29 m, and 30 m, crown settlements measured were 92.97 mm, 212.97 mm, and 215.49 mm, respectively, indicating an unstable state in the section. When the cover depth of the Qindong tunnel is 28 m, the crown settlement measures 134.46 mm, while when the cover depth is 169 m, the crown settlement is 126.77 mm. It signifies the relationship between crown settlement and stratum properties, cover depth, support parameters, and construction methods. The connection between tunnel cover depth and crown settlement is not straightforward with a single parameter. Therefore, the deformation allowance for the tunnel can be determined through statistical measurements. Once the initial tunnel support is installed, the surrounding tunnel displacement stabilizes. For the control of deformation allowance, one can use the time of closed initial support as a reference. Due to data variations and the need to establish guarantee rates, deformation allowances for loess tunnels vary based on different cover depths.
# A Portable Device for the Measurement of Venous Pulse Wave Velocity
## 3 Device Validation  
### Procedure  
In order to verify the performance of the device, vPWV measurements were carried out on four healthy subjects (two males and two females, age \(33\pm 13\) years). Each subject provided informed consent, and the experiment was approved by the Ethics Committee of the University of Turin (23 March 2015) in accordance with the guidelines of the Declaration of Helsinki. Tests were performed during resting condition, in supine position, and during a simulated increase in blood volume in the trunk, as obtained by PLR. Each subject lay in supine position for at least 30 minutes before starting the measurement in order to achieve a stable haemodynamic condition [7]. The cuff for the delivery of the pneumatic compressive stimuli was placed around the wrist, and the Doppler ultrasound probe was placed over the BV about 15 cm proximally to the elbow. An electric compressor supplied the high-pressure air. Respiratory movements and ECG were also collected and all transducers and devices were connected to the prototype as described above. In addition, all signals were also continuously sampled by an external acquisition board (Micro 1401 IImk, CED, Cambridge, UK, with Spike2 software) and off-line processed in Matlab(r), according to the original methodology [3]. After the initialisation phase, 10 vPWV values were collected, while the subject remained at rest in supine position (baseline); then, the PLR was performed by an operator manually raising the subject's leg at about 45 degrees. Then, 5 to 7 vPWV readings were taken in this position, and another 5 to 10 readings after returning to supine position (post-PLR). A time interval of 15-30 s elapsed between subsequent measurements. The whole session lasted about 45 min.
## 3 Results and Discussion  
### Prediction Model Established by Full Wavelengths  
Figure 3 shows the average of the reflectance spectra over one portion (five soybeans) for processed samples at different drying times (0, 10, 40, 50, and 70 min). Absorption peaks around 430, 535, 660, and 970 nm were observed, which were caused by carotenoid, anthocyanin, chlorophyll, and moisture contained in the vegetable soybeans [1]. At the initial periods of drying (0, 10, and 40 min), changes of color and microstructure occurred on the surface, caused by evaporation of water in the immediate surroundings of the soybean surface. These are the main factors affecting the reflectance spectra. In these periods, the discoloration and microstructural change gradually extended from slight to  
Figure 2: Framework of the binary firework algorithm (BFWA).  
moderate, which led to the decrease of the reflectance spectra. At a later stage of drying (50 and 70 min), the outer layer of soybean cells on the surface became \"unsaturated\" with moisture, and the diameter of pores and capillaries decreased sharply, resulting in increase of shrinkage and densification of the surface microstructure. This compact and brittle structure of soybeans may account for the increase in reflectance spectral value observed at 50 and 70 min of drying [1].  
Before selecting the optimal wavelengths, the PLSR model using the full wavelength spectra of soybean was built to predict the moisture content of the dried soybeans. After 10 runs, the average values of \(R_{c}\), \(RMSEC\), \(R_{p}\), and \(RMSEP\) were 0.984, 3.523%, 0.971, and 4.733%, respectively. Although the models yielded good accuracy based on the full wavelength, it was difficult to realize online detection of moisture content, due to the speed limitations in imaging acquisition and processing.
# Spiral Mobility Based on Optimized Clustering for Optimal Data Extraction in WSNs
## 4 Network Design
### Spiral Mobility  
In contrast to existing mobility patterns such as random mobility and grid mobility, spiral mobility can be taken as an asymmetrical sampling on the sensing field. Hot-spot and energy hole problems are difficult to neglect in large-scale WSNs, and as a remedy of this problem, we propose the spiral mobility pattern. Spiral mobility is denser in the inner region than the outer region, and this feature helps to reduce the hot-spot problem by covering the maximum region of the sensing field. The spiral mobility pattern consists of a constant trajectory in a spiral-shaped route over the sensing field, which starts from the center of the network. The shape of the spiral is generated by Equations (4) and (5), and the density and curve of the
## 4 Results and Analysis
### Limitations and Future Work  
Despite the promising results demonstrated by the ONSSO framework, some key limitations will benefit from further investigation. This section outlines these limitations and suggests areas for future work to enhance the framework's performance, scalability, and applicability in evolving 5G and beyond networks.
## 4 Methodology  
In this section, we describe the materials and methods used in our research. First, we present the mathematical models used to represent a communication network and its correlated failures. Next, we formulate three sequential optimization problems, which correspond to the core of the multiculture network design method. The first problem introduces diversity in the selection of node technologies by finding the maximum number of different technologies that do not share common risks. The second problem optimally selects the number of nodes of each technology that must be specified to maximize the network resilience. The third problem optimally specifies the location of each technology on each node, on a given network topology, in order to minimize the impact of a correlated failure on the entire network connectivity. Since the above-mentioned problems are NP-hard, we also introduce the search algorithms we developed for solving the optimization problems. The materials used in this paper correspond to both the eight real-world topologies that are commonly used in the literature to assess networking methods and the reliability metrics used to evaluate the results of the proposed methods. In summary, we state that the research questions guiding our work are: (i) What is the optimal number of technologies that can be employed to increase the diversity in a given network? (ii) What is the optimal number of nodes of each technology that are required to maximize the resilience of the entire network? and (iii) What is the optimal node location of each technology for maximizing the network resilience?
# Performance Optimization of Grounding System for Multi-Voltage Electrical Installation
## 4 Grounding Configurations and Optimization Techniques
### Grounding Grids and Mesh Systems  
Grounding grids improve fault current dissipation by providing multiple current paths. The grid resistance can be estimated as follows:  
\[R_{g}=\frac{\rho}{4L}\left(1+\frac{K}{\sqrt{n}}\right) \tag{15}\]  
where \(K\) is a coefficient dependent on the grid layout.  
For optimal design, IEEE Std 80-2013 suggests spacing electrodes based on the maximum step and touch voltage limits:  
\[V_{s}=\frac{I_{f}R_{g}}{A_{s}} \tag{16}\]  
where \(V_{s}\) = step voltage (V), \(I_{f}\) = fault current (A), and \(A_{s}\) = surface area of the grid (m\({}^{2}\))
## 5 Evaluation
### System Implementation and Data Preparations
#### 5.1.2 Data Pre-Processing  
We match the corresponding raw GNSS measurements, GPS locations, accuracy (from two sources), and ground truth locations by their timestamps. We further calculate the _true GPS localization errors_ as the longest radius between GPS locations and their respective ground truth locations. As explained in Section 4.1.2, we use SvPosition as an index to pack features into a snapshot. We set the snapshot (image) resolution as 18 \(\times\) 18. The following 18 features are chosen; their details can be found in [33]:  
\begin{tabular}{l l} ADR_STATE_CYCLE_SLIP & HardwareClockDiscontinuityCount \\ ADR_STATE_RESET & BiasUncertaintyNanos \\ ADR_STATE_VALID & ReceivedSvTimeUncertaintyNanos \\ STATE_SYMBOL_SYNC & Cn0 DbHz \\ STATE_MSEC_AMBICUOUS & PseudorangeRateMetersPerSecond \\ STATE_TOW_DECODED & PseudorangeRateUncertaintyMetersPerSecond \\ STATE_SUPERAME_SYNC & AccumulatedDeltaRangeUncertaintyMeters \\ STATE_BT_SYNC & DeltaRange \\ STATE_CODE_LOCK & MultipathIndicator \\ \end{tabular}  
**Remark 2**: _We note that our current experiments are rather preliminary; the data traces collected are far from comprehensive and the GNSS features are selected largely on an intuitive basis. Nevertheless, we believe they are sufficient to demonstrate the effectiveness of mining GNSS measurements and to provoke further research on related topics. In particular, crowd sensing (e.g., used to gather WiFi data for outdoor localization [16]) can help to significantly enlarge the coverage in collecting GNSS measurements._
# Research on Dynamic Path Planning of Multi-AGVs Based on Reinforcement Learning
###### Abstract  
Automatic guided vehicles have become an important part of transporting goods in dynamic environments, and how to design an efficient path planning method for multiple AGVs is a current research hotspot. Due to the complex road conditions in dynamic environments, there may be dynamic obstacles and situations in which only the target point is known but a complete map is lacking, which leads to poor path planning and long planning time for multiple automatic guided vehicles (AGVs). In this paper, a two-level path planning method (referred to as GA-KL, genetic KL method) for multi-AGVs is proposed by integrating the scheduling policy into global path planning and combining the global path planning algorithm and local path planning algorithm. First, for local path planning, we propose an improved Q-learning path optimization algorithm (K-L, Kohonen Q-learning algorithm) based on a Kohonen network, which can avoid dynamic obstacles and complete autonomous path finding using the autonomous learning function of the Q-learning algorithm. Then, we adopt the idea of combining global and local planning by combining the K-L algorithm with the improved genetic algorithm; in addition, we integrate the scheduling policy into global path planning, which can continuously adjust the scheduling policy of multi-AGVs according to changes in the dynamic environment. Finally, through simulation and field experiments, we verified that the K-L algorithm can accomplish autonomous path finding; compared with the traditional path planning algorithm, the algorithm achieved improves results in path length and convergence time with various maps; the convergence time of the algorithm was reduced by about 6.3%, on average, and the path length was reduced by about 4.6%, on average. The experiments also show that the GA-KL method has satisfactory global search capability and can effectively avoid dynamic obstacles. The final experiments also demonstrated that the GA-KL method reduced the total path completion time by an average of 12.6% and the total path length by an average of 8.4% in narrow working environments or highly congested situations, which considerably improved the efficiency of the multi-AGVs.  
AGVs; Q-learning; Kohonen; adaptive and clustering algorithms; potential energy +
Footnote †: journal:  
Given the above problems, dynamic path planning of AGVs based on special environments has become a research hotspot [3]. It is important to establish a method that can accomplish dynamic path planning of multiple AGVs in order to maximize the transportation time of AGVs in dynamic environments.
## 2 Experimental facility  
### Requirements of Sensor  
#### 2.1.1 Sensor Frequency Requirements  
When the car runs at a speed v on the road, the time that the tire passes through a certain section p of the road surface is \(t\), which is shown in Figure 1. During the time \(t\), the sensor must collect enough data to faithfully reflect the change of pore water pressure.  
The grounding dimension of the tire is length \(\times\) wide = s \(\times\) b, then the distance traveled by the tire is \(s\), t = s/v.  
The ground length of the Volkswagen MAGOTAN tires used in the article is 175.6 mm, and the speed of cars on the highway is generally 40-120 km/h. When the car passes through the interface \(P\) at a speed of 120 km/h, the elapsed time is only 5.27 ms. At least five data are needed in the process to reflect the approximate shape of the curve. Then, the sensor is required to have a high frequency. If the frequency of the sensor is 100 Hz, that is to say that 1 ms collects a data, the sensor can acquire 5 data during 5.27 ms.  
In conclusion, if the pore water pressure is expected to be recorded when the tire passes through a section at high speed, the acquisition frequency of the sensor should be above 100 Hz.  
Figure 1: Schematic diagram of a tire passing through a road section p.
# Ensemble Radar-Based Rainfall Forecasts for Urban Hydrological Applications
## 2 Materials and Methods
Data from 5 tipping bucket rain gauges and flow monitors within the urban area were used for calibration. Data from 7 depths monitors, 4 additional rain gauges (not shown in Figure 1) and 16 flow monitors are available from 2007 until 2009 within the urban area [9]. In this study data from 2008 was used for validation. [PERSON] et al. [9] provide more detailed information about the urban model, and their Figure 2 presents the location of the flow monitor used in this study. The capacity of the hydrodynamic model in simulating flows was assessed using radar and gauge data from 15 April 2008 until 31 December 2008. The period was chosen to include a wide range of events. The root mean square error (RMSE) was calculated comparing simulated flow with the measured flow. The RMSE was only computed for measured flow higher than 0.1 m\({}^{3}\)/s to exclude flows measured in dry periods and to minimise uncertainties related to the flow measurement. An overview of the events with the start date and time, duration, peak flow, maximum average rainfall and storm type is shown in Table 1. Events with high peak flow were selected, and flow forecasts were performed every 30 min, including forecasts of low, medium and high flows. In order to classify the storms as convective or stratiform, each pixel of the radar scan was classified as either stratiform or convective using to the algorithm proposed by [PERSON] et al. [54]. A storm was classified as convective if more than 3% of the pixels of the study area are convective for a period of more than 3 h. The events that did not fulfil these requirements are classified as stratiform [37]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Event Date** & **Starting Time** & **Duration (h)** & \begin{tabular}{c} **Peak Flow** \\ **(m3/s)** \\ \end{tabular} & \
\begin{tabular}{c} **Max Average Rainfall** \\ **(mm/h)** \\ \end{tabular} &
\begin{tabular}{c} **Storm** \\ **Type** \\ \end{tabular} \\ \hline
29 April 2008 & 23:30 & 6.5 & 0.3974 & 5.344 & S \\
30 April 2008 & 15:30 & 3.3 & 0.3477 & 8.511 & S \\
12 May 2008 & 04:00 & 6 & 0.5582 & 4.117 & S \\
26 June 2008 & 13:00 & 9 & 0.2869 & 11.426 & S \\
7 July 2008 & 15:00 & 4 & 0.5024 & 7.144 & S \\
29 July 2008 & 05:00 & 4 & 0.5826 & 6.444 & S \\
1 August 2008 & 01:30 & 4.5 & 0.9904 & 8.073 & C \\
20 August 2008 & 20:00 & 5 & 0.58 & 10.564 & S \\
4 October 2008 & 15:00 & 13 & 0.3378 & 8.489 & S \\
14 October 2008 & 15:00 & 5 & 0.2754 & 7.358 & C \\
8 November 2008 & 20:00 & 5 & 0.2592 & 5.412 & S \\
9 November 2008 & 13:30 & 7 & 0.563 & 18.41 & S \\
13 December 2008 & 00:00 & 9 & 0.4551 & 1.813 & S \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Event start dates, duration, peak measured flow, maximum average rainfall and storm types (S—stratiform and C—convective) for the Ilkkey urban catchment. Figure 1: Location of the urban catchment (inside the orange circle), radar locations (red squares) and rain gauge positions (blue circles) in the study region. Figure 2: Radar scan at 02:30 (a), 03:00 (b) and 03:30 (c) on 1 August 2008.
## 3 Proposed Methods
### Gradient Modeling (GM) Subnet  
From the gradient magnitude map of LR T2w image \(g_{T2}\), the GM subnet aims at selectively determining the locations high-frequency image details and facilitating the discrimination between smooth areas and non-smooth areas that are full of fine textures by the SR subnet. To fully exploit the gradient information for image super-resolution, the GM subnet is designed as a shallow densely connected convolutional network, so that it can be optimized end-to-end with the SR subnet.  
As shown in Figure 3, the gradient detector calculates the gradient map of LR T2w image in the y- and x-directions to obtain its magnitude, and then the gradient map is normalized to \([0,1]\) before being fed into the gradient modeling subnet. Specifically, we choose Sobel detector as the gradient detector.  
\[g_{T2}=\frac{1}{1+\exp(-G(x_{T2}))} \tag{6}\]  
After normalization, \(g_{T2}\) is fed into the GM subnet to produce gradient guidance, which is a set of feature maps \(G_{T2}\):  
\[G_{T2}=F_{GM}(g_{T2};\Theta_{GM}) \tag{7}\]  
where \(F_{GM}\) is the learned mapping function with parameters \(\Theta_{GM}\). As expected, the GM subnet acts similar to a feature selector that can identify and locate high-frequency image details. We use the sigmoid function as the final convolutional layer's activation function (shown in Figure 3 with a red box). Thus, the output of the GM subnet is a set of feature maps ranges \([0,1]\), which provides helpful information regarding the image areas to which the information of pixels belongs. This output will be broadcast to the SR subnet to guide the SR process.