esa-sceva/satcom-chunk-collection huggingface dataset:
# Preliminary Validation of Surface Reflections from Fengyun-3C Radio Occultation Data
## 3 Methods and Results
### Reflected Bending Angle Model of FY-3C
As described above, in order to obtain the reflected bending angle model by Equation (8), we reconstructed the bending angle profiles from the surface to 40 km altitude (blue lines) to attain the lower impact parameter and refractive index. It can be observed from the three cases that the bending angles have a sharp gradient below 2 km, where the reflections and ducting occur. Furthermore, in the right panels of Figures 7-9, the reflected bending models gradually decrease in lower impact heights, from \(R_{E}\) to curvature radius of the reference ellipsoid. The reflected bending range of variation is about 0.01 rad and the range of altitude variation is about 0.3 km. It is indicated that the reflected bending angle model is sensitive to the ducting layer and could reduce the bias below the ducting layer of GNSS RO measurements [11; 46]. Therefore, we think the reflected bending angle models of FY-3C are useful in the atmosphere near the surface. Figure 8: Bending angles of FY-3C event on 31 August 2015: in (**a**), the red curve shows the bending angles of refractivity profile from cutting height to 40 km altitude, the green dashed curve shows the bending angles by Equation (6) and the blue curve shows the recalculated bending angle by GO method from Earth’s surface to 40 km altitude; (**b**) shows the reflected bending angle model extracted by Equation (8). Figure 7: Bending angles of FY-3C event on 13 February 2015: in (**a**), the red curve shows the bending angles of refractivity profile from cutting height to 40 km altitude, the green dashed curve shows the bending angles by Equation (6) and the blue curve shows the recalculated bending angle by GO method from Earth’s surface to 40 km altitude; (**b**) shows the reflected bending angle model extracted by Equation (8).
## 1 Introduction  
### Background  
Oil leak localization and detection systems aim to protect pipeline networks from damage to protect their surroundings, organisms, and their lives from the risk of pipeline network catastrophes. Oil pipeline leak detection and monitoring systems are important because when a leak happens, they automatically update the operator with detailed alarms so that the necessary instructions and procedures can be organized and ready to mitigate spills and their durations.  
There are different groups of leak detection techniques. Some of these technologies detect product leaks by checking the variance between the volume of the steady-state situation of the system and the value of the instant inlet and outlet flow rate [1; 2]. Other techniques employ advanced computerized network systems that are used to monitor a list of different conditions of operation. The real-time transient model (RTTM) was selected as the oil pipeline monitoring model in this study due to its many benefits when compared with other techniques, such as a negative pressure wave, pressure point analysis, acoustic sensing, fiber optics, RFID, vapor sampling, capacitive sensing, infrared cameras, etc. The RTTM achieves a high level of accuracy in leak detection and localization, with appropriate leak size estimation, easy maintenance, good performance in both steady- and transient-state cases, fast processing, and the ability to handle a suitable amount of data [3; 4; 5; 6].  
Wireless sensor networks (WSNs) are distributed networks that cover multiple small, self-dominant sensing nodes that can sense, compute, and communicate with the main central server [7]. The main function of the end nodes is to work concertedly to gather and observe information from their immediate surroundings. WSNs have gained significant interest and have been employed in various domains, such as ecological observation systems, healthcare systems, the infrastructure of intelligent cities, industrial system control, smart monitoring, and intelligent surveillance systems [8; 9].  
Machine learning is a branch of artificial intelligence that involves developing algorithms that allow systems to learn from old data and then predict experiences and output results depending on the new real input data when they receive them. The quantity and quality of data used during the training process and selecting the right algorithm to deploy increase the level of accuracy and correctness of the prediction. Environmental monitoring, industrial manufacturing, speech recognition, image identification, Facebook auto-tagging, and email filtering all require machine learning [10].
# Analysis of the Most Relevant Factors for Routing in Internet of Space Things Networks
## 3 Methodology
### IoST Simulator--ST-INETMANET  
We developed an enhanced framework for OMNET++ called ST-INETMANET [66] to perform the base experiment. ST-INETMANET allows the simulation of ad hoc IoST networks using the files provided by the Orbit Generator.  
ST-INETMANET's main modules are: \"CubeSatNode\", which represents the network's basic satellite nodes; and \"Space Mobility Module\", which defines the node's movement. A \"Space Propagation Module\" simulates space-space and space-Earth communication between nodes. The \"Network Module\" implements the routing protocols.  
The \"Network Observer submodule\" captures all node information and traces when performing a simulation. Traces formatted for the OMNET++ Metrics Analyzer are stored in files called VEC, SAC, and VIC. The proposed base experiment running in the simulator is presented in Figure 2.
# Kalman Filter Using a Third-Order Tensorial Decomposition of the Impulse Response
## 2 System Identification and TOT Decomposition  
This section reviews the required background for the upcoming developments. First, the system identification scenario is presented, followed by the solution based on the conventional Kalman filter. Finally, the TOT decomposition of the impulse response is summarized.
## 5 Conclusions  
An ultra-low power energy-efficient solar energy harvesting and sensing microsystem has been proposed for wireless sensing applications. The circuit has been realized in a 0.18 \(\mu\)m CMOS process with only a 0.25 mm\({}^{2}\) die area to measure both hydrogen concentration and temperature, using fully integrated solar energy harvesting and sensor interfacing circuits. An external wireless data transceiver is used to transmit the measurement results to a base station. Four nanowire solar cells have been connected in series, and the proposed area- and power-efficient energy harvester circuit stores the energy harvested from the PV module in a NiMH microbattery. In addition, this circuit scales the power consumption and performance of the sensor by reconfiguring the operating frequency, bias currents and time interval of sensing and data transmission to guarantee autonomous operation of the sensor. This circuit consumes only 2.1 \(\mu\)W when operating at its highest performance, which is further reduced to less than 0.7 \(\mu\)W when operating at its lowest speed. Simulation results show that even under reduced illumination, the harvested energy is enough for autonomous operation of the sensor.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Illumination Level** & **V\({}_{\text{mpp}}\)(V)** & **P\({}_{\text{mpp}}\) (\(\mu\)W)** & **P\({}_{\text{charge}}\) (\(\mu\)W)** & **Efficiency** \\ \hline
100\% of AM1.5 & 1.586 & 2,718 & 2,240.8 & 82\% \\
50\% of AM1.5 & 1.622 & 1,422 & 1,127.3 & 79.2\% \\
20\% of AM1.5 & 1.604 & 561.6 & 448.4 & 79.8\% \\
10\% of AM1.5 & 1.549 & 269.5 & 221.4 & 82.1\% \\
1\% of AM1.5 & 1.289 & 20.3 & 18.4 & 89.3\% \\ \hline \end{tabular}
\end{table}
Table 5: Die area and active power of solar energy harvesting blocks.  
Figure 9: Power delivered to the battery under simulated 1% of AM1.5 illumination.
# Sensors for Sustainable Smart Cities: A Review
## 3 Results  
### Smart City  
The goals of smart cities initiatives are to develop economically, socially, and environmentally sustainable cities [5; 6; 12]. Generally, the ideal model of a smart city is based on the incorporation of the following subsystems and technologies: distributed energy generation (micro-generation) [14], smart grids (interconnected and bidirectional smart networks) [15], smart metering (intelligent measurement of energy consumption data) [16], smart buildings (eco-efficient buildings with integrated energy production systems) [17], smart sensors (intelligent sensors to collect data and keep the city connected) [18], eMobility  
Figure 1: Histograms showing the distributions of different features of the papers selected for this review: (**a**) Year, (**b**) Number of citations, (**c**) Journal Quartile and (**d**) Journal Impact Factor.  
Figure 2: Pie charts representing the distribution of the (**a**) Type of reference and (**b**) Topic of the selected references for this review.  
(implementation of electric vehicles) [19], information and communication technologies (ICT) [20] and smart citizens (key piece of a smart city) [21].  
Cities constitute a complex socio-technical system [22; 23]. In order to design the best solutions for cities, their inhabitants, social entities and governments need to be considered [2; 3].  
More pleasant spaces and places are required to live, boost competitiveness and productivity. To achieve this, the development of communication technology, such as 5G, is imperative. There also exists a growing need for initiatives of Industry 4.0 to permeate cities, since small or medium-sized enterprises (SMEs) represent the largest business fabric in developing countries [5]. In this sense, it is necessary to increase operators' access to reliable 5G infrastructure, allowing optimal deployment and economic rationality of the networks. In addition, governments are expected to provide access to resources and tools that facilitate the deployment speed and offer the necessary infrastructure for the latest generation networks [24].
# Impact of Combine Harvester Technological Operations on Global Warming Potential
## 2 Data and Methods
### Combine Harvester and Test Site Specifications  
In 2019 and 2020, field trials were conducted on the land of an agricultural company engaged in crop production. The Wideband Global SATCOM system field coordinates of the study site in 2019 and 2020 were 54.7970, 23.0079. The 2019 study used the winter wheat variety _Ada_ for harvesting, whereas _Balitus_ was used in 2020. Five samples were collected from 0.25 m\({}^{2}\) areas in five randomly selected field locations to determine the weight of 1 m\({}^{2}\) of the crop. The _Ada_ variety weighed 1702.9 \(\pm\) 209.1 g m\({}^{-2}\) (grain moisture: 12.2% \(\pm\) 1.1%), while the weight of the _Balitus_ variety was 1370.2 \(\pm\) 154.6 g m\({}^{-2}\) (grain moisture: 13.5% \(\pm\) 1.3%).  
The wheat flow mass delivered to the CH was calculated using the following formula:  
\[\text{q}=\text{CS}\times\text{HW}\times\text{WM}, \tag{3}\]  
where q denotes the wheat mass fed to the CH (feed rate) in kg s\({}^{-1}\), CS denotes the CH speed during harvesting in m s\({}^{-1}\), HW denotes the CH header width in m, and _WM_ denotes the mass of the wheat in 1 m\({}^{2}\).  
The same CH was used for the 2019 and 2020 field studies. There are already newer and higher environmental standards (i.e., Tier5) CHs. However, we chose to examine one of the most popular machines in the region at the moment. It should also be emphasized that the life cycle of a CH in the field is long, and this type of CH will work for many years to come. Table 3 presents the precise measurement specifications.  
In the field tests for both years, fuel consumption and engine load of the CHs were recorded using data from the machines' on-board computers. For each technological operation or change in harvesting speed, 10 on-board computers display values were recorded at equal 10 s intervals.
## 3 Proposed Architecture
### Security Enforcement  
The VIS provides the interfaces both for the network, service and discovery components. In this architecture design privacy is intrinsically dealt with by creating the separation, or in some cases distributed, of the knowledge of identities, intents, networks and service operations. In this architecture, different layers behave independently but share a VIS. In addition to its role of providing a privacy layer, the VIS is also a point for policy enforcement and allows for fine grain control on discovery and access to information about the VIS based on identity or intent information. For example, the VIS can be setup in a way that the separation of data ensures the communication between participants remains anonymous; while preserving the right to be part of the VIS through the identity layer (and associated credentials).
# Reliability Prediction and FMEA of Loading and Unloading Truss Robot for CNC Punch
###### Abstract  
Loading and unloading truss robot for computer numerical control (CNC) punch is widely used in the production of sheet metal parts, as its reliability level is directly related to the quality of sheet metal parts. Especially for the automatic sheet metal production line, it is urgent to predict the reliability of the loading and unloading truss robot for CNC punch. In this paper, a new method for the reliability prediction of the loading and unloading truss robot for CNC punch is proposed. The method uses the component counting method to predict the failure rate and mean time between failures (MTBF) of the electrical control system. Then, according to the MTBF value of the electrical control system, the MTBF value and failure rate of the mechanical system and pneumatic system are calculated by expert scoring method based on fuzzy theory. The MTBF value and failure rate of the loading and unloading truss robot for CNC punch are estimated, and the weak link of the loading and unloading truss robot for CNC punch is obtained. Finally, through the collected fault maintenance data records, the failure modes and effects analysis (FMEA) of the loading and unloading truss robot for CNC punch is carried out, and the reliability prediction method proposed in this paper is validated by the fault location analysis results.  
truss robot; reliability prediction; FMEA; CNC punch; MTBF Article
## 2 Main Concepts
### Tone Mapping  
Conventional display devices are limited by the fact that they work with only 8 bits per color channel, which restricts their ability to represent the full spectrum of brightness and contrast of an HDR image [57]. In order for these images to be properly displayed on screens with a reduced dynamic range, we resort to tone mapping, which compresses brightness and contrast values using nonlinear transformations, adapting the HDR content to the hardware limitations [58].  
One of the simplest but lowest quality approaches is linear scaling [59], whose only advantage is processing speed. On the other hand, the application of a logarithmic curve [15] stands out for its good relationship between final quality and computational complexity. Other techniques go further, seeking to mimic human perception by adjusting factors such as brightness and light halos [60; 61; 62]. Figure 1 illustrates the application of logarithmic tone mapping, described in Equation (1), to an HDR image, showing how the method adjusts its pixel values for viewing on an LDR device.  
Recently, [PERSON] et al. [4] proposed a zero-shot scheme to transfer a tone mapping model trained on LDR to the HDR domain using very little data. Their approach decomposes the image into tonal (brightness and intensity) and structural (details and contours) components. By processing each part separately, the technique retains subtle details while effectively adjusting brightness, resulting in a conversion that retains high dynamic range characteristics.  
\[I_{LDR}=\frac{\log(1+\mu\,I_{\mathit{ HDR}})}{\log(1+\mu)} \tag{1}\]  
where \(\mu=5000,\;I_{\mathit{LDR}}\) is the resulting LDR image, and \(\;I_{\mathit{ HDR}}\) is the input HDR image.
## 2 Materials and Methods
### LSTM Neural Network  
Long short-term memory (LSTM) neural networks are a variant of RNNs with long- and short-term memory cells. They can forget information using the forgetting gate and choose to remember or forget the long-term information [17; 20]. LSTM is a good solution to solve the problems of an RNN's gradient disappearance and explosion, as well as the lack of long-term memory capacity. This makes recurrent neural networks effective in using long-distance temporal-order information [21]. The main formulas are as follows:  
\[f_{t}=\sigma\Big{(}W_{f}\cdot[h_{t-1},\ x_{t}]+b_{f}\Big{)}, \tag{1}\]  
\[i_{t}=\sigma(W_{i}\cdot[h_{t-1},\ x_{t}]+b_{i}), \tag{2}\]  
\[\widetilde{C}_{t}=tanh(W_{\text{C}}\cdot[h_{t-1},\ x_{t}]+b_{\text{C}}), \tag{3}\]  
\[C_{t}=f_{t}\cdot C_{t-1}+i_{t}\cdot\widetilde{C}_{t}, \tag{4}\]  
\[o_{t}=\sigma(W_{o}\cdot[h_{t-1},\ x_{t}]+b_{o}), \tag{5}\]  
\[h_{t}=o_{t}\cdot tanh(C_{t}), \tag{6}\]  
where \(t\) is the time step; \(f_{t}\) is the forget gate; \(i_{t}\) is the input gate; \(o_{t}\) is the output gate; \(C_{t}\) is the final cell output; \(h_{t}\) is the final state; \(X_{t}\) is the input; \(W_{f}\), \(W_{i}\), \(W_{o}\), and \(W_{\text{C}}\) are the weights; \(b_{f}\), \(b_{i}\), \(b_{o}\), and \(b_{\text{C}}\) are the biases; and \(\sigma\) is the sigmoid function, which increases the nonlinearity of neural network algorithms.  
The LSTM network used in this study consists of three layers: an input layer, a hidden layer, and an output layer. As shown in Figure 2, the input layer includes the HF radar velocity, ERA5 dataset's 10 m standard wind, and TPX07.2 tidal data. This study tested the parameters of LSTM, compared the results through multiple experiments, and ultimately determined that the hidden layer of LSTM contains 128 neurons, while the output layer represents the true ocean current data measured by the bottom-mounted ADCP. During the training process, a bottom-mounted ADCP and towed ADCP were used as target values to determine the LSTM training parameters. In the prediction process, their respective parameters were used to validate the correction effect on radar data. Previous experiments conducted by [PERSON] et al. [31; 32] have shown that the model's correction of radar data is a function of time. Therefore, this study also conducted sensitivity experiments on the time of the input data in order to obtain the optimal tow route and towing time for correcting radar data.
## 1 Introduction
### Preliminaries  
A Unit Disk Graph (UDG) is an undirected graph which has an embedding in the plane such that two vertices are connected by an edge if and only if their Euclidean distance is at most one.  
**Definition 1**.: _A Unit Line Graph (ULG) is a unit disk graph in which all vertices have the same \(y\)-coordinate._  
Let \(G=(V,E)\) be an undirected graph. A set \(D\subseteq V\) is called a _dominating set_ if each vertex in \(V\) is either in \(D\) or adjacent to a vertex in \(D\). A set \(CD\subseteq V\) is a _connected dominating set_ if it is a dominating set such that the subgraph induced by \(CD\) is connected. We call a set \(I\subseteq V\) an _independent set_ if it does not contain two adjacent vertices. Finally a set \(VC\subseteq V\) is called a _vertex cover_ if for every edge \(e=(u,v)\) it holds that either \(u\in VC\) or \(v\in VC\).  
**Definition 2**.: _For two vertices \(u\) and \(v\) let \(d(u,v)\) be the hop-distance between \(u\) and \(v\), that is the number of edges on a shortest path between these two vertices._  
For a set of vertices \(V^{\prime}\subseteq V\) we define \(d_{V^{\prime}}\left(u,v\right)\) to be the hop-distance between \(u\in V^{\prime}\) and \(v\in V^{\prime}\) in the subgraph induced by \(V^{\prime}\). The hop-distance is not necessarily the geometric distance between two vertices. Denote by \(N^{r}(v)=\{u\in V|d(u,v)\leq r\}\) the \(r\)-neighborhood of a vertex \(v\). For ease of notation we define \(N^{0}(v):=\{v\}\), \(N(v):=N^{1}(v)\) and for a set \(V^{\prime}\subseteq V\) we define \(N\left(V^{\prime}\right)=\bigcup_{v^{\prime}\in V^{\prime}}N\left(v^{\prime}\right)\). Note that \(v\in N(v)\).  
Denote by the _locality distance_ (or short the _locality_) of an algorithm the minimum \(k\) such that the status of a vertex \(v\) (e.g., whether or not \(v\) is in a dominating or connected dominating set) depends only on the vertices in \(N^{k}(v)\).
# Software-Defined Named Data Networking in Literature: A Review
## 3 Overview and Background
### Named Data Networking
#### 3.1.2 NDN Routing  
The routing process in NDN comprises three primary stages: name resolution, discovery, and delivery. Name resolution converts a name data object into its corresponding locator, while the discovery stage routes requests to the relevant data based on their respective names. Subsequently, the delivery phase ensures the transmission of the data chunk to the requesting user. Currently, NDN employs NLSR [15] for routing, which utilizes a hierarchical naming scheme for routers and link state advertisements (LSAs). Under this scheme, each router is named based on its corresponding network, such as <network>/<site>/<router>. The protocol generates multiple routes to content names, which are stored in the RIB before being used to update the FIB, considering route cost factors [4]. More about NDN routing can be found in [16; 17; 18; 19].
## 2 Materials and Methods
### Method
#### 2.2.3 Details of Spatial-Temporal Co-Clustering Algorithm
Now that Equation (4) has been decomposed into information divergence in terms of station clustering and timestamp clustering, step 4 is to discover a new mapping for each station-to-station cluster that minimizes Equation (7):  
\[i=\text{arg}\min_{i\in\{1,\dots,k\}}D_{I}(O(T\mid s)|A(T\mid\hat{s}_{i})) \tag{7}\]  
Similarly, locate a new mapping of each timestamp to clusters of timestamps that minimizes Equation (8):  
\[A(S,T)=RO(\hat{S},\hat{T})C^{T} \tag{2}\]0  
**Step 5:** Recalculate the loss of information utilizing the revised mapping and Equation (5). If the change in information loss is less than a predetermined threshold, the new approximate matrix obtained in step 4 is the optimal co-clustering result \(O(\hat{S},\hat{T})\); otherwise, return to step 2 to begin a new cycle. The software packages we use to implement the algorithm include python3.6 (programming language), CGC version 0.7.0 (a clustering tool for geospatial applications, a software package that implements co-clustering), netcdf4 version 1.7.0 (a platform for processing NetCDF (Network Common Data Format) files), and basemap version 1.4.1 (a visualization tool). In addition, there are some simple data processing software packages, such as NumPy version 1.26.0 and Pandas version 2.2.0.
## 3 Research on BPL Decoding Algorithm for Polar Codes Based on Information Geometry
### Path Selection Criterion Based on Soft Information Centroid  
This subsection firstly introduces the concept of the \"soft information centroid\". By performing equivalent calculations on \(L\) decoding results, a path selection criterion for polar code Belief Propagation List (BPL) decoding based on the soft information centroid is proposed. The feasibility of this path selection criterion is then demonstrated through bit error rate (BER) calculations.