esa-sceva/satcom-chunk-collection huggingface dataset:
# Visual-Inertial Navigation System Based on Virtual Inertial Sensors
## 3 Virtual Inertial Navigation System Based on Deep Learning  
### Construction of Virtual Inertial Sensors Based on Deep Learning  
The construction of virtual inertial sensors involves synchronously collecting the inertial motion information of a quadruped robot using two distributed inertial sensors, and using a deep learning network model to classify the torso inertial sensor information based on gait, thereby constructing a robot foot motion model [12], as illustrated in Figure 2.  
Figure 1: Detailed implementation flowchart of visual–inertial SLAM based on virtual inertial navigation assistance.  
In this paper, we use the DeepConvGRU neural network model to classify the gait information from the inertial sensors. This model combines convolutional neural networks and gated recurrent units (GRU), replacing LSTM as the recurrent layer [13; 14], and preserves important features through gates to prevent feature loss during long-term propagation. This type of model can effectively reduce the difficulty of model training without compromising model accuracy [15], as shown in Figure 3.  
Based on the gait classification, a DenseNet-GRU hybrid model was used to model the nonlinear mapping relationship between the torso and foot inertial sensors. Corresponding virtual inertial sensor models were trained for different gaits to reduce the complexity of fitting the inertial sensor mapping relationship, thereby further improving the regression accuracy of the hybrid model. This achieved more accurate gait recognition and behavior monitoring, as shown in Figure 4.  
The DenseNet-GRU model extracts motion feature information from the inertial sensor data [16], effectively utilizing the feature information through dense connections, and learns and extracts hidden feature information from the input data. The GRU neural network further explores the temporal characteristics of inertial data [13] and inputs the extracted features into the fully connected layer to establish a nonlinear mapping relationship between the torso and foot inertial sensor information [17].
# Fusion-Based Localization System Integrating UWB, IMU, and Vision
## 2 Related Work
### Multi-Sensor Fusion Strategies  
Given the complementary strengths of VIO and UWB positioning, their integration has become a promising direction for achieving accurate and robust indoor localization. UWB, as an active positioning technique, provides absolute global position measurements, while VIO, as a passive method, ensures high-frequency relative pose estimation. Combining these modalities enables both global accuracy and local continuity.  
In recent years, various fusion frameworks have been proposed. Reference [27] introduced a tightly coupled VIO-UWB fusion scheme, where VIO and UWB raw measurements are jointly optimized to estimate the robot's pose. This approach achieves high accuracy but often requires complex, computation-intensive optimization. Reference [28] presented a loosely coupled monocular VIO system assisted by UWB, using an EKF [29] to integrate relative and absolute positioning information. EKF-based fusion is computationally efficient and suitable for real-time applications, though it may be sensitive to sensor noise models and linearization errors.  
To address scalability and modularity, an optimization-based loosely coupled framework was proposed in [30], demonstrating that UWB data can effectively reduce drift in visual localization and enhance system robustness. In addition, graph-based optimization methods have gained significant attention. Notably, the Georgia Tech Smoothing and Mapping (GTSAM) [31] framework provides a flexible factor graph structure for fusing heterogeneous sensor inputs. GTSAM enables efficient batch or incremental optimization and is increasingly used in VIO-UWB fusion systems for its ability to model measurement constraints explicitly and maintain system consistency across time.
## 2 FES for SCI
### FES Rowing and Cycling
#### 2.3.2 FES Cycling  
Among the various FES training methods, FES cycling is the most commonly available and has been studied extensively [51]. Numerous studies since the 1980s have been performed regarding FES cycling after SCI. A recent systematic review of health and fitness-related outcomes of FES cycling exercise after SCI demonstrated that FES cycling improves lower-body muscle health and increases power output and aerobic fitness [52]. FES cycling is also effective in suppressing spasticity [53] and improves measures of bone health such as bone mineral density and bone metabolic markers [52]. In contrast to FES rowing, FES cycling has the advantage that it can be performed in home-based environments [54].  
Our group has also developed a new FES cycling system that attaches to a standard wheelchair. It consists of a front-wheel-drive unit that attaches to the wheelchair and an FES control unit that enables cycling movements in those with lower-limb paralysis. FES stimulation output is performed by a controller that coordinates the contraction of the quadriceps femoris and hamstring with the paralyzed muscles of the lower limbs from the crank angle during cycling (Figure 3). We are currently verifying the effect of the system on lower limb muscle and bone health in patients with paraplegia due to SCI.  
Figure 3: A new functional electrical stimulation (FES) cycling system. The FES cycling system consists of a front-wheel drive unit (thick white arrow) that can be attached to a standard wheelchair and an FES control part (thin white arrows) that enables cycling operation for lower limb paralysis.  
Figure 2: Functional electrical stimulation (FES) rowing machine. A backrest is fixed on the seat (white arrow head). A switch on the handle bar (white dotted arrow) is installed to control the FES (white arrows) timing by the patients themselves.
## 1 Introduction
### Contribution  
To this end, the purpose of this research study was to assess three empirical temperature-based models--[PERSON] and Samani (HS), [PERSON] and [PERSON] (BC), and Okundamiya and Nzeako (ON)--for their capacity to estimate solar insolation in a tropical and mountainous environment. Statistical validation determines and compares the performance of the three models. Additionally, we proposed a new method based on the logistic regression model in order to estimate daily solar insolation according to daily temperature differences. This new approach offers an option to estimate daily solar insolations based on the temperature difference that could improve the estimation of this resource in sites with different latitudes with low computational costs. The IDEAM's (Institute of Hydrology, Meteorology and Environmental Studies, abbreviated _IDEAM_ according to its Spanish translation) Automatic Weather Stations (AWSs) located in the State of Narino, Colombia, measured the information used as input. The three environmental zones of the area, Pacific, Andean, and Amazonian, allowed evaluating the models in different weather and physiographic conditions. The database was randomly divided into two parts: the first for calibrating the models and the second for validating the modelsstatistically. Before using the empirical models, the data passed a quality control procedure to improve the reliability of the results. R-CRAN was the software used to carry out the computational process needed for the research.
## 4 Conclusions  
Analytical and numerical models were established to investigate the thermodynamic performance of an underground reservoir located in abandoned mines for A-CAES plants. Analyzing air temperature and pressure fluctuations during the operation time in A-CAES plants is essential to design the underground reservoir volume and the turbomachinery. Typical operating pressures from 5 to 8 MPa were considered in the air charging and discharging periods. A 20 mm thick sealing layer, 35 cm thick concrete lining and 2.5 m thick sandstone rock mass have been considered around the compressed air. In addition, FRP and steel have been employed as sealing layer. Unlike other research works, in which the heat transfer coefficient is considered constant during the operation time, in the present investigation a correlation based on both unsteady Reynolds and Rayleigh numbers is employed for the heat transfer coefficient.  
Figure 16: Comparison of results obtained during the first cycle with an experimental model developed by [PERSON] et al. (2020). (**a**) Air temperature; (**b**) Air pressure.  
Figure 17: Comparison of results obtained during the first cycle with a numerical model conducted by [PERSON] et al. (2015). (**a**) Air and wall temperatures; (**b**) Air pressure.  
The results obtained show greater variations in air temperature between the air compression and decompression when FRP is used as sealing layer. Thus, significant temperature variations in the sealing layer and only a 15 cm thickness of the concrete lining is affected by a temperature rise during operation when steel is employed as sealing layer. The volume of concrete affected is reduced when FRP is used as sealing layer around the fluid. Regarding the sandstone rock mass, no temperature fluctuation was observed in the simulations. In addition, the heat flux increases and the air temperature within the reservoir is lower when steel is employed as sealing layer.  
Finally, the thermal energy balance in the 30 th cycle through the sealing layer considering air mass flow rates of 0.22 and \(-\)0.45 kg s\({}^{-1}\) reached 25.27 and 20.48 kWh for FRP and steel, respectively. In general, good agreements were obtained between analytical and numerical simulations. Unlike a 1D analytical model, in the 3D CFD numerical model it is possible to analyze the distribution of the thermodynamic responses in the entire domain, assuming a significant advantage to design the reservoir.  
Conceptualization, L.A.d.P., J.M., A.B.-S., and J.M.F.-O.; Methodology, L.A.d.P., J.M., and J.M.F.-O.; Software, L.A.d.P., J.M., M.G., and J.M.F.-O.; Investigation, L.A.d.P., J.M., M.G., A.B.-S., and J.M.F.-O.; Validation, J.M., M.G., and J.F.-O; Writing--original draft preparation, L.A.d.P., J.M., and J.M.F.-O.; Writing--review and editing, J.M., M.G., A.B.-S., and J.M.F.-O.; Visualization, J.M.F.-O. and J.L.; Supervision, A.B.-S., J.L., and J.M.F.-O.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
N/A.  
The authors declare
# A 28-nm CMOS Low-Power/Low-Voltage 60-GHz LNA for High-Speed Communication
## 4 Layout Description and Expected Performance  
### Layout and EM Post-Layout Analysis  
Figure 5 depicts the layout of the 60-GHz LNA core, whose size is 445 \(\upmu\)m \(\times\) 140 \(\upmu\)m (excluding PADs for measurement setup), main labels for the LC resonant networks (i.e., transformers and MIM capacitors) and the ground (GND) plane, also included for the sake of clarity. The chip has been drawn for on-wafer measurements through GSG RF probes at both input and output (east/west positions) by using a two-port network analyzer. Multi-contact DC probes (north/south positions, not displayed in Figure 5) are instead adopted for DC biasing. As already detailed in the previous sections, on-chip ESD protection is provided at both RF input and output by means of transformers, \(T_{\text{IN}}\) and \(T_{\text{BUF}}\). A metal GND plane was implemented by using the top alucap layer, while complying with the metal density rules of the adopted technology. At mm-wave frequencies, the layout parasities highly affect circuit performance, and therefore, careful estimation is of utmost importance. In this design, the overall LNA layout, including integrated transformers (\(T_{\text{IN}}\), \(T_{\text{INT}}\), \(T_{\text{OUT}}\) and \(T_{\text{BUF}}\)), customized MIM capacitors (\(C_{2}\), \(C_{\text{L1}}\), \(C_{\text{L2}}\) and \(C_{\text{L3}}\)), transistor connections, DC paths and the metal ground plane, has been EM-simulated with ADS MomentumTM by using the substrate layer description provided by TSMC.  
It is worth noting that despite the high computational complexity of such EM simulation, this is mandatory to properly estimate all the couplings taking place in the LNA,  
Figure 4: Differential MIM capacitor: layout (**a**) and equivalent circuit (**b**).  
Figure 5: Layout of the proposed LNA.  
thereby helping to identify and eliminate potential sources of instability. However, a full EM simulation of the complete LNA layout was performed only at the final stage of the design process. Prior to this, iterative EM simulations of individual LNA sections--particularly the LC resonant networks--were conducted to guide the design according to the simplified procedure illustrated in Figure 6.
# New Taipei City Smart Pavement Management Center and Road Maintenance Analysis
## 2 Intelligent Inspection Planning  
Traditional methods have evolved to include smart software and real-time data transmission to record road damage. This shift has turned inspection planning into an intelligent process, consisting of five main components:
# A Packet Content-Oriented Remote Code Execution Attack Payload Detection Model
## 4 Methodology  
### System Overview  
The overview of the proposed PCO-RCEAPD model is as Figure 6 shows. First of all, we collect attack payloads related to XXE, ELi, and IDSER from code repositories available on the internet, such as GitHub, to construct the raw dataset. After feature extraction, the ELi and IDSER dataset is used to train a machine learning (ML) model to determine whether a string is an RCE attack payload. Secondly, we set up experimental environments to simulate RCE attacks using these payloads and collect network packets. Then, the collected network packets are preprocessed, including Base64 decoding, URL decoding, and JSON parsing. Thirdly, for packets containing XML data, we can determine whether they are XXE attacks after XML data restoring, Entity Use-Def chain construction, and tail node behavior analysis. For packets that do not contain XML data (i.e., non-XML data), we process them, extract features, and send them to the ML model to determine whether they are related to ELi and IDSER. Finally, the results from the XML detector and the ELi and IDSER detector are integrated to determine whether the target network packet contains an RCE attack payload.
# A Comprehensive Survey of Isocontouring Methods: Applications, Limitations and Perspectives
## 7 Discussion
### Marching Squares and Marching Cubes Algorithms  
Overall, the marching squares and marching cubes algorithms provide a robust method to calculate isovalues within a given data set. The main application of the process is in imaging techniques, such as for medical purposes. The original data are therefore structured as image recordings and divided into pixels. It is not possible to vectorize the algorithm, as the data cubes have to be analyzed individually, which has a significant impact on the overall speed of the algorithms. Since structured data in grid form must be available for the application of the algorithm, transferability is only possible to problems that incorporate exactly these types of data structures. This limits the applicability of the algorithm to predominantly imaging problems. Furthermore, the results of the marching cubes algorithms are mainly tested using visual inspections, or, in some cases, a comparison is carried out with the original marching cubes algorithm of [PERSON] and [PERSON].
# ErrorExplainer: Automated Extraction of Error Contexts from Smart Contracts
## 2 Background
### Standard Proposals on Error Messages  
In Ethereum, the following standards are suggested to address the problems in smart-contract error reporting. However, adopting these standards remains limited because both on-chain contracts and off-chain clients must comply with them.  
* **Status Codes (ERC-1066):** ERC-1066 [11] defines a set of status codes to return the result of the transaction execution, as in HTTP codes. To translate the code into a human-readable description, a client must maintain a lookup table.
* **Custom Errors (ERC-838):** To provide more informative error messages, ERC-838 [12] introduces a custom error definition to the ABI with named error types. It allows developers to define custom error messages that can be returned when a transaction reverts. However, decoding requires the client to know the error signature; otherwise, the error message remains raw data.
* **Error Definitions on Tokens (ERC-6093):** In ERC-6093 [13], standardized error definitions are proposed for well-known token standards (i.e., ERC-20 [4], ERC-721 [14], ERC-1155 [15]) in the custom error format. Still, users can receive consistent error messages only if every token contract implements the standard.
* **Localized Error Messages (ERC-1444):** Even if an error message is returned, it is generally in English and unsuitable for global users. ERC-1444 [16] suggests a lookuptable mechanism that converts status codes or error identifiers to human-readable localized error messages in different languages.
# Cloud-Based Single-Frequency Snapshot RTK Positioning
## 2 Methodology
### Snapshot RTK Processing Workflow  
The general workflow of cloud-based GNSS positioning was described in previous works [9] and this architecture was further investigated and adapted to achieve high-precision positioning in [12]. Figure 1 illustrates the basic workflow considered in this article.  
GNSS signals are received by the antenna and then processed by the snapshot receiver, which is similar to the front-end part of a conventional receiver that mainly performs the tasks of converting the RF signal to a stream of digital bits and down-converting the signal to baseband or an Intermediate Frequency (IF). The resulting output file is the snapshot data that will be transmitted to the cloud platform by the cellular network module. This data set contains 2 streams of digital bits; one corresponds to the in-phase samples and the other corresponds to the quadrature samples. The size of this particular file is a main focus of this paper. After the snapshot samples arrive at the cloud server, they are further processed in an SRTK engine together with other assistance data collected through different cloud  
Figure 1: General workflow of cloud-based GNSS positioning.  
services in order to estimate the PVT solutions of the snapshot receiver. Such assistance data include rough coordinates of the receiver that may be provided by the mobile network module, approximate time information that can be accessed through internet, reference measurements collected from a nearby GNSS base station and navigation data that are distributed by multiple organizations nowadays.  
The specific steps inside the implemented SRTK engine are shown in Figure 2. The snapshot samples first go through an assisted acquisition process that generates the primary results of code delay, Doppler frequency offset and carrier phase for each acquired satellite. Note that before this step the number of candidate satellites to acquire have been reduced by a filtering process based on the assistance data. From here, the SRTK engine workflow can be divided into two paths. The upper panel, shaded in red color, is a typical Coarse Time Navigation (CTN) filter that only provides a Single Point Positioning (SPP) solution with meter level accuracy, more details about its implementation can be found in numerous studies [14; 15; 16; 17]. The second path, shaded in green color, represents the unique implementations of SRTK. It includes a measurement generation step combined with a typical RTK navigation filter. With the help of reference measurements collected from a nearby station, a much more precise RTK PVT solution is obtained. When no reference data are available, the SRTK is disabled and only SPP outputs will be generated. Results from the coarse time SPP filter contain useful Time Of Week (TOW) information that is needed for the measurement generation process of SRTK; thus, the upper path is always computed first and a valid SPP PVT solution is necessary for the second path of SRTK workflow.
## 4 Calculation of Electrode Coefficient  
In array lateralog measurements, the calculation of the electrode coefficient is a critical step for ensuring the accuracy of resistivity measurements. Utilizing the standardized model shown in Figure 2, with a borehole diameter of 8 inches, mud resistivity of 0.1 \(\Omega\cdot\)m, and formation resistivity of 10 \(\Omega\cdot\)m, without the influence of surrounding rocks and mud invasion, the principle for determining the coefficient, \(K\), is that the measured apparent resistivity under homogeneous formation conditions must equal the true resistivity, \(Rt\).  
Based on the structural principles of the instrument, focusing treatment can be applied. The specific process involves meshing and matrix resolution to obtain a new potential distribution, which is crucial for the precise calculation of the electrode coefficient. The electrode coefficient for each mode can be derived using the following formula:  
\[Rt=K_{i}\frac{U_{M1(RLAi)}}{I_{0(RLAi)}} \tag{6}\]  
\[K_{i}=Rt\frac{1}{U_{M1(RLAi)}} \tag{7}\]  
\(K_{i}\): Electrode coefficient for the \(i\)-th logging mode.  
\(R_{i}\): True resistivity in the model.  
\(U_{M1(RLAi)}\): Potential of the primary electrode in the \(i\)-th logging mode.  
\(I_{0(RLAi)}\): Current of the primary electrode in the \(i\)-th logging mode (for numerical simulation in this paper, for convenience in calculation, \(I_{0(RLAi)}=1\)).  
Through these techniques, the spatial potential distribution for different modes can be derived, ultimately yielding the electrode coefficients necessary for accurate resistivity measurements. The calculated electrode coefficients are shown in Table 1 below.  
The comparison between the electrode coefficients presented in the instrument parameter table and those derived from our modeling calculations indicated that the discrepancies for all coefficients were within 2%. This level of precision substantiates the validity of the constructed model, as evidenced by the computed electrode coefficients.
## 3 Results and Discussion
### Comparison of Energy Cost of CP Versus Other AOPs  
We have demonstrated that CP can effectively treat PFOS in a short period of time due to its high oxidation ability. However, considerations of its feasibility must also include electrical costs. The electric power consumption of a CP device with an aeration pump is 0.15 kWh.  
We calculated the EE/O value and energy cost for the CP process to be 117 kWh/m\({}^{3}\) and 10.6 USD/m\({}^{3}\), respectively. The energy cost was determined by multiplying the obtained EE/O by the average US electricity rate (0.09 USD/kWh) obtained from Site-WiseTM Version 3.1 [71]. The EE/O values and energy costs for the CP and other AOPs are summarized in Table 5.  
Generally, an EE/O value under 2.5 kWh/m\({}^{3}\) is considered practical and economical for water treatment [76]. However, due to the strong C-F bond in PFOS, higher power consumption and longer reaction times are required to achieve a high removal efficiency,  
Figure 3: Variations in the acute toxicity of PFOS and its degradation intermediates in the aeration-assisted CP system.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Process** & **Target** & **Rate Constant** & **EE/O** & **Energy Cost** & **Reference** \\  & **Pollutant** & **(k, h\({}^{-1}\))** & **(kWh/m\({}^{3}\))** & **(USD/m\({}^{3}\))** & **Reference** \\ \hline Electrochemical & PFOS & 0.3 & 935 & 84.5 & [72] \\ (Ti/RuO\({}_{2}\)/stainless steel) & & & & & \\ \hline Enhanced-contact & & & & & \\ (EC) Plasma & & & & & \\ \hline Photochemical & & & & & \\ (UV/Fe\({}^{3+}\)) & & & & & \\ \hline Oxidation & & & & & \\ (Nanocrystalline BDD) & & & & & \\ \hline Ultrasound & PFOS & 1.6 & 273 & 24.7 & [75] \\ \hline Aeration-assisted CP & PFOS & 3.1 & 117 & 10.6 & This study \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of energy cost of CP and AOPs used to treat PFOS.  
leading to higher EE/O values for the AOPs. Therefore, for the cost-effective treatment of PFOS, CP could be considered a viable alternative.