esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Localization Algorithm Performance
### Position Accuracy
Hence, the numerical values can only serve as coarse assessment of the actual error since the quality of the reference solution is substantially impaired. In data set 2, the maximum 3-D position error originates from driving through a tunnel. The two tunnel passages (one in each data set) are examined more closely in the following paragraph. Figure 6 demonstrates the benefits of odometry aiding on the position error in GNSS-denied environments such as tunnels. In data set 1 (Figure 6a), the horizontal position error without odometry aiding grows to approximately 17 m and its estimated standard deviation to approximately 15 m inside the tunnel. Once the first GNSS pseudo-range observations are received again (roughly 38 s/450 m after the tunnel entrance), the error and its estimated standard deviation quickly fall to the same levels as before the tunnel. When odometry measurements are included, the maximum horizontal position error is approximately 1.5 m and the maximum value of the estimated standard deviation is approximately 1.6 m. Both with and without odometry, the estimated standard deviations correlate very well with the actual errors. It should be noted that this tunnel features a 90\({}^{\circ}\) left-hand turn between 30 s and 35 s and is different from the tunnel in data set 2, which is longer (approximately 1000 m) and consists of a series of elongated left- and right-hand turns. The differences in the actual error  
Figure 6: Horizontal position error and its estimated standard deviation during passages through different tunnels. Multi-frequency/multi-constellation algorithm, with and without odometry aiding. (**a**) Tunnel in data set 1. (**b**) Tunnel in data set 2. Note the different scaling of the two vertical axes. during the passage through that second tunnel (Figure 6b, 0-55 s/0-1000 m) are not as large: 15 m without odometry compared to 10 m with odometry. The estimated standard deviation rises to approximately 29 m without the inclusion of odometry measurements and approximately 3 m with odometry aiding. However, a few seconds after the end of the tunnel, the horizontal position error peaks up to more than 50 m without odometry aiding before it sinks to pre-tunnel levels. This is due to erroneous pseudo-range observations. Since the estimated horizontal position standard deviation is approximately 25 m at the time, the signal plausibility check operating at the 3\(\sigma\)-level cannot identify the incorrect measurements. When odometry is turned on, the estimated horizontal standard deviation is sufficiently low (approximately 3 m) to be able to exclude the erroneous pseudo-range observations, resulting in a maximum error of just approximately 10 m.
# Tool Wear Monitoring for Complex Part Milling Based on Deep Learning
## 4 Results and Discussion
### Testing of Deep Neural Network  
The trained deep learning model is used for tool wear prediction on the testing set. The average error in percentage is used to evaluate the performance of the trained model, which is calculated as  
\[error=\frac{1}{m}\sum_{i=1}^{m}\frac{\left|VB_{i}^{*}-VB_{i}\right|}{VB_{i}} \tag{15}\]  
where \(VB_{i}^{*}\) is the predicted tool wear value, \(VB_{i}\) is the measured tool wear value, and \(m\) is the total number of the testing samples.  
The testing result is shown in Figure 13. The average error of the 24 testing samples is 8.2%, as per Equation (15). In the testing samples, the maximum relative error occurs in the test NO.13, with 64.8 \(\upmu\)m larger than the measured value. In this case, the new tool has just begun to cut, with the measured flank wear of 25.7 \(\upmu\)m. Besides, the maximum absolute error occurs in the test NO.15, with 82.3 \(\upmu\)m less than the measured value. In this case, the cutting edge is seriously worn and the measured flank wear reaches 402.9 \(\upmu\)m, far beyond the ISO tool wear criterion of 300 \(\upmu\)m. Except for the two extreme cases shown above, where the actual tool wear value is too small or too large, the trained deep learning model predicts well for all the testing samples. For the cases where the tool wear is too small or too large, the model has a larger prediction error because the training set obtained from the experiment covers few samples of the tool wear with such extreme values, and thus the rules learnt in the model under these situations are somewhat under-fitting. The proposed deep learning model can be more accurate with more varied training data through additional experiments. This will be investigated in future research work. In this study, the testing result shows that the proposed model works well in most cases and this proves the generalization ability of the trained deep learning model.  
Then, the radial basis function neural network (RBF NN) is implemented on the same dataset as the baseline to compare it with the performance of the proposed method. It turns out that the average error of RBF NN is 13.8%. Compared with the RBF NN, the proposed method shows higher accuracy in the tool wear prediction of varying cutting depth milling.  
Figure 13: Testing result in 24 samples of the trained deep learning model.
## 5 Intelligent Tire Force Estimation Method
### RBF Neural Network
To establish a high-accuracy intelligent tire force estimation model, three RBF neural network models with different input vectors are designed and trained based on various combinations of sensor types, quantities, and targeted configurations, as summarized in Table 4. The sidewall area of the tire serves as a common sensitive response area for both longitudinal and lateral forces. In Model 1, a single acceleration sensor is installed in one sidewall area. Model 2 installs displacement and acceleration sensors in the left and right sidewall areas, respectively. Building upon Model 2, Model 3 adds a displacement sensor in the crown area. The estimated longitudinal and lateral tire forces for Model 3 are shown in Figure 14a and 14b, respectively. The estimation results demonstrate that the RBF neural network, determined through the global sensitivity analysis of tire forces, can effectively and accurately estimate both longitudinal and lateral forces in intelligent tires. Figure 13: Intelligent tire force estimation model based on the RBF neural network. To evaluate the generalization performance of each RBF neural network based on the intelligent tire force estimation model, the mean relative error (MRE) on the test set is used as an approximation of the generalization error. The MRE results for each model are presented in Table 5. Model 3 exhibits the best generalization performance with MREs for longitudinal and lateral forces estimation both within 2%, specifically 1.42% and 1.10%, respectively. Models 1 and 2 show inferior generalization performance compared to Model 3, with MREs for both longitudinal and lateral forces remaining within 10%, still indicating relatively high estimation accuracy. Therefore, it can be concluded that increased diversity in the input vector types of the RBF neural network model leads to improved generalization performance and estimation accuracy.
# Intra-Plot Variable N Fertilization in Winter Wheat through Machine Learning and Farmer Knowledge
## 1 Introduction
For this purpose, one or more variables related to yield (vegetation index, elevation, soil properties, etc.) are selected [13] and a model is then established that relates the variables with yield, which is the property to be estimated. Subsequently, based on the N extractions per unit produced, the fertilization maps are calculated, using an unsupervised multivariate classification algorithm, and auxiliary variables are classified, creating the site-specific management zones [14]. Many farmers of the Basque Country (Spain) have the machinery and devices required to apply the VRF technique, but the lack of technical knowledge to make fertilization prescription maps is an impediment to its implementation. Vegetation indices can be obtained from multispectral satellite images. The recent launches of the Sentinel-2 (S2) satellite constellation by the European Space Agency (ESA) may potentially increase the adoption of precision agriculture techniques by small- and medium-sized farmers [14]. Two twin S2 satellites (A + B) have been specifically designed to meet the needs of the agricultural community and researchers. The images, offered free of charge by the ESA through its Copernicus platform, have a high spatial resolution, a high number of multispectral bands, and a short revisit period [15]. To monitor crop cover, yield, and health, visible (blue, green, and red) bands and near-infrared (NIR) regions of the electromagnetic spectrum are used [16]. Strong absorption of red energy and strong reflection of NIR energy are characteristics of healthy crops [17]. In recent years, the application of machine learning algorithms in precision agriculture has increased sharply [18]. It is interesting to establish models that estimate yield from easily obtainable auxiliary variables such as vegetation indices, terrain elevation, etc. Sensor data analysis using machine learning algorithms has made it possible to handle complex interactions that happen in commercial plots, enabling data-driven decision-making. A combination of different data sources and a machine learning model to obtain high-precision yield maps was used by [PERSON] et al. [19]. [PERSON] et al. [20] utilized the data obtained from an optical satellite and environmental information to forecast wheat yield. If the use of yield monitors was more widespread, it would be possible to train and test algorithms to make an early yield estimation. Unfortunately, the use of yield monitorsis not widespread in Europe [21], which hinders the collection of data to train and test algorithms on commercial farming plots. The objective of the present study was to make variable nitrogen fertilization maps in plots without previous yield information. For this purpose, a methodology was developed that allows combining the farmer's knowledge with the use of available information--in this case yield, the normalized difference vegetation index (NDVI), and a digital elevation model (DEM)--and its statistical analysis.
# Optimization of Nonspherical Gold Nanoparticles for Photothermal Therapy
## 4 Conclusions
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\lambda\) **(nm)** & **Shape** & \([R_{min},R_{max}]\) & \(R_{mean}\pm\sigma_{R}\) & \([L_{min},L_{max}]\) **(nm)** & \(L_{mean}\pm\sigma_{L}\) **(nm)** \\ \hline \multirow{3}{*}{800} & nanospheroid & [3.5, 3.8] & \(3.7\pm 0.1\) & [43, 80] & \(61\pm 10\) \\  & nanocylinder & [2.9, 3.2] & \(3.1\pm 0.1\) & [34, 63] & \(48\pm 8\) \\  & nanorod & [3.3, 3.6] & \(3.5\pm 0.1\) & [38, 72] & \(54\pm 9\) \\ \hline \multirow{3}{*}{808} & nanospheroid & [3.6, 3.9] & \(3.7\pm 0.1\) & [44, 82] & \(62\pm 10\) \\  & nanocylinder & [3.0, 3.3] & \(3.1\pm 0.1\) & [35, 65] & \(50\pm 8\) \\  & nanorod & [3.4, 3.6] & \(3.5\pm 0.1\) & [39, 73] & \(57\pm 9\) \\ \hline \multirow{3}{*}{820} & nanospheroid & [3.7, 4.0] & \(3.9\pm 0.1\) & [45, 84] & \(64\pm 10\) \\  & nanocylinder & [3.1, 3.4] & \(3.2\pm 0.1\) & [36, 66] & \(51\pm 8\) \\  & nanorod & [3.5, 3.7] & \(3.6\pm 0.1\) & [40, 74] & \(57\pm 9\) \\ \hline \multirow{3}{*}{1064} & nanospheroid & [5.6, 6.2] & \(5.9\pm 0.2\) & [64, 122] & \(93\pm 15\) \\  & nanocylinder & [4.8, 5.3] & \(5.1\pm 0.2\) & [52, 99] & \(75\pm 12\) \\ \cline{1-1}  & nanorod & [5.2, 5.7] & \(5.5\pm 0.2\) & [56, 106] & \(80\pm 13\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Min, max, mean, and std values of aspect ratio \(R\) and length \(L\), ensuring that the volume absorption coefficients are larger than 90% of the maximum coefficients, at four typical excitation wavelengths (800, 808, 820, and 1064 nm). The refractive index of tissue is 1.44.
# An Improved Low-Density Parity-Check Decoder and Its Field-Programmable Fate Array Implementation  
[PERSON]  
1 School of Physics and Electronic Information, Yantai University, Yantai 264005, China; 178541144558163.com (H.-Y.W.); [EMAIL_ADDRESS] (S.S.)1 School of Physics and Electronic Information, Yantai University, Yantai 264005, China; 178541144558163.com  
were proposed, dividing the parity-check matrix into multiple regularly patterned submatrices. Their storage and addressing characteristics significantly simplify hardware implementation, enhancing practical utility. As a result, QC-LDPC codes have quickly been adopted into various mobile communication standards. Standards across diverse communication fields have been developed, including the DVB-S2 standard for satellite broadcasting [14], the IEEE 802.16 standard for wireless metropolitan area networks [15; 16], and the CCSDS standard for deep space communication.  
Research on LDPC codes with floating-point calculations tends to require significant hardware resources for implementation. Therefore, a quantized Min-Sum decoding algorithm using amplified, rounded floating-point numbers was proposed. Building on the nature of quantization and the structure of the NMS decoding algorithm, this paper presents an improved NMS decoding algorithm with correction factor transfer. This improvement enables some eligible LDPC decoding implementations to reduce computational overhead without compromising performance.  
The remainder of this paper is structured as follows. Section 2 introduces the NMS algorithm and describes the structure of LDPC codes in the IEEE 802.16e standard. In Section 3, a Normalized Min-Sum algorithm with transfer correction factors is proposed based on the NMS algorithm, and theoretical comparisons demonstrate reduced computational overhead under certain conditions. Section 4 details the architecture of the FPGA implementation for an LDPC decoder, which utilizes an enhanced algorithm tailored for the IEEE 802.16e standard. Section 5 validates the algorithm's feasibility using MATLAB and simulation of the FPGA decoder via the VIVADO software platform, confirming that the improved algorithm achieves performance comparable to the original. Section 6 summarizes the conclusions of the paper.
# Optimization of Processing Parameters to Increase Thermal Conductivity of Rice Straw Fiber Film
###### Abstract  
Biodegradable mulching film (BMF) is a promising alternative to petroleum-based plastic mulching film. Thermal conductivity is an important quality factor of BMF that affects the heat transfer between ambient to soil and plant growth. The objective of this research was to enhance the thermal conductivity of fiber film through an environmentally friendly agent and optimized processing conditions. Response surface methodology (RSM) was used to optimize the processing conditions. With optimized process conditions of 70 g/m\({}^{2}\) basis weight, 1.5% wet strength agent content, 0.5% neutral sizing agent content, 15% charcoal addition ratio, and 55 \"SR beating degree, the films showed satisfactory thermal conductivity (0.0714 W/m\(\cdot\)K) and high dry and wet tensile strengths (33.4 and 12.2 N). The addition of charcoal increased the thermal conductivity of the film by 34.31%. This promising result shows the biodegradable fiber film is able to increase soil temperature and meet the required temperature for crop growth.  
rice straw fiber film; response surface methodology; central composite design; mechanical strength; thermal conductivity +
Footnote †: journal: Applied Physics Letters
## 2 Operation and Control
### Operation Support Tools  
The operation and security in the \(SG\) require applications, which will serve the system operator. Another need is the improvement of the economic dispatch. Together with the previous one, simulation environments will help decision making. Operators' control centers focus their success on applications with a solid control structure. Potential subareas will be explained:  
* \(SG\) requires applications capable of working online, and whose objective is to provide the operator with security and obtain an optimized operation. Other aspects of interest are monitoring and forecasting.
* Optimization and simulation tools are essential, and these will be used for medium and long-term decision making.
* The systems and control structure must be 100% reliable.  
The applications of SGs are time-dependent, [PERSON] et al. [99] present the limit time for SGs applications: maximum 1 second for transient stability management and power frequency regulation; maximum one hour for economic dispatch, generation control, power flow, security analysis, fault analysis and voltage stability studies; maximum one week for demand, weather prediction and unit commitment; maximum one months for demand prediction, maintenance planning, hydro planning and fuel planning; and maximum one year for generation expansion planning, reliability checks, scenario analysis and production cost modelling. [PERSON] et al. [100] divide the applications for SGs in: consumer side; transport and distribution side; and suppliers side; all the previous sections are dependent on the time to be used by the applications.  
After the review of the works, the author has considered grouping them in the following subsections: Agents and MAS, Distortion in the Electrical Grid, Catastrophes in the Electrical Grid and Online Tools and Simulators.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Summary of Fault** \\ **Detection,** \\ **Replacement and** **Quality of Service** \\ \end{tabular} } & \begin{tabular}{c} **Operation and Control: Distributed Energy Resources** \\ \end{tabular} \\ \cline{2-5}  & \begin{tabular}{c} **Demand and** \\ **Microgeneration** \\ **Management** \\ \end{tabular} & **Microgrids** & \begin{tabular}{c} **Developing the** \\ **Technology for the** \\ **Incorporation of New** \\ **Energy Services** \\ \end{tabular} & \begin{tabular}{c} **Aggregating** \\ **Distributed** \\ **Energy Resources** \\ \end{tabular} \\ \hline \
\begin{tabular}{c} **Problems and** \\ **Challenges** \\ \end{tabular} & [75; 76; 78; 82] & - & [83; 84] & [79; 81; 85] \\ \hline
\begin{tabular}{c} **Applied** \\ **Methodology** \\ \end{tabular} & [86; 88; 90; 93] & [95; 96] & [89; 91; 92; 97; 98] & [94] \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of Distributed Energy Resources.
## 4 Experimental Results
### Subsurface Defect Shape Mapping  
In this subsection, we describe placing the PEC sensor on the other side of the specimen to simulate a subsurface defect. Defect 1 on the subsurface was detected using the proposed method in the direction of magnetic flux. The outline image of subsurface defect 1 is shown in Figure 14. The experimental results illustrate that subsurface defect shape mapping could also be realized. According to the estimation formula for defect length, the estimated length of subsurface defect 1 was 12 mm, and the estimated width was 7.5 mm.
## 3 Proposed Method  
The key goal of the proposed solution, as shown in Figure 1, is to enable the system operator to avoid extensive model training when (i) the network environment changes, or (ii) there is a need to move detectors from one network to another. The proposed solution comprises the following building blocks:  
* Network probe, which captures network traffic in the form of network flows,
* Sketchy feature vectors (SFV) extraction, which calculates feature vectors over a predefined time window,
* Anomaly detection and threat identification, which is responsible for detecting anomalies in the observed traffic and categorizing them as a known threat,
* Domain adaptation module, which is intended to bring the traffic coming from a different network onto a feature space where the anomaly detection and threat classification were trained,
* Dashboard, which is intended to visualize various traffic characteristics for the identified anomalies and threats.
# Enhancing Robotic-Assisted Lower Limb Rehabilitation Using Augmented Reality and Serious Gaming
## 4 Proposed Methodology for the Lower Limb Rehabilitation Treatment  
The effectiveness of the rehabilitation treatment depends on various factors that can influence outcomes. Specifically targeting the rehabilitation of the lower limb for bed-ridden patients, a methodology was developed in collaboration with medical experts, encompassing the following steps:  
1. Identification of target groups;
2. Identification of targeted movements in correlation with the game modes;
3. Sensor placement;
4. Determining the number of movements per set and establishing optimal rest intervals during treatment.
# Robustness Analysis on Graph Neural Networks Model for Event Detection
## 5 Experiments
### Model Performance on the Original Data  
The experimental results on the original data are shown in Table 3, where MDL-EEGCN refers to EE-GCN with the separate introduction of proposed multi-order distance labels (MDL), and A-EEGCN refers to EE-GCN enhanced by the edge representation update method based on attention weights.  
The results show that the A-MDL-EEGCN we proposed is better than MOGANED and GatedGCN on R and F1 and than GCN-ED, JMEE, and EE-GCN on all metrics, illustrating A-MDL-EEGCN can achieve better performance than the previously proposed GNN-based ED models. We further analyze the effect of the two enhancement methods.  
Although MDL-EEGCN is better than MOGANED on R and F1, it is worse than EE-GCN on R and F1. By checking the edge representation tensor **EM** of MDL-EEGCN, we find that this is because the l2 norm (regarded as the association score of word pair) of the embedding vector of edge \"distance: 2\" is larger than some 1-order distance edges. Thus we believe that when new edge types are introduced into EE-GCN, it is difficult for the edge representation update method of EE-GCN to distinguish the importance of different edge types in the edge update, but instead dilutes the original semantic, then degrades the performance. A-EEGCN improves on P and F1 compared to EE-GCN, confirming the effectiveness of the edge representation update method based on attention weights. A-MDL-EEGCN outperforms A-EEGCN and MDL-EEGCN on R and F1, meanwhile, its **EM** accurately embodies the difference of each order distance edge importance, demonstrating the positive effect of MDL and the necessity of combining the two methods we propose in this paper.