esa-sceva/satcom-chunk-collection huggingface dataset:
# Ensuring Agricultural Sustainability through Remote Sensing in the Era of Agriculture 5.0
## 4 Sensors
### Satellite
Another important aspect of using satellite technology is the digital boundaries of crop fields that are a pre-requisite. Areas with intensive agriculture have solved this, but, for small crop areas (>1 ha), it persists. The 5 th revolution is playing its part here. Recently, a novel method, \"DESTIN\", of crop field delineation was reported with an accuracy of 95-99%, fusing temporal and spatial information acquired from WorldView and Planet satellite imagery [101]. Such types of studies are opening up the way for digitalizing agriculture and making it sustainable. This new era is not just about processing acquired data it is also about optimizing it for improved-quality products. For example, a recent study on formulating optimal segmentation parameters to obtain precise spatial information of agricultural parcels that normally suffer over- or under-segmentation is an evident demonstration of this idea [102]. This is not just limited to the optimization of satellite imagery, it is a way to achieve automation and implementation of these technologies on all scales.
# Satellite Measured Ionospheric Magnetic Field Variations over Natural Hazards Sites
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AGW & Atmospheric Gravity Wave \\ ASI & Italian Space Agency \\ ASM & Absolute Scalar Magnetometer \\ AW & Acoustic Wave \\ CDF & Common Data Format \\ CDSM & Coupled Dark State Magnetometer \\ CNSA & China National Space Administration \\ CSES & China Seismo-Electromagnetic Satellite (Zhangheng-1) \\ Dst & Disturbance Storm Time Index \\ EQ & Earthquake \\ FGM & Fluxgate Magnetometer (FGM1 and FGM2 for CSES HPM) \\ GNSS & Global Navigation Satellite System \\ GPS & Global Positioning System \\ HDF5 & Hierarchical Data Format version 5 \\ HPM & High Precision Magnetometer \\ ISGI & International Service of Geomagnetic Indices \\ LAIC & Lithospheric Atmospheric Ionospheric Coupling \\ LEO & Low Earth Orbit \\ TEC & Total Electron Content \\ USGS & United States Geological Survey \\ UTC & Coordinated Universal Time \\ VFM & Vector Field Magnetometer \\ VLF/LF & Very Low Frequency / Low Frequency \\ \end{tabular}
## 2 Related Work
### Anomaly Detection
Recent studies have shown that LSTM outperforms Transformer in some tasks, but Transformer [40; 41; 42] replaces recurrent neural network models in some cases, such as LSTM. The performance of both LSTM and Transformer can be further improved by the semantic information of logs, so they can be integrated into a whole to deal with the instability of log anomaly detection with lower computational expenses, enhanced accuracy, and higher efficiency of anomaly detection.
# Detection and Localisation of Abnormal Parathyroid Glands: An Explainable Deep Learning Approach
## 3 Results
### Parametrisation of ParaNet
#### 3.4.1 Fine-Tuning the Trainable Layers of the VGG19 Component  
Learning is incrementally allowed starting from the final convolutional layer. It is observed that allowing the last convolutional layer to be trainable is preferable (Table 5, because more trainable layers cause underfitting.  
The reader should note that the number of trainable layers can also depend on the size of the training dataset. Hence, this experiment defines the optimal number of trainable convolutional layers under specific data availability.  
Training the entire network from scratch led to an apparent underfitting problem, as observed by the obtained accuracy (55.7%). On the other side, performing a pure transfer learning procedure by freezing the entire network and letting it work with predefined weights yielded an accuracy of 87%. The latter results imply that the initial training on the ImageNet [20] challenge was beneficial. The learned features from non-medical images  
Figure 8: ParaNet parameter tuning results.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Total Trainable Convolutional Layers** & **ACC (\%)** \\ \hline
0 (transfer learning) & 87.0 \\
1 & 94.8 \\
2 & 93.2 \\
3 & 75.0 \\
4 & 67.6 \\
5 & 67.7 \\
6 & 57.6 \\ All (training from scratch) & 55.7 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Accuracy of ParaNet variations in distinguishing between normal and abnormal scans when altering the total number of trainable convolutional layers.  
were adequate to distinguish 87% of the thyroid dataset, whilst the last convolutional layer played a vital role in increasing this accuracy to 94.8% when becoming trainable.  
This behaviour is expected because the particular medical imaging problem does not involve any underlying and nontrivial features contributing to the outcome. Therefore, DL is not expected to discover new knowledge but to extract standard image features and perform the classification. In this particular problem, the changes in distinct features in shape, texture, and volume of findings between the three images determine the outcome.
## 3 Results and Discussion
### Influence of Projectile Material
In all three sets of tests, the titanium-alloy projectile perforated the target plate, indicating that the ballistic-limit velocity is below 105 m/s for a titanium-alloy projectile. Six sets of tests were carried out using the CFRP projectiles. In two sets of tests, the projectile rebounded, whereas the projectile perforated the target in the four remaining sets of tests. At impact velocities of 118 and 131 m/s, the target was not perforated, and the CFRP projectile rebounded. At higher impact velocities, 157-172 m/s, the 2D triaxial braided composite targets suffered damages of a similar pattern. The triaxial target is perforated by the CFRP projectile at 157 m/s, but not at 131 m/s, so the ballistic limit is between these two velocities. Based on this analysis, the triaxial braided composite target plate has significantly different ballistic-limit velocities for the three types of projectiles made of different materials but of similar mass and equivalent diameter. The ballistic-limit velocity of the CFRP, gelatin, and titanium-alloy projectile is between 131 and 157 m/s, above 213 m/s, and below 105 m/s, respectively. The target has the highest ballistic limit for the gelatin projectile, followed by the composite material blade-like projectile, and then by the titanium-alloy metal projectile with the lowest ballistic-limit velocity.
## 2 Materials and Methods
In order to minimise the influence of the electrode impedance, the voltage measurements can be performed between electrodes 1 and 2, or alternatively, between electrodes 3 and 2, according to Figure 2. From the equivalent electrical circuit of Figure 3, the voltage measurement evaluates the equivalent impedance from electrodes 1 and 2, which consists of the sample impedance \(Z_{L2}\) in series with electrode 2's impedance \(Z_{e}(2)\). We assume that the sample is homogeneous and impedance variations occur equally both in \(Z_{L1}\) and \(Z_{L2}\). In this sense, we can evaluate the electrical properties of the sample by observing \(Z_{L2}\). The value of the complex bioimpedance (\(Z_{L2}\)), measured in ohms (\(\Omega\)), observed between electrodes 1 and 2 can be estimated by the ratio of the measured voltage \(V_{CH2}\) (channel 2) to the \(I_{L}\) current, according to  
\[Z_{L2}\approx\frac{V_{CH2}}{I_{L}}\ =\ R_{sh}\cdot\frac{V_{CH2}}{V_{CH1}} \big{/}\theta_{2}-\theta_{1} \tag{1}\]  
where \(\theta_{1}\) and \(\theta_{2}\) are the angles of the measured voltage phasors \(V_{CH1}\) and \(V_{CH2}\), respectively. In this way, it is possible to determine the components of magnitude \(|Z_{L2}|=R_{sh}\cdot(V_{CH2}/V_{CH1})\) and phase \(\theta_{L}=(\theta_{2}-\theta_{1})\), from the impedance \(Z_{L2}\). The behaviour of the impedance \(Z_{L2}\) concerning the variation of the excitation frequency (\(f\)), ranging from 10 Hz to approximately \(2.3\cdot 10^{6}\) Hz in this experiment, is described by the Cole-Cole model [35].
## 3 Experimental Results and Evaluations  
To analyze the effect of the adaptive cylindrical neighborhood approach on the homogeneity of the laser point attributes, a test with simulated data was carried out in Section 3.1. After then, several experiments using airborne and terrestrial laser data were conducted to verify the performance of the proposed methodology in Section 3.2 to Section 3.3. The relevant issues, including data description, visual inspection of segmentation results, quantitative evaluation, and comparison with other approaches will be addressed sequentially. Additionally, the sensitivity of the thresholds utilized in this study is evaluated in Section 3.4.
## 4 RNN Structures Used for Traffic Prediction
### Hybrid Models Including RNN Techniques
#### 4.4.3 RNNs + CNNs  
When constructing deep learning spatiotemporal traffic prediction models, it is necessary to consider the characteristic graph structure of many transportation networks. Generally, passenger flow activities occur on specific transportation networks rather than simple Euclidean spaces. Modeling with non-graph structures may result in the loss of useful spatial information. CNNs are typically used to handle data with Euclidean spatial structures, such as regular grids. However, for non-Euclidean spaces, such as graph-structured data, the utility of CNNs is not as evident. To explore the spatial properties of non-Euclidean transportation networks, some scholars have adopted mathematical graph theory approaches, modeling transportation networks as graphs. This approach effectively depicts the connectivity between traffic nodes and opens new avenues for introducing convolutional operations. In recent years, GNNs have been widely employed to capture  
Figure 6: Example of a hybrid RNN and CNN model.  
spatial correlations in transportation networks. Figure 7 shows an example of a hybrid RNN and GNN model.  
[PERSON] et al. [42] proposed a T-GCN model, which integrates the advantages of GCNs and GRUs. This model can simultaneously capture the spatial and temporal dependencies of traffic data. Furthermore, it incorporates encoder-decoder architecture and temporal sampling techniques to enhance long-term prediction performance. [PERSON] et al. [75] proposed a novel spatiotemporal GNN for traffic flow prediction, which comprehensively captures spatial and temporal patterns. This framework provides a learnable positional attention mechanism, enabling effective information aggregation from adjacent roads. Additionally, modeling traffic flow dynamics to leverage local and global temporal dependencies demonstrates strong predictive performance on real datasets. Related studies also aim to reduce the data volume processed by neural networks to improve accuracy. For example, [PERSON] et al. [76] proposed a hybrid deep neural network based on a GNN and LSTM. Additionally, they introduced a time-correlation-based data dimensionality reduction technique to select the most relevant sets of road links as inputs. This approach effectively reduces the data volume, enhancing prediction accuracy and efficiency. The model performs well in short-term traffic flow prediction and exhibited good predictive capability in forecasting four-hour long-term traffic flow. Moreover, the proposed time correlation-based data dimensionality reduction technique effectively addresses prediction problems in large-scale traffic networks.
# Development and Testing of an Active Noise Control System for Urban Road Traffic Noise
## 2 Characterization of Road Traffic Noise  
### Traffic Noise Collection  
To understand the variation laws of road traffic noise in terms of frequency, intensity, time, and different spaces, the UMIK-1 measuring microphone with a built-in sound card was used in this experiment, and the overall road traffic noise was measured using the roadside method. The test process refers to Sound environment quality Standard GB3096-2008 [34] and Technical specifications for environmental noise monitoring Routine monitoring for urban environmental noise HJ640-2012 [35]. At each monitoring point,the UMIK-1 microphone is used to collect the sound intensity of the road traffic noise for 20 min continuously, that is, the curve of the sound pressure amplitude varying with time.  
Five test points were set up in the road network of Beijing; the points were selected on the side of the road and on the pedestrian bridge of the straight section to avoid the interference of intersections, traffic lights, fixed sound sources, crowds, reflectors, and other influencing factors. The distance between the microphone and the roadside was 20 cm, and the vertical distance between the microphone and the road was 1.5 m. The specific location and environmental parameters of the test points are shown in Figure 1.  
After the road traffic noise is collected using the UMIK-1 measuring microphone, the Smaart v8 software is used to analyze the data. The manufacturer of the UMIK-1 measuring microphone is miniDSP, a technology company in Hong Kong. This microphone has a frequency response range of 20 Hz-20 kHz and a sampling rate of 48 kHz. It is a 6 mm electret omnidirectional microphone. The Smaart v8, developed by an acoustic test and measurement company called Rational Acoustics LLC in Woodstock, CT, USA, is a widely used tool for audio measurement and analysis. This paper uses the latest version, smaart v8. In order to analyze the frequency distribution law of road traffic noise, the frequency spectrum of the noise is analyzed.
## 4 The Results of the Experimental Studies
The decrease in the average loads on the cutter when using the cross-cutting pattern compared to the staggered pattern is:  
\[\left(1-\frac{P_{zc}}{P_{\rm{2s}}}\right)\cdot 100\%=\left(1-\frac{1910}{2564.8 }\right)\cdot 100\%=34\%, \tag{10}\]  
where \(P_{zc}\)--average cutting force when using the cross-cutting pattern for potash mass, \(P_{zc}\) = 1910 N; \(P_{\rm{2s}}\)--average cutting force when using the staggered-cutting pattern for potash mass, \(P_{\rm{2s}}\) = 2564.8 N.  
The decrease in the root-mean-square deviation of loads on the cutter during the implementation of the cross-cutting pattern compared to the staggered pattern is:  
\[\left(1-\frac{\sigma_{cc}}{\sigma_{cs}}\right)\cdot 100\%=\left(1-\frac{1093. 2}{1300.3}\right)\cdot 100\%=16\%, \tag{11}\]  
where \(\sigma_{cc}\)--dispersion of loads on the cutter during the implementation of the cross-cutting pattern, \(\sigma_{c.c. }\)= 1093.2 N; \(\sigma_{cs}\)--dispersion of loads on the cutter during the implementation of the staggered cutting pattern, \(\sigma_{cs}\) = 1300.3 N.  
The decrease in the number of fraction of \(\varsigma\) = 0.25 size>>, which are hard to enrich, when the cross pattern is implemented in comparison with the staggered pattern is:  
\[\left(1-\frac{M_{-0.25c}}{M_{-0.25s}}\right)\cdot 100\%=\left(1-\frac{1.7}{2. 94}\right)\cdot 100\%=42\%, \tag{12}\]  
where \(M_{-0.25c}\)--mass content of the fractions, which are hard to enrich, when the cross-cutting method is implemented, \(M_{-0.25,c}\) = 1.7%; \(M_{-0.25,s}\)--mass content of the fractions, which are hard to enrich, when the staggered-cutting method is implemented, \(M_{-0.25,s}\) = 2.94%. The calculated frequencies of the formation of large stable and small intermediate cleavages, in accordance with Equations (3) and (4), are:  
\[f_{1}=\frac{V_{c}}{t}=\frac{0.1}{0.035}=2.86\;{\rm Hz}; \tag{13}\]  
\[f_{2}=\frac{V_{c}}{3h_{c}}=\frac{0.1}{0.015}=6.67\;{\rm Hz}. \tag{14}\]  
The calculated values of the frequencies are close to the values obtained experimentally (see Figure 5d), which amounted to 3 Hz and 8 Hz, respectively.
## 4 Discussion
Also, the computation of the fragment reorientation navigation was simplified. Firstly, it was assumed that the center of rotation during reorientation was the femoral head center. Secondly, navigating the complex reorientation of the fragment by aligning the actual marker surface to the targeted marker surface may have been too difficult without displaying quantitative information like rotational or translational deviations. Moreover, our evaluation was based on pre-/ postoperative CT comparison without including a separate technical evaluation of the registration accuracy or efficacy of the in situ navigation. The development of a 3D preoperative planning approach which does not rely on 2D measurements will be tackled in future work. Although various patient-specific factors could be included in such an approach, for example, joint loading, range of motion simulation and different imaging modalities, the fundamental question of which 3D factors contribute to a successful correction is still unanswered. Furthermore, an accurate, task-specific guidance for fragment reorientation and a robust method for continuous motion compensation shall be investigated. Moreover, it would be interesting to evaluate the influence of drift caused by AR devices on surgical navigation accuracy in a systematic manner. Successful improvements will then allow for an ex vivo study including a higher number of cadavers and comparison to the conventional method. In order to validate PAO AR guidance on pathological cases a clinical study will become necessary.
## 6 Results and Discussion
### Simulation Parameter  
Except for the variable parameter of each simulation, the simulation parameters are shown in Tables 1 and 2. \(a_{i}\), \(f_{i0}\), \(\theta_{u}\), and \(\theta_{\text{g}}\) obey the random distribution. K is the Ricean factor, which is associated with \(\theta_{u}\), \(\theta_{\text{g}}\) and obeys the normal distribution, \(\mu_{\text{K}}\) is the mean, \(\sigma_{\text{K}}\) is the standard deviation. The main references for parameter setting are [11; 17; 21; 26; 27]. To validate JODRA-PGLMC, it is compared with other baseline schemes. The baseline schemes are as follows. The joint offloading decision and resource allocation scheme based on random decision, Lagrange multiplier method, and satellite-terrestrial cooperation(JODRA-RDLMC). The joint offloading decision and resource allocation scheme based on potential game, Lagrange multiplier method, and non-cooperation (JODRA-PGLMNC). The scheme that only considers computation offloading, others are the same as our scheme (OCO). The scheme only considers local computing (OLC). All simulation results are obtained by running MATLAB for 2000 times and taking the average value.
## 2 Development of the Pre-Tuning Machine  
### System Structure  
The pre-tuning machine has two main components, as shown in Figure 2: a measurement module for cavity parameter testing (left) and a mechanical adjustment module for  
Figure 1: CSNS-II Linac layout.  
shape correction (right). Both sections can work alone or together as needed. The SRF cavity moves between areas on motorized rails.  
Full automation from frequency measurement to tuning ensures fast, precise operation. Sensors continuously transmit real-time data to the control software, enabling dynamic adjustments. Special algorithms decide on tuning steps for each cavity unit, reducing human input. This live feedback makes tuning both faster and more accurate.
# Coverage Optimization of WSNs Based on Enhanced Multi-Objective Salp Swarm Algorithm
## 3 Multi-Objective Optimization Deployment Model
### Obstacle Handling Description
#### 3.3.2 Obstacle Handling  
In this paper, diamond and triangular obstacles are introduced within a square monitoring area, as shown in Figure 2. When optimizing the deployment algorithm, the nodes that fall within the obstacles need to be processed as follows:  
* When a node is located within a diamond-shaped obstacle (e.g., Node A): To minimize power consumption caused by excessive node movement distance, a vertical line is drawn from Node A to the nearest boundary of the diamond-shaped obstacle. The intersection point between the perpendicular line and the boundary is denoted as point A'. Node A is then repositioned at point A' as its corrected location.
* When a node falls within a triangular obstacle (e.g., Node B): A vertical line is drawn from Node B to the slant side of the triangle. The intersection point between the perpendicular line and the slant side is denoted as point B'. Node B is then repositioned at point B' as its corrected location.
###### Abstract  
An interferogram obtained from a transparent plate contains information on the profiles of both surfaces and on the thickness variation. The present work is devoted to the processing of interferograms of this type. The processing technique is based on a 36-step algorithm developed by the authors for characterization of transparent plates having approximately equal reflections from both sides. The algorithm utilizes weighted multi-step phase shifting that enables one not only separately to extract the front and rear surface profiles together with the thickness variation of the tested plate but also to suppress the coupling errors between the higher harmonics and phase-shift deviation. The proposed measuring method was studied on a wavelength tunable Fizeau interferometer. The tested sample had an optical thickness and surface profile deviations equal to 0.51 \(\upmu\)m, 1.38 \(\upmu\)m and 0.89 \(\upmu\)m, respectively. According to the results obtained using 10 repeated measurements, the root mean square (RMS) errors for determining both surface profiles did not exceed 1.5 nm. Experimental results show that the setup and presented 36-step algorithm are suitable for the measurement of a transparent plate of arbitrary thickness.  
Keywords:Fizeau interferometry; wavelength tuning; separation of interferograms; characterization of a transparent plate; 36-step algorithm +
Footnote †: journal: Applied Physics Letters B
## 2 Materials and Method  
### Materials  
The CFA sample was obtained from a coal-fired power plant in Anhui Province, China. The main elements of CFA sample were analyzed using XRF (X-ray Fluorescence Spectrometer) (ZSX-Primus, Tokyo, Japan), and the results are shown in Table 1. The highest contents in the CFA sample are Si and Al, as well as some Fe and Ca.  
The analysis of REEs in the CFA sample was conducted by digestion in a microwave digester and tested using ICP-MS (Inductively coupled plasma-Mass Spectrometry) (ICAP-Q, Waltham, MA, USA). REE contents of the CFA sample were shown in Figure 1. The average content of total REEs reaches 455 ppm (mg\(\cdot\)kg\({}^{-1}\)), with a standard deviation of 4.4 ppm. The major REEs in CFA were found to be the light REEs (LREEs), such as La, Ce, and Nd. The average content of total heavy REEs (HREEs) is about 132 ppm (standard deviation is 1.5 ppm), among them, Sc and Y account for the highest. The ratio of heavy and light REEs was 2:5.  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Element** & **Si** & **Al** & **Fe** & **Ca** & **Na** & **K** & **Mg** & **O** & **Other** \\ \hline Content/\% & 26.08 & 14.66 & 2.04 & 0.63 & 0.49 & 1.02 & 0.24 & 44.43 & 10.41 \\ \hline \hline \end{tabular}
\end{table}
Table 1: XRF analysis of a power plant sample in CFA.  
Figure 1: Content of REEs in CFA sample activation.  
The reagents required for the experiment, including Na\({}_{2}\)CO\({}_{3}\), NaOH, and HCl, were all of analytical grade from Sinopharm Chemical Reagent Co., Ltd., Shanghai, China, the deionized water was of EW--I purity from Shandong Youwei Chemical Co., Ltd., Jinan, China, and the CO\({}_{2}\) (\(\geq\)99.999%) was from Dalian Special Gases Co., Ltd., Dalian, China.