esa-sceva/satcom-chunk-collection huggingface dataset:
# Preventing Overturning of Mobile Cranes Using an Electrical Resistivity Measurement System
## 1 Introduction
These include work planning that accounts for the crane's operational space [9; 10], the use of radio frequency identification, building information modeling simulation, real-time monitoring systems using linear regression models to assess working conditions and the surrounding environment [11; 12; 13; 14; 15], and control mechanisms based on mathematical models of lifts and outriggers [16; 17; 18]. Additional research has focused on predicting overturning by measuring outrigger loads [19; 20; 21], using sensors to prevent overloading [22; 23], diagnosing faults and analyzing the causes of overturning [24; 25], and implementing visual programs to reduce worker cognitive load [26]. These studies significantly contribute to the efficient operation of mobile cranes and help mitigate the overturning risks during operation. However, most of these studies address issues that occur during crane operation and do not sufficiently consider ground subsidence-related accidents. Therefore, there is a critical need for research aimed at predicting ground subsidence before crane operation to prevent environmental overturning accidents. Ground stability is generally evaluated through various geophysical explorations, such as electrical resistivity tomography, seismic surveys, and borehole investigations, conducted before construction work [27]. However, the causes of ground subsidence, such as earthquakes, underground mining, groundwater extraction, and other subsurface activities, can originate far from construction sites and impact them over time [28; 29; 30]. In particular, the ground at construction sites often consists of sand, gravel, or other porous materials, which can give rise to hydrological corridors enabling water to infiltrate easily and cause ground subsidence. Ground subsidence is not a random event but rather a predictable phenomenon, and its early prediction can help prevent accidents [31; 32]. However, predicting ground subsidence at construction sites is challenging, as real-timeanalysis of ground conditions requires time, manpower, and cost resources. Therefore, addressing this issue through targeted research and technological solutions is essential. This study focuses on preventing overturning accidents of mobile cranes caused by ground subsidence by developing an Electrical Resistivity Measurement System (ERMS). The ERMS can be mounted on a mobile crane and allows for quick and easy measurement of ground electrical properties. Additionally, the ground safety algorithm was applied to qualitatively assess the risk of ground subsidence. The ERMS was tested under various field conditions to enhance efficiency and usability. The results demonstrate that the developed ERMS is applicable to the field and has significant potential to reduce the risk of accidents due to ground subsidence during mobile crane operations. This system is expected to be important in creating a safe working environment for mobile crane operations.
# News Recommendation Method Based on Candidate-Aware Long- and Short-Term Preference Modeling
## 4 Experiments
### Parameter Analysis  
The following parameters are analyzed in this section: the dimension of the word embedding W, the number of multi-head cross attention heads H, and the number of layers L.  
**The dimension of word embedding W.** As shown in Figure 9, the dimension of word embedding can affect the performance of the model. When W gradually increases, model performance keeps increasing and achieves optimal performance at W = 400. This may be due to the small dimension of word embeddings, which prevents the model from fully learning the semantic features in news, resulting in low model performance. While W is  
Figure 8: Ablation study on fine-grained short-term user preference encoder.  
greater than 400, there is a decrease in the performance of our model. One possible reason for this is that increasing W can easily cause the model to fall into over-fitting, resulting in less-than-optimal model performance.  
**The number of multi-head cross attention heads H.** Through Figure 10, it can be found that an appropriate number of attention heads is highly important for mining textual information. When H is too small, the information embedded in the text may not be fully mined, which may lead to low model performance. However, when H is too large, the performance of the model decreases instead. This may be due to the fact that there may be a large portion of consistency in the focus of attention between the title and the abstract of the news, and the redundant attention heads will focus on the portion of the title and the abstract that is consistent with each other, resulting in a degradation of the model performance.  
**Number of graph layers L.** From Figure 11, it can be seen that setting the appropriate number of layers efficiently results in improved model performance. When L changes from 0 to 3, the overall performance of the model keeps rising, which indicates that the appropriate number of layers improves performance by allowing the model to adequately capture remote dependencies; however, model performance declines when L is further increased to 4, which may be due to the fact that as the number of layers is increased, the embedding of the user and the clicked news is over-propagated in the graph, which makes the embedding of the user and the clicked news tend to be homogenized, leading to the phenomenon of over-smoothing. For our proposed method, setting L to 3 achieves the best performance.  
Figure 10: Effect of word embedding dimension W on model performance. (**a**) Variation in AUC and NDCG@10 at different H; (**b**) variation in MRR and NDCG@5 at different H.  
Figure 9: Effect of word embedding dimension W on model performance. (**a**) Variation in AUC and NDCG@10 at different W; (**b**) variation in MRR and NDCG@5 at different W.
###### Abstract  
A sun-tracking solar array is an effective solution to the increasing energy demand of spacecrafts. However, the driving torque fluctuation of the solar array driving assembly (SADA) and its oscillatory motion lead to the micro-vibration problem of the spacecraft. In this article, a disturbance torque model of the sun-tracking solar array, which takes its friction and flexibility into consideration, is established. Furthermore, a test platform was built to measure the disturbance force/torque, and a solar array simulator (SAS) was designed to replace the solar array in the ground experiments. Finally, the disturbance torque of the SADA-driven SAS is simulated, and the model is validated by comparing the simulation results with the experimental results. These results show that the frequency error of the model is less than 0.648%, and the amplitude error of the corresponding frequency is less than 22.33%, which indicates that the proposed model can effectively predict the disturbance torque generated by the sun-tracking solar array in orbit. The research provides theoretical guidance for the system optimization design and micro-vibration suppression of spacecrafts.  
sun-tracking solar array; micro-vibration; disturbance torque; solar array simulator +
Footnote †: journal: Applied
# Low-Photon Counts Coherent Modulation Imaging via Generalized Alternating Projection Algorithm
## 3 Numerical Simulations
### Far-Field Simulation  
We also validated the effectiveness of the CMI-GAP algorithm for the far-field geometry. In the simulations, a 632.8 nm laser source was assumed. The sample to modulator  
Figure 4: Convergence curve of the two algorithms in near-field geometry under 3 levels of photon flux. The RMS using our proposed algorithm converges relatively fast and reaches a lower RMS value.  
Figure 3: Error metrics comparison of reconstruction in our simulation for a near-field dataset when \(N=10^{8}\), \(10^{7}\), \(10^{6}\), \(10^{5}\). Bar charts: PSNR comparison of reconstructed amplitudes under two algorithms. Line graphs: RMS comparison of reconstructed amplitudes under two algorithms.  
distance was 30.8 mm. The modulator was placed on the front focal plane of a Fourier transform lens with a focal length of 30 mm. At the back focal plane of the lens, the detector was located.  
Reconstructed amplitudes and phases are shown in Figure 5. Figure 6 shows the RMS and PSNR against the illumination photon numbers of \(N\). The iteration number of the traditional algorithm is about 500 and 50 with CMI-GAP. A clear deviation in the image quality can be seen when \(N\) is below \(10^{6}\), similar to the near-field case. In contrast, CMI-GAP produces significant enhancement in reconstruction quality, with as much as 20 dB and three times improvement in PSNR and RMS, respectively. The RMS bears a similar behavior as the near-field geometry. Figure 7 demonstrates the convergence curve when \(N\) are \(10^{6}\) and \(10^{7}\). Generally speaking, the CMI-GAP algorithm has a relatively low RMS value and a faster convergence. The vibration of the convergence curve of the CMI algorithm was due to the algorithm switching, i.e., the HIO and DM algorithm are switched at specified iterations to promote convergence.  
Figure 5: Reconstruction of the sample given far-field CMI simulated data at varying photon flux. In the lower right corner of each image is a larger version of the middle region. CMI-GAP produces significant enhancement on reconstruction quality.
# Design of a SIW Variable Phase Shifter for Beam Steering Antenna Systems
## 1 Introduction
As \(\phi\) changes from 0 to \(2\pi\), the phase of the CP antenna's co-pol radiation will change by \(2\pi\), while \(r\) and \(\theta\) remain constant. A single layer SIW version of a \(4\times 4\) Butler matrix system is presented in [18], achieving scanning angles of \(\pm 25^{\circ}\). A broadband beam steering antenna for 5G applications is introduced in [4]. This design includes a \(4\times 4\) Butler matrix which applies progressive phase shift between microstrip antenna elements. Since a Butler matrix has some constant phase differences at the output, a signal can be directed only to some specific angles. Reference [6] demonstrates a new method of using band-pass filters as phase shifters or tunable filters by using coupled microstrip resonators loaded with a varactor diode in the filter structure. A \(1\times 4\) steerable dielectric resonator antenna array at C / X band is presented in [19]. Inkjet-printed barium strontium titanate (BST) thick-film is applied as phase shifters. Integrated metal-insulator-metal varactors are used for tuning the phase. This antenna system achieves a \(\pm 30^{\circ}\) beam steering range. Firstly, this paper proposes an SIW Ku-band variable phase shifter. This phase shifter is a combination of a 3 dB coupler with an isolation better than 30 dB at the mid-band frequency of 17 GHz and 22 dB in the entire operating bandwidth, with two varactor diodes as electrical phase control elements. Then, an antenna array consisting of two Vivaldi antennas is introduced for designing a beam steering antenna system. The proposed beam steering system is designed on a single SIW layer which makes it a low-profile structure with minimum losses. The system has a 2 GHz bandwidth with a reflection coefficient better than \(-10\) dB between 16 GHz and 18 GHz and \(50^{\circ}\) controllable beam steering capability. Therefore, the design criteria consisting of reflection coefficient better than 10 dB, 10 dB gain, \(50^{\circ}\) scanning angle with cross polarization better than 20 dB, which are normally required for tracking systems and other possible scanning applications, are achieved successfully.
# Remote Sensing for Land Administration
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Source** & **Title** & **County** & **Applications** & **Techniques** & **Data** \\ \hline \multirow{4}{*}{Park and Song} & **Discrepancy Analysis for** & \multirow{4}{*}{South Korea} & Automated land & \multirow{4}{*}{Convolutional neural network (CNN);} & \multirow{4}{*}{UAV hyperspectral cadastral map} \\  & **Detecting Candidate Parcels** & & & & \\  & **Requiring Update of Land** & & & & \\  & **Category in Cadastral Map** & & & & \\  & **Using Hyperspectral UAV** & & & & \\  & **Images: A Case Study in Jeonju,** & & & & \\  & **South Korea** & & & & \\ \hline \multirow{4}{*}{[PERSON] et al.} & **Innovative Remote Sensing** & \multirow{4}{*}{Kenya} & \multirow{4}{*}{Land tenure and \(\,\)cadastral mapping} & UAV survey; Machine & \multirow{4}{*}{UAV RGB; sketch maps; human Interventions; Questionnaizes; Group Discussion} & \\  & **Methodologies for Kenyan Land** & & & & \\  & **Tenure Mapping** & & & & \\  & **T**eure & & & & \\  & **Bridging the Semantic Gap** & & & & \\  & **between Land Tenure and EO** & \multirow{4}{*}{North Korea} & Land tenure and \(\,\)cadastral mapping: land & Research synthesis; & \multirow{4}{*}{HSI and aerial RGB (Google Earth); Landsat?; academic literature} \\  & **Methodological Undernings** & & & & \\  & **for a Geospatially Informed** & & & & \\  & **Analysis** & & & \\ \hline \multirow{4}{*}{[PERSON] et al.} & **Application of Deep Learning** & \multirow{4}{*}{Ehioipia, **Towards 3D Indoor Cadastral Change Detection from Point Clouds**} & Land tenure and \(\,\)cadastral updating & Image segmentation & Aerial RGB UAV \\  & **T**eure & & & & \\  & **T**eure & & & \\  & **T**eure & & & \\  & **T**eure & & & & \\ \hline \multirow{4}{*}{[PERSON] et al.} & **Towards 3D Indoor Cadastre Detection from Point Clouds** & \multirow{4}{*}{The Netherlands} & Land tenure and \(\,\)cadastral updating & Point cloud change detection techniques & Point clouds \\  & **T**eure & & & & \\  & **T**eure & & & \\ \hline \multirow{4}{*}{[PERSON] et al.} & **Towards an Underground Utilities 3D Data Model for Land Administration** & \multirow{4}{*}{Singapore} & Land tenure and \(\,\)cadastral mapping: land & \multirow{4}{*}{3D modelling and visualisation} & \multirow{4}{*}{Pegasus Two} \\  & **Land Administration** & & & & \\  & & & & \\ \hline \multirow{4}{*}{[PERSON] et al.} & **Deep Fully Convolutional Networks for Cadastral Boundary Detection from UAV Images** & \multirow{4}{*}{Rwanda} & Land tenure and \(\,\)cadastral mapping and updating & Machine learning; Fully Convolutional Network & \multirow{4}{*}{UAV RGB; Globalized Probability of Boundary (gPb)} \\  & **T**eure & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Remote sensing applications for land administration presented in this issue.
## Methods
### Sample Size Calculation  
A sample size calculation was carried out by the difference between 2 independent groups with G*Power 3.1.9.2 software and based on the IR distance (cm) of a pilot study (\(n\) = 20) with 2 groups (mean \(\pm\) SD): 10 elite basketball players (1.7 \(\pm\) 0.8 cm) and 10 amateur basketball players (1.1 \(\pm\) 0.3 cm). For the sample size calculation, 1-tailed hypothesis, an effect size of 0.99, an \(\alpha\) error probability of 0.05, a power (1-\(\beta\) error probability) of 0.80, and an allocation ratio (N2/N1) of 1 were utilized. Therefore, a total sample size of 28 subjects, 14 for each group, was calculated. Considering 15% of possible participants lost to follow-up, a total sample of 32 subjects, 16 in each group, was considered.
# Design of Static Random-Access Memory Cell for Fault Tolerant Digital System
## 1 Introduction  
As technology scales down, there have been significant developments in current durability and a substantial reduction in the parasitic capacitances of transistors. This leads to improvements in the speed, power consumption, and area of digital circuits. On the other hand, because the amount of charge representing a logic value for a digital circuit is significantly reduced, the voltage of nodes in circuits can be more easily changed. In other words, the signals in the digital circuits have become highly sensitive to external noise sources. Specifically, the radiation-induced energetic ionized particles in the atmosphere usually contain charge sufficient to flip the data of the memory in the digital circuit. Thus, it is more likely to result in a logical error for the circuit operation.  
The most vulnerable digital circuit component to such a fault is the static random-access memory (SRAM) cell, which is a hardware component that stores data in logical values. In the conventional 6 T SRAM cell, the simple cross-coupled inverter configuration means that the stored data can be easily flipped by the radiation-induced charge particle. To enhance the fault tolerance, various device-level, layout-level, circuit-level, or system-level design techniques are developed. Among these various approaches, the most promising one is to revise the topology of the SRAM cell at the circuit level to make its storage nodes less sensitive to external charge shocks, while keeping its initial stored voltage.  
There have been numerous radiation-hardened SRAM designs proposed. In order to make the SRAM cell robust to the radiation-induced charge noise, it is typical to employ extra transistors in a more complex manner compared to 6 T SRAM. This inevitably incurs overhead in terms of the area, power, speed, and operation stability. It is valuable to conduct a comparative analysis on the various SRAM cell designs in terms of various performance aspects.  
The rest of this paper is organized as follows. In Section 2, the background for the error observed in the circuit components due to radiation-induced noise and different design approaches for radiation hardening design are considered. In Section 3, the various SRAM cell designs are introduced in detail. In Section 4, the SRAM cell designs introduced in Section 3 are compared and discussed in various respects.
## 2 Materials and Methods
### Laboratory Setting--Dataset
#### 2.1.1 Experimental Participants  
Ten participants, with a mean age of 25 \(\pm\) 3, participated in this study. All participants were either students or staff members of the National University of Singapore (NUS). They were exclusively male, skilled in video games, right-handed, and in good health without any psychological or pharmaceutical issues. The study protocol received approval from the local Ethics Committee, and all participants provided written informed consent. Additionally, participants received compensation of $200 for their involvement in the experimental protocol. One of the 10 available participants had been excluded from the analysis for technical issues in the recorded data.
## 5 Calculation of the Loss Contributions
### Losses Generated in the Insulating Layers  
The dielectric losses produced in the three insulating layers of the cable are computed as follows [41]:  
\[Q_{ins}=\sum_{w}^{3}\Big{(}2\ \pi\ f\ d_{stat}\ tan\delta_{ins}\ Capacitance_{m}\ V _{rms}^{2}\Big{)} \tag{35}\]  
\[Capacitance_{m}=2\ \pi\ \varepsilon_{ins}\ \varepsilon_{0}\text{In}\bigg{(} \frac{R_{ext_{ins}\ \pi}}{R_{int_{max}\ \pi}}\bigg{)} \tag{36}\]where \(tan\delta_{ins}\) and \(\varepsilon_{ins}\) are the loss tangent and permittivity of the selected dielectric, respectively, \(\varepsilon_{0}\) is the vacuum permittivity, and \(Capacitance_{w}\) is the capacitance of the \(m\)th insulating layer of a cable having inner and outer radii \(R_{int_{ms}}\) = and \(R_{ext_{ins}}\) =, respectively, calculated as shown in Section 4.3. In this study, Tyvek PE was adopted as the insulating material instead of the more commonly utilized laminated polypropylene paper (PPLP), as the dielectric losses of PPLP are higher [58], and these might play a significant role in an HVAC cable.
# Deep-Learning Multiscale Digital Holographic Intensity and Phase Reconstruction
## 3 Experiment and Result Analysis
### Evaluation Index  
The root mean square error (RMSE) is a frequently applied loss function in deep learning. In a dataset, it is the square root of the mean of the squared differences between the actual and predicted values. It offers an intuitive way to gauge the difference between the actual data and real values. The formula for RMSE is Equation (7):  
\[RMSE=\sqrt{\frac{1}{N}\sum\limits_{i=1}^{N}{{{{\left({{\hat{y}_{i}}-{y_{i}}} \right)}^{2}}}}}\enspace, \tag{7}\]  
When training neural networks with multi-task learning and multi-objective optimization, it is necessary to balance multiple loss functions [36]. The loss functions for the intensity and phase images are:  
\[L_{I}=\sqrt{\frac{1}{N}\sum\limits_{i}^{N}{{{\left\|{{\hat{I}_{i}}-{I_{i}}} \right\|}^{2}}}}\enspace, \tag{8}\]  
\[L_{P}=\sqrt{\frac{1}{N}\frac{N}{i}\left\|{{{\hat{P}_{i}}-{P_{i}}}}\right\|^{2 }}\enspace, \tag{9}\]  
The equation uses \({\hat{I}_{i}}\) and \({I_{i}}\) to represent the real intensity image and reconstructed intensity image, respectively, additionally \({\hat{P}_{i}}\) and \({P_{i}}\) represent the real phase image and reconstructed phase image, respectively. The symbol \(i\) denotes the number of training data samples, and \(N\) represents the size of the sampling. To balance multi-task optimization and strengthen the relationship between the intensity and phase images, the loss function computes a weighted sum to allow backpropagation through the neural network.  
If the Y-Net network model is optimized for two tasks, the loss function is as follows:  
\[L_{IP}=\lambda_{1}L_{I}+\lambda_{2}L_{P}\enspace, \tag{10}\]  
Reference [20] states that for Y-Net, the loss function weight values are \(\lambda_{1}\) = 0.01 and \(\lambda_{2}\) = 1. When optimizing the Mimo-Net network model for the six tasks, the loss function can be expressed as Equations (11):  
\[L_{IP}=\lambda_{1}L_{I1}+\lambda_{1}L_{I2}+\lambda_{1}L_{I13}+\lambda_{2}L_{P1 }+\lambda_{2}L_{P2}+\lambda_{2}L_{P3}\enspace, \tag{11}\]  
Here, \(L_{I1}\), \(L_{I2}\) and \(L_{I3}\) represent the mean squared errors of intensity images with scales of 256 \(\times\) 256, 128 \(\times\) 128, and 64 \(\times\) 64, respectively, \(L_{P1}\), \(L_{P2}\) and \(L_{P3}\) represent the mean squared errors of phase images with scales of 256 \(\times\) 256, 128 \(\times\) 128, and 64 \(\times\) 64, respectively. The weight of the intensity loss function is denoted as \(\lambda_{1}\), and the weight of the phase loss function is denoted as \(\lambda_{2}\). During training, it is necessary to minimize the  
Figure 4: Local feature-extraction process. From 256 \(\times\) 256 scales to 128 \(\times\) 128 scales to 64 \(\times\) 64 scales.  
comprehensive loss of all tasks by assigning different weights to each task. This helps to achieve the minimum total loss function \(L_{IP}\).  
A set of optimal weights is chosen as \(\lambda_{1}\)= 0.0005, \(\lambda_{2}=0.01\) after continuous testing and training.