esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Defining the origin of ground deformation, which can be a very challenging task, may be approached through several investigative techniques. Ground deformation can originate in response to both natural (e.g., tectonics) and anthropic (e.g., groundwater pumping) contributions. These may either act simultaneously or be somewhat correlated in space and time. For example, the location of structurally controlled basins may be the locus of enhanced human-induced subsidence. In this paper, we investigate the natural and anthropic contributions to ground deformation in the urbanized area of the inner Sarno plain, in the Southern Apennines. We used a multidisciplinary approach based on the collection and analysis of a combination of geomorphological, stratigraphical, structural, hydrogeological, GPS, and DInSAR datasets. Geomorphological, stratigraphical, and structural data suggested the occurrence of a graben-like depocenter, the Sarno basin, bounded by faults with evidence of activity in the last 39 ka. Geodetic data indicated that the Sarno basin also experienced ground deformation (mostly subsidence) in the last 30 years, with a possible anthropogenic contribution due to groundwater pumping. Hydrogeological data suggested that a significant portion of the subsidence detected by geodetic data can be ascribed to groundwater pumping from the alluvial plain aquifer, rather than to a re-activation of faults in the last 30 years. Our interpretation suggested that a positive feedback exists between fault activity and the location of area affected by human-induced subsidence. In fact, fault activity caused the accumulation of poorly consolidated deposits within the Sarno basin, which enhanced groundwater-induced subsidence. The multidisciplinary approach used here was proven to be successful within the study area and could therefore be an effective tool for investigating ground deformation in other urbanized areas worldwide.  
active fault; fault-controlled landform; GNSS; DInSAR; groundwater pumping; Sarno basin +
Footnote †: journal: remote sensing
# MCA-YOLOV5-Light: A Faster, Stronger and Lighter Algorithm for Helmet-Wearing Detection
## 3 Method
### MCA-YOLOv5-Light Safety Hat Wearing Detection Model
#### 3.2.2 Channel Pruning and Fine-Tuning of the Safety-Helmet-Wearing Detection Model  
At the 2017 ICCV Conference in the Field of Computer Vision, [PERSON] et al. [2017] presented a paper proposing a pruning algorithm for pruning convolutional neural networks in a simple but rather efficient manner. For the VGGNet network model, the network-slimming and pruning algorithm reduced its model size by 20 times and the computational operation by 5 times, whereas the pruned network model did not significantly reduce the accuracy. Drawing on the idea of this network-slimming pruning algorithm, this paper designs a pruning algorithm for a lightweight object detection network, which has pruned the convolutional channels in the detection network and can be directly used for object detection networks based on the convolutional neural network. At the same time, the pruned resulting model does not require the use of specialized hardware or the underlying library and can be directly used for rapid detection tasks.  
The detailed process of the pruning algorithm proposed in this paper is shown in Algorithm 1 and Figure 5. It uses the scaling factor of the BN layer as the measure of model pruning and the convolution channel below the preset threshold to reduce the volume size of the algorithm by the pruning operation, then train the trimming algorithm to recover the accuracy and finally obtain a lightweight detection network. Figure 5 shows the specific process of the channel pruning algorithm. After the model is sparsely trained, the lower convolution channel (yellow in the figure) will be pruned. Because the network structure of the pruning model changes compared with the original model, whereas the neural network parameters learned by the original network structure do not change, the target detection power of the pruning model is reduced, and the mapping power of the pruning model is low. The pruned model is trained with fine-tuning when the model can relearn the neural network parameters according to the current network structure, thus recovering the model's detection accuracy and improving the mapping.  
In conclusion, the MCA-YOLOv5-Light safety-helmet-wearing detection model mainly includes three stages: sparse training, model pruning, and model recovery. The entire pruning process is shown in Figure 6. In this paper, the MCA-YOLO v5 model is first sparsely trained, then the channel pruning operation is performed on the network model according to the pruning strategy and finally fine-tuning training is performed to obtain the final lightweight compression model.
# Research on the Tie Cable Replacement Method of Half-through Tied-Arch Bridge
## 5 Design and Implementation of the Temporary Replacement System  
Given there is little existing research on the temporary replacement device for the tie cables of half-through tied-arch bridges, this paper puts forward a design scheme for the temporary substitution system. The temporary substitution system consists of three parts, as shown in Figure 11: the steel anchor box, the support frame, and the temporary tie cable.  
The steel anchor box is welded with a Q345C steel plate with a thickness of 2.0 cm. The interior is separated and fixed by steel plates and is equipped with tie bar channels, which can anchor two temporary tie cables at the same time. The steel anchor box is anchored in the end transverse beam through M16 anchor bolts. The structure of the steel anchor box is shown in Figure 12.  
The support frame is also welded with a Q345C steel plate, which is used to support the temporary tie cable to prevent excessive sag. To prevent the tie cable and the support frame from being damaged due to friction, rubber pads are placed on the support frame. The supporting structure is shown in Figure 13.  
The temporary tie cable adopts epoxy coating 19 \(\times\)\(\varphi^{s}\) 15.2 steel strand, with the strength of 1860 MPa. The steel strand is wrapped with an HDPE protective layer, which can effectively prevent corrosion. After the steel anchor box, support frame, and temporary  
Figure 11: Temporary substitution system of the third Lingjiang bridge.  
Figure 12: Design of the steel anchor box.  
Figure 13: Design of the support frame.  
tie cables were installed, the temporary tie cables were tensioned by the jack. The tensioning method consisted of symmetrical tensioning of the upstream and downstream tie cables, as shown in Figure 14.  
Due to the damage to the tie cable of the third Lingjiang bridge, the temporary tie cables were first installed and tensioned for safety reserve, and then the tie cables will be replaced according to the best scheme.
## 2 Detection Methodology
### Method for Detecting Physiological Signals--HRV  
Heart rate variability (HRV) refers to the variation in the duration of successive heart-beats in individuals. This biological process is influenced by the modulation of the Autonomic Nervous System (ANS). HRV is thus considered a measure of neurocardiac function that reflects heart-brain interactions and autonomic nervous system (ANS) dynamics [7].  
The autonomic nervous system (ANS) can be subdivided into the sympathetic system (SNS), whose role is to excite, and the parasympathetic system (PNS), whose action is inhibitive [8]. The heart is dually innervated by the autonomic nervous system. The relative sympathetic increases cause the time between heartbeats (the inter-beat interval) to become shorter, and relative parasympathetic increases cause the inter-beat interval to become longer [9]. We therefore have two antagonistic systems that adapt the cardiac rhythm to change. States of emotional excitation modify this equilibrium and can be measured through changes in the HRV [10]. Hence, heart rate variability (HRV), which is used in evaluations of the autonomic nervous system (ANS), is used to evaluate human emotions [11]. Research shows that mental illnesses such as depression and anxiety are often accompanied by reduced HRV [12; 13]. Lower HRV has been associated with higher levels of affective instability [14], a trait known to be associated with poor psychological health [15], low self-esteem [16], and depression [17]. Furthermore, [PERSON] et al. [18] reported that higher HRV might be a predictor of greater mental health resilience [19]. This suggests that a decrease in HRV indicates a lack of good health.  
In this research, the NeXus-10 MKII biofeedback system (Figure 1) is employed to facilitate the rapid detection of HRV data of different homeowners in their own residences after the interior design projects are finished. The NeXus-10 MKII employs a non-invasive finger clip-on apparatus and light-based photoplethysmography (PPG) technology to assess blood volume pulse (BVP). BVP is a highly efficient and extremely low-risk method for rapidly determining heart rate and relative blood circulation. In Taiwan, this technology has been widely used in smartwatches and smart bracelets that anyone can buy on the market.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Symbol** & **Positive Affects** & **Symbol** & **Negative Affects** \\ \hline P1 & Active & N1 & Jittery \\ P2 & Excited & N2 & Distressed \\ P3 & Enthusiastic & N3 & Irritable \\ P4 & Interested & N4 & Nervous \\ P5 & Alert & N5 & Upset \\ P6 & Inspired & N6 & Scared \\ P7 & Strong & N7 & Hostile \\ P8 & Proud & N8 & Ashamed \\ P9 & Determined & N9 & Guilty \\ P10 & Attentive & N10 & Afraid \\ \hline \hline \end{tabular}
\end{table}
Table 1: The items of psychological affects in PANAS.
# Hardware Platform-Aware Binarized Neural Network Model Optimization
## 5 Experimental Results and Discussion  
In this section, some experiments are conducted to demonstrate the effectiveness of the proposed method. GPUs, RRAM, and FPGA are target platforms to find optimal BNNarchitectures. Depending on the type of target platforms, the corresponding Hardware Cost Estimation is prepared for the sensitive resources.  
For FPGA devices, the BNN architecture is implemented on the Ultra96 evaluation Board, which includes an xczu3 eg-sbva484-1-e Ultrascale+ MPSoC. In particular, the Process Subsystem (PS) comprises a quad-core Arm Cortex-A53 application processing unit and a dual-core Arm Cortex-R5 real-time processing unit. In the programmable logic (PL), there are 141,120 flip-flops, 70,560 LUTs, 360 DSP slices, and 7.6 Mbits block ram. The register-transfer level (RTL) design is simulated on Synopsys VCS, then synthesis and implementation are executed on Vivado 2018.3. All experiment results describing the number of LUTs, flip-flops, and estimated power consumption is collected from Vivado's report. For RRAM devices, as introduced in previous sections, an error simulation is prepared for the training process and inference period. Meanwhile, for the GPU experiments and training task, we use RTX 2080 ti to find the model with the best accuracy on this device.
## 5 Conclusions  
This contribution summarizes the specific five-system, real-time POD strategy based on the hourly updated ultrarapid orbit prediction method; particularly, an a priori cuboid model is introduced to enhance the five-parameter ECOM1 model for BDS-3 satellites. One year of observations from 21 iGMAS stations and 100 IGS/MGEX stations are processed for POD of multi-GNSS. External orbit comparison, hourly orbit boundary discontinuities and SLR validations are conducted, and it is shown that the mean 1-D RMS values with comparison to the GFZ final multi-GNSS orbits are approximately 3.7 cm, 10.2 cm, 5.8 cm, 5.7 cm, 4.1 cm and 25.1 cm for GPS, BDS IGSOs, BDS MEOs, GLONASS, Galileo and QZSS NONE GEOs, respectively. It is also shown that the mean 1-D RMS values of the hourly updated ultrarapid orbit boundary overlapping comparison are approximately 1.6 cm, 6.9 cm, 3.2 cm, 2.7 cm, 1.8 cm and 22.2 cm for GPS, BDS IGSOs, BDS MEOs, GLONASS, Galileo and QZSS NONE GEOs, respectively. The results of the hourly orbit boundary discontinuities for BDS and QZSS indicate that there still exist some mismodeled or unmodeled force errors which should be further investigated. As to the SLR validation, the mean RMS value for the four MEOs of BDS-3 is approximately 4.53 cm, and the biases are approximately \(-\)4 cm and 3 cm for BDS-3 SECM and CAST MEOs, respectively. The mean RMS value of the BDS-2 four satellites is approximately 4.73 cm, and the biases are approximately 1-2 cm.  
Conceptualization, writing--original draft preparation, writing--review and editing and methodology, B.T., Y.Y., Q.A. and J.Z.; software, B.T.; validation, Q.A.; data curation, B.T. and Q.A.; formal analysis, Q.A. and J.Z.; funding acquisition, J.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (Grant No. 42004022). Our sincere thanks go to the Crustal Dynamics Data Information System, Chinese Meridian Project and the International GNSS Monitoring and Assessment System for providing GNSS data; Deutsches GeoForschungsZentrum Analysis Center for providing multi-GNSS final orbit products; EUROLAS Data Center for providing SLR tracking data. We thank the Astronomical Institute of the University of Bern (AIUB) for providing Bernese GNSS Software version 5.2, [PERSON] and [PERSON] for providing GNUPLOT software, and School of Ocean and Earth Science and Technology at University of Hawai'i System for providing GMT Software. The authors declare no conflict of interest.
## 3 Strong Coupling Regime: Analytical Approximation  
The wave function for the single-particle state of Hamiltonian is given by:  
\[|\Psi(t)\rangle=\sum_{p}\bigl{(}a_{p}(t)\bigl{|}a_{p}\bigr{\rangle}+b_{p}(t) \bigl{|}b_{p}\bigr{\rangle}\bigr{)}, \tag{1}\]  
where \(\bigl{|}a_{p}\bigr{\rangle},\bigl{|}b_{p}\bigr{\rangle}\) are Wannier functions of the atom with number \(p\) in the excited and ground states, respectively [53], and \(a_{p}(t),b_{p}(t)\) the unknown variables, which are satisfy the system differential equations:  
\[i\frac{\partial a_{p}}{\partial t}=\Bigl{(}\frac{\omega_{0}}{2}-p\Omega_{B} \Bigr{)}a_{p}+t_{a}\bigl{(}a_{p+1}+a_{p-1}\bigr{)}-\Omega_{R}\cos\omega t\cdot b _{p}, \tag{2}\]  
\[i\frac{\partial b_{p}}{\partial t}=-\Bigl{(}\frac{\omega_{0}}{2}+p\Omega_{B} \Bigr{)}b_{p}+t_{b}\bigl{(}b_{p+1}+b_{p-1}\bigr{)}-\Omega_{R}\cos\omega t\cdot a _{p}. \tag{3}\]  
In this section, we will consider Equations (2) and (3) analytically, assuming for simplicity that the exact resonance condition is fulfilled (\(\omega=\omega_{0}\)).
## 4 Results and Discussion
### Soil Pressure Distribution of Abutment Backfill  
When the earthquake ground motion is inputted along the longitudinal direction of a bridge with a skew angle, the resultant force of soil pressure behind the abutment does not pass through the mass center of the bridge, which makes the superstructure twist and lead to the non-uniform distribution of soil pressure behind the abutment. The development of soil pressure behind the abutment during one typical cycle in a time-history analysis is shown in Figure 12 below. At first, the bottom left portion of the abutment at the obtuse corner gets in contact with the soil, and creates soil pressure as shown as the shaded area in stage (I) in Figure 12. The soil pressure gradually develops until it covers the whole abutment, as shown in stage (III). Then the top right portion of the abutment at the acute corner starts to move away from the soil, and the soil pressure there becomes zero, as shown in stage (IV). More top right portions of the abutment move away from the soil, and finally the soil pressure distribution in stage (IV) will be similar to stage (I). During this process, the soil pressure behind the abutment at the obtuse corner is always greater than the soil pressure at the acute corner.  
Figure 11: Maximum rotation of bridge deck.  
The maximum soil pressure behind abutment from time-history analyses on all SIAB models with varying skew angles and backfill compactness are shown in Figure 13 below. It is shown in Figure 13 that when the skew angle is 0\({}^{\circ}\), the soil pressure behind the abutment at the obtuse corner is equal to that at the acute corner. As the skew angle increases, the soil pressure behind the abutment decreases, but the pressure at the acute corner decreases much faster, so the soil pressure at the obtuse corner is always higher than the acute corner, and the ratio between them (O/A ratio) increases with the skew angle. As the backfill compactness increases, the soil pressure behind the abutment at the obtuse corner increases, but the pressure at the acute corner changes little, so the O/A ratio increases with the backfill compactness.  
Figure 12: Development of soil pressure behind abutment.  
Figure 13: _Cont._  
It is also shown in Figure 13 that although the soil pressure behind the abutment under pulse type ground motions is generally smaller than non-pulse type ground motions, the O/A ratio is higher. When the skew angle is 45\({}^{\circ}\) and the backfill is densely compacted, the O/A ratio is 4.5 under non-pulse type ground motions and as high as 170 under pulse type ground motions. Therefore, the distribution of the soil pressure behind a skewed abutment is non-uniform, which is more obvious under pulse type ground motions, with the increase of the skew angle and soil compactness.
# Dose Calculation Algorithms for External Radiation Therapy: An Overview for Practitioners
## 2 Dose Calculation Algorithm for Photon Beams
### Monte Carlo Methods  
The MC calculation method has been for a long time the gold standard for validating dose calculation algorithms used for radiation therapy in clinical practice [24]. However, their long computation time made these codes incompatible with routine clinical treatment planning. In the last decade, the development of powerful hardware, coupled with the implementation of accurate condensed history techniques [25] and variance reduction techniques [26], allowed for the introduction of commercial planning systems based on MC methods [27; 28].  
The distinguishing feature of MC is tied to the stochastic description of the interactions between primary or secondary particles and the medium. The cross sections of the considered processes define the probability density distributions of the interactions that are randomly sampled.  
The initial condition of the simulation is represented by a pre-filled phase space of the photon beam line.  
Particles paths are then divided into steps of length given by the specific mean free path \(\lambda\) of the particle in the medium; for every step, a random number generator is used to select the occurring interaction. The amount of energy lost in each interaction and all secondary particles produced in the process are recorded. Afterwards, secondary particles are also tracked, and the subsequent interactions recorded. This process is continued until the energy of each particle goes down below the cut-off energy value, when residual kinetic energy is deposited locally. Each simulation process (history) has to be repeated \(N\) times and the statistical uncertainty of the computation depends on how many particle histories are simulated, and usually decreases as \(N^{-1/2}\). This may result in a very intensive calculation, depending on the desired accuracy or the complexity and size of the geometry. Finally, the dose is computed by summing all the energy releases that occurred in every mass element. There are many Monte Carlo codes that have been developed for particle transport and those most frequently used in RT beam modeling include BEAM [29], GEANT4 [30], EGS [25], PENELOPE [31] and XVMC [32]. MC-based dose calculation codes all have similar structures. First, a MC code must define a set of descriptive data in order to depict the system: it reproduces set-up characteristics, modeling the LINAC head with precise details of the component dimensions, geometry, locations and material composition. For the patient portion, CT data provide morphological and chemical information in terms of mass density, electron density and atomic composition, which are required to calculate the proper radiation transport in the tissue.
## 3 Future CONERSTONE Platforms
### Electronic-Photonic Integration  
Integration of photonics and electronics is one of the key subjects for the further adoption of Si photonics technologies in the marketplace. Traditionally, integration between photonics and electronics is often classified as the physical coupling approaches between the photonics and electronics devices, such as wire-bonding, flip-chip bonding or monolithic integration. However, we believe this subject should be expanded to a synergistic design concept, in which the photonics and electronics devices should be closely codesigned in every possible aspect, including the operation speed, power efficiency, footprint, signal integrity and physical connection approaches. Two of our recent design examples [150; 151] demonstrate the advantages of this design philosophy.