esa-sceva/satcom-chunk-collection huggingface dataset:
# Positioning Methods and the Use of Location and Activity Data in Forests
## 2 Types of Positioning Technologies
### Barcodes and QR Codes  
Asset and inventory tracking tools such as one-dimensional barcodes, two-dimensional barcodes (QR codes), and Near Field Communication (NFC) tags, that are used ubiquitously for local and global commerce also provide a method of positioning and navigation in forests. QR codes are similar to barcodes but store information in two dimensions in order to expand data storage capacity [4]. NFC tags are based on RFID technology and offer two-directional short-range wireless communication [144]. Users can interact with these technologies via the sensors or camera in mobile devices, both to record and retrieve information [144; 145; 146], such as local maps or nearby landmarks [147; 148; 149]. Activated codes or tags can also be used to establish a known time and location of access (e.g., Google Analytics) and thereby inform map-matching of resources across multiple waypoints.  
Barcode tracking technologies such as QR codes are used in forestry to monitor log load information from stump to mill (Figure 9) [3; 4]. QR codes are applied to log ends printed on paper [3] or marked directly on the wood with lasers or paint [4; 150]. With higher storage capacities than simple, one-dimensional barcodes [4], QR codes can store load details such as harvest unit location, species and product sorts, bill of sale documents, or environmental certification status [3; 150]. Information can be accessed with a mobile device like a smartphone [150]. QR codes are cost-effective, accurate, weather-resistant, and will not hinder downstream processing [3; 150]. In addition to storing data, they can trace where products have been throughout each phase of the supply chain [4]. When used in conjunction with cloud-based data management systems, QR codes can be used to track logs in real-time [3; 150]. Thus, QR codes can help monitor the movement of resources to improve efficiency and logistical planning, as well as to document chain of custody [3; 4; 150].  
QR codes have also been used for wildland fire applications to disperse information in communities faced with fire. For example, QR codes printed on business cards or flyers can connect the public to online resources with fire status updates, such as social media websites and webcams [151].
# Lagrangian Modeling of Marine Microplastics Fate and Transport: The State of the Science
## 4 Numerical Lagrangian Models
### TrackMPD
Currently, TrackMPD can read hydrodynamic parameters off-line from some models such as ROMS in only a rectangular structured mesh. Moreover, hydrological properties are interpolated spatially at each time step, and then the hydrodynamic model assigns the values to particles located in the corresponding grid cells of TrackMPD [61].
## 2 Antenna Array and Design Method
### Feeding Network  
In this design, a planar substrate truncated microstrip line-to-DIL (Figure 1b) was employed as a feeding network [49]. It is a substrate-truncated microstrip line with two additional metal strips. The length of the tapered microstrip line (ty) and metal strip width (s) with inter-element spacing (g) was adjusted based on the optimized data and generalized empirical equations discussed in [49] for the taken substrate and the frequency band.
# Characteristics of HEMP Radiation Field on Typical Transport Aircraft with Frequency Sweep Method
## 3 Results
### Magnetic Field Strength Distribution  
Then, at different times, \(t\), the distribution of induced magnetic field strength \(H\) formed by the HEMP radiation field on the aircraft is analyzed. In Figure 7 schematic illustrations of the magnetic field strength \(H\) on the aircraft at different times \(t\) with the frequency sweep method, are presented.  
Figure 5: Time-domain and frequency-domain waveforms of the HEMP radiation field with \(E=1\) V/m.  
At the time \(t=0\), the HEMP with the oblique incidence as the incident angle \(\beta=45^{\circ}\) just reaches the aircraft. Although the electric field strength distribution \(E(t)\) is zero, the change rate of \(E(t)\), and hence the magnetic field strength \(H\), are quite large. Due to the oblique incidence of the HEMP field, the part of the aircraft that is far away from the incident field has not been illuminated in Figure 7a. Therefore, the magnetic field strength distribution is formed on both sides of the wing, and is larger near the radiation field.  
For the time \(t=10\) ns, almost the entire aircraft is irradiated by the HEMP field. At this time, the field strength is the largest, and the magnetic field strength distribution is also significant. In addition, it can be seen from Figure 7b that the magnetic field strength \(H\) is stronger at the sharp position of the aircraft than that at other positions. In Figure 7c,d, with the time \(t\) increased (\(t=30\) ns\(\sim\)400 ns), the HEMP radiation field intensity gradually decreases, and the magnetic field strength \(H\) also gradually decreases. The order of the magnitude of the peak magnetic field strength \(H\) is around 70 dBA/m. Therefore, the induced magnetic field strength of the aircraft is related to the HEMP radiation field strength and the change rate of the electric field strength. It is also quite large at the shape position of the aircraft which is more likely to generate the coupling effect.
# Analysis and Application of Lining Resistance to Water Pressure in Tunnel through Karst Cave
## 2 Failure Mode of Grouting in Tunnels Crossing High-Pressure Karst Caves
### Analysis of Tunnel Safety Thickness for Different Calculation Cases
#### 3.2.1 Analysis of Safety Thickness of the Surrounding Rock  
For the case of the hidden cavity on the side of the tunnel, the safety thickness of the surrounding rock was analyzed by the above model. The calculation results are shown in Figure 15, from which it can be seen that the surrounding rock thickness of 1 m could only resist 0.2 MPa water pressure, and the surrounding rock thickness of 2 m could already resist 0.6 MPa water pressure, which doubled the thickness of the surrounding rock and  
Figure 14: Acoustic emission diagram of the model shown in Figure 12 at different steps: (**a–h**).  
Figure 13: Numerical calculation results of the damage model of the composite surrounding rock under the influence of a water-rich cave: (**a–h**).  
increased the resistance to water pressure by three times. When the surrounding rock thickness increased to 10 m, the resistance to water pressure reached 3.1 MPa, compared with the surrounding rock thickness of 1 m, the surrounding rock thickness increased by 10 times, and the resistance to water pressure increased by 15.5 times. The water pressure resistance increased by 15.5 times compared with the 1 m thickness of the surrounding rock. It can be found that a thicker surrounding rock led to a stronger hydraulic resistance. The growth process of this showed a linear trend, and the growth rate was almost a constant. This is because, on the one hand, the thicker surrounding rock body had a greater bearing capacity; on the other hand, the influence range of the cavity would have been weakened with the increase in distance between the cavity and the tunnel.
## 4 Test Result
### Results and Discussion
The reliability function R(t) was the complement of the cumulative failure function \(F(t)\), therefore, \(R(t)=1-F(t)=e^{-\lambda\cdot t}\). This reliability model clearly shows the side effect problem. Figure 13 is a result of applying the SER values obtained from the emulation tests and beam tests of boards similar to the Zynq-7000 series to the reliability model [34, 36, 45, 46]. Other emulation studies have shown that the AVF is underestimated due to the existing side effect problem, resulting in higher reliability compared to the actual beam test results. On the other hand, using the ACMFI tool developed in this study, it was possible to obtain similar beam test results by mitigating the side effect problem.
## 4 Results
### Thermal Vacuum Test (TVT)  
The thermal vacuum test was focused on simulating the satellite operation in space conditions, the worst hot and cold in a vacuum environment. Two main objectives were verifying the functionality of the camera payload and the feasibility of running the classification algorithm at the satellite system level. The external solar panel board of KITSUNE experienced two thermal cycles between \(-30\) and \(+50\) degrees Celsius, which, based on the BIRDS project [34] and ISO 19683 Space systems--design qualification and acceptance tests of small spacecraft and units. Overall, both objectives met the mission requirements without anomalies observed during TVT.
# A Lightweight Module for Feature Fusion Based on Their Dimensions
## 3 Materials and Methods
### Transposed Convolution  
Transposed convolution, also referred to as deconvolution, is a widely used operation in convolutional neural networks. It aims to upsample images or feature maps and increase their spatial resolution. Transposed convolution is the inverse of ordinary convolution in terms of computation. In ordinary convolution, output feature maps are obtained by sliding a smaller filter window across the input image. In transposed convolution, a larger filter window is used to slide across the output feature maps and recover a larger input image, achieving upsampling [47; 48]. Transposed convolution is a common technique in image super-resolution tasks, which allows the conversion of low-resolution images into high-resolution ones [49]. In semantic segmentation networks with encoder-decoder structures, transposed convolution functions in the decoder part to gradually restore the high-resolution of images and obtain semantic segmentation outcomes [50]. In addition, transposed convolution is also widely utilized in the generator modules of generative adversarial networks, which can amplify feature maps and generate high-resolution images [51]. Transposed convolution is mainly used to upsample and recover the resolution, which is widely applied in image processing, semantic understanding and generative models. However, transposed convolution suffers from the issues of feature information loss during the recovery process, which limits its usage in the classification field. Nevertheless, taking into account that transposed convolution can recover feature information, and matches well with the concept of \"feature information fusion\" in this study. In this work, transposed convolution is applied to the image classification field.
## 3 Simulation of Two Objects in Different Longitudinal Planes: Mixing of Opaque and Phase Objects
Moreover, the analysis of the patterns of the reconstructed objects leads to the size and the 2D-shape of the objects. As classically observed with in-line holography, the reconstructed objects are surrounded by fringes due to the twin image. They \"scramble\" the reconstruction. Nevertheless, the reconstruction remains possible and a quantitative analysis can be carried out from the reconstructed objects. Intensity profiles of these phase and opaque reconstructed objects are plotted in Figure 5a,b, respectively. The size of the phase object can be estimated from the distance between both discontinuities which are indicated by two arrows in Figure 5a. For the opaque object, the width between both discontinuities of the intensity profile represents its size. From the intensity profile of the two objects and their magnification factors (\(5.3\) and \(6.8\), for the phase and opaque objects, respectively), the real size of the phase object along x-axis is \(24.8\)\(\upmu\)m and the real size of the opaque object along x-axis is \(20.1\)\(\upmu\)m, respectively. Size measurements correspond to the original sizes of the two simulated objects in Figure 2. For different values of the phase shift introduced by the irregularly-shaped phase object, the transverse reconstructed intensity profiles for \(\varphi\) = \(0.25\)\(\uppi\), \(0.5\)\(\uppi\), and \(\uppi\) are presented in Figure 6 (in solid line, dotted line and dashed line respectively). The curves have been plotted on the same figure. A continuous background has been added on these plots to separate them for clarity (it should be suppressed for a quantitative analysis). A reconstructed \"phase jump\" is clearly observed on the transverse profiles of Figure 6: it is the abrupt transition between points A and B (points plotted in the case \(\varphi\) = \(\uppi\)). The amplitude of the discontinuity is directly linked to the phase shift introduced by the object. It has been shown that parameter \(\frac{y_{A}-y_{B}}{y_{c}}\) is a monotonic function versus \(\varphi\) for a circular object [13]. \(y_{A}\), \(y_{B}\) and \(y_{c}\) are normalized intensities measured on the three points A, B and C of the transverse profile defined in Figure 3.0 The present simulator allows to evaluate this parameter for any complex phase object. The method is efficient when \(0.2\)\(\uppi\) < \(\varphi\) < \(1.6\)\(\uppi\). In other cases, the discontinuity is too small to be observed experimentally. Parameter \(\varphi\) can be recovered from estimated parameter \(\frac{y_{A}-y_{B}}{y_{c}}\). A comparison of experimental results with simulation allows to validate the determination of the phase shift introduced by the object.
## 3 Analytical Model
### Distribution of Serving BS Distance  
**Lemma 2**.: _Corresponding to the user type, the Probability Density Function (PDF) of the distance \(r_{l}\) between a typical UE \(l\) and its serving BS can be expressed as Equation (4), respectively:_  
\[\begin{array}{l}f_{m_{u}}\left(r_{l}\right)=\frac{2\pi r_{l}\lambda_{m}}{A_{ m_{u}}}\exp\left[-\pi r_{l}{}^{2}\left(\lambda_{m}+\left(B_{p}\hat{P}_{p} \right)^{2/\alpha}\lambda_{p}\right)\right],\\ f_{p_{c}}\left(r_{l}\right)=\frac{2\pi r_{l}\lambda_{p}}{A_{p_{c}}}\exp\left[- \pi r_{l}{}^{2}\left(\lambda_{p}+\left(\hat{P}_{m}\right)^{2/\alpha}\lambda_{m }\right)\right],\\ f_{p_{c}}\left(r_{l}\right)=\frac{2\pi r_{l}\lambda_{p}}{A_{p_{c}}}\left\{ \exp\left[-\pi r_{l}{}^{2}\left(\lambda_{p}+\left(B_{p}^{-1}\hat{P}_{m}\right)^ {2/\alpha}\lambda_{m}\right)\right]\right.\\ \left.-\exp\left[-\pi r_{l}{}^{2}\left(\lambda_{p}+\left(\hat{P}_{m}\right)^{2 /\alpha}\lambda_{m}\right)\right]\right\}.\end{array} \tag{4}\]  
Proof: See Appendix B.
## 4 Discussion
### Strengths and Limitations  
This study has several strengths: a relatively large sample size, a survey performed in different geographical areas across Europe, and the use of standardized procedures (questionnaire and checklist). Data on socio-demographics, psycho-social work environment, and perceived environmental quality were collected by a validated questionnaire. IEQ was assessed using both crude IEQ items (satisfaction with perceived control over temperature, ventilation, shading from the sun, light and noise, as well as satisfaction with overall thermal comfort, noise, light, and indoor air quality), and with 14 detailed indoor environmental parameters (e.g., layout, noise within the building, noise from building systems, noise outside the building, air movement). Some limitations, however, should be noted. Caution is needed when interpreting results, because data on IEQ were self-reported. Consequently, a potential (recall) bias cannot be excluded and this type of surveys do not always capture IEQ issues. A combination of objective and subjective measurements would be useful for assessing IEQ. Another limitation is the cross-sectional study design. Therefore, no causality of the identified relations can be confirmed.
# Measurement of the Resonant Magneto-Optical Kerr Effect Using a Free Electron Laser
## 6 Demonstration of TR-RMOKE with a Soft X-ray FEL
The sample is a thin-film structure composed of Ta (2 nm)/Gd\({}_{21}\)(Fe\({}_{90}\)Co\({}_{10}\))\({}_{79}\) (20 nm)/Ta (10 nm)/SiO\({}_{2}\). Solid lines show cosine fitting using Equation (11) for the data points that were obtained under application of +H (orange) and \(-\)H (purple). Inside the graphs, schematic diagrams of the time evolution of the Fe magnetic moment that were deduced from the time-dependent Kerr rotation angle are shown. At the delay time of \(-\)100 fs, **H** pointed in the same direction as the magnetic moment of the Fe sublattice. Figure 10: Schematic diagram of seeded FEL at Free Electron laser Radiation for Multidisciplinary Investigations (FERMI)@Elettra. Femtosecond pulses are used as seeds for the FEL and as a pump source for the TR-RMOKE measurements. The measurement chamber was directly connected on the downstream side of the chamber for the Diffraction and Projection Imaging (DiProI) station. (Inset) Details of the TR-RMOKE measurements. An Al filter was inserted to prevent the pump pulses from entering the unit composed of the analyzer (Mo/Si multilayer) and the detector (MCP).
# Real-Time Human Activity Recognition on Embedded Equipment: A Comparative Study
## 4 Implementation on Embedded Equipment
### Performance Metrics and Measuring Tools
#### 4.4.1 Definitions  
* **Processing time**: Also known as execution time, refers to the amount of time it takes for a program, or a specific task to complete its execution. It is a measure of how long a particular operation or set of operations takes to run from start to finish. In this work, the processing time is related to how long time the tasks of sampling, segmentation, and classification take to run. In the context of programming and hardware platforms, the calculations must be completed before a new event occurs. It is therefore a question of comparing times.
* **Memory usage**: Refers to the amount of computer memory (RAM or Random Access Memory) that a program is using during its execution. Memory is a crucial resource in computing, and efficient memory usage is essential for optimal performance. Here are key points related to memory usage:
* _RAM (Random Access Memory)_: This is the volatile memory that a computer uses to store data that is actively being used or processed. It is faster than long-term storage (like hard drives or SSDs) but is volatile, meaning it loses its contents when the power is turned off.
* _Memory Allocation_: Programs dynamically allocate and deallocate memory as needed. Inefficient memory allocation or memory leaks can lead to increased memory usage over time. There is an important difference from one language to another: in C the programmer must manage this allocation/deallocation himself (more complicated, risk of error, but complete mastery); unlike Python where there is a garbage collector (fewer errors, but potentially not optimal).
* _Memory Profiling_: Techniques and tools such as memory profiling are used to measure and analyze how a program uses memory. This is crucial for identifying potential memory leaks and optimizing memory usage.
* **Energy consumption**: Refers to the amount of electrical energy that a device or system consumes during its operation. It is a critical consideration in modern computing, particularly in portable devices and data centers. These machines sometimes have mechanisms to vary the processor frequency (and therefore consumption) depending on the calculation load.
# Subsampling Algorithms for Irregularly Spaced Autoregressive Models
## 6 Conclusions  
In this paper, we investigated the technique of computationally feasible subsampling in the context of irregularly spaced time series data. We proposed practical algorithms for implementing the **opt**, **iboss**, and **thin** methods for the IS-AR(1) model. The numerical re  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(m\) & **r** & **unif** & **opt** & **iboss** & **thin** & **full** \\ \hline \multirow{6}{*}{\(10^{4}\)} & 500 & 0.744 & 1.46 & 0.999 & 2.17 & 17.2 \\  & 1000 & 1.48 & 2.07 & 1.34 & 2.87 & 17.8 \\  & 1500 & 2.14 & 2.91 & 1.67 & 3.33 & 17.8 \\  & 2000 & 3.0 & 3.51 & 2.01 & 3.88 & 17.4 \\  & 2500 & 3.38 & 4.12 & 2.51 & 4.22 & 17.5 \\  & 3000 & 4.15 & 4.29 & 2.96 & 4.63 & 17.9 \\ \hline \multirow{6}{*}{\(10^{5}\)} & 500 & 0.781 & 3.1 & 4.34 & 10.6 & 141 \\  & 1000 & 1.52 & 3.7 & 4.3 & 11.0 & 147 \\  & 1500 & 2.3 & 4.45 & 4.46 & 11.6 & 147 \\  & 2000 & 3.03 & 4.84 & 4.67 & 12.0 & 147 \\  & 2500 & 3.48 & 5.69 & 4.67 & 12.6 & 147 \\  & 3000 & 4.42 & 6.71 & 5.16 & 13.1 & 150 \\ \hline \multirow{6}{*}{\(10^{6}\)} & 500 & 1.25 & 19.5 & 30.5 & 79.6 & 1244 \\  & 1000 & 2.14 & 19.0 & 29.8 & 79.8 & 1207 \\ \cline{1-1}  & 1500 & 3.15 & 19.9 & 30.4 & 80.6 & 1224 \\ \cline{1-1}  & 2000 & 4.16 & 21.3 & 30.3 & 81.1 & 1165 \\ \cline{1-1}  & 2500 & 5.01 & 21.4 & 31.7 & 82.1 & 1177 \\ \cline{1-1}  & 3000 & 5.65 & 22.1 & 29.9 & 82.4 & 1274 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average estimation times (ms) over 30 repetitions across different estimation methods, with full-data sizes \(m\) and subsample sizes \(r\) under the setting \(\phi=0.5\) and \(\sigma=1\).  
sults demonstrated that the proposed subsampling methods outperform the naive uniform subsampling approach with improved estimation efficiency and show significant benefits in reducing the computation time compared with the full-data estimation.  
While our work is currently focused on the IS-AR(1) model, it highlights the potential of optimal subsampling methods for time series data. In future research, these techniques can be extended to more complex models, such as IS-AR(\(p\)) for \(p>1\). Typically, as the number of parameters increases, optimal subsampling methods become even more effective in reducing computation times. We hope that our work paves the way for further exploration of subsampling strategies in time series analysis.  
Conceptualization, J.L. and Z.W.; methodology, J.L. and Z.W.; formal analysis, J.L. and Z.W.; investigation, J.L. and Z.W.; writing--original draft preparation, Z.W.; writing--review and editing, N.R. and H.W.; supervision, N.R. and H.W.; project administration, N.R. and H.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability StatementData used for the paper are computer-generated. Codes for generating the data are available upon reasonable request.  
The authors declare no conflicts of interest.