esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive 3D Imaging for Moving Targets Based on a SIMO InISAR Imaging System in 0.2 THz Band
## 2 Architecture of the Terahertz SIMO InISAR Imaging System
### FMCW Signal Model and De-chirp processing  
Without considering the envelope information, suppose the radar works under the \"stop-go\" mode, the FMCW signal output from the transmitting antenna is expressed as:  
\[s_{T}(t_{m},\tau)=\text{rect}(\frac{\tau}{T_{p}})e^{i(2\pi f_{c}t+\tau_{T}\tau^ {2})} \tag{1}\]  
where  
\[\text{rect}(\frac{\tau}{T_{p}})=\left\{\begin{array}{l}1|\tau|\leq T_{p}/2\\ 0|\tau|>T_{p}/2\end{array}\right. \tag{2}\]  
where \(f_{c}\) denotes the carrier frequency, \(\gamma\) means the chirp rate, \(T_{p}\) means the chirp period. \(t=t_{m}+\tau\) means the full time, where \(t_{m}\) is the slow time and \(\tau\) denotes the fast time which  
Figure 1: Block diagram of the single input and multi-output (SIMO) system.  
Figure 2: Prototype of the SIMO antenna array.  
varies within a chirp period. Denoting \(B\) as the bandwidth, then its value is determined as \(B=\gamma T_{p}\).  
Suppose an ideal point scatterer, whose instantaneous range to the radar at slow time \(t_{m}\) is \(R(t_{m})\). Ignoring the amplitude variation, the reflected signal arriving at the receiving antenna is expressed as:  
\[s_{R}(t_{m},\tau)=e^{i[2\pi f_{c}(t-\frac{2R(t_{m})}{c})+\pi\gamma(\tau-\frac{2 R(t_{m})}{c})^{2}]} \tag{3}\]  
where \(c=3\times 10^{8}m/s\) denotes the propagation speed of electromagnetic wave.  
To receive the wide-band signal with lower sampling frequency, the system adopts a de-chirping receiver scheme which mixes the received signal with the LO signal to produce the IF signal. After demodulation and residual video phase (RVP) correction, the output signal from the IF module is expressed as:  
\[s_{IF}(t_{m},\tau)=e^{-j2\pi(f_{c}+\gamma\tau)\frac{2R(t_{m})}{c}} \tag{4}\]  
It can be seen in (4) that the output IF signal of a point scatter is a single-frequency signal, and the following equation holds:  
\[f_{IF}=\gamma\frac{2R(t_{m})}{c} \tag{5}\]  
where \(f_{IF}\) means the frequency of the IF signal. It's revealed that the frequency of the IF signal is proportional to the target range, so the sampling frequency \(f_{s}\) can be determined by the expected detecting rang window. More detailed derivation can be found in [7].  
Next, the range compression is accomplished by applying Inverse Fourier Transform (IFT) along the fast time, resulting in the high-resolution range profile (HRRP), which is expressed in the following form:  
\[Y(t_{m},r)=\text{sinc}[\frac{2B}{c}(r-R(t_{m}))]e^{-j\frac{4R}{\lambda}R(t_{m})} \tag{6}\]  
where \(\lambda=c/f_{c}\) means the wavelength of the carrier frequency.  
Equation (6) indicates that the point spread function (PSF) of a single scatterer in the HRRP is a SINC function, the range information can be obtained from the HRRP according to the range cell in which the peak appears. As long as the wave path difference of scatterers from a target is larger than a range resolution cell, they will be projected into different range cells in the HRRP.
# The Application of Satellite Image Analysis in Oil Spill Detection
## 5 Conclusions
Available online: [[https://www.nornickel.com/news-and-media/press-releases-and-news/updates-on-the-clean-up-operation-following-diesel-spill-in-norlisk/](https://www.nornickel.com/news-and-media/press-releases-and-news/updates-on-the-clean-up-operation-following-diesel-spill-in-norlisk/)]([https://www.nornickel.com/news-and-media/press-releases-and-news/updates-on-the-clean-up-operation-following-diesel-spill-in-norlisk/](https://www.nornickel.com/news-and-media/press-releases-and-news/updates-on-the-clean-up-operation-following-diesel-spill-in-norlisk/)) (accessed on 17 February 2022). * _Siberian Heatwave of 2020 almost Impossible without Climate Change. Available online: [[https://www.worldweatheratribution.org/siberian-heatwave-of-2020-almost-impossible-without-climate-change/](https://www.worldweatheratribution.org/siberian-heatwave-of-2020-almost-impossible-without-climate-change/)]([https://www.worldweatheratribution.org/siberian-heatwave-of-2020-almost-impossible-without-climate-change/](https://www.worldweatheratribution.org/siberian-heatwave-of-2020-almost-impossible-without-climate-change/)) (accessed on 17 February 2022). * _[PERSON] et al. (2022)_ [PERSON]; [PERSON]; [PERSON] A Review of the Latest Trends in the Use of Green Ammonia as an Energy Carrier in Maritime Industry. _Energies_**2022**, _15_, 1453. [CrossRef]
* _[PERSON] et al. (2022)_ [PERSON]; [PERSON]; [PERSON] How Do Government Policies Promote Green Housing Diffusion in China? A Complex Network Game Context. _Int. J. Environ. Res. Public Health_**2022**, _19_, 2238. [CrossRef]
* _[PERSON] et al. (2022)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] The Impact of Energy Development of the European Union Euro Area Countries on CO\({}_{2}\) Emissions Level. _Energies_**2022**, _15_, 1425. [CrossRef]
# Object Detection for Industrial Applications: Training Strategies for AI-Based Depalletizer
###### Abstract  
In the last 10 years, the demand for robot-based depalletization systems has constantly increased due to the growth of sectors such as logistics, storage, and supply chains. Since the scenarios are becoming more and more unstructured, characterized by unknown pallet layouts and stock-keeping unit shapes, the classical depalletization systems based on the knowledge of predefined positions within the pallet frame are going to be substituted by innovative and robust solutions based on 2D/3D vision and Deep Learning (DL) methods. In particular, the Convolutional Neural Networks (CNNs) are deep networks that have proven to be effective in processing 2D/3D images, for example in the automatic object detection task, and robust to the possible variability among the data. However, deep neural networks need a big amount of data to be trained. In this context, whenever deep networks are involved in object detection for supporting depalletization systems, the dataset collection represents one of the main bottlenecks during the commissioning phase. The present work aims at comparing different training strategies to customize an object detection model aiming at minimizing the number of images required for model fitting, while ensuring reliable and robust performances. Different approaches based on a CNN for object detection are proposed, evaluated, and compared in terms of the F1-score. The study was conducted considering different starting conditions in terms of the neural network's weights, the datasets, and the training set sizes. The proposed approaches were evaluated on the detection of different kinds of paper boxes placed on an industrial pallet. The outcome of the work validates that the best strategy is based on fine-tuning of a CNN-based model already trained on the detection of paper boxes, with a median F1-score greater than 85.0%.  
machine learning; deep learning; CNN; artificial intelligence; robotics; object detection; industrial depalletization +
Footnote †: journal: Applied Sciences
# Automatic Detection of Ballast Unevenness Using Deep Neural Network
## 4 Conclusions  
The algorithm presented in this article, based on deep neural networks, allows for the detection of ballast unevenness in compliance with a regulation introduced by the Polish railway lines. The unevenness of the ballast surface is one of the indicators of its condition. For this reason, a regulation was introduced by the Polish Railways specifying the maximum threshold of ballast unevenness. The algorithm presented uses track images which are scanned by a LASER triangulation system installed on a track inspection vehicle managed by the Polish railway line authorities. One of the key elements of the algorithm is the deep neural network used to detect and segment the sleepers from the intensity image. Two networks were tested: Mask R-CNN and YOLACT with two types of backbones: Resnet101 and Resnet51. Due to its higher accuracy (\(AP=0.8901\), IoU = 0.78004 and Pixel Accuracy = 0.8063), the Mask R-CNN network with Resnet101 was selected. Because the determination of unevenness of the ballast based on the track image boils down to an instance segmentation process (areas of unevenness of the ballast undergo detection and segmentation), the quality of the detection of ballast irregularity was verified with the help of the \(AP=0.901\) measure, while the quality of the segmentation of the ballast was checked using the IoU = 0.93 and Pixel Accuracy = 0.91 measures. The algorithm presented in the article, using Mask R-CNN, makes it possible to effectively assess the level of unevenness of the ballast, which is correlated with the state of the ballast. The proposed system allowed for the effective implementation of the regulations introduced by the Polish Railways regarding ballast evenness. It can be an alternative to other existing systems.  
Conceptualization, P.B. and P.L.; methodology, P.B. and W.N.; validation, W.N.; formal analysis, P.B. and P.L.; investigation, P.B.; resources, P.L.; data curation, P.B., P.L. and W.N.; writing--original draft preparation, P.B. and P.L.; writing--review and editing, W.N.; visualization, P.L. and W.N.; supervision, P.B.; project administration, P.B.; funding acquisition, W.N. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors would like to thank [PERSON], head of the Diagnostic Measurements Department of the Office of the Diagnostic Centre of PKP Polskie Linie Kolejowe S.A., for providing the images from the ballast survey.  
The authors declare no conflicts of interest.
# Mapping a Guided Image Filter on the HARP Reconfigurable Architecture Using OpenCL
## 4 Benchmarking OpenCL Performance on HARP
### Shared Virtual Memory  
A third aspect that is considered, is the use of SVM. In the OpenCL framework, data is communicated between the host and the kernel by reading from and writing to allocated memory areas. There are two different types of memory areas: memory buffers and SVM.  
Figure 5: Bandwidth of reading src when executing kernel cache read for ulong16 pointer types. The bandwidth for both the kernel with (blue) and without (red) the volatile keyword is displayed.  
When using memory buffers, the data is copied to a specially allocated part of the RAM memory that is called a memory buffer. The host can only access memory buffers through the OpenCL API. Moreover, the memory location of the memory buffers depends on the accelerator architecture. In a PCIe-based CPU-FPGA architecture, both the processor and the FPGA have their own DRAM memory. In this type of architecture, the memory buffer is in the FPGA DRAM memory. The data that is in the CPU DRAM is therefore copied from CPU DRAM to FPGA DRAM, after which the FPGA reads from the FPGA DRAM. This is the conventional operation in a traditional PCIe-based CPU-FPGA platform.  
In a shared memory architecture such as the HARPv2 platform, on the other hand, both the original data and the OpenCL buffer are stored in the shared DRAM. SVM is a paradigm in which the original data can be accessed by both the OpenCL kernel and by the host. The host and the FPGA can access the SVM-allocated memory area by using conventional memory operations. Therefore, data is not duplicated, as illustrated in Figure 1.
## 2 Results and Discussion
### Method Performance and Validation  
The analytical efficiency of the developed analytical method using SPME based on prepared PDMS needles followed by GC-MS was evaluated to validate its performance. This validation was based on several parameters, such as the linearity coefficient, limits of detection and quantitation (LOD, LOQ), accuracy, and precision, as shown in Table 1. These evaluations were conducted at different levels of concentration to validate the SPME-GC-MS method. The external standard calibration curve was constructed using a series of standard solutions sequentially analyzed in order from low to high concentration. Each concentration was analyzed in triplicate. The linearity of the SPME method was studied by extracting the aqueous solutions spiked with pesticides in concentrations between 0.05 and 1000 ng mL\({}^{-1}\). The obtained correlation coefficients (R\({}^{2}\)) varied from 0.9990 to 0.9999. On the other side, the limits of detection and quantitation (LOD and LOQ) were calculated using the signal-to-noise ratio for each compound. As is reported in Table 1,the LOD and LOQ values were in the ranges from 0.3 to 2.5 ng mL\({}^{-1}\) and from 0.9 to 7.5 ng mL\({}^{-1}\), respectively.  
The precision of the method was evaluated by evaluation of the relative standard deviation (RSD %) of the concentration calculated in intra-day and inter-day experiments, after preparation of a standard solution of all pesticides at a constant concentration (100 ng mL\({}^{-1}\)). The intra-day relative standard deviation (RSD %) value varied from 0.8%, for diazinon, to 12.2%, for \(\lambda\)-cylahothrin. Furthermore, most of the intra-day RSD values were less than 20%. Additionally, the inter-day RSD values varied from 3.9%, for malathion, to 16.5%, for diazinon. The RSD values of the intraday and inter-day experiments were below 20%, which is acceptable for extraction by SPME.  
The relative recoveries were evaluated by extraction and analysis of water samples spiked with a known concentration of the studied pesticides. Two different concentration levels of pesticides were selected, 50 and 250 ng mL\({}^{-1}\), and each spiked solution was extracted and injected in triplicate. The recovery at the 50 ng mL\({}^{-1}\) level varied from 77.56%, for deltamethrin, to 116.93%, for chlorpyrifos, while, at 250 ng mL\({}^{-1}\), the values were between 89.36%, for \(\lambda\)-cylahothrin, and 116.23%, for deltamethrin. For the studied pesticides, the recoveries for the two concentration levels were between 70 and 120%. These results confirm the good accuracy of the extraction method based on SPME and GC-MS.
## 5 Study on Single-Parameter Control of Synchronization in Incremental Launching Construction
### Stress Changes Due to Unsynchronized Vertical Jacking  
The state of unsynchronized vertical jacking under the maximum cantilever condition in jacking construction is simulated by applying forced displacement in the Z direction (vertical bridge direction) to the base of the left pad in the model, and analyzing the stress changes in the steel box girder members under this state. The proposed vertical jacking height differences are: 5 mm, 10 mm, 15 mm, 20 mm, and 25 mm for a comparative analysis.  
When there is a difference in the vertical jacking height between the right and left jacks, the girder will rotate around the bridge axis. From Table 1, it can be seen that the base supports the reaction force of the right-side pad (the side with a smaller vertical jacking height) in the occurrence of unsynchronized vertical jacking, the side with the smaller vertical jacking height is subjected to a larger support reaction force, and the size increases with an increase in the jacking height difference. Vertical jacking height difference is shown in Figure 9.  
From Figure 10, it can be seen that the maximum equivalent stress values of each steel box girder component increase with the vertical jacking height difference due to the occurrence of the bias load phenomenon. It can also be noticed that there is a difference in the sensitivity of the steel box girder components to unsynchronized vertical jacking. The stress levels in the longitudinal stiff ribs of the base plate and diaphragms increase faster, indicating that are were more sensitive to unsynchronized vertical jacking. In contrast, the unsynchronized vertical jacking between the roof plate and the longitudinal stiffening ribs of the web plate does not have a significant effect on their stress levels due to their long distance from the contact area. The above pattern is consistent with that described in the thirtieth document [30]. The reliability of the model is verified. When the vertical jacking height difference is 15 mm, the overall stress level of the beam is low, and when the height difference reaches 20 mm, the maximum equivalent stress of the base plate reaches 244.45 MPa, which exceeds the permissible stress of Q345 qD steel, and is prone to structural instability and damage. Therefore, 15 mm is initially considered as the vertical jacking height difference control threshold for the single-parameter control of jacking synchronization.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Difference in Vertical Jacking Heights/mm** & **0** & **5** & **10** & **15** & **20** & **25** \\ \hline Reaction force/KN & 1368.12 & 1434.25 & 1492.39 & 1537.88 & 1563.82 & 1570.09 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Change in support force on the lower side of vertical jacking.  
Figure 9: Schematic diagram of vertical jacking height difference.
# Lightweight CoAP-Based Bootstrapping Service for the Internet of Things
###### Abstract  
The Internet of Things (IoT) is becoming increasingly important in several fields of industrial applications and personal applications, such as medical e-health, smart cities, _etc._ The research into protocols and security aspects related to this area is continuously advancing in making these networks more reliable and secure, taking into account these aspects by design. _Bootstrapping_ is a procedure by which a user obtains key material and configuration information, among other parameters, to operate as an authenticated party in a security domain. Until now solutions have focused on re-using security protocols that were not developed for IoT constraints. For this reason, in this work we propose a design and implementation of a lightweight bootstrapping service for IoT networks that leverages one of the application protocols used in IoT : _Constrained Application Protocol_ (CoAP). Additionally, in order to provide flexibility, scalability, support for large scale deployment, accountability and identity federation, our design uses technologies such as the _Extensible Authentication Protocol_ (EAP) and _Authentication Authorization and Accounting_ (AAA). We have named this service CoAP-EAP. First, we review the state of the art in the field of bootstrapping and specifically for IoT. Second, we detail the bootstrapping service: the architecture with entities and interfaces and the flow operation. Third, we obtain performance measurements of CoAP-EAP (bootstrapping time, memory footprint, message processing time, message length and energy consumption) and compare them with PANATIKI. The most significant and constrained representative of the bootstrapping solutions related with CoAP-EAP. As we will show, our solution provides significant improvements, mainly due to an important reduction of the message length.  
Keywords:IoT; bootstrapping; CoAP; EAP; AAA; lightweight +
Footnote †: journal: Sensors
# Inverse Design of Distributed Bragg Reflectors Using Deep Learning
## 2 Materials and Methods
### Data Generation  
The desire for relatively small-scale data generation requires careful attention to the choice of samples, to ensure enough diversity to train a useful model and simultaneously encapsulating enough similar data that accurate input-output mapping can occur. The examination of possible material depths within the chosen range yielded 46,656 samples, where material depths were constrained to multiples of 10 nm. The intention was to split these data in half, where alternating samples were taken, so that both the SNN and IDNN could be trained on similar yet unique data. Following this, information regarding refractive indices (n) and extinction coefficients (k) was compiled from an online refractive index database [18] for the two materials. Data were taken for wavelengths between 380 and 780 nm, over which range both materials had a negligible k value. The raw data were then processed through a simple program _wavelengths.m (see data availability for information on accessing code)_, which linearly extrapolated between the data points to produce uniformly spaced refractive index values for each material.  
Finally, these values were input to a program based on the works by [PERSON] [20], _tmm-Comp.m (see data availability for information on accessing code)_, along with a file containing the structural combinations, and the TMM used to evaluate each case. The output of this program was the reflectance spectrum of each structure within the visible light range.
## 3 Results
### Test of the Algorithm on Real Seismic Data  
Spectral analysis was done to understand the seismic data spectral properties as part of the data quality control process. The seismic data is zero phased with a positive reflection coefficient for high impedance layers. The bandwidth of the wavelet was determined to be 80 Hz from the spectral plot. The peak frequency of the data ranges from 22 Hz to 35 Hz, while the dominant frequency is 31 Hz. Several wavelets were extracted from the seismic and wells by varying the frequencies, time windows, and frequency filtering algorithms. The same procedure described for the synthetic trace model above was applied to the entire volume, which resulted in an expanded bandlimit of 170 Hertz as shown in Figure 17 below.  
The seismic trace at the well (Inas-1) intersection was extracted from both the enhanced and original trace for further comparison. In Figure 18, the synthetic seismogram generated from the same well was compared to both traces. The enhanced trace (Track 2) has a shorter wavelength, better resolution, and a stronger correlation to the synthetic seismogram (Track 1) than the original trace (Track 3). An overlay of both traces on the same track (Track 4) reveals a considerable difference in the magnitude of the reflection strengths (reflection coefficients), with the exception of a few reflectors where the magnitudes are equal, as indicated by the dotted black circles.  
Figure 17: Inas Field seismic data spectrum showing the original and enhanced spectral bandwidths.  
Figure 18: Comparison of the original (Track 3) and enhanced (Track 2) traces to the synthetic seismogram (Track 1). Track 3 shows an overlay of both traces. The horizontal black lines are the formation (correlation) tops across the synthetic and traces, and they match the seismic traces in time.  
At the reservoir interval, the resolution limit (or tuning thickness) of the seismic data was calculated to be roughly 31 m. Upon application of the algorithm, the resolution limit was substantially reduced to 10 m without changing the real reflection signals as shown in Figure 19a,b.  
Figure 20 also shows the seismic sections from the original (A) and improved (B) data. For instance, Figure 20B displays a channel-filled deposit (enclosed by the dotted red circle), whereas the channel is clearly hidden in Figure 20A due to insufficient resolution.  
Figure 19: A section of the original seismic volume at dip line—5215 (**a**) and a section through the improved seismic volume at dip line—5215 (**b**). The resolution of the enhanced volume is visibly improved and captures more geologic details when compared to the original data.  
Unresolved thin beds in the bandlimited data are shown to be well-resolved in the improved data (compare the white arrows in both sections, A & B). The composite trace blends the thin layer with other layers into a single loop when a bed is below the seismic tuning thickness. The dotted green arrow in Figure 20B indicates a stratigraphic pinch-out, but looks continuous in Figure 20A.