esa-sceva/satcom-chunk-collection huggingface dataset:
# A Testbed for GNSS-Based Positioning and Navigation Technologies in Smart Cities:
## 4 Hansel Services  
All the RESTful services implementing the technologies introduced in Section 2 are mapped in the high-level scheme in Figure 1 and described in this section. A set of ancillary tools (e.g., VIZ, CAS) were contextually designed and included in the _testbed_ services to complement the framework by supporting research, development, and optimization activities on current and further GNSS and non-GNSS technologies.  
Figure 1: HANSEL _testbed_ high-level services scheme. Grey boxes in the set on the left represent Docker containers embedding the services running on the _testbed_ server. The boxes included in the set on the right shows the three AndroidTM Applications with the links to the respective service.  
All the services can be configured and monitored in real-time through the _testbed_ url through a front-end service which provides a Graphic User Interface (GUI) for the whole framework, namely Front-end/Back-end Service (FBS).
## 3 Proposed Method
### Adaptive Channel Feature Fusion
#### 3.4.2 Multi-Scale Channel Feature Fusion  
We propose the ACFF module to process the acquired multi-scale feature maps. The module consists of an ACS module and two convolutional layers. Its structure is shown in Figure 2f. Using different sizes of convolutional kernels can extract features at different scales. Here, we use a convolutional layer containing 64 convolutional kernels of sizes \(7\times 1\) and \(5\times 1\) to extract local time-domain features and ultimately achieve ACFF at different scales. For the multi-channel feature map \(x_{1}\), assuming the size of \(C\times W\times H\), the whole process is formulaically expressed as  
\[f_{1}(x_{1})=\sum_{c=1}^{C}\sum_{r=1}^{7}x_{1}(c,\ i,j+r-1)W_{1}(c,k,r) \tag{16}\]  
\[f_{2}(x_{2})=\sum_{c=1}^{64}\sum_{r=1}^{5}x_{2}(c,\ i,j+r-1)W_{2}(c,k,r) \tag{17}\]  
\[y=f_{2}(f_{1}(x_{1})) \tag{18}\]  
where \(W_{1}\) and \(W_{2}\) denote the convolution kernels of the two convolutional layers, respectively, and \(x_{1}(c,i,j)\) denotes the value of input \(x_{1}\) in the c-th channel of row \(i\) and column \(j\). \(r\) denotes the convolution width. \(k\) denotes the number of convolution kernels, which is the number of output channels of the convolutional layer. Finally, after two convolution layers, the output feature is \(y\).  
In conclusion, the ACFF module performs multi-scale feature extraction and fusion of multi-channel feature maps. It can reduce the amount of computation while retaining important spatial information. Moreover, it can improve the efficiency and interpretability of the model, remove redundant information, and realize the fusion of features at different scales.
## 1 Introduction
### Twinbot Project  
The aim of the TWINBOT. ([[http://www.irs.uji.es/twinbot](http://www.irs.uji.es/twinbot)]([http://www.irs.uji.es/twinbot](http://www.irs.uji.es/twinbot))) project is to overcome the current limitations of cooperative interventions in the field of underwater robotics, enabling the operator to supervise robotic operations through a visual-guided control-based human-robot interface and to perform complex tasks while avoiding cognitive fatigue that brings low-level manual control, thus augmenting the autonomy and intelligence of the vehicles. A significant issue in this project is the necessity to communicate the robots in a wireless way, which requires the study of advanced underwater communication techniques.  
To accomplish this goal, it is necessary to evaluate the operator's interaction with the robot team, composed of a pair of I-AUVs (Intervention Autonomous Underwater Vehicles), which are intended to perform cooperative operations in complex underwater scenarios.  
Moreover, the cooperative underwater twin robots are intended to grasp and transport a big object (e.g., pipe) without the use of any umbilical and with the help of a multimodal human-robot interface (Figure 2). A third robot can be used to provide support such as enhanced point of views to the robot team and operator.  
The transmission of the images to the operator without the use of umbilical is possible thanks to an advanced compression algorithm with region-of-interest selection, which allows fo adaptation of  
Figure 1: Cooperative intervention at a European Center for Nuclear Research (CERN) experimental area.  
the image size to the currently available bandwidth via radio frequency and sonar channels, obtaining appropriated image quality with image sizes of several hundreds of bytes [5]. Current efforts are trying to extend the system to Visual Light Communications (VLC) modems.  
In order to enable partners experimentation before integration in the field, a set of simulation tools have been designed. UWSIM-NET [6] allows the simulation of cooperative interventions including mathematical models of real radio and sonar underwater transceivers, experimenting their network protocols through the NS3 platform as well. The realistic simulation used in this article takes as a basis the experience obtained from UWSIM-NET development. Moreover, previous projects such as FP7 TRIDENT [7] and MERBOTS [8] (Figure 3), in collaboration with the University of Girona (UdG) and the University of Balearic Islands (UiB), have allowed the current state of the research.
## 2 Related Work
### Vehicle Detection
#### 2.1.3 Single-Stage Method  
Single-stage detectors use dense sampling to classify and localize objects in a single step, unlike two-stage detectors. The inference speed of single-stage models is generally faster than two-stage detectors, as they do not have to generate regional proposals. This makes them more suitable for real-time applications, such as detecting vehicles in real time.  
[PERSON] [2] proposed YOLO algorithm, in which classification and bounding box regression are conducted simultaneously. At that time, most convolutional neural networks were significantly slower than YOLO. In a later version, YOLOv3 [3] utilized FPN-like feature pyramids to improve performance for smaller objects. And so far, YOLO series algorithms have developed many versions. Moreover, one single-stage detection model named the single-shot multibox detector (SSD) [4] uses a convolutional neural network to detect and classify objects in a picture in real time. LRTA-SP [17] effectively improves the efficiency and accuracy of small target detection in infrared videos under complex backgrounds by combining low-rank tensor approximation with saliency priors, utilizing a spatiotemporal tensor model, multi-rank constraints, and an alternating projection algorithm.
## 3 New Generation Sensor Web Enablement
### Evolvement of SWE Information Model  
The SWE information model comprises a set of standards which define data models primarily for the encoding of sensor observations as well as sensor metadata. For this purpose, the first generation of SWE contained three specifications: Observations & Measurements (O&M) [53, 54], the Sensor Model Language (SensorML) [55], and the Transducer Markup Language (TML) [56]. Figure 2 shows the evolvement of the first generation SWE information model to its current state. In the new generation SWE, O&M 1.0 which is used for the description of measured sensor data evolves to O&M 2.0 as described in Section 3.2.2. Also, the SensorML 1.0 standard advances to version 2.0. Although the work on SensorML 2.0 is still in progress, we outline the expected changes in Section 3.2.3. Further, the SWE Common data model, which defines data types shared by multiple SWE specifications, is extracted from the SensorML standard and is provided as a standalone specification called SWE Common 2.0 (Section 3.2.1).  
TML supports the encoding of sensor data as well as metadata by focusing on data streaming. TML has only been rarely used in practice and has not been further evolved so far. In the new generation of SWE specifications, TML is not referenced anymore and recent conversations in OGC's SWE working  
Figure 2: Evolvement of the SWE information model. _Green boxes_: specifications approved as standards (or in standardization process); _Beige boxes_: discussion papers; _Solid arrows_: “evolvement to”; _Dashed arrows_: “dependent on”.  
group showed that there is no urgent demand in TML and a retirement of the standard is in discussion. Hence, the authors do not see TML as part of the new generation SWE.  
Another new specification is the Event Pattern Markup Language (EML) [57]. It is used to define event patterns as processing rules for Complex Event Processing (CEP) and Event Stream Processing (ESP). These processing techniques can be implemented within services such as the Sensor Alert Service or the Sensor Event Service (Section 3.3.4).  
Next, the advancements of the SWE information model are analyzed. Table 1 summarizes the main changes from the first to the new generation of the SWE information model. These changes are detailed in the next sections.
# Benefits and Lessons Learned from the Sentinel-3 Tandem Phase
## 6 Understanding Inter-Sensor Differences
### OLCI Radiometric Differences and Homogenization  
For OLCI, the homogenization of the L1 radiance must be performed to adjust for the slight spectral differences between the two sensors. This homogenization corresponds to the smile correction step of the level 2 processing and is more sensitive for spectral channels having either high gradient of the solar irradiance (400 nm), fine absorption features (O\({}_{2}\) and H\({}_{2}\)O absorption bands), and/or sensitive ground spectral signatures (e.g., red-edge at 709 nm).  
Cross-comparisons over the homogenized reflectances provide evidence of a persistent radiometric bias between 2% (in the blue) and 1% (in the NIR) between the two sensors, see Figure 5. The S3A harmonization model was established to harmonize the products and validate the benefits for L2 products [21]. Further investigations highlight the necessity of performing flat-fielding prior to harmonization.
## 3 Methods
### Physical Layer of OFDM Communication System
#### 3.1.1 Wireless Channel  
A simplified wireless channel is well specified in [44; 46]. The time-domain received signal over a wireless channel can be defined as  
\[y_{cp}(n)=x_{cp}(n)\otimes h(n)+w(n), \tag{5}\]where \(y_{cp}(n),\ x_{cp}(n),\ h(n),\ w(n)\), and \(\otimes\) are the received signal, transmitted signal, channel impulse response, AWGN, and convolution operator, respectively, in the time domain. The frequency-domain received signal is obtained by DFT transformation of (5) as  
\[Y(k)=X(k)\cdot H(k)+W(k), \tag{6}\]  
where \(Y(k),\ X(k),\ H(k),\ W(k)\), and \(\cdot\) are the received signal, transmitted single, channel impulse response, AWGN, and element-wise product, respectively, in the frequency domain. AWGN with zero mean Gaussian distribution and uniform spectral density is often used to model the additive noise (for example, electric noise, thermal noise, interference) in the OFDM system. Therefore, the AWGN, \(w(n)\), from Gaussian distribution with zero mean and standard deviation, is given by  
\[w(n)=\begin{cases}\sigma\cdot\mathcal{N}_{N}(0,\sigma),\ For\ real\\ \sigma\cdot[\mathcal{N}_{N}(0,1)+j\mathcal{N}_{N}(0,\sigma)],\ For\ complex\,\end{cases} \tag{7}\]  
where \(\mathcal{N}_{N}(0,\sigma)\) is the Gaussian noise vector with 0 mean and standard deviation \(\sigma=\frac{N_{0}}{2}\), and \(N_{0}\) is the power spectral density.  
Signal transmitted over wireless channel experience reflection, diffraction, and scattering, leading to multiple versions of the same signal arriving at the receiver with different amplitude, phase, and delay. This phenomenon is known as the multipath fading effect and can be modelled as linear finite impulse-response (FIR) filter give as [47]  
\[y_{t}=\sum_{l=0}^{L-1}h_{l}\cdot x_{(t-l)},\ \text{where,}\ h_{l}=\sum_{k=1}^{K} \sqrt{\Omega_{k}}z_{k}sinc\left(\frac{\tau_{k}}{T_{s}}-l\right), \tag{8}\]  
vector \(\Omega\) and \(\tau\) are the power-delay profile (PDP) of fading process, and \(z_{k}\) and \(T_{s}\) are the complex-valued variable and sampling period of the discrete signal, respectively. \(L\) is the filter length chosen, such that \(|h_{l}|\) is small for \(l\geq L\) or \(l<0\). The effect of different channels on frequency-domain symbols of the proposed 16-QAM OFDM system is shown in Figure 2.  
For the multipath Rayleigh Fading channel, the real and imaginary part of \(z_{k}\) are independent and identically distributed (IID) Gaussian random variables, therefore \(|z_{k}|^{2}\) follows the Rayleigh Fading distribution. \(K\) is the number of paths in multipath fading channel and if \(K=1\) it is said to be flat fading with only on path, whereas, if \(K>1\) it is frequency selective fading with multipath interference. For  
Figure 2: Effect of Various Channel Models on 16-QAM Modulation Using LS Channel Estimation at \(\text{E}_{b}/\text{N}_{0}\) 15 dB under (**a**) the AWGN Channel and (**b**) the Rayleigh Fading Channel with AWGN.  
considered simple AWGN channel and Rayleigh Fading channel for the OFDM-based image communication system.
# Application-Specific Integrated Circuit of an Inter-IC Sound Digital Filter for Audio Systems
###### Abstract  
In digital audio systems, filters and equalizers are essential modules for audio improvement at the input and output stages. Due to their computational complexity, most audio tasks are processed with digital signal processors. Due to the fact that latency in audio systems is a critical specification and audio trends require higher sample rates, noise canceling, and bigger data sizes, having an independent high-resolution equalizer would reduce the computational power needed for audio systems. This research had the goal of designing and implementing a hardware architecture for a configurable filter bank based on finite impulse response (FIR) filters and a noise-cancellation stage with an inter-integrated circuit (I2C) communication interface, which allows the filter configuration. The system was implemented as a standalone integrated circuit (IC) for which its inputs were the inter-IC sound (I2S) bus control signals. The digital audio system was optimized to perform one-cycle convolutional operations by implementing a vector-vector arithmetic logic unit. Furthermore, this applied research provides the register transfer level description and the functional verification of the digital design, the system-on-chip (SoC) implementation in TSMC 180 nm technology, and the post-silicon validation with a printed circuit board for testing the output digital signals of the system.  
audio equalizer; noise cancellation; digital filter; FIR; I2S 2023
## 4 Application of the New Method in Fault Diagnosis of Rolling Bearing  
### Fault Mechanism and Characteristic Frequency  
When the rolling bearing is running, the main sources of vibration excitation include the following items: the error caused by installation and manufacture, the change of the axle center caused by the working load, the deformation of the contact position caused by a heavy load, and so on.  
Figure 6: Simulation results of different methods.  
These vibrations generate considerable noise, making it difficult to distinguish the vibration impact produced by the damaged part through the load area from them.  
The frequency of the impact is the corresponding fault characteristic frequency, which can be calculated from the rotation speed of the shaft, the size of the bearing and the location of the damage point. The key parameters are as follows: \(z\) is the number of rolling elements, \(d\) is the diameter of the rolling body, \(D\) is the diameter of bearing, \(\alpha\) is the contact angle, \(f_{r}\) is the frequency of the inner ring, and \(f_{a}\) is the frequency of the outer ring. The following is the calculation formula of the fault characteristic frequency in three cases of inner ring damage \(f_{i}\), outer ring damage \(f_{o}\) and rolling element damage \(f_{b}\).  
\[f_{i}=\frac{z}{2}\left|f_{a}-f_{r}\right|(1+\frac{d}{D}cos\alpha). \tag{11}\]  
\[f_{o}=\frac{z}{2}\left|f_{a}-f_{r}\right|(1-\frac{d}{D}cos\alpha). \tag{12}\]  
\[f_{b}=\frac{D}{2d}\left|f_{a}-f_{r}\right|(1-\frac{d^{2}}{D^{2}}cos^{2}\alpha). \tag{13}\]
## 3 Results
### Amplification during Absorbed Dose Variation  
The signal that was collected by an active area of 756 mm\({}^{2}\) (60 chips) was 89.23% linear to dose. The sensitivity that was attained with this area was 0.05503 mV/mGy. Nonetheless, the signal obtained with an active area of 1512 mm\({}^{2}\) (120 chips) was 90.58% linear to dose, hence a 1.52% increment in signal linearity to dose--as a result of increasing the active area by 756 mm\({}^{2}\). Similarly, there was an 89.3% increase in the sensitivity, i.e., the sensitivity increased from 0.05503 mV/mGy to 0.10417 mV/mGy. Therefore, a 1 mm\({}^{2}\) increase in the active area resulted in a 0.002% and a 0.12% linearity to dose and sensitivity increase, respectively, as in Figure 7a.  
Figure 6: A graph of signal augmentation with (**a**) Number of LED chips/sensitive area and (**b**) Amplifier Board amplification, during tube voltage variation (kVp).  
On the other hand, there was a 3.01% reduction in the linearity of the signal to dose when the amplifier board was applied for signal amplification. Nevertheless, sensitivity increase of 38.10% was observed; thus, the sensitivity increased from 0.10417 mV/mGy to 0.14386 mV/mGy. Therefore, amplification while using the amplifier board produced a 3.01 \(\times\) 10\({}^{-9}\)% reduction in the signal linearity to dose for every voltage gain. Nonetheless, every voltage gain produced a 3.81 \(\times\) 10\({}^{-8}\)% increase in the sensitivity to dose, as illustrated in Figure 7b.
# Anaerobic Digestate from Biogas Plants--Nuisance Waste or Valuable Product?
## 2 Materials and Methods
### Pot Experiment  
The pot experiment was conducted in an experimental greenhouse of the Warsaw University of Life Sciences. Soil samples (0-25 cm soil layer) were collected in Skierniewice (51\({}^{\circ}\)96'48\" N, 20\({}^{\circ}\)15'92\" E). The research covered soil fertilized with nitrogen, phosphorus, and potassium fertilizers without limiting since 1923. The soil can be described as Luvisol (FAO 2006). The pots were filled with 15 kg of soil and mixed with different DGBPs. DGBPs were applied in a dose corresponding to 170 kg N ha\({}^{-1}\) (0.68 g N pot\({}^{-1}\)). Doses of particular nutrients and heavy metals introduced to the soil with DGBPs are presented in Table 1.  
Figure 1: Matrix pelletizer (Testmer, Reguly, Poland).  
Figure 2: The digestate-based products (DGBPs): liquid fraction from DG1 and DG2 (LF1, LF2), solid fraction from DG1 and DG2 (SF1, SF2), pellets from DG1 and DG2 (PDG1, PDG2), and pellets from SF1 and SF2 (PSF1, PSF2).  
The fertilizer effect of the analyzed DGBPs was compared to that on control objects, which were not subject to fertilization. The experiment was arranged as a completely randomized design with four replications. The location of the pots was randomized daily. The test plant was maize of the Bosman cultivar cultivated for green forage. The pots were irrigated with distilled water up to a constant moisture at 60% water-filled pore space. Water was applied to the entire surface of the pots. The experiment was conducted in controlled growth conditions that included a day/night cycle of 16/8 h, with a day/night temperature of 25/19 \({}^{\circ}\)C and artificial lighting to complement daylight. After biomass harvest of maize, samples were weighed before and after drying (in an oven set at 60 \({}^{\circ}\)C) to determine their fresh and dry matter.  
Estimation of the Distribution of DM, FM, and Nutrients Flowing from Digestates into Solid Fraction (SF) and Liquid Fraction (LF)  
The distribution of dry matter (DM), fresh matter (FM), and nutrients flowing from digestates into solid fraction (SF) and liquid fraction (LF) were calculated as fol-lows according to the formulas:  
\[\mathrm{DM_{dstLF}}=\frac{\mathrm{FS_{DM}-DG_{DM}}}{\left(\frac{\mathrm{FS_{ DM}}}{\mathrm{FD_{DM}}}\right)-1} \tag{1}\]  
\[\mathrm{DM_{dstSF}}=\mathrm{DG_{DM}-DM_{dstLF}} \tag{2}\]  
\[\mathrm{FM_{dst(LF\ orSF)}}=\frac{\mathrm{DM_{dst(LF\ orSF)}}}{\mathrm{DM_{ cont(LF\ orSF)}}} \tag{3}\]  
\[\mathrm{N_{dst(LF\ orSF)}}=\mathrm{FM_{dst(LF\ orSF)}}\times\mathrm{N_{cont}} \tag{4}\]  
where:  
\(\mathrm{DM_{dstLF}}\)-distribution of DM (dry matter) into LF; \(\mathrm{DM_{dstSF}}\)-distribution of DM into SF; \(\mathrm{FM_{dst(LF\ or SF)}}\)-distribution of FM into LF or SF; \(\mathrm{N_{dst(LF\ or SF)}}\)-nutrients flow into LF or SF; \(\mathrm{N_{cont}}\)-nutrient content in LF or SF; \(\mathrm{FS_{DM}}\)-DM content in FS; \(\mathrm{DG_{DM}}\)-DM content in DG; \(\mathrm{LF_{DM}}\)-DM content in LF.
###### Abstract  
Ground-Based Synthetic Aperture Radar (GBSAR) has non-contact, all-weather, high resolution imaging and microdeformation sensing capabilities, which offers advantages in applications such as building structure monitoring and mine slope deformation retrieval. The Circular Scanning Ground-Based Synthetic Aperture Radar (CS-GBSAR) is one of its newest developed working mode, in which the radar rotates around an axis in a vertical plane. Such nonlinear observation geometry brings the unique advantage of three-dimensional (3D) imaging compared with traditional GBSAR modes. However, such nonlinear observation geometry causes strong sidelobes in SAR images, which makes it a difficult task to extract point cloud data. The Conventional Cell Averaging Constant False Alarm Rate (CA-CFAR) algorithm can extract 3D point cloud data layer-by-layer at different heights, which is time consuming and is easily influenced by strong sidelobes to obtain inaccurate results. To address these problems, this paper proposes a new two-step CFAR-based 3D point cloud extraction method for CS-GBSAR, which can extract accurate 3D point cloud data under the influence of strong sidelobes. It first utilizes maximum projection to obtain three-view images from 3D image data. Then, the first step CA-CFAR is applied to obtain the coarse masks of three-views. Then, the volume mask in the original 3D image is obtained via inverse projection. This can remove strong sidelobes outside the potential target region and obtain potential target area data by intersecting it with the SAR 3D image. Then, the second step CA-CFAR is applied to the potential target area data to obtain 3D point clouds. Finally, to further eliminate the residual strong sidelobes and output accurate 3D point clouds, the modified Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm is applied. The original DBSCAN method uses a spherical template to cluster. It covers more points, which is easily influenced by the strong sidelobes. Hence, the clustering results have more noise points. Meanwhile, modified DBSCAN clusters have a cylindrical template to accommodate the data's features, which can reduce false clustering. The proposed method is validated via real data acquired by the North China University of Technology (NCUT)-developed CS-GBSAR system. The laser detection and ranging (LiDAR) data are used as the reference ground truth to demonstrate the method. The comparison experiment with conventional method shows that the proposed method can reduce 95.4% false clustered points and remove the strong sidelobes, which shows the better performance of the proposed method.  
c 2023 by the authors.  
disruped under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)]([https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/))).  
c 2023137164. [[https://doi.org/10.3390/app13127164](https://doi.org/10.3390/app13127164)]([https://doi.org/10.3390/app13127164](https://doi.org/10.3390/app13127164))