esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Mission and Satellite Design
### Imaging Modes
#### 2.3.2 Wide-Swath Mode  
The wide-swath mode operates the ScanSAR imaging technique in the burst or scanning mode and uses beam steering in the elevation direction to provide a larger image swath than that of the high-resolution (stripmap) mode. Owing to the pulse repetition frequency (PRF) timing relationship, the PRF limits the selection of the image swath. Moreover, three adjacent subswaths are continuously illuminated and combined into an image with a large swath to obtain an SAR image with a wide swath. However, the scanning ScanSAR yields a reduced synthetic aperture period than that of the stripmap mode, and the azimuth resolution is degraded. The schematic of the ScanSAR is presented on the right-hand side of Figure 2. As observed, this uses the same antenna beam as the high-resolution mode and  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **High-Resolution Mode** & **Wide-Swath Mode** \\ \hline Access region & 15\({}^{\circ}\)\(\sim\)35\({}^{\circ}\) & 15\({}^{\circ}\)\(\sim\)35\({}^{\circ}\) \\ Swath width & \(\geq\)5 km & \(\geq\)15 km \\ Resolution & \(\leq\)1 m @ 25\({}^{\circ}\) & \(\leq\)4 m @ 25\({}^{\circ}\) \\ NESZ & \(\leq\)\(-\)14 dB @ 25\({}^{\circ}\) & \(\leq\)\(-\)16 dB @ 25\({}^{\circ}\) \\ Peak sidelobe ratio & \(\leq\)\(-\)17 dB & \(\leq\)\(-\)17 dB \\ Integrated sidelobe ratio & \(\leq\)\(-\)12 dB & \(\leq\)\(-\)12 dB \\ Range ambiguity ratio & \(\leq\)\(-\)17 dB & \(\leq\)\(-\)17 dB \\ Azimuth ambiguity ratio & \(\leq\)\(-\)17 dB & \(\leq\)\(-\)17 dB \\ \hline \hline \end{tabular}
\end{table}
Table 2: S-STEP SAR payload key performance parameters.  
covers a width of approximately 5 km in all of the beams. Upon considering a 1% overlap between the adjacent beams, the wide-swath mode combines three adjacent subswatths to construct the required width of 15 km. In addition, this mode significantly reduces the power consumption by reducing the transmit duty cycle (<15%) and provides a resolution of 4 m with <160 MHz signal bandwidth.
## 4 Results
Figure 7e displays the IERs as a function of the lipid concentrations at different system gains. The IERs measured using 33 and 12 dB decreased from 6.65 \(\pm\) 0.23 to 3.97 \(\pm\) 0.10 and from 6.21 \(\pm\) 0.29 to 3.39 \(\pm\) 0.07, respectively, when the lipid concentrations were increased from 0% to 30%, respectively. Evidently, the proposed IER largely reduces the dependency of intensity measurement on the system gain factor and the measurement error, as shown in Figure 7f. Figure 7: Results of the EMD-based parameters as a function of lipid concentration obtained at various system gains. (**a**) \(C_{1}\) image intensity; (**b**) Measurement error for the \(C_{1}\) intensity; (**c**) \(C_{2}\) image intensity; (**d**) Measurement error for the \(C_{2}\) intensity; (**e**) IER; (**f**) Measurement error for the IER. The results indicated that the IER is less affected by the system gain because its measurement error is much lower than the errors for the \(C_{1}\) and \(C_{2}\) data. In particular, we found that the measurement errors further decreased when the gain increased, as shown in Figure 8. Figure 9 shows the B-mode and the corresponding \(C_{1}\) and \(C_{2}\) images of the simulated mass measured using different ultrasound frequencies. With an increaseinfrequency, the speckle patterns became more meticulous, resulting in an improved spatial resolution of theimage. The quantitative analysis of the image intensity and the IER as a function of ultrasound frequency is shown in Figure 10. The \(C_{1}\) intensity decreased from 48.6 \(\pm\) 6.94 to 24.2 \(\pm\) 1.08 when the ultrasound frequency increased from 5 to 8 MHz, corresponding to a mean decreasing rate of \(-\)16.67% per MHz. Forthe same frequency range, the \(C_{2}\) intensity decreased from 10.1 \(\pm\) 0.70 to 6.41 \(\pm\) 0.29 with a mean decreasing rate of \(-\)11.67% per MHz. Compared to the \(C_{1}\) and \(C_{2}\) intensities, the IER decreased from 4.90 \(\pm\) 0.93 to 3.78 \(\pm\) 1.33 in accordance with a mean decreasing rate of \(-\)8.67% per MHz. Figure 8: Measurement error as a function of system gain for various lipid concentrations. The measurement error decreases when the system gain increases. A higher gain is suggested to improve the measurement error of the IER. Figure 9: The B-mode and the corresponding \(C_{1}\) and \(C_{2}\) images of the simulated mass measured using different ultrasound frequencies from 5 to 8 MHz. ROI: region of interest.
###### Abstract  
The aim of this study is to assess the diagnostic performance of strain elastography (SE) versus 2D shear-wave elastography (2D-SWE) by providing a head-to-head comparison of the two methods. Ninety-four thyroid nodules were evaluated using conventional ultrasound (B-mode) and SE, namely, real-time elastography (RTE) with a Hitachi Preirus machine (Hitachi Inc., Tokyo, Japan) and consecutively, 2D-SWE with SuperSonic Mach30 equipment (Supersonic Imaging, Axen-Provence, France). The results were compared in all cases to the pathology reports. Out of the 94 nodules, 29 (30.9%) were malignant. Both SE and 2D-SWE parameters proved to have excellent diagnostic quality, with comparable results. The mean elasticity index was the best parameter for the 2D-SWE (AUC 0.912); for a cut-off value of 30.5 kPa, it predicts thyroid malignancy with a sensitivity of 79.3%, specificity of 95.38%, NPV of 91.2% and PPV of 88.5%. The best parameter for SE was the strain ratio (cutoff > 3.9; sensitivity 82.7%; specificity 92.3%; AUC 0.905). When integrated in the ultrasound risk algorithm, both elastography methods improved the diagnostic performance: AUC 0.764 vs. 0.886 vs. 0.861 for B-modes: B-mode + 2D-SWE vs. B-mode + SE. We concluded that elastography adds diagnostic value in predicting malignancy, both when Hitachi RTE and SuperSonic 2D-SWE were used.  
Keywords:risk stratification; thyroid cancer; strain; 2D shear wave; SWE; thyroid elastography +
Footnote †: journal: Medical Image and Image Processing
## 4 Simulation Results
### Reliability of Preliminary Read Operation  
As detailed in Section 3, the accuracy of the SIMPLY and sFALSE operations depends heavily on the ability to reliably read the stored data. In other words, the robustness of the SIMPLY logic scheme is strongly related to the sensing margin at node \(V_{\text{G}}\) during the preliminary read operation. For this reason, the simulation analysis focuses on the impact of process variations on \(V_{\text{G}}\) during the preliminary read operation.  
Figures 5 and 6 show \(V_{\text{G}}\) statistical distributions for both DMTJ- and SMTJ-based implementations of SIMPLY and sFALSE schemes at temperatures of 300 K and 77 K. The SIMPLY results are shown for different input combinations: **P** = **Q** = '0' (\"gray/black\" distribution in Figure 5a,b), **P\(\
eq\)Q** (\"orange/red\" distribution in Figure 5a,b), and **P** = **Q** = '1' (\"green/blue\" distribution in Figure 5a,b).  
The read margin (RM) is defined as the difference between the average VG values for the **P** = **Q** = '0' and **P** \(\
eq\)**Q** cases. As shown in Figure 5a, the SMTJ-based SIMPLY operating at 300 K presents an RM (RM\({}_{3\sigma}\)) of about 51 mV (36 mV). The RM improves when the SIMPLY operates at 77 K, reaching RM\({}_{3\sigma}\) values as high as 95 mV. A similar RM\({}_{3\sigma}\) of about 90 mV is obtained for the DMTJ-based SIMPLY operating at 77 K, as shown in Figure 5b. From Figure 6, the RM\({}_{3\sigma}\) of the SMTJ (DMTJ)-based at 300 K and 70 K are 63.8 mV (65.9 mV) and 146.3 mV (141.7 mV), respectively. The RM\({}_{3\sigma}\) improvement at 77 K is more than 2\(\times\) compared to the 300 K operating condition.
# Production Plant and Warehouse Automation with IoT and Industry 5.0
## 1 Introduction
### Industry 4.0
#### 1.2.4 Research Methodology and Selection Criteria  
The major intention behind this research is to discuss the automation of production plants and warehouses using IoT. The 4 IR has brought about tremendous change towards smart industries using IoT [3]. In this paper, the authors analyze the methods and technologies proposed by other researchers to better understand how autonomous machinescan be efficiently used in Industry 5.0, which further minimizes human intervention by introducing robots in the industries.  
For this review paper, the authors have performed a thorough examination of research that has already been performed. For the enhanced understanding of the effective usage of Industry 5.0, the authors have performed a broad analysis of the available material [23]. The authors have briefly discussed Industry 4.0 and IoT to set a strong base for our paper. Then, the authors have discussed the main pros and cons of the different technologies, frameworks, and approaches suggested or discussed in the papers under review.  
Comprehensive research is performed regarding all the selected material by reviewing different journals, websites, and conferences. These materials have been gathered from Google Scholar by entering keywords, such as \"IoT in the industry\", \"smart industry and IoT\", \"industrial 5.0\", and \"industrial automation and robots\". Table 1 shows the resources accessed for an in-depth knowledge about industrial automation, IoT, Industry 4.0, and 5.0.  
\"Internet-oriented\" refers to the technologies and protocols implemented to ensure the improvised networking of the physical devices and their connectivity on the internet. \"Things-oriented\" refers to the electronic devices (for instance, sensors, actuators, RFID, LTE, Z-WAVE, NFC, UWB, and M2M) connected to the internet. The semantic-oriented type deals with the challenges of data management, which occurs due to the immense information shared by the smart devices. Due to the improvements to wireless technologies, the IoT has gained significant popularity and has garnered the attention of the SCM community [24]. IoT has greatly contributed to industrial automation and allowed the incorporation of industrial sensor networks for various SCM functions. The term \"Industry 4.0\" has also been coined by the German economic development agency (GTAI), to indicate the 4 th Industrial Revolution, which is conceptually described as assimilating the IoT technology to automate the industrial manufactories process and enhance the intercommunication of the machines. This concept has proven to be a vital success criterion for providing various business benefits, such as the optimization of business operations and value chain activities. Therefore, the IoT will continue to enable various functions and operations in various industries, to make them as efficient and effective as possible [25; 26].
## 3 Material and Methods  
### Data Set Description  
The well-known database \"Competition IV 2a\" [31] is employed to train then to test our method. It enables the comparison of our results with those from state-of-the-art methods.  
The dataset includes recordings of EEG signals for 9 subjects when they were comfortably sitting and performing MI tasks. Data are taken from 22 EEG (named Fz, FC3, FC1, FC2, FC4, C5, C3, C1, Cz, C2, C4, C6, CP3, CP1, CPz, CP2, CP4, P1, Pz, P2, POz) and 3 EOG channels. The electrodes are arranged according to the standard 10 to 20 system across the scalp of the person.  
The subjects accomplish four MI tasks. These tasks represent the Left hand, Tongue, Feet, and Right Hand. Also, data are split up into short runs. It is worth noting that each run carries 48 trials of each activity of the MI.  
The collection of the data consists of two sessions in two days, where the session comprises six runs with a short break between them. Thus, for each MI activity, a total of 288 trials is collected.  
Each session is started by recording approximately 5 min of EEG data to estimate the influence of the 3 EOG channels. This recording is divided into 3 blocks: (1) 2 min with eyes open (looking at a fixing cross on the screen), (2) 1 min with eyes closed, and (3) 1 min with eye movements (Figure 1). Due to technical issues, the EOG block is shorter for Subject 4 and only contains the eye movement condition. The timing of data acquisition is shown in Figure 2. At the start of a test (t = 0 s), a fixing cross appears on the black screen. In addition, a short acoustic warning tone is emitted. After 2 s (t = 2 s), a cue in the form of an arrow pointing left, right, down, or up (corresponding to one of the 4 classes) appears and remains on screen for 1.25 s. This prompted subjects to perform the desired MI task until the fixation cross disappeared from the screen at t = 6 s. Finally, a short break with a black screen is used.  
Furthermore, the data were sampled at a frequency of 250 Hz and were filtered by a band pass-filter between 0.5 Hz to 100 Hz. In order to remove line noise, another 50 Hz notch filter was utilized.
# Towards a Low-Cost Precision Viticulture System Using Internet of Things Devices
## 4 System Architecture
### Flow Diagrams of the Processes
#### 4.3.1 Monitoring Process  
When the system needs to monitor the area, the different sensors forward the data packets to the monitoring nodes. The sensors are connected with the monitoring node through wires and one monitoring node can have multiple sensors. When the monitoring node receives the data, it checks if there is any difference from the previous time each sensor report data. This is necessary to minimize the required data transmissions. In this way, the data that are new and different from the previous time will be forwarded to the relay node, while the data that are the same with previous times will be ignored. The diagram of the monitoring process is shown in Figure 3.
## 3 Results and Discussion
The parameter used for the data analysis has been the reference value of 300 Bq/m\({}^{3}\) indicated by the Italian Legislation, Legislative Decree 101/2020 [22], which, in turn, implements the European directive 59/2013 EURATOM [23]. In the schools of Avellino and Benevento provinces, mean annual radon activity concentration values lower than the reference level were obtained. This can probably be justified by the prevalent presence of siliciclastic and alluvial/coastal rocks characterized by a low concentration of \({}^{238}\)U. Of the eight school buildings in the Caserta province, only two, CE05 and CE08, have annual mean values of 417 \(\pm\) 20 and 320 \(\pm\) 18 Bq/m\({}^{3}\), respectively. Investigating the location of the schools on a geological map, CE05 falls within a border area between the provinces of Naples, Caserta, and Benevento, characterized by volcanic rocks and many faults. On the other hand, CE08, on the northern borders of the province of Caserta, is located on a fault in the Roccamonfina complex, also made of volcanic rocks. The 26% of schools in the Napoli province show values higher than 300 Bq/m\({}^{3}\) and, as can be seen, falls in the area between Vesuvius and Campi Flegeri and in particular also on faults (e.g., NA08 1060 \(\pm\) 31 Bq/m\({}^{3}\)). In the municipality of Salerno, all schools presented values lower than the reference one being a mixed area of alluvial/coastal, siliciclastic, and carbonate rocks, however, only one school, SA06 337 \(\pm\) 49 Bq/m\({}^{3}\), is located on the border with the municipality of Naples on volcanic rocks. All results agree with the data reported on the radon potential map (Figure 1c). School buildings with a high concentration of radon gas activity have been subjected to more investigations to plan remedial actions, such as passive ventilation which is widely adopted due to its effectiveness [24], which is part of a wider range of solutions that are normally used [25; 26; 27]. Techniques for reducing indoor radon levels range from simply increasing the overall ventilation of the investigated environment to sealing radon entry points (e.g., cracks in the foundation, gaps around a sump pump), or also installing an active soil depressurization system that actively draws radon out from under the basement and vents it outside where it quickly dilutes to harmless levels. The students learned that the first and effective remedial action, and one that is immediate to implement, is the increase of the ventilation rate. This aspect highlights the importance of the RadioLab project in disseminating knowledge of radioactivity and the related hazards in order to make students aware of how to apply safety measures and take necessary action to control  
Figure 1: Comparison among (**a**) the maps of indoor radon activity concentration in schools, (**b**) geological map and fault network of the Campania region (adapted from [21]), and (**c**) radon potential map (adapted from [17]). risk and hazards. In conclusion, it is worth mentioning that the obtained map in Figure 1a is part of a larger project of indoor radon mapping in Campania [28].
# Dual-Dimensional Gaussian Splatting Integrating 2D and 3D Gaussians for Surface Reconstruction
## 3 Methods
### Gaussian Splatting  
To render a Gaussian, it is necessary to compute the intersection between the ray and the Gaussian in its local coordinate system. The Gaussian value at the intersection point \(\mathbf{p}=(u,v,w)\) is computed as follows:  
\[G=\exp\biggl{(}-\frac{\|\mathbf{p}\|^{2}}{2}\biggr{)}=\exp\biggl{(}-\frac{u^{ 2}+v^{2}+w^{2}}{2}\biggr{)}. \tag{4}\]  
In contrast to 2 DGS, which uses only flat 2D Gaussians, our method employs both 2D and 3D Gaussians, necessitating more complex computations. To address this issue, we adopt the dual Plucker representation, following the approach of [PERSON] et al. [33]. Given an image-space coordinate \((x,y)\), the corresponding ray can be represented as the intersection of two mutually orthogonal homogeneous planes, defined as the x-plane \(\mathbf{h}_{x}=(-1,0,0,x)^{T}\) and the y-plane \(\mathbf{h}_{y}=(0,-1,0,y)^{T}\). Given the world-to-screen transformation matrix \(\mathbf{W}\), these planes are transformed into the Gaussian's local space as follows:  
\[\begin{split}\mathbf{h}_{u}=(\mathbf{W}\mathbf{H}^{*})^{-1} \mathbf{h}_{x}=(\mathbf{W}\mathbf{H}^{*})^{T}\mathbf{h}_{x},\\ \mathbf{h}_{v}=(\mathbf{W}\mathbf{H}^{*})^{-1}\mathbf{h}_{y}=( \mathbf{W}\mathbf{H}^{*})^{T}\mathbf{h}_{y}.\end{split} \tag{5}\]  
Here, \((\mathbf{W}\mathbf{H}^{*})^{T}\) is equivalent to \((\mathbf{W}\mathbf{H}^{*})^{-1}\) when applied to homogeneous planes [34].  
The intersection of the two planes in the local space is expressed as a dual Plucker line \(\mathcal{L}^{*}=(-\mathbf{m}:-\mathbf{d})\). In this representation, \(\mathbf{d}\) represents the direction of the line, obtained as the cross product of the plane normals, while \(\mathbf{m}\) is the moment vector derived from the plane normals and offsets. The two components of the line are computed as follows:  
\[\begin{split}\mathbf{d}=\left(h_{u}^{0},\ h_{u}^{1},\ h_{u}^{2} \right)\times\left(h_{v}^{0},\ h_{v}^{1},\ h_{v}^{2}\right),\\ \mathbf{m}=h_{u}^{3}\cdot\left(h_{v}^{0},\ h_{v}^{1},\ h_{v}^{2} \right)-h_{v}^{3}\cdot\left(h_{u}^{0},\ h_{u}^{1},\ h_{u}^{2}\right).\end{split} \tag{6}\]  
The intersection between the ray and the Gaussian in the local space is defined as the orthogonal projection of the Gaussian center onto the line \(\mathcal{L}^{*}\)[33]. The projected point \(\mathbf{p}\) is given by the following:  
\[\mathbf{p}=\frac{\mathbf{d}\times\mathbf{m}}{\|\mathbf{d}\|^{2}},\quad\| \mathbf{p}\|^{2}=\frac{\|\mathbf{d}\times\mathbf{m}\|^{2}}{\|\mathbf{d}\|^{4} }=\frac{\|\mathbf{m}\|^{2}}{\|\mathbf{d}\|^{2}}. \tag{7}\]  
The intersection point \(\mathbf{p}\) enables both Gaussian value computation and depth retrieval via the homogeneous transform.
## 2 Materials and Methods
### Metagenome Data Integration  
The raw reads from the metagenome sequencing were used to generate clean reads by removing adaptor sequences, trimming and removing low-quality reads (length < 50 bp, or with a quality value < 20 or having N-bases) using the fastp [18] ([[https://github.com/OpenGene/fastp](https://github.com/OpenGene/fastp)]([https://github.com/OpenGene/fastp](https://github.com/OpenGene/fastp)), version 0.20.0, accessed on 1 December 2020) on the free online platform of Majorbio's Cloud Platform ([[https://cloud.majorbio.com/](https://cloud.majorbio.com/)]([https://cloud.majorbio.com/](https://cloud.majorbio.com/)), accessed on 1 December 2020). Megahit (parameters: kmer_min = 47, kmer_max = 97, step = 10) ([[http://www.l3-bioinfo.com/products/megahit.html](http://www.l3-bioinfo.com/products/megahit.html)]([http://www.l3-bioinfo.com/products/megahit.html](http://www.l3-bioinfo.com/products/megahit.html)), version 1.1.2, accessed on 6 December 2020) based on the principle of succinct de Bruijn graphs was used to assemble the optimized sequences [19]. Among the splicing results, contigs \(\geq\) 300 bp were selected as the final assembly result. MetaGene was used to predict the open reading frames (ORFs) of contigs in the stitching results ([[http://metagene.cb.k.u-tokyo.ac.jp/](http://metagene.cb.k.u-tokyo.ac.jp/)]([http://metagene.cb.k.u-tokyo.ac.jp/](http://metagene.cb.k.u-tokyo.ac.jp/)), accessed on 11 December 2020) [20].  
The predicted ORFs with lengths over 100 bp were retrieved and translated to amino acid sequences using the NCBI translation table ([[https://www.ncbi.nlm.nih.gov/Taxonomy/taxonomyhome.html/index.cgi?chapter=tgencodes#SG1](https://www.ncbi.nlm.nih.gov/Taxonomy/taxonomyhome.html/index.cgi?chapter=tgencodes#SG1)]([https://www.ncbi.nlm.nih.gov/Taxonomy/taxonomyhome.html/index.cgi?chapter=tgencodes#SG1](https://www.ncbi.nlm.nih.gov/Taxonomy/taxonomyhome.html/index.cgi?chapter=tgencodes#SG1)), accessed on 15 December 2020). CD-HIT ([[http://www.bioinformatics.org/cd-hit/](http://www.bioinformatics.org/cd-hit/)]([http://www.bioinformatics.org/cd-hit/](http://www.bioinformatics.org/cd-hit/)), version 4.6.1, accessed on 15 December 2020) was used to classify all sequences with 90% sequence identity and 90% coverage as nonredundant gene catalog [21]. Reads after quality control were mapped to the representative genes with 95% identity using SOAPaligner ([[https://github.com/ShujiaHuang/SOAPaligner](https://github.com/ShujiaHuang/SOAPaligner)]([https://github.com/ShujiaHuang/SOAPaligner](https://github.com/ShujiaHuang/SOAPaligner)), version 2.21, accessed on 20 December 2020) [22], and gene abundance in each sample was evaluated.