esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of Low-Cost 3D Laser Scanning Technology
## 4 Problems of a Moving 2D LiDAR
### Problem II: Real-Time Performance
If a moving 2D LiDAR is used to scan a pedestrian walking through the field of view, the shape of the collected 3D point cloud may not be a human body, but a ribbon. The shapes of the moving objects cannot be shown by 3D point clouds collected by a moving 2D LiDAR, while the shapes of the moving objects can be shown by images collected by a camera. If a commercial 3D LiDAR, rather than a moving 2D LiDAR is used, things are much easier. The time required for one 3D scan of a commercial 3D LiDAR is generally very short (for example, the 3D scan frequency of Velodyne VLP-16 (Puck) is 5-20 Hz, and a 3D scan takes up to 0.2 s). Therefore, the 3D point cloud of the general moving objects in the environment can be built accurately by a commercial 3D LiDAR. Thus, the method of eliminating dynamic pixels used in the field of visual SLAM can be used, dynamic 3D point clouds can be recognized and eliminated by comparing similar frames [102], deep learning and YOLO [103] can also be used to recognize moving objects, and then the 3D point clouds that show the static background of the environment can be built. In [44; 45; 46; 47], the solution on how to deal with moving objects in the environment with the use of commercial 3D LiDAR (such as 3D LiDAR produced by Velodyne) is focused on. When it comes to the solution of how to deal with moving objects in the environment with the use of a moving 2D LiDAR, there are few related studies. In [42], the method to model the dynamic environment by a slow scanning LiDAR (that is, the frequency of 3D scanning is lower than 1 Hz) was studied. The prototype used is this literature is a pitching 2D LiDAR. By the method in [42], in the case of low update rate of 3D point cloud frame, dynamic 3D point clouds which corresponding to the moving objects in the environment are recognized and eliminated. It is worth noting that this method is applicable for non-rigid moving objects (that is, the shapes of the objects may be changeable), such as pedestrians. In [43], a pitching 2D LiDAR is used to identify and follow the leader in hilly terrain. For the recognition of moving objects of a moving 2D LiDAR, the method of identifying the leader in this literature may be worth learning. Besides, in [104; 105; 106], moving objects are recognized, according to the 2D maps collected by 2D LiDARs.
# Freshness Monitoring of Packaged Vegetables
## 4 Intelligent Packaging (IP)
### Data Carriers
#### 4.3.2 Barcodes  
Barcodes are considered one of the most popular data carriers due to its simplicity of use and low cost. These codes carry information about products through lines with different widths separated by spaces, and with numbers written underneath. The Universal Product Code (UPC) barcode is mostly used now. Barcodes are used to track the location of every package along the supply chain as each one has a unique UPC. This type of data carriers can also be integrated with sensors and indicators to check the freshness of packaged vegetables through erasing or amending the barcode to make it unreadable.  
There are many other examples that uses barcode labeled antibodies as biosensors. For example, a barcode capable of sensing the presence of pathogens and identifying whether a product is contaminated or not, known as the \"Food Sentinel System\", is shown in Figure 17a. This system uses two barcodes, one for the information about the product which is readable by barcode reader, and another for contamination code symbol which is usually unreadable in case of safe food. The antibodies are labeled and made in the form of a membrane attached to a part of the barcode. When contamination is detected, one barcode will be unreadable and the other readable. This is because lines appear when antigens bind to the antibodies on the barcode or disappear due to their dissociation from the substrate in case of bacterial metabolite being present [30; 105; 195; 196]. This system uses labeled antibodies on the membrane of the barcode to identify specific antigens. Another version of this technology is based on turning the membrane containing antibodies into red ink when a pathogen attaches to it, and so would not be readable by scanners. An illustration of this indicator is shown in Figure 17b [196; 197].  
A microbial TTI placed over barcode, known as TRACEO(r) has the labelled barcode impregnated with lactic acid bacteria that changes the color of the barcode from transparent to opaque due to time expiration or indication of high temperature exposure of the package. Thus, this barcode will become unreadable showing that produce is no longer safe to consume [105].
## 3 Results and Discussion
### Oil Recovery  
Oil recovery was slightly higher in the experiments that did not involve CO\({}_{2}\) injection, as displayed in Figure 11 and Table 4. Higher recovery occurred during the second and third steps of core flooding processes and could be attributed to pore blocking, disjoining pressure, wettability alteration, and interfacial tension reduction. The cores used in the experiments had high permeability. In such cases, the flow of the injected fluids tended to move from the inlet to the outlet through the widest channel. This phenomenon meant that oil droplets in the smaller pore channels were not produced, especially as the cores were initially oil wet. This phenomenon was observed at the end of the LSW flooding when the oil stopped being produced and the differential pressure almost stabilized, as previously displayed in Figure 7.  
When the nanofluid injection was initiated, the pore-plugging effect of the nanoparticles occurred with the first few PVs, which caused the nanofluid to flow into the pores in which oil was trapped. This phenomenon was evident by the increase in the differential pressure and absence of oil in the effluent with the first few PVs. The attachment and spreading of nanoparticles onto the pore surface allowed the nanoparticles to exert disjoining pressure on oil droplets, separating them from the rock surface. The attachment of nanoparticles also alters the wettability of the calcite from oil wet to intermediate wet. The wettability alteration test result is displayed in Figure 12, and it is consistent with the results of [PERSON] et al. [10]. Finally, interfacial tension reduction effectively increased oil recovery because of the presence of both the CTAB surfactant and Al\({}_{2}\)O\({}_{3}\) nanoparticles themselves.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Experiment’s** & \begin{tabular}{c} **Oil Recovery Due** \\ **to Low-Saliinity** \\ **Water Injection (\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Oil Recovery Due to** \\ **Nanofluid and** \\ **Sweeping Fluid** \\ **Injections (\%)** \\ \end{tabular} &
\begin{tabular}{c} **Overall Oil** \\ **Recovery** \\ \end{tabular} \\ \hline Nanofluid alternating CO\({}_{2}\) injection & 40.03 & 10.93 & 50.93 \\ Nanofluid injection & 38.79 & 14.13 & 52.92 \\ Low-pH nanofluid injection & 37.77 & 15.11 & 52.88 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Oil recovery results of each experiment.  
Figure 11: Cumulative oil recovery during all core flooding experiments.  
In the experiments with greater permeability damage, more nanoparticles were retained in the core, and more nanoparticles spread on the surface, which enhanced the pore plugging, disjoining pressure, and wettability alteration effects. Therefore, oil recovery was enhanced in the cases in which the permeability damage was higher.
## 2 Research Aim  
The aim of this article is two-fold. First, to present a methodological framework for delineating previously unknown subsurface archaeological features using very high-resolution satellite imagery (at a sub-meter scale). An emphasis is placed on the application of edge-detection spatial filtering and spectral-enhancement indices, particularly those looking at vegetation marks and iron oxide levels. Second, to construct a realistic georeferenced 3D model of the buried structures detected using remote sensing techniques. This 3D reconstruction is primarily intended to help scientists and the public to conceptualize what the buried structures may have looked like in antiquity. The present research is highly interdisciplinary in nature, integrating various aspects of archaeology, geospatial sciences, computer science, and earth science for one sole goal: to digitally conserve a threatened cultural heritage site.
# Spatiotemporal Feature Enhancement for Lip-Reading: A Survey
## 5 Research Trends
### Integration of Event Cameras and SNN  
The integration of event cameras and SNN offers a novel concept for processing event-driven data for enhancing spatiotemporal feature extraction. Event cameras capture changes in scenes at high temporal resolutions, providing a wealth of information about lip movements. However, the challenge lies in effectively processing this event-driven data with SNNs, which operate on different principles compared to traditional neural networks. Researchers must develop methods to bridge the gap between the data generated by event cameras and the computational requirements of SNNs. This includes addressing issues related to data representation, noise reduction, and the design of SNN architectures that can efficiently learn from event-based inputs. As this integration unfolds, it promises to unlock new levels of responsiveness and accuracy in lip-reading systems.
# FedQAS: Privacy-Aware Machine Reading Comprehension with Federated Learning
## 3 Proposed Approach
### Private Data Pipeline Module  
To train a model with a high level of accuracy, machine reading comprehension models require large datasets to 'learn' from, however, data might be sensitive and private. To preserve data privacy, different anonymization techniques have been used. The most relevant are k-anonymity [31], l-diversity [32], and t-closeness [33]. In k-anonymity, specific columns (e.g., name, religion, sex) are removed or altered (e.g., replacing a specific age with an age span). L-diversity and t-closeness are extensions of k-anonymity, which are used to protect attribute disclosure, these anonymization techniques are applied before data is shared for training. However, with the rise of AI, this form of anonymizing personal data is not enough to protect privacy because the data can often be reverse-engineered using machine learning to re-identify individuals [34]. In question answering systems, there might be sensitive documents, personal data that needs to be processed for MRC task, without exposing data. To handle this issue, we propose a question answering  
Figure 3: A paragraph from Wikipedia and three associated questions together with their answers, taken from the SQuAD dataset.  
Figure 2: Overview of the FedQAS architecture. The FedQAS approach is organized in three main logical layers, the first one is for collaborative privacy-preserving training, the second one is a federated question answering manager, and the third is for incremental learning and private data generation.  
system based on federated learning methodology to protect data leakage and ensure secure collaborative training. The proposed system follows a federated learning paradigm in which participating clients are required to train their local models and then share the gradient (model parameters) for an eventual aggregation strategy in a central server. This approach ensures input data privacy, enables collaborative training, low-cost training by distributing the workload across clients instead of training a large model individually, sharing the local learning model within the alliance (training clients) without compromising private data, and improving local learning by using incremental learning and local data generation pipeline.
## 3 Results
### Q-PCR Validation of the Gene Expression Levels Related to Flavonoid Biosynthesis  
To further confirm the reliability and repeatability of the transcriptome data, this study conducted a Q-PCR analysis on the seven structural genes involved in the biosynthetic pathway of flavonoids. The findings revealed a consistent relative expression of these genes with their corresponding FPKM values obtained from the transcriptomic data (Figure 8).  
Figure 6: Correlation network diagram of DEGs and DAFs shared by the three ecotypes. Blue lines indicate a negative correlation between DEGs and DAFs, while gray lines indicate a positive correlation. Octagons, triangles, and circles are used to represent structural genes, TFs, and flavonoid metabolites, respectively.  
Figure 7: Flavonoid biosynthesis pathway in response to low-temperature stress in sandrice. The square and circle represent enzyme and flavonoid metabolite, respectively, and the three adjacent columns represent the three different ecotypes: DL, AEX, and DK. Red indicates that enzymes or flavonoid metabolites of the cold-treatment group are up-regulated compared to the control group, blue indicates that those are down-regulated, and white indicates that those had no significant change. Bolded black circles indicate more abundant metabolites. PAL, phenylalanine ammonia lyase; 4 CL, 4-comarate-CoA ligase; C4H, Cinamate-4-hydroxylase; CHS, Chalcone synthase; CHI, Chalcone-flavanone isomerase; FNS, flavone synthase; CYP5B1, Flavonoid 3′-monooxygenase; F3H, Flavanone 3-dioxygenase; FLS, Flavanol synthase; UGT7B1, UDP-glycostransferase 76B1; OMT, O-methyltransferasezerase; DFR, Dihydroflavonol-4-reductase; ANR, Anthocyanidin reductase; LAR, Leucanthocyanidin reductase; and RT, Anthocyanidin-3-O-glucose rhamnosyltransferase.
# Analysis of the Near-Ground Wind Field Characteristics during Typhoon Soulik
## 4 Quantitative Analysis of Fluctuating Wind Properties
### Variations in the Crest Factor  
The instantaneous intensity of fluctuating wind can be described by the crest factor, which has the following expression:  
\[g_{u}=\left(\overline{U_{t_{g}}}-U\right)/\sigma_{i} \tag{17}\]  
where \(\sigma_{i}\) represents the longitudinally fluctuating wind velocity standard deviation and \(\overline{U_{t_{g}}}\) represents the average highest velocity of the wind for that interval of time \(t_{g}\).  
Figure 13 shows the correlation among gust factor, average wind speed, and height. From Figure 13a, at heights of 15, 27, 53, 67, and 82 m, the variations in the peak factor with respect to the average velocity of the wind are comparatively dispersed. As the average wind velocity is below 6 m/s, the peak factor diminishes as the average wind speed rises and thereafter stabilizes at a value of around 1.5. From Figure 13b, it can be seen that there is a progressive decline in the peak factor as the observation height increases. The peak factor has an average value of 1.87 at 15 m and an average value of 1.67 at 82 m.  
To study whether fluctuating wind speeds under the influence of a typhoon follow a Gaussian distribution, Figure 14 depicts the probability density distributions of longitudinal fluctuating wind speeds at heights of 15, 53, and 82 m. Gaussian function distribution curves are obtained using moment estimation. From the figure, it can be observed that the probability density distributions of longitudinal fluctuating wind speeds align well with Gaussian function curves, indicating that the probability density distribution of fluctuating wind speeds during Typhoon Soulik conforms to a Gaussian distribution. Meanwhile, the probability density distributions of transverse and vertical fluctuating wind speeds are consistent with Gaussian distributions which are not shown in this paper.  
Figure 13: The change in crest factor: (**a**) Average wind speed and (**b**) Height.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Typhoon** & **Height (m)** & \(a\) & \(b\) \\ \hline  & 15 & 0.862 & 1.291 \\ _Soulik_ & 53 & 0.759 & 1.203 \\  & 82 & 0.718 & 1.361 \\ \hline \end{tabular}
\end{table}
Table 5: Fitting parameters \(a\) and \(b\).
# A Machine Learning Approach for Solar Power Technology Review and Patent Evolution Analysis
## 4 Solar Technology Patent Review and Analysis
### Statistical Analysis of Patent Metadata
The leading International Patent Classification (IPC) is H02S which involves converting infrared radiation, visible light or ultraviolet light to generate electrical power. The second IPC, H02J, relates to circuit devices or systems for power supply or distribution, and electrical energy storage systems. The third IPC, H01L, is related to semiconductor devices or electric solid devices not included in other categories. The fifth most important IPC, F24J, relates to heat generation devices not included in other categories. Detailed IPC statistics show that the leading IPC is H02S004044, which is a method of utilizing thermal energy such as a system generating both warm water and electricity. The secondleader, H02J00735, is a photosensitive battery. The third is H02S004042, which is a cooling method, the fourth is H02J000700 which is circuit device for charging or depolarizing a battery pack or for supplying power from a battery pack to a load. These statistics show that the solar power technology is trending toward transforming solar power into thermal energy by hydroelectric systems. Both H02S and H02J IPCs account for the most patent technologies. A01G appears in the top IPCs but not in the top rank of total IPCs which relate to horticulture, flower cultivation and watering systems. Plant cultivation is becoming one of the most popular applications of solar power. More detailed IPC analysis shows that H02S004042, H02S004044 and H02J00735 are the three dominant technologies which are also in the top three IPCs. In addition to IPC, the statistical results of the Cooperative Patent Classification (CPC) system was also analyzed [66]. Since 2013, The European Patent Office (EPO) and the United States Patent and Trademark Office (USPTO) officially implemented the CPC, which has become a global patent classification system. The leading CPC Y02E001060 relates to thermal-PV hybrids technologies, the second CPC Y02E001050 relates to PV energy, the third CPC Y02E001044 relates to heat exchange systems, and the fourth most important CPC relates to solar thermal, hybrid systems, PV systems with concentrators and solar thermal energy. The CPC trend of solar technology shows that development is gradually turning to thermal-PV hybrids technologies instead of individual PV or thermal technologies commonly used over the past 10 years.
## 4 Discussion
Processing and combining multispectral infrared images that were acquired by a variety of satellite sensors with different spatial characteristics and acquisition times allowed for us to derive the radiant heat flux from 28 March at 01:10 to 1 April at 20:30, finding a peak of thermal activity of \(\sim\)1.1 GW on 31 March at 12:45 (Figure 3e). Poor weather conditions, as well as the limited time duration and small magnitude of the event, did not allow for the calculation of the TADR and volume of the lava overflow erupted on 28 March, but only that erupted from 30 to 31 March, obtaining a peak of TADR of \(\sim\)2.3 m\({}^{3}\) s\({}^{-1}\) on 31 March at 12:45 and a volume of \(\sim\)53 \(\times\) 10\({}^{3}\) m\({}^{3}\) for the 30-31 March overflow. Because it emplaced in 23 h, the Mean Output Rate (MOR) results \(\sim\)0.65 m\({}^{3}\) s\({}^{-1}\). Using a ML approach, we were able to estimate a cumulated area over which the two lava overflows emplaced, which amounts to 94,500 m\({}^{2}\). From DEM difference, we retrieved a total average bulk volume of \(\sim\)179 \(\times\) 10\({}^{3}\) m\({}^{3}\) emplaced between 8 October 2019 and 7 April 2020, which reduces to a DRE volume of \(\sim\)136 \(\times\) 10\({}^{3}\) m\({}^{3}\) when considering an average lava vesicularity of 25% [111]. This value includes four overflows, which all occurred in the same area, on 18 January, 3 February, 28 March and 30-31 March 2020. Comparing it to the satellite-derived estimate for 30-31 March, we found a DRE cumulative volume of \(\sim\)83 \(\times\) 10\({}^{3}\) m\({}^{3}\) emitted during the three previous events. Due to the comparable duration (a few hours) of the overflows occurred on 18 January, 3 February, and 28 March, it is plausible to divide this volume equally, obtaining \(\sim\)27.5 \(\times\) 10\({}^{3}\) m\({}^{3}\) per each eruptive episode. Summing up this value with the one derived from multispectral infrared satellite images for the 30-31 March, a lava volume of \(\sim\)80.5 \(\times\) 10\({}^{3}\) m\({}^{3}\) could have been emitted during 28 March-1 April, eventually providing an average thickness of 0.85 m.
# A Combined Elevation Angle and C/N0 Weighting Method for GNSS PPP on Xiaomi MI8 Smartphones
## 2 Correlation of CMN, Elevation Angle and C/N0
In summary, it is obvious that the CMNs have a negative correlation with the elevation angle and C/N0. In contrast, their correlation to the C/N0 is stronger than the elevation angle. Figure 5 shows the relationship between the C/N0 and elevation angle for quad-constellation GNSS at L1/G1/B1/E1 frequencies. A second-order polynomial fitting curve is employed to reflect the correlation between the C/N0 and elevation angle. As can be seen, their correlation is obvious since the C/N0 increases as the elevation angle increases. Figure 4: CMN mean value statistics against the elevation angle (**a**) and C/N0 (**b**). Figure 3: CMN of dual-frequency GPS and Galileo satellites against elevation angles (**a**) and C/N0 (**b**) at L1/E1 frequencies.
## 3 Characterization of Invariant Relative Orbits
### A Guidance Strategy for the Injection into a Bounded Orbit  
The model defined in the previous section allows us to derive a simple guidance strategy to transfer a deputy satellite moving along a relative drift trajectory into a boundedorbit about the chief satellite. At a given time \(t\), the deputy satellite is moving along a drift trajectory if \(w_{1}(t)\
eq 0\) and \(z_{1}(t)\
eq 0\). The satellite can be transferred into a bounded orbit after introducing \(\delta w_{1}\) and \(\delta z_{1}\) such that  
\[\begin{cases}w_{1}(t)+\delta w_{1}=0\\ z_{1}(t)+\delta z_{1}=0\end{cases} \tag{35}\]  
The conditions set by Equation (35) can be expressed in terms of relative position and velocity components and the instantaneous change in velocity required to establish the boundedness condition can be derived. First, the inverse of Equation (29) allows expressing \(\delta w_{1}\) and \(\delta z_{1}\) as a function of the instantaneous changes in \(P_{x}\) and \(P_{y}\), indicated as \(\delta P_{x}\) and \(\delta P_{y}\).  
\[\begin{cases}\delta w_{1}=-2\dfrac{c_{2}}{c_{2}c_{3}-c_{1}^{2}}\delta P_{x}- \dfrac{c_{1}}{c_{2}c_{3}-c_{1}^{2}}\delta P_{y}=-w_{1}(t_{0})\\ \delta z_{1}=-2\dfrac{c_{1}}{c_{2}c_{3}-c_{1}^{2}}\delta P_{x}-\dfrac{c_{3}}{ c_{2}c_{3}-c_{1}^{2}}\delta P_{y}=-z_{1}(t_{0})\end{cases} \tag{36}\]  
Then Equation (36) can be solved in \(\delta P_{x}\) and \(\delta P_{y}\) and the \(\Delta v\) components can be determined, introducing Equation (4) in the solution.  
\[\begin{cases}\delta v_{x}=\dfrac{c_{3}w_{1}(t_{0})-c_{1}z_{1}(t_{0})}{2}\\ \delta v_{y}=c_{2}z_{1}(t_{0})-c_{1}w_{1}(t_{0})\end{cases} \tag{37}\]  
Finally, it is possible to determine the instant in which to operate the maneuver to minimize the total \(\Delta V=\sqrt{\delta v_{x}^{2}+\delta v_{y}^{2}}\). Recalling Equations (32), it is straightforward to determine that the time \(t^{\star}\) minimizing \(\Delta V\) is  
\[\begin{cases}t^{\star}=\dfrac{1}{4\kappa}\log\dfrac{z_{1}^{2}(t_{0})\bigg{(} \dfrac{c_{1}^{2}}{4}+c_{2}^{2}\bigg{)}}{w_{1}^{2}(t_{0})\bigg{(}c_{1}^{2}+ \dfrac{c_{3}^{2}}{4}\bigg{)}}&\leftrightarrow\quad z_{1}^{2}(t_{0})\bigg{(} \dfrac{c_{1}^{2}}{4}+c_{2}^{2}\bigg{)}>w_{1}^{2}(t_{0})\bigg{(}c_{1}^{2}+ \dfrac{c_{3}^{2}}{4}\bigg{)}\\ t^{\star}=t_{0}&\leftrightarrow\quad z_{1}^{2}(t_{0})\bigg{(}\dfrac{c_{1}^{2} }{4}+c_{2}^{2}\bigg{)}<w_{1}^{2}(t_{0})\bigg{(}c_{1}^{2}+\dfrac{c_{3}^{2}}{4} \bigg{)}\end{cases} \tag{38}\]