esa-sceva/satcom-chunk-collection huggingface dataset:
# Behavior and Damage Characterization of Impulsively Loaded Cross-Laminated Timber Panels
## 2 Materials and Methods
### CLT Panel Specimens  
Three-laminae and five-laminae SPFS CLT panels produced by SmartLam and manufactured to meet the ANSI/APA PRG 320-12 V4 grade were used to produce specimens. The CLT panels supplied by SmartLam measured 4 ft (1.219 m) \(\times\) 8 ft. (2.438 m) and test specimens were cut from these panels. Test specimens were cut to measure approximately 16 in (406.4 mm) \(\pm\)\(\dfrac{1}{8}\) in (3.175 mm) wide and the lengths were cut to achieve the span-to-depth ratio of approximately \(5\leq L/h\leq 7\). The laminae were face-glued with polyurethane adhesive and finger joints were used for the lamellas where required. Laminae thicknesses, \(t_{l}\), are approximately 1.375 in (35 mm) with lamella widths, \(w_{l}\), at approximately 4 in (101.6 mm) yielding \(w_{l}/t_{l}=3\). Figure 1 shows the specimen cutting in process. Four plywood base plates, 4 in (101.6 mm) \(\times\) 16 in (406.4 mm), were adhered at the points where contact was made with the steel rods in the clamping assembly. A handheld two-pin moisture meter, DelmHorst BD-10, with a hammer electrode, DelmHorst 26-Es, was used to take moisture content readings at multiple locations for each specimen. The average moisture content for the specimens ranged from approximately 8% to 9%.
# Finite Difference Algorithm on Non-Uniform Meshes for Modeling 2D Magnetotelluric Responses
## 2 Governing Equations  
### Electromagnetic Equations  
Considering a right-handed coordinate system, with \(z\)-axis pointing downwards and \(x\)-axis along geo-electrical strike, and assuming time dependence as \(e^{-\mathrm{i}\omega t}\) and neglecting displacement currents, electromagnetic Maxwell's equations can be expressed in the frequency domain as [21]  
\[\
abla\times\mathbf{E}=\mathrm{i}\omega\mu\mathbf{H}, \tag{1}\]  
\[\
abla\times\mathbf{H}=\sigma\mathbf{E}. \tag{2}\]  
where \(\mathbf{E}\) is electric field and \(\mathbf{H}\) is magnetic field. \(\omega\) is an angular frequency (\(\omega=2\pi/T\), \(T\) is a period). \(\mu\) is the magnetic permeability in free space and \(\mu=\mu_{0}=4\pi\times 10^{-7}\) H/m. \(\sigma\) is the electric conductivity in (S/m) and is varied only in the direction of the \(y\)-axis and \(z\)-axis; i.e., \(\sigma=\sigma(y,z)\).  
For a 2D conductivity structure assuming the \(x\)-axis is the geo-electrical strike direction (i.e., \(\partial\mathbf{E}/\partial x\equiv 0\) and \(\partial\mathbf{H}/\partial x\equiv 0\)), expanding the curl operators in Equations (1) and (2), the governing Maxwell's equations are given by  
\[\left\{\begin{array}{ll}\frac{\partial E_{x}}{\partial y}-\frac{\partial E_ {y}}{\partial z}=\mathrm{i}\omega\mu H_{x},&\\ E_{y}=\frac{1}{\sigma}\frac{\partial H_{x}}{\partial z},&\text{TM-mode}\\ E_{z}=-\frac{1}{\sigma}\frac{\partial H_{x}}{\partial y}.&\end{array}\right. \tag{3}\]  
and  
\[\left\{\begin{array}{ll}\frac{\partial H_{x}}{\partial y}-\frac{\partial H_ {y}}{\partial z}=\sigma E_{x},&\\ H_{y}=\frac{1}{\omega\mu}\frac{\partial E_{x}}{\partial z},&\text{TE-mode}\\ H_{z}=-\frac{1}{\omega\mu}\frac{\partial E_{x}}{\partial y}.&\end{array}\right. \tag{4}\]  
These two modes are commonly referred to as transverse magnetic (TM) mode and transverse magnetic electric (TE) mode. We calculate the magnetic field \(H_{x}\) (TM-mode) or electric field \(E_{x}\) (TE-mode) parallel to the geo-electrical strike of the conductivity model. According to Equations (3) and (4), the associated partial differential equations can be written as  
\[\frac{\partial}{\partial y}(\frac{1}{\sigma}\frac{\partial H_{x}}{\partial y })+\frac{\partial}{\partial z}(\frac{1}{\sigma}\frac{\partial H_{x}}{\partial z })+\mathrm{i}\omega\mu H_{x}=0, \tag{5}\]  
\[\frac{\partial}{\partial y}(\frac{1}{\mathrm{i}\omega\mu}\frac{\partial E_{x}} {\partial y})+\frac{\partial}{\partial z}(\frac{1}{\mathrm{i}\omega\mu}\frac{ \partial E_{x}}{\partial z})+\sigma E_{x}=0. \tag{6}\]  
According to these relations, the magnetotelluric impedances for the TM and TE modes are  
\[Z_{yx}=Z_{TM}=\frac{E_{y}}{H_{x}},\quad Z_{xy}=Z_{TE}=\frac{E_{x}}{H_{y}}. \tag{7}\]The associated apparent resistivities and impedance phases can be calculated as  
\[\rho_{a,ij}=\frac{1}{\omega\mu}\big{|}Z_{ij}\big{|}^{2},\quad ij=xy,\quad ij=yx, \tag{8}\]  
\[\phi_{ij}=\arctan\left[\frac{\text{Im}(Z_{ij})}{\text{Re}\big{(}Z_{ij}\big{)}} \right]. \tag{9}\]
## 3 Design, Implementation and Test of the DXN Engine
### Why DXN  
Note that some of the scenarios to the issues raised here are illustrated in detail in Sections 3.4.1-3.4.3.  
Figure 1 shows a screenshot of SNs' generated/sent data received by the host server in our deployed LoRaWAN IoT network (for example, the first column in the screenshot contains the SN address to communicate with the relevant SN, the second column contains the address of the end node in case the message is being forwarded, plus other information such as energy level to estimate its remaining battery life, RSSI, location, etc.). If these kinds of data are fed to an AI-based engine, there is potential to enhance the QoS of the network by continuously analysing the collected data. An AI engine will be able to make decisions that can be fed back to the SNs to adapt/change their behaviour according to the  
connectivity status of the network at that time. This will overcome certain networking QoS issues, such as:  
1. When more than one middle/bridging SN is available nearby in the vicinity of an SN that is either blocked or out of GW reach, DXN will decide which of these middle/bridging SNs is used to forward through the messages of the blocked node. This will balance the connectivity amongst middle/bridging nodes without overusing certain bridging SNs more than others.
2. By analysing the transmission configurations used by each SN (for example, the used Spreading Factor (SF) or the used LoRaWAN frequency channel), DXN can change these configurations or update the frequency channels list for any SN that is affected by a harsh signal environment to prevent jitter or packet loss.
3. To maintain a healthy IoT network with minimum duty cycle restrictions, DXN can adjust the sleep/active times for each SN in the network based on its current transmission/relevance status, or the environment status. The rhythm of SN activity may change in some networks from time/environment to another, especially in agricultural applications, for example.
4. Furthermore, to point (3) above, by analysing the trend of the collected data, the activity of SNs could be predicted, and then \"activity\" will become an effective factor in SNs clustering for better network management, and load-balancing on any chosen middle/bridging node.
5. The AI engine productivity could be increased further by including simple extra information data within the SNs packets as inputs that will have a huge impact. However, including such extra information, data should be based on a considerate study to balance between the effort of the SNs and the improvement impact on the connectivity of the whole network. Examples of such data are:  
* Security, adding security details to help detecting/preventing certain attacks.
* Priority, including priority details to help deciding the response/process time compared to others.
* Type, including the SN type in the hybrid deployments, to help utilise each SN type efficiently.
* Location, including location details to help make decisions that suit certain areas, such as deploying extra or fewer SNs.  
Figure 1: Screenshot of the database used by DXN at the host server.
###### Abstract  
An unprecedented number of wildfire events during 2019 throughout the Brazilian Amazon caught global attention, due to their massive extent and the associated loss in the Amazonian forest -- an ecosystem on which the whole world depends. Such devastating wildfires in the Amazon has strongly hampered the global carbon cycle and significantly reduced forest productivity. In this study, we have quantified such loss of forest productivity in terms of gross primary productivity (GPP), applying a comparative approach using Google Earth Engine. A total of 12 wildfire spots have been identified based on the fire's extension over the Brazilian Amazon, and we quantified the loss in productivity between 2018 and 2019. The Moderate Resolution Imaging Spectroradiometer (MODIS) GPP and MODIS burned area satellite imageries, with a revisit time of 8 days and 30 days, respectively, have been used for this study. We have observed that compared to 2018, the number of wildfire events increased during 2019. But such wildfire events did not hamper the natural annual trend of GPP of the Amazonian ecosystem. However, a significant drop in forest productivity in terms of GPP has been observed. Among all 11 observational sites were recorded with GPP loss, ranging from -18.88 gC m\({}^{-2}\) yr\({}^{-1}\) to -120.11 gC m\({}^{-2}\) yr\({}^{-1}\), except site number 3. Such drastic loss in GPP indicates that during 2019 fire events, all of these sites acted as carbon sources rather than carbon sink sites, which may hamper the global carbon cycle and terrestrial CO: fluxes. Therefore, it is assumed that these findings will also fit for the other Amazonian wildfire sites, as well as for the tropical forest ecosystem as a whole. We hope this study will provide a significant contribution to global carbon cycle research, terrestrial ecosystem studies, sustainable forest management, and climate change in contemporary environmental sciences.  
A Amazon; 2019 Amazon Fire; GPP; CO: fluxes; MODIS; Brazil +
Footnote †: journal: Academic Editors: [PERSON], [PERSON] and [PERSON]
# A New Approach to Information Extraction in User-Centric E-Recruitment Systems
## 6 Conclusions and Future Work  
In this research, SAJ extracts context-aware information from job descriptions by exploiting Linked Open Data, job description domain ontology, and domain-specific dictionaries. SAJ enriches and builds context between extracted entities to minimize the information loss in the extraction process. It combines various processes together to achieve context-aware information extraction and enrichment from job description in e-recruitment. SAJ segments the text into predefined categories using a self-generated dictionary. Natural Language Processing (NLP) and a dictionary help in identification of entities. The extracted entities are enriched using Linked Open Data, and job context is built using a job description domain ontology. The enriched and context-aware information is stored in the knowledge base built using Linked Open Data principles. The evaluation has been performed on a data-set of 860 jobs, verified by HR experts. The initial assessment was conducted by a comparison between manually verified data and system extracted entities. SAJ achieved an overall f-1 of 87.83 %. In comparison with other techniques, OpenCalais and alchemy API, SAJ performed the best. OpenCalais was able to extract job titles and job requirements while Alchemy API was only able to extract job titles. SAJ can facilitate searching and retrieval, scoring and ranking of job candidates. In the future, the plan is to extend this work for automatic dictionary learning. This will enhance the dictionary by the addition of new rules learned from the text.  
Conceptualization, M.N.A.A. and K.L.; Methodology, M.N.A.A.; Software, M.N.A.A.; Validation, S.K. and K.L.; Writing--Original Draft Preparation, M.N.A.A.; Writing--Review & Editing, M.N.A.A., S.K., K.L. and A.M.K.; Visualization, M.N.A.A.; Supervision, S.K. and K.L.; Project Administration, S.K. and K.L.  
This research received no external funding.  
The authors declare no conflict of interest.  
Figure 11: Comparative analysis among SAJ and OpenCalais for requirements.
# An Overview of the Applications of Earth Observation Satellite Data: Impacts and Future Trends
## 3 Results
### RSIF for Different Satellites
The Sentinel and Gaofen satellite missions will have the most launches, and Sentinel data will become the most widely used EO satellite data. Data from the Chinese Gaofen missions are projected be widely applied. The RSIF value for Gaofen clearly stands out above the values for the other Chinese satellites, but there is a considerable gap compared to Sentinel, Landsat, and MODIS. Therefore, in the rest of this review, the similarities and differences between the  
Figure 3: Annual RSIF for Earth observation satellites (the graphic inside is a zoom of dense area, and the right figure is the result based on the linear extrapolation). application of Sentinel, Landsat, MODIS, Gaofen, and WorldView data, as described in the literature, will be discussed. The development of these excellent satellite missions will assist in the development of remote sensing applications and the findings of this article will promote the development of EO satellites.
# UAV Detection and Tracking in Urban Environments Using Passive Sensors: A Survey
###### Abstract  
Unmanned aerial vehicles (UAVs) have gained significant popularity across various domains, but their proliferation also raises concerns about security, public safety, and privacy. Consequently, the detection and tracking of UAVs have become crucial. Among the UAV-monitoring technologies, those suitable for urban Internet-of-Things (IoT) environments primarily include radio frequency (RF), acoustic, and visual technologies. In this article, we provide a comprehensive review of passive UAV surveillance technologies, encompassing RF-based, acoustic-based, and vision-based methods for UAV detection, localization, and tracking. Our research reveals that certain lightweight UAV depth detection models have been effectively downsized for deployment on edge devices, facilitating the integration of edge computing and deep learning. In the city-wide anti-UAV, the integration of numerous urban infrastructure monitoring facilities presents a challenge in achieving a centralized computing center due to the large volume of data. To address this, calculations can be performed on edge devices, enabling faster UAV detection. Currently, there is a wide range of anti-UAV systems that have been deployed in both commercial and military sectors to address the challenges posed by UAVs. In this article, we provide an overview of the existing military and commercial anti-UAV systems. Furthermore, we propose several suggestions for developing general-purpose UAV-monitoring systems tailored for urban environments. These suggestions encompass considering the specific requirements of the application scenario, integrating detection and tracking mechanisms with appropriate countermeasures, designing for scalability and modularity, and leveraging advanced data analytics and machine learning techniques. To promote further research in the field of UAV-monitoring systems, we have compiled publicly available datasets comprising visual, acoustic, and radio frequency data. These datasets can be employed to evaluate the effectiveness of various UAV-monitoring techniques and algorithms. All of the datasets mentioned are linked in the text or in the references. Most of these datasets have been validated in multiple studies, and researchers can find more specific information in the corresponding papers or documents. By presenting this comprehensive overview and providing valuable insights, we aim to advance the development of UAV surveillance technologies, address the challenges posed by UAV proliferation, and foster innovation in the field of UAV monitoring and security.  
Keywords:anti-UAV; UAV detection; UAV tracking; UAV dataset; anti-UAV systems +
Footnote †: journal: _sciences_  
_Applied_  
_Accepted: 10 October 2023_  
_Keywords_: 15 September 2023  
_Review_  
UAV Detection and Tracking in Urban Environments Using Passive Sensors: A Survey
## 2 Numerical Model Settings
### Gas Explosion Model
#### 2.2.3 Simulation Setup of Leakage  
(1) Mesh settings  
The meshing was carried out using ANSYS 19.0 MESH software with an unstructured mesh, with the number of meshes varying from 214533-398134 depending on the working conditions, with an average mesh mass of 0.82.  
In addition, a grid independency test was completed. Take the spacing of 50 m between the gas baffle as an example, when the mesh size is equal to 0.2 m, the element number is 200280, and the peak pressure at the gas baffle is 1.7 MPa. The size of the grid continues to decrease, and the peak pressure does not change obviously, so the grid is considered to be independent. When the mesh size is 0.2 m, grid division will not affect the calculation result.  
(2) Solution setup  
Fluent calculations are based on a pressure solver, which uses the Large Eddy Simulation (LES) turbulence equation and standard wall functions. The combustion model uses the Eddy-Dissipation (ED) model based on turbulent combustion. The SIMPLE solution methods is used. The method of unsteady state calculation is adopted, the time step is 0.001 s, the number of iterations is 30, the number of steps is 600, and the calculation of the gas chamber explosion process is 0.6 s.  
(3) Boundary and initial conditions  
The boundary conditions and initial conditions are set as follows. 1 Boundary conditions: the walls around the gas cabin and the pipes are wall surfaces. 2 Initial conditions: the methane-air premixing zone has a temperature of 300 K, a methane volume fraction of 9.5%, and an oxygen volume fraction of 21.2%. At the initial moment, a spherical high-temperature zone is set up at the left end of the gas chamber with an initial temperature of 1600 K, a CO\({}_{2}\) mass concentration of 0.1456, and an H\({}_{2}\)O mass concentration of 0.11925.  
(4) Basic assumptions  
Combined with the actual engineering situation and the need for numerical simulation calculations, the numerical simulation of the explosion process requires the following assumptions:  
1. The combustible gas explosion process is an irreversible single-step reaction. 2. Gas to meet the ideal gas state. 3. Methane-air premixing zone mixing uniformity. 4. The gas chamber walls are adiabatic, rigid, non-slip, and do not exchange energy with the outside world.
## 3 Results and Discussion
### Long-Term Operation of SDCMD Process  
Long-term operation of the SDCMD process was conducted at 0.5 m/s of permeate recirculation velocity, 600 rpm of mixing intensity, 55 \({}^{\circ}\)C of feed temperature, and 20 \({}^{\circ}\)C of permeate temperature. A temperature in feed wastewater was replenished regularly to maintain a constant water level in the feed tank. Permeate flux from the membrane decreased slowly with operation time, but it was dropped rapidly after 72 h of operation (Figure 5A). Mass transfer coefficients of organics and nutrients are also shown in Figure 5B. The concentration of each component was measured immediately after taking the samples. As mentioned above, ammonia nitrogen in feed wastewater can be passed through the membrane as form of ammonia gas which can be continuously transferred into permeate water. The mass transfer coefficient associated with ammonia nitrogen was estimated about 131.9 \(\pm\) 12.1 mg/m\({}^{2}\)-h during 180 h operation. The concentration of ammonia nitrogen in MD permeate was similar to the concentration of it from the feed wastewater after 24 h continuous SDCMD operation.  
From the SDCMD process, the transfer of organic compounds such as VFA can occur through membrane. The results show that the mass transfer of VFA through MD membrane slightly increased and reached at 59.5 \(\pm\) 4.8 mg/m\({}^{2}\)-h at the end of the long-term experiment due to volatilization of VFA as discussed [29]. In contrast, the SDCMD process showed excellent rejection of phosphorus yielding relatively low mass transfer coefficient which is about 1.3 \(\pm\) 0.4 mg/m\({}^{2}\)-h as compared to organics and ammonia nitrogen. Therefore, it appeared that membrane wetting did not affect membrane performances significantly during the long-term operation of SDCMD.  
A reduction in permeate flux from the MD membrane is caused by membrane fouling formed, as evidenced by scanning electron microscope (SEM) images and energy dispersive X-ray (EDX) analysis. External fouling on the membrane surface became more serious during longer SDCMD operation. Figure 6 shows the presence of carbon (C), nitrogen (N), oxygen (O), magnesium (Mg), phosphorus (P), and calcium (Ca) ions on the membrane surface as detected by EDX analysis using fouled membrane (Figure 6D). The presence of C, O, and N peaks is often representative of organic fouling [52]. The detection of Ca, P, and Mg ions on membrane can also support the presence of inorganic precipitates such as CaCO\({}_{3}\), MgCO\({}_{3}\), and Ca\({}_{3}\)(PO\({}_{4}\))\({}_{2}\)[53]. Among these inorganic scales, the CaCO\({}_{3}\) and Ca\({}_{3}\)(PO\({}_{4}\))\({}_{2}\) are thought to be dominant as external resistance against permeate flux through MD membrane. Obviously, high rejection of phosphorus across SDCMD process as shown in Figure 5B is not only caused by the membrane rejection but also due to the formation of inorganic precipitates on the membrane surface [54; 55].  
Figure 5: Variation of permeate flux (**A**) and mass transfer coefficients (**B**) in long-term operation.
## 4 Discussion
### Limitations and Future Perspectives  
While this study provides insights into the fracture resistance of provisional restorations, several limitations should be acknowledged. Firstly, the experimental protocol involved only static loading to assess fracture resistance. Although this method offers a controlled means of evaluating mechanical performance, it does not fully replicate the complex biomechanical conditions experienced in the oral cavity, where restorations are subjected to dynamic, cyclic forces over extended periods. The absence of fatigue loading may limit the generalizability of the findings to real-world clinical scenarios. Secondly, the study design included only one representative material for each manufacturing method. This approach, while practical for initial comparison, may not yield information regarding the mechanical behaviors exhibited by different materials within the same category. Variations in composition, microstructure, and processing parameters across commercially available provisional materials could lead to significantly different outcomes.  
In light of these limitations, further research is necessary to improve the clinical applicability of the results. Future studies should integrate thermomechanical aging procedures, such as cyclic loading and thermal cycling, to more accurately simulate the oral environment and assess the long-term durability of provisional restorations. Additionally, including a wider variety of materials within each manufacturing category would enable a more thorough investigation of material-dependent mechanical properties, leading to stronger, more generalizable conclusions about their suitability for clinical use. Such in-depth analyses are crucial for supporting informed, evidence-based decisions regarding the selection of materials for provisional restorative treatments.