esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Characteristics of Sandstone under Coupled Static-Dynamic Loads after Freeze-Thaw Cycles
## 2 Experimental and Rock Specimens
### Freeze-Thaw Treatment  
The sandstone specimens were water-saturated before the freeze-thaw weathering treatment. Specimens were saturated by a vacuum saturation device for 12 h and then placed in a TDS-300 automatic freeze-thaw cycle testing machine (made by the Shanghai Shenrui Test Equipment Manufacturing Limited Company, Shanghai, China). In the freeze-thaw weathering treatment, saturated specimens were placed into the freeze-thaw machine and conditioned at \(-20\)\({}^{\circ}\)C for 4 h. Then, they were allowed to thaw for 4 h at 20 \({}^{\circ}\)C. In this work, five experimental groups were carried out with the number of freeze-thaw cycles at 0 (no treatment), 20, 60, 100, and 140 respectively.  
The five groups of samples, B21-B25, C21-C25, D21-D25 and E21-E25, underwent 140 freeze-thaw cycles. In order to obtain the influence of freeze-thaw cycle on the mass of the samples, the mass of each saturated sample was measured after every 20 freeze-thaw cycles, shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Density (kg/m\({}^{3}\))**} & \multirow{2}{*}{**P-Wave Velocity**} & **UCS (Uniaxial** & \multirow{2}{*}{**Elastic Modulus**} & \multirow{2}{*}{**Poisson’s Ratio**} \\  & **(m/s)** & **Compressive** & & \\  & **Strength (MPa)** & & & \\ \hline
2360 & 3750 & 75.0 & 15.7 & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Static mechanical properties of sandstone.  
Figure 1: Static stress–strain curves of sandstone used in this study.  
It can be seen from Table 2 that after 140 freeze-thaw cycles, the mass of saturated sandstone samples increases as the number of freeze-thaw cycles increases. The mean mass of the samples in group E increases the most, reaching 0.96%. The variation of the average mass for the saturated samples in groups B, C, D, and E as shown in Figure 3 present non-linearly with the freeze-thaw cycles. This is due to the anisotropy of the rock and the degree of grain spalling on the rock surface during the freeze-thaw process.
transmission, and other interactive multimedia services need optical fiber as a transmission medium owing to its excessive capacity and huge bandwidth [9; 10; 11]. As the need arises to send huge capacity data across long transmission range, the optical communication industry starts to use wavelength division multiplexing (WDM) that is used to ship diverse signals through a fiber [12]. With the help of employing this technology in long haul optical communication system (LHOCS), the transmission capacity can be increased several folds. However, on the other side, this WDM technology induces extra unwanted frequencies, which badly interrupt the overall performance of LHOCS. The noises generated due to these unwanted induced frequencies are named as nonlinear effects (NLEs) [13; 14]. These NLEs are classified into two types including scattering related NLEs [15; 16] and refractive index related NLEs [17; 18]. The scattering related NLEs are known as Stimulated brillion scattering (SBS) [19] and stimulated Raman scattering (SRS) [20]. On the other hand, the refractive index related NLEs include four wave mixing (FWM) [21], self-phase modulation (SPM) [22] and cross phase modulation (XPM) [23]. These aforementioned types have severe effects on performance of LHOCS. However, FWM has a strong impact on LHOCS compare to other NLEs because of WDM technology and less channel spacing [24]. In WDM systems, several optical signals are transmitted, having less frequency spacing among them, over a single mode fiber (SMF). Hence, as a result, the transmitting optical signals are mixed with each others and causes the generation of extra unwanted signals. This phenomena is known as FWM. This effect becomes adverse as the number of user increases in LHOCS. Therefore, existing system of optical communication system (OCS) is not capable of countering FWM effectively in long haul transmission. The FWM nonlinear effects are the most common limiting factors in long haul transmission, the accumulated chromatic dispersion (CD) and polarization mode dispersion (PMD) have also been reported in several references to cause adverse effects in optical fiber transmission. CD is a severe problem in WDM systems, as different wavelengths travel through the same fiber. The long-haul transmission give rise to the accumulated CD [25] in a wavelength-dependent setup, which can be compensated by using slope compensation but it causes an increase in FWM nonlinear distortions [26]. As compared to linear accumulation of CD, the PMD accumulates stochastically along the fiber length and causes distortion and reduction in peak power for long-haul fiber-optic transmission with high data rates [27]. Therefore, an improved OCS framework is a much needed requirement, which has the capability to overcome the effects of CD, PDM, and FWM.
## 2 Historical Data
### Synthetic Aperture Radar
This is in contrast to fully government-funded and operated satellites, which are moving to free and open data access, like the previously mentioned Landsat program. The trend is increasingly moving in this direction (e.g., European Copernicus Sentinel series, which includes SAR imagers), as there is ongoing difficulty with pricing levels and user uptake for commercial providers. SAR satellites do not operate a program of regular long-term observations unless they are tasked and paid to do so. All data available are in satellite-specific archives, and the discovery of data frequently relies on requests directly to the commercial satellite operators. There are few tools for online/offline searching of historic archives, and the image search requires communication with satellite customer service desks. For example, ESA's ERS and ENVISAT data can be searched using the EOLI-SA tool ([[http://earth.esa.int/EOLI/EOLI.html](http://earth.esa.int/EOLI/EOLI.html)]([http://earth.esa.int/EOLI/EOLI.html](http://earth.esa.int/EOLI/EOLI.html))). An easily searchable database for Canadian SAR data back to 1995 was recently made available to the public ([[https://neodf.nrcan.gc.ca](https://neodf.nrcan.gc.ca)]([https://neodf.nrcan.gc.ca](https://neodf.nrcan.gc.ca))); the SAR images, however, are not freely available. Canadian Arctic RADARSAT-1 data are available at the Canadian Cryospheric Information Network through the Polar Data Catalogue ([[https://polardata.ca/](https://polardata.ca/)]([https://polardata.ca/](https://polardata.ca/))). More polar-specific resources for access to freely available SAR data are also available via Polar View ([[http://polarview.aq](http://polarview.aq)]([http://polarview.aq](http://polarview.aq))). Recent coordination of SAR satellite acquisition and tasking for the polar regions was undertaken during the 2007-2009 International Polar Year through the Space Task Group. This group has now transitioned to be the EC-PORS Polar Space Task Group (Executive Committee-Panel of Experts on Polar Observations, Research and Services), and SAR activities are specifically addressed by its SAR Coordination Working Group ([[http://www.wmo.int/pages/prog/sat/pstg_en.php](http://www.wmo.int/pages/prog/sat/pstg_en.php)]([http://www.wmo.int/pages/prog/sat/pstg_en.php](http://www.wmo.int/pages/prog/sat/pstg_en.php))).
## 4 Discussion
#### Limitations  
This study has some limitations that should be addressed. The spring-mass model, employed to calculate running mechanics, assumes that the leg behaves as a linear spring during the stance phase, suggesting a linear relationship between force and displacement. However, this assumption oversimplifies the system, as human tissues, such as muscles and tendons, demonstrate non-linear mechanical properties [23]. In addition, the spring-mass model captures only the vertical direction of motion during running. However, the horizontal and lateral direction of motion may also influence the running mechanics. Finally, to calculate the mechanical parameters during treadmill running, it is crucial to capture as accurately as possible the contact and flight time of the gait cycle, the running velocity, and the anthropometric measures. These parameters are paramount for the overall accuracy of the stiffness data obtained with the sine wave method [23]. The latter may not influence our results, as the methods employed in the current study align with those used in other research assessing the same parameters.
## 5 Experimental Verification  
### Experimental Design  
In order to verify the effectiveness of the method proposed in this work, we use Ubuntu and Matconvnet [20] to implement the aforementioned convolution kernel attention model and combine it with the classic SiamFC tracking framework to form a new visual object tracking method, namely SiamFCCA. The proposed method is presented in Figure 10. In this work, the performance of the proposed SiamFCCA is tested by using the OTB [21; 22] and VOT [23] benchmark datasets, and the results are compared with those of SiamFC and other existing object tracking methods. OTB and VOT are the two main benchmark datasets in the field of object tracking. By testing on the two benchmark datasets, the performance of the object tracking method can be reflected. The major implementation details of the model are as follows:  
Figure 8: The proposed attention module weighting results for each convolution kernel (“soccer” video sequence).  
Figure 9: The proposed attention module weighting results for each convolution kernel (“jump” video sequence).  
(1) Backbone convolutional neural network: We use AlexNet as the basis for building the Siamese network and remove the boundary expansion operations on the pooling and convolutional layers. The resolution of the object template is 127 \(\times\) 127, and the size of the candidate area is twice that of the object template.  
(2) Convolution kernel attention module: In this work, we calculate the convolution kernel weights of the three convolution layers, i.e., Conv3, Conv4 and Conv5, using the initial frame. These are used as the weights of all object image features and search area features in the later stages.  
(3) The Siamese network still uses a similar training method used by SiamFC. During the training process, the two branches use the same parameters. Please note that the convolution kernel attention module does not participate in the training process.
# Thermo-Mechanical Coupled Analysis of Electric Vehicle Drive Shafts
## 3 Results
### Experimental Validation  
To validate the simulation results, external temperature measurements were conducted using a test setup as shown in Figure 8. The experiments utilized the Drive Shaft Vibration Performance Tester (TSK, Tochigi, Japan), with the test samples subjected to specified conditions: a torque of 700 Nm and a rotational speed of 500 RPM at the designated angular displacement. Two temperature measurement methods were applied during the rotation of the samples. First, a non-contact laser thermometer (BS-05T, OPTEX, Otsu, Japan) was used to measure the temperature. Second, a thermal imaging camera (FLIR E8 XT, FLIR, Wilsonville, OR, USA) was employed to simultaneously record the temperature at the center of the external joint on the outer race of the BSJ, where temperature changes were accessible for monitoring. The measurements, which are summarized in Table 4, reveal a discrepancy of approximately 6% between the experimental and simulated values, validating the accuracy and reliability of the proposed analytical procedure and methodology. This result demonstrates that the simulation approach effectively captures the thermal behavior of the system under the given operating conditions.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Source** & **Value [\({}^{\circ}\)C]** & **Error Rate [\%]** \\ \hline Simulation & 61.09 & \\ Non-Contact sensor & 65.24 & 6.37 \\ Thermal Imaging Camera & 64.7 & 5.59 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Temperature comparisons among sensors and simulation.  
Figure 7: Resultant temperature. (**a**) Resultant temperature of all parts. (**b**) Resultant temperature of outer race. (**c**) Resultant temperature of cage wall. (**d**) Resultant temperature of ball.
# A Deep Convolutional Neural Network for Detecting Volcanic Thermal Anomalies from Satellite Images
## 2 Materials  
### Volcanoes  
We investigated 9 different volcanoes (see Figure 1): Cumbre Vieja (La Palma, Spain), Etna (Italy), Pico do Fogo (Cape Verde), Volcan de Fuego (Guatemala), Geldingadalir (Iceland), Kilauea (Hawaii, USA), Klyuchevskaya (Kamchatka, Russia), Pacaya (Guatemala) and Stromboli (Italy). All these volcanoes were characterized by frequent eruptive activity in the 2000s (Table 1). We selected these sites for their different volcanic settings and eruptive styles, enabling a more comprehensive evaluation of the capabilities and limitations of infrared remote sensing for detecting and tracking eruptive activity.  
Cumbre Vieja, Etna, Volcan de Fuego, Klyuchevskaya and Stromboli are stratovola-noes with different types of eruptions. Cumbre Vieja is characterized by explosive activity and lava flows. Etna has a continuous degassing activity, with frequent effusive and explosive eruptions. Persistent activity at Volcan de Fuego is distinguished by prolonged periods of lava effusion, frequent yet moderate explosions, and intermittent episodes of paroxysmal activity. Klyuchevskaya is characterized by recurring moderate-volume explosive and effusive eruptions without significant periods of inactivity. Stromboli is dominated by a persistent (strombolian) activity, interspersed with major eruptions and paroxysms. In addition, Pico do Fogo, Geldingadalir, Kilauea and Pacaya are volcanoes with distinctive landforms. Pico do Fogo is a caldera with continuous activity in the past centuries and recent lava flow eruptions. Geldingadalir is a fissure vent with an effusive activity and sporadic lava fountains. Kilauea is a shield volcano with a summit caldera, characterized by frequent lava flow eruptions at the summit and flanks alternating with periods of lava lake activity and, finally, Pacaya is a large volcanic complex with strombolian eruptions, lava flow extrusions in the caldera moat, and occasional larger explosive eruptions [from Smithsonian Institution, Global Volcanism Program. Available online: [[https://volcano.si.edu/](https://volcano.si.edu/)]([https://volcano.si.edu/](https://volcano.si.edu/)), accessed on 18 November 2022].
## 3 Methods
### Temporal Smoothing of NDVI Values  
A 2-iterative original cubic spline smoothing technique (implemented in the R environment [32]) was applied to filter out spurious NDVI values (MODIS and Sentinel-3) introduced by residual cloud cover and/or by geolocation errors. This technique assumes that residual cloud cover decreases NDVI values. Therefore, in a first step, the smoothing method fitted a spline to the original data. Then, the distance (difference) between the fit and the original values created weights so that the original values above the spline fit received high weights and the values below the initial fit received weights equal to 0. Finally, the smoothed NDVI was generated by the second cubic spline fit that uses these weights. The applied method is a modification of [PERSON] et al. (2004) [33] who used the Savitsky-Golay filter (instead of spline fit) in the iterative NDVI smoothing.
# Structural Optimization of Slotting Nozzle Used to Improve Coal-Seam Permeability
## 2 Structural Optimization of Slotting Nozzle Design  
### Structural Parameters Affecting Performance of Slotting Nozzle  
A conical straight slotting nozzle is most commonly used in coal-seam hydraulic slotting, as shown in Figure 1. Due to its special working environment, the total length of the slotting nozzle is limited, and its special runner structure needs selection of different structural parameters from those of a conventional nozzle. The structure of a slotting nozzle comprises total length \(L_{0}\), outlet diameter \(D\), stable segment length \(L\), convergent angle \(\theta\), and straight segment length \(l\).  
According to calculations using a 75 mm drill pipe commonly used in coal mines, the maximum length of the slotting nozzle is 26 mm. At present, the rating of high-pressure pumps commonly used in coal mines is 31.5 MPa, the flow rate is 200 L/min, and so the diameter of the slotting nozzle outlet was determined to be 3 mm. Hence, the three factors that affect the performance of slotting nozzle are stable segment length \(L\), convergent angle \(\theta\), and straight segment length \(l\). Reasonable values of these three factors will influence the slotting ability.
## 3 Design and Implementation of the WSN Based AST System
### The Distributed Synchronization Protocol for AST Measurement  
Since the designed wireless sensor nodes support IEEE 802.15.4/ZigBee [9,10] implementation, this paper presents a reliable cluster-star network topology protocol for distribution AST applications to ensure real-time data acquisition and transmission when different loads are applied. The IEEE 802.15.4/Zigbee protocol stack is being considered as a promising technology for low-cost low-power wireless sensor networks. However, the current IEEE 802.15.4/Zigbee specifications restrict the synchronization in the beacon-enabled mode to star-based networks which only support simple small wireless synchronization applications [11].  
The cluster-star network topology adopted for scalable wireless sensor network based AST system is shown in Figure 1. The main advantage of the star topology is its simplicity for synchronization. The cluster-star topology has only a PAN coordinator; however, it differs from the star topology in that all nodes make up a two layer framework. In the clusters, cluster heads can communicate with any other device within its radio range to a star topology. In the top layer, the unique PAN coordinator only communicates with the cluster heads to a star topology. The topology can completely support low-power, multi-point, and heterogeneous operations with a distributed synchronization mechanism. With the Guaranteed Time Slot (GTS) mechanism and beacon-enabled mode, the cluster-star network topology protocol is modeled on the OPNET Modeler [12]. Figure 7 (a) shows the cluster-star network node model. In order to validate the function of the protocol, the simulation is conducted by 16 nodes and four cluster heads. Figure 7 (b) shows the simulation experiments setup. The adopted distributed synchronization mechanism is based on beacons and acknowledgments frames. The active time of the clustered WSN is divided to some time slots which are dedicated to package transmission in only one link every time. The CSMA/CA scheme is used to reliably send command and data frames. Because the sent frame requires an acknowledgment, the sender must wait for it before sending a new message. The wait or the retransmission mechanism consists on a timer that is activated after a transmission that requires an acknowledgment. The synchronized data transmission sequence chart is shown in Figure 8. Figure 9 shows the simulation result for network delay with 16 nodes and four cluster heads. The average times of end-to-end delay in the two layer framework both approximate 5 milliseconds.  
Figure 8: Data transmission sequence chart.  
Figure 7: (a) The cluster-star network node model. (b) Simulation experiments setup.  
Figure 9: The simulation result for network delay with 16 nodes and four cluster heads.
## 4 Simulations and Discussion  
Based on the established theoretical model, the simulation was carried out using matlab software. Here, the influence of the oblique incidence angle on the characteristics of the MSON was analyzed. In addition, the voltage-controlled liquid crystal was selected for phase compensation of oblique incidence. By analyzing the characteristics of the MSON before and after compensation, a MSON with high aspect ratio that can move in the focal plane was obtained. Finally, the relationship between the moving distance of the MSON and the oblique incidence angle was established.
# City Information Modeling: State of the Art
## 4 CIM Applications
### Typical Applications
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Phase** & **City** & **Practice for CIM** & **Application** \\ \hline \multirow{8}{*}{Planning and design} & & Build a 3D city model using LiDAR and oblique photogrammetry, and create its semantic model in CityGML format. & Establish a citizen interaction platform based on CIM to improve the decision making for city planning. \\ \cline{2-4}  & & Create a CIM platform by integrating GIS and BIM, in which the big data of planning and design are collected. & Simulate some potential plans in the CIM platform to support the decision making for urban planning. \\ \hline \multirow{4}{*}{Construction} & & Load the BIMs and the IoT data into a 3D GIS platform to form a CIM for South New Town, Twanjing. & Monitor the construction site using CIM to manage the construction progress timely. \\ \cline{2-4}  & & The Nanyang Technological University (NTU) campus [56], Singapore & Build a CIM of the NTU campus for visualization, virtual testing, and building performance optimization. & The EcoCampus was built using CIM, which helps achieve optimal performance in existing buildings for energy savings. \\ \cline{2-4}  & & & A total of 50,000 sensors will be installed in the CIM to manage public utilities (e.g., light, water, waste, parking, and transportation) effectively. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Typical applications of CIM in the entire life cycle of cities. be more plentiful. The openness of Helsinki's CIM model deserves to be emulated by all other cities. Although Helsinki has invested a lot in modeling [66; 61], its CIM model is still not complete. Without the dynamic IoT data, the CIM model is difficult to guide urban operation and maintenance. In the future, Helsinki can connect the CIM model with the IoT system to build a more complete model and better serve urban operation and maintenance. 2. NTU's EcoCampus  
A CIM model of the NTU campus was established in the NTU's flagship EcoCampus project, which includes over 200 buildings with a built area of 1.1 million m\({}^{2}\)[56]. In the EcoCampus project, a lot of sensors (e.g., light and temperature sensors) were installed in the building so that the CIM model can collect real-time monitoring data by using these sensors to make decisions on energy saving [67; 68; 69; 70]. Besides, the CIM model can also be visited online so that the data in the CIM model can be shared for other applications. The main advantage of the CIM model in the NTU's EcoCampus is the connections with IoT sensors. In this way, the dynamic energy data are collected in the CIM model so that some in-depth analyses can be carried out for optimizing the energy management on the campus. However, the application of the CIM model should not be limited to energy saving, and more applications can be performed for improving the operation and maintenance of the campus. With the development of the CIM model, more corresponding applications will be performed.