esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Analysis of Factors Affecting the Spread of HBNG Leakage under Normal Ventilation Conditions  
#### 3.2.1 HBNG Leakage Pattern under Normal Ventilation  
Figure 7 depicts the diffusion distribution condition of case 4 situations at various time periods under normal ventilation circumstances. As demonstrated in Figure 7, owing to ventilation, the presence of HBNG in the space above the compartment to the left of the leakage point is almost negligible. With the exception of monitoring point 8 near the air inlet, the concentration of HBNG increases quickly to 0.01 vol before gradually stabilizing in the area to the right of the point of leakage. At 150 s, the gas doped with hydrogen had dispersed toward the vent. The release of the HBNG was hindered by the limited vent size, resulting in the attainment of the alert concentration throughout the entire downwind area at 380 s. This shows mechanical ventilation increases HBNG diffusion in the gas compartment, and the typical ventilation wind speed is not enough to eliminate the jet column, so raising it is important for gas compartment safety and stability.  
Figure 8 illustrates the dispersion pattern of the flow lines that arise from a gas compartment leakage under typical ventilation conditions in Case 4. The graphic demonstrates that ventilation gas flow is the primary factor influencing the leakage process. Over time, the gas flow line becomes increasingly level and linear. Mechanical ventilation increases HBNG diffusion in the gas compartment, and the typical ventilation wind speed is not enough to eliminate the jet column. Raising it is thus important for natural gas compartment safety and stability.  
Figure 7: Normal ventilation natural gas compartments leakage concentration distribution of pure methane natural gas (HBR = 15%), (**a**) t = 10 s, (**b**) t = 50 s, (**c**) t = 150 s, (**d**) t = 380 s. Natural ventilation natural gas compartments leakage concentration distribution of pure methane natural gas (HBR = 0%), (**a**) t = 6 s, (**b**) t = 30 s, (**c**) t = 150 s, (**d**) t = 360 s.
## 1 Introduction
### Use Case 1: Classifying Low-Grade Epilepsy-Associated Brain Tumors  
Dysembryoplastic neuroepithelial tumor (DNET) and ganglioglioma (GG) are slowly growing tumors composed of both glial and neuronal cell elements and, histopathologically, are often difficult to classify [10] (see Figure 1).  
Figure 1: Histopathologic findings in DNET (**left** part) and Ganglioglioma (**right** part). The histopathological pattern can be hard to tell apart in some cases.  
They account for 1-2% of all brain tumors and do not metastasize or spread beyond the primary site of origin. These tumors occur mainly in children and young adults with long-standing drug-resistant epilepsy. The average age at seizure onset was 12 years in 984 GG and 14 years in 565 DNET when reviewing a large European cohort of 9523 patients who underwent epilepsy surgery. Seizures are commonly focal with or without secondary generalization, and neurosurgical resection has proven as the most successful treatment option. Malignant transformation has been reported for the group of GG [11; 12], whereas DNET rarely show this behavior [13]. Therefore, a precise histopathological diagnosis and differentiation of these two tumor entities is important for clinical patient management [14]. The problem is that even in specialized medical centers the inter-rater agreement on the diagnosis accounts for only 40% of these tumors [10]. The DL task was to develop, therefore, a binary classifier distinguishing between the two entities.
## 3 Deployment Overview
### Hardware  
Both the nodes and the gateway are implemented using the OpenMote-B board [13; 14], as shown in Figure 3a. The OpenMote-B board, which has a TelosB form factor (64 \(\times\) 33 mm), is equipped with a Texas Instruments CC2538 SoC (System on Chip) and a Microchip AT86 RF215 radio transceiver. The CC2538 [15] includes an ARM Cortex-M3 micro-controller (32 MHz, 32 kB RAM, 512 kB Flash) and a radio transceiver compatible with the IEEE 802.15.4-2006 standard. The AT86 RF215 [16] is a dual-band (sub-GHz and 2.4 GHz) radio transceiver compatible with the IEEE 802.15.4-2015 standard, which supports all multi-rate PHY options defined in the IEEE 802.15.4g-2012 amendment (i.e., SUN-FSK, SUN-OQPSK, and SUN-OFDM).  
For the nodes, the OpenMote-B was coupled with the OpenMote-Sensors board, shown in Figure 3b. The OpenMote-Sensors is a carrier board equipped with the Bosch BME280 [17] and the Texas Instruments OPT3001 [18] sensors. The Bosch BME280 sensor can measure temperature, relative humidity, and pressure, whereas the OPT3001 can measure ambient light. The nodes were enclosed in a custom 3D-printed case that exposes the sensors and the SMA antenna connectors, but hides the USB connector for protection. Each node was powered by a pair of 2\(\times\) AA batteries connected in series, providing 2.4 V and containing a nominal charge of 2000 mAh. The laboratory measurements previous to the deployment showed an average consumption of 0.2 mA during operation (described in Section 3.3), allowing the boards to remain operative for about one year.  
The gateway, depicted in Figure 4, is composed of a Raspberry Pi 3B+ SBC (Single Board Computer) connected to three OpenMote-B boards that act as IEEE 802.15.4g SUN receivers and a Huawei E3531 HSPA+ mod  
\begin{table}
\begin{tabular}{c c c c} \hline
**EUI-64** **(Last 2 bytes)** & **Distance** **(m)** & **Height** **(m)** & **Cluster** **Group** \\ \hline
56–53 & 34.0 & 12.0 & Close \\ \hline
55–AD & 63.0 & 2.0 & Close \\ \hline
55–E4 & 80.0 & 6.0 & Close \\ \hline
55–99 & 115.1 & 2.0 & Medium \\ \hline
55–DD & 115.1 & 6.0 & Medium \\ \hline
55–65 & 115.1 & 10.0 & Medium \\ \hline
56–OB & 115.1 & 14.0 & Medium \\ \hline
56–32 & 172.5 & 2.0 & Far \\ \hline
55–B3 & 221.4 & 2.0 & Far \\ \hline
55–63 & 224.4 & 2.0 & Far \\ \hline
63–0A & 273.5 & 2.0 & Far \\ \hline \end{tabular}
\end{table}
Table 1: Node identifier (i.e., last 2 bytes of EUI-64), distance to gateway (meters) and installation height (meters). Notice that for evaluation purposes nodes are grouped into clusters according to their distance to the gateway (i.e., close, medium, and far), as labeled in the fourth column.  
Figure 3: OpenMote-B (**a**) and OpenMote-Sensors (**b**) boards.  
an internal PSU (Power Supply Unit), which translates the 220 VAC to 5 VDC. The gateway runs GNU/Linux and executes a Python script that reads the packets from each OpenMote-B receiver, converts the raw payload to a JSON payload, and forwards it to the cloud back-end using the MQTT (Message Queue Telemetry Transport) protocol.
# Domain-Invariant Few-Shot Contrastive Learning for Hyperspectral Image Classification
## 3 Proposed Method
### MSAAN Model
#### 3.3.1 Spectral Feature Extraction Module  
MSAAN adopts a progressive spectral dimensionality reduction strategy. Through four consecutive 3D convolution layers subsequently passed through a max pooling layer, we effectively compress the original high-dimensional spectral data. This design allows the network to retain crucial spectral information while reducing computational complexity. Finally, using an adaptive max pooling operation, we completely compress the spectral dimensions to produce a compact 2D feature representation. Specifically, the convolution kernel sizes of our four convolution layers are (9,1,1), (7,1,1), (5,1,1), and (3,1,1), with paddings of (4,0,0), (3,0,0), (2,0,0), and (1,0,0), respectively, and strides of (4,1,1), (3,1,1), (2,1,1), and (1,1,1). The max pooling layer has a kernel size of (4,1,1) and a stride of (4,1,1).
## 4 Simulation Results  
In this section, we develop a simulator in C++ for the evaluation of the proposed algorithm. To investigate the efficiency of energy conservation and the accuracy of monitoring environment, LEACH and 2 TC-cor are compared in the experiments. HNA [29] denoted as the estimated value for the sensing intervals in which half of the nodes die is used as the metric to evaluate the efficiency of energy conservation. As to the accuracy of monitoring environment, some check points are predefined in order to examine the difference between the forecasting/representative readings and the concrete data readings. The difference is used as the metric of the accuracy of monitoring environment. Details of the simulation environment are given in Table 1. The nodes are randomly distributed in a square region and the sink node is located in the center of the region. Once the cluster members gather the data readings (sense the environment), they report to cluster head within the sensing interval. Particularly, the sensing interval is set to five seconds in order to speed up the observation of the simulation. Besides, the pre-specified basis function (\(F\)) is associated with k value to predict the sensing environment in our scenario. A scenario is simulated 30 times to produce more reliable results and the average of results is taken.
## 2 Conspectus of Watermarking Technology
### Watermark Requirements  
The critical requirements that a watermarking method must have are as follows [35]. First, embedding capacity: it must have capable of storing data of sufficient capacity to protect the copyright of digital contents. Second, robustness: the embedded watermark in a cover image needs to be able to resist various attacks such as compression of images and image processing. Third, security: attackers must not be able to easily access the embedded watermark. Fourth, unrecognizable: It should be possible to hide the presence of watermarks by preventing distortion of marked images in case of embedding a watermark in the cover image. Fifth, blind: it should be possible to recover the watermark without reference to the original image.
## Materials and Methods
### Quasi-Static Tensile Tests  
The quasi-static tensile experiments were conducted based on DIN 53504 and the dumbbell-shaped test specimens (S2) were punched from the received \(\sim\)2 mm thick plates. These specimens possess a width of 4 mm and were clamped at a distance of 43 mm. The tests were conducted with a Zwick universal testing machine (Zwick Roell Z001, Test expert, Ulm, Germany) utilizing a 1 kN load cell, at a constant crosshead speed of 200 mm/min according to the testing standard. The non-contact strain measurements based on the digital image correlation (DIC) technique were implemented using two cameras and the Mercury RT software (version 2.5, 2017) (Sobriety s.r.o., Kurim, Czech Republic). Each material grade was characterized implementing five specimens to ensure reproducibility. Based on the results, stresses at 50 and 100% of strain (\(\sigma_{50}\) and \(\sigma_{100}\)), as well as stress at break (\(\sigma_{\rm B}\)) and elongation at break (\(\varepsilon_{\rm B}\)) were determined in average values along with standard deviation.
# Effect of PODE on Emission Characteristics of a China VI Heavy-Duty Diesel Engine
## 4 Conclusions  
In this paper, the effects of blending PODE on emission characteristics of an inline, six-cylinder, turbocharged and intercooler Weichai WP12.460 heavy-duty diesel engine equipped with a common rail fuel injection system were studied. Furthermore, the effects of the blending ratio of PODE (10%, 20% and 30%), the engine loads (25%, 50%, 75% and 100%), and the EGR rate (5%, 10%, 15% and 20%) have been discussed. The experimental results of this study are as follows.  
(1) Blending PODE can further reduce CO, HC and NOx emissions. In addition, adding PODE can also reduce PN emissions at low and high engine loads. Under 100% load, PN emissions of DP30 are 45.0% lower than that of D100, and NOx emissions are reduced by 10.8%.  
(2) With an increase in the EGR rate, the CO and HC emissions of the engine increase. The addition of PODE reduces CO emissions, but the impact on HC emissions is not obvious.  
(3) With an increase in the EGR rate, NOx emissions are significantly reduced, PN emissions gradually increase, and PN emissions increase rapidly at a high EGR rate. Additionally, PN emissions are always at a low level of 10\({}^{6}\) ppm with DP20 and DP30 blends, which is more than 85% lower than that of diesel.  
(4) In general, blending PODE can achieve a simultaneous reduction in PN and NOx at various EGR rates. Even if PN emissions sometimes rebound with DP30 blends, the quantity is still lower than that of pure diesel.  
In the current work, all results are obtained from the engine bench test. However, the road test is also very important. In the future, we will conduct further road tests to explore the emission performance of PODE/diesel blends under complex conditions.  
Investigation, Z.Z.; data curation, X.Z., H.G.; writing--original draft preparation, Y.W.; writing--review and editing, Y.Z.; visualization, Y.Z.; supervision, S.L.; project administration, Y.W. and S.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The work in Xi'an Jiaotong University was supported by the National Natural Science Foundation of China (Grant Nos. 52176128 and 91741110) and the Shaanxi Provincial Key R&D Program (Grant No. 2019 ZDLGY15-10).  
The authors declare no conflict of interest.
# NaYF4 Microstructure, beyond Their Well-Shaped Morphology
## 2 Experimental Section  
### Typical Synthesis of the Er\({}^{3+}\)/Yb\({}^{3+}\)/Gd\({}^{3+}\)-NaYF\({}_{4}\) Nanorods  
GdCl\({}_{3}\)-6H\({}_{2}\)O (99.99%), YCl\({}_{3}\)-6H\({}_{2}\)O (99.99%), YbCl\({}_{3}\)-6H\({}_{2}\)O (99.99%), ErCl\({}_{3}\)-6H\({}_{2}\)O (99.99%), NaOH (98%), NH\({}_{4}\)F (98%), NaF (98%) and oleic acid (90%) were all purchased from Sigma-Aldrich and used as starting materials without any further purification.  
Synthesis where adapted from the protocols of [23; 24], which consists in solvothermal precipitation of chloride lanthanide salts and NH\({}_{4}\)F in a water/ethanol/oleic acid mixture. Experimental conditions are shown on Table 1 for samples labeled NRXX for nanorods sample N\({}^{\circ}\)XX. In a typical experiment (e.g., sample NR11), 30 mmol (1.2 g) of NaOH in 5.6 mL of water were mixed with 20 mL of ethanol (EtOH) and 20 mL of oleic acid (OA) under stirring. To the resulting mixture were successively added 0.75 mmol (228 mg) of YCl\({}_{3}\)6H\({}_{2}\)O, 0.27 mmol (104.6 mg) of YbCl\({}_{3}\)6H\({}_{2}\)O, 0.03 mmol (11.4 mg) of ErCl\({}_{3}\)6H\({}_{2}\)O, 0.45 mmol (167 mg) of GdCl\({}_{3}\)6H\({}_{2}\)O and 7.7 mmol (278 mg) of NH\({}_{4}\)F dissolved in 12 mL of water. The solution was then transferred into a 75 mL autoclave and heated at 200 \({}^{\circ}\)C for 2 h under stirring. In some cases, the NPs were heated for a longer time (2.5, 3 or 24 h instead of 2 h) to remove the \(\alpha\)-NaYF\({}_{4}\) NPs and also a sealed glass tube has been used to test higher pressure synthesis. After cooling down to ambient temperature, the resulting nanoparticles were precipitated by addition of 50 mL of ethanol, collected by centrifugation, washed with water  
Figure 1: Three SEM images of NaYF\({}_{4}\) NPs (from left to right NR13, NR12 and NR11).  
and ethanol several times. They were finally dried and kept as a powder. For the optical experiments, we manipulate the nanorods (NRs) as aqueous dispersion to ease the manipulation and observations. In this case, a functionalization by ligand exchange is needed to ensure the good dispersion in water. About 20 mg of NaYF\({}_{4}\) @oleic acid NPs are sonicated and centrifuged several times with 2 mL aqueous citrate solution (0.2 M), washed with EtOH and water to remove remaining oleic acid molecules, finally the nanorods are well dispersed in water.
## 3 Peak-Shaving Costs and Economic Dispatch Model
### Modeling Peak-Shaving Costs
#### 3.1.2 Wear-and-Tear Cost  
In DPSW and DPSC periods, substantially low-level power output result in the very large axial thermal stress on turbines, which will cause low-cycle fatigue life loss and creep loss due to the long-term exposure of metal parts in a high temperature [26]. We consider the turbine wear-and-tear cost in terms of the low-cycle fatigue life loss and creep loss. For low-cycle fatigue life loss, the temperature field and stress field of turbines are simulated to obtain the axial thermal stress \(\sigma_{eq}\)[27], and the total strain range of turbines \(\Delta\varepsilon\) is obtained by  
\[\Delta\varepsilon=\frac{(1+\mu)\sigma_{eq}}{1.5M} \tag{2}\]  
where \(\mu\) is a Poisson's ratio, \(\mu\) = 0.3; \(M\) is the elastic modulus, and \(M\) = 172.8 \(\times\) 10\({}^{3}\) MPa. Then, the Manson-Coffin Equation (3) is used to implicitly derive the number of cycles to failure \(N(P_{i,t})\).  
\[\Delta\varepsilon=A[2N(P_{i,t})]^{d}+B[2N(P_{i,t})]^{e} \tag{3}\]  
where \(d\) is the fatigue strength index; \(e\) is the fatigue ductility index. As suggested in [28], the constant parameters are settled as \(A\) = 0.00332, \(d\) = \(-\)0.06974, \(B\) = 0.6264, and \(e\) = \(-\)0.7553. Afterward, the low-cycle fatigue life loss is calculated as  
\[q_{1}=\frac{1}{2N(P_{i,t})}^{\tau} \tag{4}\]  
where in the later section, \(\tau_{1}\) is used for DPSW period, \(\tau_{2}\) for DPSC period, and \(\tau_{2}>\tau_{1}\).  
For creep loss, Larson-Miller Equation (5) is used to calculate creep time \(t_{ul}\)  
\[L\big{(}\sigma_{eq}\big{)}=10^{-3}G[C+\lg(t_{ul})] \tag{5}\]  
where \(L\big{(}\sigma_{eq}\big{)}\) is a Larson-Miller parameter [29]; \(G\) is the operating temperature (\({}^{\circ}\)C); \(C\) is a material constant value; and \(C\) = 20. With regard to \(t_{ul}\) in Equation (5), the creep loss rate \(q_{2}\) is obtained as Equation (6).  
\[q_{2}=\frac{1}{t_{ul}} \tag{6}\]  
Finally, the linear cumulative damage theory is used to calculate the total wear-and-tear cost as  
\[W_{\text{cost}}(P_{i,t})=(q_{1}+q_{2})S_{unit} \tag{7}\]  
where \(S_{unit}\) is the purchase price of coal-fired units.
# Modeling Dew Computing in DISSECT-CF-Fog
## 3 The Proposed Model for Dew Computing  
High distances between the IoT devices and fog/cloud nodes can cause critical increases in service response times. Furthermore, the lack of internet access also requires effective solutions to provide continuous service. If a smart device has sufficient computing and storage capacity, the service can be migrated to the associated smart device. In most cases, the capacities of end user devices, which can also be local PCs or tablets, are more limited than the fog nodes serving them; therefore, computational tasks may take more time to complete. The requested service should only be installed on a smart device if the increased response time due to distance is expected to be less than the response time of the application running on the device. Such migration and further CPU-related tasks can affect the battery life of the device and may increase its energy consumption dramatically, or slow down the performance of other tasks.  
DISSECT-CF-Fog has a generic IoT device representation, which transmits the information collected by the sensors to the processing units. To model Dew-related use cases realistically, this representation was extended to provide various behaviors, including service migration to mobile devices and temporary internet access limitation. To be as realistic as possible, the simulator also supports several types of device mobilities: (i) _random_ motion moves at random speeds between the minimum and maximum values in a random direction in a circle of radius \(r\) for a period \(t\), and then at the \(t+1\) moment, the speed and direction may change. When the device reaches the edge of its moving environment, the device is turned back according to the current degree of motion. (ii) _Deterministic_ motion is capable of moving at a given speed based on predefined geographic positions \(l_{1},l_{2},\ldots,l_{n}\) in the specified order. When the device reaches position \(l_{i}\), the next destination, if any, is \(l_{i+1}\). If there are no more positions, the device stops. Finally, (iii) _GPS_ movement deals with real GPS data. The coordinates are read from the files in a corresponding timestamp. Besides these movements, devices can be placed in static positions; in this case, the initial geographic coordinates of the device did not change during the simulation.
## 2 Materials and Methods
### Sample Preparation  
The sediment samples were collected from the upstream areas of 14 weirs: Gangcheon, Yeju, and Ipo in Han River; Nakdan, Gumi, Chilgok, Gangjung, Dalsung, and Hapcheon in Nakdong River; Seijong, Gongju, and Baekje in Geum River; and Seungchon and Juksan in Yeongsan River. All showed the representative characteristics of the sediments along each weir. The sediment samples were finely ground and dried in an oven for a homogeneous mixing of the sample. The sediment samples were prepared with a grain size of <100 \(\upmu\)m and blended with starch for adhesion of the powdered sample. The powdered raw samples and compressed pellets were compared to evaluate the measurement accuracy [14]. The LIBS signals of the powder samples were less intense than those of the corresponding compressed pellets. The powder samples were ablated with a high-energy laser pulse. The shockwave produced scattered the sample powder, and the laser pulse was absorbed in front of the sample because of the flying debris. Therefore, the density and compactness of the sample had a significant effect on the measurement efficiency. Each sediment sample (0.6 g) containing starch was compressed under 10 tons of force for 3 min to be pelletized to a diameter of 13 mm.  
The reference concentrations of Cd, Cr, and Pb in the sediment samples were assessed by using Inductively Coupled Plasma-Optical Emission Spectroscopy (ICP-OES) (ICP-5800, Agilent, Santa Clara, CA, USA). The analysis was performed by the National Instrumentation Center for Environmental Management (NICEM) at Seoul National University, South Korea ([[https://nicem.snu.ac.kr](https://nicem.snu.ac.kr)]([https://nicem.snu.ac.kr](https://nicem.snu.ac.kr)), accessed on 1 September 2018). Table 1 lists the results. The ground sample (0.2 g) was placed in 100 mL perfluorinated acid (PFA) in a Teflon beaker and mixed with 6 mL of HF and 3 mL of HNO\({}_{3}\) at 25 \({}^{\circ}\)C for more than 2 h. Two milliliters of HClO\({}_{4}\) (Merck, Kenilworth, NJ, USA) were then added, and the mixture was covered with a Teflon lid, heated on a hot plate, and completely evaporated to dryness. Until the dried sample showed a white or pale yellow color, the above procedure was repeated to completely decompose the sample. HNO\({}_{3}\) (1%) was added to dissolve the residue in the beaker. The solution volume was adjusted to 10 mL, appropriately diluted, and measured using ICP-OES.  
Figure 1: Study site.