esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### DR-Modified P(S-DVB) Microparticles  
DR-modified porous P(S-DVB) microparticles were synthesized by following the routes in Figure 7. The porous P(S-DVB) microparticles were first sulfonated by concentrated sulfuric acid to form negatively charged porous microparticles, and the positively charged DR was coated onto the surface of porous microparticles via electrostatic interaction. The electrostatic interaction was completely transformed into stable covalent bonds after UV light treatment. The DR was first converted into its phenyl cationic form after releasing N\({}_{2}\) upon UV irradiation, then a S\({}_{\text{N}}\) 1 type of nuclear displacement by sulfonate occurs [33]. The specific surface areas of DR-modified porous P(S-DVB) microparticles (396 m\({}^{2}\)/g) and the distribution of pore size are shown in Figure S1.  
As shown in Figure 8, the characteristic absorption peaks of sulfonic acid groups at 1180 and 1089 cm\({}^{-1}\) indicate the successful sulfonation of porous P(S-DVB) microparticles. The absorption  
Figure 6: Nitrogen adsorption–desorption isotherms of P(S-DVB)-3 (**a**) and P(S-EDMA) (**b**). Insets are the distributions of pore size.  
Figure 7: Schematic illustration of the diazoresin (DR) modification of P(S-DVB).  
peak at 1265 cm\({}^{-1}\) can be assigned to the stretching vibration of N-H derived from DR; the peaks at 1450 cm\({}^{-1}\) and 1500 cm\({}^{-1}\) were assigned to the stretching vibration of the aromatic ring of DR, demonstrating the presence of DR after the modification.
## 2 Materials and Methods
### DNA Extraction
#### 2.2.2 DNA Extraction from Fish Tissue  
High-Quality DNA Isolation from Fish Tissue  
DNA was extracted according to [PERSON] et al. [30]. In brief, 100 mg muscle tissue was used as the starting material and incubated for 1 h at 65 \({}^{\circ}\)C in 0.5 mL of buffer 1 (1.2% (\(w/v\)) CTAB, 60 mM Tris, 10 mM Na\({}_{2}\)-EDTA, 0.8 M NaCl, 3-mercapto-1,2-propanediol 0.1% (\(v/v\)) and proteinase K (final concentration 0.5 mg/mL, pH 8.0)). After centrifugation for 10 min at 10.625\(\times\)\(g\), the supernatant was washed twice with 500 \(\mu\)L chloroform. Then, two volumes of buffer 2 (1% (\(w/v\)) CTAB, 50 mM Tris, 10 mM Na\({}_{2}\)-EDTA, pH 8.0) were mixed with the supernatant. After centrifugation, 400 \(\mu\)L of buffer 3 (1 M NaCl, 10 mM Tris, 1 mM Na\({}_{2}\)-EDTA, pH 8.0) were added to the precipitate. The extract was then incubated at 65 \({}^{\circ}\)C for 10 min and subsequently precipitated with 400 \(\mu\)L of isopropanol. After another for 10 min at 10,625\(\times\)\(g\) centrifugation, the precipitate was washed twice with 500 \(\mu\)L of ethanol (70% (\(v/v\))). The dried precipitate was dissolved in 100 \(\mu\)L Millipore water.  
Point-of-Care-Suitable DNA Isolation from Fish Tissue  
The specimen was sampled with a medex cytbrush (medesign I.C. GmbH, Linden, Germany) by swiping the tissue five times. The brush was incubated in 100 \(\mu\)L extraction-buffer (100 mM KCl, 10 mM Tris-HCL, 0.1 mM EDTA, 1 mM DTT, 0.1% Triton X-100) for 10 min at an ambient temperature (approx. 20 \({}^{\circ}\)C).
# Dynamic Cooperative MAC Protocol for Navigation Carrier Ad Hoc Networks: A DiffServ-Based Approach
## 4 DiffServ-Based Dynamic Cooperative MAC Protocol  
In this section, we introduce a DiffServ-based MAC protocol (DDC-MAC) and its key features for QoS-provisioning and adaptation according to different types of service. The proposed protocol consists of two compatible sub-protocols: (i) time-driven-based periodic communication, denoted as a PC sub-protocol, and (ii) event-driven-based on-demand communication, denoted as an OC sub-protocol.  
Figure 2: Transmission modes with adaptive antenna.
# Containerised Application Profiling and Classification Using Benchmarks
## 7 Discussion  
Understanding the computational needs of a given application, can create knowledge that can be exploited not only in the process of delivering the required QoS, but also, as an IaaS to recommend and offer solutions that take under consideration the application resource requirements. The research and work conducted in this paper challenges the process of profiling and extracting key metrics in order to classify the containerised applications to known benchmark categories, to ultimately guide the hardware selection process. In this process the main research challenges were that the profiling and classification system should work for a substantial variety of benchmarks and applications and more importantly for different hardware configurations used in a containerised environment (Docker). As it is shown and described in the experimentation (Section 6) the hardware diversity impacted significantly the accuracy of the classification algorithms, with the leading algorithm showing almost a 5% accuracy drop, and others even reaching 50%. Even without the consideration of the workload the profiler system reached a notable 89% of accuracy. This phenomenon shows how strongly under consideration the environment in which the profiling is conducted should be taken. Given the diversity of hardware and benchmarks used in the experimentation 85% accuracy is significantly high, although there is always margin for improvement.  
Due to the diversity off applications as well as the benchmarks that are created to simulate them, different classification models for each application category can be investigated. In this way the organisation of the general application type classes can create an initial classification before the usage of the profiler in order to precisely identify which is the most representative benchmark. This process requires a lot of data in order to train the different models and also very strict and precise categorisation of the known benchmarks. In addition, it is important to mention that given the diversity of the benchmark types,  
Figure 8: Accuracy of classification algorithms with the external dataset.  
there are benchmarks that can be deployed easily and produce consistent results, and there are benchmarks that require configurations and multiple containers to run. These type of benchmarks in the automatic process of profile extraction can produce very different profiles because of the malfunction in the benchmarks deployment. These profiles produce noise and confuse the algorithm. Having a mechanism that discards bad profiles can also create a more robust and stable classification system.
## 4 Choice of Optimal Bottoming Cycle Configuration  
In the next step of the study, an assessment of different configurations of the ORC bottoming thermodynamic cycle has been made. The criteria used for choosing pressures and temperatures for each configuration are those that allow to maximize the power produced by the cycle for each level of output of the engine. At this stage, the constraints related to the real geometrical dimensions of the heat exchangers are not explicitly taken into account; they will be introduced in the next step, in view of the economic evaluation of the chosen solution.  
The following parameters have been kept constant for all engine load:  
* cycle maximum pressure 20 bar (in order to avoid stressing pipes and components);
* cycle maximum temperature 290 \({}^{\circ}\)C (in order to obtain a slight overheating above the toluene saturation temperature at 20 bar (260 \({}^{\circ}\)C), and below the degradation limit for the same fluid);  
Figure 5: ORC efficiency vs. maximum cycle pressure, for different engine loads (cooling water temperature equal to 38 \({}^{\circ}\)C).  
Figure 6: ORC power vs. condensation temperature, for different engine loads and maximum pressure equal to 20 bar.  
* condensing mass flow rate 80 kg/s;
* maximum oil temperature 350 \({}^{\circ}\)C;
* maximum gas temperature 468 \({}^{\circ}\)C;
* overall heat transfer coefficient constant for all exchangers, equal to 850 W/m\({}^{2}\)K (average value recommended by Aspen(r) for the used fluid);
* no pressure drops in the bottoming cycle components (consistently with a simplified initial analysis).  
The performance of the simple cycle, as described in Section 3, will be compared below (by fixing toluene as working fluid) with those of three other more complex configurations of the energy recovery cycle, obtained by considering a preheating of the working fluid with the water of the HT engine cooling circuit, the internal regeneration for the ORC and two combined ORC cycles in cascade.
## Appendix A Tubular Reactor Model  
Consider the (single-species axial-dispersion) tubular reactor model [26; 28; 29]  
\[\partial_{t}T =D\partial_{s}^{2}T-\bar{v}\partial_{s}T-\eta(T-T_{c})+\delta \rho(c)e^{-\gamma/T} \tag{11a}\] \[\partial_{t}c =D\partial_{s}^{2}c-\bar{v}\partial_{s}c-\rho(c)e^{-\gamma/T} \tag{11b}\]  
with Danckwert's boundary conditions  
\[D\partial_{s}T(0,t)-\bar{v}T(0,t) =-\bar{v}T_{e},\quad D\partial_{s}T(0,t)=0 \tag{11c}\] \[D\partial_{s}c(0,t)-\bar{v}c(0,t) =-\bar{v}c_{e},\quad D\partial_{s}c(0,t)=0. \tag{11d}\]  
In (11) \(T,c:[0,1]\times\mathbb{R}_{+}\to\mathbb{R}\) with \(T(s,t)\) representing the reactor temperature at \(s\in[0,1]\) and time \(t\geq 0,c(s,t)\) the concentration, \(D\) being the diffusion coefficient, \(v\) the superficial velocity (assumed to be constant), \(\eta\) the heat interchange coefficient between the cooling jacket with temperature \(T_{c}(s,t)\) (that can be used as a control input [27]), \(\delta\) the adiabatic temperature rise, \(\rho:\mathbb{R}_{+}\to\mathbb{R}_{+}\) the reaction rate function, \(\gamma\) the Arrhenius coefficient, and \(T_{e},c_{e}\) the (constant) feed temperature and concentration, respectively.  
In the following, we focus on the reactor behavior close to the equilibrium temperature and concentration profiles \(T^{*},c^{*}:[0,1]\to\mathbb{R}_{+}\) satisfying  
\[0 =D\partial_{s}^{2}T^{*}-\bar{v}\partial_{s}T^{*}-\eta(T^{*}-T_{c })+\delta\rho(c^{*})e^{-\gamma/T^{*}}\] \[0 =D\partial_{s}^{2}c^{*}-\bar{v}\partial_{s}c^{*}-\rho(c^{*})e^{- \gamma/T^{*}}\]  
with the associated boundary conditions. Introducing the deviation variables  
\[\tau=T-T^{*},\quad x=c-c^{*}\]  
and using a first order approximation of the nonlinear terms it holds that  
\[\rho(c)e^{-\gamma/T}=\rho(c^{*}+x)e^{-\gamma/(T^{*}+\tau)}\approx e^{-\gamma /T^{*}}\left(\rho(c^{*})+\rho(c^{*})\frac{\gamma}{T^{*2}}\tau+\rho^{\prime}(c^ {*})x\right).\]  
Considering in a first approximation that the temperature deviation can be neglected (e.g., due to some appropriate control through the cooling jacket temperature [27]) one obtains for the concentration dynamics in a neighborhood of the equilibrium profile \(c^{*}\) the following dynamics  
\[\partial_{t}x=D\partial_{s}^{2}x-vx-rx\]  
with \(r(s)=\rho^{\prime}(c^{*}(s))e^{-\gamma/T^{*}(s)}\) for \(s\in(0,1)\) and the associated boundary conditions. This model is equivalent to the one in (1), assuming a particular reaction rate distribution \(r\) with reaction rate maximum determined by the coefficients \(k_{1}\).
## 2 Related Background
### Wind Forecasting  
Currently, weather predictions are mostly conducted by numerical analysis using physical simulation models. The major global models used are the GFS (Global Forecast System) and ECMWF (European Centre for Medium-Range Weather Forecasting), whereas local simulations are conducted using other models such as WRF (a regional model that is used in this research) and ICON (Icosahedral Nonhydrostatic). NWP relies on physical conservation laws, specifically the ideal gas law. It calculates the future state of weather systems by computing spatial and temporal derivatives of the weather system [9]. Numeric forecasting is a cyclical process, and its main stages are as follows:  
1. Synthesis of measured environmental and simulated data that describes the state of the atmosphere is used as input (initial and boundary conditions) for simulation software.
2. Four-dimensional (3D space + time dimension) simulation of the future atmospheric state is performed using atmospheric model simulators.
3. Postprocessing of simulation output produces a forecast.
4. Data assimilation of model output and newly measured atmospheric properties are used to create initial conditions for the next run of the model.  
The forecasting cycle has been used since the 1960s [10; 11]. Since then, immense improvements in predictability have been achieved by better measurement of atmospheric quantities [8] and increased processing power, enabling high-resolution simulation [12]. Atmospheric modelers focused most of their attention on stage 4 of the prediction [10; 13; 14; 15; 16] cycle (data assimilation). However, even this improved weather predictability does not meet operational forecasting needs. Currently, the ability to perform both long-range weather prediction and short-range weather forecasts or nowcasting remains limited. Meteorological systems are chaotic due to atmospheric instability, which introduces many variables and degrees of freedom into the forecasting equations [17; 18].  
Small-scale forecasting plays an ever-greater role in competitive sailing. The use of very high-resolution data, attributed to current simulation tool capabilities, has allowed models to exhibit higher performance. Prior research using super-high-resolution WRF simulation improved wind-direction prediction but struggled with reliability on specific days due to initialization and lateral boundary conditions [19; 20; 21].  
Forecasting for sailing events is divided into site characterization, which aids sailors in event preparation by understanding typical weather conditions [22; 23; 24], and high-resolution short-term forecasting [20; 24]. [PERSON] and [PERSON] [23] recently proposeda method that groups typical days at a venue into clusters, aiding tactical decisions through a call book based on these clusters.
## 2 Materials and Methods
### Data Sources and Processing
#### 2.2.2 Meteorological Data  
Temperature data from 2001 to 2019 were collected from the daily land surface temperature (LST) product (MOD11A1) ([[https://lpdaac.usgs.gov/products/mod11a1v006/](https://lpdaac.usgs.gov/products/mod11a1v006/)]([https://lpdaac.usgs.gov/products/mod11a1v006/](https://lpdaac.usgs.gov/products/mod11a1v006/)), accessed on 28 September 2021) provided by NASA, with a spatial resolution of 1000 m and a conversion factor of 0.02. The function to obtain the product is ee.ImageCollection(\"MODIS/006/MOD11A1\") in GEE. The GEE platform can also perform calculations of annual average temperature based on these obtained data.  
Precipitation data from 2001 to 2019 were obtained from Climate Hazards Group Infrared Precipitation with Station data (CHIRPS) ([[https://www.chc.ucsb.edu/data/chirps](https://www.chc.ucsb.edu/data/chirps)]([https://www.chc.ucsb.edu/data/chirps](https://www.chc.ucsb.edu/data/chirps)), accessed on 28 September 2021) provided by University of California, Santa Barbara (UCSB), with a spatial resolution of 0.05 arc degrees. The function to obtain the dataset is ee.ImageCollection(\"UCSB-CHG/CHIRPS/DAILY\") in GEE. The GEE platform can also perform calculations of annual average precipitation based on these obtained data.
## 4 SF-PPP Performance Analysis of Android Mobile Phones  
The MGEX stations utilize geodetic-grade GNSS receivers, which provide better data quality than the Android devices and are beneficial to SF-PPP performance. In addition,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & & **East** & **North** & **Up** \\ \hline \multirow{4}{*}{Positioning error (m)} & \multirow{4}{*}{GRAPHIC} & Mean & 0.01 & \(-\)0.01 & \(-\)0.03 \\  & & STD & 0.08 & 0.07 & 0.18 \\  & & RMS & 0.08 & 0.08 & 0.18 \\ \cline{2-6}  & \multirow{4}{*}{Uncombined} & Mean & 0.01 & 0.00 & \(-\)0.03 \\  & & STD & 0.08 & 0.07 & 0.16 \\  & & RMS & 0.08 & 0.08 & 0.17 \\ \hline \multirow{2}{*}{Convergence time (min)} & GRAPHIC & Mean & 30.3 & 18.1 & 30.2 \\ \cline{2-6}  & Uncombined & Mean & 16.1 & 14.4 & 26.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistical values of the positioning error and convergence time for the GRAPHIC model and the uncombined model.  
Figure 3: Distribution of convergence time for the GRAPHIC model and the uncombined model in the E, N, and U directions, from top to bottom.  
currently, most mobile Android terminals usually cannot produce reliable and continuous dual-frequency or triple-frequency observations owing to the chip and antenna limitation; the SF-PPP technique is a more feasible approach for precise positioning or location-based applications. Thus, SF-PPP with Android smartphones is the focus of this section.  
The Huawei P40 smartphone is adopted for this study, which utilizes Android release version 10.0 and the latest Kirin-990 chipset. It can track dual or triple-frequency signals for different satellite systems, explicitly L1/L5 for GPS, B1I/B1C/B2a for BDS, E1/E5a for GALILEO, and L1/L5 for QZSS. However, the data continuity on the second frequency band (L5/B2a/E5a) is severely interrupted owing to channel limitations. Thus, the data from the first frequency band are our focus. To avoid environmental impacts, we collected 4 h of data on the rooftop of a tall building inside Wuhan University at a sampling rate of 1 s on 26 October 2020. Two P40 smartphones were utilized to record GNSS measurements during the same period but with different antennas; one was the P40 built-in antenna, while the other was an external geodetic antenna (Huawei helped adapt the P40 for connection with external antennas). In this way, the impacts of the built-in antenna could be compared and analyzed. In this section, the quality of the GNSS observations from the smartphone is analyzed in terms of carrier-to-noise density ratio (C/N0), pseudorange noise, and carrier phase noise. Then, the result of SF-PPP is examined.
## 2 Materials and Methods
### Construction of Moisture Content Prediction Model  
In this study, we aim to construct a prediction model for outgoing corn moisture content by utilizing various methods. Specifically, we employ the Multiple Linear Regression (MLR) model, the Extreme Learning Machine (ELM) method, which excels in handling complex nonlinear problems, and the Long Short-Term Memory (LSTM) neural network, a method belonging to the deep learning category.
# Three-Dimensional Imaging via Time-Correlated Single-Photon Counting
## 4 Conclusions  
In summary, a 3D imaging scheme based on TCSPC technology has been proposed and demonstrated. In the scheme, we adopt the range-gated technology to reduce environmental noise and improve the SSIM of imaging results by 10 times. At the same time, the TOF algorithm and photometric stereo algorithm are used for three-dimensional imaging of objects. Experiments show that the proposed scheme can realize imaging with a lateral resolution of 512 \(\times\) 512 and an axial resolution of 4.2 mm. Finally, the proposed imaging scheme has the potential to be widely used in imaging in complicated environments, especially in remote, haze and underwater.  
Conceptualization and methodology, H.Z., Y.Z. and C.F.; software, C.F. and G.W.; validation, C.F., H.Z. and H.C.; formal analysis, G.W.; investigation, Y.H.; resources, H.C.; data curation, C.F.; writing-original draft preparation, C.F.; writing-review and editing, C.F., H.Z., Y.Z. and J.L.; visualization, H.C. and J.L.; project administration and Supervision, J.S. and Z.X. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Shanxi Key Research and Development Project (Grant No. 2019 ZDLGY09-10); Key Innovation Team of Shaanxi Province (Grant No. 2018 TD-024) and 111 Project of China (Grant No.B14040).  
The authors declare no conflict of interest.
## 3 Methodology of TSDCA-BA Model
### Structural Design of the TSDCA-BA Model  
#### 3.1.1 Single-Frame Statistical Feature Extraction Module  
Figure 2 shows the overall architecture of the proposed Extract stats module. To enrich the frame-level representation, this module computes statistical features for each time step individually by calculating the mean, standard deviation, maximum, and minimum across the feature dimension of each frame. Formally, given the input feature tensor \(\mathbf{x}\in\mathbb{R}^{B\times T\times F}\), where \(B\) is the batch size, \(T\) the number of time steps, and \(F\) the number of features per frame, the statistics for the \(b\)-th sample at the \(t\)-th time step are computed as described in Equation (1):  
\[\begin{split}\mu_{b,t}&=\frac{1}{F}\sum_{f=1}^{F}x_{ b,t,f},\quad\sigma_{b,t}=\sqrt{\frac{1}{F}\sum_{f=1}^{F}\left(x_{b,t,f}-\mu_{b,t} \right)^{2}},\\ \max_{b,t}&=\max_{1\leq f\leq F}x_{b,t,f},\quad\min _{b,t}=\min_{1\leq f\leq F}x_{b,t,f}.\end{split} \tag{1}\]  
Then, these four statistics are concatenated along the feature dimension, resulting in a statistical feature tensor with shape \((B,T,4)\). By concatenating the statistical features with the original features, the module effectively enriches the representation of each frame, enhancing the model's ability to perceive the distribution of features within each frame. This facilitates the subsequent network in more accurately capturing and understanding the variations and differences of frame-wise features.