esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Route Maintenance  
We analyze route maintenance by counting Unreachable Nodes (URNs), which run out of energy or have no routes to the gateway. Since URNs cannot forward data, URNs indicate data-forwarding capability for medical monitoring sensors. Figure 8 shows URNs in three protocols, and LBMR's URNs are the fewest before most of nodes run out of energy. Because of single next-hop design, nodes in AODV cannot adapt to route failure quickly while encountering large amount of network traffic, and thus URNs increase quickly. URNs in AOMDV are much fewer because of alternative route design. But alternative routes in AOMDV must be disjoint so URNs in AOMDV increases more than 20 quickly. In LBMR, since nodes can quickly choose their best upper-layer nodes to reach the gateway, URNs in LBMR is none most of time, and thus LBMR provides the best performance in terms of route maintenance.  
Besides, the initial number of URNs is very high in AODV and AOMDV because nodes require sending RREQ to establish routes to the gateway and this process takes a long time. In LBMR, the route establishing time is very short for nodes within layer 2. For nodes beyond layer 2 in the grid topology, they require a little time to join the network but the process time is much shorter than AODV and AOMDV from Figure 8a. Thus, the route establishing time in LMBR is also shorter than the time in AODV and AOMDV.  
Because route maintenance is more challenging in random topology than in grid topology, Figure 8a can clearly illustrate route maintenance effort in three protocols. AODV's URNs quickly increases quickly while AOMDV's URNs gradually increase. LBMR's URNs keeps none in the beginning and remains under 20 after node failure occurs. From Figure 8b, nodes in LBMR rejoin the network quickly, even when URNs keep increasing. LBMR' URNs remain low until most of the  
Figure 8: Route maintenance. (**a**) Grid topology (total nodes is 85); (**b**) Random topology (total nodes is 100).  
nodes run out of energy in the end. Therefore, LBMR ensures the most nodes participating in medical monitoring services than AODV and AOMDV in the case that nodes have sufficient energy.
# Modelling and Assessment of a New Triple-Frequency IF1213 PPP with BDS/GPS
## 3 Results and Discussion
### BDS/GPS PPP Performance
#### 3.3.2 Kinematic Mode  
This section describes four BDS/GPS kinematic PPP models for positioning. In Figure 9, the kinematic PPP results differ from the BDS/GPS static PPP results. Ignoring the GPS PIFCB significantly affects the convergence time. Additionally, the triple-frequency PPP models in addition to the TF-F model make the convergence much faster than the DF PPP model.  
Figure 10 and Table 7 show that the positioning performance of the models is similar, except for the TF-F model, where the horizontal positioning accuracy is better than 2.0 cm and the U-direction accuracy is better than 4.0 cm. In terms of convergence time, the TF-C  
Figure 9: Positioning error of the four BDS/GPS kinematic PPP models (DOY121, 2022). The stations are (**a**) CUSV, (**b**) DGAR, (**c**) GAMG, (**d**) MIZU, (**e**) SEXG and (**f**) MOBS, respectively.  
model is improved by 46.2%, 33.5% and 35.1% in the E, N and U directions, respectively, and the TF-CF model is improved by 49.6%, 32.4% and 36.1%, respectively.
## 2 Materials and Methods
### Wastewater Treatment Plant Typical Processes
The last stage is the tertiary treatment, which is similar to the drinking water treatment process, the resulting water quality being close to drinking water quality. Firstly, a chemical compound (usually alarm, Al\({}_{2}\)(SO\({}_{4}\))\({}_{3}\), but polyaluminum chloride or ferric chloride, FeCl\({}_{3}\), can also be used) is injected into the wastewater in order to remove the phosphorus. In some plants, the phosphorus removal can be implemented during different stages, such as the large sedimentation tank, during biological treatment, or later, in the clarifier tank. If the phosphorus is removed by the biological treatment, the chemical treatment becomes an auxiliary method. Then, the water passes through a sand filter and a charcoal filter before entering a disinfection tank, where a mixture of chlorine and sodium hypochlorite is added. Lastly, the water is sent into the discharge tank where sodium bisulfite is used in order to chemicallyochlorinate the water because residual chlorine is toxic to aquatic species. The water exiting the tertiary treatment is released into the natural environment in rivers, lakes, or other local waterways. Another important periodic process that takes place during tertiary treatment is the filters cleaning, in which the sand and charcoal filters are washed with air and water, the resulting sludge being sent to the sludge treatment process. Each of the primary, secondary, and tertiary treatment stages produce sludge, which is also processed inside the WWTP, during the sludge treatment process. Firstly, the sludge enters the thickening procedure, which is conducted inside a sludge thickener, an equipment resembling a clarifier tank with an added stirring mechanism. Then, the sludge goes through the organic matter digestion process, which reduces the amount of organic matter in the sludge. Three different digestion options can be used: Aerobic digestion, anaerobic digestion, and composting. The digestion process can produce biogas (a mixture of CO\({}_{2}\) and methane), which can be used at the plant for powering equipment. The last sludge treatment process is dewatering, in which the sludge is commonly placed in drying beds. The dried sludge is either burned in incinerators, sent to landfills or used as fertilizer in agriculture.
## 2 Channel Measurement and Simulation  
This section examines wireless channel characteristics in high-speed rail environments to optimize communication performance. A two-path model is developed, considering direct and reflected transmission and window penetration loss. Channel measurements at 2.6 GHz are conducted inside the train, with the data used to calibrate a ray-tracing (RT) simulation. The accuracy of the RT model is validated through comparisons of theoretical calculations, measurement results, and simulation data. Finally, large-scale simulations generate a high-precision path loss dataset across various seat positions and heights, supporting wireless communication optimization in HSR scenarios.
## 4 Fingerprinting-Based Positioning with Spatial Side Information
### Pattern-Matching Methods
#### 4.4.3 Pattern-Matching Method 3-MultFingIn-PosOut  
The pattern-matching method 3-MultFingIn-PosOut uses spatial side information on the fingerprints. It produces a position estimate by aggregating the fingerprints from all of the sub-tiles in the fingerprint domain. The aggregation of the fingerprints is carried out using an FFNN. This method is depicted in Figure 10d.  
The method consists of calculating a position estimate from all of the sub-tiles. In the training phase, an FFNN with \(N_{ST}\times N_{F}\) inputs takes all of the fingerprints from all of the sub-tiles of a fixed tile at once as the input. The input to the NN is a tensor of fingerprints \(\mathcal{F}\), constructed with \(N_{ST}\) vectors of \(N_{F}\) fingerprints each one. The NN is trained as a regression model to map the input tensor of fingerprints to a reference ground truth position associated with the tile.  
In the testing phase, the FFNN takes as its input all of the fingerprints \(\mathcal{F}\) from a moving tile and produces as its output a position estimate with the coordinates \(\hat{x}_{\mathcal{F}}\) and \(\hat{y}_{\mathcal{F}}\), consistent with the reference position used in the training phase.
## 3 Design Approach and Methodology
### Application Design and Development
#### 3.2.1 Prototype  
The development process first consisted of planning and prototyping of the application and its contents to ensure that a consensus was reached on what the expected outcomes would be. After gathering initial requirements from stakeholders (clients and supervisors), a prototype was created using Framer and subsequently Unity (see Table 2). A minimum viable product (MVP) was used as a strategy to benchmark the outcomes of the application with the core rudimentary features required, which best represented the purpose of the application. The features for the MVP were acquired from use case scenarios and feedback from the client (UWC Medical Bioscience department).
# Information-Centric Network-Based Vehicular Communications: Overview and Research Opportunities
## 4 An Overview of Research Challenges
### SDN-Based Vehicular ICN  
SDN carries a new idea to the Cloud [21] by presenting resource separation where all resources are assembled and supervised by software. Unlike legacy physical server model, SDN takes into account resource separation and allows for hardware resources as discrete and flexible elements. With the deployment of SDN, the concept of client-server system is exterminated, but only information of various hardware is coded and processed. The administrator of a Cloud architecture then deploys a particular client-server environment, for example, logical clients and servers, which brings a high level of flexibility to the Cloud environment. However, this phenomenon needs essential physical resources so that to connect various devices through a high-speed communications channel to deal with the physical disaggregation of resources [21].  
Moreover, SDN is an evolving idea that isolates data plane from control plane, where the controller collects information from network nodes and provides an abstract view of the network. In addition, all SDN applications can access the SDN controller, where the applications deploy various network services [20]. In the context of ICN-based VANET, SDN provides scalability, manageability, and a universal view of the network. Among different ICN modules, in-network caching is one of the most popular approach [22], which improves the content availability ratio and decreases the content retrieval delay. SDN is the most suitable choice for the cache improvement as it helps in distinguishing diverse kinds of cached contents. However, this linkage of ICN and SDN in the VANET environment is a challenging task due to vehicles' mobility.  
An SDN-based VANET scheme is proposed in [23], where different wireless nodes are considered as SDN switches, which share the network bandwidth in a centralized control plane. This technique seems suitable for the SDN-based VANET, but it does not consider the most promising paradigm, i.e., ICN. Thus, an intelligent and flexible method is required for the ICN-based VANET communication paradigm to share network resources with smooth content mobility and caching.
# An Efficient Dynamic Load Balancing Scheme Based on Nash Bargaining in SDN
## 5 Simulation Experiment
### Simulation Environment Settings
#### 5.2.1 Load Balancing Degree  
MOS, UMS, and LBSNB are run in the two network topologies, and their load conditions are compared and analyzed. The comparison results of the load balancing degree are obtained from Formula (7), as shown in Figures 3 and 4.  
From the results of the load balancing of the controllers in both networks, it can be seen that when the switch request rate is low, the load balancing degree of the three algorithms is basically the same, indicating that the controller in the network does not overload. When the request rate continues to increase (300 KB/s-800 KB/s), the load balancing degree of the three algorithms shows a downward trend, indicating that the network controller begins to overload. It can be seen from Figures 3 and 4 that UMS performance is the worst, MOS is second, and the algorithm is optimal. Because UMS does not quantify the migrated switch, it is easy to make the target controller's load too high. MOS is solved based on the genetic algorithm and obtains the approximate optimal solution. The proposed algorithm can obtain a fair Pareto optimal solution based on the Nash bargaining model. When the  
Figure 4: Load balancing degree in the Columbus network.  
Figure 3: Load balancing degree in the OS3E network.  
switch request rate reaches its maximum, the load balance is the lowest, indicating that all controllers are overloaded. At this time, all three algorithms are invalid, and the controller needs to be added to relieve the load pressure.
# Low Temperature Plasma Strategies for
## 2 Materials and Methods
### Xylella fastidiosa Growth Conditions and Plasma Treatments
#### 2.4.2 Treatment Cells with Plasma Activated Water (PAW)  
Deionized water (DIW) (18.2 M\(\Omega\cdot\)cm at STP) was obtained from a Millipore Direct-Q 3 UV system. A volume of 500 mL of DIW were stored in a container in an ambient atmosphere for one day. This procedure enabled the concentrations of gases dissolved within the DIW to equilibrate with the air, which ensured constant concentrations of gasesbetween experiments. Three mL of DIW were transferred to a 50 mm-diameter petri dish inserted in the SDBD discharge chamber and treated for 15 min. The free surface of the water was approximately 3 mm far from the SDBD HV electrode. The PAW was then immediately used to resuspend bacteria to a final concentration of 10\({}^{7}\) CFU mL\({}^{-1}\). The LIVE/DEAD(r) BackLight(tm) (Molecular Probes) viability kit was used to assess the viability of bacteria cells treated with PAW. The kit contains a vial of microsphere suspension and two nucleic acid dyes SYTO 9 and propidium iodide (PI) that allow distinguishing live cells with intact plasma membranes (green) from dead bacteria with compromised membranes (red). Bacteria suspensions were incubated at room temperature for 15 min in the dark in a solution of equal volumes of the two stains. Photomicrographs were taken on a Nikon E800 microscope using a fluorescein isothiocyanate (480/30 excitation filter, DM505 dichroic mirror, 535/40 emission filter) and tetramethylrhodamine isocyanate (546/10 excitation filter, DM575 dichroic mirror, 590 emission filter) fluorescence filter sets.
# Assessment of Earthquake Destructive Power to Structures Based on Machine Learning Methods
## 5 Convolutional Neural Network
### CNN Architecture  
The architecture of some classical the CNN model, such as LeNet-5 [53], AlexNet [13], and Visual Geometry Group (VGG) [54] models are adopted as references to build the CNN architecture for ground motion evaluation. The CNN structure used in this study is illustrated in Figure 12.  
All ground motion records are down sampled to 50 Hz and processed to 20 s signal sequences according to the method presented in Section 4.1. Therefore, 1000 data points of each ground motion are taken as the input for the proposed CNN model. The maximum response displacement of the structure under the action of ground motion is taken as the output. Adam algorithm was adopted as the optimization algorithm [55]. The loss function of MAE is adopted, the learning rate is set to 0.05, the maximum training epoch is set to 200, and the batch size is set to 4000.
# Network Visualization of Cooperative Games
## 2 Preliminaries  
### Cooperative Games  
The definition of the following subjects can be found in [37].  
A cooperative \(n\)-person game with transferable utility, or a TU-game, is a pair \((N,v)\), where \(N=1,\ldots,n\) is the set of players and \(v\) is a real-valued function.  
Given a TU-game \((N,v)\), the characteristic function \(v\) is a real-valued function defined by [38] on the power set of \(N\), denoted as \(2^{N}\). The function \(v\) assigns a value to each coalition \(S\subseteq N\), representing the total worth or wealth that the coalition \(S\) can achieve by cooperating. Formally, the characteristic function \(v\) is defined as \(v:2^{N}\rightarrow\mathbb{R}\).  
The characteristic function \(v\) of a TU-game \((N,v)\) must satisfy \(v(\emptyset)=0\), meaning that the value of the characteristic function for the empty coalition is zero.  
The value \(v(S)\) of a characteristic function \(v\) in a TU-game \((N,v)\) represents for any coalition \(S\subseteq N\) the total worth that the members of \(S\) can achieve by cooperating.  
We will denote the cardinality \(|S|\) of \(S\) as \(s\), and the vector space of all TU-games with the set of players \(N\) will be represented by \(G^{N}\).  
Given \((N,v)\in G^{N}\), \(i\in N\), and \(S\subseteq N\setminus\{i\}\), the marginal player contribution \(i\) to the coalition \(S\) in the game \(v\) is the difference \(v(S\cup\{i\})-v(S)\).  
Considering the definition of a game \((N,v)\in G^{N}\), a player \(i\in N\) is defined as a _null player_ in \((N,v)\) if \(v(S\cup\{i\})-v(S)=0\) for all \(S\subseteq N\setminus\{i\}\).  
Once \((N,v)\in G^{N}\), two players \(i,j\in N\) are defined to be _symmetric_ in \((N,v)\) if \(v(S\cup\{i\})=v(S\cup\{j\})\) for all \(S\subseteq N\setminus\{i,j\}\).  
Symmetric games in \((N,v)\in G^{N}\) are those that fulfill \(v(S)=v(T)\) for \(S,T\subseteq N\) with \(s=t\).  
A point solution, or an allocation rule, for \(n\)-person TU-games is a function \(\phi:G^{N}\rightarrow\mathbb{R}^{n}\), where \(\phi_{i}(N,v)\) represents the outcome for player \(i\) in game \((N,v)\in G^{N}\).  
The author of [18] introduced a very relevant point solution for TU-games, the so-called Shapley value, that is defined as in Equation (1).  
\[Sh_{i}(N,v)=\sum_{S\subseteq N\setminus\{i\}}\frac{(n-s-1)!s!}{n!}[v(S\cup\{ i\})-v(S)],\ \ i\in N \tag{1}\]  
We will denote the vector of all Shapley values for a game \((N,v)\in G^{N}\) as \(Sh=\{Sh_{i}(N,v)\in\mathbb{R}|i\in N\}\).  
Given that the exact calculation of the Shapley value is exponential, polynomial approximations can be used for large-scale problems, as defined in [19; 20].  
Initially formulated in [36], and extended in [21; 22], for a finite set \(S\subseteq N\) with cardinality \(|S|=s\) in a TU-game \((N,v)\), the interaction index for elements \(i,j\in S\) is defined in Equation (2), with \(\zeta_{k}=(s-k-2)!(k)!/(s-1)!\)  
\[I_{i,j}(v)=\sum_{k=0}^{s-2}\zeta_{k}\sum_{\begin{subarray}{c}K\subseteq S \setminus\{i,j\}\\ |K|=k\end{subarray}}(v(\{i,j\}\cup K)-v(\{i\}\cup K)-v(\{j\}\cup K)+v(K)) \tag{2}\]
## 2 Experimental Setup
### Experimental Apparatus  
The present experiments were carried out in an improved CVCB device, and schematic was shown in Figure 2. Experimental setup equipped a highspeed Schlieren photography system, a time synchronization system, an ignition system, an image acquisition system, a pressure data acquisition system, a heating system and a gas exchange system as well as NSSs.  
The combustion bomb of CVCB is a cylindrical cavity. Its inner diameter is 100 mm, its length is 230 mm and its volume is 2.32 L, which is similar to gas storage but with smaller size. In order to allow the light rays to penetrate front-back of CVCB, both racetrack shaped optical quartz glasses are set on both sides as optical windows. Their length are 230 mm and width are 80 mm. However, their thickness is different, the front glass is 100 mm and the back glass is 50 mm. The shadow region in Figure 2 is an optical region, which is 160-mm long and 80-mm wide. The combustion chamber is heated by electrical heating elements with total power of 2 kW. The entire vessel is uniformly heated to the specified temperature. Its temperature is kept constant with uncertainty of 4 K by a closed-loop feedback controller. The CVCB can sustain about maximum pressure of 10 MPa. An 8 MPa pressure relief valve is installed in the CVCB to keep safety. The spark plug (Bosch R6, Shanghai, China) is mounted on the left end wall to ignite mixtures and ignited duration is 0.7 ms. In order to record the pressure of in-cylinder, the pressure transducer (100 kHz, Kistler 6113 B, Shanghai, China) is arranged on the top of CVCB. The distance from the pressure transducer to the right wall is 130 mm. The in-cylinder pressure is acquired by pressure data acquisition system with the uncertainty of 0.005 MPa. The exhaust gas is scavenged by the gas exchange system. The flame images are captured and recorded by high-speed Schlieren photography system to observe the process of flame acceleration and  
\begin{table}
\begin{tabular}{c c c} \hline
**Test Item** & **Test Standard** & **Results** \\ Compressive strength/MPa & ISO 527 & 135.5 \\ Tensile modulus/GPa & ISO 527 & 10.224 \\ Percentage of breaking elongation /\% & ISO 527 & 1.48 \\ Melt flow index/g-(10 min)\({}^{-1}\) (260 \({}^{\circ}\)C, 2.16 kg) & ISO 1133 & 11.52 \\ Density/g-cm\({}^{-3}\) & ISO 1183 & 1.282 \\ Shrinkage ratio/\% & ASTM D 995 & 0.6-0.8 \\ Shore hardness/HD & ISO 868 & 85 \\ Volume resistivity/\(\Omega\)-cm & ASTM D257 & 6.57 \(\times\) 10\({}^{15}\) \\ Thermal conductivity/w\(\cdot\)m\({}^{-1}\).K\({}^{-1}\) & ISO 22007 & 0.39 \\ \hline \end{tabular}
\end{table}
Table 1: Properties of NSSs.  
Figure 2: Schematic of experiment setup.  
propagation. The ignition system, pressure data acquisition system and image acquisition system are simultaneously triggered by the time synchronizing system.
## 3 Results  
### Overall GPP Performance at Daily Time-Scale  
The comparison between rain gauge measurements and gridded rainfall product estimates reveals large differences between the six GPPs (Figure 3). However, all statistical metrics follow a similar pattern. Overall, IMERG data correlate best with the rain gauge data: highest R\({}^{2}\), the slope of the regression closest to 1, lowest RMSE and MAE. ARC2, PERSIANN, and RFEv2 perform rather similarly, though RFEv2 tends to have a better slope and MAE and PERSIANN a better R\({}^{2}\) than the two other products. ERA5 and, especially, CHIRPS perform worst.  
Figure 3: Comparison of ground-based precipitation data with 6 GPPs at a daily time scale from September 2018 to August 2020 based on 4 indicators: (**a**) Coefficient of determination, (**b**) Slope of linear regression, (**c**) Root Mean Square Error, (**d**) Mean Absolute Error. Each box plot is based on 2 years of data from 14 rain gauges in the Ankavia watershed. ‘A’ refers to the point-to-grid approach (grey boxes and whiskers) and ‘B’ to the point-gridded approach (red boxes and whiskers) (see Figure 2). Box edges correspond to the 25 th (Q1) and 75 th (Q3) percentiles. Whiskers extend to Q1-1.5 IQR (lower bound) and Q3+1.5 IQR (upper bound), with IQR = Q3–Q1 (Inter-Quartile Range). Points outside the box are outliers.  
Overall, the statistical indices are generally similar or slightly better using the point-gridded approach compared to the point-to-grid approach (Figure 3). As a result, the point-gridded approach was used for the remainder of the analyses.