esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Interannual and Seasonal Time Series
#### 3.1.3 Soil Respiration  
The temporal variations of RS in all three sites are displayed in Figure 1c, which depicts a strong seasonality during the study period across all sites. RS showed a relationship with precipitation patterns (Figure 1d), although the seasonal responses and magnitudes at sites were different (Table 3). RS magnitudes during the dry season were similar across sites with minor responses to precipitation events during the winter and early wet season. During the wet season, RS varied from 0.06 to 6.25 umol CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\) in the OG site, while for MS and ES sites it ranged from 0.05 to 4.25 umol CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\) and from 0.10 to 4.25 umol CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\), respectively. As expected for seasonally dry forests, the peak of RS occurred in the wet season (June-October). During this period, RS rates in the OG site were consistently higher than in the MS and ES sites, reaching a mean annual rate of 5.20 \(\pm\) 0.25 umol CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\), while for the MS and ES sites the mean annual rates were 4.52 \(\pm\) 0.26 umol CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\) and 4.56 \(\pm\) 0.32 umol CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\), respectively (Table 3). In 2015, the RS peak for the OG sites was in October, whereas for the MS and ES sites it was in August; in 2016 and 2017 all sites presented their peaks in August and July, respectively. By the end of the wet season (October) of 2015 and 2016, RS rates in the OG site were sustained for a longer period, while those in the MS and ES sites decreased soon after the moisture availability in the system halted (Figure 1c).  
Annual C emission estimates throughout the study period in all the sites are displayed in Table 4. The mean annual budget for the entire observation years were 868.80 \(\pm\) 70.95 g C m\({}^{-2}\) in the OG, followed by 731.96 \(\pm\) 30.40 g C m\({}^{-2}\) for the ES site and 718.66 \(\pm\) 48.54 g C m\({}^{-2}\) in the MS site. Meanwhile, the main contribution of the seasonal C emissions to annual budgets was in the wet season, which ranged from 54% to 67% in the OG site, while for the MS site it was from 57% to 72%, and from 54% to 70% in the ES site.  
Figure 1: Interannual and seasonal variation of soil temperature (TS\({}_{\text{plot}}\); (**a**)), volumetric content water (SWC\({}_{\text{plot}}\); (**b**)), precipitation (PPT; (**c**)) and soil respiration (R\({}_{\text{S}}\); (**d**)) from 2015 to 2019 in an old-growth (OG), mid-secondary (MS) and early-secondary (ES) tropical dry forest in northwestern Mexico. The ES site was not sampled during 2019. Each point at the plots is the mean \(\pm\) standard error for TS\({}_{\text{plot}}\), SWC\({}_{\text{plot}}\) and R\({}_{\text{S}}\), while PPT bars are monthly sums. The shaded areas show the wet season in the tropical dry forest.
## 2 Study Area and Data
### Data

Table 2: Basic parameters of Sentinel-1A image.
## 1 Introduction
### Extraction of Discontinuity Sets
#### 2.3.3 Extraction of Discontinuity Sets Based on the Improved Naive Bayes Classifier  
The procedure for extracting discontinuity sets using the improved Naive Bayes classifier is outlined as follows:  
Step 1: The training set is selected using the method proposed in Section 2.3.2 and the percentage \(P(y_{j})\) of training points belonging to each category within the training sets is calculated.  
Step 2: Based on the training sets, Equation (9) is utilized to calculate the conditional probability \(P(x_{i}\,|\,y_{j})\) that the \(i\)th feature of the point \(X\) takes the value of \(x_{i}\), provided that it belongs to each discontinuity set \(y_{j}\).  
Step 3: The probability \(P_{j}\) that the sample \(X\) belongs to each category is calculated by substituting \(P(y_{j})\) and \(P(x_{i}\,|\,y_{j})\) derived from Step 1 and Step 2 into Equation (7). The distribution curve of logarithmic probability is plotted (Figure 7), from which it can be seen that the logarithmic probability of the points after the curve inflection point is higher and tends to be level. So, the logarithmic probability of the points at the curve inflection point is selected as the minimum logarithmic probability (_minLPR_), and the points with logarithmic probability less than \(minLPR\) are removed as noise points. Substituting the probability \(P_{j}\) of the remaining points into Equation (8) yields \(h_{nb}(X)\) to complete the classification of the entire point cloud. At this point the procedure for extracting the discontinuity set using the improved Naive Bayes classifier terminates, where the points with the same category are those belonging to the same discontinuity set. So, the high accuracy extraction of the discontinuity set was achieved using the improved Naive Bayes classifier.  
Figure 6: (**a**) Training points selection; (**b**) training sets generation.  
Figure 7: Logarithmic probability distribution curve of point classification. LPR: Logarithmic probability of point belonging to a category; n: point index.  
The extraction results of discontinuity sets are shown in Figure 8.
# Smartwatch-Based Monitoring and Alert System for Factory Operators Using Public Cloud Services
## 3 Materials and Methods
### The Implementation of the Proposed Solution
#### 3.2.3 Implementing Functions in AWS Lambda  
The system incorporates multiple AWS Lambda functions, which handle client requests via REST and WebSocket APIs, enabling an event-driven architecture. AWS Lambda, a serverless computing service, executes code in response to events without the need for server management. By leveraging these functions, we offloaded processing from the main server, decentralized data exchange between application components, and enhanced response times for client requests. The Lambda functions implemented for this project were created through the AWS console using the following general steps:  
* A new function is created with a descriptive name and the programming language used for execution is specified (Node.js 20.x, in this case). * The function code is written directly in the AWS console and can be subsequently tested for correctness. Following the outlined steps, we implemented 10 Lambda functions, each serving a distinct purpose within the system. Each Lambda function operates as a serverless microservice, with the \"handler\" function acting as the entry point triggered by the AWS Lambda infrastructure in response to an event. The _handler_ function receives an _event_ object as a parameter, which contains all the information about the triggering event, as well as a _context_ object that contains information about the Lambda execution and lifecycle management functions. Through this, the specific logic for processing the event is implemented. At the end of the processing, the function returns a response that can include an HTTP status code and a response body (usually a JSON object) to indicate the result of the execution. In the presentation of each function, we will mention both the implementation details and their role, and in the following sections, their exact utility within the implemented functionalities will be observed in Table 4.  
To support these actions, several utility functions were implemented. The _sendMessage_ function sends messages to specific connections, while _broadcastUpdate_ delivers messages to all active connections. _broadcastToSpecificConnections_ targets specific connections linked to a given employeeId. To manage connections, _getAllConnections_ retrieves all entries from the WebSocketConnections table, _deleteConnection_ removes a connection from this table, and _addEmployedl_ associates an employeeId with a specific connection. Additionally, _findConnectionsByEmployedl_ fetches connections linked to a particular employeeId, and _removeEmployedFromConnection_ detaches an employeeId from its associated connections.
# A Tiered Control Plane Model for Service Function Chaining Isolation
## 3 The Architectural Model
### The Control Plane--Tiered Tunnel Automation
#### 3.2.1 Tier 1--Datacenter Sharing  
The first tier exists in a global IP Virtual Private Network (IPVPN) where the NFV providers share their control plane attributes such as the network controller and their external Transport Links (Figure 7). The address of each network controller is announced to all peer controllers in the VPN, along with routing capabilities and connection properties in order to define how to connect to them. This information about the association between domains and their Transport Links is required in order to ensure routing support of SFC headers between the domains. In addition, since encrypted tunnels to a destination VNF are configured on the control plane, each controller must know which controllers to connect with, in order to set up the Virtual Links (VL) and consecutively inner Encrypted Links (EL).  
Figure 6: Additional Service Function Chaining layer.  
Figure 7: Multiple levels of communication channels.  
For this purpose, every peer in the VPN serves as a proxy for the aforementioned information to others with normal BGP route distribution algorithms, so this information can be further used to set up a full mesh of Transport Links between datacenter domains (Figure 8). Figure 8 also shows an example of Provider A and C establishing a Transport Link between them based on the route information sent by proxy from Provider B.  
A new multi-protocol BGP (mBGP) [17] address family is required because the BGP peers announce new types of IP routes that must not be mixed with regular BGP IP routes. This applies both to Transport Links and Network Controller routers. Consequently, new Address Family Identifiers (AFI) [17] and new Subsequent Address Family Identifiers (SAFI) [17] have to be defined by the Internet Assigned Numbers Authority (IANA), to ensure a global identification of which IP routes a network controller is responsible for. Furthermore, the use of a standard Resource Public Key Infrastructure (RPKI) [18] with BGP to secure the origin of the BGP speaker is required for distributing the public keys of each network controller, in order to provide identification, confidentiality and integrity of the network controllers and distributed routes.
## 2 Overall Analysis Approach and Implemented Methods
### Overview of the Analysis Method
The structural sizing takes both the insulation foam mass and the internal volume occupied by LH\({}_{2}\) into account; therefore, at the end of this step the tanks' groups are fully defined in terms of geometries, masses, and internal volumes;
* The aircraft empty weight is updated and, for those cases in which tank installation causes limitations of the cabin length available for passengers, the same is completed for payload;
* Once the aircraft take-off weight is updated and the initial embarked LH\({}_{2}\) is known, a time-marching simulation of the flight mission is performed considering that at each time step all the non-empty tanks contribute equally to feed the engines;
* The time-marching simulation is coupled at each timestep with thermodynamic analyses that evaluates, for each tank, masses, pressures, temperatures, etc., of both liquid and gaseous hydrogen. These analyses allow to estimate the mass flow of evaporated hydrogen to be expelled in the atmosphere through the venting valve;
* By summing up the LH\({}_{2}\) flow going to the engines and the vented gaseous hydrogen flow, the hydrogen consumption is simulated through the following flight phases: preparation for flight (holding time between LH\({}_{2}\) fuelling and engine start), taxi-out, take-off run, take-off path, climb, and cruise. The mission ends once one of the following conditions is reached:  
If the cruise is successfully completed, the considered tank layout is associated to a set of output data including flight range, maximum number of passengers, aircraft empty weight, burnt and vented LH\({}_{2}\). The following paragraphs provide a detailed description of all the above-mentioned steps and their implementation in a MATLAB code with most relevant results. Figure 6: Scheme of the analysis method workflow.
## 3 Proposed TH-CPSK-CEMC System
### Demodulation  
For demodulation, we employ a soft decision decoding method to process signals transmitted via CPSK. Initially, we assign a value \(L_{n}(m)\) for each time slot \(n\) and position \(m\). The aggregation of these values across all time slots for a given position is then calculated as follows:  
\[S(m)=\sum_{n=0}^{N_{p}-1}L_{n}(m), \tag{5}\]  
where \(S(m)\) represents the cumulative value for position \(m\). Subsequently, we identify the position with the highest aggregated value, which indicates the most likely transmitted position. This detected position, denoted as \(\hat{m}\), is determined by the following equation:  
\[\hat{m}=\underset{m\in\{1,\dots,M\}}{\operatorname{argmax}}S(m). \tag{6}\]  
In the context of Equation (6), \(\hat{m}\) is then compared to the initially transmitted position \(m\). A match between the detected and transmitted positions signifies the successful detection of the transmitted signal. Conversely, any discrepancy is categorized as a detection error, indicating a potential misinterpretation of the transmitted information due to factors such as the MC channel's effect or multiuser interference.
# Assessment of Daytime HONO Emission Source from Asphal Surface to Urban Air
## 3 Results  
### UV Irradiance Dependency  
HONO production on asphalt shingle surfaces was evaluated along with changes in UV intensity, NO\({}_{2}\), and VOC concentrations. As three factors were taken into account, we evaluated one factor at a time while keeping all other factors fixed to typical values. Figure 2 shows the observed HONO production on the asphalt surface with varying UV intensities in the constant flow chamber reactor with humidified air, with 100 ppbv of NO\({}_{2}\) and 500 ppbv of toluene. The observed HONO production ranged from zero to a maximum of 1.4 \(\times\) 10\({}^{9}\) molecules cm\({}^{-2}\) s\({}^{-1}\) at UV (295-385 nm) irradiance of 11.3 W/m\({}^{2}\), which is typical daytime UV irradiance during the summer in Korea. The error bars indicate standard deviations of triplicate readings. Interestingly, HONO production increased sharply within a very narrow range under very low light conditions.  
In this experiment, no emission of HONO was observed in the dark, confirming that the proposed reaction is activated with photon fluxes. However, the UV light threshold to initiate photolytic HONO production on the asphalt surface was very low. Even at the lowest UV irradiance at 0.7 W/m\({}^{2}\), substantial formation of HONO over 6 \(\times\) 10\({}^{8}\) molecules cm\({}^{-2}\) s\({}^{-1}\) was observed. This UV intensity was close to that observed in the early morning or late afternoon. Observed efficient HONO production under this low-light condition implies that HONO emission from the asphalt surface is an important additional OH source near the ground, especially in the early morning and late afternoon when other OH sources are limited.  
Figure 2: HONO production rate as a function of total UV irradiance. Input concentrations of NO\({}_{2}\) and toluene were fixed as 100 ppbv and 500 ppbv, respectively.  
HONO production peaked at 11.3 Wm\({}^{-2}\) and then decreased slowly with UV intensity. The decrease in HONO production rate at higher UV intensities is likely due to two reasons. One is increasing HONO decomposition, which is proportional to the actinic flux [32]. Another is the deactivation of photo-produced reduced species on the asphalt surface by photo-oxidant species, which are formed simultaneously during high light intensity [24]. Hereafter, we fixed the integrated UV (295-385 nm) intensity at 11.3 Wm\({}^{-2}\) to test the effects of NO\({}_{2}\) and VOCs on HONO production.
## 2 BDS/GNSS for Earth Observations
### GNSS-Reflectometry  
GNSS reflectometry (GNSS-R) is a technique that utilizes the reflective properties of GNSS signals for monitoring environmental conditions on the Earth's surface. This method can examine the variations between the signal transmitted directly from the satellite and the reflected signal from the surface and infer information as to the characteristics of the surface. The application of GNSS-R technology is widespread in the fields of environmental remote sensing, including hydrology, ice and snow, ocean environment, and vegetation coverage. GNSS-R technology is a remote sensing technology based on the principle of GNSS signal reflection. It uses the reflected GNSS signals from the surface of the ground or other objects and obtains information such as ground features and environmental parameters. Its principle is based on the reflection-based characteristics of GNSS signals upon reflection from the surface. For example, the characteristics of the reflected signals--such as delay, amplitude, phase, and frequency--vary according to the physical properties of the surface (like soil moisture, wave height, and ice thickness). By analyzing these variations, relevant information about the surface environment can be obtained. The benefits of GNSS-R include extensive coverage, functionality in all weather conditions, reduced expenses, and the capability to utilize current GNSS satellite signals for environmental observation through developing a vital instrument among remote sensing applications. GNSS-R can be classified into three distinct categories: ground-based GNSS-R, airborne GNSS-R, and space-borne GNSS-R. This classification is grounded in the varying methods and platforms of signal reception and the specific application contexts in which these systems operate. To illustrate this concept more concretely, we can consider ground-based GNSS-R, particularly its application for measuring sea surface height and water level change. The fundamental operating principle underlying this technique is depicted in Figure 2, which provides a visual representation of how ground-based GNSS-R is effectively working in this capacity.  
Figure 1: PWV variation trend in different regions of China from GNSS observations (Reprinted from Ref. [26]).
# Prediction of the Yield Strength of RC Columns Using a PSO-LSSVM Model
## 2 Pseudo-Static Cyclic Test and Yield Point  
### Pseudo-Static Cyclic Test  
The pseudo-static cyclic test, pseudo-dynamic test and simulated seismic shaking table test are three test methods which are commonly used for testing the seismic performance of building structures and their components. Therein, the pseudo-static cyclic test is an approach which records the whole process from the elastic stage to the failure or damage by controlling the load or displacement for low circumferential reciprocal cyclic loading [20; 45]. The pseudo-static test is widely adopted by researchers in the field of study of the seismic performance of structures or components relying on its advantages, such as the better adaptability of the site and experimental equipment, which is convenient for conducting full-scale experiments, and low loading rate that allows detailed observation of the whole process of specimen failure.  
Under the action of low circumferential cyclic load, a hysteresis loop denoting the relationship between the load and the corresponding deformation of the structure is formed during a loading and unloading process, and the series of hysteresis loops formed after multiple cycles of loading constitute the hysteretic curves of the structure, namely the load-displacement curves. Then, the skeleton curve of the specimen shown in Figure 1 can be obtained by connecting the peak points of each hysteretic curve of the specimen, which reflects the relationship between the force and deformation of the experimental model. The skeleton curve is a comprehensive reflection of the seismic performance of the structure; it thus can be an important basis for analyzing the elastic-plastic dynamic response of the specific structure. For the concrete specimen, its skeleton curve is taken as the envelope connected by the peak points of the first cycle at each loading level of each load-displacement curve, i.e., the envelope of the hysteretic curve.  
In recent years, many experimental studies on the seismic performance of RC columns have been conducted by using the pseudo-static cyclic test [46; 47]. The influences of relevant factors on their seismic performance are investigated. The load-displacement curve can be obtained through the experimental test, and it reflects the nonlinear performance, energy dissipation characteristics and ultimate damage mechanism of the structure or component.
## 2 Application of the Space Syntax Analysis to an Urban Context
### Correlations between Traffic Volumes and Spatial Indicators  
To assess which of the four analysed spatial indicators (\(I\) and \(Ch\) for the axial analysis; \(I\) and \(Ch\) for the angular segment analysis) best fit the Pontedera traffic volumes, a correlation has been made between traffic volumes data (see Section 2.4) and the values of the four analysed spatial indicators (see Section 2.3). A linear regression analysis has been performed using the least squares method and then calculating the coefficient of determination (\(R^{2}\)). For example, in Figure 7, the correlations between the logarithm values of the pedestrian traffic volumes measured in the 40 roads and the values of the four analysed spatial indicators is shown. The use of the natural logarithm has been adopted on the base of studies conducted on English cities in Nineties [47].  
In Table 3, the \(R^{2}\) coefficient for the correlations between the logarithm values of the pedestrian and motorised traffic volumes observed in the 40 roads and the values of the four analysed spatial indicators are shown. Among the four analysed indicators, the \(I\) index of the axial analysis has been chosen because it shows the highest \(R^{2}\) values both in the case of pedestrian and motorised traffic volumes (\(R^{2}=0.69\) and \(R^{2}=0.59\), respectively).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Group** & \(\mathbf{I}\) & **Number of Roads** & **Number of Selected Roads** \\ \hline A & \(2.48\leq I\leq 2.16\) & \(37\) (\(12.2\%\)) & \(5\) (\(12.2\%\)) \\ B & \(2.16<I\leq 1.84\) & \(38\) (\(12.5\%\)) & \(5\) (\(12.5\%\)) \\ C & \(1.84<I\leq 1.52\) & \(69\) (\(22.5\%\)) & \(9\) (\(22.5\%\)) \\ D & \(1.52<I\leq 1.20\) & \(46\) (\(15.0\%\)) & \(6\) (\(15.0\%\)) \\ E & \(1.20<I\leq 0.88\) & \(76\) (\(25.0\%\)) & \(10\) (\(25.0\%\)) \\ F & \(0.88<I\leq 0.56\) & \(39\) (\(12.8\%\)) & \(5\) (\(12.8\%\)) \\  & Tot. & \(305\) (\(100.0\%\)) & \(40\) (\(100.0\%\)) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Range of values of the \(I\) index for the selection of the 40 roads.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Road ID**} & \multicolumn{2}{c}{**Traffic Volumes**} & \multirow{2}{*}{**Road ID**} & \multicolumn{2}{c}{**Traffic Volumes**} \\ \cline{2-5}  & **Pedestrian** & **Motorised** & & **Pedestrian** & **Motorised** \\ \hline
1 & 85 & 80 & 21 & 18 & 34 \\
2 & 183 & 178 & 22 & 45 & 51 \\
3 & 60 & 64 & 23 & 7 & 5 \\
4 & 69 & 61 & 24 & 17 & 18 \\
5 & 104 & 98 & 25 & 23 & 25 \\
6 & 21 & 15 & 26 & 24 & 37 \\
7 & 36 & 43 & 27