esa-sceva/satcom-chunk-collection huggingface dataset:
# Responses of Forest Carbon Cycle to Drought and Elevated CO2
## 3 Elevated CO2 Effect on Plants and Ecosystem
### How Plants Respond to Elevated CO2
#### 3.1.1 Stomatal Conductance  
The stomatal aperture is determined by the turgor pressure of the guard cell [45], CO2 enrichment stimulates the guard cell by changing the ion channel activities or the ion composition inside and outside the cell membrane [46]. The response of stomata to atmospheric CO2 concentration varies among species, mainly owing to genetic traits and stomatal acclimation to humidity [4]. A reduced stomatal conductance (\(g_{s}\)) under the enriched CO2 explains the decrease in leaf transpiration and the increase in water use efficiency, but it does not seem to limit photosynthesis. Whether reduced \(g_{s}\) restricts photosynthesis is indicated by the ratio of intercellular to atmospheric CO2 concentration (Ci/Ca). The decrease in stomatal conductance appears to limit plant photosynthesis if the value of Ci/Ca in elevated CO2 is less than that in normal ambient CO2. However, [PERSON] et al. [4] examined experiments with different species and found that there is no significant difference in Ci/Ca between plants grown in ambient CO2 and elevated CO2,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Location** & **Period** & & **Forest Carbon Variations** & **Causes** & **Reference** \\ \hline Global & Site-dependent & \(\bullet\) & Reduction of GFP and Re, neutral NPP & & & \\  & Site-dependent & \(\bullet\) & Response (drought without heat) & & & \\  & & Reduction of GFP and NPP; increase of Re (drought with heat) & & & & \\  & & & & & \\ \cline{3-5} California & 2001-2015 & \(\bullet\) & Loss of 188.4 Tg carbon storage & \(\bullet\) & Tree mortality & \\  & & & & & \\  & & & & & \\ North American forests & Site-dependent & \(\bullet\) & Reduction of NPP in deciduous forests & \(\bullet\) & Seasons & \\  & & & & & \\  & & & & & \\  & & & & & \\ Northern Europe & Dry summer of 2018 & \(\bullet\) & Reduction of GPP & \(\bullet\) & Precipitation deficit & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._which proves that the reduced \(g_{s}\) does not inhibit plant photosynthesis but enhances the WUE of plants.
## 2 Architecture of Dima System
### System Layer  
The system layer describes the functional subsystems. From the perspective of system design, each subsystem has its own specifications, including resource requirements (i.e., computation and storage), connectivity requirements, specific capacity and other properties of relevance for the DIMA architecture design such as their Design Assurance Level (DAL) or specific hardware dependencies. As shown in Figure 2, DIMA system is broken down into a hierarchical tree of subsystems, with the root node representing the entire system [25]. Typical subsystems in the aircraft can be Bleed-Air System (BAS), Overheat Detection System (OHDS), Pneumatic System (PS) and Ventilation Control System (VCS) [26]. The purpose of this layer is to capture the system logic, tasks and connections, as well as peripherals and interfaces for functional subsystems.  
Each of the subsystems in the system layer is composed of functions from an architectural point of view. Such an abstraction in form of functions is beneficial to reduce the complexity of the system. Typical functions of the aircraft include but are not limited to: aileron control, multi-function spoilers control, ground spoilers control, rudder control and brake function. For example, an aileron controller function is in charge of monitoring and actuating flight control surfaces. It may consist of a sensor acquisition module, a monitor module, and an actuator controller module. These modules are enabled by a set of hardware components in the hardware layer, including CPMs, RDCs and sensors. More discussions of these hardware components will be given in Section 2.2.  
Figure 1: System architecture of DIMA. The design of system layer, hardware layer and installation layer is linked with a group of constraints and quality measures.
# Recognition of Blinks Activity Patterns during Stress Conditions Using CNN and Markovian Analysis
## 3 Materials and Methods  
### Eye Aperture Timeseries Extraction  
The eye aperture timeseries and the corresponding eye-related features (e.g., eye blinks, IBI, eye blinks duration) were extracted from facial videos. The facial videos had a sampling frequency of 30 fps. The pre-processing phase involved histogram equalization for contrast enhancement and face detection. The face detection was performed based on the Viola-Jones algorithm [14]. Active appearance models (AAM) [15] have been employed to estimate the 68 2-dimensional facial landmarks. In each eye, 6 landmarks were segmented tracking the perimeter of each eyeball. The 2D coordinates \((x_{i},y_{i})\) were used to compute the area enclosing these 6 landmarks according to [PERSON]'s theorem representing the eye aperture  
\[A=\frac{1}{2}\sum_{i=1}^{N}|x_{i}y_{i+1}-y_{i}x_{i+1}| \tag{1}\]  
where \(N\) = 6 and \(x_{N+1}=x_{1}\), \(y_{n+1}=y_{1}\).  
The eye aperture timeseries' artefactual spikes (large narrow spikes mainly caused by the instant loss of eye-tracking) were suppressed using wavelet Independent Component Analysis (wICA) [16] and lowpass filtered with cutoff frequency \(f_{c1}\) = 10 Hz, in order to  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Experimental Phases and Tasks** & **Duration (Min)** & **State of Affect** \\ \hline
**Social Exposure** & & \\
1.1. Neutral (reference) & 2 & N \\
1.2. Baseline Description & 2 & N \\
1.3. Interview & 2 & S \\
**Emotional recall** & & \\
2.1. Neutral (reference) & 2 & N \\
2.2. Recall stressful event & 2 & S \\
**Mental Tasks** & & \\
3.1. Reading letters/numbers & 2 & N \\ (reference) & & \\
3.2. Stroop-color word test (SCWT) & 2 & S \\
3.3. PASAT Task & 2 & S \\
**Stressful videos** & & \\
4.1. Relaxing video & 2 & R \\
4.2. Adventure video & 2 & S \\
4.3. Psychological pressure video & 2 & S \\ \hline \hline \end{tabular} Note: Intended affective state (N: neutral, S: stressful, R: relaxed).  
\end{table}
Table 2: Experimental phases, tasks, duration and induced affective state employed in the 3 rd experiment.  
remove highly varying segments coming from AAM instability. The procedure of eye aperture time series extraction is presented in Figure 1.
# SCATTER PHY: An Open Source Physical Layer for the DARPA Spectrum Collaboration Challenge
## 5 Conclusions  
The paper described the design, features, and benefits of SCATTER PHY, which is an open-source physical layer that uses a filtered form of OFDM as its waveform.  
The main features included using Google's Protobuf to define the message format for interaction with higher layers of the system and the ability to transmit/receive two different types of packets, which can include a synchronization signal, control data, and user data. The PHY layer communication operated with various modulation and coding schemes, several different channel bandwidths, and automatically improved the carrier frequency offset correction by utilizing the OFDM CP in addition to the synchronization subframe detected in the signal. SCATTER PHY minimized OOBE by utilizing custom-built FPGA based FIR filters on the transmitter side, configured for each channel bandwidth at run-time. Additionally, two independent physical interfaces were available, which effectively enabled the usage of full-duplex and theoretically double the possible throughput achieved with one interface.  
Figure 13: CPU profiling of SCATTER PHY for a PHY Tx BW of 4.5 MHz.  
The described benefits were verified experimentally. The throughput achieved with actual physical transmissions was compared to a theoretical maximum, defined by using the entire available time for communication with an effective duty cycle of 100%. The verification showed that using full-duplex had no impact on the achieved throughput in each interface. Spectrometer measurement results showcased the capabilities of the FPGA based FIR filter, resulting in negligible OOBE, which in turn increased channel utilization and facilitated communication in neighboring channels by other radios. Using CP based CFO estimation and correction was demonstrated to outperform the sync-signal based correction in all cases, at the expense of higher processing time. Finally, the packet reception rate for all types of transmitted signals was presented, showing that SCATTER PHY could successfully decode user data at SNR values as low as 0 dB.  
**Author Contributions:** Conceptualization, methodology, software, validation, investigation, formal analysis, data curation, and writing, original draft preparation: F.A.P.d.F., D.S., and P.M.; writing, review and editing: R.M., and I.J.; resources, supervision, project administration, and funding acquisition: X.J., and I.M.  
**Funding:** This work was funded by the European Union's Horizon 2020 research and innovation program under Grant Agreement No. 732174--Orchestration and Reconfiguration Control Architecture (ORCA) project.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# User Perception of Facebook App Data Access: A Comparison of Methods and Privacy Concerns
## 1 Introduction  
Social media users are concerned about privacy. In the study we present here, only four out of 120 subjects reported they were \"Not at all\" concerned about privacy on Facebook. However, despite a wide range of media reports about the consequences of oversharing and stories about how data can be used in unintended ways, people continue to interact, share data, and use apps online despite their concerns.  
Are users continuing to share because they are aware of the privacy risks and have made an informed choice about what they are comfortable sharing, or are they operating under false assumptions or without the knowledge they need to make an informed choice?  
In this study, we focus specifically on the personal information the Facebook apps can access on a user's profile. These apps serve as an example and case study for broader questions about user understanding of privacy in social media.  
We set out to answer three research questions:  
RQ1: How informed are users regarding the privacy risks of using Facebook apps?  
RQ2: Will viewing additional information about what Facebook apps can access affect the way users feel about the related privacy risks?  
RQ3: Are some methods of sharing this information more effective at educating users about the information that apps can access?  
To answer these questions, we conducted a between-subjects experiment with 120 subjects. Subjects completed a survey indicating their concern about Facebook privacy and their beliefs about what data Facebook apps could access. Then, they were divided into conditions where they viewed either the Facebook privacy policy or the Take This Lollipop interactive horror film (Figure 1). Then, subjects re-took the survey so we could judge if their feelings and beliefs had changed.  
We found substantial increases in privacy concerns and in the number of personal data attributes that our subjects believed apps could access. However, we also found that many users were still unclear about what data Facebook apps could access.
# Wide Area Key Distribution Network Based on a Quantum Key Distribution System
## 1 Introduction
Additionally, the relay nodes need encryption actions, decryption actions and storage of function, which increase the complexity of the system. In [25], [PERSON] and [PERSON] proposed a re-encryption key distribution scheme for relay QKD system, which is introduced in Section 2.1. Compared with BBN key relay protocol, the re-encryption scheme reduces the damage in case of the compromised node and reduces the storage memory of keys for relay nodes. However, in re-encryption scheme, the relay nodes still need to keep the XOR (exclusive OR) value of the QKD keys secretly, and the successive communications between adjacent relay nodes is also required. In 2013, \"Beijing-Shanghai Line\" of quantum secrecy communication is based on the public-XOR-key scheme, where each trusted relay node publicly announces the XOR key it holds, enabling the endpoints to share a key. Compared with the re-encryption scheme, the scheme simplifies the system's complexity and eases the traffic of the relay nodes. In 2014, [PERSON] et al. proposed a simplified trusted relay (STR) protocol for point-to-point [26]. It is similar to public-XOR-key scheme. The two endpoints of STR are connected by a series of trusted relays that announce the XOR keys, but the rest of the classic QKD post-processing needs to be performed based on data from its original terminal, which comes at the cost of a low key rate. Contributions: considering the key rate and the requirements of the long-distance communications with multiple endpoints, the public-XOR-key scheme is more efficient and practical than the BBN protocol, re-encryption protocol and STR. We extend the chain structure of the public-XOR-key scheme with two endpoints to the complex network with multiple endpoints. Our contributions are as follows. * Our scheme can reduce the memory complexity and heavy traffic of communications for relay nodes in real implementation. It is appropriate for the remote communications of the complex networks with multiple endpoints. Compared with other key distribution schemes based on relay nodes, our scheme reduces the complexity of the system and eases the traffic of the relay nodes. * Compared with the re-encryption schemes that does not announce the XOR value, our scheme not only improves the actual performance and simplifies the complexity of the system, it also does not weaken the security of the system. To analyze the security, we build the threat model and the security model of the public-XOR-key scheme. We rigorously demonstrate that the scheme is as secure as the re-encryption scheme under the same adversary model. In addition, we analyze the information leakage of practical QKD network from the perspective of [PERSON]'s ciphertext-only attack model by exploring the theory of unicity distance and applying it to the model of practical QKD networks.
## 3 Results and Discussion
### Performance Index Analysis
As can be observed, the MFNN-BA model had better performance than the MFNN-WA model, since most of the results has smaller RSRs than that of the MFNN-WA model. 3 NSE and PBIAS (Figures 12 and 13)  
There are general efficiency ratings for NSE and PBIAS as follows [20]:  
\[\text{Very good : }0.75\leq NSE\leq 1.00,PBIAS<\pm 15,\]  
\[\text{Good : }0.65\leq NSE\leq 0.75,\pm 15\leq PBIAS<\pm 30,\]  
\[\text{Satisfactory : }0.50\leq NSE\leq 0.65,\pm 30\leq PBIAS<\pm 55,\text{ and}\]  
\[\text{Unsatisfactory : } NSE\leq 0.50,PBIAS\geq\pm 55.\]  
It can be seen that the performance of ANFIS-BA was very good and good. In fact, NSE and PBIAS were separately computed for each month and then classified based on these efficiency ratings. To a large extent, the MFNN-WA model had unsatisfactory results. However, the MFNN-BA had a  
Figure 11: Standard deviation ratio (RSR) computations for different models. better performance than the other MFNN-WA models because the good and satisfactory ratings for the MFNN-BA model were more than those of the MFNN-WA model. Generally, the training level had a better performance than the testing level (Figures 12 and 13). Previous studies showed the application of the ANFIS model with other optimization algorithms [15]. For example, a genetic algorithm was used to improve the ANFIS model, and SSL was simulated. In fact, the ANFIS parameters were considered as the initial population of chromosomes [33]. Another study used the ANFIS model with particle swarm optimization (ANFIS-PSO) [34]. In fact, the ANFIS parameters were considered as the particle positions. Then, the velocity and position were updated for each level. The results of the current paper were compared to the ANFIS-PSO and ANFIS-GA models developed in previous studies, as seen in Figure 14. The Taylor diagram shows better performance of the ANFIS-BA model compared to the ANFIS-PSO and ANFIS-GA models. The Taylor diagram is based on standard deviation, correlation coefficient, and normalized RMSE. The ANFIS-BA model was closer to the observed or reference point compared to the other models. Thus, it had better performance than the other models. Figure 12: Computation of percent bias (PBIAS). Figure 9:0 Taylor diagram for the different models. Figure 9:1 Computation of Nash–Sutcliff efficiency (NSE).
# Performance Estimate of a Spin-Stabilized Drag Sail for Spacecraft Deorbiting
## 3 Spinning Drag Sail Dynamics
The set \(\mathcal{D}\) divides the osculating orbit into \(N-1\) time intervals in each of which the value of \(\rho\) is kept constant (equal to the average of the values calculated at the edges of each interval), then the iterative procedure is restarted with a new osculatingorbit defined by the MEOEs obtained at \(t=\widetilde{t}+\widetilde{T}\). In the simulations, a value \(N=5\) was chosen. The components of the perturbative acceleration due to Earth's oblateness \(\mathbf{a}_{P_{2}}\) are given in the RTN frame by Equation (13). When the instantaneous values of MEOEs are substituted into Equation (13), the components of \(\mathbf{a}_{P_{2}}\) are determined for each \(t\geq t_{0}\). Finally, the acceleration generated by the solar radiation pressure is obtained from Equation (14), which is conveniently written in the ECI frame. The Sun-spacecraft unit vector \(\mathbf{\hat{s}}\) in the ECI frame is calculated as a function of the spacecraft instantaneous orbital parameters by applying the algorithm discussed in Ref. [67]. The normal unit vector \(\mathbf{\hat{n}}_{\text{SRP}}\) is selected by applying the procedure detailed in Equation (15) and recalling that the components of \([\mathbf{\hat{n}}]_{\text{ECI}}\), given by Equation (18), are constant. The eclipse factor \(\eta\) is determined from \([\mathbf{\hat{s}}]_{\text{ECI}}\) and \([\mathbf{r}]_{\text{ECI}}\), the latter being calculated by substituting the MEOE into Equation (20). The perturbative term \([\mathbf{a}_{\text{PSRP}}]_{\text{ECI}}\) can then be calculated and its components in the RTN frame are given by \([\mathbf{a}_{\text{PSRP}}]_{\text{RTN}}=\mathbb{R}[\mathbf{a}_{\text{PSRP}}]_{\text{ ECI}}\), where \(\mathbb{R}\) comes from Equation (17).
## 2 Mathematical Model of Stepless Capacity Control System
### Computational Fluid Dynamics (CFD) Model of Reciprocating Compressor
#### 2.2.1 Flow Control Equations  
Any flow problem is governed by the three conservation laws of mass, momentum and energy. The differential equation is as follows:  
\[\frac{\partial\rho}{\partial t}+\
abla\bullet(\rho\overset{\rightarrow}{ \boldsymbol{U}})=0 \tag{6}\]  
\[\frac{\partial\left(\rho\overset{\rightarrow}{\boldsymbol{U}}\right)}{ \partial t}+\
abla\bullet(\rho\overset{\rightarrow}{\boldsymbol{U}} \overset{\rightarrow}{\boldsymbol{U}})=\rho\overset{\rightarrow}{ \boldsymbol{f}}+\
abla\bullet(T_{ij}\overset{\rightarrow}{\boldsymbol{e}} \overset{\rightarrow}{\boldsymbol{e}}\overset{\rightarrow}{\boldsymbol{e}} ) \tag{7}\]  
\[\frac{\partial}{\partial t}\left[\rho\left(e+\frac{U^{2}}{2}\right)\right]+ \
abla\bullet\left[\rho\left(e+\frac{U^{2}}{2}\right)\overset{\rightarrow}{ \boldsymbol{U}}\right]=-\rho\overset{\rightarrow}{\boldsymbol{f}}\bullet \overset{\rightarrow}{\boldsymbol{U}}-\
abla\left(\overset{\rightarrow}{ \boldsymbol{U}}\bullet T_{ij}\overset{\rightarrow}{\boldsymbol{e}} \overset{\rightarrow}{\boldsymbol{e}}\overset{\rightarrow}{\boldsymbol{e}} \right)-\rho\overset{\bullet}{\boldsymbol{q}}-\lambda\Delta T \tag{8}\]  
where, \(\rho\) represents the density, \(U\), \(\overset{\rightarrow}{\boldsymbol{U}}\) represents the velocity, \(\overset{\rightarrow}{\boldsymbol{U}}\) is vector, \(T_{ij}\overset{\rightarrow}{\boldsymbol{e}}\overset{\rightarrow}{\boldsymbol{ e}}\) represents the surface stress tensor, \(\overset{\rightarrow}{\boldsymbol{f}}\) represents the body force, \(\overset{\bullet}{\boldsymbol{q}}\) represents the internal heat source, \(\lambda\) represents the coefficient of thermal conductivity, and T represents the temperature.  
In order to close the above equation, the state equation of ideal gas needs to be supplemented:  
\[P=R\rho T \tag{9}\]  
where, \(P\) represents the pressure, \(R\) represents the gas constant.  
For the compressible gas under extreme conditions such as non-high temperature and high frequency sound waves, the constitutive equation is:  
\[P_{ij}=-p\delta_{ij}-2\mu(S_{ij}-\frac{1}{3}S_{kk}\delta_{ij}) \tag{10}\]  
where, \(-p\delta_{ij}\) represents the thermodynamic pressure, \(2\mu S_{ij}\) represents the stress deviator tensor, and \(-\frac{2}{3}\mu S_{kk}\delta_{ij}\) represents theIsotropic viscous stress.
# Achieving Low Latency Communications in Smart Industrial Networks with Programmable Data Planes
###### Abstract  
Industrial networks are introducing Internet of Things (IoT) technologies in their manufacturing processes in order to enhance existing methods and obtain smarter, greener and more effective processes. Global predictions forecast a massive widespread of IoT technology in industrial sectors in the near future. However, these innovations face several challenges, such as achieving short response times in case of time-critical applications. Concepts like in-network computing or edge computing can provide adequate communication quality for these industrial environments, and data plane programming has been proved as a useful mechanism for their implementation. Specifically, P4 language is used for the definition of the behavior of programmable switches and network elements. This paper presents a solution for industrial IoT (IIoT) network communications to reduce response times using in-network computing through data plane programming and P4. Our solution processes Message Queuing Telemetry Transport (MQTT) packets sent by a sensor in the data plane and generates an alarm in case of exceeding a threshold in the measured value. The implementation has been tested in an experimental facility, using a Netronome SmartNIC as a P4 programmable network device. Response times are reduced by 74% while processing, and delay introduced by the P4 network processing is insignificant.  
P4; data plane programming; IoT; industrial communications; time-critical IEEEexample:BSTcontrol