esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Efficiency of a Decode-and-Forward Multiple-Relay Network with Rate Adaptive LDPC Codes +
## 3 Power Allocation in Dual-Hop Networks
### Equal Power Allocation (EPA)  
Equal power allocation to all transmitting nodes reduces the computational complexity of a wireless network, keeping in view the simplicity of the power allocation algorithm. In this Section, we investigate the equal power allocation in two contexts:  
1. Equal power allocation, without code rate adaptation (EPA-I). In this case, we assume that the same code rate is applied on both hops. Hence, the relays do not perform any temporal buffering because of the same input and output data rates. This assumption makes the implementation of such a network straightforward. For EPA-I there is no power adaptation and no code rate adaptation on the relays but equal transmit power is allocated to every transmitter regardless of its needs;
2. Equal power allocation with code rate adaptation on relays based on feedback from destination (EPA-II). EPA-II is also equal power allocation but code rate adaptation is done here, i.e., a higher code rate can be adapted if channel conditions are better. With bad channel conditions, a lower code rate is chosen to send the data from the relays. However, the transmit power at relays remains the same. For EPA, in Equation (9) \(P_{i}\) denotes the transmit power of the \(i^{th}\) relay, under the constraint that: \[P_{i}=P_{i}^{max},\ \ \forall i,\] (10)  
where \(P_{i}^{max}=P_{s}\), yielding \(P_{i}=P_{s}\) for all \(i\).
## 4 Analysis of the Main Objectives
### Limitations and Effectiveness of Virtual Reality  
Patients often respond positively to VR rehabilitation due to its engaging and enjoyable nature, sense of control, personalized approach, feedback mechanisms, safety features, and innovative appeal. But not all patients may respond equally to VR interventions, as individual preferences, abilities, and cognitive impairments can influence treatment effectiveness [10]. Additionally, some patients may experience side effects such as motion sickness or cybersickness, which can limit the duration and intensity of VR sessions [31]. In this review, however, no adverse reactions were reported by the researchers, and there were no dropouts due to any difficulties encountered in the use of VR technology devices. Still, one notable advantage of VR compared with other therapies is its ability to provide personalized rehabilitation programs in order to prevent the effectiveness of the treatment. Through customizable scenarios and feedback mechanisms, therapists can adjust the intensity and difficulty of tasks to match the patient's abilities, facilitating gradual progress and improving treatment outcomes. Moreover, VR offers a safe environment for patients to practice activities that may be challenging, especially for people with MS or who otherwise find such activities inaccessible in their lives, such as walking on uneven terrain or navigating obstacles [32]. Also, traditional rehabilitation exercises can be repetitive and monotonous, leading to low adherence rates. VR addresses this issue by providing interactive and enjoyable activities that capture the patient's attention. By making therapy sessions more engaging, VR increases motivation and encourages patients to adhere to their treatment regimen. VR platforms can incorporate cognitive training exercises specifically designed to target cognitive impairments commonly associated with MS, such as attention, memory, processing speed, and executive function. These exercises can be adapted to the individual's cognitive abilities and progressively adjusted to challenge their cognitive skills, promoting cognitive improvement over time [10; 32]. Although it closely matches the treatment outcomes for balance disorder rehabilitation compared to other traditional rehabilitation methods, VR technology used in the rehabilitation of MS patients provides concurrent benefits for other symptoms faced by MS patients and contributes overall to improving the quality of life of these patients.
# Dynamic Graph Convolutional Crowd Flow Prediction Model Based on Residual Network Structure
## 4 Experiments and Analysis
### Experimental Environment and Parameter Settings  
The NVIDIA Tesla T4 graphics card was used for the experiments in this study.  
To assess the effectiveness of the experimental models, we followed the commonly used methods in the field of crowd flow prediction. The Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) were adopted to assess the errors between the predicted values and the ground truth values. The equations for RMSE and MAE are shown in Equations (13) and (14), respectively:  
\[RMSE=\sqrt{\frac{1}{n}{\sum\limits_{i=0}^{n}}{\left({Y_{i}-\hat{Y}_{i}}\right) ^{2}}} \tag{13}\]  
\[MAE=\frac{1}{n}{\sum\limits_{i=0}^{n}{\left|{Y_{i}-\hat{Y}_{i}}\right|}} \tag{14}\]  
where \(n\) is the number of samples, \(Y_{i}\) is the ground truth value, and \(\hat{Y}_{i}\) is the predicted result of the model:  
Based on the grid search method, the optimal parameters for crowd flow prediction using Res-DGCN are determined and presented in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **TaxiNYC** & **BikeNYC** \\ \hline Number of historical time intervals & 5 & 5 \\ Loss function weights \(\beta\) & 5 & 70 \\ Huber function parameter \(\alpha\) & 15 & 30 \\ Number of DGCN layers & 4 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model parameter settings.  
Figure 5: (a) BikeNYC outlier histogram; (b) TaxiNYC Outliers Histogram.  
The historical time interval of 5 indicates that the model uses the past five hours of historical data to predict the result for the next hour, which is a single-step prediction. For instance, to predict the crowd flow data at time step 5, the input consists of the crowd flow data at time steps 0, 1, 2, 3, and 4. For the experiment, the dataset is split into three sections: the training set, validation set, and test set, with corresponding temporal fractions of 80%, 10%, and 10%.
## 3 Results
### Daily Physical Activity
The participants overall spent 3.7 h (SD 1.8) standing, 1.4 h (SD 0.8) walking, 3.8 min (9.0) cycling, 7.0 h (SD 3.1) in sedentary activities, and had 43 (SD 27) sit to stand transfers. The oldest age group had the highest daily average sedentary time, and the youngest age group had a higher average cycling time compared to the rest of the age groups (Table 2). Men and women had the same average daily walking time, standing time, and number of sit to stand transfers. For the individual age groups, walking time and number of sit to stand transfers differed slightly between men and women. Women had a higher average standing time than men in the higher age groups, but a lower average standing time than men in the lower age groups. Furthermore, men had a higher average daily cycling time compared to women overall and the majority of the individual age groups. Moreover, men had a higher average sedentary time compared to women (7.2 h, SD 3.2 vs. 6.7 h, SD 3.1), which also differed when examining the individual age groups (Table 3). \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Dimension** & **Total** & **18–29** & **30–39** & **40–49** & **50–59** & **60–69** & \(\geq\)**70** \\ \hline Parameter & Mean (SD) & Mean (SD) & Mean (SD) & Mean (SD) & Mean (SD) & Mean (SD) & Mean (SD) \\ \hline Frequency & & & & & & & \\ Steps, n & 6095 (3351) & 6563 (3894) & 5688 (3827) & 6028 (3612) & 6086 (3785) & 6874 (2954) & 5406 (2254) \\ \hline Intensity & & & & & & & \\ Cadence, steps/min & 98.5 (9.0) & 99.6 (6.4) & 103.4 (7.8) & 100.4 (5.5) & 98.9 (10.8) & 97.9 (9.5) & 94.8 (8.9) \\ Very low intensity, \% & 75.6 (13.3) & 69.7 (16.8) & 75.8 (13.2) & 73.2 (13.7) & 74.3 (14.4) & 74.7 (11.4) & 82.5 (8.7) \\ Low intensity, \% & 13.0 (6.8) & 15.9 (8.8) & 12.7 (6.4) & 13.8 (7.4) & 13.6 (6.7) & 13.2 (5.7) & 10.1 (5.4) \\ Moderate intensity, \% & 6.8 (4.3) & 8.2 (5.7) & 6.4 (4.3) & 7.4 (4.3) & 7.1 (5.1) & 7.1 (3.6) & 5.0 (2.6) \\ High intensity, \% & 4.7 (3.3) & 6.1 (3.2) & 5.0 (3.3) & 5.6 (3.1) & 4.9 (3.7) & 5.0 (3.2) & 2.4 (1.7) \\ \hline Time & & & & & & & \\ Wear time, h & 12.2 (3.7) & 11.3 (4.2) & 10.4 (3.5) & 10.3 (3.8) & 11.3 (3.6) & 12.9 (3.0) & 15.0 (1.8) \\ Standing, h & 3.7 (1.8) & 3.3 (1.9) & 3.6 (1.7) & 3.3 (1.6) & 3.7 (2.2) & 3.9 (1.9) & 4.0 (1.6) \\ Walking, h & 1.4 (0.8) & 1.5 (0.9) & 1.3 (0.9) & 1.4 (0.9) & 1.4 (0.9) & 1.6 (0.7) & 1.3 (0.5) \\ Cycling, h & 3.8 (9.0) & 8.0 (12.6) & 1.5 (3.2) & 3.3 (11.6) & 2.7 (6.1) & 3.7 (7.6) & 4.4 (9.5) \\ Sedentary, min & 7.0 (3.1) & 6.2 (3.0) & 5.5 (2.8) & 5.6 (2.7) & 6.1 (3.0) & 7.3 (2.7) & 9.7 (2.4) \\ \hline Type & & & & & & & \\ Sit to stand transfers, n & 43 (27) & 42 (21) & 43 (28) & 38 (15) & 44 (32) & 43 (18) & 47 (38) \\ Cycle rotations, n & 183 (480) & 415 (759) & 83 (201) & 175 (649) & 127 (282) & 149 (298) & 208 (517) \\ \hline \end{tabular} Data are presented according to the FITT model (frequency, intensity, time, and type); women; SD, standard deviation.  
\end{table} 
Table 2: Daily Physical Activity According to Age Group.
# Comparison of SNAP-Derived Sentinel-2A L2A Product to ESA Product over Europe
###### Abstract  
Sentinel-2 is a constellation of two satellites launched by the European Space Agency (ESA), respectively on 23 June 2015 and 7 March 2017, to map geophysical parameters over land surfaces. ESA provides Level 2 bottom-of-atmosphere reflectance (BOA) products (ESA-L2A) for Europe, with plans for operational global coverage, as well as the Sen2 Cor (S2C) offline processor. In this study, aerosol optical thickness (AOT), precipitable water vapour (WVP) and surface reflectance from ESA-L2A products are compared with S2C output when using identical input Level 1 radiance products. Additionally, AOT and WVP are validated against reference measurement. As ESA and S2C share the same input and atmospheric correction algorithm, it was hypothesized that they should show identical validation performance and that differences between products should be negligible in comparison to the uncertainty of retrieved geophysical parameters due to radiometric uncertainty alone. Validation and intercomparison was performed for five clear-sky growing season dates for each of three ESA-L2A tiles selected to span a range of vegetation and topography as well as to be close to the AERONET measurement site. Validation of S2C (ESA) products using AERONET site measurements indicated an overall root mean square error (RMSE) of 0.06 (0.07) and a bias of 0.05 (0.09) for AOT and 0.20 cm (0.22 cm) and the bias was \(-\)0.02 cm (\(-\)0.10 cm) for WVP. Intercomparison of S2C-L2A and ESA-L2A showed an overall agreement higher than 99% for scene classification (SCL) maps and negligible differences for WVP (RMSE under 0.09 and \(R^{2}\) above 0.99). Larger disagreement was observed for aerosol optical thickness (AOT) (RMSE up to 0.04 and \(R^{2}\) as low as 0.14). For BOA reflectance, disagreement between products depends on vegetation cover density, topography slope and spectral band. The largest differences were observed for red-edge and infrared bands in mountainous vegetated areas (RMSE up to 4.9% reflectance and \(R^{2}\) as low as 0.53). These differences are of similar magnitude to the radiometric calibration requirements for the Sentinel 2 imager. The differences had minimal impact of commonly used vegetation indices (NDVI, NDWI, EVI), but application of the Sentinel Level 2 biophysical processor generally resulted in proportional differences in most derived vegetation parameters. It is recommended that the consistency of ESA and S2C products should be improved by the developers of the ESA and S2C processors.  
Sentinel-2; Sen2 Cor; ESA-L2A; S2C-L2A
## 3 Concepts, Models and Technologies
### Software-Defined Network (SDN)  
An SDN, software-defined network, is by definition an emerging, dynamic, cost-effective, manageable and highly adaptable architecture; in short, an ideal solution for the dynamic and broadband nature of real-world applications. The SDN architecture separates the network control and packet forwarding functions, allowing for network control to be directly programmable on switches and routers, and the infrastructure, in turn, to be made a new abstraction for network applications and services, according to the definition of the Open Network Foundation itself.  
The advent of SDN initially brought _Openflow_[12] a way for researchers to program the API and open network interface of switches from various worldwide manufacturers, creating a parallel research network that was later used for business or production environments in the cloud. Although SDN is present in IoT research, its adoption remains  
Figure 2: Cloud, Fog and Intelligent Edge Computing (IEC).  
moderate, and the fundamental drivers for this gradual implementation are not well understood. Current studies tend to prioritize the technical aspects of SDN integration with Edge Computing and Fog Computing, highlighting a significant need for further research into the motivations behind SDN adoption in IoT, as seen in the articles [13; 14; 15].  
Fog and Edge computing architectures leverage and complement their implementations with SDN technology, which offers lower costs (as it operates under an open source model) and enhanced security through separation of the data and control planes in network SDN switches. Additionally, the large-scale adoption of SD-WAN, replacing MPLS-VPN for communication between Smart Buildings (SBs) or between an SB and its operations center within a Smart City or a federated SB neighborhood, further reinforces this integration.  
Thus, in the present study, the focus on SDN is not on determining superiority or inferiority between these technologies but rather on emphasizing the cooperation among integrated architectures: Fog, Edge and SDN (SD-WAN).  
This integration brings several benefits, such as reduced latency, enhanced security and an open source application development platform with APIs supported by a diverse developer community using multiple and common programming languages, including Java, Python, C and C++. All these elements work in a distributed computing framework, utilizing SDN switches and cloud providers' microservices, which enable container orchestration mechanisms within a multi-cloud and SDN environment.
## 4 Automated Online Traffic Collection, Sample Construction, and Correlation
### Cross-Platform Traffic Collection and Sample Construction
#### 4.2.2 Linux  
As a server-level OS, the Linux platform has the particular condition of persistently high traffic and multiple users. We obtain the \"UID-PID\" (User IDentifier) tree relationship by monitoring the generation and invocation of the kernel state process in real-time to isolate the same application traffic of different users.  
Figure 4: A3C-Windows sample collection and construction.  
In addition, there are some differences in the system mechanics between Linux and Windows. Linux is an OS that relies entirely on the file system, even though some of the files under the path are virtual and exist in memory. Therefore, to meet the continuous high concurrency under Linux, similar to Windows, we obtain the mapping relationship between Linux in-memory process and network-related information. However, since Linux does not have a direct port-PID mapping, we do the correlation by reading the _inode_ intermediate state. Finally, the continuous association of \"port-PID-process name-application-user\" is realized.  
The A3C-Linux sample collection and construction procedure is demonstrated in Figure 5.  
As seen from the flowchart, there is not much difference between A3C-Linux and A3C-Windows during the initialization phase. However, since Linux depends on the file system, the ETT and EUT that should be obtained from the API in Windows need to be fetched from TCP and UDP in the /proc/net path to obtain the inode in Linux. Since the nature of the /proc path is to save all kinds of process-related information, we need to find the PID matching the inode through regular matching to achieve PP mapping.  
Like Windows, semi-automated and fully-automated sample collection builds are easy to implement. However, if the device deploying A3C-Linux is a server, the server port  
Figure 5: A3C-Linux sample collection and construction.  
is multiplexed (multiple clients communicate through the same server port). Therefore, bidirectional port numbers must be considered when associating processes with traffic.
## 2 Data and Methods
### Input Data: The Western Mediterranean OPerational (WMOP) Model  
A high resolution OGCM of the western Mediterranean region provides input data for the SWOT simulator. We used the WMOP model [31] developed at SOCIB (Balearic Islands Coastal Observing and Forecasting System). More specifically, we consider a 7-year free run simulation of the model spanning the period 2009 - 2015, with spatial coverage from the Strait of Gibraltar to the Sardinia Channel (Figure 2). WMOP is a regional configuration of the Regional Oceanic Model System (ROMS) model [32] with a spatial resolution of approximately 2 km. WMOP is forced with high resolution atmospheric forcing (HIRLAM model from the Spanish Meteorological Agency AEMET), with temporal resolution of 3 h and spatial resolution of 5 km. These features make WMOP a suitable choice to evaluate the potential of SWOT-derived SSH data to resolve mesoscale processes in the western Mediterranean Sea. The presence of fine scale features of a few kilometers is illustrated in Figure 2. In Figure 2, we show snapshots of model relative vorticity (normalized by f) for days corresponding to pass 15 (Figure 2, left) and pass 168 (Figure 2, right) of cycle 2 of the SWOT orbit (see Figure 3).  
Figure 1: Flowchart of the SWOT simulator procedure.
## 4 Results and Discussion
### Estimation of the Gas/Liquid Interfacial Area  
It was observed during the performance of the experiments that the foam created above trays decreased when the pressure was increased. The effect of pressure increase on the reduction of the foam increased when the inlet gas flow rate was increased. For the estimation of the gas/liquid interfacial area, the validated rate-based model was used. Figure 6 shows the estimated gas/liquid interfacial area calculated by [PERSON]'s (1982) correlation [23] when the pressure is increased. As can be seen in Figure 6, at 15 Nm\({}^{3}\)/h of the inlet gas flow rate, the gas/liquid interfacial area distinctly decreases when pressure is increased up 0.23 MPa, whereas the gas/liquid interfacial area slightly decreases when the pressure is increased between 0.23-0.3 MPa. In contrast, the gas/liquid interfacial area significantly decreases when pressure is increased between 0.2-0.3 MPa at 20 Nm\({}^{3}\)/h of the inlet gas flow rate. The trend may be explained when consideration is taken of the fact that the gas/liquid interfacial area depends on superficial velocity, according to [PERSON]'s (1982) correlation [23]. The gas/liquid interfacial area increases when the superficial velocity of the gas is increased, and vice versa. With a constant inlet mass flow rate of the gas, the superficial velocity decreases when pressure is increased ([PERSON] 1996) [12], which leads to a decrease in the interfacial area according to [PERSON] (1982) [23].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Pressure (MPa)** & **Inlet Gas Flow Rate (15 Nm\({}^{3}\)/h)** & **Inlet Gas Flow Rate (20 Nm\({}^{3}\)/h)** \\ \hline
0.2 & 1.195\% & 0.262\% \\
0.21 & 0.856\% & 0.018\% \\
0.22 & 0.711\% & 0.308\% \\
0.23 & 0.551\% & 0.511\% \\
0.24 & 0.591\% & 0.764\% \\
0.25 & 0.6182\% & 1.063\% \\
0.26 & 0.528\% & 1.379\% \\
0.27 & 0.104\% & 1.678\% \\
0.28 & 0.008\% & 1.785\% \\
0.29 & 0.085\% & 2.017\% \\
0.3 & 0.062\% & 2.271\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: The relative error between the values calculated using the rate-based model and those obtained from empirical measurement at inlet gas flow rates of 15 and 20 Nm\({}^{3}\)/h.  
Figure 6: Effect of the pressure on the gas/liquid interfacial area at inlet gas flow rates of 15 and 20 Nm\({}^{3}\)/h.
## 4 Results and Discussion
### Spectral Signature of Vegetated Structures
This provides a correct validation for the images from 2016 (Figure 8). Comparing these field data with the oldest image from our dataset (2013), a good match is observed with the knowledge of species composition and patch positions from the most recent data. For this reason, we consider that our ground truth data are also valid for the image of April 2013. Figure 7: Salt marsh evolution at the study site of Andernos between 1949 and 2016. Dates up to 2004 correspond to airborne aerial photographs, and the image from 2016 was acquired by drone. Two zones of _Spartina maritima_ dominance are indicated by red boxes, and _Spartina anglica_ dominance patches are indicated by the blue box in the drone image. The bigger red box corresponds to the patch considered in the text as the main marsh structure. The appearance of the invasive _Spartina_ is indicated in the 1993 image by the blue arrow. Figure 8: Overlay of field GNSS marsh vegetation contours (26 October 2016) over (**a**) the drone image (21 June 2016); (**b**) the Pleiades image from 25 April 2013; and (**c**) the Pleiades image from 10 October 2016. Yellow contours correspond to patches dominated by the native _Spartina maritima_, red contours correspond to patches of the invasive _Spartina anglica_, and the orange contour delineates an intrusion zone of other types of vegetation. Image a is displayed in true color, while images b and c correspond to a RGB composite, with independent contrast enhancements to highlight the vegetated features of interest.