esa-sceva/satcom-chunk-collection huggingface dataset:
# A Microservices-Based Control Plane for Time-Sensitive Networking
## 4 Tests and Discussion of Results
### Performance Analysis
#### 4.3.2 Hardware Configurations Comparison  
In this case, we used GLPK as an ILP solver to determine how much the hardware affects timing to obtain a solution to the scheduling problem. We considered RAM size, the number of processor cores, and the operating frequency. In Figure 15, each graph refers to a different number of streams; it should be noted that each graph has a vertical axis that covers different ranges. The figure shows that the best results were obtained using the M5 Zn.large instance, although the difference with the local machine is not too large. The worst results were those obtained with the T3.small instance, which presents much greater solution times than the other two platforms. The T3.small instance has completely different hardware characteristics than the other two: it has much less RAM, fewer processor cores, and, perhaps the most important feature, its processor frequency is much lower than the other two platforms. On the other hand, the local machine and the M5 Zn.large instance only differ in their clock frequency, since they have the same number of cores and the same RAM.  
This test indicates that the hardware characteristics significantly affect the time to obtain a result. Tables 3-5 show the average, maximum, and minimum values for each test  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**GLPK**} & \multicolumn{4}{c}{**Gurobi**} \\ \cline{2-10}
**Streams** & **Average** & **Min** & **Max** & **Std. Dev.** & **Average** & **Min** & **Max** & **Std. Dev.** \\ \hline
15 & 4.768 & 1.895 & 27.772 & 5.621 & 0.620 & 0.542 & 1.410 & 0.182 \\
20 & 23.113 & 10.431 & 55.086 & 12.329 & 0.758 & 0.700 & 0.926 & 0.047 \\
25 & 58.503 & 30.810 & 80.636 & 13.622 & 0.978 & 0.909 & 1.439 & 0.111 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Seconds needed to find a solution for GLPK vs. Gurobi, depending on the number of streams.  
Figure 14: Time to find a solution: GLPK vs. Gurobi.
# Experimental and Numerical Investigation on Ballast Flight from Perspective of Individual Particles
## 3 Results and Discussions
### Simulation Results
#### 3.2.3 Influence of Wind Speed  
The pressure difference on the ballast surface of each group at wind speeds of 10 m/s, 20 m/s, 30 m/s, and 40 m/s respectively are shown in Figure 18.  
Figure 16: Contour of air pressure on the ballast surface.  
Figure 17: Streamline around the ballast particle.  
In Figure 18, the scattered points in dotted circles are the computational results of the air pressure difference, and the coordinates of the dotted circles are the average pressure difference of the six ballast models. It can be seen that the pressure difference of the ballast increases with the wind speed. The average pressure difference increases 373 Pa, 643 Pa, and 860 Pa respectively when the wind speed increases from 10 m/s to 40 m/s, and increases 14.9 times in total. The increasing range of the pressure difference also increases gradually with the wind speed. That is, there is a nonlinear relationship between the pressure difference and the wind speed, which is fitted by the power function. The fitting curve as well as the regression equation of each ballast is shown in Figure 18. The pressure difference of the ballast is proportional to the square of the wind speed based on the analysis of regression, which can be expressed with the power function as  
\[P=kv^{2} \tag{8}\]  
where \(v\) is the wind speed, \(k\) is the fitting coefficient. The value of \(k\) is related to the shape and windward direction of ballast. The ellipsoid ballast (B15_1) with long side facing the wind has the largest \(k\) of 1.3799, and the oblate ballast (C15) has the smallest \(k\) of 1.1360. The ballast with a higher \(k\) is easier to fly when the ballast mass is similar.  
It can be seen from Figure 18 and Equation (8) that the wind speed has a significant effect on ballast flight. Therefore, with the increasing of the train running speed, it is necessary to take measures to reduce the air pressure above the ballast bed, which could improve the stability of the ballast on the ballast bed surface and then avoid ballast flight. In case of special natural conditions such as strong wind, freezing weather, and heavy snow that might induce ballast flight, the running speed of train must be limited.
# Sampling-Based Motion Planning for Free-Floating Space Robot without Inverse Kinematics
## 2 Motion Equations of an FFSR
### Basic Equations of Motion in Position Form  
The basic equations of motion in position form only considers that the geometric relationship between rigid bodies belongs to the FFSR. From the base to the \(n^{th}\) link, the attitude transformation matrix between the fixed coordinate systems of adjacent rigid bodies is \({}^{i-1}\mathbf{R}_{i},i~{}=~{}1,2,\ldots,n\), and it is a function of the joint angle: \({}^{i-1}\mathbf{R}_{i}~{}=~{}f_{1}(\theta_{i}),i~{}=~{}1,2,\ldots,n\). \({}^{I}\mathbf{R}_{0}\) denotes the attitude transformation matrix from the base coordinate system to the inertial coordinate system. It is a function of the base attitude angle \({}^{I}\mathbf{R}_{0}~{}=~{}f_{2}(\mathbf{\Psi}_{b})\). Through the chain transformation, the attitude of each rigid body can be obtained, and it can be described by the attitude transformation matrix from the coordinate system of each rigid body to the inertial coordinate system:  
\[{}^{I}\mathbf{R}_{i}=^{I}\mathbf{R}_{0}{}^{0}\mathbf{R}_{1}{}^{1}\mathbf{R}_{2}\ldots{}^{i-1} \mathbf{R}_{i} \tag{1}\]  
As mentioned before, the configuration space for FFSRs includes joint space and base attitude space. Therefore, the configuration parameter of FFSRs includes \(\mathbf{\Theta}~{}=~{}\left[~{}\begin{array}{cccc}\theta_{1}&\theta_{2}&\ldots& \theta_{n}\end{array}\right]^{T}\) and \(\mathbf{\Psi}_{b}\). The above formula reflects the mapping relationship between the attitude of each rigid body and the configuration parameter. Let \(\mathbf{r}_{0}\) denotes the centroid position vector of the base; the centroid position vector of each rigid body can be obtained recursively from \(\mathbf{r}_{0}\):  
\[\mathbf{r}_{i}\ =\ \mathbf{r}_{0}+\mathbf{b}_{0}+\sum_{j\,=\,1}^{i-1}\big{(}\mathbf{a}_{j}+\mathbf{b} _{j}\big{)}+\mathbf{a}_{i},i\ =\ 1,2,\ldots,n \tag{2}\]  
The position vector of the end of each rigid body is still recursively obtained from \(\mathbf{r}_{0}\):  
\[\mathbf{p}_{i}\ =\ \mathbf{r}_{0}+\mathbf{b}_{0}+\sum_{j\,=\,1}^{i}\big{(}\mathbf{a}_{j}+\mathbf{b} _{j}\big{)},i\ =\ 0,1,2,\ldots,n \tag{3}\]  
Vectors can be added only if they are expressed in the same coordinate system. Therefore, the above vectors must be expressed in the inertial coordinate system when performing the calculation. Representations of \(\mathbf{b}_{i},\mathbf{a}_{i}\) in the inertial space are denoted as \({}^{i}\mathbf{b}_{i},{}^{i}\mathbf{a}_{i}\). \({}^{i}\mathbf{b}_{i},{}^{i}\mathbf{a}_{i}\), depending on the configuration parameter, so in order to calculate \(\mathbf{r}_{i}\) and \(\mathbf{p}_{i},\mathbf{r}_{0}\) and the configuration parameter must be known. In Section 2.3, we will show how to calculate \(\mathbf{r}_{0}\) when the configuration parameter is given.
# Real-Time Webcam Heart-Rate and Variability Estimation with Clean Ground Truth for Evaluation +
## 5 Experiments and Results
### In-Depth Analysis  
#### 5.3.1 Effect of Window Length on Heart Rate Computation  
Considering the choice of window length over which heart rate is computed is important. In principle, estimating the average heart rate over larger time windows is an easier task than estimating instantaneous heart rates over shorter windows. This is because variations in the inter-beat intervals caused by falsely detected and missed peaks average out over a large window length, thereby giving the evaluation a higher tolerance to incorrect peak detections. To illustrate this, Figure 6 plots the factor by which the error rate changes when using different window lengths with respect to the long-term average heart rate error over the whole video. It can clearly be seen that error rates increase exponentially with decreasing window lengths for most datasets. The datasets less affected by this are the ones on which errors caused by other factors (like movement, compression, etc.) overshadow the effect of window length (e.g., ECG-Fitness, MAHNOB-HCI). This experiment illustrates the severity by which the chosen window size can affect the heart rate estimation accuracy and this should be taken into consideration during comparative evaluations.  
Figure 6: Heart rate estimation error rate (MAE) ratio with varying time-window lengths with respect to average long-term heart rate estimation error over the full video. The graph shows that the task of HR estimation becomes exponentially harder with decreasing window lengths. For example, error rate on PURE over 2-s window is 7 times worse than over the full video.  
The rest of the experiments in this paper are performed with a window length of 16 s for heart rate estimation.
# Transitioning to Society 5.0 in Africa: Tools to Support ICT Infrastructure Sharing
## 1 Introduction
### Aims and Objectives  
The aim of this research was to validate the concept of--and identify the potential of--telecoms infrastructure sharing opportunities in Nigeria through the development of supporting ICT tools and their application to a real case study.  
The specific objectives of this research study were as follows:  
* To review arguments and evidence that identifies the current problems associated with the lack of BTS infrastructure sharing and related problems linked to CO2 emissions in Nigeria;
* To develop mathematical models and a related optimization and decision support tool to assist with mast colocation decisions involving BTSs;
* To apply the developed mathematical and optimization approaches to a case study to provide initial supporting evidence that colocation of BTSs, if conducted appropriately using decision support tools, can reduce costs and CO2 emissions while maintaining adequate network service in Nigeria.
## 1 Introduction
The third method extrapolates the void ratio- or volumetric strain-dilatancy (\(e\)-\(D\) or \(\varepsilon_{\text{v}}\)-\(D\)) trend to the critical state condition of a zero dilation rate \(D=0\), where \(D=\text{d}\varepsilon_{\text{v}}^{p}/\text{d}\varepsilon_{q}^{p}\) and the superscript \(p\) indicates the 'plastic' component of the strain increment [24; 29; 30; 31]. The \(e\)-\(D\) or \(\varepsilon_{\text{v}}\)-\(D\) data set from the minimum dilation rate \(D_{\text{min}}\) (because of the compression positive convention) to the end of the test is used for extrapolation. Simple linear extrapolation has been consistently adopted in the last two methods, making the methods attractive for estimating the critical state void ratio \(e_{\text{cs}}\). There nevertheless remains a lack of knowledge about which of the \(e\)-\(\eta\), \(e\)-\(D\), and \(\varepsilon_{\text{v}}\)-\(D\) data sets is best suited for linear extrapolation and how dependent the goodness-of-fit of the linear function is on the size (or the start point) of the data set. In this paper, a state-dependent constitutive model for cohesionless soils developed by [PERSON] [32] is used to perform a series of numerical simulations of drained triaxial compression tests on Toyoura sand. Multiple sets of \(e\), \(\varepsilon_{\text{v}}\), \(\eta\), and \(D\) data are derived from the simulations. The accuracy of the critical state void ratios estimated from linear extrapolation of the \(e\)-\(\eta\), \(e\)-\(D\), and \(\varepsilon_{\text{v}}\)-\(D\) data sets is examined. The data set best suited for linear extrapolation is determined, and then the effect of its size on the goodness of fit of the linear function is evaluated using experimental data freely available from [PERSON]'s homepage on the torsten-wichtmann.de website [33]. Guidance is obtained on the start point of the data set required to reach a certain high level of the coefficient of determination.
## 3 Methodology  
Figure 1 shows the block diagram of the proposed sleep stage classification framework. First, the power spectral density is calculated for individual channels of the EEG data for both the genders. After that, ResNet is trained using the PSD of individual channels as input, to identify different sleep stages based upon input from individual channels that is then combined in subsequent stages. The ResNet is individually trained to identify sleep stages in each gender, and its accuracy is also calculated individually for both genders.
## 3 Results and Discussion
### Flow Field Analysis for Multi-Chamber Model
#### 3.1.1 Overall Flow Field Distribution
This uneven loading results in a significant reduction in the overall filtration efficiency of the system. The pressure contours shown in Figure 6b indicate that the pressure near the outermost wall along the air inflow in each chamber is higher, as the kinetic energy of the high-velocity airflow is converted into pressure energy upon impacting the wall. The pressure in Chamber 1 is the lowest, followed by Chambers 4, 3, and 2. Despite variations in internal pressure, the inlet-outlet pressure drops for the chambers are nearly identical: PC1 = 105.02 Pa, PC2 = 104.83 Pa, PC3 = 104.31 Pa, and PC4 = 105.15 Pa. These results suggest that the inlet-outlet pressure drops are consistent, despite the internal pressure variations across the chambers. To investigate airflow distribution in different regions of each chamber along the direction of the inlet airflow, cross-sections along the \(Z\)-axis were analyzed, with the results shown in Figure 7.  
It can be observed that the airflow velocity near the filter bag bottom and close to the walls is higher, leading to accelerated wear in these regions. In the central section of the filter chamber, both velocity and pressure distributions are relatively uniform, indicating that airflow within the middle section of the filter bag remains stable. At the outlet end near the tubesheet, the internal-external pressure difference of the filter bags is larger compared to the middle section, resulting in an increase in airflow velocity. This is due to the upward airflow encountering the tubesheet, causing the conversion of kinetic energy into pressure energy. The internal-external pressure difference across the filter bag shows a distinct pressure jump, reflecting the resistance characteristics of the filter bag as a porous medium. A comprehensive analysis of the flow field distribution at various cross-sections of the multi-chamber model reveals several consistent patterns across the chambers. In the filter chamber, the central region exhibits a relatively uniform velocity field, while the areas near the bag bottoms and chamber walls experience higher flow velocities. This variation is primarily due to the high-velocity jet entering the hopper, which encounters obstruction by the outer hopper walls. This causes the jet to diffuse laterally, generating an upward vortex that increases the flow velocity near the side walls. Meanwhile, the central region of the chamber maintains lower velocities. This uneven flow field leads to non-uniform airflow between the filter bags, resulting in varying degrees of wear on each bag. Additionally, lateral vortices may form beneath the bags, accelerating their deterioration and complicating the detection of damaged bags. This makes it more difficult to determine the optimal time for bag replacement, further affecting the overall performance and longevity of the filter system.
# A Method to Optimize Routing Paths for City-Pair Airlines on Three-Layer Air Transport Networks
## 3 Routing Paths Optimization of City-Pair Airlines
#### Objective Function  
In this subsection, we will introduce the objective function of routing path optimization, which is defined as follows:  
\[Obj=\alpha\cdot Dis+\beta\cdot TL+\gamma\cdot SL \tag{2}\]  
where \(\alpha\), \(\beta\), \(\gamma\) are balance factors and \(\alpha+\beta+\gamma=1\). Because of the difference between the orders of magnitude of the three objectives, it is difficult to choose the balance factors, so the three objectives are normalized in our work.  
_Dis_ is the total distance of the optimized routing paths for all airlines.  
\(TL\) is the total traffic load of \(NL\) time intervals, which is calculated as follows:  
\[TL=\sum_{t=0}^{NL}tl_{t} \tag{3}\]  
where \(tl_{t}\) is the traffic load of a time interval \([t,t+T]\), which represents the maximum traffic flows across all air route segments in a time interval \([t,t+T]\) based on the flight operation network \(G_{FN}\), that is, \(tl_{t}=\max_{c\in R(G_{ARN})}e^{t}\).  
\(SL\) is the total segment load of all air route segments, which is calculated as follows:  
\[SL=\sum_{c\in R(G_{ARN})}sl_{c} \tag{4}\]  
Figure 5: Optimization flow of routing paths for airlines.  
where \(sl_{e}\) is defined as the segment load, which represents the maximum traffic flows on air route segment \(e\in R(G_{ARN})\) of \(NL\) time intervals based on the flight operation network \(G_{FN}\); that is, \(sl_{e}=\max_{0\leq t\leq NL}e^{t}\).  
In the definitions of \(TL\) and \(SL\), a variable \(e^{t}\) is introduced. The \(e^{t}\) is defined as the air traffic flows on the air route segment \(e\) in time interval \([t,t+T]\). Because of the departure airport and departure time for each flight in \(G_{FN}\) are given, we can calculate the arrival time of each air route segment across the optimized routing path by using historical flight times based on machine learning.
## 2 Literature Review
### Automated Safety Monitoring Systems for Real-Time Environments
They placed four UWB anchors at the corner of a 30 \(\times\) 10 m field. The system tracks the location of workers carrying UWB tags and sends an alarm if the location is within the \"red area\" representing hazardous zones on the map. Prior research with the Zebra Sapphire DART UWB system in a construction-like scenario was able to achieve tens of centimeters of localization accuracy [13; 35]. The UWB system used in this study, however, requires wires to connect the anchors to the hub making it inappropriate for real-world construction scenarios. There is a newer generation of fully wireless UWB systems which have not been extensively tested in a construction environment. UWB localization based on Ubisense chip was reported to have achieved tens of cm of accuracy even with mobile objects [36; 37]. Researchers have reported accuracy of up to 2 cm in industrial applications with Decawave UWB system [18] and some other work has reported Decawave UWB to be superior to BeSpoon UWB [38]. Thus, the newer generation of UWB systems holds a lot of promise in introducing precise location tracking but they have not been customized and tested for the unique constraints of construction sites and environments.
###### Abstract  
Subcarrier free-space optical (FSO) systems using coherent recovery techniques at the receiver have acquired growing research interest in recent times. However, their optimal performance is diminished by the non-perfect synchronization of carrier frequency and phase, which is mainly due to phase noise problems. Moreover, turbulence and pointing error effects further deteriorate the overall performance. However, relay transmission schemes can extend the coverage distance and offer substantial improvements over fading conditions. In this respect, we consider a serially relayed network using decode-and-forward relays, and investigate its performance by means of average symbol error probability and mean outage duration. Turbulence is modeled by the recently unified \(M\)(alaga) distribution, which constitutes a very general statistical model that accurately describes the irradiance fluctuations from weak-to-strong turbulence conditions. Additionally, the presence of non-zero boresight pointing errors due to misalignment between the transmitter-receiver pair is considered, while the effect of phase noise is specified by a Tikhonov distribution. A comparison between single line-of-sight and serially relayed FSO configurations is provided as well. Novel approximated mathematical expressions are deduced, which are proved to be accurate enough over a wide range of turbulence strengths and signal-to-noise values. Finally, proper numerical results are presented and validated by Monte Carlo simulations.  
free-space optics (FSO); multi-hop transmission; atmospheric turbulence; pointing errors; phase noise +
Footnote †: journal: Applied Physics Letters
## 1 Introduction
### The VICINITY Project  
The goal of the VICINITY project is to develop a platform that connects isolated IoT infrastructures into one global ecosystem called virtual neighborhood where users can select to which other systems their smart objects should be connected in a peer-to-peer network. The platform automatically supports interoperability from technical up to semantic level. The semantic interoperability of smart objects coming from different operators and using different standards is enabled with the semantic model integrated into the VICINITY platform [3].  
The VICINITY architecture offering interoperability \"as a service\" is shown in Figure 1. It is based on a decentralized, bottom-up and cross-domain approach that resembles a social network, where users can configure their setups, integrate standards according to the services they want to use and fully control their desired level of privacy in a P2P (peer-to-peer) network.  
At the end of the project, VICINITY's approach will be demonstrated on four pilot sites coming from the following different domains: energy, building automation, health, and transport. VICINITY's potential to create new, cross-domain services will be demonstrated by value added services, such as micro-trading of demand-side management capabilities, AI-driven optimization of smart urban districts and business intelligence over IoT.  
The focus of the work presented in this paper is to build a virtual environment for simulation and validation of IoT infrastructures and their use cases in real life scenarios before their real deployment. Here, a smart energy use case at one of the VICINITY pilot sites, located in Tromso, Norway, is used for demonstration.
## 2 Related Works
### Activation-Based Methods
#### 2.4.3 Ablation-CAM  
In Ablation-CAM [8], the authors replaced the use of gradient information in Grad-CAM when estimating the importance of each activation channel \(A^{k}\) using different types of scores: \(\alpha_{k}^{c}=\frac{y^{c}-y_{k}^{c}}{y^{c}}\), where \(y^{c}\) is the prediction score of the class \(c\) with the original image and \(y_{k}^{c}\) is the score obtained by removing the \(k\)-th channel from the activation by setting the values to the zero value. Then, the attribution map is generated as follows, similarly to Grad-CAM: \(I_{\text{Ablation-CAM}}=\mathcal{S}\Big{(}\mathcal{T}\Big{(}\text{ReLU}\Big{(} \sum_{k}\alpha_{k}^{c}A^{k}\Big{)}\Big{)}\Big{)}\). It has been shown that Ablation-CAM can produce better attribution maps than Grad-CAM.