esa-sceva/satcom-chunk-collection huggingface dataset:
# Low-Cost Solution for Kinematic Mapping Using Spherical Camera and GNSS
## 3 Results  
In this section, results from various methods of evaluating point cloud accuracy and quality are presented. First, the relative accuracy of the measured point cloud is evaluated by comparing the clouds; then, the section deals with the absolute accuracy of the measured cloud relative to the ground checkpoints measured by GNSS RTK. The last topic in this  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & **Number** & **Total Computation** & **St. Dev. Y of** & **St. Dev. Z of** & **St. Dev. Z of** & **St. Dev. XYZ** \\  & **of Images** & **Time of Dense** & **GCPs [mm]** & **GCPs [mm]** & **GCPs [mm]** & **GCPs [mm]** \\ \hline Fleming & \multirow{2}{*}{406} & \multirow{2}{*}{6 h 30 min} & \multirow{2}{*}{22} & \multirow{2}{*}{6} & \multirow{2}{*}{39} & \multirow{2}{*}{46} \\ Square & & & & & \\ Lipová street & 1394 & 8 h 10 min & 48 & 35 & 118 & 132 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Agisoft Metashape processing parameters.  
chapter is the evaluation of the quality of the data obtained from the Insta360 X3 camera. Here, the quality of the original data output is also compared with the AI-edited data.
# _Sci_
## 4 Discussion
### Modifying Satellite ICDS and KPPs for Laser Communications
#### 4.3.1 Key Performance Parameter (KPP) #1: Adaptive Optics  
Regardless of the recommendation selected, the first key performance parameter to address regards optics. Due to unpredictable and changing atmospheric conditions, there is a need for adaptive optics somewhere in the system. The optics are better positioned on the terrestrial end of the system to remove vibration- and jitter-induced bit error rate (which will have to be examined for air and ship platforms) along with addressing adaptive transmit power and beam broadening [37]. The optics must also compensate for various sources of optical noise, including shine from the sun, moon, stars, and other high-irradiance objects [60].
# Smart Cities of the Future as Cyber Physical Systems: Challenges and Enabling Technologies
## 7 Use Cases
### Cooperation among CPSs  
To investigate the cooperation occurring among CPSs, we analyzed the possible interactions among vehicles moving in a city and the traffic light subsystem. The smart city is made of several subsystems, each controlling a specific aspect of life (traffic congestion, air pollution, emergency requests, traffic light status, and more). A strong cooperation among such subsystems would enable the vehicles to agree on a path that is able to reduce the air pollution and limit traffic congestion. Each vehicle can be seen as an autonomous CPS that should be put in relation with the surrounding environment managed by the smart city. The cooperation occurs if the traffic management and the traffic light subsystems interact, exchanging info and allowing the actuation of specific actions.  
Figure 14: Example of a function-based pipeline on cyber physical system devices through exploitation of a platform offering an edge-based FaaS system: (**a**) The administrator injects the functions generated by pipeline to the devices. (**b**) The workflow is activated when the triggering event occurs.  
Figure 13: Typical pipeline definition workflow originated by an administrator.  
The proposed architecture exploits the computing continuum approach that was previously described, taking advantage of the interactions among several CPSs. In this way, the computation load can be distributed on the smart city Infrastructure Nodes (SCI Nodes in Figure 15).  
In this scenario, cloud computing facilities act as a \"_mediator_\"/_\"data broker_\" between SCI Nodes and Vehicular Nodes. Each vehicle receives all the information related to the segments of street it is traveling (the status of the traffic lights, traffic condition, length of the street segment, and max speed allowed) and computes the optimal speed profile to be adopted. This information is sent to a Simulink-based \"power-train model\" to evaluate and compare the traversing time, fuel consumption, and greenhouse gas emissions against the _New European Driving Cycle_ standard [48].  
As shown in Figure 16a, the cooperation among CPSs reduced the traversing time from 1180 s to less than 900 s. The fuel consumption was also decreased from an average value of 6 to 4.5 L/100 km (see Figure 16b).
# Energy-Aware Control of Error Correction Rate for Solar-Powered Wireless Sensor Networks
## 4 Experimental Results and Discussion
### Experimental Environment  
The performance evaluation was carried out using Solar-Castalia [20], which is a simulator specially designed for solar-powered WSNs. The harvested solar energy used in the simulation was based on real data [21] taken from actual solar-powered motes. The weather varied in a cycle with a length of one day, and sunny, cloudy or rainy days were randomly selected. Figure 5 shows sample data of energy harvested over two days. For the moving average shown in Equation (1), the value of \(\theta\) was set to 0.5 due to the characteristics of solar energy as explained in Section 3.2.1, and the update period was 1 h. The payload of the RS block was composed of 32 symbols, and the size of each symbol was 8 bits. That is, the payload size of the RS block was 256 bits. The sensing rate of each node was 256 bits per round. In addition, the nodes were randomly arranged and experimented with on various network sizes and error environments by varying the field size, number of nodes and error rate. Details of the experimental conditions can be found in Table 3.  
Figure 5: Sample part of energy input according to time.  
The blackout time for each network was also measured while increasing the number of nodes. Figure 7 shows the results for the accumulation of all blackout time for each network, where the numbers of nodes are deployed. Note that because the results show the accumulated values of all nodes, it is natural that the blackout time increases as the number of nodes increases. The part we are most interested in is the linear form of increasing lines, especially for the EA-RS scheme. This means that even though EA-RS uses the most complicated algorithm among all schemes, the overhead per node does not change, regardless of the network size, because it is a fully-localized scheme. Assuming that a certain scheme is not localized, the sum of the blackout times will increase exponentially as the number of nodes increases, which means that the scheme has no scalability. Therefore, from the results of this experiment, it can be confirmed that the EA-RS scheme has a high scalability.
## 3 Kinetic Energy Control
### Optimal Kinetic Energy Control  
Figure 5 shows the WGs with the proposed optimal KE control. To consider the trade-off between the WG output smoothing and the generating efficiency, we optimize the WG reference by the following optimization problem:  
\[P_{\rm{ipu}}^{\rm{ref}}=\arg\min J(P_{\rm{ipu}}^{\rm{mppt}},f(\omega_{\rm{ipu}} )\cdot\Delta P_{\rm{ipu}}), \tag{17}\]  
where  
\[J(P_{\rm{ipu}}^{\rm{mppt}},f(\omega_{\rm{ipu}})\cdot\Delta P_{\rm{ipu}})=(P_{ \rm{ipu}}^{\rm{mppt}}-P_{\rm{ipu}}^{\rm{ref}})^{2}+q((P_{\rm{ipu}}^{\rm{mppt}} -f(\omega_{\rm{ipu}})\cdot\Delta P_{\rm{ipu}})-P_{\rm{ipu}}^{\rm{ref}})^{2}. \tag{18}\]  
The roles of the first term and the second term in (18) keeps the generating efficiency and smooths the WF output, respectively. It is clear that the WF can be optimally operated, handling the trade-off, when each WG output is regulated to its reference \(P_{\rm{ipu}}^{\rm{ref}}\) that is obtained from (17). In (18), \(q\) is a nonlinear weight that is given by  
\[q=K_{\rm{q}}(|\Delta P_{\rm{WFpu}}|)^{\alpha}, \tag{19}\]  
Figure 4: The standard moving average kinetic energy control.  
where \(K_{\text{q}}\geq 0\) and the natural number \(\alpha\) are weight and exponent weight, respectively. Weight \(q\) becomes small when \(\Delta P_{\text{WFpu}}\) approaches zero. It implies that the generating efficiency increases when the fluctuation of the WF output is small, because the second term in (18) is low. Figure 6 shows the relation between \(\Delta P_{\text{WFpu}}\) and \(q\) (\(K_{\text{q}},\,\alpha\)). Although it is difficult to find the best values of \(K_{\text{q}}\) and \(\alpha\) analytically, these weights can be determined based on the technical requirement and Figure 6. For example, when \(\Delta P_{\text{WFpu}}\geq 0.1\) p.u., \(q\) has to be large in order to guarantee the technical requirement \(\Delta F_{\text{g}}(t)\leq 10\%\). Therefore, \(K_{\text{q}}\) and \(\alpha\) in (19) should be determined to satisfy the condition that \(q\) is large when \(\Delta P_{\text{WFpu}}\geq 0.1\) p.u.  
The solution \(P_{\text{ipu}}^{\text{ref}}\) of (17) can be easily obtained by solving \(\frac{\text{d}I}{\text{d}P_{\text{ipu}}^{\text{ref}}}=0\) because (18) is a convex function of \(P_{\text{ipu}}^{\text{ref}}\). The solution is  
\[P_{\text{ipu}}^{\text{ref}}=\frac{P_{\text{ipu}}^{\text{mppt}}+q(P_{\text{ipu }}^{\text{mppt}}-f(\omega_{\text{ipu}})\cdot\Delta P_{\text{ipu}})}{1+q}. \tag{20}\]  
The calculation time of (20) is short because (20) is a purely algebraic calculation. Therefore, this method does not require any special software (CPLEX and Gurobi, etc.) or numerical optimization (genetic algorithm and particle swarm optimization, etc.).
## 4 Real-Time Implementation and Experimental Set-Up

Table 2: Performance of proposed controllers for PMSM-SCSs. In the following, the real-time testing of the proposed PMSM-SCS based on the DTC strategy is presented in the constant-flux case and in the variable-flux case, as presented in Section 3, where numerical simulations were performed. Real-time performance was obtained by implementing the constant-flux PMSM control structure shown in Figure 2 using PI-EOA and FOPI controllers, and the variable-flux PMSM control structure shown in Figure 3, using SMC- and FOSMC-type controllers. Note that the controller structures presented in the Matlab/Simulink numerical simulations are preserved in the real-time implementation, but the 1/s integrators expressed in the continuous domain are equated to integrators in the discrete domain by using a bilinear Tustin transform.
## 6 Conclusions  
Several experiments were conducted to compare the performance of a high-end commercial receiver in the presence of both narrowband and wideband jamming signals. The advantages of multi-constellation over GPS-only were explored by repeating the same scenario while enabling or disabling the GLONASS constellation. The impact of jamming signals was explored when targeting the GPS band and GLONASS band. It has been concluded that the use of multi-constellation could provide a potential solution for the jamming problem, specifically in terms of solution availability and reliability. Furthermore, jamming the GPS signal would result in higher uncertainty with respect to the solution provided by the receiver as the DOP values increase compared to jamming the GLONASS band. In addition, the positioning solution was much more deteriorated when jamming the GPS band compared to jamming the GLONASS band due to the better coverage and geometry of the GPS constellation in North America.  
[PERSON] proposed the idea, designed the simulation, analyzed the data and wrote part of the paper. [PERSON] and [PERSON] contributed to the idea and wrote part of the paper. A.N. reviewed the paper. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors declare no conflict of interest.
# Kernel Learning by Spectral Representation and Gaussian Mixtures
## 3 Approximating Stationary Kernels
### Learning Locally Stationary Kernel, GaBaSR
#### 3.2.1 GaBaSR Algorithm  
Based in the previous ideas, we have the following high level description of the algorithm.  
1. Learn all the parameters for the Gaussian mixture \(\rho(\mathbf{\omega})\): * Let \(\{\pi_{k},\mathbf{\mu}_{k},\Sigma_{k}\}_{k=1}^{K}\) be the current parameters of the Gaussian Mixture Model (GMM), where \(\pi_{k}\) is the prior probability of the \(k\)th component, \(\mathbf{\mu}_{k}\) is the mean and \(\Sigma_{k}\) is the covariance matrix of the \(k\)th component, then the GMM is given by \(\rho(\mathbf{\omega})=\sum_{k=1}^{K}\pi_{k}\mathcal{N}(\mathbf{\mu}_{k},\Sigma_{k})\), here the output will be the new sample parameters for the GMM \(\rho(\mathbf{\omega})\).
2. Take \(M\) samples from \(\rho(\mathbf{\omega})\), i.e., \(\mathbf{\omega}_{i}\sim\rho(\mathbf{\omega}),i=1,2,...,M\) for the spectral representation. * Here the input are the parameters of the GMM, and the frequencies \(\mathbf{\omega}_{i},i=1,...,M\), and the output will be the new frequencies sampled.
3. Approximate the kernel as \[\kappa_{1}\bigg{(}\frac{\mathbf{x}+\mathbf{x}^{\prime}}{2}\bigg{)}\kappa_{2}\big{(} \mathbf{x}-\mathbf{x}^{\prime}\big{)}\approx\frac{1}{M_{1}M_{2}}\mathbf{\varphi}(\mathbf{x}) ^{T}\mathbf{\varphi}^{-}(\mathbf{x}^{\prime})\]
4. Predict the new samples: 1. If the task is a regression use: \[f(\mathbf{x})=\mathcal{N}(\mathbf{\beta}^{T}\mathbf{\varphi}(\mathbf{x}),\sigma^{2})\] 2. If the task is a classification use: \[f(\mathbf{x})=\frac{1}{1+\exp(-\mathbf{\beta}^{T}\mathbf{\varphi}(\mathbf{x}))}\]  
In this work, we use a Markov Chain Monte Carlo (MCMC) algorithm, the Gibbs sampler [25], to learn and predict new inputs. The final entire process is described in the following subsections.
## 2 The Study Area
### Terrestrial Laser Scanner--TLS  
In the last decade, geometric techniques have been developed that do not require any physical contact with the measured object. These include terrestrial photogrammetry, TLS, and UAV [51; 52]. TLS produces a point cloud (X,Y,Z) made up of millions of points measured on the surface of an object with a precision of a few millimetres, photographic information of its texture and with the possibility of 3D modelling, all in a short time after data acquisition. This technique has been used in high-mountain geomorphological studies and is of great use in the study of glaciers, in which field conditions are complex and narrow, and sites very difficult to access [53; 54; 55].  
As with photography, TLSs are \"line of sight\" instruments, i.e., they collect what they see from a single point. Multiple takes must therefore be made from different stations to guarantee full coverage of a structure [56]. In the collection of information, there are uncertainties of less than \(\pm\)2 mm in each scan, and the fusion of all the scans including support from the GNSS does not surpass \(\pm\)3 cm of uncertainty.  
TLS was introduced as a method for monitoring the dynamic of the Corral del Veleta rock glacier in the 2012 survey. To carry out such a dense measurement, the TLS used was a Faro Focus X330, which measures distances in a range of 0.6 m to 330 m with nominal precision of \(\pm\)2 mm at a distance of 50 m under normal conditions of light and reflectivity.  
The vertical field of vision (FOV) has an amplitude of 300\({}^{\circ}\) and the horizontal FOV a range of 360\({}^{\circ}\).  
The high mountain site places constraints on TLS working conditions. In order to make as many scans as possible in the shortest possible time with optimal resolution, the points to scan were planned in order to maximize the information collected while trying to minimize occlusions. To date, four annual scanning operations have been carried out on the Corral del Veleta rock glacier (2012, 2014, 2016, and 2019) with a mean of fourteen placements per year, in which over 500 million points with 3D information were obtained in each survey.
###### Abstract  
Trackers for activity and physical fitness have become ubiquitous. Although recent work has demonstrated significant relationships between mental effort and physiological data such as skin temperature, heart rate, and electrodermal activity, we have yet to demonstrate their efficacy for the forecasting of mental effort such that a useful mental effort tracker can be developed. Given prior difficulty in extracting relationships between mental effort and physiological responses that are repeatable across individuals, we make the case that fusing self-report measures with physiological data within an internet or smartphone application may provide an effective method for training a useful mental effort tracking system. In this case study, we utilized over 90 h of data from a single participant over the course of a college semester. By fusing the participant's self-reported mental effort in different activities over the course of the semester with concurrent physiological data collected with the Empatica E4 wearable sensor, we explored questions around how much data were needed to train such a device, and which types of machine-learning algorithms worked best. We concluded that although baseline models such as logistic regression and Markov models provided useful explanatory information on how the student's physiology changed with mental effort, deep-learning algorithms were able to generate accurate predictions using the first 28 h of data for training. A system that combines long short-term memory and convolutional neural networks is recommended in order to generate smooth predictions while also being able to capture transitions in mental effort when they occur in the individual using the device.  
Despite these challenges, research on the use of wearables in education is expanding. Researchers have proposed the use of wearables for varied purposes, such as detecting a student's mood [9], detecting a student's hand motions [10], or predicting students' cognitive load and mental focus [11]. While these cases represent only specific areas of work in the field, they highlight three important themes: detecting mood, detecting movements, and predicting cognitive outcomes.  
In this study, we focused on the third theme, predicting cognitive outcomes from data collected via wearables. Specifically, we sought to investigate if it was possible to predict the mental effort of a learner based on the physiological parameters collected from a wearable, and to study the efficacy of the system to detect transitions in mental effort from one activity to another. We believe that this work can build toward the creation of sensor-based tools that can help students self-regulate their own learning [11].
# CommC: A Multi-Purpose COMModity Hardware Cluster
## 3 System Architecture
### Security  
A publicly accessible cluster requires stringent measures to minimize its attack surface and protect against unauthorized access. In the configuration that we propose, the firewall is set to allow only a small number of ports, which have been intentionally randomized, rather than using the default well-known ports. SSH access via the root account is completely disabled, and VNC functionality is restricted to the local network to reduce external visibility. All internet-bound traffic is routed through Cloudflare, which serves as a reverse proxy and secures data transmissions with SSL encryption. Furthermore, user accounts are grouped according to role-based permissions, ensuring that privileges are granted only as needed and preventing inadvertent or malicious misuse. Multi-tenancy challenges are managed by allocating separate virtual machines with dedicated storage to each user, thereby containing any potential security incidents within isolated environments.
## 4 Experiments
### Visualization Result  
We provide a visualization of some key frames finally selected by the proposed method. Figure 4(a) shows the images corresponding to key frames A to H of the action \"hand waving\". In frames A and B, the subject gradually raises her left hand. By frame C, the subject's left hand has reached the starting position for waving and begins to wave. Additionally, the curve shows that the subject completed four cycles of waving, with the amplitude decreasing gradually, which aligns with the key frames selected by the model. After key frame refinement and alignment, more frames unrelated to the action type were removed, and more detailed information can be added to minimize information loss. We also show the relationships between these frames by presenting the attention maps learned in our proposed model in Figure 4(b). From the figure, we can observe that the relationship weights between these manually selected key frames, which we consider representative, are generally large, indicating the effectiveness of the temporal graph optimization. Another notable observation is frame C, the starting position of the action, showing strong relationships with many other key frames, underscoring its significance.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Methods**}} & \multicolumn{2}{c}{**w/o KFRA (\%)**} & \multicolumn{2}{c}{**w/KFRA (\%)**} \\  & **Top 1** & **Top 5** & **Top 1** & **Top 5** \\ \hline \multirow{2}{*}{All} & X-sub & 92.3 & 98.2 & - & - \\  & X-view & 96.3 & 99.4 & - & - \\ Uniform & X-sub & 92.1 & 98.3 & 92.1 & 97.9 \\ sampling & X-view & 95.6 & 98.6 & 95.8 & 98.7 \\ Salient & X-sub & 92.2 & 98.1 & **92.5** & **98.9** \\ posture (ours) & X-view & 96.1 & 98.8 & **96.9** & **99.7** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accuracies (%) of different baseline methods. The best results for each split are indicated in bold.
## 3 Experimental Setup
### Scenario 1: DRL-Based Scenario at the Signalized Intersection
#### 3.2.3 Reward Definition  
A reward is an important part of reinforcement learning because it determines the goal of the model after training. In reinforcement learning, after the agent performs an action a in state s, it obtains a reward r from the environment. Through the reward, the agent understands the result of the action and improves the model for the next action choices.  
Our main goal in this model is to optimize traffic signals and increase the operational efficiency of the intersection. In our case, we define the reward based on the change in the cumulative waiting time of all vehicles. The waiting time of each vehicle is recorded when the vehicle speed is below or equal to 0.1 m/s before the intersection. The agent will observe all vehicles twice in a green time interval to sum up the cumulative waiting time. The first observed is the beginning of the green phase, and the second is the end of the green phase.  
Let \(w_{i,t}\) denote the waiting time of the ith observed vehicle when this vehicle enters the model to the beginning of the green time. Moreover, \(w_{i,t}\) is the waiting time of this vehicle when it enters the model to the end of the green time interval. The equation to determine the cumulative waiting time of all vehicles at the beginning and the end of each phase is as follows:  
\[\text{W}_{t}\ =\ \sum_{i\ =\ 1}^{N}w_{i,\ t}\text{ and }\text{W}^{\prime}_{t}\ =\ \sum_{i\ =\ 1}^{N}w^{\prime}_{i,\ t} \tag{6}\]  
If the vehicle speed is greater than 0.1 m/s, its waiting time is over, and it is not added to the cumulative time. Since our aim is to increase the efficiency of the intersection, the model reward is based on the reduction in total cumulative waiting time. The reward function in our model is shown below:  
\[\text{r}_{t}\ =\ \text{W}_{t}\ -\ \text{W}^{\prime}_{t} \tag{7}\]  
In this model, the reward is a negative cumulative waiting time, because the cumulative waiting time of all vehicles at the end of the green time will be greater than the cumulative waiting time of all vehicles at the beginning of the green time. Moreover, the  
Figure 8: Partial detection.  
agent will choose the appropriate actions (changing the duration of green time and the phase order) to minimize the cumulative waiting time.