esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Distortion and Perceptual Measures
### Perceptual Measures in Information Theory
#### 2.2.1 Total Variation Distance  
The TV distance defined in the following is widely used in information theory. Given two distributions \(P_{X}\) and \(P_{Y}\) defined on the same \(\sigma\)-algebra \((\mathcal{X},\mathcal{F})\), the TV distance is defined as  
\[\mathcal{D}_{\text{TV}}(P_{X},P_{Y})=\sup_{S\subseteq\mathcal{X}}|P_{X}(S)-P _{Y}(S)|.\]  
The TV distance is equivalent to the \(L_{1}\) norm when the alphabets are finite, making it a natural choice in the Lebesgue space, i.e., when \(P_{X}\) and \(P_{Y}\) are probability mass functions,  
\[\mathcal{D}_{\text{TV}}(P_{X},P_{Y})=\frac{1}{2}\|P_{X}-P_{Y}\|_{1}=\frac{1}{ 2}\sum_{x\in\mathcal{X}}|P_{X}(x)-P_{Y}(x)|. \tag{2}\]  
From Equation (2) and the triangle inequality for real numbers, we have the triangle inequality  
\[\mathcal{D}_{\text{TV}}(P_{X},P_{Y})\leq\mathcal{D}_{\text{TV}}(P_{X},P_{Z})+ \mathcal{D}_{\text{TV}}(P_{Z},P_{Y}).\]  
This implies that the TV distance does not tensorize. In fact, we have the following folklore knowledge:  
\[\mathcal{D}_{\text{TV}}(P_{X_{1}X_{2}},P_{Y_{1}Y_{2}})\leq\mathcal{D}_{\text{ TV}}(P_{X_{1}},P_{Y_{1}})+\mathcal{D}_{\text{TV}}(P_{X_{2}},P_{Y_{2}})- \mathcal{D}_{\text{TV}}(P_{X_{1}},P_{Y_{1}})\cdot\mathcal{D}_{\text{TV}}(P_ {X_{2}},P_{Y_{2}}). \tag{3}\]
## 4 Discussion
### Prosthetic Use  
Prosthetic use frequency seems to be correlated with the user group (see Figure 2). Twenty-five percent of current users wear prosthetics all day long (12-16 h), while no past users reported wearing it for this duration. The reported mean usage time was 8.95 h, which aligns with previous studies [(43)]. Prosthetic use frequency for past users was generally lower, with 50% of them wearing prosthetics for only 1-3 h per day.  
The reported use type also appears to be associated with the user group (see Figure 3). Approximately 41.67% of current users reported using their prosthesis for almost everything, while this rate was only 25% for past users. Similarly, 25% of past users reported only using the prostheses at home, which is a pattern that was not reported by any current users.  
Regarding the environment, the most common environment for current users is at work (see Figure 4), followed by social interactions and nightlife. Less frequent usage occurred at the pool/beach and during sports activities, which is coherent because most prosthetic arms cannot be wetted. Past users reported lower use frequency in all of these environments. The only environment in which both user groups reported nearly equal use frequencies was \"only in the family environment\". This high prosthetic use rate at home for past users may indicate that past users could have experienced social stigma associated with their use. These users might have preferred only using their prostheses in the family setting because it is sometimes more discreet to conceal stumps than to wear a body-powered prosthesis that requires wearing a harness over clothing. Other explanations may be related to prolonging the prosthesis's life span for cost-related reasons or to functional considerations like wearing it to perform household tasks.  
Although our findings in this section suggest an association between usage patterns and user group (current user or past user), it is important to acknowledge the potential influence of other factors. For instance, factors like the possibility of receiving the appropriate training for prosthetic use could influence both use type and the compositions of user groups. Therefore, alternative explanations may be considered before interpreting the reasons that lie behind the observed patterns.
## 6 Results
### Execution History and Performance Results  
This subsection presents the execution history of the proposed genetic algorithm variants in the 15-Location (Figure 4) and 25-Location (Figure 5) problems, focusing on the evolution of fitness, diversity, travel distance, and delivery time across generations.  
Figure 4: Execution history analysis for the 15-instance problem. (**a**) Fitness evolution shows GAAM-TS achieving superior optimization. (**b**) Diversity evolution demonstrates GAAM-TS maintaining higher diversity and avoiding premature convergence. (**c**) Distance evolution highlights GAAM-TS achieving the most significant travel distance reduction. (**d**) Time evolution confirms GAAM-TS minimizing delivery time more effectively than the other variants.  
\begin{table}
\begin{tabular}{l l l l l} \hline
**Variant** & **Distance (km)** & **Time (min)** & **Comp. Time (s)** & **Diversity** \\ \hline Standard GA & 153.286 & 340.595 & 55.552 & 0.65 \\ GA-AM & 147.173 & 335.338 & **54.528** & 0.67 \\ GAAM-TS & **122.381** & **300.090** & 1759.560 & **0.73** \\ \hline \end{tabular}
\end{table}
Table 3: Performance comparison for 25 locations.
## 4 Stability Evaluation
### Islanding Mode  
In this way, the Lyapunov function could be assumed as:  
\[V_{isl}\Big{(}S_{isl},\ \widetilde{\Theta}\Big{)}=\frac{1}{2}\ S_{isl}^{2}+ \frac{1}{2}\ \xi_{isl}\ \widetilde{\Theta}^{2} \tag{24}\]  
Corresponded to the pervious subsection, the subscript '\(isl\)' represents the islanding operation mode and for the uncertainties, we should have: \(\ \widetilde{\Theta}=\Theta-\ \hat{\Theta}\). So that, the first order derivative is mathematically calculated as:  
\[\frac{d}{dt}V_{isl}\Big{(}S_{isl}(t),\ \widetilde{\Theta}(t)\Big{)}=S_{isl}(t) \frac{d}{dt}(S_{isl}(t))+\ \xi_{isl}\ \widetilde{\Theta}(t)\frac{d}{dt}\ \widetilde{\Theta}(t) \tag{25}\]  
where according to (13) to (16), we have:  
\[\begin{split}&\frac{d}{dt}V_{isl}\Big{(}S_{isl}(t),\ \widetilde{\Theta}(t)\Big{)}=S_{isl}(t)\big{(}b_{p}u_{gsw}+m(t)\big{)}+\ \xi_{isl}(\Theta(t)-\Theta(t))\frac{d}{dt}\ \widetilde{\Theta}(t)\\ &\qquad=S_{isl}(t)\big{(}-\ \hat{\Theta}(t)sign(S_{isl}(t))+m(t) \big{)}-\xi_{isl}(\Theta(t)-\Theta(t))\frac{|S(t)|}{\xi_{isl}}\\ &\qquad=S_{isl}(t)m(t)-\Theta(t)|S_{isl}(t)|\leq|S_{isl}(t)|(|m( t)|-\Theta(t))\leq 0\end{split} \tag{26}\]  
Therefore, the control approach is stable is islanding operation mode.
## 4 Overview of GNSSs
### Galileo
#### 4.2.1 GALILEO System Architecture  
The architecture of the Galileo system consists of space and ground segments, with the possibility of specifying additional stand-alone sub-segments with specific services in the ground segment. The user segment is considered a separate component, the implication being that this segment will interact with all other system components [79].
# Leaking Gas Source Tracking for Multiple Chemical Parks within An Urban City
## 4 Experiments and Discussion
### Analysis of the Performance of the Self-Attention Module and SVM  
As a universal module, the self-attention mechanism and SVM are widely used in various research fields. We also evaluate their performance in both situations with and without sensor failure, as shown in Table 2.  
SVM only achieves an accuracy of 28.32%, which means it is really hard for SVM to track the leaking source in this case. It confuses SVM classifying 79 potential leaking sources in different weather conditions with only 61 sensors' concentration data as input. These are the conclusions from several experiments:  
1. The 79 sources are divided into four groups since there are four chemical parks macroscopically. Then, we use the same 61 sensors' data to train a linear-SVM model tracking from which chemical park the gas is leaking. It achieves an accuracy of 99+% on the test set.
2. The accuracy of RBF-SVM is 2% lower than that of linear-SVM using the same data for training and testing.
3. With the same configuration, the linear-SVM reaches a training accuracy of 95.9% while its test accuracy is 28.5% tracking 79 sources separately.  
The attention-based fully connected model finally reaches an accuracy of 98.67% while the model without attention reaches 98.86%, which is 0.19% higher. On the surface there is little gap between the performances of the two models with and without attention, the attention-based model uses more parameters but achieves a worse result. Under the situation that some sensors fail, this gap becomes wider, up to 1.69%. However, the introduction of the attention mechanism aims to solve the sensor failure problem through finding the inner relationship among the sensors' data and recovering the original  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Model**} & **Without Sensor Failure** & **With 10\% Sensor Failure** \\  & **(Acc.)** & **(Acc.)** \\ \hline SVM & 28.32\% & / \\ Attention-based model & 98.67\% & 94.93\% \\ Fully connected model & 98.86\% & 96.62\% \\ \hline \hline \end{tabular} All the models above are trained for 3000 epochs.  
\end{table}
Table 2: The test accuracy of three models with and without sensor failure.  
Figure 10: Histogram of large concentrations.  
concentration distribution. However, it fails to complete the work obviously. This may result from the concentration data not being suitable for normalization. The data from some popular research fields, such as CV and NLP, in which the attention mechanism works, are always normalized before feeding them into the model. As shown in Figure 7, after applying data normalization the models perform worse.
# A Self-Organizing Area Coverage Method for Swarm Robots Based on Gradient and Grouping
## 6 Experiment and Analysis
### Self-Organizing Coverage Experiment Based on Grouping  
Figures 14 and 15 show the process of self-organizing covering the 'K' area and the 'house' area. There are 603 robots and 942 robots were used respectively, and the number of groups was 3. In Figure 16, a total of 2000 robots are divided into 10 groups to self-organize the 'K' area.  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter Name** & **Describe** & **Value** \\ \hline \(d_{safe}\) & Safe distance & 1 \\ \(d_{cover}\) & Coverage distance & 1, 2, 4, 6, 8, 10 \\ \(r\) & Coverage radius & 4 \\ \(\mathbf{v}\) & Velocity & 0.1 \\ \(R_{c}\) & Communication distance & 15 \\ \hline \end{tabular}
\end{table}
Table 2: Coverage experiment parameter setting.  
Figure 12: System operation when the failure robot stops outside the area to be covered.  
Figure 13: Operation of the system when the failure robot stops in the area to be covered.  
Different colors of the robot in the figure represent different gradients of the robot (due to the limited number of colors in Netlogo software, 14 different colors are taken as a cycle, i.e., the colors of 0 gradient and 14 gradient are the same, and so on). The gradient-based method can self-organize the coverage of the target area, and the speed of coverage can be improved by the grouping method. Although the experiment demonstrates the effectiveness of the method proposed in this section to cover the target area, some gaps cannot be completely covered at the junction of each group due to the robot conflict between the groups.
# Review of Advances in Active Impulsive Noise Control with Focus on Adaptive Algorithms
## 2 Fractional Lower-Order Power-Based Algorithms
### p-Power Algorithms
Scholars have sought to enhance the performance of this type of algorithm by employing methods such as normalized step size, filter convex combination and integration with projection algorithms to improve convergence speed and steady-state error. However, these improvement measures cannot prevent the subsequent increase in algorithm complexity.
# An Improved Mixed AC/DC Power Flow Algorithm in Hybrid AC/DC Grids with MT-HVDC Systems
## 4 Results and Discussions
### Case 4: AC/DC Power Flow Considering Droop Control Strategy and Converter Outage  
As stated in Section 1.3, the droop control strategy is an efficient way of controlling MT-HVDC systems that can improve the PF of the AC and DC grids after an outage. In the fourth case, it is assumed that all converter stations in the studied system are equipped with the droop controllers. Table 12 illustrates the detailed information of the droop settings for each converter station. Tables 13-16 show the results of PF calculations both in all AC and DC buses and in branches in case of the outage of converter station #1 and considering droop control settings provided in Table 12. It should be noticed that the algorithm is converged in 0.45 s and in three iterations.  
Compared to Case 3, in this case, the contribution of the generator connected to bus 2 to power generation is decreased. As the outage of converter station #1 is considered, compared to Case 3, the PF on the DC lines is decreased. It should be noted that as the performance of each converter station is based on the pre-determined droop parameter, the PF on the DC lines should be in a way that the total power losses on the DC lines become zero.
## 3 Method
### Model Overview
#### 3.2.6 Generalized Zero-Shot Classification  
The classification process of the proposed method in this paper is shown in Figure 2. First, the decoder of CVAE generates unseen visual features with unseen classes of semantic embeddings, \(a_{u}\), and Gaussian noise, \(z\). Then, the generated visual features of the unseen classes and the real features of the seen classes are further fed into semantic decoder \(Dec\) and the disentanglement module to extract the corresponding semantic-consistent features. Finally, the semantic-consistent features output by \(Dec\) are concatenated with the semantic-consistent features decomposed by the disentanglement module to obtain the transformed and enhanced semantic-consistent discriminative features \(h_{s}^{s}\oplus h_{r}^{s}\) and \(h_{s}^{u}\oplus h_{r}^{u}\); they are then used to train the generalized zero-shot classifier, \(f^{GZSL}\).  
Figure 2: Classification architecture. A feature transformation is performed by concatenating (\(\oplus\)) the semantic-consistent features, \(h_{s}\), of the output of the disentanglement module with the semantic-consistent features, \(h_{r}\), of the output of semantic decoder module, \(Dec\). The transformed discriminative features are then used for GZSL classification.
## 4 Discussion
Additionally, our experiments demonstrated that high-resolution observations combined with an eddy-resolving multi-scale model could improve simulation skills towards capturing multiscale flow dynamics, which is fundamental in the understanding of mass and energy transport mechanisms in the oceanic environment. The GLIDER project demonstrated that a suite of autonomous vehicles can collect physical oceanographic parameters alongside biological data across multiple ecosystem components in the coastal Arctic. The productive waters of the LoVe region are shared by ecological important low (phytoplankton and zooplankton) and high (marine mammals) trophic groups and an increasing number of human stakeholders. High spatial and temporal resolution data collected with a small environmental footprint and made available by the GLIDER data management platform make it possible to capture ecosystem dynamics, important towards the realization of an ecosystem-based management approach in the region and elsewhere. The integration between the current underwater observatory system in LoVe and gliders should be prioritized to form an ecosystem observatory module network able to provide unique marine research and monitoring capabilities. Conceptualization, L.C., S.L.B., K.H.C., J.C., M.D., K.D., S.F.-P., P.G., G.G., H.D., D.P., J.P., G.P., K.S., S.T., V.T. and S.D. ; methodology, L.C., A.S.A., M.A., K.B., S.L.B., K.H.C., M.D., K.D., S.F.-P., P.F., G.G., G.G., I.G., D.H., T.K., J.P., G.P., K.S., A.K.S., L.T., S.T. and V.T. ; software, K.B., J.C., G.F., P.G., G.G., T.L., O.A.N., J.P., G.P., K.S., A.K.S., S.T. and V.T. ; validation, K.B., S.L.B., K.H.C., M.D., S.F.-P., P.F., G.G., D.H., T.K., J.P., G.P., K.S., A.K.S. and V.T. ; formal analysis, A.S.A., M.D., P.G., G.P. and L.T. ; investigation, A.S.A., M.A., K.B., S.L.B., K.H.C., M.D., S.F.-P., P.G. and L.T. ; resources, I.G., D.H. and D.P. ; data curation, A.S.A., G.P., K.S., A.K.S., L.T. and S.T. ; writing--original draft preparation, L.C., H.A., M.D., P.G. and A.K.S. ; writing--review and editing, L.C., H.A., A.S.A., M.A., K.B., S.L.B., K.H.C., J.C., M.D., K.D., S.F.-P., P.F., G.F., G.G., I.G., D.H., T.K., T.L., O.A.N., D.P., J.P., G.P., K.S., A.K.S., L.T., S.T., V.T., H.L. and S.D. ; visualization, L.C., H.A., M.D., G.F., P.G., T.L., J.P., G.P. and A.K.S. ; supervision, G.P. ; project administration, L.C., M.A., K.D. and S.D. ; funding acquisition, L.C., I.G. and S.D. All authors have read and agreed to the published version of the manuscript. This research was carried out as part of the GLIDER project, which was funded by the DEMO2000 research program (Norwegian Research Council and ConocoPhillips Skandinavia AS, project 269188, \"Unmanned ocean vehicles, a flexible and cost-efficient offshore monitoring and data management approach\"). The authors declare that this study received partial funding from ConocoPhillips Skandinavia AS. This funder was not involved in the study design, collection, analysis, interpretation of data, or the decision to submit this article for publication.
# No Free Lunch--Characterizing the Performance of 6 TiSCH When Using Different Physical Layers
###### Abstract  
Low-power wireless applications require different trade-off points between latency, reliability, data rate and power consumption. Given such a set of constraints, which physical layer should I be using? We study this question in the context of 6 TiSCH, a state-of-the-art recently standardized protocol stack developed for harsh industrial applications. Specifically, we augment OpenWSN, the reference 6 TiSCH open-source implementation, to support one of three physical layers from the IEEE802.15.4g standard: FSK 868 MHz which offers long range, OFDM 868 MHz which offers high data rate, and O-QPSK 2.4 GHz which offers more balanced performance. We run the resulting firmware on the 42-mote OpenTestbed deployed in an office environment, once for each physical layer. Performance results show that, indeed, no physical layer outperforms the other for all metrics. This article argues for combining the physical layers, rather than choosing one, in a generalized 6 TiSCH architecture in which technology-agile radio chips (of which there are now many) are driven by a protocol stack which chooses the most appropriate physical layer on a frame-by-frame basis.  
Keywords: 6 TiSCH; performance evaluation; OpenWSN; testbed; technology agility +
Footnote †: journal: Sensors
# Augmenting CCAM Infrastructure for Creating Smart Roads and Enabling Autonomous Driving
## 2 Level-5 Autonomous Driving--An Overview of the Relevant Concepts
### Autonomous Driving Core Layers  
To achieve the L-5 AD, we believe that a vehicle needs to drive fully autonomously in a mixed traffic environment, e.g., complex urban road traffic environment, and meet the requirements of all aforementioned use-case groups. Therefore, the L-5 enabled AV needs more sophisticated versions of the basic layers, i.e., perception, planning, and control [10], as shown in Figure 1.  
* **Perception** layer captures the context of the vehicle's environment, which may be carried out through different techniques such as: fusion of the on-board sensory data, information exchanges with other vehicles and infrastructure, localization, etc.
* **Planning** layer processes involve actions regarding mission, behavioral, motion, space-time, and dynamic environment planning.
* **Control** layer executes the planned actions through traditional and prediction-based control, i.e., steering, breaking, and trajectory tracking controls.  
However, the higher levels of vehicle automation are proportional to the effective implementation of functions and operations specific to the aforementioned three layers. Research community and industrial partners are striving to achieve the solutions with the desired sophistical levels.
## 4 Opposite Normalized Trust-Region Reflective (ONTRR) Algorithm  
### Main Routine of the Proposed ONTRR Algorithm  
To overcome the above shortcomings of the standard TRR search, this paper adds two concepts, opposition-based learning (OBL) [34] and min-max normalization [35], to formulate the proposed ONTRR algorithm. The concept of OBL is used to produce a better initial point for the TRR search, while min-max normalization is applied to eliminate the negative effects arising from different magnitudes of model parameter values and thus to drive the derivative-dependent TRR search in an efficient manner. These two amendments are interspersed in the main routine illustrated in Algorithm 1. As can be seen from Algorithm 1, the proposed ONTRR algorithm begins by inputting the only control parameter MaxNFEs and specifying the default random seeds for solving the parameter extraction problem over 1000 independent runs. This is followed by:  
Step 1: Random opposite initialization: generate a random point and its opposite point to obtain the initial point of the subsequent iteration loop. Step 2: min-max normalization processing: generate the unitary surrogates of model parameter values to update the distances to the normalized boundary. Step 3: Construct the 2-D subspace by Cholesky factorization and the preconditioned conjugate gradient method. Step 4: Determine the trial step \(s\) from three directions via 2-D eigenvalue decomposition and one-dimensional reflective path search. Step 5: Perturb the trial point to ensure it is not on the boundary and thus strictly feasible, and then return it to the original space for evaluating the error function. Step 6: Update the trust region radius with Equations (13) and (19). Step 7: Decide to accept or reject the trial point and update the Jacobian matrix, gradient and 2-D subspace for the next iteration. The iteration loop incorporating Steps 2-7 is repeated until the terminate criterion MaxNFEs is reached. In the following subsections, the details of Steps 1-4 are explained.
## 2 Design of the Proposed RDA with the EBGs  
Figure 1a demonstrates a wireless power transmission scenario of the type used in a typical indoor environment, such as an office or a living room. Inside the room, there are electronic devices, such as laptops and mobile devices, which serve as power receivers. They are located randomly at the different positions of the users and are assumed to be able to move freely. These electronic devices can be charged wirelessly from the transmitter by broadcasting pilot signals. In response to the pilot signals, the transmitter transmits power wirelessly back in the reverse direction to the electronic devices. The wireless power transmitter is a RDA which can be mounted on the ceiling or walls. In this work, the RDA is a CP microstrip patch antenna array incorporated with EBG structures, as illustrated in Figure 1b. The microstrip patch antenna is designed using the Koch fractal shape for circular polarization operation and to reduce the size. The mushroom-like EBGs are used to suppress the mutual coupling effects between antenna elements, therefore enhancing the performance of the proposed CP RDA.  
Figure 1: (**a**) Depiction of the wireless power transmission scheme in a typical indoor environment; (**b**) Configuration of the proposed CP RDA. CP RDA: Circularly polarized retrodirective array.