esa-sceva/satcom-chunk-collection huggingface dataset:
# Rendering Strategy to Counter Mutual Masking Effect in Multiple Tactile Feedback
## 2 The Masking Function
### Results  
To calculate changes in the AL, the unmasked ALs (investigated in our previous work [14]) were subtracted from the corresponding masked ALs. The upper part of Table 2 shows the mean change in the AL of the EV in the presence of the masker, MV (Experiment I). A two-way repeated-measures analysis of variance (ANOVA) showed that the effect of both frequency and intensity of the masker (MV) on changes in the AL of the EV was significant (\(p<0.05\) for frequency and \(p<0.001\) for intensity). However, the interaction term was not significant (\(p=0.328\)). A multiple paired-sample \(t\)-test with a Bonferroni correction found no difference between 120 Hz and 180 Hz (\(p=1.000\)). We also performed a two-tailed one-sample \(t\)-test on all data points to clarify whether the masking effect was present. When the masker frequency was 120 Hz or 180 Hz, a significant masking effect was noted at masker intensity of 15 dB SL or higher. When the masker frequency was 270 Hz, a significant masking effect appeared at a masker intensity of 10 dB SL or higher. To obtain the masking function of the EV, a linear fitting with the least square method was applied. The resulting curves are depicted in the left part of Figure 3. Data points that did not differ significantly from each other were fitted into a single line. We obtained two linear regression lines (one for 120 Hz and 180 Hz, and the other for 270 Hz). Both results show a strong linear relationship (adjusted R-squared \(>0.99\)). The lower part of Table 2 shows the results of experiment II: the mean change in the AL of the MV in the presence of the masker, EV. For experiment II, the same statistical analysis was performed as for experiment I.  
Figure 2: Experimental procedure and stimulus sequence within a three-interval, forced-choice (3 IFC) paradigm. The test stimulus was randomly applied at one of the three intervals.  
The results indicate that the changes in the AL of the MV depend on the frequency of the MV and intensity of the EV (\(p<0.01\)), but a mixed trend was not observed ((\(p=0.111\)). When evaluated for frequency, no difference was found between 180 Hz and 270 Hz (\(p=1.000\)). Unlike the results of experiment I, significant masking effects were observed at all masker intensities (\(p<0.001\) for all data points). We obtained two linear regression lines (one for 120 Hz, and the other for 180 Hz and 270 Hz). As shown in the right part of Figure 3, both lines show a strong linear relationship (adjusted R-squared \(>0.99\)).
## 2 Related Works  
This section presents studies on path planning and SLAM optimization, highlighting advances and limitations. Table 1 summarizes the primary algorithms, applications, and constraints. Table 2 compares key characteristics, illustrating MARS-SLAM's contributions to completion detection and uncertainty reduction.  
\begin{table}
\begin{tabular}{p{42.7 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline  & **Reference** & **Approach** & **Main Algorithm** & **Application** & **Limitations** \\ \hline \multirow{4}{*}{**Local**} & [29] & Inertial SLAM and Planning & Inertial SLAM, Entropy & Inertial SLAM, Entropy & Aerial environments & Focus on UAVs, does not address confined environments \\ \cline{2-6}  & [30] & Path Planning & Modified D* & Dynamic environments & Focus only on navigation, without exploration optimization \\ \cline{2-6}  & [31] & SLAM and Path Planning & GMapping, Electron SLAM, A* & Indoor rescue & Does not optimize exploration in dynamic scenarios \\ \hline \multirow{4}{*}{**Local**} & [32] & Structure Prediction & P-SLAM, Particle Filter & Indoor environments & Limitation in dynamic environments \\ \cline{2-6}  & [33] & EM Exploration and Pose SLAM & Adapted EM, segmentation & Unknown environments & Increase in uncertainty in complex scenarios \\ \cline{2-6}  & [34] & DL-RRT Exploration & DL-RRT, AP, BO & Unknown environments & Manually adjusted parameters \\ \cline{2-6}  & [35] & 3D Exploration with FIT-SLAM & FIT-SLAM & 3D environments & Computational complexity of voxel mapping \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of methods related to path planning and SLAM optimization, highlighting the approaches, main algorithms, application environments, and their limitations.  
Figure 1: Mind map illustrating the key components of the MARS-SLAM methodology.
# Detection of Denial of Service Attack in Cloud Based Kubernetes Using eBPF
## 1 Introduction
In Kubernetes, such attacks pose a major concern given that it usually contains multiple container applications running on [11]. Traditional DDoS countermeasures cause significant overhead on the Kubernetes cluster. Therefore, it is necessary to devise a solution to observe and detect the traffic of DDoS attacks efficiently. We have proposed an eBPF solution for the observation and detection of DDoS to address the problems identified in the current cloud orchestration tool like Kubernetes. The proposed solution is very lightweight and scalable on multiple clusters, and one of the most important features is that it is very fast so that we can monitor every aspect of the incoming packets. In this research, the proposed DDoS solution using eBPF resulted in a negligible overhead for the virtual machines (VMs) installed on the various Kubernetes Cluster nodes. We summarize the contribution of this study as follows:  
* To analyze the state-of-the-art cloud solutions and identify the most relevant problem in the current cloud Kubernetes solutions. * To propose and implement an effective solution by which we can detect and observe the Distributed Denial of Service attack in the Kubernetes cluster using eBPF. * To validate and measure the evaluation metric based on overhead while detecting and observing the DDOS attack using eBPF and without it. The rest of the article is structured as follows. Section 2 reviews the related works. The architecture of the proposed solution is presented in Section 3. The suggested architecture's implementation phases are elaborated on in Section 4. Section 5 provides a discussion of the results. Finally, we conclude the study in Section 6.
## 7 Practical Applications  
These results can assist sports scientists, medical staff, and coaches in understanding the variability in relative speed and acceleration profiles to design individualized training programs tailored to the specific positional demands of each soccer player. For example, the load for central defenders and midfielders is often underestimated due to their relatively lower maximum physical capacities. For instance, the use of percentages in relative individualization could facilitate the direct comparison of external load variables with different devices. This is particularly relevant for accelerometer variables, considering the variability among devices or different brands.  
Regarding the rehabilitation process, during the gradual return of the player to the field, both physiotherapists and fitness trainers have questioned whether the player is ready for group training sessions and subsequently returning to competition. Measuring external load in an individualized manner, based on the player's current maximum physical capabilities, could optimize quantification in each phase of rehabilitation to ensure performance and minimize the risk of relapse or recurrence. Additionally, our results could provide semi-professional Spanish soccer players with reference information about the maximum physical capabilities they need to achieve to reintegrate into training or competition.  
Utilizing individualized speed and acceleration profiles based on the maximum capacities of semi-professional soccer players can significantly alter positional characteristics by considering both the initial speed and action intensity. This approach facilitates the design of new strategies for monitoring external load and enhances the functional specificity of training by quantifying variables with relative thresholds. Additionally, relative individualization through the use of percentages allows for direct comparison of external load variables across different devices.  
Conceptualization, C.C., J.A.P., J.P.-B. and S.L.J.-S.; methodology, C.C., J.A.P., J.P.-B. and S.L.J.-S.; validation, C.C., J.A.P., J.P.-B. and S.L.J.-S.; formal analysis, C.C., J.A.P., J.P.-B. and S.L.J.-S.; investigation, C.C. and S.L.J.-S.; data curation, C.C., J.A.P., J.P.-B. and S.L.J.-S.; writing--original draft preparation, C.C., J.A.P., J.P.-B. and S.L.J.-S.; writing--review and editing, C.C., J.A.P., J.P.-B. and S.L.J.-S.; supervision, C.C., J.A.P., J.P.-B. and S.L.J.-S.; project administration, S.L.J.-S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of the European University of Madrid (reference number: CIPI/18/195).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
###### Abstract  
Heat collecting elements (HCEs) are the core components in the parabolic trough collector (PTC) system because photothermal conversion of the whole system occurs in the HCEs. However, considerable heat loss from the HCEs at high operating temperature exerts seriously negative impact on the photothermal conversion efficiency of the PTC system and subsequent application systems. To effectively reduce the heat loss and thus enhance the overall performance of the PTC system, in our previous work, we proposed three kinds of novel HCEs by partially depositing different IR-reflector coatings on the inner and outer surfaces of the glass envelope. The infrared (IR)-reflector of actual transparent conductive oxide (TCO) film, IR-reflector with a fixed cutoff wavelength of 2.5 \(\upmu\)m, and the IR-reflector with optimal cutoff wavelength showed extremely effective roles in the reduction of heat loss in HCEs. In this paper, the comprehensive energy and exergy performances of these three novel HCEs in a real 72 m small-scale PTC system are further investigated by the mathematical models established. Additionally, the comparisons among overall performances of the proposed HCEs under different direct solar irradiances are also carried out. The results show that the simulated data yields good consistence with the experimental results, and that all three of the novel HCEs achieve superior overall performance compared with the conventional HCEs. The PTC system installing the novel HCEs with the IR-reflector coating which possesses the optimal cutoff wavelength has the best energetic and energetic efficiencies, which are significantly improved by 25.2% and 28.1% compared with the conventional HCEs at the solar irradiance of 800 W/m\({}^{2}\) and inlet temperature of 580 \({}^{\circ}\)C. Moreover, the proposed novel HCEs have a much superior performance at lower solar irradiance. The performance-enhanced PTC system will play a significantly positive role in the performance improvement of the heating and cooling of buildings in the future.  
parabolic trough collector; CSP; solar receiver; efficiency; reflector +
Footnote †: journal: Applied Soft. 2020, 10, 3744; doi:10.3390/app10113744
# Noise Modeling to Build Training Sets for Robust Speech Enhancement
## 2 GAN: Basic Principles and Evolved Architectures  
This paper focuses on developing an advanced GAN for noise modeling and constructing paired training sets. In this chapter, we briefly present the basic principles associated with GANs and the most successful recent architectures.  
In its original formulation, a GAN [4] comprises a pair of adversarial neural networks, namely a generative model \(G\) that captures the data distribution for a set of input data and a discriminative model \(D\) that estimates the probability that a sample originates from a set of training data rather than \(G\). The generator is trained to create samples that will try to fool the discriminator and an adversarial game is played between the two networks.  
A GAN is used for data generation and given a dataset \(\{(x_{1},z_{1}),(x_{2},z_{2}),(x_{3},z_{3})\cdots(x_{N},z_{N})\}\) comprising \(N\) signal pairs, a target signal \(x\) and a random signal \(z\  
(1) By drawing upon a combined U-Net and LSTM block, NM-GAN can learn complex noise distributions. The generator uses a symmetrical U-Net, where the encoder is a stack of convolutional and pooling layers. This strategy affords to extract high-level features from the input noise. Moreover, the decoder has the same structure as the encoder but, in reverse, effectively mapping the low-resolution feature maps from the encoder to full-size input noise feature maps. This network structure better extracts the input data characteristics than other GANs as it fully exploits the information of various features.  
(2) NM-GAN operates end to end without involving any hand-crafted features and without considering explicit assumptions about raw noise.  
(3) NM-GAN operates directly in the time domain, permitting integrated modeling of the phase information and considering contextual information.
## 6 Existing Routing Protocols
### Geographic and Energy-Aware Routing (GEAR)  
Location based routing protocols for sensor network need location information of all the sensor nodes to calculate the distance between any two nodes. GEAR [17, 21] is a location based routing protocol which uses GIS (Geographical Information System) to find the location of sensor nodes in the network. According to this protocol, each node stores two types of cost of reaching the destination: estimated cost and learning cost. The estimated cost is a combination of residual energy [22] and distance to destination. The learned cost is a modified estimated cost and it accounts the routing around holes in the network. When a node does not have any closure neighbours towards the target region, a hole occurs. In case where no holes exit, the estimated cost is equal to the learned cost. The GEAR protocol only considers a certain region rather than sending the interests to the whole network as happens in Directed Diffusion [18] and thus restricting the number of interests. There are two phases in this protocol:  
Phase-I: In this phase, packets are forwarded towards the target region. After receiving a packet, a node searches for a neighbor which is closer to the target region then itself. The neighbor is then selected as the next hop. If there are more than one suitable nodes then there exists a hole and in this case one node is picked to forward the packet based on the learning cost function.  
Phase-II: In this phase, the packets are forwarded within the region. If the packet reaches the region, it is diffused in that region by either recursive geographic forwarding or restricted flooding. If the sensors are not densely deployed, then restricted flooding is used and if the node density is high, then geographic flooding is used. In geographic flooding, the region is divided into four sub regions and four copies of the packet are created. This process continues until the regions with only one node are left.
## 4 Discussion
### Limitations and Future Considerations  
This study was limited by a couple of factors. The high sample rate (1200 Hz) used for force plate data collection may have caused the entropy analysis to be biased with the redundancy of data in the time series. Additionally, the sample was small and one of convenience, so it is likely the data were underpowered. A larger number of participants to collect data from, a reduced data collection sampling rate, and collection of data over a greater amount of time per trial, such as a minute or two instead of 30 s, as was conducted for this work, would be beneficial for future studies. Additionally, this study was affected by the inherent limitations of ApEn. The values chosen for m and r have a significant effect on the result of ApEn. Although numerous values for each were tested in this study, a more in-depth analysis into how the ApEn of COP data is affected by varied m andr-values would be pertinent. ApEn can also become biased with a large sample size [52], which was a consideration in this study and contributed to the data collection limitations mentioned above.  
The results of this study lead to many interesting avenues for future examination. For example, comparing the results of ApEn with the results of sample entropy to determine if, by avoiding the limitations of ApEn, sample entropy can give more information about postural control. Another future consideration could be completing an analysis of data from non-neurotypical participants, such as those suffering from concussion, to learn how the techniques investigated in this work can determine differences in the postural control mechanisms of healthy and injured participants. Finally, including an analysis of the angular acceleration of the COP and COM signals to identify how COP oscillations pivot as the two signals move toward and away from one another could be utilized to learn more about the postural control system during quiet standing. Namely, considering the distance between the COP and COM over time could lead to interesting findings as it is directly proportional to the angular acceleration, but does not require any derivative calculations.
# Temperature State Awareness-Based Energy-Saving Routing Protocol for Wireless Body Area Network
## 3 System Model
### Thermal Model  
In order to perceive the node temperature state, it is necessary to calculate the node temperature rise rate. The temperature rise rate can be calculated by the [PERSON] bioheat equation [PERSON] (1964), as shown in Formula (4).  
\[\mathrm{\rho\ T_{s}\ dT/dt=K\
abla^{2}T-b(T-T_{b})+\rhoSAR+P_{c}}. \tag{4}\]  
Here, \(\mathrm{T_{s}}\) represents the specific heat of human tissue, \(\mathrm{dT/dt}\) represents the rate of temperature increase, \(\mathrm{K\
abla^{2}T}\) indicates the temperature increase due to the thermal conductivity of human tissue, \(\mathrm{b(T-T_{b})}\) is the heat caused by blood perfusion in the human body, and \(\mathrm{Pc}\) refers to the sensor heat caused by the circuit. In the analysis of this paper, consider the typical power consumption of a conventional sensor circuit. \(\rhoSAR\) represents the antenna radiation absorption, the specific absorption rate (SAR) measures the rate at which the human body absorbs heat [PERSON] (2008), and its calculation formula is as follows.  
\[\mathrm{SAR=\sigma E^{2}/\rho}. \tag{5}\]  
where the conductivity of human tissue is denoted using \(\sigma\), the induced electric field is denoted using \(\mathrm{E}\), and \(\rho\) is used to represent tissue density.  
The nodes are in space, with some treatment of the bioheat equation. Finite difference time domain (FDTD) is used in heating applications [PERSON] (2008), FDTD discretizes the modeling of time and space by discretizing the entire network space into small grids, each represented by a pair of coordinates, with the following results:  
\[\begin{split}&\mathrm{T^{m+1}(i,j)=[1-(\delta_{t}b/\rho T_{s})-(4 \delta_{t}\ K/\rho T_{s}\delta^{2})]\ T^{m}(i,j)+(\delta_{t}/T_{s})\ SAR+(\delta_{t}b/\rho T _{s})\ T_{b}+\\ &(\delta/\rho T_{s})\ P_{c}+(\
# Cooperative Communication Based Protocols for Underwater Wireless Sensors Networks: A Review
## 6 Challenges and Future Trends in Cooperative UWSNs  
The main takeaway from the reviewed literature and the presented results is that utilizing cooperation leads to a substantial improvement in both throughput and reliability compared to the single-path scenario. In UWAC where signal attenuation, scattering, and multipath effects pose significant challenges, cooperative communication and multi-hop architectures can markedly enhance performance. These methods can extend the range of communication, improve data rates, and increase the robustness of the network against node failures or environmental interference.
# In-Orbit Attitude Determination of the UVSQ-SAT CubeSat Using TRIAD and MEKF Methods
## 4 Results
### Results with MEKF Method  
The second method, described in Section 3.2, should correct the signals from noise and gyrometer biases. The Sun LOS and nadir directions are computed in the body reference frame from the attitude matrix. Their components in this frame are shown in Figure 9.  
Figure 8: First lights of the Sun LOS (**a**) and nadir (**b**) components in the satellite body frame based on TRIAD method on 2 March 2021 (representative period in the case of measurements at 30 s intervals almost continuously, with eclipse phase in gray).
# Estimating Airway Resistance from Forced Expiration in Spirometry
###### Abstract  
Spirometry is the gold standard to detect airflow limitation, but it does not measure airway resistance, which is one of the physiological factors behind airflow limitation. In this study, we describe the dynamics of forced expiration in spirometry using a deflating balloon and using this model. We propose a methodology to estimate \(\zeta\) (zeta), a dimensionless and effort-independent parameter quantifying airway resistance. In N = 462 (65 \(\pm\) 8 years), we showed that \(\zeta\) is significantly (\(p<0.0001\)) greater in COPD (2.59 \(\pm\) 0.99) than healthy smokers (1.64 \(\pm\) 0.18), it increased significantly (\(p<0.0001\)) with the severity of airflow limitation and it correlated significantly (\(p<0.0001\)) with airway resistance (r = 0.55) and specific conductance (r = \(-0.60\)) obtained from body-plethysmography. \(\zeta\) also showed significant associations (\(p<0.001\)) with diffusion capacity (r = \(-0.64\)), air-trapping (r = 0.68), and CT densitometry of emphysema (r = 0.40 against % below \(-950\) HU and r = \(-0.34\) against 15 th percentile HU). Moreover, simulation studies demonstrated that an increase in \(\zeta\) resulted in lower airflows from baseline. Therefore, we conclude that \(\zeta\) quantifies airway resistance from forced expiration in spirometry--a method that is more abundantly available in primary care than traditional but expensive methods of measuring airway resistance such as body-plethysmography and forced oscillation technique.  
Keywords:spirometry; airflow limitation; airway resistance; specific airway conductance; COPD; body-plethysmography; forced expiration; alveolar pressure; emphysema; computed tomography; air-trapping +
Footnote †: journal: Applied
# Improving Spatial Reuse of Wireless LAN Uplink Using BSS Color and Proximity Information
## 3 Proposed Method
### Protocol Details
#### 3.2.3 Protocol Operation
A large margin makes nodes conservative, which reduces collisions but at the same time reduces parallel transmissions. On the other hand, a small margin makes nodes more aggressive, which increases parallel transmissions but also collisions. In the simulations, we study the impact of margin on the protocol performance. There are methods to improve the accuracy of a pathloss model, such as tuning the pathloss exponent to better reflect the current environment [40]. With a more accurate model we may choose a smaller margin and further improve the protocol performance. It is out of scope of this paper and left as a future work. Figure 6: A flow diagram illustrating the operation of the proposed method.