esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Experimental Setup
#### 2.3.1 rPMS Application  
For rPMS, stimulation was applied to the tibialis anterior (TA) muscle on the paretic side. A small support was placed under the foot to maintain slight TA contraction. After identifying and marking the midpoint of the TA muscle belly, the stimulation coil (figure-of-eight, air-cooled; Magstim Rapid2) was positioned parallel to the muscle fibers. Motor threshold (MT) was determined as the lowest stimulation intensity that elicited a visible muscle contraction [16; 17]. Stimulation was then delivered at 10% above MT in 40 trains of 3-s stimulation, with a 19-s rest, totaling approximately 2400 pulses over 15 min.
## 3 Results and Discussion
### Influence of Sample Time
This indicates that the improvement is significant compared to the scatter. From a production engineering point of view, it is also of interest how long the classification process takes for the whole print head with 768 nozzles, as the machine is not able to produce during classification. 30,000 samples correspond to 0.83 s for each tested nozzle or 480 s for the whole print head. Therefore, in the following a second sample number will be proposed, which also takes the necessary sampling time into account. It may be necessary to maximize the classification success or to save time in an industrial application, depending on the specific printing geometry. Therefore, a compromise must be found at which the number of samples can be kept as low as possible, but at the same time the success rate can be kept sufficiently high. According to Figure 7, it seems reasonable to define this mark at 10,000 samples. The success rate here has a value of about 72%. A z-test has failed to reject the hypothesis that the results at 10,000 samples and 40,000 samples originate from the same distribution up to a 35% significance level, which indicates similar success rates. Furthermore, the success rate is already in an approximately stable range, which has a safe distance to the lower boundary of 2500 samples. By setting the limit at a sample number of 10,000, it is possible to reduce the recording time (testing time for the whole print head: 160 s) without having to accept a deterioration in the success rate of the ANN.
## 2 Materials and Methods
### Trend Analysis  
Trends are calculated by fitting linear functions for time series of annual LUE and iWUE* for each pixel using ordinary least-squares regression (OLS). Trends are computed with 95% confidence interval (here after 95% CI) using the Student's t-test. The t-test compares a calculated t with a critical tc value for a stipulated significance level and N-2 degrees of freedom (N = 29; representing here the annual time series length: 1982-2010). No significant trends (\(\left|\text{t}\right|<\text{tc}\)) at 95% CI) are labeled in maps with grey and slate grey colors for negative and positive trends respectively. Trends for fAPAR, GPP and ET during 1982-2010 are also calculated from annual values following the same method as for LUE and iWUE*. All trends are weighted by their average over the whole period in order to compute the result in \"%\" of each parameter.
## 2 Related Work
### Visualisation of Semantic Data  
The visualisation of semantic data, stemming from document corpora, presents distinctive challenges due to its heterogeneous and dynamic nature. Conventional fixed metadata structures are inadequately equipped to manage such intricacies.  
Existing research [2] has identified distinctive characteristics of tools for Linked Data visualisation, categorising them based on various interaction paradigms and types of represented information.  
In terms of interaction paradigms, the following are highlighted:  
**Tabular interaction paradigm**: This paradigm organises information about a single resource in a tabular format, enabling users to explore specific attributes, such as media files, descriptions, and links to related resources [5; 14; 15].  
**Node-link interaction paradigm**: In this paradigm, resources are represented as nodes or boxes, connected by arcs, which represent relationships. Users navigate the graph by traversing these connections [16; 17; 18].  
**Visual query composition**: These interfaces simplify the creation of SPARQL queries using graphical elements [19; 20].  
Regarding the types of represented information, the distinctions are made as follows:  
**Data visualisation**: These tools employ graphical representations to enhance data comprehension [21].  
**Model visualisation**: Tools in this category specialise in illustrating data models, including schemas and ontologies, offering users insights into the underlying data structures [3; 22].  
**Data to model visualisation (schema extraction)**: These tools deduce ontology schemas from RDF triples using SPARQL queries [23; 24].  
These categories of tools provide various modes of interaction with Linked Data. However, it is essential to note that SKATEBOARD goes beyond these paradigms and categories, adopting a dynamic approach based on the selected entity type. For instance, SKATEBOARD offers interactive maps for locations, reveals details such as birth dates, birth places, and key information for individuals, and presents publication data and authors for books. Furthermore, SKATEBOARD customises its visualisation based on the entity type, allowing users to decipher underlying data models and promoting in-depth exploration of semantic information. As research continues, Linked Data interfaces are expected to become increasingly sophisticated, but SKATEBOARD is at the forefront, offering users a richer and more-intuitive experience.
# Energy and Environment-Aware Path Planning in Wireless Sensor Networks with Mobile Sink
## 4 Formulation for Optimal Solution
### Optimal Routing Problem
If there is no cluster head in the candidate set \(CCHN_{x}\) which satisfies this condition, the \(FCHN_{x}\) will not have a member. Therefore, such constraints are used to ensure the presence of members in set \(FCHN_{x}\); otherwise, drop control is called. Constraints in (66) and (67) ensure that every cluster head \(y\) reaches the mobile sink. Any cluster head \(y\) must be on at least one path to the sink. The constraint in (68) is utilized to avoid cycles; for the same source cluster head \(s\) and urgent message \(u\), the use of any cluster head \(y\) as a relay node has a cost of 1. Constraints in (69) through (72) ensure that only one cluster head \(y\) meets the minimum relaying delay criterion while urgent data packets are delivered to the mobile sink on time. The constraints in (73) through (76) ensure energy consumption balance to prolong network lifetime. The only cluster head \(y\) that has the highest value of \(ECL_{y}(t)\) must be selected from the final set of the cluster head neighbors of cluster head \(x\). Constraints in (77) through (80) ensure that the cluster head \(x\) picks only one cluster head \(y\) from its final cluster head neighbor set that meets the maximum final environmental impact metric condition so that data packets are sent to the mobile sink in safe paths away from hazards. The constraint in (81) is utilized to ensure that the decision variable \(\omega_{xy}^{su}\) is enforced to 1 when: (1) the cluster head \(y\) reaches mobile sink, (ii) it has the minimum value of \(Rd_{y}\) compared with other cluster head neighbors, and (iii) it has the maximum value of \(EIM_{y}^{i}(t)\) and \(ECL_{y}(t)\) compared with other cluster head neighbors. Constraints in (82) through (83) are redundancy constraints where all \(\sum\limits_{x,y\in CH^{t}}d_{xy}^{su}\) and \(\sum\limits_{p\in P_{y}^{i}}\alpha_{y}^{p}\) must be greater than or equal to 1. The constraint in (84) ensures the decision variables \(\left\{d_{xy}^{su},\alpha_{y}^{p},\omega_{xy}^{su},c_{y},o_{y},b_{v},e_{y},v_{n},N_{y}\right\}\) are equal to 0 or 1.
## 3 Numerical Simulations and Experiment Validation
### Simulation Results and Analysis
This result aligns with the theoretical analysis and provides a reliable basis for porosity estimation using measured frequency domain characteristics. As shown in Figure 6a, the theoretical SAW velocity-porosity curve derived from Equation (5) is compared with the simulation results, where each scatter point represents the actual porosity and the corresponding SAW velocity obtained from a set of numerical simulations. The porosity range considers spans from 0% to 15%, and the fitting between the simulated data and the theoretical model yields a high coefficient of determination (\(R^{2}=0.9426\)), indicating strong agreement between the theoretical prediction and the numerical results. Figure 6b further validates the effectiveness of the proposed method by comparing the porosity values estimated from simulated SAW velocities with the actual porosities used in the numerical models. This comparison demonstrates a similarly high level of agreement, with an \(R^{2}\) of 0.9426 and a mean absolute error (MAE) of 0.7919%, confirming that the proposed approach can reliably and accurately estimate porosity based on SAW velocity measurements. Figure 4: SAW signals for simulation models with varying porosity levels: (**a**) porosity = 1.29%; (**b**) porosity = 1.29%; (**c**) porosity = 1.29%; (**d**) porosity = 1.29%.
# ESL: A High-Performance Skiplist with Express Lane
## 2 Background
### Skiplist
#### 2.1.1 Cache-Conscious Skiplist  
Some research efforts were made to fundamentally reduce cache misses in a skiplist structure. In the following works, researchers focused on reducing cache misses by merging multiple nodes.  
**Cache-Sensitive Skiplist (CSSL)**[24] is a variant of a skiplist, which reduces the number of cache misses by merging the multiple nodes in the index levels into a single array, denoted as a _fast lane_. The fast lane is a consecutive array that contains all nodes, with the exception of the data level, which contains all data in the skiplist. As the nodes are in a single array and stored in a sorted manner, the data structure is more cache-friendly. CSSL includes another array, denoted as the _proxy lane_, which consists of the data stored at the data level. Since the proxy lane is the array, it accelerates the process of finding a target data node not found at higher levels. However, because the nodes of both index levels and data levels in a skiplist are stored in separate arrays, numerous shift operations are required for node insertion or deletion. As shown in [24], CSSL delays the fast lane and proxy lane updates to mitigate the shift operation overhead. However, it leads to the periodic reconstruction of the fast lane and proxy lane, which may hinder overall performance.  
**Parallel in-memory Skiplist (PSL)**[25] is a skiplist that merges multiple nodes into a node that is similar to the B+-tree node. This structure enables reducing the number of cache misses in the traversing skiplist. The Parallel in-memory Skiplist also defers updates of higher index levels and rebuilds the structure when the number of updates reaches the threshold using background threads. Since it periodically rebuilds the whole index structure in an asynchronous manner, the background threads can be the performance bottleneck.  
Figure 1: Skiplist structures.
## 6 Experimental Simulation and Analysis of the Results
### Performance Gains from Cross-Layer Optimization  
To investigate the benefits gained by the cross-layer information sharing in the PEAFC-CL protocol, we compared the PEAFC(No-CL) protocol with the PEAFC-CL protocol in terms of network lifetime and packet loss ratio based on simulations. Cross-layer technology is not applied to the PEAFC(No-CL) protocol, eliminating the need to acquire channel efficiency parameters. Only the residual energy is  
Figure 11: (**a**) Round when the first node dies _vs._\(a\); (**b**) Packet loss ratio _vs._ number of nodes for different protocols.  
Figure 12: Packet loss ratio _vs._ number of nodes for different values of \(\xi\).  
cited as the reference factor for the CH selection. In Figure 13a, the first node death is taken as an objective for investigation. The first node death time of the network based on the PEAFC-CL protocol is extended by 15% on average compared with that of the network based on the PEAFC(No-CL) protocol. Figure 13b indicates that the packet loss ratio of the PEAFC-CL protocol is 20%-30% lower than that of the PEAFC(No-CL) protocol. With increasing number of nodes, the PEAFC-CL protocol is more competitive in terms of the network packet loss ratio because the link status on the MAC layer and variations of the signal intensity on the physical layer are fed back to the network layer, thus providing the network layer with specific references during cluster construction and data transmission and promoting the transmission performance.  
The PEAFC-CL protocol offers two major contributions: one is the design of a clustering network protocol self-adaptive to and aware of channel fading changes, and the other one is the adjustment of the receiving and sending power of nodes according to fading situations. In realistic environments, particularly situations with channel fading, the PEAFC-CL protocol can adapt to channel fading changes and complete adaptive networking, which increases the robustness of network topology; it can also improve gains regarding data transmission rate and throughput in the environment of a large-flow network. Second, through the control of node power, it is largely able to avoid unnecessary energy consumption caused by excessively large or small node power during fluctuation of channel fading, thus improving the energy utilization efficiency and extending the service life of the network.
## 4 Challenges in Rainfall Estimation by CMLs
### \(\gamma\)-\(R\) Relationship  
As shown in Equations (6) and (7), accurate rainfall inversion can only be guaranteed if the \(\gamma\)-\(R\) relationship is close to linear or if rainfall is uniform on the path of CMLs. Although existing CMLs, most of which operate at 15 to 40 GHz, have a parameter \(b\) that can be approximated as 1, with the growing demand for bandwidth, higher-frequency CMLs will have to pay more attention to this problem [160], which will introduce considerable inherent errors into the rainfall estimation. This phenomenon is mitigated in the case of short CMLs because the variability of their path rainfall tends to be negligible.  
Another source of uncertainty worth noting is the influence of DSD on the \(\gamma\)-\(R\) relationship. For CMLs outside of about 30 GHz, the dependence of the \(\gamma\)-\(R\) relationship on DSD is getting higher (Figure 4), i.e., the agreement between the scatter corresponding to \(\gamma\)-\(R\) and the fitted curve decreases, resulting in lower accuracy in rainfall estimation [18]. It has been shown that long-range spatial averaging of DSDs over the paths of CMLs can reduce rainfall estimation errors because the \(\gamma\)-\(R\) relationship over the paths becomes closer to the fitted power law as the path length increases [171; 172]. In addition, considering different DSDs for stratiform and convective rainfall, the accuracy of rainfall inversion of the \(\gamma\)-\(R\) relationship for different rainfall types was analyzed by [PERSON] et al. [160]. The results show that stratiform rainfall always exhibits a negative bias above 10 GHz, whereas convective rainfall exhibits a negative and positive bias below and above 50 GHz, respectively. Fittingpower law relationships to different types of rainfall separately can provide more accurate rainfall estimates [47; 84], but it requires additional information about the rainfall type.
## 2 Resolution-Improved Near-Virtual-Image-Mode Light-Field Display
Assuming \(g_{a}\) denotes the distance between the aperture  
Figure 1: (a) Configuration of the resolution-improved light-field display using near-virtual-image mode and (b) virtual image of the aperture. and lens arrays, \(g^{\prime}_{a}=f_{ga}/(f-g_{a})\) gives the distance between the virtual image plane of the aperture array and lens array. The magnification of the aperture is given by  
\[M_{a}=g^{\prime}_{a}/g_{a}=f/(f-g_{a}). \tag{2}\]  
To observe the virtual pixel images at a constant interval, the width of the virtual aperture image, denoted by \(w^{\prime}_{a}\), should be equal to the lens pitch. Therefore, assuming that \(w_{a}\) denotes the aperture width, the following equation should be satisfied:  
\[p=M_{a}w_{a}=nM_{p}q. \tag{3}\]  
The display parameters of the proposed light-field display are described. An elementary image is displayed on a flat-panel display corresponding to each lens. The pitch of the elementary images should be \(p_{e}=(1+g_{p}/l)p\) to maximize the viewing zone at a distance of \(l\) from the lens array, and \((p_{e}/q)\times(p_{e}/q)\) gives the resolution of the elementary image. When \(X\times Y\) denotes the resolution of the flat-panel display, \((nXq/p_{e})\times(nYq/p_{e})\) gives the resolution of the 3D images. \(\Phi=2\tan^{-1}(p_{e}/2g_{p})\) determines the viewing zone angle. The light efficiency is \(\eta=(w_{a}/p_{e})^{2}=(nM_{p}q/M_{a}p_{e})^{2}\), and the screen size of the display is given by \(Xq\times Yq\). The divergence of rays is discussed to evaluate the blurs of the 3D images generated by the proposed technique. Figure 2 shows the ray divergence of the proposed technique. A ray from the virtual pixel images passes through the aperture. \(w_{1}^{np}\) denotes the width of the central region, where the light intensity is constant because the width of the virtual aperture image is equal to that of the multiplied virtual pixel images. \(w_{2}^{np}\) denotes the width of the region where rays exist, which expands as light propagates. When \(z\) denotes the propagation distance from the lens array, \(w_{1}^{np}\) and \(w_{2}^{np}\) are given by:  
\[w_{1}^{np}=nM_{p}q, \tag{4}\]  
\[w_{2}^{np}=nM_{p}q+2 nM_{p}qz\ /\ g^{\prime}_{p}. \tag{5}\]  
Because \(n=1\) in the original near-virtual-image mode, the divergence of the rays in the high-resolution system exceeds that in the original system. As \(z\) increases, \(w_{2}^{np}\) of the proposed technique increases more rapidly compared with that of the original near-virtual-image mode because \(g^{\prime}_{p}\) in the proposed technique is made smaller than that in the original technique to reduce the magnification. Thus, although the proposed system improved the resolution of the generated 3D images, the images are blurred when they are displayed far from a screen plane.
# Micro-Nanobubble Technology in Surface Cleaning and Defouling
## 4 Biofouling Mitigation and Advanced Cleaning Technologies
### Healthcare and Biofilm Remediation
Complete water flux recovery was limited by the persistent nature of the extracellular polymeric substances, despite the fact that it successfully addressed both irreversible and reversible biofilm formation. To improve the long-term performance, the authors advised combining chemical, biological, and physical cleaning techniques. They also recommended that future research should examine how self-cleaning affects biofilms over longer periods of at least 24 h.  
[PERSON] et al. [105] described in detail the treatment of a female patient, aged 10, who had HI, a severe skin condition marked by recurrent fissures and excessive skin production. A 100 h medical trial at the dermatologist's office revealed that NBs significantly improved the effectiveness of conventional treatments. The White Water Company was founded in 2016 after the family saw positive results in the first 20 min session. This company created its first portable nanobubbler. Through skin shedding and internal moisturization, this technology provided a comprehensive solution for addressing both excessive skin production and biofilm remediation. In addition to highlighting the significance of interdisciplinary cooperation in therapeutic innovation, the report underscored the revolutionary effects of NB hydrotherapy on HI management. With numerous applications such as improving skin oxygenation and preventing infections, NB technology offered a thorough method of handling intricate problems related to HI, ultimately leading to better results for patients and their families. [PERSON] et al. [104] investigated the disinfection potential of OUFBW. The bactericidal activity of the water produced by the OUFBW generator, which contained 4-6 parts per million of ozone, was tested against various pathogenic bacteria, including antibiotic-resistant strains. Within 30 s, exposure to OUFBW decreased the bacterial viability of planktonic cultures of species like _Streptococcus pneumoniae_ and _Pseudomonas aeruginosa_ by more than 99%. Additionally, OUFBW demonstrated efficacy against bacteria that had adhered to gauze and toothbrushes. Bacterial cell wall disruption was observed by transmission electron microscopy. OUFBW showed minimal cytotoxicity in human gingival epithelial cells, which is significant. These results implied that OUFBW is a low-toxicity disinfectant with broad-spectrum bactericidal effects on human cells. Figure 15: Analysis of biofilms with/without self-cleaning at 15 V for 10 min. (**a**) CLSM images of biofilms formed on the carbon nanotube (CNT)/graphene membrane after 3 h biofouling (top) and after self-cleaning (bottom). (**b**) Biofilm volume and thickness based on CLSM images. Reproduced with permission from [107], Copyright © 2022 Elsevier B.V.
# Mobile Autonomous Sensing Unit (MASU): A Framework That Supports Distributed Pervasive Data Sensing
## 4 MASU Evaluation Using Simulations
### Simulations Setup
#### 4.1.3 Network Infrastructures  
We configured the simulator to use three different types of networks to support the communication between the nodes: Access Point (AP)-based, Terminal-to-terminal (T2T) and MANET. These wireless network infrastructures, based on the IEEE 802.11 standards, were set according to the proposals presented in [33; 34], and they were adapted to be simulated on ns-3. Table 4 shows a detail of the configuration of the ns-3 simulator for the different types of User Datagram Protocol (UDP) messages interchanged in the three network infrastructures considered.
# 4-Class MI-EEG Signal Generation and Recognition with CVAE-GAN
## 3 Methods  
In this work, firstly, we employed a data preprocessing step based on short-term Fourier transform (STFT) to convert MI-EEG signals (C3, C4, and Cz electrodes) to a set of time-spectrum images. Then, a CVAE-GAN architecture was proposed for EEG signal generation and classification from a latent learning representation, obtained from an adversarial learning process. Considering that CVAE generated images are decent but fuzzy and the ones from CGAN are clear but contain a significant change, CVAE-GAN was used as an appropriate solution. For example, when we go to generate the motor imagery EEG, CVAE-GAN can calculate the class probability of the EEG data with its additional part. Therefore, it can make use of the latent class in training data to generate samples regularized by Gaussian distributions with learnable statistic values. The proposed CVAE-GAN architecture for EEG mainly consists of three parts: (1) The encoder network, mapping the EEG sample \(x\) to a latent representation \(z\) through a convolution neural network (CNN). (2) The generator network, generating fake EEG signals with respect to a latent vector. (3) The recognition network, being trained to distinguish between real and fake EEG data and measuring the class probability of the data from the real and generated input. Then, all the parts would be guided to adjust itself.