esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Synoptic Diagrams
#### 4.1.1 First Diagram (Figure 3): Linking Objectives to Analysis  
The first diagram (Figure 3) illustrates how the study was structured based on its general objective and three specific objectives. The logical progression follows a top-down approach:  
Figure 4: Logical progression from the narrative review findings to the cutting-edge research.  
Figure 3: Synoptic diagram of the study structure: from bibliometric trends to SARS applications in the health domain.  
* Block 1: This block represents the bibliometric trends reported in Figures 1 and 2 (Section 3.1). These trends were analyzed to provide an overview of the scientific production also in relation to robotics in the health domain in general.
* Block 2: This block corresponds to the identification of thematic areas, as presented in Tables 2 and 3 (Section 3.2). This categorization allowed for the organization of the reviewed studies according to key themes, focus, and contribution, facilitating a structured analysis.
* Block 3: Building upon the thematic categorization, this block highlights the comparative side-by-side analysis of the studies. The classification was refined, with categorization as reported in Table 4 (Section 3.2), enabling a deeper understanding of the different ways SARS are applied in the health domain.
* Block 4: This block synthesizes the opportunities and barriers/limitations identified in the reviewed studies, as reported in Table 5 (Section 3.3). These findings highlight both the potential benefits of SARS' applications in the health domain and the barriers/limitations.  
This diagram provides a step-by-step visualization of the study's methodological process, from bibliometric analysis to thematic categorization, comparative analysis, and the identification of emerging opportunities and challenges.
## 4 Simulation Results and Discussion
### Results of Case Studies
#### 4.3.3 Case 3  
In all previous cases, the output power of PV and WT units was presented under predicting conditions. To evaluate the effect of predicted and real weather data on changes in the operation cost of prosumer, this case study was introduced. Real weather data were deployed for optimization. Figure 14 demonstrated the power flow of the system based on predicted and real data. As can be seen, both diagrams followed a similar pattern where the performance of the system was not changed significantly. However, for the case with predicted weather data, less electrical power was imported from the grid between 4:00 and 11:00. The trend was reversed from 12:00 to 18:00, where the case with real data imported more electricity. Between 18:00 and 3:00, the exchanged power was the same for both cases (real and predicted weather data) because the PV as the main source of energy had no output power in this period. It is important to say that the operation cost of the prosumer, when the real data was used, would be US$20.384, which shows a slight increase in comparison to the use of the predicted data for the understudy day. The difference between real and predicted operation cost was US$0.031, which is a desirable value. Therefore, the proposed EMS for the day-ahead operation of the prosumer, which considered intermittency of RESs and ESSs depreciation cost, was effective according to the obtained results.
# 2 CA-R2: A Hybrid MAC Protocol for Machine-Type Communications
###### Abstract  
Machine-to-machine (M2M) communications are becoming the most important factor shaping network traffic. However, traditional controls developed for human-generated traffic are not able to cope with new demands. Thus, hybrid MAC protocols have been proposed to make use of the combined advantages of contention and reservation. Most of them are based on a contention stage (where a variant of CSMA/CA or ALOHA is used) followed by a reservation stage (e.g., TDMA or FDMA). In this paper, we introduce 2 CA-R2, a hybrid MAC protocol for M2M communications intended to be used in the device domain. What distinguishes this proposal is that the contention stage is controlled by a conflict-resolution algorithm known as Adaptive-2C. The protocol was evaluated using a model based on a Markov chain and computer simulations. Its performance was compared with DCF, the MAC technique used in IEEE802.11 standards. Our results show significant improvements over DCF in various metrics of network performance across different traffic situations. We also evaluated the time the protocol takes to resolve an access conflict, and we observed substantial improvements in the number of stations that can be served with the same network resource (in some cases, around a 40% improvement).  
machine-to-machine communications; medium access control; wireless LAN; protocols; standards 2025  
252994
## 3 Controlled Energy Transfer
### Static-Field Induced EET
Red and black lines denote electronically excited and ground states, respectively; a yellow line denotes a virtual intermediate state. The blue dashed line indicates the applied static field, while the green dashed line denotes short-range dipole–dipole coupling. given that each is polar; \(T\) is the absolute temperature and \(k_{\rm B}\) is the Boltzmann constant. The mechanism of mutual orientation that generates the above result applies only when both donor and acceptor are polar; the inverse fourth power dependence on temperature indicates a sharp diminution of the mutual orientation effect as temperature rises, since randomising thermal motions become increasingly dominant. In this result, there is also a notable dependence on the relative internal orientation of each transition dipole with respect to the corresponding static dipole, as signified by the two factors defined as \(\Delta\mu_{z}^{2}\equiv 3\mu_{z}^{2}-\left|\mu\right|^{2}=2\mu_{z}^{2}-\mu_{x}^{2}- \mu_{y}^{2}\) in Equation (6). Evidently the effect is optimised if the transition and static dipoles have a common orientation, signifying that the relevant excited state has the symmetry of a totally symmetric representation in the relevant point group. It is worth emphasizing the unique fourth power dependence on the strength of the applied static field \(D\), which affords a sensitivity that is especially amenable to experimental validation. Wherever field-induced orientation occurs, any corrections associated with the mechanism of direct engagement with the applied field will only furnish terms with a still higher order dependence on the field strength. The above analysis has focused on a case where a uniform applied field permeates the energy transfer system. However, static field effects can also be produced by local molecular dipoles--as, for example, in structurally ordered media or at surfaces [44]--where intrinsic local fields may exert another kind of influence on observed energy transfer effects. This is the topic of the next Section.
# Therapeutic Potential of Polymer-Coated Mesoporous Silica Nanoparticles
## 3 Polymer Coated MSNs for Neurodegenerative Diseases  
The treatment of neurodegenerative diseases, such as Parkinson's, Alzheimer's, and so forth, represent a great challenge due to the existence of the BBB. However, MSNs demonstrate their potential to cross the BBB and it has been reported that polymer coated MSNs could perform better than their uncoated counterparts in terms of BBB penetration [66; 67].  
Negatively charged MSNs loaded with premiyeole were coated with chitosan and sodium alginate via electrostatic interaction for the treatment of oxidative stress associated with [PERSON]'s disease. The polymer coated MSNs showed sustained drug release and minimized initial burst release at acidic and neutral pH. Cytotoxicity assay results suggested that the toxicity of the free drug was greatly reduced upon encapsulation in coated MSNs, and the designed drug delivery system showed promising potential in preventing H\({}_{2}\)O\({}_{2}\)-induced oxidative damage in human neuroblastoma SH-SYSY cells [47]. The same group later demonstrated the ability of chitosan and sodium alginate coated hollow MSNs for the delivery of dopamine-receptor agonist promipexole [68].  
Polylactic acid (PLA)-coated MSNs, attached with low-density lipoprotein receptor (LDLR) as a ligand were evaluated for brain targeting. These targeted particles were capable to cross the BBB to deliver resveratrol in CNS. In this study, PLA was explored as a stimuli-responsive gatekeeper. It was demonstrated that PLA significantly hinders the release of drug in PBS, but in the presence of superoxide (ROS), drug release increased owing to the accelerated degradation of PLA [48].  
In an interesting study, a plasmid RhoG-DsRed was used to coat the MSNs to control the release rate of loaded curcumin and simultaneously for promoting neurite outgrowth in treating oxidative stress. Negatively charged plasmid was adsorbed on the surface of positively charged MSN via electrostatic interaction. Further, to promote cellular uptake, RhoG was mixed with cell penetrating peptide TAT, which subsequently showed higher cellular uptake in N2a cells compared to TAT unmodified MSNs. Curcumin loaded engineered MSNs significantly reduced paraquat (superoxide anion radical generator) induced signals in N2a cells [49].  
PEG coated MSNs were explored to deliver flavonoids (quercetin, myricetin and myricitrin) with strong antioxidant properties in order to improve their aqueous solubility, release characteristics, and stability. High drug loading was observed for the flavonoids (up to 27 weight %) and the loaded flavonoids were capable of suppressing H\({}_{2}\)O\({}_{2}\)-induced changes in model lipid membrane's elasticity and morphological properties. The results suggested that flavonoid loaded MSNs showed neuroprotective activity, and that atomic force microscopy could be a valuable technique for monitoring the drug-induced effects at the membrane level, which may be extended to the cellular level [50].
###### Abstract  
Purpose. This study evaluated whether 3D-based vision training (VT) with visual cortex-activated auditory frequency entrainment and bilateral orbital electrical stimulation (ES) could prevent the progression of myopia among schoolchildren. Methods. In this two-stage, randomized, crossover, single-blind study, pre- and post-logMAR visual acuity and refractive error from 27 schoolchildren with myopia (\(\leq\)\(-\)0.5 D) were evaluated among four groups: (1) sham control with no VT, frequency following response (FFR), or ES (control group); (2) 3D-based VT only (VT group); (3) VT with FFR generated through visual cortex-activated auditory entrainment (VT-FFR group); and (4) VT with FFR and bilateral orbital ES (VT-FFR-ES group). In stage 1, the intervention was administered for 30 min to all groups using a randomized crossover design. In stage 2, the intervention was administered for 30 min/day, 3 days a week, for 4 weeks to evaluate the effectiveness of intervention. Results. Compared with the pre-test, post-test logMAR visual acuity after a single intervention was not significantly different in control and VT groups, but significantly improved in the VT-FFR (\(-\)0.08 \(\pm\) 0.11) and VT-FFR-ES groups (\(-\)0.13 \(\pm\) 0.14). Compared with the pre-test, post-test refractive error by spherical equivalent in VT-FFR-ES group for the 4-week intervention was significantly (\(<\)0.001) improved (0.21 D) compared with the control group (\(-\)0.1 D). Conclusions. The multicomponent physiotherapeutic intervention of 3D-based VT with auditory FFR and bilateral orbital ES can inhibit the progression of myopia. This intervention can be used as a noninvasive physiotherapeutic approach to prevent or reduce the severity of myopia.  
binaural; children; myopic; near-sightedness; visual entrainment +
Footnote †: journal: Applied Sciences
## 4 Results and Discussion
### Performance Comparison  
Table 1 shows the comparison of the proposed framework with the existing state-of-the-art techniques. As shown in the table, the proposed framework achieved the best performance. The proposed algorithm achieved 0.834 SROCC, 0.867 PLCC and 0.680 KROCC. The proposed framework showed an improvement of 0.103, 0.133 and 0.138 for the SROCC, PLCC and KROCC, respectively. The high performance of the framework depicts that using a visual saliency module for no-reference image quality assessment is beneficial. It helps the neural network model to learn the useful information required to predict the quality of the image. The InceptionResNetV2-based model exhibited good results when compared with other neural network models. Therefore, we utilized InceptionResNetV2 as our baseline model with the visual saliency module.  
Figure 3 shows a scatter plot between the predicted quality score and the actual quality score. As can be seen, there are minimal outliers in the scatter plot. The scatter plot was very near to the ideal inclined line. This demonstrates that the visual saliency was able to find the important regions, and predicting based on those regions made the prediction more accurate, as unwanted regions leading to wrong predictions were ignored. It is important to highlight here the limitation of the proposed framework. The proposed framework intends to fail in the situation where there is no object (having no high-frequency component) in the image, as the visual saliency model will not extract the important region.
## 4 Experimental Results
### Dataset Production
#### 4.1.2 Traffic Trend Prediction Monitoring Dataset  
The monitoring dataset of the traffic trend prediction section is based on the monitoring data of multiple roads in Yangjiang, Jiulongpo District, Chongqing, at the time of 2:00 p.m. on 21 November 2021, with a total of 250 sets of data samples. Each set of pieces contains monitoring information of ten traffic situation assessment index parameter values for four roads. In this paper, road traffic trends are predicted according to the traffic situation assessment indexes. The monitoring data of traffic trend prediction dataset.
## 4 Measured Signal Processing
### Field Data Processing  
In order to better verify the performance of the algorithm in real groundwater detection signal processing, field measurement data processing examples were further developed. The experimental site we used is next to Taipingchi Reservoir in Shaoguo Town, Changchun City. The reservoir has water all year round, ensuring that there is a magnetic resonance groundwater signal for the instrument to detect. The JLMRS-Array magnetic resonance detector independently developed by Jilin University was used as the detection instrument. The experiment was carried out in the poplar forest near the reservoir. Because the surrounding area is farmland without too many electrical facilities, the noise interference was relatively small. During the experiment, 16 sets of pulse moments were set up and transmitted successively using a square coil with a side length of 100 m. The local Larmor frequency was 2337 Hz and the sampling rate was 52,734 Hz. We selected two sets of data under the fifth and seventh pulse moments to analyze the actual application effect of the algorithm. The results are shown in Figure 8. It can be seen from the processing results of the two sets of data in Figure 8 that the MP, SSA, and HMC algorithms all achieve noise suppression and MRS signal extraction, and the Larmor frequency of MRS signal extracted after algorithm processing is stable at 2337 Hz. The HMC algorithm cannot process other types of frequency interference other than random and harmonic noise, making its processing effect slightly poor, and as a result the signal-to-noise ratio only increases by 10.88 dB. The MP and SSA algorithms achieve relatively good performance, and the signal-to-noise ratio is improved by 19.92 dB and 19.16 dB, respectively. Therefore, from the shape of time domain and frequency spectrum curves as well as the numerical value of the improvement in signal to noise ratio, the MP algorithm is superior to the SSA and HMC algorithms, and obtains a good processing effect on real field-measured groundwater detection data.  
Figure 7: Comparison of denoising results of the MP, SSA, and HMC algorithms with \(T_{2}^{s}\) of 200, 300, and 400 ms: (**a**) \(T_{2}^{s}\) = 200 ms, (**b**) is the corresponding spectrum; (**c**) \(T_{2}^{s}\) = 300 ms, (**d**) is the corresponding spectrum; (**e**) \(T_{2}^{s}\) = 400 ms, (**f**) is the corresponding spectrum.
# Electro-Optic Sensor for Measuring Electrostatic Fields in the Frequency Domain
## 2 Method and Experimental Results
### Possible Sources of Measurement Error
#### 2.6.3 Temperature Drift  
Temperature variations affect birefringence. As the sensor's two crystals are installed in two different locations, they might be exposed to different temperatures which is equivalent to an additional phase retardation. This shifts the sensor's transfer function but it does not impact its amplitude. Figures 22 and 23 show experimental data taken in a climate chamber where the transfer function of the system is evaluated at different temperatures, equal to the colored dots in the temperature plots, for a rising and then falling temperature. The transfer function at a given temperature is linked by the color of the plot to the related temperature.  
Figure 21: Impact of the phase difference between the spurious second harmonic and the fundamental frequency. The calculations were done for H2 = \(-\)54 dBc.  
Figure 20: Impact of the spurious second harmonic amplitude on the measurement accuracy. H2 amplitude is referred to the fundamental frequency amplitude.  
Since the process is relatively slow compared to the foreseen measurement time, which is in the order of seconds, temperature drifts are unlikely to have resulted in a significant measurement error. However, during the initial tuning of the sensor, the effect is easily compensated by adjusting the DC bias voltage, regardless of the temperature difference between the two crystals.
# Stochastic Modelling of Selfish Mining in Proof-of-Work Protocols +
## 3 Agent-Based Model and Simulation
### ABM of Selfish Mining
#### 3.1.4 Block Propagation  
As previously described, after accepting or mining a new block, the block is broadcasted through the peer-to-peer network. In reality, block propagation times depend on many parameters: actual distance, network traffic, block size, etc. Blocks vary in size depending on the number of transactions included in them. A larger block takes longer to send from node \(i\) to node \(j\): \"size matters\" [16]. For the sake of simplicity, it is assumed that block propagation is also a Poisson process with parameter \(\lambda_{nd}^{-1}\), the same average value for all edges in the peer-to-peer network. Thus, the average time for a block to be propagated from node \(i\) to one of her peers is distributed exponentially, namely \(\sim\exp(-\lambda_{nd})\). Innumerical simulation, we use the parameter \(\lambda_{nd}\) to control the _network delay_ (or _network latency_). A larger \(\lambda_{nd}\) reflects a lower network delay, meaning that blocks will diffuse more quickly through the network.
# Spectral Domain-Based Data-Embedding Mechanisms for Display-to-Camera Communication
## 2 Display to Camera Communication System
### Frequency Coefficient Extraction  
The entire image requires DCT prior to embedding data into the frequency coefficients of \(\mathbf{I}\). Figure 3 shows the positions of the DCT coefficients for an image of size M \(\times\) N. The top left corner is the DC component representing the average color of the entire frequency-transformed region. The remaining coefficients represent the changes in color when moving from the top left to the bottom right corner of the DCT transformed image. As the DC component preserves the average color of the entire DCT image, data embedding in this region can cause visual artifacts noticeable to the normal human eye. Embedding data in the low-frequency (LF) region (with large-valued coefficients) degrades the visual quality of the image. Likewise, embedding data in the high-frequency (HF) region (with small-valued coefficients) renders the embedded data susceptible to noise present in the channel. This is because JPEG compression in the image capture process is associated with large data loss in the HF spectral region. Therefore, to ensure robust communication in the presence of various types of noises in the optical wireless channel, we embed the data into the MF coefficients. The MF regions are less perceptually significant and carry comparatively less vital information of the image than the HF and LF regions. Therefore, embedding data in this region does not noticeably impact image quality or communication performance.  
To select the MF coefficients, all elements from the M \(\times\) N-sized \(\mathbf{I}_{\mathbf{F}}\) matrix are arranged in descending order. Then, after excluding the first 10,000 coefficients underlying in the DC and LF region, the next \(m\) highest coefficients are selected for data embedding; \(m\) is the total number of input bits. Note that the number of excluded coefficients that occupy the DC component and the LF regions was determined according to the analysis of the value  
Figure 3: Discrete cosine transform (DCT) coefficients of an image of size M \(\times\) N.  
distribution of the frequency coefficients. The selected \(m\) highest coefficients positioned under the MF region are termed the coefficient vector \(\mathbf{X_{F}}\) and modeled as follows:  
\[\mathbf{X_{F}}=[\mathrm{x_{1}},\;\mathrm{x_{2}},\;\mathrm{x_{3}},\ldots,\mathrm{ x_{m}}]. \tag{1}\]
# Evaluation of Green Strategies for Prolonging the Lifespan of Linear Wireless Sensor Networks
## 1 Introduction  
### Wireless Sensor Networks and Applications  
A wireless sensor network (WSN) comprises numerous embedded nodes equipped with sensing, processing, and wireless communications functionalities strategically deployed across a designated area to observe physical or environmental conditions [1]. As distributed systems, WSNs utilize wireless communication for inter-node communication, rendering them suitable for a wide range of applications.  
In tandem with WSNs, the concept of the Internet of Things (IoT) has emerged. The IoT can be defined as the interconnection of identifiable devices within an internet network for the purpose of sensing and monitoring processes [2]. WSN is thus a central component of the IoT [3], as the IoT leverages the capabilities of WSNs to collect data about the environment and execute actions based on the analysis of the gathered data [4]. In contrast to WSNs, which do not inherently require internet connectivity, the IoT predominantly utilizes internet protocol (IP) connectivity to assign each of its components, or \"things\", a unique address. Figure 1 depicts the relationship between the IoT and WSN, illustrating a typical IoT scenario where data collection is facilitated using WSNs.  
The application areas of WSNs encompass a wide range of domains, including geographical monitoring, habitat monitoring, transportation, military systems, business processes, structural health monitoring, microclimate research, medical care, and others [5; 6].  
In a survey conducted by [PERSON] et al. [7], the applications of WSNs were classified into six primary categories (military, environmental, health, flora and fauna, industrial, and urban) based on their respective usage scenarios, as illustrated in Figure 2.