esa-sceva/satcom-chunk-collection huggingface dataset:
# Future of IoT Networks: A Survey
## 2 IoT Overview
### IoT Platform  
The role of the IoT platform is to support and execute IoT services. In the past, the platform was developed in a closed manner. However, industries and governments are developing assorted IoT platforms as open source platforms. One of the reasons for this trend is to enhance the speed to enter the market, reduce the development cost, and improve the quality of the software [13]. By opening up the software platform, many developers are given the opportunity to contribute their work. This will eventually expedite the development process and reduce the cost. Specifically, Qualcomm developed the Alljoyn platform and is now under the process of turning it into as an open platform under the AllSeen Alliance [14; 15]. Additionally, the European Union (EU) is operating the OpenIoT project to develop an open platform for IoT [16].  
Another notable aspect of the IoT platform is the way in which the services were developed. In the past, the services were executed and developed independently. Now, they are being transformed into one platform that can support various services. For example, Cisco, International Business Machines Corporation (IBM), Qualcomm, Intel, and Google are developing their home, environment, energy, and traffic supporting services as a cross-platform network that can support various services [14]. Furthermore, the EU's Seventh Framework Programme (FP7) project and the Horizon 2020, International Telecommunication Union Telecommunication Standardization Sector (ITU-T), and oneM2M platforms standardize the IoT platform as a cross-platform network.  
In addition, internal modeling of the IoT platform has been proposed [17; 18]. The core idea of the internal modeling of the IoT platform is the publisher/subscriber model. For example, MAGIC Broker 2 is an IoT platform that provides a programming interface based on the publisher/subscriber model [17]. The authors created this platform by utilizing the mobile devices, public displays, and a Web-based sensor actuator named Sense Tecnic [17].  
Finally, interaction between the platform and the devices is crucial to construct and design an IoT platform. Application Programming Interfaces (APIs) are the key components with which IoT devices can interact with the IoT platform. For example, an exemplary API for IoT platform has been developed [19]. The authors proposed an API based on TinyOS [20] named the Constrained Application Protocol (CoAP) that can enable both client and server to be integrated into the IoT environment.  
Overall, the trend for the IoT platform is primarily based on the open source platform, cross-platform, publisher/subscriber model, and well-suited APIs.
## 3 Multi Objective Optimization of SFD Parameters
### Comparison Results of SFD Parameters of the Rotor System under Three Conditions  
Table 3 is SFD optimization parameters with normal, misalignment faults, and misalignment-rubbing coupling faults. It can be found that SFD optimization parameters are different under various conditions. The optimal value of the oil film gap is at the minimum level under the normal condition, and the optimal value is increased when the misalignment fault is considered. While the optimal value is relatively reduced, the misalignment-rubbing coupling faults are considered. For the effect of elastic support stiffness, the optimal value is the largest under the normal state, and the optimal value is the same under the state of the misalignment fault and the misalignment-rubbing coupling fault, while the optimal value reduces under the normal state. For the viscosity of the oil lubrication, the optimal value is the largest under the state of misalignment-rubbing coupling fault, while the optimal value reduces under the state of misalignment fault, and the optimal value relatively increases under the normal state. The reason that SFD optimization parameters are different under various conditions may be that various conditions have different influences on the rotor system, resulting in the change of dynamic characteristics of the rotor system.  
Figure 16: Effect of SFD design parameters on the amplitude of the fundamental, 2\(\times\) fundamental and 3\(\times\) fundamental frequency of the system with the misalignment-rubbing coupling fault.  
Figure 17: Spectrum diagram of SFD optimized system with the misalignment-rubbing coupling fault.
###### Abstract  
This study investigates the mechanical and optical characteristics of silicon nitride thin films deposited with PECVD at 80 \({}^{\circ}\)C for tunable silicon-rich SiN\({}_{\text{x}}\)-SiO\({}_{\text{y}}\)-based MEMS optical cavities. Varying the deposition parameters using SiH\({}_{4}\) and N\({}_{2}\) as precursor gases for silicon-rich SiN\({}_{\text{x}}\) thin films allows us to tune the refractive index to a value as high as 2.40 \(\pm\) 0.013 at an extinction coefficient of only 0.008, an extremely low surface roughness of only 0.26 nm, and a compressive stress of about 150 MPa. We deposited 6.5-layer pairs of silicon-rich SiN\({}_{\text{x}}\)/SiO\({}_{\text{y}}\)-distributed Bragg reflector (DBR) micro-electro-mechanical system (MEMS) mirror that covers the whole 1300 and 1550 nm range. Cavity architectures of 6.5 top and 6 bottom layer-pairs were fabricated in the clean room providing a variety of cavity lengths between 0.615 \(\upmu\)m and 2.85 \(\upmu\)m. These lengths were then simulated in order to estimate the Young's Modulus of silicon-rich SiN\({}_{\text{x}}\), obtaining values from 56 to 92 GPa. One of the designs was characterised electro-thermally providing a tuning range of at least 86.7 nm centred at 1585 nm. The tunable filters are well suitable for implementation as tuning element in lasers for optical coherence tomography.  
silicon nitride; PECVD; DBR; refractive index; Fabry-Perot cavity; extinction coefficient; Young's modulus; stress gradient Article
# Cleaning Phenolic Compounds Present in Water Using Salting-Out Effect with DCA-Based Ionic Liquids
## 3 Results and Discussion
### Effect of Initial Concentration in the Extraction Yield  
The initial concentration of the compound that wanted to be extracted significantly influenced the extraction efficiency since a lower amount of the compound meant more difficult extraction. Regarding the obtained results, for the removal of 2-chlorophenol and PCM, the extraction yields were higher than for phenol and o-cresol, even at the lowest initial concentration. This behavior can be observed in Figure 2, where the extraction efficiency for the removal of the three phenolic compounds at all initial concentrations was plotted using [EMim] [DCA] as an extraction solvent with 20% of salt. Nevertheless, all data were higher than 92%, and the extraction efficiencies increased as the initial concentration increased.
# An Automatic Partition Time-Varying Markov Model for Reliability Evaluation
## 2 Materials and Methods  
### Tsallis Entropy  
Tsallis entropy is non-extensive entropy; it is an expression of entropy with exponent q proposed by Brazilian physicist [PERSON] in 1988 [33]. Its mechanism is based on the non-extensive statistics derived from the Boltzmann-Gibbs theory. The Tsallis entropy performs very well in the description of the system with long-range interactions, long-term memory effects, or systems evolving in a multifractal-like space-time. Due to the above characteristics, the Tsallis entropy has a wide range of applications in many fields, such as physics, medicine, and economics. The Tsallis entropy is concave, stable, and is limited in the rate of entropy generation per unit time [34]. Although Shannon entropy, Renyi entropy, etc., have very rich applications in the field of bearing analysis, these entropies are not good enough to describe the bearing performance degradation with vibration signal. Therefore, the Tsallis entropy is extracted as the data featured from the vibration signal in this article. The definition is as below:  
\[H_{q}^{T}=\frac{1}{q-1}(1-\sum_{i=1}^{n}p_{i}^{q}) \tag{1}\]  
where \(q\) describes the non-extension degree of the target object. In this research, it represents the long-range correlation of time series data. \(n\) is the total data number in one sample. \(p_{i}\) is the \(i\)-th value of the sample.  
The purpose of Tsallis entropy is to find a clear \(q\) value (generally not equal to 1) to describe as much as possible the phenomenon that is neither regular nor completely chaotic or random. After many experiments, we found that \(q=2\) can better describe the changing characteristics of bearing.
## 3 Innovation
### Utilization of Satellite Sensors and Machine Learning Methods
However, their limited number of Spectral bands poses challenges for the accurate measurement of soil salinity. Additionally, scholars have sought to estimate soil salinity content using spaceborne hyperspectral satellite data, including GF-5 [89] and HJ-1 [114]. Despite its advantages and potential for quantitative inversion and spatial mapping, the processing of hyperspectral imaging data is relatively complex and subject to numerous interference factors, indicating that significant challenges remain before it can be widely applied in practice [18]. Fundamentally, both types of sensors are optical and radiation detectors that are susceptible to interference from clouds and precipitation. As a result, measuring soil salinity in vegetated areas proves difficult due to the extremely limited penetration capabilities [33]. The rapid advancement of UAV low-altitude remote sensing platforms offers new methods and perspectives for soil salinization research [115]. These platforms possess high spatial resolution and frequency, which can complement spaceborne remote sensing technologies, mitigating the conflict between spatial and temporal resolution, and achieving a cost-effective trade-off between space and time [27; 60; 65; 91; 116; 117]. Currently, machine learning algorithms serve as the primary tools for predicting spatial and temporal changes in soil properties within the realm of digital soil mapping. Among these, the Random Forest algorithm remains the most favored, selected by 37.90% of researchers, followed by SVR and ANN (Figure 2). Deep learning techniques, particularly CNN, as well as the recently popular XGBoost and LightGBM algorithms, have not yet seen widespread adoption. To fully leverage the potential of deep learning, a larger training dataset is necessary; however, the current average dataset of 200 samples does not significantly bridge the gap between deep learning and more established machine learning algorithms. Furthermore, acquiring additional sample data necessitates financial support. Nevertheless, it is anticipated that the newly developed XGBoost and LightGBM algorithms will gain increased utilization over time.
## 3 Parametric Analysis and Simulation Results  
The performance of the proposed antenna is evaluated through parametric analysis by taking various antenna configurations, using a penetrated probe\(-\)fed DRA without a metasurface and later introducing 3 \(\times\) 3, 5 \(\times\) 5, and finally, 7 \(\times\) 7 unit\(-\)cell arrays of metasurface structures, as shown in Figure 4. The corresponding S\({}_{11}\) plots of Figure 4 are plotted together in Figure 5 for various N \(\times\) N metasurface configurations. It is observed from the S\({}_{11}\) plot with and without metasurface configurations in Figure 5 that the return loss obtained at 6.3 GHz for a DRA without a metasurface is \(-\)35 dB, and the bandwidth is 1.05 GHz. Adding a metasurface shifts the higher frequency band to the lower frequency band, i.e., at 5 GHz. The return loss obtained with a metasurface configuration at 5 GHz is \(-\)35 dB, and the bandwidth is 1.5 GHz, which gives the enhanced fractional bandwidth of 450 MHz and also results in the size reduction in the antenna as the resonance frequency is shifted from higher to lower by utilizing the property of the metasurface. It can also be observed from Figure 6 that the peak gain of 3.94 dBi is obtained without a metasurface configuration, and it is less when compared to the peak gain of 6.92 dBi with a metasurface configuration. It is also observed that as the array of unit cells increased, the consistency in gain across the offered bandwidth also increased. It is shown in Figure 7 that the diameter (Dr) of the DR is varied to optimize the resonance bandwidth at 5 GHz with circular polarization (which is also correlated with Figure 3). Based on a parametric and applictive study of an HFSS EM solver, the RL bandwidth obtained for Dr = 15 mm and ph = 5 mm  
Figure 3: Magnitude of E-field rotation on a modified Minkowski\(-\)based metasurface at different phases.  
is 1.5 GHz, which is higher than the bandwidth for the other Dr and ph values shown in Figures 7 and 8.  
Figure 4: Evaluation of metasurface-based antenna for different N \(\times\) N arrays of unit cells: (**a**) without metasurface, (**b**) 3 \(\times\) 3 array of unit cells, (**c**) 5 \(\times\) 5 array of unit cells, (**d**) 7 \(\times\) 7 array of unit cells.  
Figure 5: Comparison of S\({}_{11}\) parameters for various N \(\times\) N metasurface configurations.  
Figure 6: Analysis of gain plots with and without metasurfaces for the proposed configuration.  
Figure 7: S\({}_{11}\) plots for different diameters (Dr) of DR.
## 2 Materials and Methods
### Sleep Assessment  
Athletes were instructed to measuretigraphy sleep quality and heart rate during the night following each experimental session. Objectively measured sleep parameters were collected using triaxial and wrist-worn GENEActiv accelerometers (Activinsights, Cambridge, UK). The device contains a triaxial MEMS-accelerometer with a range of \(\pm\) 8 g and a sensitivity of \(\geq\)0.004 g. It records motion-related and gravitational acceleration and has a linear and equal sensitivity along three axes. The x-axis of the GENEActiv recorder corresponds to the radial-ulnar axis, the y-axis to the long axis of the radius and ulna, and the z-axis to the palmar-dorsal axis [42]. The accelerometer data were collected in 5 s epochs with a sampling frequency of 10 Hz and extracted using the GENEActiv PC Software (ver. 3.3). Then, data were processed and analyzed using the open source sleep detection algorithm in the GGIR software R-package GGIR (ver. 2.2-0) in the R environment (ver. 3.6.3) [43; 44]. This algorithm uses a novel method in which the accelerometer-derived arm angle is used to detect sleep. Estimated arm angles are averaged per 5 s epoch and used to assess changes in arm angle between successive 5 s epochs. Periods during which there is no change in arm angle >5\({}^{\circ}\) over at least 5 min are classified as bouts of sustained inactivity or potential sleep periods [43]. These thresholds have shown good sleep detection accuracy compared with polysomnography, the gold-standard sleep measuring method [44], and do not require the use of an activity diary. The following variables were derived: (1) sleep duration, (2) time in bed, (3) sleep efficiency, (4) wake after sleep onset, (5) sleep onset time, and (6) wake time [45]. Additionally, the time between placebo and caffeine intake and sleep onset time was calculated. Heart rate during the night was measured using a heart rate monitor (Polar H10, Finland). Athletes were also asked to assess their subjective sleep quality in the morning after the experimental sessions using the Karolinska Sleep Diary [46]. The questionnaire contained seven items and offered five response alternatives graded from 5 to 1 for the following parameters: (1) sleep quality (very well to very poor); (2) calm sleep (very calm to very restless); (3) ease of falling asleep (very easy to very difficult); (4) amount of dreaming (much to none); (5) ease of waking up (very easy to very difficult); (6) feeling refreshed after awakening (completely to not at all); (7) slept throughout the time allotted (yes to woke up much too early).
## 3 Upper Bound  
Having defined the \(k\)th-order predictability \(P_{k}(S)\) of \(S\), it is natural to ask when an adaptive predictor with no advance knowledge of \(S\) can achieve success rate \(P_{k}(S)\). Whenever both \(k\) and the size \(\sigma\) of the alphabet are fixed, [PERSON] and [PERSON]'s predictor [5] almost certainly achieves a success rate asymptotically approaching \(P_{k}(S)\) as \(n\) goes to infinity. If \(S\) is a randomly-chosen permutation, however, then \(P_{1}(S)=1\) but the expected success rate of any predictor without advance knowledge of \(S\), approaches 0 as \(n\) increases. In this section we show that if \(k\leq\log_{\sigma}n-3-\epsilon\), for a constant \(\epsilon>0\), then an adaptive predictor can achieve expected success rate \(P_{k}(S)\) on any sufficiently long sequence. For simplicity we assume that \(k\) is given although, in practice, a predictor should find an optimal or nearly optimal context length by itself.  
The most obvious 0 th-order predictor is the one that always guess that the next element will be the most frequent element seen so far. [PERSON] [7] randomized this predictor and obtained a predictor \(A\) whose expected success rate converges to \(P_{0}(S)\) when \(n=\omega(\sigma^{3})\). We now consider the most obvious generalization \(A_{k}\) of [PERSON]'s predictor to use contexts of a given length \(k\): after seeing a \(k\)-tuple \(\alpha\), we apply [PERSON]'s predictor to the subsequence of elements consisting of the concatenation of elements so far that immediately followed occurrences of \(\alpha\); _i.e._,  
\[A_{k}(S[1\ldots i])=A((S[1\ldots i])_{\alpha})\]  
where \(\alpha=S[i-k\ldots i-1]\) and \((S[1\ldots i])_{\alpha}\) is as defined in Section 2.  
Fix \(\epsilon>0\) and assume \(k\leq\log_{\sigma}n-3-\epsilon\). Consider the subsequences into which \(A_{k}\) partitions \(S\) before applying \(A\), and let \(\mathcal{L}\) be the subset of them that are each of length at least \(\sigma^{3+\epsilon/2}\). Notice that \(A\) achieves expected success rate \(P_{0}(S^{\prime})\) on any subsequence \(S^{\prime}\in\mathcal{L}\) so, by linearity of expectation, \(A_{k}\) achieves expected success rate at least  
\[\frac{1}{n}\sum_{\stackrel{{|\alpha|=k,}}{{S_{\alpha}\in\mathcal{ L}}}}|S_{\alpha}|P_{0}(S_{\alpha})\]  
On the other hand, the total length of the subsequences not in \(\mathcal{L}\) is less than \(\sigma^{k}\cdot\sigma^{3+\epsilon/2}<\frac{n}{\sigma^{\epsilon/2}}=o(n)\), so  
\[P_{k}(S) = \frac{1}{n}\left(k+\sum_{\stackrel{{|\alpha|=k,}}{{ S_{\alpha}\in\mathcal{L}}}}|S_{\alpha}|P_{0}(S_{\alpha})+\sum_{\stackrel{{| \alpha|=k,}}{{S_{\alpha}\
ot\in\mathcal{L}}}}|S_{\alpha}|P_{0}(S_{\alpha})\right)\] \[= \frac{1}{n}\sum_{\stackrel{{|\alpha|=k,}}{{S_{\alpha} \in\mathcal{L}}}}|S_{\alpha}|P_{0}(S_{\alpha})+o(1)\]  
Therefore, when \(S\) is sufficiently long, \(A_{k}\) achieves expected success rate \(P_{k}(S)\).  
**Theorem 1**: _If the \(n\) elements of a sufficiently long sequence \(S\) are chosen arbitrarily from an alphabet of size \(\sigma\) and \(k<\log_{\sigma}n-3-\epsilon\), for a constant \(\epsilon>0\), then \(A_{k}\) achieves expected success rate \(P_{k}(S)\)._
# Human-AI Collaboration for Remote Sighted Assistance: Perspectives from the LLM Era +
## 2 Background and Related Works
### Collaboration between Human and AI  
Despite recent advancements in CV, automatic scene understanding from video streams and 3D reconstruction remains challenging [71]. Factors such as motion blur, image resolution, noise, illumination variations, scale, and orientation impact the performance and accuracy of existing systems [71; 72]. To overcome these challenges, researchers have proposed interactive, hybrid approaches that involve human-AI collaboration [73]. One representative of this approach is the human-in-the-loop framework. [PERSON] et al. [74] incorporated human responses to increase the visual recognition accuracy. Meanwhile, they found that CV reduces the amount of human effort required. Similarly, researchers developed interactive 3D modeling in which humans draw simple outlines [75] or scribbles [76] to guide the process. They increased the accuracy of 3D reconstructions while considerably reducing human effort.  
Collaborative 2D map construction and annotation is another example of human-AI collaboration, where AI integrates and verifies human inputs. Systems have been developed for collaborative outdoor (e.g., OpenStreetMap [21]) and indoor (e.g., CrowdInside [77], SAMS [78], and CrowdMap [79]) map construction. Researchers also investigated the use of collaborative 2D map construction and annotation in supporting navigational tasks for PVI, for example, improving public transit [80] and sidewalk [81; 82] accessibility and providing rich information about intersection geometry [83]. [PERSON] and [PERSON] [83] indicated that collaborative annotations represent information requested by users with VI and compensate for information not available in current open databases.  
Although prior works support the technological feasibility of collaborative mapping and annotation, the motivation and incentives of volunteers have been a concern surrounding collaborative map construction. [PERSON] and [PERSON] [84] indicated that volunteers can be motivated by intrinsic (e.g., self-efficacy and altruism) or extrinsic (e.g., monetary return and social relations) factors. In contrast, all volunteers are equally motivated in terms of a personal need for map data.
## 8 Model Application Analysis
### Educational Robot Systems for Emotion-Aware Interaction  
The advancement of deep learning and robotics offers innovative approaches to autism rehabilitation through educational robot systems enhanced with emotion recognition capabilities. Our multimodal fusion model can be embedded into educational robots' central systems, creating an emotion-aware interaction loop. The robot's visual and auditory sensors would collect facial expressions and speech signals from the child, which would then be processed by the embedded neural network. The attention mechanism would integrate these features to produce accurate emotion classification, allowing the robot to adapt its responses through appropriate behaviors based on the recognized emotion.  
This integration could provide personalized interactions based on real-time emotional assessments, enabling robots to respond with consistency and predictability, aligning with the child's emotional needs. Such a system would reduce the burden on therapists by automating aspects of emotional monitoring while collecting valuable longitudinal data on emotional responses across different activities. The emotion-aware robot would serve as a complementary tool to human therapists, offering opportunities for structured social-emotional learning in a controlled environment that can be gradually generalized to human interactions.
## Materials and Methods
### Data Collection  
The spectrometric measurement was conducted using a Field Spec(r)3 portable analytical spectral device (ASD, Boulder, CO, USA). The sampling interval of this device over the range of 350-1000 nm is 1.4 nm with a 3 nm resolution. The spectral sampling interval of 1000-2500 nm is 2 nm, and its resolution is 10 nm. Measurements were taken between 10:30 am and 1:30 pm. During the data measurements, fiber optic probes had a 25\({}^{\circ}\) field of view all the time. Scans were taken from the height of 70 cm to the nadir position of the rapeseed canopy. Five canopy positions with balanced growth in each plot were selected for spectral data acquisition respectively. Meanwhile, a whiteboard calibration was performed every five minutes.  
LAI-2200 (LI-COR, Lincoln, NE, USA) was selected for the measuring of the rapeseed leaf area index. The measurement time was synchronized with the spectral data acquisition. Five sampling points were selected evenly along two diagonal lines of each planting plot. Furthermore, each point was measured five times to calculate the average.  
According to the above method, 180 samples of rapeseed growth cycle from 2017 to 2018 were obtained.