esa-sceva/satcom-chunk-collection huggingface dataset:
# Sitting Posture Recognition Systems: Comprehensive Literature Review and Analysis
## 6 Challenges and Future Recommendations
### Future Directions  
There are several works that have been performed in sitting posture detection in the literature. The researchers in this work aim to improve accuracy, usability, and applicability. Researchers suggest increasing hardware, data volume, and posture classes and enhancing classification algorithms to achieve these aims. In addition to that, some of the researchers are planning to work on real-time applications for healthcare, entertainment, and traffic. Using a small number of sensors and multiple posture detections are the other challenges in this area.  
* The researchers plan to expand the dataset to include more participants with diverse health situations and ages. Additionally, they aim to realize a complete posture detection system based on ML algorithms with a feedback system to warn users of bad posture detection.
* By employing multi-sensors and a neural network algorithm, sitting posture can be more accurately monitored, and ambient influences may be avoided. The multi-sensor approach offers multi-dimensional analysis and action recognition with enhanced range and accuracy.
* Optimizing sensor layout to improve resolution, exploring the system's capability to recognize additional sitting postures related to health risks, and testing the system on individuals with various physical characteristics.
* Development of personalized correction solutions tailored to individual users based on their specific sitting habits, body characteristics, and ergonomic needs.
* Customizing sensor placement based on individual user characteristics, such as height and build, to ensure accurate pressure readings and symmetrical sensor positioning.
* Conducting clinical validation studies to evaluate the effectiveness of the correction seat system in preventing and reducing musculoskeletal disorders associated with prolonged sitting.
* Focus on enhancing the user experience of the correction seat system through ergonomic design, intuitive interfaces, and customizable settings to promote user engagement and adherence.
* Investigate the integration of sensing textiles with smart furniture or seating solutions to create an interconnected system that promotes healthy sitting habits and provides holistic posture monitoring.
* Explore opportunities to integrate the smart chair solution with healthcare professionals or ergonomic experts to provide personalized recommendations and interventions for users with specific posture-related issues akin to [147].  
* Explore ways to analyze and interpret long-term data collected from the smart chair system to identify trends, patterns, and insights into users' sitting behaviors and posture habits. A summary of recommendations is provided in Table 12.
## 3 System Overview
### Probabilistic Fingerprint
#### 3.3.2 Conditional Probabilistic Observation Model  
Most work ignores the unobserved RSS value in the runtime observation, while we investigated that the missing APs also provide extra beneficial information in deciding the user position because of their \"_unseen_\" properties. This paper presents a conditional probabilistic likelihood observation function, by taking advantages of the invisibility of APs, referring to the likelihood of observing _a particular invisible AP set_.  
The hypothesis is that, if an AP cannot be scanned for the whole training data collection within cell \(r\), then an online observation contains RSS value from that AP would have low probability belongs to that cell. In other words, if an observation contains RSS values from the APs that _should not to be seen in cell \(r\)_, then the probability of being located in cell \(r\) given the observation would be lower. This is expected as the APs with no RSS readings are less probable to be heard within the same area.  
By splitting the observation \(O\) into \(O_{RSS}\), the RSS measurements for the visible APs, and \(O_{Z}(I)\), a binary indicator variable for APs, \(O_{Z}(i)=1\) if AP \(i\) is invisible and 0 otherwise. In this case, we define the _particular invisible AP set \(I\)_ as:  
\[I=\cap(\textit{Invisible APs }r,\textit{Visible APs }O) \tag{14}\]  
The observation probability would be:  
\[P(O|r) = P(O_{RSS},O_{Z}(I)|r) \tag{15}\] \[= P(O_{RSS}|O_{Z}(I),r)P(O_{Z}(I)|r) \tag{16}\]  
\(P(O_{RSS}|O_{Z}(I),r)\) matches the online observation with the probabilistic fingerprint discussed in Section 3.3.1. \(P(O_{Z}(I)|r)\) is a likelihood of observing RSS from the invisible AP sets of cell \(r\).  
\[P(O_{Z}(I)|r)=\prod\limits_{i=1}^{P}P(O_{Z}(i)|r) \tag{17}\]  
\[P(O_{Z}(i)|r)=\frac{O_{Z}^{\prime}(i)}{\sum\limits_{r\in R}O_{Z}^{\prime}(i)} \tag{18}\]  
where \(P=|I|\). \(O_{Z}^{\prime}(i)\) is the invisibility of AP \(i\) at cell \(r\), and \(\sum\limits_{r\in R}O_{Z}^{\prime}(i)\) is the invisibility of AP \(i\) over all cells \(R\).
# Beyond 5G Fronthaul Based on FSO Using Spread Spectrum Codes and Graphene Modulators
## 1 Introduction
### Related Work
Moreover, a bi-directional fiber-FSO-5G millimeter-wave and sub-THz converged system were demonstrated in Ref. [100]. The system uses an orthogonally polarized dual-carrier to achieve downlink transmissions up to 40 Gb/s/100 GHz while uplink at 10 Gb/s/28 GHz, supporting large traffic capacity. In Ref. [101], the authors experimentally demonstrated a 400 Gb/s FSO mode-division-multiplexing link under turbulence mitigation with adaptive optics by multiplexing four Laguerre-Gaussian modes using both radial and azimuthal indices, each carrying a 50-Gbaud quadrature-phase-shift-keyed signal. Subsequently, in Ref. [102], a 4 Gb/s 16-QAM turbulence-resilient self-coherent FSO link using a pilot tone and an array of photodiodes was demonstrated by the authors. At the transmitter, a Gaussian beam was transmitted with a frequency offset so that the beams have a modal coupling similar to high-order Laguerre-Gaussian modes, while the array of photodiodes increased the overall bandwidth at the receiver side. Furthermore, the authors experimentally demonstrated a mid-infrared FSO link using wavelength division-multiplexing, mode division-multiplexing, and a combination of both in Ref. [103]. Three wavelengths were multiplexed on a single polarization, with each wavelength carrying two orbital angular momentum beams. Since each beam carries 50 Gb/s QPSK data, the system achieved a total capacity of 300 Gb/s, which represents an increase of around 30 times (\(\sim\)30\(\times\)) compared to the single channel and beam link in Ref. [104]. By its turn, a FSO link at 10 Gb/s using a unipolar quantum opto-electronic system at room temperature was demonstrated in Ref. [104]. Moreover, the authors experimentally demonstrated an adaptive multi-modal FSO transceiver to increase the robustness of FSO communications under atmospheric turbulence in Ref. [105]. Accordingly, by applying time-division multiplexing techniques combined with a spatial light modulator under the turbulence effect, an aggregate data rate of 590 Gb/s for five transmit modes and two polarizations was successfully achieved. In contrast, the authors experimentally demonstrated the mitigation of atmospheric turbulent effects in an FSO system with a 3 \(\times\) 1 MISO (multi-input single-output) transmitter based on OAM modes in Ref. [106]. They showed the feasibility of the system operating under intensity-modulated direct-detection transmitting three OAM modes with 39.06 Gb/s over a 1.2 m free-space link at 1550 nm. Lastly, the authors experimentally demonstrated a single multi-plane light convertor to mitigate turbulence-induced crosstalk and demultiplexed channels for a 1550 nm to FSO link with two OAM multiplexed channels, each carrying a 100 Gb/s-QPSK signal in Ref. [107].
## 2 Different Methods of Hydrogen Generation
### Latest Hydrogen Production Process Advances
#### 2.3.5 DAE Technology  
DAE technology is a new technology that produces hydrogen directly from air, wherever on Earth, without the need for any other freshwater source. The direct air electrolyzer (DAE) collects and transforms air moisture, even at 4% humidity. This electrolyzer has two flat plates that serve as the anode and cathode, Figure 13. A porous material, such as a melamine sponge or sintered glass foam, is sandwiched between the two plates. This medium is immersed in a hygroscopic ionic solution, which is a chemical capable of absorbing moisture from the air. Connect it to an energy source, expose it to air, and at the cathode and anode, hydrogen and oxygen begin to be released [56].  
Figure 12: HYSATA technology [54].  
2.3.6. Gallium-Aluminum Composite Technology  
[PERSON] et al. (2022) discovered a novel approach to manufacture hydrogen from water at room temperature by producing a gallium-aluminum composite that can react with water under normal conditions (Figure 14). As the concentration of gallium in the composite grows, so does the production. Hydrogen might be utilized to power fuel cells or internal combustion engines, and could then replace gasoline. The hydrogen is created in an amazingly simple way: water is added to a liquid alloy of aluminum and gallium to make hydrogen, which can then be immediately fed into an engine. The approach causes a chemical reaction in water that divides the oxygen and hydrogen, releasing hydrogen in the process. It may be used with any available water supply, including wastewater, commercial drinks, and even ocean water [57]. Table 2 illustrates the optimal mixing conditions along with different sources of gallium, aluminum and water, providing valuable insights into the best combination for achieving desired results.
## 4 Measurements of the Actual Structure  
### Measurement Methodology  
A proprietary method was used to measure the existing building structure, combining static measurements made using traditional surveying methods with real-time displacement analysis.  
This method of measurement is called the dynamic-static method (DSM), which can be useful not only in the analysis of structural displacements of wooden churches, but also of other historic buildings prone to dynamic deformations. The method consists of combining long-term tachymetric measurement with short-term recording of tilts using a set of inclinometers (for details see Refs. [37; 38]). Its advantage is the possibility of transforming relative tilt readings into vector components of displacements based on tachymetric data related to external reference points.  
Long-term research was carried out in a one-month cycle using the tachymetric technique with reflective targets arranged as in Figure 5 (green squares). Points 1, 2, 7, and 8 are located in the rear part of the presbytery, and points 4, 5, 6, and 9 in the corners of the church nave. Additional point 3 was located on the beam under the choir. Two sights [ref. 1, ref. 2] located outside the studied area of the displacements served as references. Three components of the Cartesian coordinate system were determined for each point, which allowed for the description of both horizontal and vertical components of the displacement of each point (Figure 5).  
Figure 5: Location of points for geodetic measurements.  
For the measurements, an automated Total Station Leica TCRP 1201+ with an angle measurement accuracy of 1'' and a distance measurement accuracy of 2 mm was selected, along with targets made of reflective film. For a distance of about 6 m, this accuracy yields an RMSE standard error of horizontal displacement determination of no more than 0.05 mm.  
For short-term measurements, one of the typical monitoring techniques was used, based on inertial sensors (inclinometers); these were located at points 6, 7, 8, and 9 and are marked with blue circles in Figure 5. Inclinometers measured and recorded inclinations in a 2 s cycle. The results of static measurement were used to check the stability of inclinometer readings.  
In order to assess the impact of external factors on the stability of the structure, a weather station was installed near the tested facility to measure the temperature inside and outside the church, the strength and direction of the wind, and the amount of precipitation. Meteorological measurements were recorded in a 10 min cycle. On this basis, the inclination measurement results were also grouped into 10 min blocks, indicating the minimum and maximum inclination of each sensor in each block of data.
# Stochastic Weight Averaging Revisited
###### Abstract  
Averaging neural network weights sampled by a backbone stochastic gradient descent (SGD) is a simple-yet-effective approach to assist the backbone SGD in finding better optima, in terms of generalization. From a statistical perspective, weight-averaging contributes to variance reduction. Recently, a well-established stochastic weight-averaging (SWA) method was proposed, which featured the application of a cyclical or high-constant (CHC) learning-rate schedule for generating weight samples for weight-averaging. Then, a new insight on weight-averaging was introduced, which stated that weight average assisted in discovering a wider optima and resulted in better generalization. We conducted extensive experimental studies concerning SWA, involving 12 modern deep neural network model architectures and 12 open-source image, graph, and text datasets as benchmarks. We disentangled the contributions of the weight-averaging operation and the CHC learning-rate schedule for SWA, showing that the weight-averaging operation in SWA still contributed to variance reduction, and the CHC learning-rate schedule assisted in exploring the parameter space more widely than the backbone SGD, which could be be under-fitted due to a lack of training budget. We then presented an algorithm termed periodic SWA (PSWA) that comprised a series of weight-averaging operations to exploit such wide parameter space structures as explored by the CHC learning-rate schedule, and we empirically demonstrated that PSWA outperformed its backbone SGD remarkably.  
deep neural network; stochastic gradient descent; stochastic weight-averaging; generalization; wide optima; learning rate +
Footnote †: journal: Medical  
denoted by \(w_{\text{ssoA}}\), as the final output of SWA. A pseudo-code to implement SWA is shown in Algorithm 1, which outputs a running average of the sampled weights per \(c\) iterations.  
```
0: weights \(w_{\text{ssoA}}\), learning-rate schedule, cycle length \(c\), number of iterations \(n\)
0:\(w_{\text{ssoA}}\)
1:\(w\gets w_{\text{ssoA}}\); \(w_{\text{ssoA}}\gets w\).
2:for\(i\gets 1,2,\ldots,n\)do
3: Compute current learning rate \(\alpha\) according to the learning-rate schedule.
4:\(w\gets w-\alpha\bigtriangledown\mathcal{L}_{i}(w)\) (stochastic gradient update).
5:if mod(i,\(c\))=0 then
6:\(n_{\text{ssoA}}\gets i/c\) (number of models averaged).
7:\(w_{\text{ssoA}}\leftarrow(w_{\text{ssoA}}\cdot n_{\text{ssoA}}+w)/(n_{\text{ssoA }}+1)\).
8:endif
9:endfor
10:return\(w_{\text{ssoA}}\)
```  
**Algorithm 1** Stochastic Weight Averaging (SWA)  
A common insight to explain SWA's success is that the local optima it discovers are located at the boundary of a high-quality basin region in the DNN weight parameter space. Conducting weight-averaging over such optima then resulted in a wider optimum that is closer to the center of the basin region [5], and a wider optimum leads to better generalization [5; 8].
# High-Precision Pointing and Tracking System Design for Near-Space Balloon-Based Optical Observation
## 2 Methods and Analysis  
### System Overview  
The near-space-based coronal observation system mainly consists of a high-altitude balloon, a parachute, and an observation cabin, as shown in Figure 1. The high-altitude balloon uses a zero-pressure balloon, which is mainly composed of polyethylene material and charged with helium gas. The function of a parachute is to reduce the descent speed and protect the observation cabin. The observation platform is a key component of the entire system and includes a power supply system, fly control system, measurement and control system, stable pointing and tracking system, and payload control system.  
This paper mostly focuses on the stable pointing and tracking system in a coronal observation system. The stable pointing and tracking system of the near-space spherical coronal observation platform includes two parts: the primary platform stable pointing system and the secondary precise tracking system, as shown in Figure 2. The primary platform stable pointing system is equipped with an inertial navigation module, including a gyroscope, a sun sensor, a set of actuators, and an attitude control module. The secondary precise tracking system consists of a high-precision two-dimensional turntable and a precision sun-tracking camera. The coronal observation payload is installed on the two-dimensional turntable, and the tracking camera is installed on the payload to ensure that the optical axis of the precision tracking camera and the optical observation equipment is parallel.  
The collaborative workflow of the primary and secondary platforms in the stable pointing and tracking system is shown in Figure 3. The attitude control module on the primary platform drives the reaction momentum flywheel module and anti-twist module based on the information output by the sun sensor. With the support of high-precision inertial navigation and gyroscope data, the azimuth angle is stabilized within an error range of \(\pm 1^{\circ}\) of the sun. After the primary platform rotates into place, the status signal is sent to the secondary platform. The high-precision two-dimensional turntable adopts a design of two axes and two frames, providing stable pointing functions for the azimuth and  
Figure 1: Near-space-based coronal observation system.  
Figure 2: Diagram of the stable pointing and tracking system.  
pitch angles of the optical payload. The two-dimensional turntable adjusts the azimuth and pitch angles based on the offset data output from the precision tracking camera, enabling precise pointing and tracking of the sun.
## 4 Applications
### Multiferroism  
The magnetoelectric (ME) multiferroics (MF) materials are defined as those possessing simultaneously ferroelectric and ferromagnetic properties and exhibit linear coupling. They can present them in a single phase or in composites and layered thin films. The interest for this property is mainly due to that magnetic properties could be controlled by electric fields, which would simplify the devices and the energy needed, for example, in process of write/read information.  
There are scarce single phase materials that can present simultaneously ferroelectric and ferromagnetic properties above room temperature [78]. An example is BiFeO\({}_{3}\), an antiferromagnetic and ferroelectric material with a Curie temperature close to 1100 K and Neel temperature \(T_{N}\) near 625 K [79].  
Aurivillus-type structures have been proposed as good candidates for use as single phase multiferroic materials because of their high ferro-paraelectric phase transition temperatures and the ease hosting of \"magnetic cations\" in the structure. The research has been focused on compositions with cobalt and iron, for which weak or strong ferromagnetic behavior above room temperature have been found. Many articlesshow that phases containing Fe and Co have simultaneously ferroelectric and ferromagnetic properties above room temperature. Relative high values of Pr between 13 and 16 \(\mu\)C/cm\({}^{2}\) have been achieved in Bi\({}_{6}\)Fe\({}_{2-x}\)Co\({}_{x}\)Ti\({}_{3}\)O\({}_{18}\) solid solutions and Bi\({}_{5}\)Ti\({}_{3}\)FeO\({}_{15}\) compositions [80; 81; 82]. A common characteristic is that the ferroelectric saturation is hard to achieve. However, the works that show magneto-electric effect (that is, a real interaction between two orders, which is the property to be explored) are scarcer to be found [83; 84; 85]. In those works, very weak coupling is shown but they prove that at least a way is open in this direction. In fact, multiferroic applications are now far from the practical point of view (not only for Aurivillius compounds).
# RoPT: Route-Planning Model with Transformer
## 4 Methodology
### Spatial Feature Aggregation Module  
Given that the road network is fundamentally a graph, within the RoPT framework, GCN is employed to capture the spatial representations of nodes by aggregating the features of both their direct and indirect neighbors. Specifically, the influence of direct neighbors is derived from the adjacency matrix of nodes. And the influence of indirect neighbors is obtained by iterative multiplication of the adjacency matrix. By stacking multiple layers of GCN, indirect influence from distant nodes is aggregated to enrich the latent spatial representations of current node. The specific calculation process is shown in Formula (3).  
\[M_{S}^{(i)}=\left\{\begin{array}{l}\left(\overset{\sim-\frac{1}{2}}{A} \overset{\sim-\frac{1}{2}}{D}M_{S}^{(i-1)}W^{(i-1)}\right)\text{ {if }}i=n\\ \\ BN\left(\sigma\left(\overset{\sim-\frac{1}{2}}{A}\overset{\sim-\frac{1}{2}}{D} M_{S}^{(i-1)}W^{(i-1)}\right)\right)\text{ {else}}\end{array}\right. \tag{3}\]  
\[\overset{\sim}{D}=M_{D}+I,\overset{\sim}{A}=M_{A}+I,M_{S}^{(0)}=M_{Emb} \tag{4}\]  
Figure 1: The framework of RoPT (including spatial feature aggregation module with GCN, sequence feature aggregation module based on Transformer, and next node prediction module).  
where \(M_{S}^{(i)}\in\mathbb{R}^{n\times h}\) means the output of layer \(i\) in this module, \(M_{Emb}\in\mathbb{R}^{n\times e}\) is the node-embedding matrix to be learned, \(I\) represents the identity matrix, \(A\) represents the node adjacency matrix \(M_{A}\in\mathbb{R}^{n\times n}\), \(D\in\mathbb{R}^{n\times n}\) donates the degree matrix obtained from the adjacency matrix, \(W^{(i)}\in\mathbb{R}^{n\times n}\) donates the weight matrix of layer \(i\), \(W^{(0)}\), \(W^{(1)}\),..., \(W^{(l)}\) are learned with this model, and \(\sigma\) is an activation function. In addition, \(BN\) indicates a Batch Normalization function, to improve the stability of model training. Since there are other operations after GCN, \(BN\) and \(\sigma\) are not used in the last layer of this module. The final output of this module can be viewed as the spatial potential representations of all nodes \(M_{S}\in\mathbb{R}^{n\times h}\).
###### Abstract  
In connection with subsonic jet noise production, this study investigates acoustic noise reduction in micro turbojet engines by comparing ejector and chevron nozzle configurations to a baseline. Through detailed statistical analysis, including assessments of stationarity and ergodicity, the current work validates that the noise signals from turbojet engines could be treated as wide-sense ergodic. This further allows to use time averages in acoustic measurements. Acoustic analysis reveals that the chevron nozzle reduces overall SPL by 1.28%, outperforming the ejector's 0.51% reduction. Despite the inherent challenges of Schlieren imaging, an in-house code enabled a more refined analysis. By examining the fine-scale turbulent structures, one concludes that chevrons promote higher mixing rates and smaller vortices, aligning with the statistical findings of noise reduction. Schlieren imaging provided visual insight into turbulence behavior across operational regimes, showing that chevrons generate smaller, controlled vortices near the nozzle, which improve mixing and reduce noise. At high speeds, chevrons maintain a confined, high-frequency turbulence that attenuated noise more effectively, while the ejector creates larger structures that contribute to low-frequency noise propagation. Comparison underscores the superior noise-reduction capabilities of chevrons with respect to the ejector, particularly at high-speed. The enhanced Schlieren analysis allowed for new frame-specific insights into turbulence patterns based on density gradients, providing a valuable tool for identifying turbulence features and understanding jet flow dynamics.  
subsonic jet; chevron nozzle; ejector nozzle; Schlieren image analysis; statistics; acoustics