esa-sceva/satcom-chunk-collection huggingface dataset:
# Sitting Posture Recognition Systems: Comprehensive Literature Review and Analysis
## 6 Challenges and Future Recommendations
### Sensor-Related Challenges  
* The current state of sitting position detection employs costly high-resolution pressure sensor arrays or invasive wearable/visual sensors, which prevents widespread deployment [87; 145].
* Many sensor-based systems rely on specific sensor devices, which can hinder widespread adoption and limit flexibility in real-world environments [58].
* Proper placement of inertial sensors or pressure sensors on the body or chair to capture relevant data for posture detection can be challenging. Incorrect sensor placement may lead to inaccurate readings and unreliable results. There are no scientifically proven guidelines for the optimal number of sensors used and their placement. There is a tradeoff between area coverage and the number of sensors used. Discrete pressure sensors can be strategically placed at specific points on the seating surface, allowing for customization based on the user's anatomy and seating preferences. This flexibility in sensor placement enables targeted monitoring of key pressure points, such as the seat and backrest, to provide detailed posture feedback. The pressure sensor array provides the benefit of more comprehensive and detailed information about the distribution of pressure across the seated surface due to its high spatial resolution [74; 101].
* Ensuring the monitoring system is portable, comfortable to wear, and does not restrict movement is important for user acceptance. Designing wearable devices that are discreet and practical for daily use presents a challenge.
* Balancing the need for accurate sensing capabilities with user comfort and wearability to ensure that the textiles are comfortable to wear for extended periods [136].
* Integrating multiple sensors, such as the pressure and the infrared sensor arrays, into a cohesive system can be challenging. Ensuring that the sensors work together seamlessly to capture relevant data for sitting posture recognition without interference or data loss may have been a challenge [54; 96; 56].
* Combining data from different modalities, such as pressure and infrared sensors, to accurately recognize sitting postures requires sophisticated data fusion techniques. Challenges may have arisen in developing and optimizing algorithms for fusing multimodal data effectively [87; 96; 108].
* Collecting and processing a large amount of data from sensors in real-time can be computationally intensive. Efficient data processing techniques are required to handle the volume of data generated by the sensors [137].
* Existing sensor-based methodologies, such as video-based and wearable sensor-based systems, raise privacy concerns due to the intrusive nature of the sensors [146].  
* While some systems achieve high accuracy in recognizing sitting postures, there are limitations in terms of accuracy range and usability, with accuracy ranging from 80% to 99.1% [73; 108; 137].
* Limited sample size is one of the challenges faced by the researchers [25; 90; 97].
## 3 DoFs Angles Estimation
### Estimation of the Elbow Joint Angles  
The position and orientation of the elbow joint, as it is the distal point of the upper arm, can be estimated from the upper arm position and orientation as shown in Section 3.2. Following a similar approach for the motion data, which are collected by the sensor node that is attached to the human's forearm, the orientation of this segment is extracted in terms of roll, pitch and yaw angles. The kinematic model variables that relate to the elbow joint are theangles \(\theta_{4}\) and \(\theta_{5}\). The elbow joint revolute DoFs can be estimated from the orientation angles, taking into account the representation of Equation (11) for the Tait-Bryan angles, as:  
\[\theta_{4} =\text{atan2}(-\mathcal{R}(3,1),\mathcal{R}(3,3)) \tag{13}\] \[\theta_{5} =\text{atan2}(-\mathcal{R}(1,2),\mathcal{R}(2,2)) \tag{14}\]  
By replacing these estimated angles' values in the transformation matrix \(T_{4}^{6}\), the posture of the forearm is extracted with respect to the \(O_{3}x_{3}y_{3}z_{3}\) coordinate frame (Figure 1).
# Deep Learning Methods in Image Matting: A Survey
## 2 Datasets
### Building Matting Datasets  
The primary challenge in creating an image matting dataset is obtaining high-quality alpha mattes for images. The alpha matte labels the opacity value of each pixel, which cannot be directly discerned by the human eye. Consequently, opacity values are obtained through the following methods: (1) using chroma key: a scene is initially set up with the foreground and camera fixed while capturing the natural image. Subsequently, a screen is placed between the natural background and the foreground and is set to alternate be tween red, green, and blue, capturing images of the foreground against different color backgrounds. The chroma key is subsequently applied to obtain the alpha mattes from the images, and the final alpha matte is computed by taking the average of these alpha mattes. High-quality alpha mattes can be obtained from different natural scenes by repeating this procedure; (2) using image processing softwares (e.g., Photoshop [43]): the alpha matte obtained by this method is rough and influenced by human subjectivity; and (3) using current state-of-the-art image matting algorithms: this approach enables the rapid attainment of high-quality outcomes. However, the performance of the algorithm constrains the quality of the obtained alpha mattes, and the quality of the alpha mattes obtained from different types of natural images varies significantly.  
Additionally, trimaps should be included in an image matting dataset alongside alpha mattes. There are two main methods of generating trimaps: (1) manually annotating: the trimaps generated through this approach demonstrate high accuracy, but the process is exceedingly costly; and (2) using alpha mattes: this method utilizes image processing techniques, such as dilation and erosion, to obtain trimaps from alpha mattes, which is a low-cost approach. However, the trimaps generated by this method may have a lower accuracy compared with those generated by manually annotating, as shown in Figure 5.  
Due to the high cost of obtaining alpha mattes, some datasets obtain the foreground by using the alpha matte and then compositing it with different backgrounds to generate new images and expand the dataset. Although this method can quickly increase the size of the dataset, datasets that contain a large number of composited images with compositing artefacts may affect the generalization ability of models in applications [9; 10; 19].
# An Architecture for the Performance Management of Smart Healthcare Applications
## 4 The PRIMUS Architecture
### Network Model
One slice can have more than one application and user associated at the same time. However, organizations have defined that at most eight network slices may serve one end-user at a time [5,6]. Thus, let \(u_{num}\) be the respective user identification \(u\) using \(num\) network slices in parallel. When \(num\geq 8\), PRIMUS removes the user \(u\) from the slice \(s\) and reallocates the resources for another slice. In case an \(app\) uses more resources than allowed, or it is harming other \(app\), PRIMUS defines a threshold duration time \(t_{d}\). Hence, the \(app\) has a timing \(t_{d}\) until it makes the resource available. Otherwise, when exceeding \(t_{d}\), PRIMUS removes the slice and the application associated, i.e., it removes the associated traffic, application, and user, making resources available. PRIMUS marks all slice \(s\) created with the Slice/Service Type \(SST\) and Slice Differentiator \(SD\). The \(SST\) refers to the network slice behavior in terms of features and services. There are three main types of \(SST\): (1) Enhanced Mobile Broadband (eMBB) with high data rates, (2) Ultra-Reliable Low-Latency Communications (URLLC) with more critical data traffic, and (3) High-Density Massive Internet of Things (mIoT) with a massive amount of users and devices. Thus, s-health intensive care and remote surgery applications fit into the URLLC slice type. Based on this, the \(SST\) values are \(SST=1\) for eMBB, \(SST=2\) for URLLC, and \(SST=3\) for mIoT. Note that URLLC slices can not be removed because it involves critical applications. Thus, when occurring resource overloading, PRIMUS reallocates network resources or, in extreme situations, removes a mIoT or eMBB slice. The \(SD\) is optional information that complements the \(SST\) to differentiate among multiple network slices of the same \(SST\). For instance, \(SD\) serves to identify extreme critical care applications from typical s-health applications. Finally, PRIMUS has full visibility and control of the end-to-end slice and its performance to maintain the overall requirements of the applications. PRIMUS computes a set of performance variables \(P=(1,...,p)\) (e.g., packet loss rate and latency) to analyze the slice performance and create dynamic virtual routes. Such performance variables define the state of the physical structure for establishing or adapting network slices to meet predefined requirements.
## 2 Related Work
### Sensors and Ranging Technology
#### 2.5.2 Radar  
Radar stands for (Radio Detection and Ranging). Radar is a \"sensor integrated into vehicles for different purposes like adaptive cruise control, blind spot warning collision warning and collision avoidance\", as defined by [36].  
Radar uses radio waves to detect the presence and location of objects around the forklift, even in adverse weather conditions or low-visibility environments. It is particularly effective for detecting objects that might not be visible to cameras or LiDAR.  
Oculii, as shown in Figure 3, is a company at the forefront of AI-powered radar perception software. They have unveiled FALCON and EAGLE, commercially available 4D imaging sensors boasting the highest resolution in the industry.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Aspect** & **IMU (Inertial Measurement Unit)** & **RTK (Real-Time Kinematic)** & **GNSS (Global Navigation Satellite System)** \\ \hline
**Purpose** & \begin{tabular}{l} Measures velocity, \\ orientation, \\ gravitational forces \\ \end{tabular} & \
\begin{tabular}{l} Enhances GNSS \\ positioning accuracy \\ \end{tabular} &
\begin{tabular}{l} Provides positioning \\ anywhere on Earth \\ \end{tabular} \\ \hline
**Components** & \begin{tabular}{l} Accelerometers, \\ gyroscopes, \\ sometimes \\ magnetometers \\ \end{tabular} & \
\begin{tabular}{l} Base station, \\ rover/mobile unit \\ \end{tabular} &
\begin{tabular}{l} Satellites orbiting \\ Earth \\ \end{tabular} \\ \hline
**Applications** & \begin{tabular}{l} Aircraft, drones, \\ spacecraft, robotics \\ \end{tabular} & \
\begin{tabular}{l} Land surveying, \\ precision agriculture, \\ construction \\ \end{tabular} &
\begin{tabular}{l} Navigation for \\ vehicles, \\ smartphones, marine \\ vessels, aviation, \\ outdoor recreational \\ activities \\ \end{tabular} \\ \hline
**GPS-Denied Usage** & Yes & No & No \\ \hline
**Accuracy** & \begin{tabular}{l} Provides orientation \\ and movement data \\ \end{tabular} & \
\begin{tabular}{l} Centimeter-level \\ accuracy with \\ corrections \\ \end{tabular} &
\begin{tabular}{l} Typically accurate to \\ within a few meters \\ under open sky \\ conditions \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of sensor technologies for autonomous forklift manufacturing and design.  
Figure 3: Oculii imaging 4D radars.
## 3 Results
### Inter-Observer Reliabilities  
The Bland-Altman analysis revealed a good agreement of the \(\Gamma\) measurement between the two graders (Figure 6). Specifically, for the right eyes, the mean inter-observer difference between the two graders was 0.005\({}^{\circ}\), and the 95% limits of agreement were between \(-0.16^{\circ}\) and \(0.17^{\circ}\). For the left eyes, the mean inter-observer difference between the two graders was 0.05\({}^{\circ}\), and the 95% limits of agreement were between \(-0.17^{\circ}\) and \(0.27^{\circ}\). The ICCs for the right eyes and left eyes were 0.999 (\(p<0.001\)) and 0.998 (\(p<0.001\)), respectively.  
Figure 5: The peak D\({}_{\rm max}\) distribution in the 768 scans from the 16 right eyes conducted by two graders.  
Figure 6: Bland–Altman plot of the inter-observer agreement of the \(\Gamma\) measurements between the two graders for the right eyes and left eyes. The solid black line represents the mean of the \(\Gamma\) measurements difference between the two graders; the two dashed red lines represent the mean of the difference \(\pm\) 1.96\(\times\)standard deviation of the \(\Gamma\) measurements difference.
# Demonstration of High-Speed Optical Transmission at 2 \(\upmu\)m in Titanium Dioxide Waveguides
## 4 Conclusions  
To conclude (thanks to embedded metal gratings) we have been able to efficiently couple an incident light beam into a TiO\({}_{2}\) waveguide in the 2-um spectral range. This particular design allows us  
Figure 6: **(a)** Eye diagrams for the back-to-back configuration and after transmission in the 1.65 μm wide waveguide (panels 1 and 2 respectively). In both cases, eyes diagrams were recorded for error-free measurements; **(b)** Bit-Error-Rate (BER) as a function of optical signal-to-noise ratio (OSNR) for the two previously described waveguides. On the graph, the black points of measurements are associated with back-to-back configuration. The crosses are used for BER measurements for TiO\({}_{2}\) devices (red for the 1.65-μm SM wide waveguide and blue for the 8-μm wide MM waveguide).  
to demonstrate, for the first time, an error-free transmission of a 2-\(\upmu\)m optical data stream at 10 Gbit/s in a 575-\(\upmu\)m long TiO\({}_{2}\) waveguide. A full set of BER measurements has been performed with a fair comparison between a single-mode 1.65-\(\upmu\)m wide waveguide and a multimode 8-\(\upmu\)m wide waveguide insensitive to the polarization. No significant difference was then observed. With the future progress of the emitter/receiver stages, we believe that the present component will also be able to handle higher transmission speeds.  
This study paves the way to integrated photonics at 2 \(\upmu\)m, and introduces titanium dioxide as a serious candidate for photonics from the visible to the mid-infrared range. With technological progress and the maturity gain that can be expected in the near future for fiber and optoelectronics solutions operating around 2 \(\upmu\)m, we are confident in the possibility of involving longer TiO\({}_{2}\) waveguides up to a few centimeters in length.  
This work is financially supported by PARI PHOTCOM Region Bourgogne, by Carnot Arts Institute (PICASSO 2.0 project), by the Institut Universitaire de France, and by FEDER-FSE Bourgogne 2014/2020. The research work has benefited from the PICASSO experimental platform of the University of Burgundy. We thank [PERSON] for advices and fruitful discussions on the grating design issues.  
[PERSON], [PERSON], [PERSON] and [PERSON] conceived, designed and performed the experiment of optical transmission; [PERSON], [PERSON], [PERSON] and [PERSON] designed and fabricated the optical waveguides and the associated gratings. [PERSON], [PERSON] and [PERSON] took part in the numerical modelling and interpretation. All the authors contribute to the analysis of the results and to the writing of the manuscript. [PERSON] and [PERSON] supervised the project.  
The authors declare no conflict of interest.
## 5 Conclusions
Future experiments should enhance the use of equipment to better simulate real-world scenarios and increase sample sizes. Additionally, future studies should include scenarios from small and medium-sized cities with varying economic development levels to validate the generalizability of the evaluation method. In the design of the multi-task comparative experiment, the paper selected a large-scale comprehensive transportation hub as a comparative experimental object. In subsequent research, it is advisable to contemplate selecting scenes from small and medium-sized cities across different regions and levels of economic development. This approach will help verify and evaluate the potential universality of the experimental program. * Data Collection and Analysis: The experimental scheme solely gathered survey evaluation data and eye movement data, employing only fuzzy calculations for the traveler visual attention index, which constrained the precision of the analysis. The information obtained from surveys is limited, and the sample size acquired is low. To enhance precision, more precise indicators should be derived by combining eye movement data and expanding the sample size. In-depth studies could incorporate the collection of electroencephalogram (EEG) and electrocardiogram (ECG) data to objectively reflect the evaluation results. Additionally, it is crucial to diversify the sample size across different demographic groups (including the elderly, the young, and special groups) to discuss and analyze potential evaluation differences. In conclusion, this paper addresses the limitations of real technical conditions and traditional investigation methods by proposing a visual attention-based evaluation scheme using panoramic video. Through the capture of real scenes in panoramic videos and the creation of experimental scenes for replication, combined with survey and eye-tracking device data, the paper provides a more objective evaluation of the visual setting effect of guidance signage within transportation hubs. This approach offers a fresh perspective for the design and optimization of guidance signage, with significant theoretical and practical implications. Conceptualization, C.Z. and S.Z. ; methodology, C.Z. ; validation, C.Z. ; formal analysis, C.Z. and S.Z. ; investigation, C.Z. ; data curation, C.Z. ; writing--original draft preparation, C.Z. ; writing--review and editing, C.Z. ; visualization, C.Z. ; supervision, C.Z. and S.Z. ; funding acquisition, S.Z. All authors have read and agreed to the published version of the manuscript. This research is funded by the Fundamental Research Funds for the Central Universities [grant number 22120240086]. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Data are contained within the article in Sections 2 and 3. **Acknowledgments:** We would like to express our gratitude to all the participants who took part in this questionnaire survey. **Conflicts of Interest:** The authors declare no conflicts of interest.
## 6 Mathematical Representations  
### Magnetorheological Damper as a Lumped, Hard Non-Linearity  
Magnetorheological (MR) fluids are materials that respond to an applied magnetic field with a drastic change in the rheological structure. In the absence of a magnetic field, an MR fluid remains in the liquid phase. However, under a magnetic field, its viscosity varies according to the magnitude of the magnetic field applied up to the point of exhibiting solid-like characteristics. The strength of an MR fluid can be explained by the shear yield stress. The alteration of viscosity is continuous and reversible with respect to the applied magnetic field. As a consequence, MR fluid devices have the capacity of being fast-response interfaces between electronic devices and mechanical systems [42; 43]. Although MR fluids are traditionally described by partial differential equations, from a lumped control systemsperspective, MR fluids can be investigated as hard non-linearities. MR fluid dampers are characterized by a large damping force and low power consumption. That is why they are used in vibration control systems.  
Hence, to evaluate the potential of MR dampers in vibration control and observe the advantages, a model should be developed in order to represent their behavior. The main problem of MR dampers is their non-linear nature and the hysteresis of their force-velocity response. Therefore, an adequate characterization of an MR damper is a challenge. The model used in this work was a modified version of the one presented in [48] because it considers an accurate estimation of the damping force when the applied current is 0 A, as well as regarding: viscous damping, stiffness, and the non-linearities (friction and hysteresis). When the applied current is equal to zero, the force-velocity (FV) diagram is a sigmoidal curve accurately described by a hyperbolic tangent [48] (see Figure 4).  
The equations that model the MR damper in Figure 4 are:  
\[F_{D}=F_{P}+F_{MR} \tag{1}\] \[F_{P}=k_{P}z+c_{p}\dot{z}+f_{f}tanh(x_{friction}z+v_{friction}\dot{z})\] (2) \[F_{MR}=c_{MRpostyield}tanh(x_{preyield}z+v_{preyield}\dot{z}) \tag{3}\]  
where:  
\(F_{D}\) is the MR damping force;  
\(F_{P}\) is the damping force when the current is 0 A;  
\(F_{MR}\) is the damping force due to the MR fluid changes;  
\(k_{p}\) is the stiffness coefficient;  
\(c_{p}\) is the damping coefficient;  
\(f_{f}\) is the friction coefficient;  
\(v_{friction}\) and \(x_{friction}\) define the sigmoidal behavior of the friction component;  
\(c_{\xi}=c_{MRpreyield}\), \(k_{\xi}=k_{MRpreyield}\);  
\(c_{*}=c_{MRpostyield}\) is the damping coefficient due to electric current;  
\(v_{preyield}\) and \(x_{preyield}\) define the sigmoidal force component due to the MR fluid changes;  
\(z\) is the vertical damper displacement;  
\(I\) stands for the applied current in the MR damper.
###### Abstract  
Twisted light beams such as optical angular momentum (OAM) with numerous possible orthogonal states have drawn the prodigious contemplation of researchers. OAM multiplexing is a futuristic multi-access technique that has not been scrutinized for optical satellite communication (OSC) systems thus far, and it opens up a new window for ultra-high-capacity systems. This paper presents the 4.8 Tbps (5 wavelengths \(\times\) 3 OAM beams \(\times\) 320 Gbps) ultra-high capacity OSC system by incorporating polarization division multiplexed (PDM) 256-Quadrature amplitude modulation (256-QAM) and OAM beams. To realize OAM multiplexing, Laguerre Gaussian (LG) transverse mode profiles such as LG00, LG140, and LG400 were used in the proposed study. The effects of the receiver's digital signal processing (DSP) module were also investigated, and performance improvement was observed using DSP for its potential to compensate for the effects of dispersion, phase errors, and nonlinear effects using the blind phase search (BPS), Viterbi phase estimation (VPE), and the constant modulus algorithm (CMA). The results revealed that the proposed OAM-OSC system successfully covered the 22,000 km OSC link distance and, out of three OAM beams, fundamental mode LG00 offered excellent performance. Further, a detailed comparison of the proposed system and reported state-of-the-art schemes was performed.  
OSC; OAM; QAM; DSP; TLBC