esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Open Radio Access Networks: Challenges, Research Directions, and Open Source Approaches
## 6 Challenges and Future Research Directions
### O-RAN Alliance Architecture Issues
It is concluded that ZF can reduce the channel interference in its interface so that the high transmission bitrate can fulfill the low latency requirements by utilizing the advantage of low latency in the FHI. However, ZF implementation still needs several further research. Even though ZF can reduce channel interference and the low latency requirements can be fulfilled, it is still unexplained whether the channel status information (CSI) also has met the low latency requirements for URLLC. O-Cloud might not be mentioned as frequently as other parts of the O-RAN architecture in research papers. However, there is research that proposed deployment strategies for the virtualized O-RAN units in the O-Cloud [87]. With embedded intelligence, O-RAN is considered a SON. O-RAN system has to maintain its performance and availability autonomously while maintaining the quality of service. Self-healing is a key feature in intelligently handling and managing failures and faults in the network. The proposed optimized deployment strategy aimed to minimize the network's outage while complying with the performance and operational requirements. Binary integer programming was used to optimize the placement of the VNFs and their redundant ones. The model evaluation showed that the proposed strategy could maximize the network's overall availability. AIMLFW is a new project within OSC led by Samsung and focuses on implementing AI and machine learning (AI/ML) into the 5G system. The project was initiated to address the need for End-to-end AI/ML framework Management and Platform functions. Since it is the newest project within OSC, the current status is still in the early phase of defining an AI/ML workflow on OSC.
# Telepresence Social Robotics towards Co-Presence: A Review
## 2 Co-Presence Taxonomy
### Immersive Qualities
#### 2.1.6 Audio Quality  
As mentioned earlier, audio modalities guarantee higher levels of social presence. The audio channel should provide bidirectional communication between the remote and local users to exchange messages. Recognition of a person's voice plays an important role in person identification, contributing to the sense of co-presence [78]. Voice transmission is expected to be fluid without cuts or delays. Telepresence robots quite often make use of an array of microphones to acquire spatial sound, enabling the remote user to identify the direction of the sound source [79] or simply detect the movements of the local user.
## 2 BLRFS Principles
### Intrinsic Noise Reduction in the BLRFS Technique  
The problem of noise reduction in the band-limited reference-free speckle spectroscopy becomes important in the case of wide-band spectral selection of the multiple-scattered fluorescence radiation. Due to a strong suppression of stochastic interference under the condition \(l_{c}/\left\langle\Delta s\right\rangle<1\), the evaluated statistical moments \(\left\langle I_{s}^{2}\right\rangle/\left\langle I_{s}\right\rangle^{2}\) and \(\left\langle I_{s}^{3}\right\rangle/\left\langle I_{s}\right\rangle^{3}\), which are associated with scan-induced intensity fluctuations (the \"desired signal\"), may be comparable with the similar values related to the noise component of the detected signal. This component can be considered as a non-correlated contribution to the \"desired signal\" \(I_{s}\) caused by interactions of an idealized partially coherent light with the examined system. The noise contribution \(I_{n}\) is related to a variety of noisy factors (a detector noise, random fluctuations of the incoming laser radiation, etc.) and can be assumed as having the zero average and, therefore, the detected signal can be presented as the sum of the \"desired\" value \(I_{s}\) and a noisy fluctuation component (an additive model of noise):  
\[I=I_{s}+\delta I_{n} \tag{4}\]  
Thus, the first-, second-, and third-order statistical moments of the detected signal can be expressed as:  
\[\left\langle I\right\rangle=\left\langle I_{s}\right\rangle; \tag{5}\]  
These formulas evidently follow from the absence of correlations between \(I_{s},I_{n}\) and the relationships \(\left\langle\delta I_{n}\right\rangle=0\); \(\left\langle\left(\delta I_{n}\right)^{3}\right\rangle=0\). Consequently, the normalized statistical moments have the following forms:  
\[\begin{array}{l}\frac{\left\langle I^{2}\right\rangle}{\left\langle I \right\rangle^{2}}=\frac{\left\langle I_{s}^{2}\right\rangle}{\left\langle I _{s}\right\rangle^{2}}+\frac{\left\langle\left(\delta I_{n}\right)^{2} \right\rangle}{\left\langle I\right\rangle^{2}}=M_{2}+\frac{\left\langle\left( \delta I_{n}\right)^{2}\right\rangle}{\left\langle I\right\rangle^{2}};\\ \frac{\left\langle I_{s}^{3}\right\rangle}{\left\langle I_{s}\right\rangle^ {3}}=\frac{\left\langle I_{s}^{3}\right\rangle}{\left\langle I_{s}\right\rangle ^{3}}+3\frac{\left\langle\left(\delta I_{n}\right)^{2}\right\rangle}{\left\langle I \right\rangle^{2}}=M_{3}+3\frac{\left\langle\left(\delta I_{n}\right)^{2} \right\rangle}{\left\langle I\right\rangle^{2}}.\end{array} \tag{6}\]  
These relationships give the basis for recovery of the normalized statistical moments of non-corrupted intensity fluctuations from a sequence of \(I\) values obtained in the course of sample scanning, and the reference values \(\left\langle\left(\delta I_{n}\right)^{2}\right\rangle/\left\langle I\right\rangle ^{2}\) obtained in pre-calibration experiments.
## 1 Introduction
The specific objectives were to (1) extract spectral data from a region of interest (ROI) in hyperspectral images of chicken breast fillets acquired in the range of 400-1000 nm; (2) identify optical wavelengths from the whole spectra that are most correlated to the WHC classification; (3) extract textural features from the first principal component (PC) image by gray-level co-occurrence matrix (GLCM); (4) combine optimal spectra with textural data by feature level fusion; and (5) develop partial least square-discriminant analysis (PLS-DA) classification models based on the spectral data, textural data, and a mixture of optimal spectra and textural data.
# Practical Methodology for a Three-Dimensional-Printed Hybrid Desalination System
## 2 Materials and Methods
### Computational Fluid Dynamics Study  
Since the solutions in flow reactors are in a liquid state, it is essential to study the hydrodynamic phenomena occurring inside them. These phenomena such as mass transport and distribution, pressure profiles, and the presence of stagnation zones play a crucial role in the reactor's performance, as they directly influence the ion exchange processes. Different hydrodynamic processes and their relationship with the reaction mechanisms of the devices are deeply studied [25; 26; 27; 28; 29; 30]. Hydrodynamic phenomena in RED and ED systems can be addressed in different ways, from the study of particle behavior to the analysis of flow in specific areas of the system. The equations that describe these phenomena are complex, and although three-dimensional simulations provide a more accurate representation, their implementation requires many computational resources. In this study, a more efficient approach was chosen using two-dimensional simulations that allowed for evaluating the velocity profile in the contour of the proposed geometries, to analyze the flow distribution inside the reactor. Computational fluid dynamics (CFD) simulations were employed to evaluate the proposed reactor geometries. Eight different flow velocities were tested to determine the most efficient pump flow rate. This study focuses on the physical limitations of pump velocities achievable for the proposed RED-ED reactor dimensions.  
The simulations were performed using COMSOL Multiphysics 5.3 software, under the license provided by the Engineering Institute of UNAM. One of the most widely used mathematical models for hydrodynamic studies is the Navier-Stokes model, which consists of a set of partial differential equations that comprise the fluid motion equations for each coordinate component [31]; the momentum transport equation, Equation (1), and the continuity equation, Equation (2), of the Navier-Stokes model for an incompressible Newtonian fluid at steady state, were used.  
\[\rho(u\cdot\
abla)u=\
abla\cdot\left[-p\vec{v}+\mu\left(\
abla u+(\
abla u)^{T }\right)\right]+F+\rho g, \tag{1}\]  
\[\rho\
abla\cdot(u)=0, \tag{2}\]  
where \(\
abla\) is the fluid density, \(u\) is the velocity, \(p\) is the pressure, \(\vec{v}\) is the identity matrix, \(\mu\) is the viscosity, \(T\) is the temperature, and \(F\) represents the external forces.
## 3 Results and discussion
### Validation of the Analytical Procedure  
System suitability tests were conducted to ensure the performance of the chromatography system with respect to capacity factor (\(k^{\prime}\)), relative retention (\(\alpha\)), theoretical plate number (\(N\)), resolution (\(Rs\)),  
Figure 1: Representative HPLC chromatograms of (**A**) standard solution of all analytes and (**B**) HPYJT sample at 214 nm. (**1**) MULA, (**2**) AMY, (**3**) LIQA, (**4**) LIQ, (**5**) NAR, (**6**) HES, (**7**) RA, and (**8**) GA.  
and symmetry factor (\(S\)). The system and all parameters were found to be suitable for performing the analysis (Table S2). The \(r^{2}\) values of the eight marker components showed good linearity from 0.9996 to 1.0000, and LOD and LOQ values of these compounds were also calculated to be 0.09-0.48 \(\upmu\)g/mL and 0.27-1.46 \(\upmu\)g/mL, respectively. These results are summarized in Table 1. Recovery tests, which were conducted to ensure the validity of the method, showed that all the analytes were found with recovery rates from 97% to 103.50% and RSD values of \(\leq\)2.50% (Table 2). To evaluate repeatability, the results were analyzed six times, and the RSD values of retention time and peak area showed excellent repeatability of \(\leq\)0.11% and \(\leq\)0.71%, respectively. Intra- and interday precisions also showed good RSD values below 2.94% (Table 3). The validation parameters reported above confirm that the developed HPLC analytical method is suitable for simultaneous quantification of HPYJT. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Analyte** & \begin{tabular}{c} **Linear Range** \\ **(\(\upmu\)g/mL)** \\ \end{tabular} & \begin{tabular}{c} **Regression Equation \({}^{\text{a}}\)** \\ **y = ax + b** \\ \end{tabular} & \(r^{2}\) & \
\begin{tabular}{c} **LOD** \\ **(\(\upmu\)g/mL)** \\ \end{tabular} &
\begin{tabular}{c} **LOQ** \\ **(\(\upmu\)g/mL)** \\ \end{tabular} \\ \hline \hline MULA & 1.56–100.00 & \(y=26254.19x+7269.63\) & 0.9998 & 0.35 & 1.06 \\ AMY & 1.56–100.00 & \(y=6226.21x+3363.42\) & 0.9996 & 0.09 & 0.27 \\ LIQA & 1.56–100.00 & \(y=14121.49x+5547.89\) & 0.9999 & 0.24 & 0.72 \\ LIQ & 0.78–50.00 & \(y=18895.76x+3098.35\) & 0.9999 & 0.14 & 0.42 \\ NAR & 1.56–100.00 & \(y=16761.20x+6157.72\) & 0.9999 & 0.48 & 1.46 \\ HES & 1.56–100.00 & \(y=15716.44x+6639.20\) & 0.9999 & 0.31 & 0.95 \\ RA & 0.78–50.00 & \(y=20714.33x+830.82\) & 1.0000 & 0.11 & 0.33 \\ GA & 1.56–100.00 & \(y=8011.17x+2326.31\) & 0.9999 & 0.24 & 0.72 \\ \hline \hline \end{tabular}  
* \(y\): peak area (mAU) of each analyte; \(x\): concentration (\(\upmu\)g/mL) of each analyte.  
\end{table} 
Table 1: Linear range, regression equation, \(r^{2}\), LODs, and LOQs for the eight analytes (\(n=3\)).
# Application of Improved Sliding Mode and Artificial Neural Networks in Robot Control
## 2 Application of Improved Sliding Surfaces and Artificial Neural Networks in Robotics
### Estimating Nonlinear Components Using Artificial Neural Networks
We choose the following Lyapunov function:  
\[V=\frac{1}{2}\mathbf{s}^{T}\mathbf{s} \tag{24}\]  
where \(\mathbf{s}=\dot{\mathbf{e}}+\lambda\mathbf{e}\). Taking the time derivative of \(V\):  
\[\dot{V}=\mathbf{s}^{T}\dot{\mathbf{s}} \tag{25}\]  
The sliding surface is defined as:  
\[\mathbf{s}=\dot{\mathbf{e}}+\mathbf{\lambda}\mathbf{e} \tag{26}\]  
Taking the derivative:  
\[\dot{\mathbf{s}}=\ddot{\mathbf{e}}+\mathbf{\lambda}\dot{\mathbf{e}} \tag{27}\]  
From the system dynamics, we have:  
\[\ddot{\mathbf{e}}=\ddot{\mathbf{A}}_{W}\mathbf{X}+\mathbf{B}_{W}\mathbf{U}+\mathbf{D}_{f}-\ddot{\mathbf{ \Gamma}}_{d} \tag{28}\]  
Substitute \(\ddot{\mathbf{e}}\):  
\[\dot{\mathbf{s}}=\ddot{\mathbf{A}}_{W}\mathbf{X}+\mathbf{B}_{W}\mathbf{U}+\mathbf{D}_{f}-\ddot{\mathbf{ \Gamma}}_{d}+\mathbf{\lambda}\dot{\mathbf{e}} \tag{29}\]  
Substitute the control law \(U_{\text{ISMC-Tanh-RBF}}\):  
\[\mathbf{U}=-\frac{1}{2}B_{W}^{-1}\Big{[}2\Big{(}\ddot{\mathbf{A}}_{W}\mathbf{X}+\mathbf{D}_{f} -\ddot{\mathbf{\Gamma}}_{d}\Big{)}+(\mathbf{\lambda}+2\mathbf{\alpha})\dot{\mathbf{e}}+\mathbf{ \alpha}^{2}\mathbf{e}+\mathbf{\eta}\text{tanh}\Big{(}\frac{\mathbf{s}}{\mathbf{e}}\Big{)}+ \mathbf{\kappa}\mathbf{s}\Big{]} \tag{30}\]  
Substitute \(\mathbf{U}\) into \(\dot{\mathbf{s}}\):  
\[\dot{\mathbf{s}}=\ddot{\mathbf{A}}_{W}\mathbf{X}+\] \[\dot{\mathbf{B}}_{W}\Big{(}-\frac{1}{2}\mathbf{B}_{W}^{-1}\Big{[}2\Big{(} \ddot{\mathbf{A}}_{W}\mathbf{X}+\mathbf{D}_{f}-\ddot{\mathbf{\Gamma}}_{d}\Big{)}+(\mathbf{\lambda }+2\mathbf{\alpha})\dot{\mathbf{e}}+\mathbf{\alpha}^{2}\mathbf{e}+\mathbf{\eta}\text{tanh}\big{(} \frac{\mathbf{s}}{\mathbf{e}}\big{)}+\mathbf{\kappa}\mathbf{s}\Big{]}\Big{)}+\mathbf{D}_{f}- \tag{31}\]  
\[V=\frac{1}{2}\mathbf{s}^{T}\mathbf{s} \tag{24}\]0  
Simplify the expression:  
\[V=\frac{1}{2}\mathbf{s}^{T}\mathbf{s} \tag{24}\]1  
\[V=\frac{1}{2}\mathbf{s}^{T}\mathbf{s} \tag{24}\]2  
\[V=\frac{1}{2}\mathbf{s}^{T}\mathbf{s} \tag{24}\]3  
Substitute \(\dot{\mathbf{s}}\) into \(\dot{V}\):  
\[V=\frac{1}{2}\mathbf{s}^{T}\mathbf{s} \tag{24}\]4  
Since \(\mathbf{s}=\dot{\mathbf{e}}+\mathbf{\lambda}\mathbf{e}\):  
\[V=\frac{1}{2}\mathbf{s}^{T}\mathbf{s} \tag{24}\]5  
Simplify \(\dot{V}\):  
\[V=\frac{1}{2}\mathbf{s}^{T}\mathbf{s} \tag{24}\]6  
\[V=\frac{1}{2}\mathbf{s}^{T}\mathbf{s} \tag{24}\]7  
Since \(\text{tanh}\big{(}\frac{\mathbf{s}}{\mathbf{e}}\big{)}\) is bounded by \(-1\) and \(1\), and assuming \(\mathbf{\alpha},\mathbf{\eta},\mathbf{\kappa}>0\):  
\[V=\frac{1}{2}\mathbf{s}^{T}\mathbf{s} \tag{24}\]8With proper choices of \(\alpha,\eta,\kappa\), the derivative of the Lyapunov function \(\dot{V}\) is negative semi-definite, ensuring that \(V\) does not increase over time. This guarantees the stability of the system, and that the error, \(e\), and its derivative, \(\dot{e}\), will converge to zero.
# Extensive Experiments on Opportunistic Routing in Wireless Sensor Networks
## 5 Testbed Deployment and Experimentations
### Average End-To-End Delays  
As depicted in Figure 8, the delay plots follow the beacon overhead plots (see Figure 4) as the beaconing process is basically a source of packet forwarding delays. The end-to-end delays vary linearly with \(\alpha\). The scenarios MED_ADAP and INFR have low end-to-end delays that slightly increase with \(\alpha\). This witnesses a good trade-off between energy saving and delay reduction. In MED_N_ADAP, delays are rapidly increasing with \(\alpha\).  
A good visualisation to understand the packet delay sources is illustrated in Figure 9 presenting the times for sending and receiving packets of a sample image for \(\alpha=20\). Obviously, the colored area represents the source of end-to-end delays. In MED_N_ADAP scenario, plots have high amplitude, spikes correspond to some routers that are blocked because of either network congestion or duty cycle. As explained above, these facts are alleviated by the duty cycle adaptation (in MED_ADAP) and the traffic regularisation (in INFR).  
In general, while the traffic regularisation is dependant on the application (not possible in multimedia scenarios in particular), the duty cycle adaptation is an effective method to reduce delays. Also, as we will see hereafter, this adaptability is of a less impact on the energy consumption.
## 4 Discussion
### Changes in Leg Stiffness  
The results of the leg stiffness in the current study are comparable to the findings of only one previous study [55], in which ALS, RLS and RSI were measured in adolescent team sports players (both girls and boys) of competition age group U16. However, the population in the current study was heterogenous with a large age range (EG age: 20.05 \(\pm\) 4.43 yrs., CG age: 20.04 \(\pm\) 4.88 yrs.). The comparison of ALS values of our pre-intervention testing in the EG (25.98 kN\(\cdot\)m\({}^{-1}\)) and in the CG (23.04 kN\(\cdot\)m\({}^{-1}\)) with girls' category U16 from the above-mentioned cohort study (U16: 24.60 \(\pm\) 5.00 kN\(\cdot\)m\({}^{-1}\)) indicate lower ALS in players from EG and similar ALS in players from CG. RLS values in both groups (EG: 32.67, CG: 27.58) are lower than in female team sports players from the U16 group (34.10 \(\pm\) 6.80). This may be a factor of the range of anthropometrics between the studies; in our study, players had different anthropometrics (EG height: 166.54 \(\pm\) 4.46 cm, EG body mass: 64.65 \(\pm\) 6.44 kg, CG height: 166.83 \(\pm\) 7.30 cm, CG body mass: 69.83 \(\pm\) 12.84 kg) to U16 group players (height: 166.10 \(\pm\) 5.80 cm, body mass: 59.10 \(\pm\) 7.90 kg) thereby influence any relative measures. Although higher values in both groups of rugby players could be expected due to leg stiffness development with age between 17-20 yrs. [23], the absence of differences could be attributed to the different performance levels [53], as elite players were observed in the study by [PERSON] et al. [55], while amateur players participated in the current study. In terms of leg stiffness comparison of players from these studies, it should also be considered that both groups of rugby players in the current study were heterogenous with a large age range. Comparable results were found in a healthy population aged from 21 to 31 years [58] with ALS values of 26.30 \(\pm\) 6.50 kN\(\cdot\)m\({}^{-1}\). However, it has to be considered that although a similar test protocol as in the current study was applied, a different measurement device was used.  
The results of the current study are not supportive of a positive alteration in participants' measure of leg stiffness. The lack of change indicates no positive involvement in muscle-tendon unit activation and, consequently, no change in its ability to resist deformation originating during an SSC [22]. It has previously been determined that the variance in leg stiffness (-97%) is a product of contributions from the pre-activation of musculature and the stretch-reflex response of the extensor muscles in the lower limb [59]. Therefore, it is expected that any alterations to leg stiffness in our group after the intervention period may not suggest improvements to these control mechanisms. Comprising stiffness regulation around all the lower limb joints, the results do not point out the improvements in control of multi-joint movement during SSC exercise, lesser probability of excessive load of the knee passive structures as the ACL, and decreased ACL injury risk during rugby-specific movements in players [60].
# DBCAN: DFormer-Based Cross-Attention Network for RGB Depth Semantic Segmentation
## 4 Results
### Ablation Studies
#### 4.5.2 Effectiveness of Spatial-Semantic Fusion Module  
The effectiveness of the proposed Spatial-Semantic Fusion Module (SSFM) is validated through ablation experiments. Additionally, three other feature fusion strategies are considered in this study for comparison with SSFM. The first strategy involves the simple concatenation of RGB and spatial features, the second strategy employs the Local Attention Module (LAM) on RGB and spatial features, and the third strategy utilizes the Global Attention Module (GAM) on RGB and spatial features. The experimental results of these three strategies are listed in rows 1, 2, and 3 of Table 6.  
Considering the first row, the Acc, mAcc, and mIoU values are lower than those of the SSFM model on the NYUDepthv2 dataset, indicating that using the Spatial-Semantic Fusion Module effectively improves the segmentation results. Furthermore, considering the second and third rows, the Acc, mAcc, and mIoU results are higher than those of the first row, but lower than the fourth row, indicating that the simultaneous utilization of the Local Attention Module and Global Attention Module enhances the effectiveness of cross-modal fusion features.
## 4 Experimental Results
### Propeller Measurement Results
#### 4.2.3 Precision Analysis in Single-Point Measurement Mode  
The one-way ANOVA homogeneity test was employed to evaluate the measurement precision under the single-point measurement mode. The results revealed nonsignificant differences in the measured values at the second decimal place. However, the variances at the second and third decimal places differed significantly. Thus, the developed system consistently achieves precision up to the second decimal place when being operated in the single-point measurement mode (Table 6).
# A Brief Review of Machine Learning-Based Bioactive Compound Research
## 2 Cheminformatics, Bioinformatics, and Databases for Machine Learning
One of the most commonly used tools for interpreting transcriptome changes is the GSEA [40], which lists biological pathways that have been statistically significantly altered between different conditions. This knowledge-based information can be useful for narrowing down features (genes) for training because, in general, the number of features is much larger than the number of samples when building an ML model with gene expression profiling data. Another obstacle to applying machine learning technology to gene expression data is that individual gene expression data have been processed in different analysis pipelines, so unknown bias (noise) that is difficult to identify, such as batch effects, different sequencing devices, and/or various experimental conditions, is inherent to the data. To minimize this bias and provide vast transcriptome resources in a user-friendly manner, a recent study constructed a transcriptome database called ARCHS4 ([[https://maayanlab.cloud/archs4/](https://maayanlab.cloud/archs4/)]([https://maayanlab.cloud/archs4/](https://maayanlab.cloud/archs4/)) (accessed on 10 December 2021)), comprising more than 200,000 human and mouse transcripts processed through a uniform analysis pipeline [41]. In the case of DNA variants in humans, gnomAD ([[https://gnomad.broadinstitute.org/](https://gnomad.broadinstitute.org/)]([https://gnomad.broadinstitute.org/](https://gnomad.broadinstitute.org/)) (accessed on 10 December 2021)) provides an excellent and the largest source of DNA variant information identified in multiple populations via a wide variety of large-scale sequencing projects [42]. Overall, advances in cheminformatics, bioinformatics, and a variety of publicly accessible databases have emerged rapidly over the past decade, accelerating the process of interconnecting chemistry, biology, and drug development. This system will be used to expand our limited understanding of chemicals and ultimately build promising ML-based models that generate novel bioactive-like compounds, with high efficacy and low toxicity, against various diseases, including cardiovascular and metabolic diseases.