esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Transmission Experiment  
Single-lane 100 Gbps PAM4 VCSEL-based transmission over the fabricated 1 m long polymer waveguides was carried out. A schematic of the experimental setup is shown in Figure 15a. The transceiver (TX) of the VCSEL received the radio frequency signal of 53.125 Gbaud PAM4 from the pulse pattern generator (PPG) and output the modulated light at 850 nm. The modulated light passes through the waveguide and is connected to the multimode VOA. The receiver (RX) of the VCSEL received the modulated light and passed the corresponding RF signal into the bit error ratio tester (BERT) to obtain the bit error rate (BER) of the link. Figure 15b shows measured BER curves of 53.125 Gbaud PAM4 signal versus different received optical powers for the B2B link and waveguide links with various lengths. The measured BERs are well below the forward error correction limitation (BER = \(3.8\times 10^{-3}\)). Compared with the B2B link, the signal after 1 m long waveguide transmission suffers a power penalty of less than 1 dB.  
Eye diagrams of 100 Gbps PAM4 VCSEL-based transmission were obtained using a digital communications analyzer (DCA). A schematic of the experimental setup is shown in Figure 16a. Figure 16b shows the 100 Gbps PAM4 eye diagrams for the B2B link and waveguide links with different lengths when the received optical power is \(-3\) dBm, respectively. The eye diagrams open well after the propagation through a 1 m-long waveguide and the transmitter dispersion eye closure penalty quaternary (TDECQ) of the eye diagram is about 3 dB. The measurement standard for TDECQ is IEEE 802.3 cd [39] and the target symbol error rate (SER) is set to \(4.8\times 10^{-4}\).  
Figure 14: (a) Normalized S\({}_{21}\) response curves under the underfilled launch condition and (b) the overfilled launch condition.  
Figure 15: (a) Experimental setup for 53.125 Gbaud PAM4 transmission and (b) BER curves for the B2B and waveguide links with different lengths.
## 3 Results
### Experiments with Datasets Obtained from CMM  
To assess the proposed method's practicality, we measured a metal hemispherical shell resonator, as depicted in Figure 16b. The measurements were carried out in a controlled environment, as shown in Figure 16a, using a Hexagon CMM.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Algorithm** & **Mean Value of Results** & **Standard Deviation of Results** \\ \hline \multirow{3}{*}{1} & A & 1.00057719429339 & 1.7 \(\times\) 10\({}^{-3}\) \\  & B & 1.0000000000003 & 2.0 \(\times\) 10\({}^{-14}\) \\  & A & 0.01539504919669 & 7.6 \(\times\) 10\({}^{-6}\) \\  & B & 0.01538487054931 & 3.0 \(\times\) 10\({}^{-14}\) \\  & A & 3.39553011862261 & 9.5 \(\times\) 10\({}^{-2}\) \\  & B & 3.33251761616355 & 0.8 \(\times\) 10\({}^{-14}\) \\  & A & 0.00766080588045 & 5.5 \(\times\) 10\({}^{-7}\) \\  & B & 0.00766019493788 & 0.2 \(\times\) 10\({}^{-14}\) \\ \hline \hline \end{tabular}
\end{table}
Table 10: Ablation experiment results (units: mm).  
Figure 16: Measurement of a metal hemispherical shell resonator surface with a Hexagon CMM. (**a**) The Hexagon CMM and environment. (**b**) A metal hemispherical shell resonator sample was used for the spherical surface measurement.  
For the measurement of the specimen, we selected five cross-sections on the surface. As detailed in Table 11, one hundred and thirty measurement points were taken. The iterative process convergence, reconstruction model of the dataset, and evaluation results are presented in Figures 17 and 18.  
Ten replication experiments were conducted for the measurement data. The sphericity error obtained by the CMM is 0.008396638 mm. The results of these ten experiments are presented in Table 11, indicating a mean result of 0.00747188442066 mm, with a standard deviation of 2.3 \(\times\) 10\({}^{-14}\) mm. The spherical center obtained by our algorithm is (0.00218039158405 mm, \(-\)0.00226432610011 mm, \(-\)23.9943439405 mm). Our algorithm demonstrated an 11% improvement in accuracy compared to the CMM result.
# Unsupervised Learning in Precision Medicine: Unlocking Personalized Healthcare through AI
## 2 Artificial Intelligence
### Unsupervised Learning: Clustering
#### 2.2.1 K-Means Clustering  
K-means clustering [19] is a widely used partition-based clustering technique designed to segment a dataset into k distinct clusters. K-means aims to minimize the sum of squared distances between each data point and its assigned cluster centroid, thus the geometric centers of data points in a given space. Let \(X=\{x_{1},x_{2},\cdots x_{n}\}\) represent the dataset with \(n\) data points. The goal is to partition the dataset into \(k\) clusters \(\mathrm{C_{1}},\mathrm{C_{2}},\cdots\mathrm{C_{k}}\) and minimize the following objective function:  
\[\sum_{i=1}^{k}\sum_{x_{j}\in\mathrm{C_{i}}}\left\|x_{j}-c_{i}\right\|^{2}\]  
where \(c_{i}\) is the centroid of cluster \(C_{i}\), and \(\left\|\cdot\right\|\) represents the Euclidean distance.  
The process begins with the algorithm randomly selecting k initial cluster centroids. Subsequently, each data point is assigned to the nearest centroid, resulting in the formation of k clusters based on a distance metric. The centroids of these clusters are then recalculated as the mean of all data points assigned to each cluster. This assignment and update process is iteratively repeated until the centroids stabilize, indicating that the algorithm has converged. The K-means algorithm is particularly suitable for handling large datasets with many features [21]. However, K-means needs the number of clusters specified in advance, which can be challenging if the optimal number of clusters is unknown.
## 1 Introduction
In this case, the inputs for the black box are the yaw rate, lateral acceleration, steering angle, and vehicle speed, while the VSA is the output. Estimation results are accurate as much as the training dataset contains the whole possible scenario that the system might have to deal with [96]. This method has a major drawback, consisting in the need of changing the ANN every time the system changes, forcing the user to redo the ANN training procedure. This is probably the reason why the use of ANN seems to be a second choice for VSA estimation. This is reflected in the literature production, where not so many works can be found on this approach. The paper is organised as follows: Section 2 analyses observer-based estimation, presenting the different types of observers with a specific focus on the most used ones, vehicle model applications, and GPS-aided observers. Section 3 concerns the neural network-based estimation. A detailed overview table on the existing VSA estimation methods is presented in Section 4, where concluding remarks are also given. Hardware costs, computational burden, and accuracy are also taken into account in order to assess the efficiency of each approach.
# On the Control and Validation of the PARA-SILSROB Surgical Parallel Robot
## 2 Materials and Methods
### Command and Control Architecture of the Parallel Robot PARA-SILSROB  
The command-and-control system integrates both hardware and software components, which allows it to execute the surgeon's commands and convert inputs from peripherals (3D Space Mouse devices, version Space Mouse Pro, developed by 3 DConnexion, Munich, Germany, 2001) into controlled movements. The master-slave architecture is used to restrict the robot's ability to move autonomously. Safety measures [47] are implemented through hard limits configured within the software part of the system and though signals that are either tied to mechanical emergency buttons or graphically represented in the graphical user interface (GUI).  
The robustness and low cycle times of an industrial-grade programmable logic controller (PLC) proved adequate for this purpose. This capability allows the control system to be easily scaled to accommodate additional actuators, sensors, and other devices as needed. Consequently, it provides the necessary resources for future developments while ensuring that the existing functionalities remain intact.
## 3 Results and Discussion
### SEM-EDX Analysis  
The SEM analysis clearly showed the presence of the synthesized AuNPs. NPs ranging from 25 to 40 nm with an average size of 32 \(\pm\) 1.1 were observed in the surface morphology of AuNPs. It was noted that smaller sized particles were almost spherical in shape, and some of them were aggregated (Figure 4a). Similar results were found in [PERSON] et al. [38]: the SEM micrographs showed aggregates of _Zingiber officinal_ synthesized AuNPs, and the particles were in the range of 15-25 nm and are not in direct contact even within the aggregates, indicating the stabilization of NPs by the capping agents. The EDX analysis shown in Figure 4b revealed that a strong signal of Au peak was observed at approximately 3.6 keV, which is typical for the absorption of metallic gold nanocrystallites due to SPR  
Figure 4: (**a**) Scanning electron microscope (SEM) micrograph showed the synthesized of AuNPs using _E. prostrata_ leaf aqueous extract (\(\times\) 40,000) and (**b**) EDX analysis showing the chemical composition of synthesized AuNPs.  
Figure 3: Fourier transform infrared spectroscopy (FTIR) analysis of synthesized AuNPs
## 1 Introduction  
The failure of metallic materials under fatigue loading conditions is governed by the initiation and propagation of cracks. To investigate the fatigue behavior, different methods are used to measure crack lengths. The optical measurement with a travelling microscope [1, 2] gives only the crack length on one surface and can only be automated with great effort. Aside from the light optical measurement of crack length, infrared thermography [3, 4] and digital image correlation [5] are also used. However, these methods require special and expensive equipment and afford a complicated evaluation. An automated integral measurement of the crack length, especially for compact tension and single edge notched bending-specimen, can be undertaken by measuring the crack opening displacement [6, 7]. Since the 1950s the direct current potential drop method (DCPDM) has been widely used to determine the crack length in fatigue experiments on specimens [8, 9, 10, 11, 12, 13, 14] and components [15]. The DCPDM can easily be automated and thus enables crack propagation experiments under stress intensity controlled conditions to be carried out automatically [16, 17]. The alternating current potential drop method (ACPDM) uses the skin effect which is why this method is very sensitive to changes at the specimen surface and is suitable for detection of small surface cracks only. Accordingly, the ACPDM is more complicated to use and requires expensive equipment [18, 19]. Although the DCPDM is less sensitive to short crack propagation, it was successfully used to detect crack initiation in fatigue experiments [20, 21].  
In this paper, some practical aspects of crack length measurement in fatigue tests on metallic materials will be presented and discussed. In the second part of the article, the benefits of a multiple potential drop measurement for the detection of cracks will be presented. This method offers an improved resolution compared to a single measurement and also enables the localization of the crack initiation site.
## 3 Methods
### Calibration
#### 3.3.3 Estimating the Scale  
We now analyze the optimized histogram illustrated in Figure 4. The narrow peaks in this histogram correspond to the rails. Our goal is to measure the distance between the two rails of the _ego_-track. To achieve this, we first group the clusters in the histogram using the DBSCAN algorithm [37] and identify those representing the rails of the _ego-track_. Given that the camera is mounted on the _ego-train_, it is positioned _close_ to the _ego-track_ in the _ego_\(y\)-coordinate. Leveraging this information, we identify the two cluster centers closest to the LoS \(y\)-coordinate at the camera's position, projected onto the ground plane. These cluster centers are designated as the _ego-rails_, with \(y\)-coordinates \(y_{l}^{\sigma\tau}\) for the left rail and \(y_{r}^{\sigma\tau}\) for the right rail.  
The final scaling factor is then computed based on the distance between \(y_{l}^{\sigma\tau}\) and \(y_{r}^{\sigma\tau}\), ensuring that this distance matches the known track gauge, \(\Delta_{S}\):  
\[s=\frac{\Delta_{S}}{y_{l}^{\sigma\tau}-y_{r}^{\sigma\tau}}\,. \tag{16}\]  
Using this scaling factor, we can now convert an unscaled point cloud \(\mathbf{P}_{c}\) to a metric-scaled point cloud \(\mathbf{P}_{c,m}\):  
\[\mathbf{P}_{c,m}=s\cdot\mathbf{P}_{c}\,. \tag{17}\]
# On Coding and Decoding Reconfigurable Radiation Pattern Modulation Symbols
## 4 Conclusions  
IM is an elegant approach to maximizing the number of bits transmitted through a reconfigurable radiation pattern device. An IM-based methodology for selecting suitable reconfigurable radiation patterns for transmitting binary information is presented in this paper. Radiation pattern symbols are used directly to encode information bits. The radiation symbols transmitted are selected according to an algorithm that maximizes the orthogonality between each set of symbols, considering the BER as the leading figure of merit. The portion of covered space and the number of receiving antennas are considered as variables with a high impact on the BER. In this work, we evaluate the effect of the measurement conditions for the first time, giving a reference for the appropriate symbol selection. The impact of the position and density of \(R_{x}\) receiving antennas has a direct effect on the BER depending on the number of bits to be transmitted. Increasing the number of measuring antennas is shown to lead to a lower error probability if a higher density of symbols is encoded, as can be seen in Figure 5. Depending on the orthogonality requirements for a system, the designer can quickly adapt Algorithm 1 to increase or decrease the number of symbols transmitted. The developed methodology can be applied in any kind of reconfigurable radiation device; however, further research has to be done in hybrid systems with reconfigurable antenna arrays and reconfigurable metasurfaces, including multipath propagation and various channel fading models for static and dynamic systems.  
H.B. initiated this project. Conceptualization: [PERSON], [PERSON] and L.Z. identified the theoretical constructs and the different elements that represented the phenomenon in academic terms. Validation: [PERSON] and [PERSON] conducted the verification and validation such that the system complied with the requirements and specifications according to the intended purpose. Formal Analysis: [PERSON], [PERSON], [PERSON], [PERSON], A.M.E. and K.N.S. developed the required theoretical analysis. Resources and funding: K.N.S., H.B. and A.M.E. obtained technological, financial and economic resources. Writing, initial-draft preparations: [PERSON] and [PERSON] edited the first deliverable draft of the manuscript. Writing, review and editing: [PERSON], [PERSON], K.N.S., [PERSON] and [PERSON] re-edited the paper and performed proofreading. Supervision: [PERSON], [PERSON], A.M.E. and K.N.S. supervised the technical and scientific-quality assurance of the study. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors are thankful for the technological support of the Computer, Electrical, and Mathematical Science and Engineering (CEMSE) Division of King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.  
The authors declare no conflict of interest.
# Phase Shift Design in RIS Empowered Wireless Networks: From Optimization to AI-Based Methods
## 6 Conclusions  
In this paper, we reviewed and compared current optimization methods for solving resource allocation problems associated with RISs. We note that most of the available methods are tailored to continuous phase shift constraints and that AI-based methods are emerging as serious contenders. With the principles and properties of different algorithms explained and illustrated and future challenges analyzed, we hope that this paper will facilitate the suitable choice of algorithms for design problems involving RISs.  
**Author Contributions:** Conceptualization, Z.L., Y.-C.W. and H.V.P.; methodology, Z.L., S.W., Q.L., Y.L., and M.W.; software, Z.L., S.W., Q.L., Y.L., and M.W.; validation, Z.L., S.W., Q.L., Y.-C.W. and H.V.P.; formal analysis, Z.L., S.W., Q.L., Y.L., M.W., Y.-C.W., and H.V.P.; writing--original draft preparation, Z.L., S.W., Y.L., M.W. and Y.-C.W.; writing--review and editing, Z.L., Q.L., Y.-C.W. and H.V.P.; visualization, Z.L., S.W. and Q.L.; project administration, Y.-C.W. and H.V.P; funding acquisition, Z.L., S.W., Y.L., M.W., Y.-C.W. and H.V.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by National Natural Science Foundation of China (No. 62101349), Shenzhen Science and Technology Program (No. RCB20200714114956153) and National Science Foundation (No. CCF-1908308).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Discussion  
Previous studies in [13; 17] revealed that the spectral details in direct parts of BRIRs and the degree of reverberation (length of BRIRs) were two important cues for perceived externalization of virtual sound sources in reverberant environments. Either smoothing the magnitude spectra of direct parts or truncating the reverberant parts of BRIRs, the perceived externalization decreased substantially. In the present study, the importance of these two cues in ipsilateral and contralateral ear signals for perceived externalization was further investigated.  
As mentioned in Section 1, non-individual BRIRs are commonly used in binaural rendering systems because the measurement of individual BRIRs is not feasible for each listener in every room in normal consumer scenarios [18; 20]. To generalize display scenarios, non-individual BRIRs were used in our study to synthesize binaural signals. It can be seen that the simulated virtual sound sources rendered by original BRIRs (\"SN\" and \"RN\" conditions) were all perceived as externalized (mean externalization ratings > 2.0). The mean externalization rating was lower for a frontal sound source than a lateral sound source and generally increased as the source moved from frontal to lateral incidence angles, which was consistent with the results of the studies in [4; 35; 36].
# Multimodal Tucker Decomposition for Gated RBM Inference
## 2 Restricted Boltzmann Machines  
A restricted Boltzmann machine is a type of graphical model in which the nodes \(x=\{\textbf{v},\textbf{h}\}\) form a symmetrical bipartite graph with binary observed variables  
Figure 1: Two types of gating connections. (**a**): gated connection used to control the flow of information in a network; (**b**): gated connection implementing a multiplicative relationship between two inputs \(x\) and \(h\) to provide the output \(y\).  
(visible nodes) and binary latent variables \(\mathbf{h}\in\{0,1\}^{m}\) (hidden nodes). Each visible unit (node) is connected to each hidden unit, but there are no visible-to-visible or hidden-to-hidden connections. Importantly, RBMs are able to model the probabilistic density of the joint distribution of visible and hidden units, enabling them to generate samples similar to those of the training data onto the visible layer. This type of model is called _generative_ models. A classic RBM model is illustrated in Figure 2.  
An RBM is governed by an energy function, the energy of a joint configuration \((\mathbf{v},\mathbf{h})\) between the visible layer, and the hidden layer is given by  
\[E(\mathbf{v},\mathbf{h})=-\sum_{i\in\text{visible}}b_{i}v_{i}-\sum_{j\in \text{hidden}}c_{j}h_{j}-\sum_{ij}W_{i,j}v_{i}h_{j}, \tag{1}\]  
where \(\theta=\{W,\mathbf{b},\mathbf{c}\}\) are the parameters of the model. \(W_{ij}\) is the connection weight matrix between the visible layer and the hidden layer, and \(b_{i}\) and \(c_{j}\) are biases of the visible layer and the hidden layer, respectively.  
\(E(\mathbf{v},\mathbf{h})\) is called the energy of the state \((\mathbf{v},\mathbf{h})\). The joint probability distribution under the model is given by the Boltzmann distribution:  
\[p(\mathbf{v},\mathbf{h})=\frac{1}{\mathcal{Z}(\theta)}\exp\biggl{(}\frac{-1} {\tau}E(\mathbf{v},\mathbf{h})\biggr{)}\]  
where \(\mathcal{Z}(\theta)\) is a normalizing constant and \(\tau\) is the thermodynamic temperature (often considered as 1). \(\mathcal{Z}(\theta)\) is called a _partition function_ and is defined by summing over all possible visible and hidden configurations. Therefore, it is extremely hard to compute when the number of units is large. The partition function is represented as follows:  
\[\mathcal{Z}(\theta)=\sum_{\mathbf{v},\mathbf{h}}\exp(-E(\mathbf{v},\mathbf{h} ))\text{ for }\tau=1.\]  
Since there are no connections between two variables of the same layer, it is possible to derive an expression for \(p(v_{k}=1\mid\mathbf{h})\); this is the probability of a particular visible unit being _on_ given a hidden configuration:  
\[p(h_{j}=1\mid\mathbf{v})=\sigma\biggl{(}\sum_{i}W_{ij}v_{i}+c_{j}\biggr{)} \tag{2}\]  
This is also true for a particular hidden unit given a visible configuration:  
\[p(v_{i}=1\mid\mathbf{h})=\sigma\biggl{(}\sum_{j}W_{ij}h_{j}+b_{i}\biggr{)} \tag{3}\]  
Figure 2: Restricted Boltzmann machine.  
where \(\sigma(z)=1/(1+\exp(-z))\). This leads to a block Gibbs sampling dynamics, used universally for sampling from RBMs.
## 1 Introduction
For instance, a conceivable option to affect the concentration of the carbon-containing fraction of the process gas, and, consequently, the carburizing potential, is the additional admixture of oxygen-containing gases to the nitrogen-hydrogen feed gas. For conventional plasma nitrocarburizing treatments using gaseous carbon precursors, such as CO\({}_{2}\), a positive effect of the oxygen-containing gas was reported [13; 14]. However, the effect of an oxygen admixture to the nitrogen-hydrogen plasma of an ASPN process operating with a solid-phase carbon source on the resulting process gas composition, as well as the material response, have not yet been investigated. In addition, no long-term spectroscopic measurements accompanying the full duration of a material treatment are available. Especially in the context of an oxygen addition to the nitrogen-hydrogen feed gas, long-term spectroscopic gas diagnostic measurements can provide information on the stability of the process gas composition within the plasma-assisted process. In this work, the effect of oxygen admixtures by applying O\({}_{2}\) and CO\({}_{2}\) additions to the N\({}_{2}\)-H\({}_{2}\) feed gas was investigated within an industrial-scale cold-wall ASPN reactor. The main focus was to reduce the concentrations of carbon-containing reactive species within the resulting process gas in a controlled way. The resulting process gas composition's dependence on an oxygen admixture to the N\({}_{2}\)-H\({}_{2}\) feed gas during short-term process cycles was measured by infrared laser absorption spectroscopy (IRLAS). The time-dependent developments of species concentrations were monitored in situ by IRLAS without and with an oxygen admixture during a nitrocarburizing material treatment of ARMCO\({}^{\text{\textregistered}}\) iron. The effect of an oxygen addition on the compound layer formation and the corresponding diffusion depth of nitrogen and carbon were revealed. The study aims to contribute to the understanding of the mechanisms of plasma-based nitrocarburizing processes and the interaction of the process gas atmosphere and the material reaction.