esa-sceva/satcom-chunk-collection huggingface dataset:
# Quantitative Assessment of Upper- Limb Volume: Implications for Lymphedema Rehabilitation?
## 4 Methods for Volumetric Assessment
### Three-Dimensional (3D) Scanning Techniques  
Three-dimensional (3D) scanning techniques have emerged as an innovative tool for volumetric assessment in upper-limb lymphedema. These techniques utilize various technologies, such as laser scanning or structured light projection, to capture detailed surface information and create a digital representation of the affected limb [9; 10; 38]. 3D scanning offers a non-invasive and convenient method to assess limb volume and shape, providing valuable insights for the management of lymphedema [10].  
The principle behind 3D scanning involves projecting a light pattern onto the limb's surface and capturing the reflected light using sensors. By analyzing the distortions in the light pattern, the system can create a digital model of the limb's surface. Advanced algorithms then process this data to calculate limb volume and provide visualizations of the limb's shape and contours [38].  
One of the primary advantages of 3D scanning techniques is their non-invasive nature and ease of use. The scanning process is typically quick and comfortable for the patient, as it does not involve direct contact or radiation exposure [73]. Additionally, the digital models generated by 3D scanning provide a comprehensive visualization of the limb, allowing for a detailed assessment of volume, shape, and symmetry [74].  
Furthermore, 3D scanning techniques offer the advantage of capturing surface information, which can be particularly valuable in the assessment of lymphedema. Surface irregularities, such as swelling or fibrotic changes, can be accurately captured and quantified, aiding in the identification and monitoring of edema [42]. Moreover, 3D scanning facilitates the comparison of pre- and post-treatment measurements, enabling the objective assessment of treatment outcomes [10].  
It is important to note that different 3D scanning techniques may have variations in their equipment and processes. Laser scanning, structured light projection, and other methods each have their own advantages and limitations [10; 33; 38; 75; 76]. Factors such as scanning speed, accuracy, and resolution should be considered when selecting a 3D scanning technique for volumetric assessment in upper-limb lymphedema [75].  
Multiple studies have investigated the utility of 3D scanning techniques in assessing limb volume and shape in lymphedema [10; 38; 42; 76]. These techniques have shown good agreement with other established volumetric assessment methods and have been validated in various patient populations [10; 76].  
Altogether, this evidence highlighted that 3D scanning techniques might be considered promising tools in evaluating lymphedema and monitoring the efficacy of comprehensive rehabilitation programs. Their non-invasive nature, ability to capture surface information, and quantitative analysis capabilities represent essential advantages in the routine monitoring and the follow-up of upper-limb lymphedema, with intriguing implications for more precise and tailored therapeutic interventions.
## 3 The Proposed Learning-Based MPC Energy Management Strategy
### Dual-Layer LSTM-Based Load Power Prediction  
Unlike EVs, legged robots typically exhibit periodic hopping behavior consisting of swing, jump, and takeoff phases. These movements are carefully planned and executed, and variations in power output during the jumping cycle are often influenced by terrain and speed. This study predicts the short-term future power demand of the legged robot, leveraging historical data and incorporating gait planning strategies.  
LSTM is a special kind of recurrent neural network designed to tackle the problems associated with vanishing and exploding gradient issues. It is a more stable and reliable network that can handle long-term dependencies in sequential data. In this paper, the LSTM network is used to predict the short-term future power demand of the legged robot.  
The dual-layer LSTM-based load power prediction structure is illustrated in Figure 5. The network takes multiple inputs for accurate prediction, including the past 1 s load power data \(P_{d,k-20}\sim P_{d,k}\) (21 steps in total, sampled every 50 ms), the operation attribute at the time \(l_{12,k}\), and the attributes of the subsequent gait steps \(l_{12,k+1}\sim l_{12,k+20}\). The prediction model employs two LSTM layers, each with 128 hidden cells. The final output of the algorithm is the predicted power output for the next gait step (20 steps in total), generated via a fully connected layer.  
Figure 4: The overall framework of the learning-based MPC energy management strategy for the legged robot with the hybrid energy storage system. The MPC with a deep learning-based weighting factor adjustment is designed for energy management. The deep learning algorithm adjusts the weighting factors based on the predicted load power, which is generated from a dual-layer LSTM network.  
Figure 5: The structure of the dual-layer LSTM-based load power predictor.
# Dual-Band Frequency Selective Surface-Backed Reflectarray for High-Speed Ka-Band Satellites
## 3 Dual-Band Reflectarray System Design  
### Aperture Efficiency Analysis  
An aperture efficiency analysis is carried out to determine the physical dimensions of the reflectarray. The reflectarray antenna configuration is given in Figure 4. The reflectarray is illuminated with a horn antenna. During the measurements, OBHL180400 double-ridged horn antenna that operates in the 18-40 GHz frequency range will be used. Thus, characteristics of this antenna are used to observe the aperture efficiency. If the feed antenna is approximated with the \(cos^{\phi}\) function, \(q=11\) at 30 GHz. The far-field distance of the feed antenna is calculated as 154 mm at 20 GHz and 231 mm at 30 GHz. Since the feed antenna is meant to be positioned to the far-field region in both cases, the distance of the phase center in the z direction is chosen as \(H=250\) mm. In the reflectarray design, we aimed to direct the reflected beam towards \(\theta_{o}=20^{\circ}\) in the \(\phi=0^{\circ}\) plane. And the predefined feed antenna is placed to \(\theta_{f}=-20^{\circ}\) in the \(\phi=0^{\circ}\) plane. By the aperture analysis [30], the physical structure of the reflectarray that would work with the highest efficiency is obtained for the given requirements. Aperture efficiency (\(\eta_{a}\)), illumination efficiency (\(\eta_{i}\)) and spillover efficiency (\(\eta_{s}\)) are given in Figure 5 as the function of reflectarray diameter. Aperture efficiency, which is calculated by the product of illumination and spillover efficiencies, shows highest performance at \(D=250\) mm and \(D=280\) mm at 20 and 30 GHz, respectively. By considering the physical size of the available printed circuit boards in our laboratory, the diameter of the reflective surface for the prototype is chosen as \(D=198\) mm, which results in aperture efficiency of approximately \(\eta_{a}=0.69\) for both bands. The resulting reflective surface consists of 33 unit cells along its diameter.
# A Study on the Essential and Parkinson's Arm Tremor Classification +
## 4 Methodology
### Classification  
After calculating the parameters from the acquired data, the features of the training data are introduced to the 21 classifiers shown in Table 5. For the Tree classifiers, the maximum number of splits is 100 for the Fine, 20 for the Medium and 4 for the coarse tree while the split criterion is the Gini's diversity index. Moreover, the covariance structure for the discriminant analysis category is selected as full. Regarding SVMs, the Kernel scale for Linear, Quadratic and Cubic is automatic while for Fine Gaussian it is 0.87, for Medium Gaussian 3.5 and for Coarse Gaussian 14. For all the SVMs, the box constrained level is selected at 1 and the multi-class method is one-vs-one. Furthermore, for Medium, Cosine, Cubic and Weighted KNN the number of neighbors is 10, for Fine KNN it is 1 and lastly for Coarse KNN it is chosen at 100. The distance metric is defined as Euclidean for Fine, Medium, Coarse and Weighted KNN. In parallel, Cosine and Minkowski distances are used for the Cosine and Cubic KNN classifiers, respectively. Finally, for the Ensemble learning category, the maximum number of splits is 20 for the Boosted (ensemble method: AdaBoost) and RUSBoosted (ensemble method: RUSBoost) and 134 for the Bagged trees (ensemble method: Bag). For the subspace KNN classifier, the subspace dimension is 6 and the ensemble method is the subspace one. For all the ensemble classifiers the number of learners is 30.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Category** & **Algorithm** \\ \hline \multirow{3}{*}{Trees [52]} & Fine tree \\  & Medium tree \\  & Coarse tree \\ \hline \multirow{3}{*}{Discriminant analysis [53]} & Linear discriminant analysis (LDA) \\  & Quadratic discriminant analysis (QDA) \\ \hline \multirow{6}{*}{Support vector machine (SVM) [54]} & Linear SVM \\  & Quadratic SVM \\  & Cubic SVM \\  & Fine Gaussian SVM \\  & Medium Gaussian SVM \\  & Coarse Gaussian SVM \\ \hline \multirow{6}{*}{K-nearest neighbor (KNN) [55]} & Fine KNN \\  & Medium KNN \\ \cline{1-1}  & Cubic KNN \\ \cline{1-1}  & Weighted KNN \\ \hline \multirow{3}{*}{Ensemble learning [56]} & Boosted trees \\ \cline{1-1}  & Bagged trees \\ \cline{1-1}  & Subspace KNN \\ \cline{1-1}  & RUSBoosted trees \\ \hline \hline \end{tabular}
\end{table}
Table 5: Tested classification algorithms.
# Analysis of Interference-Fit Joints
## 3 Results of Numerical Calculations and Experimental Studies
### Experimental Verification  
Five sets consisting of a shaft and a hub with dimensional parameters corresponding to the joint with symbol J5 were tested. The test set is shown after pressing to a depth of 15 mm (Figure 12).  
Each set was subjected to a joint axial disassembly force test performed on a measuring device consisting of an MTS class 0.5 actuator equipped with a force sensor measuring the force range up to 25 kN and a displacement sensor with a range of up to 75 mm. The author's procedure, developed in the TestSuit program controlling measuring system, was applied. An appropriate mounting adapter was used to fix the assembly, as shown in Figure 13.  
Five breaking tests were carried out, recording the force and displacement values in each test until the shaft was completely withdrawn from the hub. An example of the graph obtained during one of the tests is shown in Figure 14.  
Figure 11: A graph of joint breaking forces, determined by means of formulas and numerical simulation.  
Figure 12: Set after pressing.  
The mean breaking force of the joint from the five tests carried out was 6014.0 \(\pm\) 126.6 N with a standard deviation of 45.6 which, compared to the result obtained from the numerical simulation, shows a convergence with the calculations of 3.7%, confirming the correctness of the numerical model. The confidence range was determined based on the t-Student's distribution with the confidence level of \(\alpha\) = 0.95 [21].
# The Role of PET and SPECT Imaging in Prostate Cancer Targeted Alpha Therapy: When and How?
## 5 \({}^{225}\)Ac/\({}^{213}\)Bi-PSMA TAT
### Nuclear Medicine Imaging for Patients' Selection and Follow-Up after PSMA TAT
#### 5.1.2 \({}^{68}\)Ga-PSMA/\({}^{18}\)F-PSMA
However, according to a recent paper, in patients who experience PSA progression on PSMA-based radioligand therapy, PET imaging with \({}^{68}\)Ga-NODAGA that accumulates in the region of bone turnover surrounding the PCa metastatic deposit in the bone and provides an indirect assessment of bone metastasis is a more suitable imaging modality for the detection of skeletal lesions not expressing PSMA [96], which is probably due to the mechanisms by which mCRPC becomes resistant to PSMA-based radioligand therapy with alpha- or beta-emitting radionuclides: that is, by downregulating PSMA expression that influences \({}^{68}\)Ga-PSMA uptake [68]. Further papers areneeded in order to clearly define a dedicated protocol in patients' selection and PSMA-based TAT monitoring. The fluorine-based agents developed for prostate cancer imaging include \({}^{18}\)F-DCFBC, F-DCFPyl, \({}^{18}\)F-rhPSMA and \({}^{18}\)F-PSMA-1007. The half-life makes 18F more attractive for commercial development because it can be produced centrally and distributed to satellite sites [63]. However, certain PSMA ligands such as PSMA-1007 have high hepatic background activity compared with most other PSMA ligands [97]. In the patients' selection, when \({}^{18}\)F-PSMA-1007 PET/CT is obtained for baseline imaging, caution must be exercised in using hepatic background activity as reference standard to define sufficient PSMA expression in mCRPC lesions by considering mediastinal blood-pool as alternative internal reference.
## 1 Introduction
### Study Contribution  
The Bayesian technique has been implemented in various fields, from intrusion to fault detection systems [5]. On the other hand, the majority of past safety studies in the literature review either focused on the severity [6] or the frequency of barrier crashes [7]. However, an inadequate number of studies considered both aspects of barrier crashes: crash severity and frequency. Also, none of those past studies considered geometric characteristics of barriers while performing the optimization. This is partly due to a lack of available information related to traffic barrier characteristics.  
Thus, besides considering geometric characteristics of barriers, this study is conducted to consider both the aspects of crash frequency and crash severity by using equivalent property damage only (EPDO) and by aggregating barrier crashes across various barrier IDs. In addition, although several studies and experiments have been conducted to identify recommended dimensions of barrier geometric characteristics, none of those studies have quantified the benefit that could be gained by bringing the barriers to their optimal values based on machine learning techniques.  
Other shortcomings of the past studies were the approaches taken to account for heterogeneity. Some studies used FMM to come up with the data models' distributions. However, those approaches resulted in a loss of information as they divided data into a number of datasets based on various distributions. Another approach is to use the hierarchical technique to account for the heterogeneity in the dataset. However, assignment of data to the hierarchy has been mainly subjective in studies related to traffic safety studies, where there is no clear-cut hierarchy, and the researchers assign data to the hierarchy based on educated or engineering knowledge. Thus, this study is conducted by taking into consideration the aforementioned shortcomings. As not much study has been conducted in the area of optimization in traffic safety the way we did, the readers are referred to the literature review [3].  
Figure 1: Methodological research approach.  
This paper is organized as follows: the methodology section outlines the implemented methods, while the data section details the data used in the study. The remaining sections of this study would summarize the results and conclusions.
## 5 RTIoT System Design Methodology
### RTIoT System Optimization  
In the RTIoT system, each function, represented by a task graph, can be simultaneously activated by different sources (users and sensors). This can be represented by multiple instances of a given graph. When designing an RTIoT system, one specifies the requirements for the system by identifying the following constraints:  
* Maximum number of instances of a given graph: This parameter determines the maximum load on the system in terms of the number of simultaneously activated tasks.  
Figure 7: Mapping the described processes to RTIoT system architecture.  
* Maximum processing time (deadline): This parameter specifies the maximum time that can elapse from the start of the \(\mathrm{t_{start}}\) task to the completion of the \(\mathrm{t_{stop}}\) task. For a given system, there can be multiple time constraints that define different paths in task graphs. For soft-real-time systems, a soft constraint is defined as \(\mathrm{d_{max}^{s}}\) and a hard constraint is defined as \(\mathrm{d_{max}^{h}}\). The goal of optimizing RTIoT systems is to achieve the highest possible QoS. The QoS for a single constraint can be defined as: \[\mathrm{QoS_{i}}=\begin{cases}0&\text{if $\mathrm{t_{i}}>\mathrm{d_{max}^{h}}$}\\ 1&\text{if $\mathrm{t_{i}}<\mathrm{d_{max}^{s}}$}\\ 1-\frac{\mathrm{t_{i}}-\mathrm{d_{max}^{s}}}{\mathrm{d_{max}^{s}}-\mathrm{d_{ max}^{s}}}&\text{in other cases}\end{cases}\] (1) where \(t_{i}\) is the current execution time of tasks covered by the \(i\)-th constraint. Then, the total QoS for the system can be determined as the average value of all QoS: \[\mathrm{QoS}=\sum_{i=0}^{n}\frac{\mathrm{QoS_{i}}}{n}\] (2) where \(n\) is the number of all constraints in all instances of task graphs. While intending to obtain the highest possible QoS value, the optimization of the system consists in prioritizing the execution of tasks and transmissions in such a way as to minimize any exceedance of time constrains. For this purpose, the scheduling methods described in Section 4 can be used. However, in order to be able to perform tasks and transmissions according to the optimized prioritization, it is necessary to consider the following properties of the RTIoT architecture:  
* Transmissions are carried out over shared internet links: To ensure predictable transmission times, it is necessary to develop routing methods that consider the required order and priority of transmissions.
* Individual tasks and transmissions are assigned to resources distributed over the internet. Therefore, in order to execute them in the required order, it is necessary to use time synchronization mechanisms. This can be accomplished by developing appropriate communication protocols. Assuming that the aforementioned solutions are available, the presented methodology can enable the automatic synthesis of QoS-optimized RTIoT systems. The quality of a given solution is evaluated by calculating the QoS value defined by Equation (2). This value is calculated for the worst-case scenario, i.e., assuming the maximum system load and maximum computation and transmission times.
# Enhancing the Security of Pattern Unlock with Surface EMG-Based Biometrics
## 4 Materials and Methods
### sEMG Feature Extraction
#### 4.4.9 Number of Zero Crossing (NZC)  
Number of Zero Crossing (NZC) is a time-domain feature that measures the frequency information of an EMG signal [33]. It is defined as the number of times the signal crosses zero. A threshold, \(T\), was included in the calculation to reduce the effect of noise-induced zero-crossings [33].  
\[NZC=\sum\limits_{i=1}^{N-1}[sgn(X_{i}\times X_{i+1})\cap|X_{i}-X_{i+1}|\geq T] \tag{11}\]  
\[sgn(x)=\begin{cases}1,&\text{if }x<0,\\ 0,&\text{otherwise}.\end{cases}\]
## 2 Problem Formulation
### Noncircular Signals  
For complex random variables and vectors, circularity is an important property [18]. Making full use of the properties of noncircular signals, the array aperture can be enlarged. The invariance specialityof rotation can be used to judge if a complex random sequence is noncircular [18]. More specifically, if stationary complex signal sequence \(\tilde{\mathbf{q}}\) is circular, it accords with \(\mathrm{E}[\tilde{\mathbf{q}}\tilde{\mathbf{q}}^{\mathrm{H}}]\
eq 0\) and \(\mathrm{E}[\tilde{\mathbf{q}}\tilde{\mathbf{q}}^{\mathrm{T}}]=0\). By comparison, if the sequence is noncircular, then \(\mathrm{E}[\tilde{\mathbf{q}}\tilde{\mathbf{q}}^{\mathrm{H}}]\
eq 0\) and \(\mathrm{E}[\tilde{\mathbf{q}}\tilde{\mathbf{q}}^{\mathrm{T}}]\
eq 0\), which can be utilized to increase the number of effective array elements. In addition, for zero-mean stationary complex signal sequence \(\tilde{\mathbf{q}}\), it holds the property that:  
\[\mathrm{E}[\tilde{\mathbf{q}}\tilde{\mathbf{q}}^{\mathrm{T}}]=\rho e^{i\tilde {\mathbf{q}}}\mathrm{E}[\tilde{\mathbf{q}}\tilde{\mathbf{q}}^{\mathrm{H}}] \tag{5}\]  
where \(0\leq\rho\leq 1\) and \(\psi\) are noncircular rate and noncircular phase angle, respectively, and \(\rho\) depends on the signal modulation type. In this paper, we consider the signals with maximum noncircular rate, namely, \(\rho=1\) in \(\mathbf{s}(t)\), such as BPSK and MASK modulated signals that are widely used in MIMO radar systems. It has been pointed out that the noncircular signal vector \(\mathbf{s}(t)\) in Equation (1) can be rewritten as [17; 25]:  
\[\mathbf{s}(t)=\boldsymbol{\Lambda}_{\varphi}\mathbf{s}_{r}(t) \tag{6}\]  
where \(\mathbf{s}_{r}(t)=[s_{r1}(t),s_{r2}(t),\ldots,s_{rP}(t)]^{\mathrm{T}}\) is the real-valued part of noncircular signal \(\mathbf{s}(t)\), and \(\boldsymbol{\Lambda}_{\varphi}=\mathrm{diag}([e^{i\varphi_{1}},e^{i\varphi_{ 2}},\ldots,e^{i\varphi_{P}}])\) is the noncircular phase matrix. Based on the composition and structure of \(\mathbf{s}(t)\), for the \(p\)-th target with \(t=1,2,\ldots,J\), \(\mathrm{E}[\mathbf{s}_{p}\mathbf{s}_{p}^{\mathrm{T}}]=e^{i2\varphi_{P}} \mathrm{E}[\mathbf{s}_{rp}\mathbf{s}_{rp}^{\mathrm{T}}]=e^{i2\varphi_{P}} \mathrm{E}[\mathbf{s}_{p}\mathbf{s}_{p}^{\mathrm{H}}]\) can be obtained. In addition, each element in \(\mathbf{s}(t)\) accords with Equation (5), i.e., \(\mathrm{E}[\mathbf{s}_{p}\mathbf{s}_{p}^{\mathrm{T}}]=e^{i\varphi_{P}} \mathrm{E}[\mathbf{s}_{p}\mathbf{s}_{p}^{\mathrm{H}}]\) with \(\rho=1\)[26]. This indicates that the relationship between \(\varphi_{p}\) and \(\psi_{p}\) can be written as \(\varphi_{p}=\psi_{p}/2\). With the application of signal noncircularity, \(\mathbf{X}\) in Equation (4) is expressed as:  
\[\mathbf{X}=\mathbf{CA}\boldsymbol{\Lambda}_{\varphi}\mathbf{S}_{r}+\mathbf{N} \tag{7}\]  
where \(\mathbf{S}_{r}=[\mathbf{s}_{r}(t_{1}),\ldots,\mathbf{s}_{r}(t_{J})]\) is a real-valued matrix.  
Figure 2: Difference between monostatic MIMO radar and bistatic MIMO radar.
###### Abstract  
Thyroid hormone (TH) plays an essential role in cell proliferation, differentiation, and metabolism. Experimental and clinical studies have shown a potential association between TH signaling and retinal degeneration. The suppression of TH signaling protects cone photoreceptors in mouse models of retinal degeneration, whereas excessive TH signaling induces cone degeneration, manifested as reduced light response and a loss of cones. This work investigates the genes/transcriptomic alterations that might be involved in TH-induced cone degeneration in mice using single-cell RNA sequencing (scRNAseq) analysis. One-month-old C57 BL/6 mice received triiodothyonine (T3, 20 \(\mathrm{\SIUnitSymbolMicro m}\)/mL in drinking water) for 4 weeks as a model of hyperhybridism/excessive TH signaling. At the end of the experiments, retinal cells were dissociated, and cell viability was analyzed before being subjected to scRNAseq. The resulting data were analyzed using the Sentivar package and visualized using the Loupe browser. Among 155,866 single cells, we identified 14 cell clusters, representing various retinal cell types, with rod and cone clusters comprising 76% and 4.1% of the total cell population, respectively. Cone cluster transcriptomes demonstrated the most alterations after the T3 treatment, with 450 differentially expressed genes (DEGs), accounting for 38.5% of the total DEGs. Statistically significant changes in the expression of genes in the cone cluster revealed that phototransduction and oxidative phosphorylation were impaired after the T3 treatment, along with mitochondrial dysfunction. A pathway analysis also showed the activation of the sensory neuronal/photoreceptor stress pathways after the T3 treatment. Specifically, the eukaryotic initiation factor-2 signaling pathway and the cAMP response element-binding protein signaling pathway were upregulated. Thus, excessive TH signaling substantially affects cones at the transcriptomic level. The findings from this work provide an insight into how excessive TH signaling induces cone degeneration.  
thyroid hormone; retina; photoreceptor; cone; phototransduction; scRNAseq