esa-sceva/satcom-chunk-collection huggingface dataset:
# Roboethics in Remote Human Interactions and Rehabilitative Therapeutics
## 5 iManus for Remote Rehabilitation and Learning
### iManus in the Context of Treatment Domains  
Patients who need neurorehabilitation undergo a learning process to regain as much of their lost mobility, coordination, and dexterity as possible. [PERSON]'s Taxonomy classifies the learning process into three domains: (1) cognitive, (2) psychomotor, and (3) effective [30]. The cognitive learning domain applies to activities such as critical thinking, fact recall, decision-making, and overall understanding of a subject [31; 32; 33]. The psychomotor domain involves learning applied skills, such as procedural knowledge and adaptive thinking. The psychomotor domain involves the honing of reflexes, dexterity, and purposeful, focused movement [31; 32; 33]. The affective learning domain typically emphasizes emotional intelligence and personal values [30; 32]. The interaction that iManus allows, in a way that we nowadays consider intuitive, namely teleconferencing, enables patients to manage their emotions positively as they interact with the therapist. Sharing information with their therapist in real-time and/or through an embedded messaging system relieves inner stress about overcoming recovery challenges.  
A successful therapy program should have the following characteristics: technical competence on the therapist's part, the implementation of ethical and professional values, socio-technical responsibility, patient management, and capability for self-assessment through feedback [34]. These elements all relate to one or more of the learning domains described.  
The psychomotor learning domain is where iManus has benefits to victims of brain injury, aiding them in recovery of hand motor skills. iManus also meets the above-described criteria of a successful therapy program, offering self-assessment through feedback. When patients complete hand rehabilitation tasks using iManus, they are practicing hand movements that are analyzed remotely through a sensor that offers instantaneous feedback. Patients can thereby recover their upper limb motor skills, spatial awareness, and proprioception. Patients using iManus work with a therapist, who can interpret their hand movements and offer guidance. During the COVID-19 pandemic, with many medical and therapeutic interactions transitioning to an online format, such rehabilitative programs are delivered via video conferencing. The application of new online technology can contribute to delivering quality rehabilitation once face-to-face sessions resume.
## 2 Materials and Methods
### Extractable Data  
The diagnostic performance was evaluated based on three different endpoints. Of those, invasion of tunica albuginea (TA) and invasion of urethra were provided within the eligible studies. Additionally, based on data reported by the authors, we defined a new endpoint, namely pathological T-stage \(\geq\) 2 (pT \(\geq\) 2). According to different endpoints, true positive (TP), false positive (FP), true negative (TN), and false negative (FN) values were extracted, whenever possible, from each study and reported in 2 \(\times\) 2 contingency tables. Specifically, for the endpoint consisting in pT \(\geq\) 2: clinical T-stage \(\leq\) 1 (cT \(\leq\) 1) on MRI and pT \(\geq\) 2 at final histology was defined as FN; cT \(\geq\) 2 on MRI and pT \(\leq\) 1 at final histology was defined as FP; finally, TP or TN were defined, in case of agreement between cT \(\geq\) 2 and pT \(\geq\) 2 or cT \(\leq\)1 and pT \(\leq\) 1, respectively. Subsequently, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for each endpoint. All the included studies reported pathological T-stage based on TNM 6 th or 7 th edition [8; 9].  
Other descriptive variables were extracted: study design, year of study, number of patients, median age, histology, tumor size, number of readers, use of intra-cavernous PGE1 injection, MRI machine technical features, and inter-reader agreement (K value).
# On the Accuracy of Turbulence Model Simulations of the Exhaust Manifold
## 2 Methodology
### Turbulence Models Equations
#### 2.3.4 Transition SST Model  
The SST k-\(\omega\) transition model, also known as the \(\gamma-Re_{\theta}\) model, introduces two additional transport equations for \(\gamma\) and \(Re_{\theta}\), alongside the existing SST k-\(\omega\) transport equations. This extension enhances the SST k-\(\omega\) model's capability to predict the onset and length of transition. The intermittency equation initiates local transition, while the momentum-thickness equation captures non-local flow effects, making the model sensitive to factors such as length scale, Re, pressure gradient, and freestream turbulence intensity6. Operating as a correlation-based model, it employs empirical correlations for \(Re_{\theta c}\) to activate the model or identify those instances where transition may be bypassed due to exceptionally high turbulence levels. To predict free shear flows as fully turbulent, the SST k-\(\omega\) model incorporates turbulent production limiters (e.g., [PERSON] et al. [31]) to prevent excessive turbulent kinetic energy predictions in stagnation regions.  
\[\frac{\partial\rho\gamma}{\partial t}+\frac{\partial\rho U_{j}\gamma}{\partial x _{j}}=P_{\gamma}-E_{\gamma}+\frac{\partial}{\partial x_{j}}\left[\left(\mu+ \frac{\mu_{t}}{\sigma_{f}}\right)\frac{\partial\gamma}{\partial x_{j}}\right] \tag{16}\]  
This equation represents the intermittency equation factor (\(\gamma\)) in a turbulent flow. It includes the terms for production (\(P\gamma\)), destruction (\(E\gamma\)), and diffusion.  
\[\frac{\partial\rho\widetilde{Re}_{\theta t}}{\partial t}+\frac{\partial\rho U _{j}\widetilde{Ro}_{\theta t}}{\partial x_{j}}=p_{\theta t}+\frac{\partial}{ \partial x_{j}}\left[\sigma_{\theta t}(\mu+\mu_{t})\frac{\partial\widetilde{Ro }_{\theta t}}{\partial x_{j}}\right] \tag{17}\]  
This equation represents the transport equation for the turbulent Reynolds number (\(Re_{\theta}\)) associated with thermal fluctuations. It includes the terms for production (\(P_{\theta t}\)) and diffusion.
## 3 Recommended Architecture for Industrial Software Service Components
### FM-Based Models for Service Recommendation
#### 4.2.2 Measuring Feature Importance  
Because FM uses the same weight for all combined features, incorporating unnecessary feature interactions introduces noise and diminishes the predictive efficacy. Introducing an attention mechanism can help to measure the importance of different features, making the model more interpretable. The network's input consists of an interaction vector formed by two features, and the combined information is encoded within the embedding domain; the output is the attention score corresponding to the combined features. The network responsible for attention can be characterized as follows:  
\[a_{ij}^{\prime}=\mathbf{h}^{\mathrm{T}}\operatorname{ReLU}\left(W\left(v_{i} \odot v_{j}\right)x_{i}x_{j}+b\right)\]  
\[a_{ij}=\frac{\exp\left(a_{ij}^{\prime}\right)}{\sum_{(i,j)\in\mathbb{R}_{x}} \exp\left(a_{ij}^{\prime}\right)} \tag{4}\]  
The parameters are defined as \(W\in\mathbb{R}^{t\times k}\), \(b\in\mathbb{R}^{t}\), and \(h\in\mathbb{R}^{t}\), with \(t\) signifying the dimensionality of the hidden layer within the attention network, often referred to as the attention factor. The result from the attention pooling layer is a k-dimensional vector that efficiently compresses features within the representation space through interactive operations into a single vector, uses different weights to differentiate their importance, and projects the vector onto the prediction score. The overall formulation of the introduced structure of the attention mechanism model is outlined as follows:  
\[\hat{y}_{AFM}(\mathbf{x})= w_{0}+\sum_{i=1}^{n}w_{i}x_{i}+ \tag{5}\] \[\mathbf{p}^{T}\sum_{i=1}^{n}\sum_{j=i+1}^{n}a_{ij}\langle v_{i}\odot v _{j}\rangle x_{i}x_{j}\]  
The above equation contains two parts; the first part is a linear model which weights and sums the input embedding vectors, and the second part is a nonlinear model which first performs element-wise product operation on every two input embedding vectors to obtain a new vector. These interaction vectors are then weighted, the weighted vectors are pooled by the attention scores obtained from Formula (4), and the neural weights of the prediction layer are finally weighted with \(p\) to obtain the final prediction results. The interactions of the higher-order features are not taken into account.
## 2 Materials and Methods
### The Overall Structure  
As shown in Figure 2, MSACN is a U-shape structure including an encoder and decoder components, which are multi-scale Concurrent Dilated Residual Convolution Module (CDRCM) and Multi-head Soft Attention Module (MSAM), respectively. The MSACN is taking inspiration from U-Net as a whole. In addition to the core modules parts (CDRCM and MSAM), other backbone parts refer to ResNet50. Its expansion path and contraction path also have a corresponding relationship. Thus, the feature extraction part uses the residual module to deepen the model without loss of resolution and learn more complex features to reinforce the representation ability of the model. Before inputting the feature extractor part to the cloud semantic prediction module through skip connection, the shape is changed through convolution to adapt to the prediction module, and CDRCM is used in the first convolution to expand the field of view and preserve the spatial resolution. The input layer has the richest and most primitive features, and the reflected features are not lost due to processing. Therefore, the dilated pyramid-like unit is built for the original input image to control the scale difference by the dilation-convolution factor in order to improve the shape-scale in-variance performance. A series of multi-head attention modules are added to the cloud semantic prediction module. The multi-head attention of the decoding process belongs to the soft attention mechanism. Through the combination of multiple heads soft attention blocks, the depth semantic features of the cloud are strengthened and make them more approximate to be linear separable features. The multi-head soft attention module consists of these white blocks, as shown on the right half of the structure in Figure 2. Each multi-head attention module is based on concatenated features. It receives two inputs from the encoder and decoder parts, and performs splicing and convolution operations on its output and upsampling results to ensure the depth of the network. The attention mechanism believes that features at different levels in the network have different importance, and by assigning greater weight to important features, it can inform subsequent layers to focus more on the interested information and suppress useless information. This improved method trains the network to more accurately capture the spatial location information and boundary details of clouds, thereby enhancing the model's accuracy. Through multiple operations of upsampling and attention modules, the network is able to gradually restore spatial details and perform fine boundary segmentation to improve the accuracy and clarity of cloud boundaries.
# YOLO-RRL: A Lightweight Algorithm for PCB Surface Defect Detection
## 2 Algorithm Description  
### Baseline YOLOv8  
The YOLO family of algorithms has undergone numerous iterations, with YOLOv8 representing the latest version of the YOLO family of target detection models [19]. Several popular target detection models were considered when selecting the base model, including YOLOv4 [20], YOLOv5, and SSD [21]. However, YOLOv8 was ultimately selected as the base model primarily because of its ability to achieve a favorable balance between speed and accuracy and its capacity to perform effectively in the presence of complex backgrounds and multi-scale targets. Additionally, the architectural design of YOLOv8 proved to be more aligned with the specific requirements of our application in resource-constrained environments. YOLOv8 represents an iterative enhancement in the YOLO family, integrating state-of-the-art techniques. The incorporation of deep learning enables the model to achieve superior target detection performance. The backbone feature extraction component of YOLOv8 is based on CSPDarknet, which reduces redundant gradient information by cross-stage partially connected to reduce redundant gradient information, improves gradient flow, and helps the model retain gradient information more effectively. The feature fusion part comprises FPN and PAN, which facilitate a reduction in loss during feature delivery and enhance the network's capacity to perceive contextual information. YOLOv8 employs an enhanced head architecture with an anchorless design and a segregated head for classification and localization tasks. BCE Loss [22] is employed for classification loss, while DFLLoss + CIOU Loss is utilized for regression loss.
# The Influence of Sensor Size on Acoustic Emission Waveforms--A Numerical Study
## 4 Discussion  
### Impact of Sensor Size on Signal Amplitude  
The effect of sensor size on signal response is quantified considering the amplitude of the signal received by different size sensors normalized over the amplitude of the point sensor which is considered as reference. The relative amplitude values are presented in Figure 8 for all three source directions and covering the frequency range from 50 kHz to 1 MHz.  
Figure 8: _Cont._  
Figure 7: **(a)** FFT functions of waveforms received by different size sensors (exc. frequency at 1 MHz and diagonal source); **(b)** FFT functions of waveforms received by different size sensors (exc. frequency at 50 kHz and diagonal source).  
In the surface wave propagation case (Figure 8a) and for high frequencies (up to 1 MHz), the output of the larger sensors is just a fraction of the response of the point sensor. Specifically, considering the long sensor (20 mm), the relative amplitude is equal to 3.6% of the reference. Moving towards lower frequencies, the relative amplitude is gradually restored, earlier for the medium and then for the long sensor. At 50 kHz, the relative amplitude of the long sensor is still around 0.65 indicating that even at low frequencies the signal remains significantly distorted.  
In the diagonal wave propagation case (Figure 8b), the medium sensor seems to approach the point sensor response especially at low excitation frequency ranges (from 50 to 200 kHz). This is not the case for the long sensor that receives a much lower amplitude. Indicatively, at 500 kHz excitation frequency, the amplitude of the long sensor is just 22% of the point sensor, while for 200 kHz it is just above 54%.  
Concerning the last case, where the source is placed vertically beneath the receiver (Figure 8c), relative amplitude is much closer to unity (difference between point sensor and long sensor at 1 MHz excitation frequency is about 15%). These results indicate that the sensor size effect is minimized for propagation vertical to the sensor surface in contrast to other angles of incidence.  
It is concluded that sensor size effect dominantly influences the received waveform shape in almost the whole frequency range and especially when the source does not stand directly beneath the sensor. As expected, the measurements obtained by AE on bulk materials, such as concrete where stochastic defects are included, carry an error due to sensor size effect and may provide less accurate signal amplitude. In real size structures, wave attenuation eliminates the higher frequencies, but still frequencies up to 200 kHz are commonly measured, therefore the signal amplitude can be affected by the sensor size. However, in tests done in laboratory, even higher frequencies are measured meaning that the influence of the sensor effect will be even stronger.
## 4 Results and Discussion
### Slope Displacement Analysis
#### 4.2.2 Influence of Mining, Terrain Conditions, and Rainfall on Slope Movement  
By analyzing the X, Y, and Z displacement, X-Y plane trajectory, and X-Z profile trajectory of the monitoring points in the study area, we found that:  
(1) The influence of mining was the main factor continuously driving the overburden and surface movement as the working face advanced, and the direction of ground movement was determined by the position of the advancing working face. This represented the main driving force for the formation of a slope body slip.  
(2) The influence of the terrain conditions on the ground displacement was mainly reflected in an increase in the displacement value in a certain direction, and the monitoring points were greatly affected by the topography in the downhill direction of the slope body. This provided sufficient conditions for a landslide to occur.  
(3) The effect of rainfall on the movement and moving velocity of the ground at the monitoring points was periodic, and the formation of a sliding surface of the slope body was accelerated by the infiltration of rainwater during the rainfall process.  
(4) Other effects, such as formation conditions, groundwater, and vegetation, had a lesser impact on ground movement in the study area.  
To summarize, according to the 90-day monitoring program and the analysis of the results, the dynamic process of ground movement on Luhu slope body can be divided into stages of: mining, overburden and surface movement, surface movement in the opposite direction to the advance of the working face, surface dynamic fracture generation, surface movement in the same direction to the advance of the working face, increased length of surface dynamic fractures, local non-uniform deformation, and slope body failure.
# Enhancing Precision in Magnetic Map Interpolation for Regions with Sparse Data
## 2 Improved Variogram of Kriging Algorithm for Sparse Data
### Parameters Solving of Variogram Using Nonlinear Least Squares  
It can be seen from Equation (10) that the process of solving the improved variogram is transformed into solving the parameters \(\lambda\), \(a\), and \(C\) using magnetic measurement data. The fitting problem of the improved spherical variogram model is transformed into a multiple linear regression problem, which can be solved by the Gauss-Newton nonlinear least squares algorithm. Its calculation formula is as follows:  
\[\begin{array}{ll}x&=argmin_{\Delta x}\frac{1}{2}\ \ |\ f(x+\Delta x)\ \|^{2}\\ &=argmin_{\Delta x}\frac{1}{2}\Big{\{}f(x)^{T}f(x)+2f(x)^{T}J(x)\Delta x+ \Delta x^{T}J(x)^{T}J(x)\Delta x\Big{\}}\end{array} \tag{11}\]  
where \(x=[\lambda,a,C]^{T}\),  
\[\begin{array}{ll}f(x)=\begin{cases}\gamma-C\Big{\{}(1+\lambda)\Big{(}\frac{l }{a}\Big{)}^{2}-\lambda\Big{(}\frac{l}{a}\Big{)}^{3}\Big{\}}&0\leq l\leq a\\ \gamma-C&l>a\end{cases},\quad J(x)^{T}=\begin{bmatrix}JJ_{11}&JJ_{12}&JJ_{13} \\ \dots&\dots&\dots\\ J_{n1}&JJ_{n2}&JJ_{n3}\end{bmatrix},\\ J_{11}=\begin{cases}-C\Big{\{}\Big{(}\frac{l_{1}}{a}\Big{)}^{2}-\Big{(}\frac {l_{1}}{a}\Big{)}^{3}\Big{\}}&0\leq l_{1}\leq a\\ 0&l_{1}>a\end{cases},\quad JJ_{12}=\begin{cases}C\Big{\{}2(1+\lambda)\frac{l_ {1}^{2}}{a^{3}}-\lambda\frac{l_{1}^{3}}{a^{4}}\Big{\}}&0\leq l_{1}\leq a\\ 0&l_{1}>a\end{cases},\\ J_{13}=\begin{cases}-\Big{\{}(1+\lambda)\Big{(}\frac{l_{1}}{a}\Big{)}^{2}-\lambda \Big{(}\frac{l_{1}}{a}\Big{)}^{3}\Big{\}}&0\leq l_{1}\leq a\\ -1&l_{1}>a\end{cases},\quad JJ_{n1}=\begin{cases}-c\Big{\{}\Big{(}\frac{l_{1}}{ a}\Big{)}^{2}-\Big{(}\frac{l_{1}}{a}\Big{)}^{3}\Big{\}}&0\leq l_{n}\leq a\\ 0&l_{n}>a\end{cases},\\ J_{n2}=\begin{cases}C\Big{\{}2(1+\lambda)\frac{l_{1}}{a^{3}}-\lambda\frac{l_{1} }{a^{4}}\Big{\}}&0\leq l_{n}\leq a\\ 0&l_{n}>a\end{cases},\quad JJ_{n3}=\begin{cases}-\Big{\{}(1+\lambda)\Big{(} \frac{l_{1}}{a}\Big{)}^{2}-\lambda\Big{(}\frac{l_{1}}{a}\Big{)}^{3}\Big{\}}&0 \leq l_{n}\leq a\\ -1&l_{n}>a\end{cases}.\end{array} \tag{12}\]  
Take the derivative of Equation (11), and the derivative is 0.  
\[\begin{array}{ll}J(x)^{T}J(x)\Delta x=-J(x)^{T}f(x)\\ \Delta x=-(J(x)^{T}J(x))^{-1}J(x)^{T}f(x)\end{array} \tag{13}\]  
The estimates of \(\lambda\), \(a\), and \(C\) can be computed by iterating Equation (13), and the well-fitted improved variogram model can be obtained too.
## 3 Results and Discussion  
### Synthesis and Characterization  
Scheme 1 shows the synthetic routes for new 3-CF\({}_{3}\)-phenyl substituted DAA compounds **1-3**. The precursors **(1a-3a)** of the anthracene derivatives were produced by the reaction between 2-bromoanthracene-9,10-dione and lithium salts of each broomoaryl compound, followed by acidification in a relatively high yield (i.e., 46-61%). These precursors could be readily converted into the final products **1-3** from the Suzuki-Miyama coupling with (3-CF\({}_{3}\)-phenyl) boronic acid in moderate yields (44-48%). The formation of compounds **1-3** was confirmed by \({}^{1}\)H, \({}^{13}\)C NMR, \({}^{19}\)F NMR spectroscopy (Figures S1-S6, Supplementary Materials), and elemental analysis (EA). The \({}^{1}\)H and \({}^{13}\)C NMR spectra of all DAA derivatives were in good agreement with the predicted structures. The \({}^{19}\)F NMR signals of **1-3** detected around \(\delta\)-62 ppm also indicated the presence of the CF\({}_{3}\)-phenyl unit. Moreover, the chemical shifts of the resonances associated with the proton and carbon atoms in these molecules were in the expected range.
## 5 Discussion  
The experimental outcomes obtained from the last section have shown that AdaCB outperformed the other optimizers. Compared to Adam, our proposed method employs the two bounds to clip the learning rates of Adam, so the effect of unreasonable learning rates is alleviated during training. Additionally, compared to AdaBound, the bound functions of our method are designed based on reasonable convergence range obtained by the LR test instead of empirically hand designed, which can accurately truncate the unreasonable learning rates for a certain task. Moreover, our proposed method is faster than SGD (M) with convergence speed, because our method generates the feature of adaptive learning rates in the early stage. AdaCB integrates both advantages of the adaptive gradient descent method and SGD (M), which can behave like Adam in the early stage with faster convergence speed and become SGD (M) in the end with considerable accuracy. However, our method also has shortcomings. Our method relies on the LR test to pre-specify the convergence bound range, which takes a bit of time to train the current task with a few epochs to calculate the starting point and end point of the convergence range. In future work, we will investigate to determine the convergence bound range adaptively rather than pre-specify with the LR test, so more time can be saved.