esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Study of Pressure Attenuation Effect on Tunnel Structures Subjected to Blast Loads
## 2 Experiment  
The aim was to reduce and avoid explosion pressure directly jeopardizing the safety of personnel inside a tunnel structure. This study designed three pressure attenuation models by changing the tunnel's cross-section, namely, an expansion chamber, single-orifice-plate attenuator, and double-orifice-plate attenuator, to investigate the attenuation effect, wave propagation pattern, and pressure distribution. When a blast wave passes through the tunnel, the pressure is expected to be attenuated by diffusion and detour due to the tunnels' cross section change.  
In this study, a small-sized rectangular section tunnel specimen was used to demonstrate an underground tunnel structure subjected to external explosions. The tunnel specimen was made of steel plate with a thickness of 0.5 cm, and the size of its cross-section was 30 \(\times\) 30 cm. The charge used in the explosion test was C-4 explosive. Its appearance is gray to light yellow. The density was between 1.59 and 1.60 g/cm\({}^{2}\), and the detonation speed can reach 8193 m/s.  
Two types of pressure transducer produced by PCB company were used in the field test. The first type was pencil type sensor (models: 137A21 and 137A23), and the measuring range was from 345 to 345 MPa. This type of sensor is used to measure the explosion pressure near the ground in the free field; the second type is high-frequency pressure gauge (models: 113B23, 113B27, and 113B28), and the measuring range was from 345 kPa to 69 MPa, which were used to measure the pressure in the rectangular tunnel specimens. The maximum bandwidth of the oscilloscope was 100 MHz, and the maximum sampling rate was 2 \(\times\) 10\({}^{9}\) s\({}^{-1}\).
# An Exploration of Robot-Mediated Tai Chi Exercise for Older Adults
## 3 Results
### Participants' Experience of the Robot-Led Tai Chi Exercise (from the Post-Exercise Survey)
#### 3.3.2 Attitude toward the Robot and Future Expectations  
On average, the participants rated 5.75 (SD = 3.40) for question 4 (\"Would you like to have such a robot at home?\"), showing modest acceptance, with a common reason being seeking \"a fun experience\", while many others emphasized the preference for a community-based, interactive, or socializing experience instead of at-home exercise.  
The average ratings for questions 5 (\"Would you want the robot to understand your speech?\") and 6 (\"Would you want the robot to understand your postures?\") were 6.85 (SD = 2.91) and 7.65 (SD = 1.60), respectively. Participants hoped that the robot could personalize the exercise (e.g., repeating a posture as needed) and give real-time feedback (e.g., correcting a gesture and pointing out how to improve) if the robot could understand their speech and postures. Participants only rated 5.4 (SD = 3.10) on question 7 (\"Would you want the robot to understand your facial expressions that show your emotions?\"). Some participants thought this would help the robot identify their confusion, while most people felt it unnecessary or showed concerns (e.g., \"might creep me out\").
## 2 Related Work
### Sensors and Ranging Technology
#### 2.5.4 IMU  
The Inertial Measurement Unit (IMU) is an electronic gadget that calculates the exact strength, rotational velocity and alignment of a body by utilizing a combination of accelerometers, gyroscopes and magnetometers. Autonomous forklifts rely on an IMU to sense their movement, including speed, direction and tilt. This sensor is essential for tasks like maintaining balance, tracking location and planning smooth paths. By combining IMU data with other sensors, the forklift can navigate precisely and efficiently, guaranteeing its safe and effective operation in diverse environments. IMU sensors, as shown in Figure 5, measure the forklift's orientation, acceleration and angular velocity. By continuously tracking these parameters, IMUs help in estimating the forklift's motion, mitigating the effects of vibrations, and improving overall navigation accuracy.  
Figure 4: TiM 150 Series Laser Scanner LiDAR Sensor.  
IMUs are commonly employed in the navigation systems of unmanned aerial vehicles (UAVs), satellites and landers to assist in flight control, attitude and heading reference systems. IMU-enabled GPS devices have been developed recently. An IMU-equipped GPS receiver can operate even when GPS signals are not accessible, such as in tunnels, indoors or when electrical interference is present [63; 64].
## 4 Discussion
### Analysis of Conformational Parameters and Interaction Profiles
Remarkably, \(\beta\)-pinene was the only terpene found to interact with multiple matching catalytic residues of PLA\({}_{2}\): PHE5, CYS44, and HIS47. This direct engagement with the enzyme's functional core positions \(\beta\)-pinene as a promising candidate for PLA\({}_{2}\) inhibition, despite its higher Ki value compared to limonene and menthol for this enzyme. For PLA\({}_{2}\), limonene and menthol, which show the lowest binding energies and Ki values, did not show direct \"matches\" with the listed catalytic residues, similar to AA for COX enzymes. This reinforces the idea that an optimal fit (low binding energy) can be achieved through interactions with residues forming the binding pocket, even if they are not explicitly defined as \"catalytic\" in the active site sequence list. However, their precise positioning within critical regions of PLA\({}_{2}\), as supported by their favorable thermodynamic parameters, further strengthens their candidacy as potential inhibitors. The space-filling representation in Figure 4 visually supports the deep penetration and spatial accommodation of these ligands within the PLA\({}_{2}\) catalytic pocket. Recent studies have employed virtual screening to identify natural products with inhibitory activity against enzymes involved in inflammation, such as COX-2 and 5-LOX. For instance, [PERSON] et al. (2023) conducted the molecular docking of COX-1/2 inhibitors, including celecoxib, reporting binding energies ranging from \(-6.5\) to \(-9.0\) kcal/mol for known inhibitors, indicating strong affinity to COX-2 ([PERSON] et al., 2023). In comparison, the binding energies of menthol (\(-6.42\) kcal/mol) and camphor (\(-6.48\) kcal/mol) for COX-2, and limonene (\(-5.57\) kcal/mol) for 5-LOX, observed in this study, suggest moderate-to-weak affinity. While these terpen exhibit lower binding energies than synthetic inhibitors like celecoxib, their interactions with the key catalytic residues (e.g., TYR385 and HIS388 for COX-2; PHE177 and HIS372 for 5-LOX) indicate potential competitive inhibition. The smaller molecular size and simpler structure of monoterepenes compared to synthetic inhibitors may limit their potency but offer advantages in bioavailability and reduced toxicity, positioning them as viable lead compounds for further optimization through structural modifications, such as the addition of polar groups to enhance binding affinity.
## 3 Regression Models  
### Model Class Selection  
Finding the key relationships between the phenomena under consideration is the goal of the presented statistical model. Understanding of cause-effect relationships was realized using the linear model, inverted linear model, and power model [29; 30; 32]. An important aspect is to carry out calculations for all forms of models. The following models were used in this study:  
Linear model (1):  
\[\mathrm{Y}=\infty_{0}+\alpha_{1}\;\mathrm{X}_{1}+\alpha_{2}\;\mathrm{X}_{2}+ \ldots+\alpha_{\mathrm{n}}\;\mathrm{X}_{\mathrm{n}}+\varepsilon \tag{1}\]  
Inverted linear model (2):  
\[\mathrm{Y}^{-1}=\infty_{0}\;+\infty_{1}\;\mathrm{X}_{1}^{-1}+\infty_{2}\; \mathrm{X}_{2}^{-1}+\ldots+\alpha_{\mathrm{n}}\;\mathrm{X}_{\mathrm{n}}^{-1}+\varepsilon \tag{2}\]  
Power model (3):  
\[\mathrm{Y}=\infty_{0}\;+\mathrm{X}_{1}^{\infty_{1}}+\mathrm{X}_{2}^{\infty_{2} }+\ldots+\mathrm{X}_{\mathrm{n}}^{\infty_{\mathrm{n}}}+\mathrm{e}^{\varepsilon} \tag{3}\]  
\begin{table}
\begin{tabular}{c c c} \hline
**Period of Exploitation of the** & **A = Brake System Pressure** & **B = Vehicle Load Range** \\
**Brake Pads [km]** & **Range [MPa]** & **GVW [\%]** \\ \hline \multirow{5}{*}{Y} & X\({}_{1}\): \(<\)0.15 & X\({}_{5}\): \(<\)20\% \\  & X\({}_{2}\): 0.15 to 0.24 & X\({}_{6}\): 20\% to 39\% \\ \cline{1-1}  & X\({}_{3}\): 0.25 to 0.40 & X\({}_{7}\): 40\% to 59\% \\ \cline{1-1}  & X\({}_{4}\): \(>\)0.40 & X\({}_{8}\): 60\% to 80\% \\ \cline{1-1}  & X\({}_{9}\): \(>\)80\% \\ \hline \end{tabular}
\end{table}
Table 1: Algorithm for clustering source data.  
The power model described by Equation (3) was linearized using the natural logarithm. The structure of the quasi-linear model is shown in Equation (4):  
\[\ln Y=\ln\alpha_{0}+\alpha_{1}\ln X_{1}+\alpha_{2}\ln X_{2}+\ldots+\alpha_{8} \ln X_{8}+\epsilon \tag{4}\]  
where Y--dependent variable [km], \(X_{1,2,\ldots,n}\)--explanatory variables, \(\alpha_{0}\), \(\alpha_{1},\ldots,\alpha_{n}\)--unknown parameters of the model, and \(\epsilon\)--randomness component of the model.  
The models were recognized as high-quality models after meeting the following criteria:  
* Valve of adjusted determination coefficient \(\overset{\sim}{R^{2}}>90\%\),
* full analysis of the random components of the residuals of the models--correctly verified statistically.
# Prediction Interval Adjustment for Load-Forecasting using Machine Learning
## 3 Materials and Methods
### Prediction Intervals Adjusted by Means of Association Rules Support Metric  
The prediction interval is adjusted by subtracting the value of a correspondent rule support to the \(100(1-\alpha)\%\) value when estimating the prediction interval. This adjustment occurs only when a specific rule in \(r_{p}\) matches with the inputs of a \(a_{p}\) ANN model. In this case, Equation (4) can be re-written as follows:  
\[\hat{V}_{t}(p,h)\pm z_{\delta}\sqrt{Var[\epsilon(p,h)]} \tag{9}\]  
where \(\delta=\alpha+\beta\)  
The parameter \(\beta\) is a bias to adjust the value of the \(z\)-_score_. The modified \(z\)-_score_ will decrease or not the prediction interval. The parameter \(\beta\) can take values according to the following expression:  
\[\beta=\left\{\begin{array}{cl}support(l_{i}^{*}\Rightarrow r_{i}^{*}),&\text { if }&W_{i}^{*}=l_{i}^{*}\\ 0,&\text{ Otherwise }\end{array}\right. \tag{10}\]  
where \(W_{i}^{*}\) is the quantile mapped version of the ANN model inputs \(W_{i}\). To modify the value of the \(z\)-_score_, we re-write the confidence interval probability expression as follows:  
\[100\cdot(1-(\alpha+\beta)) \tag{11}\]  
From the modified confidence interval probability shown in Equation (11), we can obtain the modified prediction interval confidence. With this value, we can look forward to its corresponding modified \(z\)-_score_ from any \(z\)-_score_ table [22]. This, in fact, gives us the corresponding \(z\)-_score_ value, such that we can modify the coverage of the prediction interval.
# Big-Delay Estimation for Speech Separation in Assisted Living Environments
## 4 TF Masking  
Widely recognized components of source separation (SS) approaches include Estimation of Signal Parameters via Rotational Invariant Techniques (ESPRIT) [77], DUET [67], Tlme Frequency Ratio Of Mixture (TIFROM) [78] and the Direction Estimation of Mixing matrlX (DEMIX) [79] algorithms. DUET-ESPRIT (DESPRIT) [80] is an extension of ESPRIT. A family of power-weighted estimators was introduced in [71], exploiting the sparsity of sources in a specific transform domain [81], in this case the TF domain. The core principle of Independent Component Analysis (ICA)-based methods is to leverage the sparsity of sources in a specific transform domain [82]. The inherent sparsity of the sources implies that they are already separated, enabling SS to be performed using a binary or hard mask. The essence of ICA-based methods lies in the exploitation of the sparsity of the sources in the transform domain [82]. If the sources are sparse, this implies that they are already separated [68]. Speech demixing can then be achieved using a binary or hard mask, as shown in Equation (26). The mask is a weighting matrix that corresponds to a filter for the target source from the mixture [20]. Hard-masking techniques assume that a TF-bin belongs to one source. According to the authors in [67], if all the TF bins that correspond to a particular source are determined, then the source is already separated.  
\[\mathbf{M}[k,\tau]=\begin{cases}1,&\text{if the TF-bin}\in\mathbf{S}_{j}\\ 0,&\text{otherwise.}\end{cases} \tag{26}\]  
Figure 2: (**–d**) IPD distribution across frequencies: A voting algorithm is used in [5] to obtain the global maxima, \(\phi=2.5\) rads. The black, green, and orange dots represent \(\phi_{\text{max}}\), located in the region where \(\tan(\phi)\) rises/falls exponentially.  
The mask, \(\mathbf{M}[k,\tau]\), has TF bins set to 1 for all components belonging to the speaker of interest, \(\mathbf{S}_{j}\). All other TF bins in \(\mathbf{M}[k,\tau]\) belong to the background interference and are set to 0. The hard-mask AdRess algorithm was extended to handle anechoic scenarios in [83]. A soft-masking variant of AdRess, is known as Reformulated-AdRess (Redress) [84] because it tackles the hard-masking short-coming of AdRess. It was motivated by prior work on single channel feature learning in the presence of background noise [85]. It was adapted for anechoic conditions in [86]. These techniques are computationally efficient soft-masking techniques for SS. Unlike hard-masking methods, which use binary decisions, soft-masking approaches generate a ratio mask \(\mathbf{M}[k,\tau]\) with values ranging between \(0\leq\mathbf{M}[k,\tau]\leq 1\). This approach distributes the energy contributions in \(\mathbf{M}[k,\tau]\) across sources, based on the likelihood of their presence in the mixture [87]. TF-masking has seen application in Automatic Speech Recognition (ASR) systems because these systems involve a step to isolate the target from its acoustic background.
###### Abstract  
Background: Implementation of intraoperative 3D ultrasound (i3D US) into modern neuronavigational systems offers the possibility of live imaging and subsequent imaging updates. However, different modalities, image acquisition strategies, and timing of imaging influence object appearances. We analyzed the differences in object appearances in ultrasound (US) and magnetic resonance imaging (MRI) in 35 cases of brain metastasis, which were operated in a multimodal navigational setup after intraoperative computed tomography based (iCT) registration. Method: Registration accuracy was determined using the target registration error (TRE). Lesions segmented in preoperative magnetic resonance imaging (preMRI) and i3D US were compared focusing on object size, location, and similarity. Results: The mean and standard deviation (SD) of the TRE was \(0.84\pm 0.36\) mm. Objects were similar in size (mean \(\pm\) SD in preMRI: \(13.6\pm 16.0\) cm\({}^{3}\) vs. i3D US: \(13.5\pm 16.0\) cm\({}^{3}\)). The Dice coefficient was \(0.68\pm 0.22\) (mean \(\pm\) SD), the Hausdorff distance \(8.1\pm 2.9\) mm (mean \(\pm\) SD), and the Euclidean distance of the centers of gravity \(3.7\pm 2.5\) mm (mean \(\pm\) SD). Conclusion: i3D US clearly delineates tumor boundaries and allows live updating of imaging for compensation of brain shift, which can already be identified to a significant amount before dural opening.  
intraoperative ultrasound; intraoperative imaging; intraoperative computed tomography; brain metastasis; brain shift; brain distortion; neuronavigation
## 2 Materials and Methods
### Models  
#### 2.2.1 Baseline Models  
Recently, many architectures have been researched and proposed to identify optimal solutions for various user demands and requirements. At present, benchmarks using similar network configurations with combined data augmentations and multi-backbone architectures are not readily available. To address this, we designed, implemented, trained, tested, and analyzed a baseline configuration, which was compared to more complex combined configurations utilizing the proposed SMA and MB techniques. A single-backbone model served as the baseline for these comparisons, as it aligns closely with state-of-the-art models. MobileNet is one such approach, introduced by [PERSON] et al. [33], as an extension of the MobileNet architecture aimed at enhancing NN efficiency. Developed to address the limitations in training speed and parameter efficiency observed in its predecessor, MobileNet represents a significant step forward in optimizing deep learning models for practical use.  
MobileNetV3 Small is a variant of the MobileNetV3 architecture, part of the broader MobileNet family, specifically designed for efficient deployment on mobile and edge devices. Introduced by Google in 2019, MobileNetV3 builds upon the foundations of the earlier MobileNetV1 and V2 models, incorporating depthwise separable convolutions from MobileNetV2 and introducing newer techniques like Squeeze-and-Excitation (SE) modules. It also leverages the NetAdapt and MnasNet frameworks for automated architecture search and optimization.  
MobileNetV3 Small is tailored for devices with very limited computational power, such as smartphones, IoT devices, and other embedded systems. By integrating efficient architectural elements like SE modules and lightweight depthwise convolutions, it achieves high efficiency in both speed and power consumption. MobileNetV3 Small has been widely adopted in applications where real-time processing on low-power devices is crucial, including mobile apps, embedded systems, and Edge Computing scenarios. It delivers SOTA accuracy while being more computationally efficient than previous models.  
The introduction of progressive learning and improved scaling in MobileNetV3 has influenced the design of subsequent neural networks, driving advancements in efficient deep learning. MobileNetV3-Large achieves 3.2% higher accuracy on ImageNet classification while reducing latency by 20% compared to MobileNetV2. MobileNetV3-Small is 6.6% more accurate than a MobileNetV2 model with comparable latency. Additionally, MobileNetV3-Large offers over 25% faster detection at roughly the same accuracy as MobileNetV2 on COCO detection.  
Figure 2: The distribution of the average brightness in the (**a**) training dataset, (**b**) test dataset, and (**c**) modified test dataset.
## 3 Joint Power Control and Resource Allocation
### Joint Power Control and Resource Allocation  
Based on Algorithms 1 and 2, a stepped alternative optimization algorithm to solve OP1 is proposed. The idea of the algorithm is as follows:  
Step 1: Give an initial power allocation ratio.  
Step 2: Utilize Algorithm 2 to obtain the optimal power splitting ratio \(\rho^{(k+1)}\) and time allocation ratio \(t^{(k+1)}\).  
Step 3: Substitute the obtained \(\rho^{(k+1)}\) and \(t^{(k+1)}\) as the initial value of Algorithm 1 to obtain the power allocation ratio.  
Finally, the optimized parameter value of OP1 is satisfied by solving the above three steps iteratively. The proposed algorithm is described below.  
```
1:Initial \(P_{1}\), \(P_{2}\)
2:While \(l=1:\) loop
3:Utilize Algorithm 2 to solve \(\rho^{(k+1)}\) and \(t^{(k+1)}\)
4:With \(\rho=\rho^{(k+1)}\) and \(t=t^{(k+1)}\), utilize Algorithm 1 to solve \(\alpha^{*,l}\), \(P_{1}^{*,l}\), \(P_{2}^{*,l}\), \(P_{R}^{*,l}\)
5:Update \(\hat{\alpha}=\alpha^{*,l}\)
6:Update \(l=l+1\)
7:End
```  
**Algorithm 3** Joint power control and resource allocation scheme
# Wind Power Forecasting with Deep Learning Networks: Time-Series Forecasting +
## 3 Wind Power Forecasting Model with Temporal Convolutional Networks
### Architectural Design for TCN  
Inspired by [20], we incorporated the convolutional network architecture involved in casual convolution with residual connections to construct a stable TCN-based prediction model for 24-72 h wind power forecasting. Dilated convolution is used to select which values of the neurons from the previous layer contribute to those in the next layer. Thus, the dilated convolution operation captures both local and temporal information.  
The dilated convolution function, \(F(s)\), is provided by [21]  
\[F(s)=(x*_{d}f)(s)=\sum_{i=0}^{k-1}f(i)\cdot x_{s-d,i}, \tag{10}\]  
where \(x_{s}\) is the current input sequence data at time \(t\), \(d\) is the dilation factor parameter, and \(f\) is a filter of size \(k\).  
The TCN model can be defined as follows [23]:  
\[x_{t}^{l}=\sigma(W_{x}^{l}\cdot F_{d}\Big{(}x_{t}^{l-1}\Big{)}+b_{x}^{l}+x_{t} ^{l-1}), \tag{11}\]  
where \(F_{d}(.)\) is the dilated convolution function of \(d\) factor, \(x_{t}^{l}\) is the value of the neuron of the \(l\)-th layer at time \(t\), \(W_{x}^{l}\) and \(b_{x}^{l}\) are the weights and bias corresponding to the \(l\)-th layer,and \(\sigma\) is the activation function. The dilated residual block in our project is detailed in Figure 2.  
The system must use the residual block to the convolutional layers when deep and large TCNs are employed in order to achieve further stabilization. As presented in Figure 3, the residual connections constituted the addition of the data input to the output before applying the activation function; the residual block (\(d=16\)) is used between each layer in the TCN to accelerate convergence and enable the training of deeper models.
# Two-Dimensional Vortex Solitons in Spin-Orbit-Coupled Dipolar Bose-Einstein Condensates
## 5 Gap Solitons in Spinor SOC BEC
### Effects of Contact Nonlinearity  
The conclusions in Section 5.1 are drawn without contact nonlinearity, i.e., for \(g=0\) in Equation (14). The effects of contact nonlinearity can be investigated numerically. The system is found to have an intrinsic stable boundary \(g_{cr}\simeq\pm 0.25\) for isotropic and anisotropic GS, respectively. Thus, the isotropic GSs are stable if \(g<0.25\), and the anisotropic GSs are stable if \(g>-0.25\) (recall that \(g<0\) and \(g>0\) correspond to repulsive contact linearity for isotropic GS and anisotropic GS, respectively), which means the GSs are completely stable when arbitrarily strong contact nonlinearity is repulsive. On the other hand, due to the critical collapse effect of the contact nonlinearity, the norm of isotropic and anisotropic solitons cannot exceed certain limits, which is determined by the norm of the Townes' soliton \(N_{T}\simeq 5.85\), resulting in an exist condition for isotropic and anisotropic GS:  
\[\begin{array}{ll}g_{iso}&<N_{T}/(\Omega N)\\ g_{aniso}&>-N_{T}/(\Omega N)\end{array} \tag{16}\]  
Between these two limits, that is \(0.25<g_{iso}<N_{T}/(\Omega N)\) and \(-N_{T}/(\Omega N)<g_{aniso}<-0.25\), the GSs are unstable and spontaneously transform into breathers in propagation [60].  
Figure 10: Effective mass for isotropic (left) and anisotropic (right) 2D GS under kick in the \(x\) and \(y\) directions [60].
# Unified Ciphertext-Policy Weighted Attribute-Based Encryption for Sharing Data in Cloud Computing
## 3 Preliminaries
### Bilinear Mapping  
Let \(\mathcal{G}_{1}\) and \(\mathcal{G}_{2}\) be two cyclic groups of prime order \(p\) with the multiplication. Let \(g\) be a generator of \(\mathcal{G}_{1}\) and \(\widehat{e}\) be a bilinear mapping. Let \(\widehat{e}\colon\mathcal{G}_{1}\times\mathcal{G}_{1}\to\mathcal{G}_{2}\) be a bilinear mapping having the following properties:  
* Bilinearity: For any \(u,v\in\mathcal{G}_{1}\) and \(a,b\in\mathbb{Z}_{p}\), \(\mathbb{Z}_{p}\) is the set of integers \([0,p-1]\). It has \(\widehat{e}(u^{a},v^{b})=\widehat{e}(u,v)^{ab}\).
* Non-degeneracy: \(\widehat{e}(g,g)\
eq 1\).
* Computability: For all \(u,v\in\mathcal{G}_{1}\), there is an efficient computation \(\widehat{e}(u,v)\).
* Note that \(\widehat{e}\) is symmetric since \(\widehat{e}(u^{a},v^{b})=\widehat{e}(u,v)^{ab}=\widehat{e}(u^{b},v^{a})\).