esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 System Model Using SVD  
When considering the SVD technique applied to a frequency-selective MIMO channel, the resulting discrete-time block-oriented system is illustrated in Figure 4. The joint-PPE and ZF-PPE schemes are analyzed over the SVD-based \((2\times 2)\) MIMO system. The advantage of utilizing SVD is that the channel is converted into a diagonal matrix. It implies that the crosstalk components, \(\mathbf{H}_{n_{\mathrm{R}}\,n_{\mathrm{T}}}\) when \(n_{\mathrm{R}}\
eq n_{\mathrm{T}}\), will be completely mitigated. The modified frequency-selective channel \(\mathbf{H}\) is defined as  
\[\mathbf{H}=\mathbf{S}\cdot\mathbf{V}\cdot\mathbf{D}^{\mathrm{H}}\enspace, \tag{23}\]  
where \(\mathbf{V}\) contains the positive square roots of the eigenvalues of \(\mathbf{H}^{\mathrm{H}}\mathbf{H}\) matrix with \((n_{\mathrm{R}}\times n_{\mathrm{T}})\) and \(\mathbf{S}\) denotes the unitary matrix of \((n_{\mathrm{R}}\times n_{\mathrm{R}})\) dimensions. The matrix \(\mathbf{D}^{\mathrm{H}}\) of \((n_{\mathrm{T}}\times n_{\mathrm{T}})\) dimension represents the conjugate transpose (Hermitian operation H) of the unitary matrix \(\mathbf{D}\). The resulting numerical matrix after SVD is the real-valued diagonal matrix \(\mathbf{V}\), which has the following form  
\[\mathbf{V}=\begin{bmatrix}\mathbf{V}_{11}&\cdots&0\\ \vdots&\ddots&\vdots\\ 0&\cdots&\mathbf{V}_{n_{\mathrm{R}}\,n_{\mathrm{T}}}\end{bmatrix}\enspace. \tag{24}\]  
The overall transmission of the system using SVD is described as  
\[\mathbf{y}=\mathbf{F}\cdot\mathbf{V}\cdot\mathbf{P}\cdot\mathbf{s}+\mathbf{F}\cdot\mathbf{\bar{w}}\enspace. \tag{25}\]  
The implication of incorporating SVD is distinct eye-openings, which is formulated as  
\[U_{\mathrm{A},\mu}=\sqrt{\xi_{\mu}}\cdot U_{\mathrm{s},\mu}\enspace, \tag{26}\]with \(U_{\mathrm{s},\mu}\) denoting the half-vertical transmit amplitude at \(\mu\)-the MIMO input and \(\sqrt{\xi_{\mu}}\) is the positive root of the eigenvalue of \(\mathbf{H}^{\mathrm{H}}\mathbf{H}\). The quality of transmission is calculated as  
\[\varrho_{\mu}=\frac{U_{\mathrm{A},\mu}^{2}}{\tilde{P}_{\mathrm{n},\mu}}=\xi_{\mu }\frac{U_{\mathrm{s},\mu}^{2}}{\tilde{\varrho}_{\mu}\cdot P_{\mathrm{n}}}\enspace. \tag{27}\]  
The advantage of incorporating SVD in the system model is transforming the channel \(\mathbf{H}\) into non-interfering and independent transmission layers with unequal gains. Please note that neither the noise power nor the transmit power are enhanced. The resulting orthogonal system model of the \(\mu\)-th layer based on SVD is shown in Figure 5.
## 1 Introduction
Although it has been shown that deep learning-based path loss models can outperform conventional models, their prediction accuracy in practice may be severely limited by the size of the measurement data set that is required for training, due to the sluggish growth rate of mmWave system deployment in urban areas. Few-shot learning methods have been introduced to address the issues of small training data sets [13; 14; 15]. These methods, however, require a machine learning model to be trained on multiple tasks of disjoint support classes to be fine-tuned on a new set of classes. Measurement data sets that can be used as training data sets, however, cannot be organized into disjoint sets of multiple tasks, and hence the few-shot learning methods cannot be directly applied in mmWave path loss modeling. In this paper, we propose a novel training method called multi-way local attentive learning which allows for learning from multiple perspectives on the same set of training samples with local attention paid to each subset of the entire dataset. Here, the entire data set is partitioned into multiple minibatches constructed by a data set partitioning scheme. Many different partitions can be made with respect to different attributes of the sample data such that a larger amount of knowledge is extracted from the same data set. The main contributions of this paper can be summarized as follows:  
* We propose a 3D-LAMS algorithm that can generate 3D images that encode the radio propagation environment between a Tx and a Rx in dense urban areas. * We propose a 3D CNN path loss model that is expected to be able to extract more effectively the three-dimensional morphological information included in the 3D LAMS images which have a large impact on the amount of path loss. * Finally, we propose a novel learning method that addresses the overfitting issue due to the scarcity of measurement data for training a CNN model. This paper is organized as follows. Section 2 presents some of the previous works on mmWave path loss modeling based on deep learning. In Section 3 the proposed methods are explained in detail, followed by experimental results with some analyses given in Section 4. Finally, we conclude our discussion in Section 5.
## 2 Design of the Proposed Feed Array
### Single-Element Design
However, the tendency of the measurement is in agreement with the simulated result. The bore-sight gain and HPBW of the single element are 5.91 dBi and 78\({}^{\circ}\), respectively, and the radiation efficiency is 85%. Figure 3 provides the reflection coefficients according to the radius of the parasitic rings (\(r_{1}\)) and transmission line lengths (\(l_{p}\)). In Figure 3a, \(r_{1}\) varies from 10 mm to 20 mm with intervals of 2 mm, and in Figure 3b, \(l_{p}\) varies from 1 mm to 2 mm with intervals of 0.2 mm. As can be seen, the reflection coefficient is more sensitive to changes in \(r_{1}\), while the \(l_{p}\) has little effect on the reflection coefficient. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Solver type & Time domain solver \\ Mesh type & Hexahedral mesh \\ Mesh size & 1/15 \(\lambda\) \\ Boundary condition & Open boundary \\ Analysis time range & 0 to 9 ns \\ \hline \hline \end{tabular}
\end{table} 
Table 2: CST simulation environment. Figure 3: Reflection coefficients of the proposed single element according to \(r_{1}\) (**a**) and \(l_{p}\) (**b**). Figure 2: Reflection coefficients of the proposed single element.
## 3 Results and Discussion
### Description of the Collected Data
Similarly, the ZYX and Angle orientations are sliced in such way that makes crack generation under the tensile test easier. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Statistical Characteristics** & **XYZ** & **XYZ** & **ZXX** & **Angle** \\ \hline std & 0.727 & 0.679 & 1.096 & **2.311** \\ mean & 13.383 & 13.265 & 3.499 & 7.079 \\
25\% & 13.158 & 12.875 & 2.719 & 5.594 \\
50\% & 13.582 & 13.497 & 3.26 & 6.343 \\
75\% & 13.893 & 13.663 & 4.017 & 7.262 \\ max & 14.353 & 14.287 & 7.451 & 12.537 \\ min & 11.18 & 10.618 & **1.917** & 4.316 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Statistical data evaluation for Elongation at break for each orientation separately. Figure 4: Distribution of tensile modulus for different orientations based on kernel distribution estimation (The straight line (1650 MPa) corresponds to the value from the EOS Balanced datasheet). The discussion on why this wide distribution is observed for the experimental results for the ZYX orientation will be provided in another article and is out of the scope of this article. In this work, different intelligent methods have been applied to discover the most important features, and obtained results are presented below.
## 3 Results
### Analysis of Variance: ANOVA  
ANOVA is a statistical method for determining individual interactions among all control parameters in Taguchi designs. It is used herein to identify parameters that significantly affect image quality characteristics among thickness, mAs, kVp, filter type, and FOV. A parameter was considered significant if the confidence level exceeded 95% [14]. Table 4 presents the ANOVA results for the five parameters. F\({}_{0.05}\) is provided for reference, since it represents a statistical confidence of 95%. ANOVA results revealed that most parameters significantly affected the values at a significance level of 0.05. Since they had higher F-values than other parameters, (A) thickness, (C) current voltage (kVp), (D) filter type, and (E) Field of View (FOV) were deemed to be significant primarily because their associated confidence levels exceeded 95%.  
Thickness was the most influential parameter, with an F-value of 224.97. Table 4 shows that the F-values of kVp, field size, and filter type were 188.35, 71.14, and 31.80, respectively. Percentage contribution is a statistical parameter that is used to compare the effects of parameters on an output. mAs was deemed insignificant because its associated confidence level was 80.25% less than those of other parameters. Therefore, thickness, kVp, filter  
Figure 3: Fish-bone-plots of (**A**) the average grades, (**B**) the standard deviations of the grades and (C) S/N value against the parameters in the 128-slice brain CT experiments. A1 (2.5 mm thickness), B3 (400 mAs), C3 (140 kVp), D1 (sharp filter), and E1 (200 mm\({}^{2}\)) yield the highest S/N among the 18 experiments.  
type, and FOV corresponded to the confidence level that is required for clinical diagnosis. The only minor parameter was mAs. The confidence level associated with the parameter \"other\" was 97.62%, which is close to 99%, revealing that other parameters affect the quality of images obtained in a 128-slice CT brain examination [6; 7; 8]. In the Taguchi design, the experiment that yields the highest S/N ratio is the best of the experiments performed but not necessarily the optimal one.
## 2 Model-Based Analysis of MSK Function
### Muscle Activation and Force Estimation  
Following inverse dynamics, individual muscle activations and forces can be estimated which balance the joint moments. Muscle activations are typically estimated using optimisation-based methods that use mathematical criteria to divide the required muscle forces at a joint between all muscles spanning that specific joint. The summed muscle forces must satisfy the constraints of the system (i.e., produce the required muscle moments to balance the joint moments subject to an objective function). Typical objective functions used within the literature are the minimisation of muscle activations to the _nth_, typically the second, power. Muscle activations and forces can be solved using various optimisation approaches, most commonly static optimization [33; 34; 35]. More complex implementations include dynamic optimisation [36; 37; 38; 34] which can account for more complex muscle tendon dynamics. Another method is computed muscle control [39; 40] which couples static optimisation with feedforward and feedback controls to approximate a set of desired kinematics (e.g., from inverse kinematics) and simultaneously estimate both muscle activations and forces. These optimisation methods can be supplemented with experimentally acquired electromyography (EMG) data to constrain estimated muscle activations [41; 42]. Calculated muscle activation and force patterns allow researchers and clinicians to imply any changes in muscle function due to different pathologies, or locomotion strategies.
# Allocating Small Transporters to Large Jobs
## 5 The Saturated Transporter Allocation Problem
### Aggregated MIP Formulation  
In the mentioned real-life applications, it is rarely the case that transporter sizes vary arbitrarily. Rather, some small number of different models or variants exist that come with different capacities. In that case, it is reasonable to exploit this by grouping transporters with the same filling time. In the following, we identify each filling time with a transporter model and rewrite \((\mathrm{SMIP})\) after grouping the transporters by their model.  
Suppose that the set of transporter models is denoted by \(\mathcal{M}\), and for each \(m\in\mathcal{M}\), there are \(M(m)\) many transporters of model \(m\). Furthermore, assume the filling time of transporter model \(m\) is denoted by \(\kappa(m)\). Introduce variables \(\overline{Y}_{m,j}\), denoting the number of transporters of model \(m\) assigned to job \(j\). Additionally, the binary variable \(\overline{X}_{m,j}\) is equal to 1 if and only if at least one transporter of model \(m\) is assigned to job \(j\).  
We denote by \(\mathbb{N}^{+}\) the set of natural numbers, including 0. Then, the aggregated mixed integer program \((\mathrm{SMIP}g)\) is as follows:  
\[(\mathrm{SMIP}g):\] \[\min_{\overline{X},\overline{Y}}\sum_{m\in\mathcal{M}}\sum_{j\in \mathcal{J}}\overline{Y}_{m,j} \tag{10}\] \[\mathrm{s.t.}\sum_{j\in\mathcal{J}}\overline{Y}_{m,j}\leq M(m), \forall m\in\mathcal{M}\] (11) \[\overline{X}_{m,j}\leq\overline{Y}_{m,j}, \forall m\in\mathcal{M},\forall j\in\mathcal{J}\] (12) \[\overline{Y}_{m,j}\leq M(m)\cdot\overline{X}_{m,j}, \forall m\in\mathcal{M},\forall j\in\mathcal{J}\] (13) \[\sum_{m^{\prime}\in\mathcal{M}}\left[\overline{Y}_{m^{\prime},j} \cdot\kappa(m^{\prime})\right]-\overline{X}_{m,j}\cdot\kappa(m)\geq\delta(j), \forall m\in\mathcal{M},\forall j\in\mathcal{J}\] (14) \[\overline{X}_{m,j}\in\{0,1\}, \forall m\in\mathcal{M},\forall j\in\mathcal{J}\] (15) \[\overline{Y}_{m,j}\in\mathbb{N}^{+}, \forall m\in\mathcal{M},\forall j\in\mathcal{J}. \tag{16}\]  
Note that the variables \(\overline{X}_{m,j}\) are only used in the constraints from Equations (14). Since these constraints are more restrictive when \(\overline{X}_{m,j}=1\), the solver will by default set \(\overline{X}_{m,j}\) to 0.  
Therefore, we do not need to fix an upper bound for variables \(\overline{X}_{m,j}=1\). Hence, we could omit Equation (12), but we leave it for the sake of clarity.  
This new program \((\mathrm{sMIP}g)\) has \(2\cdot N_{M}\cdot N_{J}\) variables and \(N_{M}+3\cdot N_{M}\cdot N_{J}\) constraints, whereas \((\mathrm{sMIP})\) has \(N_{R}\cdot N_{J}\) variables and \(N_{R}+N_{R}\cdot N_{J}\). Therefore, the size of the new program \((\mathrm{sMIP}g)\) is smaller than the size of \((\mathrm{sMIP})\) as soon as there are at least three transporters per model.  
After solving \((\mathrm{sMIP}g)\), we still need to arbitrarily assign the transporters of each model to the jobs, which can be done in \(\mathcal{O}(N_{R}+N_{J})\) time.
# Enhancement of English-Bengali Machine Translation Leveraging Back-Translation
## 2 Related Work
### Augmentation of Neural Machine Translation
#### 2.2.5 Augmentation Approach in Our Study  
Since NMT is data-driven, the aforementioned augmentation approaches are very effective in enhancing machine translation accuracy and quality. The concern is to know which approach is suitable for our study. We observe that, for low-resource language pairs, researchers either augment the parallel corpus by pseudo-sentence pairs or exploit the language data other than the parallel corpus. Back translation and forward translation are used in many research papers, combined with other modifications. In our research, we use back-translation on an English-Bengali translation model and explore the different decoding methods (sampling strategies) in back-translation, which is rare in previous research papers. Notably, top-\(k\) random sampling[35] and random sampling with temperature \(T\)[36] are the most commonly used and optimal decoding methods and are very effective in translation, as evaluated by the authors in [35; 36]. Now, it comes to why we do not employ the other augmentation approaches mentioned in this literature. To this end, we start a comparative analysis with Pre-training on Monolingual Data. We observe that it is mostly used (also more effective) in masked language modeling and next-sentence prediction than machine translation. Next, it is pivot language technique. As stated earlier, it is to employ rich resource language pairs that are different from the source and the target, such as employing English as the pivot between German and French by training a German-English model and an English-French model. Thus, it is not effective if we do not have rich datasets for two different pairs with the pivot language as a medium. The last approach is leveraging multi-modal data. In our domain, it is difficult to get such datasets, and it is also resource-demanding since we need to conduct training for the language model and vision model. To this end, we find that back-translation with sampling strategies can help augment our machine translation study respectfully.
## 2 Proposed STPA Configuration for JCAS, Its Design Pipeline, and Performance
### Proposed STPA and Its Design Pipeline
#### 2.2.3 Expanded Beam Pattern  
The radiation field of an array with isotropically radiating elements in \((u,v)\)-space can be obtained by:  
\[f(u,v)=\frac{1}{N}\sum_{n=1}^{N}\alpha_{n}e^{j\Omega_{n}}e^{j\mathbb{k}(ux_{n}+ y_{n})},u=\sin\theta\cos\phi,v=\sin\theta\sin\phi \tag{1}\]  
where \(N\) is the number of radiating elements, and \(\alpha_{n}\) and \(\Omega_{n}\) are the amplitude and phase of excitation, respectively, \(k=2\pi/\lambda\) is the wave-number at the wavelength \(\lambda\), and \(x_{n}\) and \(y_{n}\) are the positions of antennas in Cartesian coordinates. The radiation field in (1) can scan toward the angle \(\Delta_{g}=(u_{g},v_{g})\) by the proper weighting of amplitudes and phases. In the case of conventional beamforming, which is based on only phase excitation in the elements, the weights are \(\alpha_{n}=1,\Omega_{n}=-j\mathbb{k}(u_{g}x_{n}+v_{g}y_{n})\). The radiation pattern is then as follows:  
\[F(u,v;u_{g},v_{g})=\frac{1}{N^{2}}\left|\sum_{n=1}^{N}e^{j\mathbb{k}\left((u- u_{g})x_{n}+(v-v_{g})y_{n}\right)}\right|^{2} \tag{2}\]  
Figure 3: The flowchart of designing STPA.  
In order to assure that the designed STPA bears the desired SLL and beamwidth in all scanning directions \(\Delta_{g}\)s, the expanded beam pattern (EBP) is employed, as in Ref. [10]:  
\[F_{\zeta}(\tilde{u},\tilde{v})=\frac{1}{N^{2}}\bigg{|}\sum_{n=1}^{N}e^{ik\zeta( \tilde{u}x_{n}+\phi y_{n})}\bigg{|}^{2},\zeta=1+\sin(\theta_{max}) \tag{3}\]  
where \(\tilde{u}=u-u_{g}\), \(\tilde{v}=v-v_{g}\). The scanning of the beam in the whole \(0^{\circ}\leq\theta\leq 90^{\circ}\) without grating lobes is not practical, therefore the desired angle of view is \(0^{\circ}\leq\theta\leq\theta_{max}\) where \(\theta_{max}\) is the maximum of angle \(\theta\). The SLL for \(F_{\zeta}(\tilde{u},\tilde{v})\) is studied to ensure an appropriate SLL in all scanning angles.
# Hub-and-Spoke Network Design Considering Congestion and Flow-Based Cost Function
## 3 Mathematical Formulation of the Model
### Model with Flow-Based Economies of Scale Inter-Hub Transportation Cost  
In the literature, there are not many research articles that model the inter-hub transportation cost function as flow-based. Among the initial works, [PERSON] and [PERSON] [48] address this issue and model the cost function based on the traversed flow. This model is later extended by [PERSON] et al. [49], who also develop a tailored Benders decomposition algorithm to solve the developed model.  
This paper also considers a flow-based cost function for hub links, as a more precise representation of the real-world case than a constant cost function. For this purpose, we consider a piecewise linear function, in which the higher flow volume is associated with a higher discount on transportation costs. This is due to the modular capacity of different transporters. Suppose a type of aircraft with capacity \"C1\" and another type with capacity \"C2\" \"C1\". Until the number of passengers does not reach \"C1\", the type 1 aircraft is the best option, but once the number of passengers exceeds \"b\", the type 1 aircraft can be substituted with type 2, with a higher capacity. It is not unrealistic to assume that the per-person cost of aircraft type 1 is higher than the per-person cost for aircraft type 2. Therefore, we consider a piecewise linear function to represent the dependency of the cost function on the flow of each link. This is formulated in (28). Therefore, the new model HSLPT-03 is as follows:  
\[\begin{array}{c}min\sum\limits_{i}\sum\limits_{j\
eq i}\sum\limits_{k}\sum \limits_{l\
eq k}\alpha_{kl}w_{ij}C_{kl}x_{ijkl}+\sum\limits_{i}\sum\limits_{j \
eq i}\sum\limits_{k\
eq i,j}w_{ij}C_{ik}a_{ijk}\\ \hskip 14.226378 pt+\sum\limits_{i}\sum\limits_{j\
eq i}\sum\limits_{k\
eq i,j}w _{ij}C_{kj}b_{ijk}+\sum\limits_{i}\sum\limits_{j\
eq i}w_{ij}C_{ij}e_{ij}\\ \hskip 14.226378 pt+\sum\limits_{k}(F_{k}h_{k})+\sum\limits_{k}D_{k}s_{k}+\sum \limits_{k}\sum\limits_{l>k}I_{lq}y_{kl}+B*\left(\sum\limits_{m,n}P_{mn}-\sum \limits_{i,j}w_{ij}\right)\end{array} \tag{27}\]  
Subject to (4)-(20)  
\[\alpha_{kl}=\begin{cases}\alpha_{kl}^{1}&\sum\limits_{i}\sum\limits_{j\
eq i} w_{ij}x_{ijkl}<b\\ \alpha_{kl}^{2}&b<\sum\limits_{i}\sum\limits_{j\
eq i}w_{ij}x_{ijkl}\end{cases} \tag{28}\]  
In the objective function, previously used, the fixed value of \(\alpha\) is changed to \(\alpha_{kl}\) to represent the flow-based cost function coefficient.
## 4 Experimental Results
### Ion Arm  
Since there is a distinct peak in the O\({}^{+}\) kinetic energy spectrum when O\({}_{2}\) is exposed to femtosecond laser [29], we tested the momentum imaging capabilities of the ion arm with a Coherent femtosecond laser (800 nm, 25 fs, 1 kHz). The 11 mm diameter laser beam was back-focused onto the supersonic molecular beam by a mirror of 100 mm focal length. Our measurements are record at 1.5 \(\times\) 10\({}^{14}\) W/cm\({}^{2}\).  
As described earlier, we set the working condition of the electron arm first, then find the voltage setting of the ion arm to focus the ions and enlarge the ions image in SIMION simulation. For the test of the ion arm, we repeat this measurement at different voltage settings of the electron arm. Figure 5 shows the raw images of O\({}^{+}\) with the kinetic energy of 5.6 eV at different settings of the electron arm: (a): 100 eV, (b): 300 eV, (c): 600 eV, respectively. The radius of the ions image decreases gradually from (a) 18 mm, (b) 12.5 mm,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Author** & \(\mathbf{e}\)**KE [eV]** & \(\mathbf{\Delta E/E}\) & **Year** & **Reference** \\ \hline [PERSON] & 65 & 3.00\% & 2017 & [24] \\ [PERSON] & 100 & 3.80\% & 2019 & [20] \\ [PERSON] & 60 & 1.00\% & 2014 & [23] \\ [PERSON] & 100 & 1.80\% & 2009 & [19] \\ [PERSON] & 300 & 4.00\% & 2014 & [22] \\ This work & 500 & 1.50\% & 2021 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of the performance of VMI spectrometers tested by measurements.  
Figure 4: (a) The raw electron image of Ar ionized at the photon energy of 750 eV. (b) The slice at \(p_{z}=0\) of the 3D-momentum distribution after inverse Abel transformation. (c) The photoelectron spectra were obtained by angular integration of the complete 3D momentum distribution at different photon energies from 350 to 750 eV. (d) Electron kinetic energy resolution \(\Delta E/E\) and \(\Delta E\) as a function of electron kinetic energy.  
to (c) 10 mm. In this way, It is possible to monitor the alignment of molecule while taking the electron image in the future.  
Figure 6a shows the inverted momentum images (slice at \(p_{z}=0\)) of O\({}^{+}\) ions obtained by applying an inverse Abel transformation with the [PERSON] method [30] to the data in Figure 5a. The corresponding energy spectrum is obtained by integrating the momentum distribution over the polar angle between \(60^{\circ}\) and \(120^{\circ}\) with respect to the laser polarization, as shown in Figure 6b. Finally, the relative energy resolution of 4% is reached at the ion kinetic energy of 5.6 eV when the electron arm is set for 100 eV. In comparison, [PERSON] et al. reached \(\Delta E/E=5\)% at \(E_{ion}=10\) eV [20].