esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theory  
The schematic of a symmetric three-layered beam with a soft-core is illustrated in Figure 1, where \(h\) is the total height of the cross section, and \(h_{f}\) and \(h_{c}\) are the thicknesses of the face and core layers, respectively. The Euler-Bernoulli theory was used in the face layer, and it considers only the shear deformation in the core layer, owing to the bending of face layers. The concept of linear elasticity, which considers only the deformation in the X-Z plane, is employed. A core layer made of a lightweight material, whose mass can be ignored in comparison to those of the face layers, is assumed. The strain energies of the top and bottom faces can be deduced as follows [26; 27]:  
\[\mathcal{U}_{f}=\mathcal{U}_{f1}+\mathcal{U}_{f2}=\frac{1}{2}E_{f}A_{f}\int_{ 0}^{L}2\bigg{(}\frac{\partial u(x,t)}{\partial x}\bigg{)}^{2}dx+\frac{1}{2}E_{ f}I_{f}\int_{0}^{L}2\bigg{(}\frac{\partial^{2}w(x,t)}{\partial x^{2}}\bigg{)}^{2}dx \tag{1}\]  
where \(E_{f}\), \(A_{f}\), and \(I_{f}\) are the elastic modulus, area, and moment of inertia of the face layer, respectively. \(E_{f}A_{f}\) and \(E_{f}I_{f}\) are the extensional and bending rigidities in the face layer, respectively. \(L\) is the total beam length. In addition, \(\mathcal{U}_{f1}\) is the axial strain energy, and \(\mathcal{U}_{f2}\) is the bending strain energy. \(u(x,t)\) and \(w(x,t)\) are the axial and bending displacements, respectively. The shear strain energy of the core is as follows [26; 27]:  
\[\mathcal{U}_{c}=\frac{1}{2}k_{c}A_{c}G_{c}\int_{0}^{L}\bigg{\{}\frac{2}{h_{c} }u(x,t)+\bigg{(}1+\frac{h_{f}}{h_{c}}\bigg{)}\frac{\partial w(x,t)}{\partial x }\bigg{\}}^{2}dx, \tag{2}\]  
where \(k_{c}\), \(A_{c}\), and \(G_{c}\) are the shear correlation factor, area, and shear modulus of the core layer, respectively. Therefore, the total normal strain energy can be expressed by  
\[\mathcal{U}=\mathcal{U}_{f}+\mathcal{U}_{c} \tag{3}\]  
The kinetic energy of a three-layered beam with a lightweight core can be formulated as  
\[\mathcal{T}=\frac{1}{2}\int_{0}^{L}2m_{f}\bigg{(}\frac{\partial u(x,t)}{ \partial t}\bigg{)}^{2}dx+\frac{1}{2}\int_{0}^{L}2m_{f}\bigg{(}\frac{\partial w (x,t)}{\partial t}\bigg{)}^{2}dx, \tag{4}\]  
where \(m_{f}\) is the mass per unit length of each face layer [26; 27]. By employing [PERSON]'s principle,  
\[\delta\int_{t_{1}}^{t_{2}}(\mathcal{T}-\mathcal{U})dt=0, \tag{5}\]  
Figure 1: Schematic of a symmetric three-layered beam with a soft core.
## 3 Proposed QoS_mPCD Scheme Procedure
### Step 2: User Association and Resource Allocation  
Users can be associated with BS \(j\) using the user's contextual information. The association based on a minimum path loss is the most optimized approach. The deployment of the mPC is very helpful in supporting the PS user in coexisting networks. Our main objective is to provide the best resources to high-priority users, so it is a very efficient approach, i.e., using the context information of the users, to serve PS users on a priority basis, and it will also boost the performance of the overall network, because the proposed scheme is dealing with users according to priority levels. Moreover, if more PS users lie on the cell edge, they can be offloaded to the mPC, and hence, more users can be associated with the mPC. Equation (9) applies user priority constraints so users can be associated with the nearest BS. The resources are allocated to public safety users in accordance with their service priority levels [13]. After allocating resources to public safety users as a high priority, resources are distributed to these public safety users to enhance the data rate according to proportional fair scheduling. That is, all resources are first secured for public safety users with high-priority access.
# Neural Models for Measuring Confidence on Interactive Machine Translation Systems
## 4 Confidence Measures
### BiLingual Evaluation Understudy (Bleu)  
Bleu [22] was one of the first metrics that appeared in the MT field. Like many others, it tries to copy how professionals consider a group of aspects when evaluating translations. Between these aspects, Bleu attempts to reproduce the adequacy, fidelity, and fluency  
Figure 1: Example of a conventional prefix-based IPNMT session with CMs to translate a sentence from Spanish to English. At iteration 1, the user checks a word mistakenly classified as incorrect. At iteration 2, the word “a” is corrected and the system generates a new hypothesis. Non-validated hypotheses are displayed in _italics_, and accepted prefixes are printed in normal font.  
judgement. The main characteristic of Bleu, and the feature that differentiates it from the others metrics selected, is the use of multiple n-grams orders. Bleu computes the n-gram precision at each level individually, combining them and multiplying the final score by a brevity penalty. Bleu needs this brevity penalty to penalize short sentences with high precision. To calculate the intermediate rewards we wanted this penalty to remain the same so we use the next technique.  
When we proceed to calculate the metric score \(R(\hat{y}_{t},x)\) of the translation, we substitute each word from the segment \(y_{t+1...T}\) with a special token <_null_>. In this way, the translation length does not vary when we calculate the reward \(R(\hat{y}_{t},x)\) for each value of \(t\).  
We can expect good results from the CM trained with the Bleu word scores. These scores capture the quality of each word pretty well, as they take into account the correctness of the word and its context. In addition, it gives each word the same weight, unlike the metric Chr-F whose use of character n-grams gives more significant weight to the longer words.
# Spectral Line Reflectance and Fluorescence Imaging Device for Skin Diagnostics
## 3 Design of the Prototype Device
Then, the spectral un-mixing is done using linear operation:  
\[I=A\ {}^{*}I_{s}{}^{-1} \tag{2}\]  
where \(I_{s}\)(\(x\),\(y\),\(\lambda\)) is a matrix consisting of intensity values of the spectral image at the \(xy\) pixel and the given wavelength \(\lambda\), and \(I\)(\(x\),\(y\),\(\lambda\)) is spectrally-unmixed image; \(A\)(\(\lambda\)) is the correction matrix calculated from the camera photo-sensitivity curves. Next, flat-field correction for each spectral image is performed, using a special image filtering technique. The idea is to compensate for possible uneven illumination caused by a deformed skin surface caused by the device's cap pressure. To find the non-evenness function of surface illumination, a 2D median filter is applied (kernel size \(128\times 128\) pixels); then, the spectral image is divided by this function. The normalized reflectance image is calculated as:  
\[R=(I\ -I_{d})/(I_{f}\ -I_{d}) \tag{3}\]  
where \(R\)(\(x\),\(y\)) is a reflectance at each \(xy\) pixel of the spectral image, \(I_{d}\) is an image measured in dark conditions (the image values depends on the camera gain, exposure, and temperature), and _If(\(x\),\(y\))_ is the filtered image. The \(I_{d}\) spectral images are acquired before or after each measurement. Finally, the image outside the region of interest is masked with dark pixels to highlight the circular image  
Figure 4: Spectral sensitivities of the used four-band image sensor (manufacturer’s data); dotted lines represent the working wavelengths. area on the monitor screen. The processing of MS data is done by custom designed Matlab software. The software performs off-line processing of RAW image data using the algorithms described above. The user's interface (Figure 5) is designed for live showing of skin target and the recorded/stored skin images. The device can be managed by means of the touchscreen display. The software allows the manual adjustment of device parameters such as the intensity of light sources, camera gain, and exposure. Before each measurement, specific patient data and identification of the examined skin malformation are stored. The two image sets (four spectral line images and the autofluorescence image) are captured within two seconds. All recorded images are transmitted to the monitor screen. The measurement data also can be transferred to the remote server computer for more detailed analysis. Outlook of the prototype device is presented in Figure 6.
###### Abstract  
(1) Background: Metal-on-metal (MoM) total hip arthroplasty (THA) and hip resurfacing arthroplasty (HRA) was presumed to provide superior functional outcomes compared to metal-on-polyethylene (MoP) THA. (2) Methods: We compared muscle mass, power, step test asymmetry, and patient-reported outcomes between MoM THA/HRA and MoP THA. A total of 51 MoM THA/HRAs and 23 MoP THAs participated in the cross-sectional study at a mean of 6.5 (2.4-12.5) years postoperatively. Muscle mass was measured by Dual energy X-ray Absorption (DXA) scans and muscle power in a Leg Extensor Power Rig. Step test asymmetry was obtained with an Inertial Measurement Unit (IMU). The patients completed the Harris Hip Score (HHS) and the Copenhagen Hip and Groin Outcome Score (HAGOS). (3) Results: The MoM THA/HRA group had a greater inter-limb difference in hip muscle mass compared to the MoP THA group (\(p\) = 0.02). Other inter-limb differences in muscle mass and power were similar (\(p\) > 0.05). Muscle mass of the thigh and calf area and muscle power in both legs were higher in MoM THA/HRA compared to MoP THA (\(p\) < 0.009). Step test time asymmetry when ascending was lower in MoM THA/HRA compared to MoP THA (\(p\) = 0.03). HHS and HAGOS scores were similar between groups (\(p\) > 0.05). (4) Conclusion: Overall, we could not verify the hypothesis that MoM THA/HRA contributes to superior functional outcomes compared to MoP THA.  
Keywords:metal-on-metal hip arthroplasty; metal-on-polyethylene hip arthroplasty; DXA scan; muscle mass; muscle power; functional outcome test +
Footnote †: journal: Applied Sciences
## 1 Introduction
reported on the relationship of adsorption isotherms, the dependence of permeability on temperature, and the change in porosity due to dehydration [27; 28]. [PERSON] et al. simulated the combined effect of heat and moisture diffusion on transient stress [29]. [PERSON] et al. studied mass transport influenced by moisture and temperature gradients. As a result, they found that the presence of temperature gradients could induce internal pressure gradients in porous media with sufficiently low permeability [30]. Many researchers have studied a coupled thermo-chemo-mechanical model to simulate critical phenomena in concrete, in terms of hydration behavior in early age, creep, shrinkage, cracking, and so on. These models have been constructed with a thermodynamic basis, associated with the reactive porous media theory and microprestress-solidifications theory [31; 32]. Recently, a 3D chemo-hygro-thermo mechanical model was proposed to predict the degradation and reinforcement degpassivation in a bridge exposed to an aggressive environment. The results underlined the need for incorporating damage and cracking into service life prediction for reliable long-term durability assessments [33]. [PERSON] et al. developed a 2D finite element computational program, CONDUR, considering the coupled effects of moisture, heat, and carbon dioxide transport in concrete. The proposed finite element model and program can handle all aspects, including cracks, prescribed initial and boundary conditions, and related material nonlinearities [34]. In addition, [PERSON] et al. investigated that increasing temperature accelerates the chloride penetration rate in concrete and proposed coupling parameters for chloride penetration concerning temperature effects [35]. In this study, we aimed to explain and propose prediction methods for the penetration phenomena of chloride ions and other ions (Na\({}^{+}\), Ca\({}^{2+}\), K\({}^{+}\), OH\({}^{-}\)) diffusing into partially saturated concrete based on mathematical models and numerical analysis techniques. To this end, we intended to derive the interactions of chloride ions and ions (Na\({}^{+}\), K\({}^{+}\), OH\({}^{-}\)) in the concrete pore solution based on the Nernst-Planck equation. This mathematical model utilized the FEM method and the Parallel algorithm to derive the results, and the reliability of the model was verified using experimental data.
# Three Rapid Methods for Averaging GPS Segments
## 3 Three Rapid Methods
### Time Complexity  
The time complexity of the method is summarized in Table 1. Considering the number of trajectories in the set \(k\) and the maximum number of points of a trajectory \(n\), RapSeg-A takes O(\(k\)) per set. RapSeg-B takes O(\(n^{2}k^{2}\)), as it has the calculation of Medoid as the bottleneck. The actual processing time, therefore, depends on how many sets are processed by the curvy model. In our case, most sets are processed using the linear model (\(p=93\%\) for training data; \(p=95\%\) for test data), so this method is not as slow as it might first look. RapSeg-C takes O(\(Tk\)), where \(T\) is the number of times; we need to calculate M. In our case, \(T=4.00\) for training data and \(T=4.33\) for test data, on average. In the worst case, \(T\) can theoretically equal to \(n\), in the case where every point in the curve is a turning point. In summary, RapSeg-A is the fastest, and RapSeg-B is the slowest.  
Figure 11: Outlier removal.
## 3 Results
### Correlation Coefficient  
Our analysis shows that all the signals in the different categories seem to be strongly correlated (Figure 2). Moreover, there are no significant differences between the average correlation coefficient of spectral entropy among electrodes in the resting-state test for PD subjects compared to healthy controls (\(p\) = 0.09). Nevertheless, there are significant differences found in PD subjects in the motor activation test since there is an increase of the average correlation coefficient (right hand \(p\) < 0.001; left hand \(p\) < 0.001) and non-significant changes in healthy subjects (right hand \(p\) = 0.1; left hand \(p\) = 0.2).
# Study on Vibration Effects and Optimal Delay Time for Tunnel Cut-Blasting Beneath Existing Railways
## 2 Numerical Simulation of Blasting Vibration of Tunnel Crossing Under the Existing Railway
### Numerical Model and Parameters
The P-V relationship of the JWL equation is shown in Equation (1), and the parameters are selected in Table 3.  
\[\mathrm{P}=\mathrm{A}\left(1-\frac{\omega}{\mathrm{R}_{1}\mathrm{V}}\right) \mathrm{e}^{-\mathrm{R}_{1}\mathrm{V}}+\mathrm{B}\left(1-\frac{\omega}{\mathrm{ R}_{2}\mathrm{V}}\right)\mathrm{e}^{-\mathrm{R}_{2}\mathrm{V}}+\frac{\omega\mathrm{E}_{0}}{ \mathrm{V}} \tag{1}\]  
where A, B, R\({}_{1}\), and R\({}_{2}\), \(\omega\) are the parameters of the explosive material; P is the pressure; V is the relative volume; and E\({}_{0}\) is the initial specific internal energy.
## 3 Analysis of Experimental Results
### Characterization of Acoustic Emission Waveforms  
The acoustic emission system can monitor the elastic waves during the creation of fractures inside the specimen and the corresponding energy change during cyclic disturbance loading. The key points N1 and N2 are extracted according to the acoustic emission energy release characteristics. N1 is the initial period of specimen dynamic disturbance loading, and N2 is when the damage to the specimen occurs. The acoustic emission characteristics at key points N1 and N2 are extracted and the signals are transformed from the time domain to the frequency domain by using the fast Fourier transform to obtain the principal frequency eigenvalues. MATLAB is used to obtain a three-dimensional plot of amplitude-frequency-time, and then the effect of the dynamic disturbance on the acoustic emission characteristics of the siltstone is analyzed.  
Figure 13 shows the three-dimensional plot of the principal frequency characteristics obtained by the Fourier transform of the acoustic emission key points N1 and N2 of the specimens under different stresses, and the sharp points corresponding to the amplitude are the principal frequency eigenvalues. In different sets of cyclic disturbance experiments, the specimen changes from the high principal frequency at key point N1 to the low principal frequency at key point N2, and the principal frequency eigenvalues are reduced to a large extent. The high-frequency eigenvalues of the waves indicate small-scale damage to the specimen, and the low-frequency eigenvalues of the waves indicate large-scale damage to the specimen. The principal frequency eigenvalues of key point N1 ranges from 172 KHz to 203 KHz. The initial average stresses are 10.96 MPa and 10.05 MPa, corresponding to the lowest and highest principal frequency eigenvalues, respectively. The principal frequency eigenvalues of key point N2 ranges from 73 KHz to 98 KHz. The initial average stresses are 9.13 MPa and 6.39 MPa, corresponding to the lowest and highest principal frequency eigenvalues, respectively. The principal frequency values of key points of the siltstone specimens under different average stresses are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Key Point** & **5.48 MPa** & **6.39 MPa** & **7.31 MPa** & **9.13 MPa** & **10.05 MPa** & **10.96 MPa** & **11.87 MPa** \\ \hline N1 & 201 KHz & 185 KHz & 181 KHz & 179 KHz & 203 KHz & 172 KHz & 178 KHz \\ N2 & 87 KHz & 98 KHz & 85 KHz & 73 KHz & 82 KHz & 79 KHz & 87 KHz \\ \hline \end{tabular}
\end{table}
Table 2: Principal frequency values of key points in siltstone specimens at different initial average stresses.  
Figure 12: Diagram showing the relationship between different initial average stress percentages and maximum load.  
Figure 13: _Cont._
## 4 Experimental Results
### Experimental Study 1
#### 4.1.1 Results Obtained Using the Custom-Built CNN Model  
The proposed model architecture processes input images of 50 \(\times\) 50 \(\times\) 3 pixels with three color channels (RGB). The model was compiled using the binary cross entropy loss function (BCE) and trained using the Adam optimization algorithm. The BCE loss function aims to minimize the error by measuring the difference between the probabilities predicted by the model and the actual labels (IDC (1): cancer and IDC (0): non-cancer). BCE, which is compatible with the sigmoid activation function, is a standard method, especially in binary classification problems, and plays an effective role in increasing the prediction accuracy of the model. Training was conducted with 50 epochs, a learning rate of 0.001, and a batch size of 200. To prevent overfitting, an early stopping mechanism was employed, halting the training process if no improvement in validation error was observed over 10 consecutive epochs. The model achieved an accuracy of 93.80%, a precision of 84.20%, a recall of 74.76%, and an F1 score of 79.20%. On the validation dataset, the model yielded an accuracy of 93.69%. Figure 5 shows the accuracy and loss graphs of the model, respectively.
## 3 Results and Discussion
### Correlation Significance Analysis  
The existence of statistically significant relations between stormwater HM concentrations and catchment characteristics and weather conditions may deliver information on HM sources (e.g., pollution from transport, atmospheric deposition, etc.) [43; 44]. Additionally, strong correlation of individual HMs (Zn-Cu, Cr-Cu, Pb-Ni, Cd-Ni, Pb-Cd and Cr-Zn) may indicate common sources [45]. Analysis of data in Table 3 suggests that HM present in stormwater from the three catchments originate in either traffic, pollutant wash-off from impervious surfaces, or atmospheric deposition.  
Pb, Ni and _Cd_ concentrations in stormwater are positively correlated with the area of roofs (RF), roads (RD) and car parks (CP), which suggests that atmospheric deposition is not the main source of those elements. In the catchments of concern, Pb, Ni and _Cd_ originate primarily from traffic and roof covers [46]. Concentrations of HM in storm water are not determined by rainfall event duration (t\({}_{\rm r}\)) as shown by a low correlation coefficient (Table 3). They rather depend on precipitation depth (Pt\({}_{\rm tot}\), P\({}_{\rm(10)}\)). This observation complies with the results reported by [PERSON] et al. in [47]. Based on the data obtained from Paris, the researchers observed that the process of pollutant removal from the atmosphere with rainfall depends mainly on its depth rather than other characteristics. Permeable surfaces (green spaces--GS and gravel roads--GRD) within the catchment, characterised by high water retention capacity, to some extent reduce the content of trace elements in stormwater intercepted by watertight drainage systems. This observation is confirmed by the occurrence of negative correlation relationships between the analysed pollution indicators and the share of permeable surfaces in total area of the catchments.  
Stormwater quality also depends on wind velocity, which has substantial impacts on transport and deposition of atmospheric pollutants (Table 3) and may provide a reasonable explanation for the negative correlation between the catchment area and the studied HM. With respect to the analysed catchments, large areas are relatively less urbanised and have a considerable share of green spaces. In addition, the negative correlations (from \(-\)0.34 to \(-\)0.71) between wind velocity and HM concentrations in stormwater may indicate that wind has a substantial impact on the volume of dry pollutant deposition.
## 5 Experimental Results
### Tests in Ideal Static Scenarios
#### 5.3.1 Experimental Setup and Results  
We evaluate the performance of the X-NETAD application in two different static scenarios, which are illustrated in Figure 9. For each test, the transmit power, \(P_{\mathrm{t}}\), of each device was set to its maximum value, equal to \(32\) mW, and the data rate, \(R\), was fixed at \(11\) Mb/s--the rate that guarantees the minimum experimental PER, as shown in Subsection 5.2.1. According to the results presented in Subsection 5.2.2, we use the value, 60 m, as the device's transmit range, \(z\), to be used in the IF protocol. The linear node spatial density, \(\rho_{s}\), is related to the considered scenario, since it depends on the (physical) spatial distribution of the nodes. The shaping coefficient, \(c\), is set to \(5\) to balance the small number of nodes present in the system and, thus, to increase the rebroadcasting probability. In each test, the application disseminates the same archive of roughly \(100\) KB, divided in \(128\) chunks. During tests, we considered, as an expiration date for each content, \(T^{\mathrm{q}}=60\) s: the choice of this value allows significant information propagation. Finally, the waiting time before sending a _REQUEST_ message is set to \(5\) s.  
Figure 8: Experimental PER as a function of the inter-node distance, by considering \(P\mathrm{tx}=32\) mW and \(R=11\) Mb/s.