esa-sceva/satcom-chunk-collection huggingface dataset:
# A Mayfly-Based Approach for CMOS Inverter Design with Symmetrical Switching
## 4 Problem Formulation
### Case III  
In this case, the main objective is to obtain the symmetrical switching characteristics for the CMOS inverter with equal fall time (\(\mathrm{t_{f}}\)) and rise time (\(\mathrm{t_{r}}\)), and equal propagation delay times. The cost function that needs to be minimized is formulated as in Equation (21) [25; 26; 27; 28; 29; 30; 32].  
\[\begin{array}{ll}\mathrm{CF}&=\left|\left(\mathrm{t_{f}}(\mathrm{C_{L}}, \left(\frac{\mathrm{W}}{\mathrm{L}}\right)\mathrm{n})\right)-\left(\mathrm{t_ {r}}(\mathrm{C_{L}},\left(\frac{\mathrm{W}}{\mathrm{L}}\right)\mathrm{P}) \right)\right|\\ &+\left|\left(\mathrm{t_{pHL}}(\mathrm{C_{L}},\left(\frac{\mathrm{W}}{\mathrm{ L}}\right)\mathrm{n})\right)-\left(\mathrm{t_{pLH}}(\mathrm{C_{L}},\left(\frac{ \mathrm{W}}{\mathrm{L}}\right)\mathrm{P})\right)\right|\end{array} \tag{21}\]  
Subject to the following constraints as in Equations (16)-(20), (22) and (23) [25; 26; 27; 28; 29; 30; 32].  
\[\left(\mathrm{t_{pHL}}\right)_{\mathrm{min}}\leq\mathrm{t_{pHL}}\leq\left( \mathrm{t_{pHL}}\right)_{\mathrm{max}} \tag{22}\]  
\[\left(\mathrm{t_{pLH}}\right)_{\mathrm{min}}\leq\mathrm{t_{pLH}}\leq\left( \mathrm{t_{pLH}}\right)_{\mathrm{max}} \tag{23}\]  
The optimization problem depends on three variables: the load capacitance \(\mathrm{C_{L}}\), the ratio between the channel width and length of NMOS and PMOS transistor (\(\mathrm{W/L}\))\({}_{\mathrm{P}}\), and (\(\mathrm{W/L}\))\({}_{\mathrm{n}}\). The eight different design sets are considered with the lower and upper bounds of the constraints and they are shown in Table 3 for Case III.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Set Number** & \(\mathrm{C_{L}}\) **(pF)** & **(W/\(\mathrm{L}\))\({}_{\mathrm{n}}\)** & **(W/\(\mathrm{L}\))\({}_{\mathrm{P}}\)** & **\(\mathrm{t_{f}}
# Energy Generation and Attenuation of Blast-Induced Seismic Waves under In Situ Stress Conditions
## 3 Numerical Study of Blasting Seismic Energy Generation and Attenuation under In Situ Stress
### Seismic Energy Generation and Attenuation under Non-Hydrostatic In Situ Stress  
#### 3.4.1 Seismic Energy Density on the Elastic Boundary  
Under non-hydrostatic in situ stress, in order to investigate the blasting seismic energy generated in different directions, the total seismic energy on the entire elastic boundary is no longer used in this section. Instead, the seismic energy density at individual observation points is studied. The seismic energy density \(e_{\mathrm{s}}\) is given by the integral of the seismic energy flux over time:  
\[e_{\mathrm{s}}=\int_{t}\Phi\cdot ndt \tag{17}\]  
Figure 9 gives the fractured zone caused by the multiple-hole blasting under non-hydrostatic in situ stress. In this figure, the case \(\sigma_{x}\) = 3\(\sigma_{y}\) = 30 MPa is presented only as an example. The envelope of the fractured zone, namely the elastic boundary, is elliptical. The major axis of the elliptical elastic boundary aligns with the maximum principal stress direction. This accords with the case of single-hole blasting. The individual observation points A and B on the elastic boundary are selected to assess the initial seismic energy density generated in different directions. The seismic energy densities at points A and B are 9.74 kJ/m\({}^{2}\) and 14.03 kJ/m\({}^{2}\), respectively. The initial seismic energy density generated in minimum principal stress orientation is higher. The distribution of the initial seismic energy density on the elastic boundary exhibits anisotropy. Under non-hydrostatic in situ stress, the rock fracture produced in the minimum principal stress orientation is smaller. The explosive energy consumed in the rock fracture in this direction is less. As expected, more seismic energy is generated in this direction, corresponding to a higher seismic energy density.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**In Situ Stress Levels (Mpa)** & **Energy Attenuation Index** & **Seismic \(Q\)** \\ \hline
0 & 0.0940 & 82 \\
10 & 0.0729 & 102 \\
20 & 0.0597 & 116 \\
30 & 0.0443 & 120 \\
40 & 0.0578 & 98 \\
60 & 0.0648 & 90 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Seismic energy attenuation indexes and seismic Q under varied hydrostatic in situ stress levels.
## 4 Results and Discussion
A potential avenue of investigation with regards to this observation is to train the model by using specific regions of a brain slice where the lesions are known to be smaller. This is similar to an attention-based model framework which has been known to perform well [13], but it would require significantly more labeled data. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Time and DSC** & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline \multirow{2}{*}{U-Net with ResNeXt-50} & Time (seconds) & 5.6973 & 5.4851 & 5.5566 & 5.5522 & 5.5916 & 5.5049 \\  & DSC & 0.7105 & 0.8016 & 0.4080 & 0.6495 &  
When training and testing using 5-fold cross validation, the models had improved slightly with regards to the DSC (Table 6); this is most likely due to the availability of more data from different scanners. However, it is important to note that the IoU had also decreased in a few cases, especially in the case of T1. Based on the DSC of 0.5197 and an IoU of 0.3571, the 5-fold cross validation demonstrates that when using T1, the U-Net++ with ResNeXt-50 can yield better performances than other models using the same sequence. This could mean that the U-Net++ CEN with regards to Table 3 does not have significantly lower performances when using T1; thus, it is more likely the result of the random distribution of data when training and testing with the UMCL dataset. Regardless, this is a something that that could be revisited in future works. Additionally, FLAIR2 does not yield better segmentation performances after 5-fold cross validation, but it follows FLAIR relatively closely. This might suggest that the segmentation performances when using this multi-contrast combinations are dependent on the type of scanner used. Based on the performances when using FLAIR, the FLAIR\({}_{3}\) multi-contrast combination could provide a better performance since, technically, the FLAIR exponent (1.55) is slightly more than the T2 exponent (1.45). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Input** & **Model** & **DSC** & **IoU** \\ \hline \multirow{4}{*}{T1} & FPN & 0.4585 & 0.3038 \\  & Linknet & 0.4220 & 0.2730 \\  & U-Net & 0.4862 & 0.3272 \\  & U-Net++ & 0.5197 & 0.3571 \\ \hline \multirow{4}{*}{T2} & FPN & 0.5336 & 0.3704 \\  & Linknet & 0.5137 & 0.3513 \\ \cline{1-1}  & U-Net & 0.5677 & 0.4024 \\ \cline{1-1}  & U-Net++ & 0.6079 & 0.4423 \\ \hline \multirow{4}{*}{FLAIR} & FPN & 0.6246 & 0.4945 \\  & Linknet & 0.6305 & 0.4646 \\ \cline{1-1}  & U-Net & 0.6939 & 0.5355 \\ \cline{1-1}  & U-Net++ & 0.7159 & 0.5612 \\ \hline \multirow{4}{*}{FLAIR2} & FPN & 0.6048 & 0.4387 \\ \cline{1-1}  & Linknet & 0.5991 & 0.4324 \\ \cline{1-1}  & U-Net & 0.6624 & 0.4996 \\ \cline{1-1}  & U-Net++ & 0.6743 & 0.5132 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Testing metrics using combined dataset. Figure 6: Segmentation results for U-Net++ with ResNeXt-50 when using FLAIR: (**a**) sagittal FLAIR slice, (**b**) overlayed ground truth lesions (red), and (**c**) overlayed model predictions (red).
# Potential of Waste Oyster Shells as a Novel Biofiller for Hot-Mix Asphalt
## 3 Results
### X-ray Diffraction (XRD)  
X-ray diffraction (XRD) patterns of OSP, neat asphalt, and OSP-filled asphalts are shown in Figure 5. Oyster shells are biological wastes, which are mainly made up of calcium carbonate (\(\geq\)96 wt %) and minor amounts of other minerals. It can clearly be seen that the major crystalline phase of oyster shell waste is calcite (i.e., CaCO\({}_{3}\)). No peaks due to other phases were detected, indicating the high purity of raw material understudy. X-ray diffraction of neat asphalt sample (AP-5 OSP 0 wt %) was analyzed and the diffractogram is presented in Figure 5. Figure 5 shows that most of the peaks appear at around 20 value of 20\({}^{\circ}\) and 25\({}^{\circ}\). The \(\gamma\)-peak, which occurs at approximately 20 = 20\({}^{\circ}\), mirrors the packing distance of saturated structure that comes from the X-rays scattered by the aliphatic chains or the condensed naphthenic rings. It has been reported that this band arises principally from paraffins order [47]. The (002) peak or graphene peak, which appears at about 20 = 25\({}^{\circ}\), is due to the diffraction of X-rays from the stacks of aromatic sheets. The (10) and (11) peaks, or (100) and (110) reflections, originate from the _in_-plane structure of the aromatic molecules. They refer to the first and second nearest neighbors in the ring compounds. Generally, the size of an aromatic sheet is often determined from the (10) peak (20 = 40\({}^{\circ}\)), and not from (11) peak, due to its very weak intensity [48]. It is widely known that when the peaks in the XRD patterns are broader, the more short range exists in that  
Figure 4: FT-IR spectra of OSP, neat asphalt, and OSP-filled asphalts.  
structure type. This case comprises the unfilled and OSP-filled asphalts. On the other hand, when the XRD pattern peaks are sharper and narrower, the more highly crystalline phase with high degree of long-range order exists. The oyster shell powder meets this criterion. As demonstrated by XRD patterns, with the increase in OSP dosage from 5 to 15 wt %, the intensity of the CaCO\({}_{3}\) peaks gradually increased without affecting the XRD profile of the base bitumen. This suggests that OSP exists stably in the binder structure, which means that its addition did not cause any change in the crystalline phase of bitumen (governed chiefly by asphaltene compound). Accordingly, it can be assumed that the OSP has no chemical reaction in the asphalt system and exists independently with only filling effect.
## 4 Results and Discussion
### Principal Component Analysis (PCA)
Results from the initial analysis indicate the insignificant items to be X2, X6, X7, X13, Y10, Y13, Z5, and Z15. Moreover, 15 of the observed variables were categorized into a group comprising variables where the variance explained was less than 20%. Observations that explained less than 20% of variance were also considered to be insignificant. These 15 observed variables were X5, X9, X10, X11, X12, X15, Y9, Y15, Z1, Z2, Z3, Z4, Z8, Z9, and Z10. Table 4 shows a summary of the factor loadings of the variables, percentage of variance, and the Cronbach's Alpha of each latent variable. The results obtained indicate that motorcyclists in Malaysia are significantly connected with the risky behaviors, namely \"Frequently changing lanes to overtake (X1)\", \"Exceeding speed limit even feeling unsafe (X3)\", \"Riding fast on curve (X4)\", \"Racing with other vehicles (X8)\" and \"Using phone while riding (X14)\", with factor loadings of 0.580, 0.653, 0.669, 0.651 and 0.556, respectively. This is compatible with a previous study that stated that the manner or behavior of motorcyclists is one of the issues that lead to vehicle crashes [20]. Motorcyclists are also associated with their perceptions on risky riding behaviors, consisting of variables Y1, Y2, Y3, Y4, Y5, Y6, Y7, Y8, Y11, Y12, and Y14, with loading factors of 0.577, 0.790, 0.829, 0.864, 0.869, 0.900, 0.744, 0.884, 0.738, 0.832 and 0.832, respectively. This shows that the risk perception of motorcyclists in Malaysia significantly affects their riding behavior. Specifically, risk perception affects one's risky behavior and whether one takes protective action to ensure safety [21]. The question \"How risky do you think taking alcohol before riding is (Y6)\" had the highest factor loading among the risk perception statements. Moreover, motorcyclists are also related to their preferences with respect to riding behavior, such as \"Taking alcohol before riding (Z6)\", \"Run a red light (Z7)\", \"Overtaking/turning without using signal lights (Z11)\", \"Riding without wearing crash helmet (Z12)\", \"Crossing a stop-junction without fully stopping (Z13)\" and \"Using phone while riding (Z14)\". The factor loadings were 0.574, 0.509, 0.698, 0.781 and 0.693, respectively. The sensation-seeking behavior of motorcyclists negatively affects riding behavior [22]. These results are consistent with previous studies that have also stated that emotions are closely related to risky riding behavior [7; 23; 24].
# Optoelectronic Instrumentation and Measurement Strategies for Optical Chemical (Bio)Sensing
## 2 Optical Devices
### Light Sources
The currently available LEDs cover the visible spectrum, a large part of the UV range (from approximately 250 nm), and part of the IR range (up to approximately 4.5 \(\upmu\)m) [20]. The full-width half-maximum (FWHM) is usually in the range of 10 to 100 nm. The optical power is typically in the range of 1 to 170 mW. Other advantages are the possibility of direct electronic modulation at high frequencies, long life (100,000 h), small dimensions, high energy efficiency, and low cost. LEDs and fiber optics can be purchased already assembled, which guarantees perfect coupling [21]. Other light source options are laser diodes (LDs). They are available with center wavelengths in the range of 400-2000 nm and output powers from 1.5 mW up to 3 W. It should be noted that the absolute rating is of the output power, not of the drive current. Their lifetime is also longer and the price is much higher than conventional LEDs. Until recently, there were no laser diodes in the violet-blue region. The price of a laser diode in this region, at certain wavelengths, can exceed $1000. The most important factor when choosing a LD is probably wavelength. Another highly important factor is diode packaging. The \"correct\" choice of packaging is dependent on the intended use and lab requirements. Laser diodes can also be modulated, although the particularities of their characteristic light-current power curve do not make them the most suitable option. They require a minimum current value to produce the laser effect. Table 1 summarizes the most outstanding features of the considered light sources.
# RS-DETR: An Improved Remote Sensing Object Detection Model Based on RT-DETR
## 4 Results and Discussion
### Evaluation Metrics  
Commonly used metrics for evaluating remote sensing image object detection algorithms include precision (P), recall (R), mean average precision (mAP), number of parameters (Params), and floating point operations (FLOPs). Precision denotes the proportion of true positive samples in the detection results, while recall represents the proportion of positive samples correctly detected among all positive samples, with precision and recall relating to error rate and omission rate, respectively. The precision-recall curve is  
Figure 6: The number of instances for every category in the SIMD dataset.  
Figure 7: The number of instances for every category in the TGRS-HRRSD dataset.  
obtained by plotting precision on the vertical axis and recall on the horizontal axis, where the area under the curve denotes the average precision (AP) for a specific category. The mean average precision (mAP) across multiple categories represents the algorithm's overall performance on the dataset. mAP can be calculated at various IoU thresholds. In this paper, we primarily select mAP at an IoU threshold of 0.5 and the average of mAP values from an IoU threshold of 0.50 to 0.95 with a step size of 0.05. These two mAP values are represented as mAP50 and mAP, respectively. Additionally, the Params metric measures the total number of parameters to be trained in the network model, while FLOPs denotes the model's number of floating-point operations per second, measuring the computational complexity of the network. The definitions of these metrics are  
\[P=\frac{TP}{TP+FP}, \tag{7}\]  
\[R=\frac{TP}{TP+FN}, \tag{8}\]  
\[AP=\int_{0}^{1}P_{i}(R_{i})dR_{i}, \tag{9}\]  
\[mAP=\frac{\sum_{i=1}^{N}AP_{i}}{N}, \tag{10}\]  
where \(TP\) refers to true positives, \(FP\) refers to false positives, and \(FN\) refers to false negatives. \(N\) represents the total count of categories.
###### Abstract  
This study compares various methodologies for lung dosimetry in radioembolization using Monte Carlo (MC) simulations. A voxelized anthropomorphic phantom, created from a real patient's CT scan, preserved the actual density distribution of the lungs. Lung dosimetry was evaluated for five lung-shunt (LS) cases using traditional methods: the mono-compartmental organ-level approach (MIRD), local energy deposition (LED), and convolution with voxel S-values, either with local density corrections (SVOX_L) or without (SVOX_ST). Additionally, a novel voxel S-value (VSV) kernel for lung tissue with an ICRU density of 0.296 g/cm\({}^{3}\) was developed. Calculations were performed using either the ICRU lung density (Lung_296), the average lung density of the phantom (Lung_221), or the local density (Lung_L). The comparison revealed significant underestimations in the mean absorbed dose (AD) for the classical approaches: approximately \(-\)40% for MIRD, \(-\)27% for LED, \(-\)28% for SVOX_L, and \(-\)88% for SVOX_ST. Similarly, calculations with the lung VSV kernel showed underestimations of about \(-\)62% for Lung_296, \(-\)50% for Lung_221, and \(-\)35% for Lung_L. Given the high heterogeneity of lung tissue, traditional dosimetric methods fail to provide accurate estimates of the mean \(AD\) for the lungs. Therefore, MC dosimetry based on patient images is recommended as the preferred method for precise assessment of lung \(AD\) during radioembolization.  
lung shunt; internal dosimetry; Monte Carlo; radioembolization; Y90; sirt; tare; lung dosimetry +
Footnote †: journal: Applied Sciences
# Power Optimization Model for Energy Sustainability in 6G Wireless Networks
## 2 System Model  
Consider a cell-free massive MIMO system in which a number \(A\) of APs equipped with \(N\) antennas are deployed randomly in a given geographical area. A total of \(U\) users, each with single antenna, are distributed in the given area to be served by the APs. Each AP serves a subset of users which are selected based on user needs. Each AP is connected to the CPU via fronthaul. A total of \(l_{p}\) mutually orthogonal pilots are sent in the uplink to obtain the channel estimates. Channel reciprocity is assumed to obtain the downlink channel coefficients. The data transmission takes place in blocks of \(l_{c}\) samples, each with coherence time \(T_{c}\) and coherence bandwidth \(B_{c}\). The uplink data samples in each block has length \(l_{u}\), while the length of the downlink data is \(l_{d}\). Correlated Rayleigh fading is considered in this paper, where a channel between AP \(a\) and user \(u\) is given as  
\[h_{ua}\sim\mathcal{N}(0_{M},R_{ua}), \tag{1}\]  
\(R_{ua}\) defines the spatial correlation matrix between user \(u\) and AP \(a\). For uncorrelated channels, \(R_{u}=\beta_{ua}I_{N}\). In addition, \(R_{k}=diag(R_{k1},\ldotsldots.R_{kN})\) is the collective matrix. For  
Figure 1: A communication scenario with dense AP deployment serving a large number of users.  
finding the channel gains \(\beta_{ua}\), a 3 GPP urban microcell large-scale fading model is taken as [40]  
\[\beta_{ua}[dB]=-30.5-36.7 log_{10}(\frac{d_{ua}}{1m})+S_{kn} \tag{2}\]  
where \(S_{ua}\) denotes shadow fading and \(d_{ua}\) is the distance between UE \(u\) and AP \(a\).
## 3 Results and Discussion
### Slope Stability Analyses Incorporating Reinforcements
After the reinforcements were introduced, almost in every cross-section, the safety was increased to \"green\" levels. The results can be seen in Figure 7, where the displacement monitoring and numerical computations were used to present the conditions of the landfill body. After the reinforcement works were accomplished, the calculated factors of safety felt were in a safe zone (Figure 7). The western slope (cross-section II) indicates an FOS value of 1.27, but according to observation, the slope is stable, with well-established vegetation cover and no signs of potential failure to occur in the future.
# Scene Text Recognition That Eliminates Background and Character Noise Interference
## 3 Proposed Framework
### The ENBC System
#### 3.2.2 Analysis of Deep Convolutional Neural Network Models  
Through layered convolutional and pooling layers, DCNN gradually recovers low-level and high-level aspects of the picture, such as texture and geometric information, in deep learning-based word recognition algorithms for scene images. The foundational DCNN frameworks VGGNet, ResNet, Inception, and MobileNetV2 are often utilized in the picture domain. A lightweight network among them is called MobileNetV2 [55], which was introduced by Google in 2017. Its main feature is the substitution of depth-separable convolution for the conventional convolution. Depth-separable convolution can decrease the calculation to 1/8-1/9 of the original by splitting the normal convolution into 1 depth convolution and 1 point-by-point convolution (also known as \(1\times 1\) convolution).  
Figure 4: Schematic diagram of the cavity convolution. (**a**) Ordinary convolution (conv \(3\times 3\), rate1); (**b**) null convolution (conv \(3\times 3\), rate2); (**c**) null convolution (conv \(3\times 3\), rate4).  
Figure 5: ASPP structure.  
Figure 6 displays the depth-separable convolution + BN + ReLU network versus the regular convolution + BN + ReLU network.  
In this research, the MobileNetV2 architecture is used by the ENBC network, which is built on a simplified design that builds a lightweight deep neural network with high-performing trade-offs for model accuracy using highly separable convolutions. Figure 7 depicts the MobileNetV2 [56] structure, which adopts a more high-performing residual structure, suggests an inverse residual module, and converts the MobileNet v1 module's last ReLU6 layer to a linear layer.
## 4 Proposed Method for Physical Layer Performance Estimation  
As already mentioned in the introduction, in this paper we aim at evaluating the suitability of a passive method for the estimation of the physical layer performance in a wireless sensor network. Herein, by passive we mean that the nodes of a network that are already installed and active in a given environment are not involved in the monitoring process. Instead, the measurements are performed by the sniffer nodes that carry out a series of observation on the wireless channels concerning the network nodes transmissions. Then, from these measurements, estimations on the wireless links status among the operating nodes are performed.  
The rationale behind the proposed method is that the WSN nodes are mostly battery-powered and any additional activity, such as receiving signal power measurements needed in an active scenario, could compromise the network performance and lifetime. These nodes would not only need to make the measurements of the power but also perform some operations (to compute the average values for instance) and send the relevant data to a sink with additional energy consumption. Furthermore, in the current WSN market, the node prices are kept as low as possible by limiting the computational and memory capabilities, which prevents implementing most of the monitoring tasks in the nodes themselves using the available hardware.