esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### X-CT Results  
X-CT scans were run on all three samples, as detailed in Table 3. Except for sample F3 (scan H3), the porosity turned out to be very low, with pore size below the scan resolution. Furthermore, the 'high resolution' scan of sample F3 (scan H3-HR) resulted in an indistinguishable porosity distribution compared with the preceding scan H3 of the same sample, indicating that the latter had accurately captured the porosity structure of  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**Quartz**} & \multirow{2}{*}{**Calcite**} & \multirow{2}{*}{**Dolomite**} & \multicolumn{3}{c}{**Mineralogical Composition (\%)**} & \multirow{2}{*}{**2:1 Clay**} & \multirow{2}{*}{**Rutile**} & \multirow{2}{*}{**Pyroxene**} \\  & & & & & & & & & \\ \hline F1 & 46.7 & 4.7 & 29.5 & 2.4 & 10.0 & 1.0 & 3.5 & 1.6 & 0.3 \\ F3 & 43.4 & 5.4 & 25.0 & 2.8 & 10.4 & 1.0 & 3.3 & 0.7 & 8.0 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Mineralogical compositions from XRD.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & **Piece** & **Porosity** & **Bulk Density** & **Grain Density** \\  & & & \(\rho_{\rm b}\) (kg m 3)** & \(\rho_{\rm g}\) (kg m 3)** \\ \hline _Sandstone_ & & & & \\ F3 & 1 & 0.296 & 2396 & 3403 \\ F3 & 2 & 0.355 & 2091 & 3242 \\ F3 & 3 & 0.310 & 2111 & 3059 \\ _Silstone_ & & & & \\ F1 & 1 & 0.135 & 2699 & 3120 \\ F4 & 1 & 0.066 & 3088 & 3306 \\ F4 & 2 & 0.063 & 3081 & 3288 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Mercury intrusion porosimetry measurements.  
this sample. X-CT results are therefore only presented here for sample F3 and for scan H3. Figure 10A,B illustrates the pore space segmentation of X-CT scan H3 of sample F3, based on initial visual interpretation. Porosity was calculated for slices of this sample in the x-y plane (i.e., in the horizontal plane, parallel to the bedding) and varied from 0.17 to 0.20 (Figure 10C). The mean porosity for our greyscale cut-off choice is 0.18, its standard deviation being 0.006. Layers of thickness 2-3 mm with different porosity, presumably due to variations in grainsize sorting, can be recognized, porosity being related to grain-size sorting as highlighted by circles 1 and 2 in Figure 10B. In regions with lower porosity (such as circle 1), a larger range in grain size is recognized, whereby the smaller grains reduce the pore space. This ensemble of porosity values is rather less than the \(\sim\)0.24 expected, after Almaisi [23], for sandstone from a depth of \(\sim\)1900 m.  
Figure 10: Analysis of X-CT results for sample F3 based on analysis H3. (**A**) X-y plane of X-CT scan. (**B**) The image in (**A**), labelled to show segmented pore space, also showing variations in grain size sorting, indicating the range of porosity values and their vertical trend. (**C**) Calculated porosity for multiple slices through this sample parallel to the x-y plane, showing the variability in porosity that contributes to the determination of the mean value.
## 3 Results and Discussion
### Electrochemical Characterization  
The potential window of the HABDD in 0.5 M H\({}_{2}\)SO\({}_{4}\) solution by cyclic voltammetry (CV) measurement is illustrated in Figure 3. It can be seen the HABDD was an excellent anode for wastewater degradation since it had a wide potential window (\(-\)1.8-2.5 V), low-background current, and remarkable anti-corrosion stability due to almost no changes of the potential window after two weeks of the wastewater treatment. The wide potential window guaranteed high yields of hydroxy radicals (OH*) as the anode, which afforded it high performance for organic decomposition. In addition, free active radicals, such as O\({}_{2}\)* and HO\({}_{2}\)*, could be generated by the overpotential at the diamond anode [19]. In addition, hydrogen and oxygen evolution could be avoided due to the wide potential window [11].  
Figure 3: CV curve of the potential window of the HABDD in 0.5 M H\({}_{2}\)SO\({}_{4}\) solution at a scan rate of 50 mV/min (black line, initial state; red dot, after two weeks).  
Figure 2: SEM images of (**a**) a hole in the HABDD, (**b**) the HABDD surface from the top view, and (**c**) Raman of the HABDD surface.
# Real-Time Multidepth Multiphoton Microscopy Using Pixel-to-Pixel Focus-Switching
## 4 Conclusions  
Real-time multidepth label-free TPF and SHG microscopy is achieved by using pixel-to-pixel focus-switching. It provides a cost-effective and convenient solution to modify a commercial single-focal-plane multiphoton microscope for the real-time multidepth imaging of scattering specimens. At 870 nm excitation, mouse pulmonary alveoli images acquired at different focal planes distinctly reveal the microenvironment by TPF from elastin fibers and SHG from collagen fibers, providing a tool for the investigation of structure-associated diseases. Visualization of macrophages residing inside apparent alveolar lumens by autofluorescence shows that the imaging system can localize biological objects in three dimensions. Although the offset of ARM2 was set to 5 (m) in demonstrations, a range of at least \(\pm\)50 (m) can be achieved for different applications. Note that the pixel dwell time of the system is limited to 2 (s), only due to the restricted scanning speed of galvanometer-scanner. Applying a resonant-scanner (e.g., LSK-GR12, Thorlabs) will greatly improve the scanning speed in lateral directions, enabling three-dimensional monitoring of in vivo physiological processes. Meanwhile, objectives O1 and O2 can be replaced by a deformable mirror for fine-tuning beam divergence in future research.  
Conceptualization, Y.Q. and Y.X.; methodology, Y.Q., D.C., and Y.X.; software, Y.Q.; validation, Y.Q.; formal analysis, Y.Q. and Y.X.; investigation, Y.Q., D.C.; resources, Y.Q.; data curation, Y.Q.; writing--original draft preparation, Y.Q.; writing--review and editing, D.C. and Y.X.; visualization, Y.Q.; supervision, D.C., Y.X.; project administration, Y.X.; funding acquisition, Y.X. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by National Natural Science Foundation of China, grant numbers 61675057, 61975050, 61475040.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Gated Recurrent Attention for Multi-Style Speech Synthesis
## 2 Backgrounds
### Tacotron  
Tacotron [13] is a seq2 seq model equipped with attention [16; 17] designed for speech synthesis. This model consists of an encoder, an attention-based decoder, and a postprocessing network as shown in Figure 1. The encoder first encodes a one-hot vector, which represents each character in a text sequence into a continuous vector. Then, a set of nonlinear transformations called the pre-net, which includes two layers of fully connected network with ReLU activation [29] and dropout [30], is applied to each layer. A convolution bank with highway connection and bidirectional-GRU (CBHG) module summarizes contextual information in the text. The content-based attention introduced in [16; 17] is adopted for the attention-based decoder. The decoder RNNs consist of a stack of GRUs with residual connections [31]. As the training target of the decoder, a mel-scale spectrogram is used instead of a high-dimensional raw spectrogram. In the postprocessing network, another CBHG module is used to transform the mel-scale spectrogram into the linear-scale spectrogram with larger number of bands for synthesizing high quality audio. For the vocoder, the Griffin-Lim algorithm [32] and WaveNet [33] vocoder have been applied. The content-based attention mechanism has a problem that the attention scores are nearly unchanged regardless of the context for the equivalent or very similar inputs. For this reason, an attention mechanism that scores according to the attention alignment from the previous step, LSA [21], is used in an extended version of the Tacotron, also known as Tacotron 2 [15]. In Tacotron 2, a vanilla LSTM and convolutional layers are used in the encoder and decoder instead of CBHG-stacks and GRU recurrent layers. Training is performed to minimize the summed mean square error obtained before and after the post-net:  
\[\mathcal{L}_{\textit{Taco2}}=\mathcal{L}_{before-post-net}+\mathcal{L}_{after -post-net}. \tag{6}\]
# DC-DC Buck Converters with Quasi-Online Estimation of Filter Capacitor Equivalent Parameters
###### Abstract  
The article focuses on devising solutions for monitoring the condition of the filter capacitors of DC-DC converters. The article introduces two novel DC-DC buck converter designs that monitor the equivalent series resistance (ESR) and the capacitance of capacitors using a parameter observer (PO) and simple variable electrical networks (VEN). For the first scheme, the PO processes in real time the voltage at the capacitor terminals during a discharge-charge cycle. For the second scheme, the filtering is performed with two or more capacitors in parallel, and the PO processes the voltage at the terminals of each capacitor during two discharge processes without interrupting the filtering operation of the converter. The paper presents the principles and theoretical support on which the two schemes of DC-DC buck converters are based, design details regarding PO and VEN, as well as experiments performed with each of the schemes. In the experimental schemes, the PO is implemented with a microcontroller, and the parameters of some aluminum electrolytic filter capacitors are calculated in a real-time manner of about 40 ms. The calculation accuracy of the equivalent capacity is very good. Regarding the calculation accuracy of the ESR, it is shown that it depends on the fulfillment of certain ratios between the VEN resistances, on the one hand, as well as between them and the ESR, on the other hand.  
DC-DC buck converter; condition monitoring of filter capacitor; parameter observer; quasi-online estimation; real-time processing; VEN +
Footnote †: journal: applied sciences  
DO-DC buck converter; condition monitoring of filter capacitor; parameter observer; quasi-online estimation; real-time processing; VEN
# Wide-Band Interference Mitigation in GNSS Receivers Using Sub-Band Automatic Gain Control +
## 4 Test Setup
### Estimation of Interference to Noise Ratio
Fourth, a calibration value is determined to translate from the Agilent MXG signal generator's output power \(P_{t}\) to the \(I/N_{0}\). A mean value of all valid calibrations (i.e., invalid tracking data is extracted) determines the final scalar value:  
\[\text{CAL}[n,m]=\frac{(I/N_{0})_{\text{est}}[n,m]}{P_{t}[n]} \tag{16}\]  
\[\mathrm{INR}_{\mathrm{dB}}=\mathrm{ISR}_{\mathrm{dB}}+\left(C/N_{0}\right)_{ \mathrm{dB}}-10\log_{10}(B)\quad\mathrm{[dB]} \tag{10}\]0  
Figure 6 shows the values \(\text{CAL}[n,m]\) before the mean operation. The mean value is \(125.14\,\text{dBs/mW}\) and a standard deviation of \(0.95\,\text{dB}\), indicating a reliable estimate. Finally, the calibration CAL is used to translate the Agilent MXG power output \(P_{t}\) to the \(I/N_{0}\):  
\[\mathrm{INR}_{\mathrm{dB}}=\mathrm{ISR}_{\mathrm{dB}}+\left(C/N_{0}\right)_{ \mathrm{dB}}-10\log_{10}(B)\quad\mathrm{[dB]} \tag{10}\]1  
These are the final values shown in the subsequent plots as a reference. The \(I/N_{0}\) can be converted to the INR simply by using Equation (12). However, the receiver bandwidths should be known. The LC receivers are believed to have a bandwidth less than \(10\,\text{MHz}\) and the HE receiver above \(50\,\text{MHz}\), but exact values cannot be obtained without the manufacturers disclosing this information. Nevertheless, the INR cannot differ by more than \(7\,\text{dB}\) depending on the receiver bandwidth, making comparisons challenging. Table 1 compares the output power, INR and \(I/N_{0}\), as references.
# Review of the AI-Based Analysis of Abdominal Organs from Routine CT Scans
## 2 Historical Evolution
### Deep Learning Revolution (2018-2024)
#### 2.3.19. 3D Convolutional Block Attention Module Neural Network (CBAMNN)  
In 2024, [PERSON] et al. introduced a deep learning model leveraging the 3D convolutional block attention module (CBAM) to rapidly identify life-threatening solid organ injuries, including spleen, liver, and kidney damage, in trauma settings. Using abdominal CT scans from a single trauma center (2008-2017), the model classified cases as positive or negative based on organ injuries. The dataset comprised 1302 scans (87%) for training and validation and 194 scans (13%) for testing. Evaluated using AUROC, accuracy, sensitivity, specificity, and predictive values, the model delivered impressive results: spleen injury detection achieved 93.8% accuracy and 95.2% specificity; liver injury detection reached 82.0% accuracy and 84.7% specificity; and kidney injury detection achieved 95.9% accuracy and 98.9% specificity [30].
# A Study of High-Efficiency Laser Headlight Design Using Gradient-Index Lens and Liquid Lens
## 1 Introduction  
A laser headlight, representing the most advanced, cutting-edge and efficient vehicle lighting system, has higher illumination efficiency than halogen and xenon headlights, and its energy efficiency is also no less than that of an LED headlight [1]. With a small size and strong penetration, laser technology has become the mainstream for next wave vehicle lighting. Since its successful development in a laboratory in 1960, laser has been considered an extremely important scientific achievement; however, it is rarely used in lighting applications [2]. Laser automotive headlights, which exhibit the characteristics of single wavelength, high homology, high directivity and high penetration, [3] are considered to be extremely innovative and energy efficient [4, 5].  
Advanced headlight technology such as adaptive headlighting system are timely research topics. In this paper, a proposed optical design and simulation is our present approach, aimed at not only improving the headlight efficiency with laser diode, but also supporting adaptive headlighting technologies. Generally speaking, the advantage of using a laser application in headlights is that the laser diode is only one-tenth or less the size of a conventional halogen lamp. In addition, advanced technology today significantly improves the performance of various laser diodes, which saves spaceand reduces power consumption further. Laser automotive headlights produce 170 lumens per watt, as opposed to the 100 lumens per watt produced by LEDs, indicating that the luminous efficiency achieved using a laser is 70% higher than that obtained using LED headlights. In terms of penetration, laser automotive headlights can penetrate as much as 600 m, which is twice the penetration achieved when using traditional halogen, xenon and LED lights, and is considered to be energy efficient in a vehicle-proven environment. Furthermore, the small size of the laser device provides a considerable degree of freedom in the design of the vehicle [6; 7].  
Laser diodes as the light source and their simulation in this proposed research could be further analyzed from the point of view of the background of the simulation software and how it works.
## 4 Proposed Protocol
### Cluster Formation
#### 4.1.2 Steady-State Phase  
In the steady-state phase of EEUCH, each SN that has been selected as a CM has a TDMA slot assigned to it by its respective CH, which is the UAV. Each CM must transmit its data packets within its assigned slot. Once the data transmission is successful, the UAV sends an Ack to the ground SN. When the SN receives the Ack, the steady-state phase ends. When the CH acknowledges the data packets, the CM turns off its MR and enters sleep mode. This marks the completion of a single round. The duration of each round and the time between consecutive rounds depend on the application's requirements.  
Figure 4 illustrates the complete flow of a single round of a UAV in the proposed EEUCH routing protocol. The dotted lined box labeled \"Illustration of a single round of a UAV\" encloses the sequence of processes that occur during the round. The figure comprises two vertical boxes. The left vertical box, labeled \"UAV-Aerial BS\", shows all the processes that occur at the UAV, including CCA and WuC transmission. The right vertical box, labeled \"Ground Sensor Node\", demonstrates all the processes that occur at the SN, such as WuC reception, CSMA-CA, and joining request transmission. Additionally, the figure contains two horizontal boxes. The upper horizontal box, marked as \"SETUP PHASE\" in its top left corner, contains all the processes that occur during the setup phase, including cluster formation and TDMA slot allocation to the SNs. The lower horizontal box, marked as \"STEADY STATE PHASE\" in the bottom of its left corner, encloses all the processes that occur during the steady-state phase, such as data transmission to UAV and reception of Ack from the UAV. The figure demonstrates a single round of a UAV where all the required processes occur at the UAV and ground SNs are presented in distinct boxes. The cluster formation (setup phase) and steady-state phase are each enclosed in a distinct dotted box, representing the different stages of a single round of the proposed protocol.
## 5 Simulation Results
### The Path Loss Effect  
In this part of the simulation studies, the impact of path loss exponent on the network is analyzed. For the studies, the path loss exponent values of \(n=1.5\), 2 and 4 are used. Figures 12 and 13 illustrate the impact on network lifetime and remaining energy.  
As shown in Figure 12, the network lifetimes are approximately 3786, 3423, and 2582 rounds when path loss exponent values are 1.5, 2, and 4 respectively. These results indicate that an increase in the path loss exponent value leads to a significant decrease in the network lifetime.  
The impact of the path loss exponent on the network's remaining energy is illustrated in Figure 13. For the path loss exponent values of 1.5, 2, and 4, the network energy is completely depleted at rounds 3752, 3410, and 2574, respectively. This confirms the fact that  
Figure 11: Comparison of remaining energies in a large area network with a RN.  
Figure 12: The impact of the path loss exponent on the network lifetime.  
Figure 13: The impact of the path loss exponent on the network’s remaining energy.  
increasing the path loss exponent value reduces the lifetime of the network as the nodes consume more energy to transmit their data to the BS.
## 3 Suggested Techniques
### Suggested Routing Protocol
#### 3.3.2 Route Selection between CH and BS  
After the CH is selected, a path setting step is required to transmit data to the BS. Transmitting data directly from CH to BS consumes a considerable amount of energy due to the relatively large transmission distance. To solve this problem, the shortest transmission path between CH and BS is selected by applying a Dijkstra algorithm [10]. To this end, the operation process is as follows:  
Figure 6: Round when 100 nodes by weight are down.  
Nodes selected as CH transmit node location information to BS.
2. BS calculates the shortest transmission path between BSs in individual CHs by using the location information of the aggregated CH nodes.
3. BS transmits the calculated shortest path (next hop of individual CH) to the corresponding CH.
## 4 Problem Analysis and Algorithm Description  
### Problem Analysis  
In using energy-constrained UWSNs to monitor a target water area, we must ensure that the network has high coverage and full connectivity at the same time and effectively manage nodes sleep or active states to save communication energy consumption. Moreover, the network should maintain high network connectivity, which is a crucial optimization goal of our algorithm, as some nodes begin to die because of persistent network operation, that is, the network still has relatively good reliability. However, in most of the move-constrained deployment algorithms of [13; 14; 15], coverage overlaps are reduced by node depth adjustment, thus increasing the global network coverage rate. On the other hand, all nodes are in an active state during the deployment process, and this increases their network communication energy use. Meanwhile, these algorithms do not consider the average node degrees between different depth networks, subsequently affecting network reliability and connectivity rate under network operation conditions. Moreover, in view of the node move-restricted depth-adjustment algorithm based on computational geometry, through the vertical depth-adjustment of nodes in reducing network coverage redundancy, the 3D network coverage is increased, [PERSON] et al. [24] proposed the VBDA algorithm for UWSNs. However, the VBDA algorithm does not consider the network connectivity between two neighboring layers, resulting in a very poor network connectivity performance with a small \(\alpha\) (the ratio of node communication radius to sensing radius). As the network is deployed, fewer nodes are available to execute depth adjustment when the process of deployment reaches the water bottom threshold. Thereafter, network coverage performance becomes relatively inferior. In addition, the VBDA algorithm does not consider network connectivity in the deployment process. The nodes are always in the active state, and thus all nodes require more communication, thereby consuming more communication energy. The limited energy for the nodes and the difficulty of charging the batteries in the water monitoring space affect the network operator performance, including network coverage rate, network connectivity rate, and network reliability. Meanwhile, VBDA is a centralized algorithm, and its practical application is more difficult to achieve than that of our proposed system.  
Figure 3: Schematic of a 2D convex hull.  
To better solve the problem of the abovementioned VBDA algorithm, we proposed NDACS in this study. A detailed description of the NDACS algorithm is given in the next section.
## 5 Proposed Taxonomy of the Attacks, DDoS Attacks, and DDoS Detection Approaches on CC
### Taxonomy of the Attacks on CC
#### 5.1.3 Economic Denial of Sustainability (EDoS)  
A type of DDoS attack on CC that impacts the financial bottom line of victims is called an EDoS attack [44]. It is a malicious attack specific to the CC that focuses on impacting the CSP's OpEx more than the physical resources. It exploits the CC's elastic or auto-scaling characteristic by attacking targeted services, especially on the application layer, forcing maximum consumption of CC resources until the services become inaccessible. Avoiding this situation requires the CSP to keep providing additional resources to fulfill the SLA for the CCC accessibility, which increases the cost for the CSP [33; 45], resulting in EDoS [2]. Figure 3 illustrates how EDoS attacks on CC resources impact service availability.  
Figure 3: Economic denial of sustainability attack.  
Figure 2: The topology of smurf attack.  
Adversaries could trigger DDoS attacks on CC networks or resources using legitimate service requests to generate EDoS attacks [45]. In other words, the basis of this financial damage came from DDoS attacks (the result of EDoS is DDoS attacks usually, and the opposite is also true) that exploited available resources to increase the bill. The bloated cost has to be borne by the CSP or passed along to their clients.