esa-sceva/satcom-chunk-collection huggingface dataset:
# Physical Vulnerability and Landslide Risk Assessment in Tegucigalpa City, Honduras
## 3 Results
### Temporal Probability  
A total of 52 landslide episodes in neighborhoods located on the layer of colluviums and residual soils were identified, with 70% of the episodes characterized by one or two movements.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer** & – & \\ \(\mathbf{X}\) & \(\mathbf{\sigma}\) \\ \hline Colluviums and residual soils & 0.37 & 0.26 \\ Validated landslide inventory & 0.44 & 0.25 \\ Colluvium of basalts, andesites, and rhylolites & 0.42 & 0.28 \\ Residual soil from Valle de Angeles group & 0.4 & 0.23 \\ Colluvitums and residual soils & 0.3 & 0.22 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average and standard deviation of the SF < 1 probability (between 0 and 1) for the different layers.  
Figure 6: Map showing the distribution of the probability of SF < 1.  
Figure 7: Probability of SF < 1 including polygons reclassified with expert criteria.  
Based on the antecedent rainfall indicators, the return period correlated with each number of landslides was estimated. In total, values were obtained for six return periods. The temporal probability (Pt) of a landslide being triggered in any pixel of the colluvium and residual soil layer was estimated for each return period; see the results provided in Table 2.
# A 0.18 um CMOS Millimeter Wave Antenna-on-Chip with Artificial Magnetic Conductor Design
## 2 Materials and Methods
### Parameter Analysis of the Number of AMCs  
Then, this paper introduces the artificial magnetic conductor for simulation analysis to observe the influence of the number of artificial magnetic conductors on the radiation efficiency. The evolution of the number of artificial magnetic conductors and the simulation results are shown in Figures 12a-c and 13. In Figure 12a, the artificial magnetic conductor is located under the antenna, and the number was \(15\times 11\), with a bandwidth of 30.1 GHz to 78.8 GHz. There were resonance points at 39 GHz and 65 GHz, which were \(-41.9\) dB and \(-38.6\) dB, respectively. Figure 12b's artificial magnetic conductor extended from the bottom of the antenna to the front of the antenna, and the number was \(15\times 26\), with a bandwidth of 32.4 GHz to 79.9 GHz and 90.9 GHz-110 GHz. The resonance points were \(-22.1\) dB at 41 GHz, \(-21\) dB at 62 GHz, and \(-14.8\) dB at 62 GHz. Compared with Figure 12a, there was an additional 19.1 GHz frequency band. The number of artificial magnetic conductors in Figure 12c was \(30\times 26\), covered with metal_1. At this time, the bandwidth was 34.8 GHz-110 GHz. There were resonance points at 42 GHz, 72 GHz, and 95 GHz, which were \(-14.4\) dB, \(-17.4\) dB, and \(-12.6\) dB, respectively. Compared with Figure 12b, it had a maximum bandwidth of 75.2 GHz. According to the simulation results, Figure 12c had the best result when the artificial magnetic conductor increased.  
Figure 11: Simulation results of antenna branches’ evolution.  
Figure 10: Evolution of antenna branches: (**a**) Type 1; (**b**) Type 2; (**c**) Type 3; (**d**) Type 4.
# Millimeter-Wave Smart Antenna Solutions for URLLC in Industry 4.0 and Beyond
## 5 Research Opportunities and Future Directions  
Where there are challenges, there are opportunities. Millimeter-wave communication has opened many research opportunities in wide areas of communication systems. Some key opportunities and open research questions are discussed in this section.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Antenna Technology** & **Antenna Type** & **Array Configuration** & **Peak Gain** & **Size (mm \(\times\) mm)** \\ \hline
[90] & PCB & Integrated horn & Single unit structure & 14.6 & - \\
[85] & PCB & Monopole array & \(1\times 2\) & 11.6 & 20.64 \(\times\) 20 \\
[96] & PCB & SIW coplanar fed slot & Linear array & 12 & 30 \(\times\) 5 \\
[82] & PCB & T-slot planar & Single element & 8.77 & 11.7 \(\times\) 9.8 \\
[84] & PCB & CP substrate-integrated cavity & 4 \(\times\) 4 & 20 & 30 \(\times\) 30 \\
[97] & PCB & Microstrip CP array & 2 \(\times\) 2 & 16 & 20 \(\times\) 20 \\
[120] & PCB & Dielectric resonator & 2 \(\times\) 2 & 11.43 & - \\
[132] & PCB & SIW-based leaky wave & Linear array & 14.5 & 23 \(\times\) 3 \\
[127] & PCB & SIW fractal antenna & Single SIW & 4.57 & 4.1 \(\times\) 8.6 \\
[130] & PCB & SIW fractal antenna & Single SIW & 7.9 & 6.5 \(\times\) 9.6 \\
[110] & LTCC & CP SIW & 4 \(\times\) 4 & 18.2 & 18.6 \(\times\) 18.6 \\
[107] & LTCC & Parasitic microstrip patches & 4 \(\times\) 4 & 10.5 & 10.1 \(\times\) 8.5 \\
[104] & LTCC & Planar aperture & 16 \(\times\) 16 & 24.6 & 37 \(\times\) 37 \\
[111] & LTCC & Patch with SIW feed & 4 \(\times\) 4 & 16.7 & \(\approx\)20 \(\times\) 20 \\
[105] & LTCC & Patch & 4 \(\times\) 4 & 17.1 & 13 \(\times\) 13 \\
[114] & LTCC & U-slot patch & 4 \(\times\) 4 & 16 & 14 \(\times\) 16 \\
[115] & LTCC & Helical & 4 \(\times\) 4 & 14 & 12 \(\times\) 10 \\
[103] & On-chip & Yagi & - & -8 & Chip size 1.1 \(\times\) 0.95 \\
[98] & On-chip & MEMS based & 9 \(\times\) 9 & 23.3 & 24.75 \(\times\) 24.75 \\
[99] & On-chip & Folded slot silicon integrated & - & 3.9 & - \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of various 60 GHz antenna designs available in the literature.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Antenna Type** & **Advantages** & **Disadvantages** \\ \hline Microstrip and PCB & Compact, low cost, easy fabrication, & High substrate loss, conductor and dielectric loss, \\ antennas & light weight, easily integrable & feed radiation issues, impedance matching \\  & with other RF circuitry & issues, bandwidth issues for thick substrates \\ On-chip integrated & Compact, low power, light weight, low profile & Low gain, low efficiency, high radiation losses, \\ antennas & and multifunctional & complex fabrication, and complex design rules \\ Leaky wave and surface & Low fabrication cost, planar tunability, & Low efficiency usually due to traveling wave, \\ wave antennas & no requirement of phase shifters usually & scanning angle varies with frequency, complex design considerations \\ \hline \hline \end{tabular}
\end{table}
Table 3: Categories of 60 GHz mmWave antennas with benefits and drawbacks.
# Allocating Small Transporters to Large Jobs
## 5 The Saturated Transporter Allocation Problem
### Greedy Algorithms  
In this section, we develop three greedy algorithms for \((\mathrm{sTAP})\), which are inspired by algorithms used for the multiple knapsack problem.  
In order to illustrate the connection between \((\mathrm{sTAP})\) and multiple knapsack problems, below, we use the wording of knapsack instances to formulate the goal of \((\mathrm{sTAP})\). When solving a multiple knapsack problem, the goal is to pack items with given weight and value in knapsacks to maximize the total packed value without exceeding the weight capacity of any knapsack. Using the same wording, when solving the saturated transporter allocation problem, the goal is to pack items with given weight and constant value of 1 in knapsacks to minimize the total packed value while ensuring the following minimum capacity conditions: in every knapsack, the total packed item weights excluding the biggest one exceeds a minimum total weight.  
In [27], the authors propose, among others, two heuristics to solve the multiple knapsack problem under additional assignment restrictions. Their first greedy heuristic consists of taking the items in non-increasing order and assigning them to the first eligible knapsack. Their second algorithm takes knapsacks in an arbitrary order and successively solves single knapsack problems using the still unassigned items.  
Following these ideas, our first algorithm assigns transporters to the jobs one job after the other, whereas the second and third algorithms assign transporters one by one to the best suited job. These heuristics are later also extended to the general version of \((\mathrm{TAP})\) in Section 6 and are computationally evaluated along with other algorithms and MIPs in Section 7.
## 3 Hardware and Software Setup
### Hardware Setup  
The hardware used to deploy and validate our design was a high-end laptop hosting an Intel Core i7-10750H CPU and an NVIDIA GeForce GTX 1650 Ti. The specifications of the CPU architecture are summarized in Table 1.  
The NVIDIA GeForce GTX 1650 Ti is based on the Turing architecture and features 1024 CUDA cores optimized for parallel processing tasks. Designed primarily for gaming laptops and compact desktops, this graphics processing unit (GPU) integrates several features that enhance its suitability for algorithm acceleration in computational research. The main specifications of the NVIDIA GeForce GTX 1650 Ti are gathered in Table 2.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Processor** & Intel Core i7-10750H \\ \hline
**Base Clock Speed** & 2.60 GHz \\ \hline
**Max Turbo Frequency** & Up to 5.00 GHz \\ \hline
**Number of Cores** & 6 \\ \hline
**Number of Threads** & 12 \\ \hline
**Lithography** & 14 nm \\ \hline
**Cache** & 12 MB Intel Smart Cache \\ \hline
**Memory Type Support** & DDR4-2933 \\ \hline
**Max Memory Size** & 64 GB \\ \hline
**Integrated Graphics** & Intel UHD Graphics \\ \hline
**TDP** & 45 W \\ \hline
**Socket** & FCLGA1200 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of Intel Core i7-10750H CPU.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Architecture** & Turing Architecture (Turing TU117 GPU) \\ \hline
**Single Multiprocessors** & 16 \\ \hline
**CUDA Cores** & 1024 \\ \hline
**Base Clock** & 1350 MHz \\ \hline
**Boost Clock** & 1485 MHz \\ \hline
**Memory** & GDDR6 Memory with Varying Capacities (typically 4 GB) \\ \hline
**Memory Interface** & 128-bit \\ \hline
**Memory Bandwidth** & Varies, typically around 192 GB/s \\ \hline
**TDP** & Around 55-80 W \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specifications of NVIDIA GeForce GTX 1650 Ti.
## 3 Particle Filter with Weight Adjustment Design for the Compensation of Sensor Noise and Time Delay
### Operator Based Control System Design
When the number of particles that violate the \(3\sigma\) rule reaches a certain proportion \(\textit{prop}\) of the total particles, it can be considered that the actual large noise has likely affected the current measurement adversely. At this time, exponential weights \(\omega_{exp,t}^{i}\), taking the place of the original absolute weights \(w_{t}^{i}\) at low likelihood due to large noise, are assigned to those particles outside the \(3\sigma\) range. For particles within the \(3\sigma\) range, the particle weights remain as the original absolute weights. Overall, particle weights are calculated with Equation (18). After that, the exponential weights and absolute weights are normalized to \([0,1]\) in Equation (19) for the subsequent state estimation and measurement prediction. \[\widetilde{\omega}_{t}^{i}= \begin{cases}\omega_{exp,t}^{i}=exp(w_{t}^{i})&| \textit{res}_{t}^{i}|>3\sigma\\ w_{t}^{i}&|\textit{res}_{t}^{i}|\leq 3\sigma\end{cases}  \tag{18}\]  
\[\widetilde{\omega}_{t}^{i}=\frac{\widetilde{\omega}_{t}^{i}}{\sum_{i=1}^{N} \widetilde{\omega}_{t}^{i}} \tag{19}\]  
In summary, the particle filter with weight adjustment algorithm is shown in Algorithm 1.
## 5 Conclusions  
DL methods are significantly more efficient in Kyphosis illness identification and diagnosis than traditional computing technologies. The DNN model outperformed all other ML models regarding classification accuracy, achieving 87.64% and 87.72% with stratified 10-fold and 5-fold cross-validation, respectively. These results exceed the most recent analysis in various academic publications. If we compare the experimental results with the literature with the same dataset, our proposed work improves the accuracy by 2.53% and 1.22% based on the 5-fold and 10-fold cross-validation, respectively. The DNN model is the best suitable for the diagnosis of Kyphosis in comparison to other methods presented in this work and the literature. Compared to ANN, the numerous layers in DNN enable models to learn complicated features and carry out more demanding computational tasks more efficiently. Furthermore, DNN is more effective in training time as they can learn faster.  
It is advised that when a patient has a clinical procedure, the DNN model be trained to detect and foreseek Kyphosis disease. Medical experts can use this study's findings to correctly predict if a patient will still have Kyphosis after surgery. We propose that DL should be adopted and utilized as a crucial and necessary tool throughout the broad range of resolving biological queries. Future research can explore more DL methods to increase the accuracy of Kyphosis disease prediction.  
**Author Contributions:** Conceptualizations, A.S.C. and U.K.L.; methodology, A.S.C., U.K.L. and A.K.G.; software, A.S.C., P.M. and K.R.; validation, A.S.C., F.H. and I.K.; formal analysis, A.S.C., F.H., I.K. and K.R.; data curation, A.S.C., P.M. and R.R.G.; writing--original draft preparation, A.S.C. and U.K.L.; writing--review and editing, A.K.G., P.M. and R.R.G.; visualization, A.S.C., F.H., I.K. and K.R.; supervision, A.S.C., U.K.L. and P.M.; project administration, F.H., I.K. and K.R.; funding acquisition, F.H. and I.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not Applicable.  
**Informed Consent Statement:** Not Applicable.  
**Data Availability Statement:** Not Applicable.  
**Acknowledgments:** The authors thank Princess [PERSON] Abdulrahman University Researchers Supporting Project number (PNURSP2023R236), Princess [PERSON] Abdulrahman University, Riyadh, Saudi Arabia.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Analysis
#### 2.2.5 Volatile Organic Compounds Analysis  
The volatile organic compounds were determined by solid phase microextraction-gas chromatography-mass spectrometry (SPME-GC/MS), according to [PERSON] et al. (2018). Briefly, volatile organic compounds (VOCs) were extracted from 5 g of a finely-ground sample in a 20-mL glass vial closed with an aluminium cap equipped with a PTFE-septum. Samples were incubated for 15 min and then VOCs were collected using a divinylbenzene/carboxen/polydimethylsiloxane (DVB/Carboxen/PDMS) Stable Flex SPME fibre (50/30 \(\upmu\)m; 2-cm long) (Supelco, Bellefonte, PA, USA).  
The SPME fibre was exposed to headspace for 30 min. The conditioning and exposure were carried out at 60 \({}^{\circ}\)C [25]. The fibre was inserted into the injector of a single quadrupole GC/MS (TRACE GC/MS, Thermo-Finnigan, Waltham, MA, USA) set at 250 \({}^{\circ}\)C, 3 min in splitless mode, keeping the fibre in the injector for 30 min to obtain complete fibre desorption.  
The GC programme conditions were the same as those described by [PERSON] et al. [24]. The GC was coupled with a Varian CP-WAX-52 capillary column (60 m \(\times\) 0.32 mm; coating thickness 0.5 \(\upmu\)m). The transfer-line and the ion source were both set at 250 \({}^{\circ}\)C. The filament emission current was 70 eV. A mass range from 35 to 270 \(m\)/z was scanned at a rate of 1.6 amu/s. The acquisition was carried out by electron impact, using the full scan (TIC) mode. Three replicates (\(n=3\)) were run per sample. The VOCs were identified in three different ways: (i) comparison with the mass spectra of the [PERSON] library (version 2.0-11/2008); (ii) injection of authentic standards; and (iii) calculation of the linear retention index (LRI) and matching with reported indexes [26; 27; 28]. Data were expressed as the peak percentages of the total VOCs.
# Performance Optimization of Object Tracking Algorithms in OpenCV on GPUs
## 2 Background and Motivation
### Previous Studies on Optimizing GPU Kernels for Computer Vision Algorithms
In general, loop unrolling improves performance by reducing executions of such instructions as incremental, comparative, and branch [30] and by exposing instruction-level parallelism if no loop-carried dependency exists. In OpenCL, all loop body statements can be re-written manually for loop unrolling. Furthermore, loop unrolling can simply apply by writing #pragma unroll in front of the loops, as shown in Lines 14 and 19 in Figure 4.  
Figure 4: Leveraging local memory and loop unrolling of OpenCL in the gaussianBlur5 optical flow kernel function [29]. **Vectorization. ** The SIMD unit supports computations with a vector (an array of primitive data types, such as integer or float) [27]. Hence, by transforming an array of values into a vector, multiple values can be computed at once in parallel. In our target GPU architecture, an SIMD unit supports up to 16 integer/floating-point operations in parallel. To alleviate vectorization, sometimes, the structure of an array is transformed to make it more fit to the vector instructions [3].
# Performance Evaluation of IMERG GPM Products during Tropical Storm Imelda
## 4 Conclusions
Overall, between the products, the Early-run was found to be better than the Late- and Final-run products for capturing the storm precipitation. Thus, it can be opened that the processing algorithm that used the monthly gauge analysis to develop the Final-run failed to capture extreme events that occurred for more than a few days. Furthermore, better precipitation estimates for all stages of the GPM products will be crucial in hydrological analysis and modeling during extreme events, such as hurricanes, tornadoes, and tropical storms. This conclusion is supported by earlier studies on IMERG-GPM products during extreme precipitation events [35; 68; 69; 53]. Conceptualization, S.S., D.G. and H.O.S. ; methodology, S.S. and D.G. ; software, D.G. ; validation, S.S., D.G. and H.O.S. ; formal analysis, S.S.; investigation, D.G. ; resources, H.O.S. ; data curation, D.G. ; writing--original draft preparation, S.S.; writing--review and editing, S.S. and H.O.S. ; visualization, S.S. and D.G. ; supervision, H.O.S. All authors have read and agreed to the published version of the manuscript. This research was partially funded by U.S. Army Research Office (Grant W912 HZ-14-P-0160) and a grant from UTSA (University of Texas at San Antonio) Open Cloud Institute (OCI) entitled \"Prediction of Crash Incidence and Severity on Texas Roadways\". Not applicable. The data presented in this study are available in [[http://pmm.nasa.gov/data-access/downloads/gpm/](http://pmm.nasa.gov/data-access/downloads/gpm/)]([http://pmm.nasa.gov/data-access/downloads/gpm/](http://pmm.nasa.gov/data-access/downloads/gpm/)) (accessed on 26 May 2021) and [[http://data.eol.ucar.edu/](http://data.eol.ucar.edu/)]([http://data.eol.ucar.edu/](http://data.eol.ucar.edu/)) (accessed on 26 May 2021). The aforementioned funding and supports are committedly acknowledged. The authors declare no conflict of interest.
# AI-Assisted Multi-Operator RAN Sharing for Energy-Efficient Networks
## 6 Implementation, Results, and Analysis  
In this section, we implemented and evaluated the energy performance of the TowerCo's RANaaS RAN-sharing scenario seen in Figure 8. We used the AI system proposed in [22] to assist each MNO in adapting their logical RAN resources. Following this, we looked at the energy savings' probability under three scenarios and studied the impact of the operator's requirements over 24 h. This analysis considered the energy-saving features activated under different scenarios and the extent to which the operator's decisions overlapped. These simulations were carried out using a proprietary network-level evaluation tool.
## 3 Results and Discussion
### Adsorption of Reactive Orange 16 (RO 16) as an Indicator for Fabric Sorption Properties
#### 3.3.2 Influence of Adsorbent Mass on Dye Removal and Fabrics' Adsorption Potential for RO 16  
The effect of adsorbent mass on the \(R\) of RO 16 and fabrics' adsorption potential (in further text: \(q\)) for the same dye was investigated by putting 100 mL of RO 16 aqueous solution (\(c_{0}\) = 25 mg/L) in contact with different amounts (0.25, 0.50, 0.75, or 1.00 g) of raw or chitosan treated jute fabrics. The results presented in Figure 7a show that increments of the adsorbent mass (with the exception of J2.0% CS) give rise to an increase in dye \(R\). The different shape of J2.0% CS curve is the consequence of different dye-binding mechanisms, reflecting different interactions between dye molecules and fabric surface, which is described further. By increasing the adsorbent mass by 4 times (from 0.25 to 1.00 g), the dye \(R\) by J, J0.5% CS, J1.0% CS, and J2.0% CS increased for 2.3, 2.0, 1.4, and 1.8 times, respectively. This behavior is expected since, with increasing the adsorbent mass, there is a higher number of available adsorption sites capable of interacting with RO 16, leading to the augmentation in the extent of dye removal [42; 43].  
Although the \(R\) of RO 16 increased with increasing the adsorbent mass, the fabrics' \(q\) conversely declined (Figure 7b). This downward trend can be explained by the fact that when larger amounts of adsorbent are used, the fabrics' adsorption sites remain partially unsaturated during the adsorption process in the presence of constant solution volume, resulting in reduced \(q\)[44]. Similar observations for the adsorbent mass vs. \(R\) of RO 16 and adsorbent mass vs. \(q\) have been reported for different lignocellulosic-based adsorbents [29; 30; 33; 45; 46].  
Figure 7: The effect of adsorbent mass on: (**a**) RO 16 removal (\(R\)) and (**b**) adsorption potential (\(q\)) of studied adsorbents (\(c_{0}\) = 25 mg/L, \(t\) = 21 h, \(T\) = 25 \({}^{\circ}\)C, pH of 2.0 (for J, J0.5% CS, and J1.0% CS) and 4.0 (for J2.0% CS), the mass of adsorbent 0.25–1.00 g).  
From Figure 7b, it is apparent that when 0.25 g of J, J0.5% CS, J1.0% CS, or J2.0% CS was immersed in 100 mL of RO 16 aqueous solution, fabrics' \(q\) accounted for 3.49, 4.09, 6.15, and 5.08 mg/g, respectively. Based on the findings of this section, in further experiments, the adsorbent mass of 0.25 g was fixed for effective dye adsorption.
_Appl. Sci._**2015**, \(5\), 622-636; doi:10.3390/app5030622  
_applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Article_  
**Critical Nodes Identification of Power Systems Based on Controllability of Complex Networks**  
**[PERSON], [PERSON], [PERSON] and [PERSON] ***  
Department of Electrical Engineering, Northeastern University, Shenyang 110819, China;  
E-Mails: [EMAIL_ADDRESS] (Y.-S.L.); [PERSON]DRESS] (D.-Z.M.);  
[PERSON]] (H.-G.Z.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +86-24-8368-3907; Fax: +86-24-8368-9605.  
Academic Editors: [PERSON] and [PERSON]  
_Received: 18 July 2015 / Accepted: 14 September 2015 / Published: 22 September 2015_  
This paper proposes a new method for assessing the vulnerability of power systems based on the controllability theories of complex networks. A novel controllability index is established, taking into consideration the full controllability of the power systems, for identifying critical nodes. The network controllability model is used to calculate the minimum number of driver nodes (\(N_{D}\)), which can solve the computable problems of the controllability of power systems. The proposed approach firstly applies the network controllability theories to research the power systems' vulnerability, which can not only effectively reveal the important nodes but also maintain full control of the power systems. Meanwhile, the method can also overcome the limitation of the hypothesis that the weight of each link or transmission line must be known compared with the existing literature. In addition, the power system is considered as a directed network and the power system model is also redefined. The proposed methodology is then used to identify critical nodes of the IEEE 118 and 300 bus system. The results show that the failure of the critical nodes can clearly increase \(N_{D}\) and lead a significant driver node shift. Thus, the rationality and validity are verified.  
**Keywords:** controllability of networks; power systems; node identification; structural controllability; vulnerability