esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Source Heterogeneous Kernel Mapping in Software Defect Prediction
## 3 Research Methodology
### Feature Mapping Strategies  
To introduce the feature mapping strategies and MSHKM algorithm, we first identify the target project. Then, we select several source projects in the heterogeneous scenario where the data feature spaces of the source projects and target project differ. The details of the projects and datasets are presented in Section 4.1.  
There are two mapping strategies that can be used when conducting a feature space transformation to deal with multi-source HDP.  
1. [label=(0)]
2. [label=(0)]
3. [label=(0)]
4. [label=(0)]
5. [label=(0)]
6. [label=(0)]  
==ERROR: No output for this page==
\begin{table} \
\begin{tabular}{c||c c c c c c} \hline
**Target** & **WPDP** & **CCA+** & **HDP-KS** & **CTKCCA** & **MHCPDP** & **MSHKM** \\ \hline CM1 & 0.818 & 0.549 & 0.528 & 0.967 & 0.507 & 0.935 \\ MW1 & 0.800 & 0.667 & 0.672 & 0.965 & 0.712 & 0.923 \\ PC1 & 0.876 & 0.582 & 0.584 & 0.795 & 0.547 & 0.915 \\ PC3 & 0.824 & 0.544 & 0.643 & 0.462 & 0.593 & 0.937 \\ PC4 & 0.885 & 0.631 & 0.553 & 0.369 & 0.569 & 0.950 \\ AR1 & 0.999 & 0.583 & 0.513 & 0.959 & 0.533 & 0.875 \\ AR3 & 0.910 & 0.709 & 0.756 & 0.904 & 0.728 & 0.871 \\ AR4 & 0.881 & 0.715 & 0.647 & 0.918 & 0.755 & 0.943 \\ AR5 & 0.990 & 0.798 & 0.884 & 0.710 & 0.742 & 0.864 \\ AR6 & 0.830 & 0.601 & 0.529 & 0.952 & 0.552 & 0.965 \\ Apache & 0.721 & 0.511 & 0.514 & 0.662 & 0.527 & 0.958 \\ Safe & 0.877 & 0.648 & 0.593 & 0.894 & 0.636 & 0.935 \\ ZXing & 0.539 & 0.460 & 0.423 & 0.561 & 0.454 & 0.908 \\ EQ & 0.792 & 0.572 & 0.493 & 0.513 & 0.521 & 0.940 \\ JDT & 0.762 & 0.585 & 0.571 & 0.320 & 0.615 & 0.972 \\ LC & 0.746 & 0.554 & 0.531 & 0.927 & 0.597 & 0.934 \\ ML & 0.754 & 0.455 & 0.519 & 0.324 & 0.528 & 0.939 \\ PDE & 0.613 & 0.570 & 0.570 & 0.315 & 0.533 & 0.969 \\ ant1.3 & 0.6\end{tabular}\end{table}
+++  
Additionally, Figures 8-10 show the boxplots of results of WPDP, CCA+, HDP-KS, CTKCCA, MHCPDP, and MSHKM, in terms of Pd, Pf, and GM, respectively. The plots show that MSHKM had the highest mean values for Pd and GM, as well as the lowest mean values for Pf. Moreover, according to the distribution in the figures, the effect of MSHKM was more stable than those of the other five methods.  
According to the above results, we can answer RQ2 and RQ3. For RQ2, the number of best results for MSHKM was higher than that for CCA+, HDP-KS, CTKCCA, and MHCPDP, except for the Pf of CTKCCA. However, all mean values of MSHKM were better than those of CTKCCA. This suggests that MSHKM performs better and is more stable than CTKCCA. For RQ3, both the numbers of best results and mean values of MSHKM were higher than those of WPDP.  
Based on the above experimental results, we can draw the following conclusion. The proposed MSHKM is superior to the existing HDP method, including CCA+, HDP-KS, and CTKCCA. In addition, MSHKM significantly outperforms the multiple-source HDP method, MHCPDP, and improves by at least 50.57%, 78.73%, and 43.61%, in terms of Pd, Pf, and GM, respectively. Moreover, MSHKM outperforms WPDP and improves by 17.09%, 65.46%, and 16.65%, in terms of Pd, Pf, and GM, respectively.
###### Abstract  
Avocado (_Persea americana_ Mill.) is a widely cultivated fruit known for its nutritional benefits, with the seed representing a significant portion of the fruit that is often discarded as waste. In the Dominican Republic, the cultivar Semil 34 represents 58% of the national production. This study aimed to explore the potential of Semil 34 avocado seed (AS) as a source of bioactive compounds with applications in the food industry. We conducted the chemical characterization of the seed extract, focusing on its total phenolic content, total flavonoids, and antioxidant capacity. High-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (HPLC-QTOF-MS) was employed to identify key phytochemicals, including phenolic acids and flavonoids, that were responsible for the antioxidant properties of the extract. The hydroalcoholic extract of the Semil 34 seeds exhibited an antioxidant capacity of 1743.3 \(\pm\) 52.3 \(\mu\)M Trolox/g extract, total phenolic content of 25.86 \(\pm\) 2.17 mg gallic acid equivalents/g extract, and total flavonoid content of 2.09 \(\pm\) 0.10 mg quercetin equivalents/g extract. However, the extract's antioxidant capacity was found to be sensitive to pH changes, suggesting the need for stabilization when used in acidic or basic food matrices. The present work identified 53 compounds in the Semil 34 seed extracts; among these, 23 are being reported for the first time in avocado seeds. This study demonstrates the potential of the avocado seed as a source of bioactive compounds and hence a functional ingredient, supporting its value in sustainable production and its possible contribution to environmental goals by reducing waste in the avocado industry.  
Keywords:phytochemicals; HPLC-QTOF-MS/MS; avocado seed extract; Semil 34; bioactive compounds; agri-food byproducts; flavonoids; antioxidants +
Footnote †: journal: Applied sciences
# IoT-Driven Resilience Monitoring: Case Study of a Cyber-Physical System
## 5 Cyber-Physical System
### Energy Digital Twin: Virtual World Interactive Model
#### 5.3.1 Dynamic Environment Service  
For the present case study, the DES service is delegated to gather environmental data detected by the sensors installed on the SPVS, along with meteorological data retrieved by the DSPM, using a Web API Connector. Regarding this former aspect, DES collects basic environmental parameters like temperature, pressure, and humidity, but also additional weather information, including cloud coverage and solar irradiance relative to the location where the SPVS is installed. The DES service processes the collected data to perform predictions on temperature and irradiation using ML approaches. Finally, it assimilates solar radiation metrics, which encompass values related to Direct Radiation, Diffuse Radiation, and Direct Normal Irradiance.
# Time-Dependent Performance of a Multi-Hop Software Defined Network
## 5 Network Model  
Consider a network composed of four SDN switches, S1-S4; see Figure 9. Their parameters are the same as the switch in Example 1, except for the SDN switch S4, which is twice as fast. Therefore \(\mu_{1}=\mu_{2}=\mu_{3}=2628.8\) packets/sec while \(\mu_{4}=5257.6\) packets/s. At all the switches, the squared coefficient of variation of service time is identical to the value \(C_{Bi}^{2}=0.33\).  
Figure 9: The example network being considered.  
Similarly to Example 1, the network's performance is investigated during 1 s. Host 1 is sending packet flows of intensity \(\lambda_{01}\) to Host 2, and the traffic rate is changing in the range 500-2500 packets/sec, as shown in Figure 10. Host 4 generates traffic at rate \(\lambda_{02}\), as shown in Figure 10, which is forwarded to Host 2 via the SDN switches \(S2\) and \(S4\).  
As in Example 1, the squared coefficient of variation of interarrival times in the flows \(\lambda_{01}\) is \(C^{2}_{A1}=C^{2}_{01}=1.02\), or \(C^{2}_{A1}=4.08\) or \(C^{2}_{A1}=8.16\). The second input traffic \(\lambda_{02}\) at \(S2\) has only one parameter \(C^{2}_{A2}=C^{2}_{02}=1.02\).  
The SDN controller alters--if needed--the routing to balance the load of nodes every 100 msec; in this example, it refers to the routing probabilities \(r_{12}\) and \(r_{13}\); see Figure 11.  
Figures 12 and 13 illustrate the decomposition of the network model. They present the flows \(\lambda_{i}(t)\) given by Equation (21) and the squared coefficients of variation \(C^{2}_{Ai}(t)\) received from Equations (23) and  
Figure 11: Routing probabilities \(r_{12}(t),r_{13}(t),r_{14}(t)\).  
Figure 12: Input flows \(\lambda_{i}(t)\) for stations \(S1\ldots S4\).  
Figure 13: Squared coefficients of variation \(C^{2}_{\dot{A}i}(t)\) for stations \(S1\ldots S4\).  
Figure 16: Total loss probability for path \(S1-S3-S4\) for different \(C^{2}_{A1}\).  
Let us also consider a **simple example of optimization**. Suppose, as previously, that station \(S1\) is forwarding a flow \(\lambda_{01}\) packets to nodes \(S2\) and \(S3\). Station \(S2\) is additionally receiving from Host 4 a flow of \(\lambda_{02}^{(loc)}\) packets. The controller is changing routing every \(\Delta=100\) msec and needs to determine the routing probabilities for the nearest \(\Delta\), knowing the current parameters of flows at the beginning of the interval, as well as the current queue distributions at \(S1\), \(S2\), and \(S3\) representing previous behaviour of the network. The goal is to minimize the mean backlog \(\Psi\) at \(S2\) and \(S3\) during \(\Delta\):  
\[\min_{r_{12},r_{13}}\left\{\Psi=\frac{1}{\Delta}\int_{0}^{\Delta}[E[N_{2}(t)]+ E[N_{3}(t)]]dt\right\}.\]  
We compute \(E[N_{2}(t)]\), \(E[N_{3}(t)]\) for \(t\in\Delta\) and minimize \(\Psi\) by the choice of \(r_{12}\), \(r_{13}=1-r_{12}\); see Figure 18.
## 5 HTS Tapes Studies Using Scanning Electron Microscope  
### Research Methodology  
In order to observe the changes in the internal structure, the HTS tapes were examined using a field emission scanning electron microscope (FE-SEM). The chemical composition was analyzed using the INCA OXFORD Energy-Dispersive X-ray Spectroscopy (EDS). The measurement stand is shown in Figure 7. The following measurement conditions were used during the tests:  
* A scanning probe current intensity of 8 mA;
* An electron beam acceleration voltage of 7-8 kV.  
Figure 8 shows the HTS tape samples embedded in conductive resin.
# DDTree: A Hybrid Deep Learning Model for Real-Time Waterway Depth Prediction and Smart Navigation
###### Abstract
The predictions of depth in waterways are obtained through satellite remote sensing images and real-time sensors; for instance, when performing monitoring of oil spills [10]. Since almost all in-flight vessels are equipped with automatic identification systems (AIS), the analysis of full AIS data and the development of deep learning models could support the improved prediction of waterway depth. In this paper, a hybrid decision tree-deep neural network (DDTree) model is constructed to predict safe waterway depths for ships to increase the precision and timeliness of the waterway depth data and provide references for safe marine navigation and route selection. The rest of this paper is organized as follows. In Section 2, the conceptual model for the analysis of the actual waterway depth data (from Global Mapper) and the AIS data are introduced, and the proposed DDTree model is presented. In Section 3, the results for prediction of waterway depth are presented and the model's performance is analyzed. Finally, Section 4 presents the conclusions of the paper.
# A Random Subcarrier-Selection Method Based on Index Modulation for Secure Transmission
## 3 Performance Analysis
### Spectral Efficiency and Computational Complexity Analysis  
* Spectral Efficiency Analysis One OFDM symbol was taken for reference. According to the principle of RSCS-IM, an OFDM symbol included \(N_{1}\) bits information, which was defined as \[N_{1}=N_{T}log_{2}\frac{N_{s}}{N_{T}}+N_{T}log_{2}M,\] (16) where \(N_{T}\) is the number of antennas, \(N_{s}\) represents the number of total subcarriers and \(M\) is the modulation order. While for PSS Plus RP, the counterpart was \[N_{2}=N_{T}log_{2}M.\] (17)  
Figure 5: Block diagram of the FDA OFDM-IM system.  
It was obvious that \(N_{1}-N_{2}=N_{T}log_{2}\frac{N_{s}}{N_{T}}>0\), so RSCS-IM promoted the spectrum efficiency. Meanwhile, we could see that a large \(N_{s}\) implied a higher spectrum efficiency; however, larger \(N_{s}\) also implied higher computational complexity. We could select the value of \(N_{s}\) and \(N_{T}\) to meet our requirement in a certain scenario.
* Computational Complexity Analysis According to the principles of PSS Plus RP and RSCS-IM shown in Figures 2 and 4, respectively, we analyze the computational complexity of these two schemes in this subsection. The computational complexity was evaluated in terms of the real-valued operations, including real-valued multiplication, real-valued additions, and real-valued modulo operations. According to the principles, we gave the complexity of these two methods shown in Table 2, where \(N_{s}\) and \(N_{T}\) denote the number of total subcarriers and antennas, respectively, and \(N\) is the number of loops of block interleaving.  
From Table 2 we could clearly see that the number of modulo operations in RSCS-IM was less than that of PSS Plus RP. As for the addition operation, in general, \(N_{T}<4N\) and \(log_{2}\frac{N_{b}}{N_{T}}<N_{T}\), so \(N_{T}(log_{2}\frac{N_{s}}{N_{T}}-1)<4N(N_{T}-1)\). Moreover, \(log_{2}\frac{N_{s}}{N_{T}}<10\) in most cases. \(log_{2}\frac{N_{b}}{N_{T}}(log_{2}\frac{N_{s}}{N_{T}}-1)/2\) and \(N\) had the same order of magnitude. Thus, we could see that the number of multiplications in these two schemes were basically equal. Overall, the complexity of RSCS-IM was lower.
# Probing Dust and Water in Martian Atmosphere with Far-Infrared Frequency Spacecraft Occultation
## 5 Conclusions  
Signal attenuation due to dust activity in the 1-10 THz frequency band is investigated under various scenarios of dust activity derived from remote sensing observations. The contribution of dust due to signal attenuation is found to be within the estimates of the observable range from the current state of technology in radio occultation experiments. A method for retrieving the dust mass loading from the signal attenuation in far-infrared transmission that can provide high-resolution data of dust mass mixing ratios in various dust opacity conditions is proposed. This method provides a significant improvement over existing measurements of the opacity of Martian dust. Moreover, the estimation of dust opacity and temperature simultaneously improves the existing capability to predict them during EDL events. The sample return mission [62] will consist of complex operations that would require strong communication support and accurate data on Martian meteorology. An orbiter-based infrastructure for supporting communication can be utilized to retrieve information on the abundance of water vapor, dust opacities, and thermal structure of the surface environment. The high-resolution data retrieved from such a constellation can be leveraged for temperature and dust opacity assimilation in Mars GCMs.  
Conceptualization, A.B. and C.L.; methodology, A.B.; resources, C.L. and D.S.; writing--original draft preparation, A.B.; writing--review and editing, C.L., N.O.R., S.K.A. and D.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.
# Modulus Stretch-Based Circular SAR Imaging with Contour Thinning
## 2 Analysis of Imaging Algorithms
### Backprojection Algorithm
This appears on the image as a contrast stretch transformation of the image, leaving only the highlights and greatly reducing the lower brightness. The usual contrast stretch transformation includes gamma transformation, piecewise linear transformation, histogram equalization and so on. Through the above analysis, \(\psi(\cdot)\) is constructed as the modulus stretch function, which enhances the high modulus and weakens the low modulus.
# Authenticated Multicast in Tiny Networks via an Extremely Low-Bandwidth Medium
## 2 Problem Statement  
Problem 1: _For tiny networks with a very narrow communication channel, we seek to address the following problems:_  
1. _How can the shared keys between the sender and multicast group members be derived without resorting to asymmetric methods such as Diffie-Hellman key exchange, where the messages are relatively long bit strings?_
2. _In reference to the message authentication mechanism for a multicast group, how can the asymmetry in key knowledge between the sender and the receivers be preserved, as is the case of digital signatures, while keeping the total overhead as small as possible?_  
The asymmetry mentioned above is crucial when facing an internal adversary threat. Answering the questions posed by Problem 1 is the main objective of this paper.  
Figure 3: Internal and external attacks on multicast communication.
## 5 Conclusions  
Accurate and rapid auto-focus methods based on image quality assessment were studied in this paper, and the accuracy and sensitivity of various IEFs on different scenes were compared. Further, in order to improve the performance of IEFs, the ES-DAFW method was proposed based on the existing focus window selection method and was compared with the traditional method. Based on the above experiments and analysis, we summarize as follows:  
1. In many image scenes collected by telescope, the Tenengrad function has high sensitivity (slope) near the peak value, which can better reflect the defocusing process of telescope system imaging. It is thus suitable as the estimation function of a telescope system;
2. From the experimental results of different focusing window methods, it can be seen that the ES-DAFW method can provide higher sensitivity and more accurate results for the auto-focus process, especially for a sparse image, when compared with the whole-image focusing window and the traditional center focusing window selection methods. At the same time, it has the advantages of simple calculation and can obviously shorten the time required for auto-focusing. These results promise significant applications to auto-focusing in other telescopes with EOTSs.  
[PERSON] and [PERSON] were involved in leading the research project, funding acquisition, conceptualization, and writing--review and editing. [PERSON] designed and conducted the experiments and wrote up this work. [PERSON] and [PERSON] were involved in writing--review and editing. All authors have read and agreed to the published version of the manuscript. This research was funded by National Natural Science Foundation of China (61775030), Aeronautical Science Foundation of China (2016018001). It was also funded by Sichuan Science and Technology Program, (2019Y0167 and 2019 YFG0307). The authors declare no conflict of interest.
# Efficient Resource Allocation for Beam-Hopping-Based Multi-Satellite Communication Systems
## 3 Offline Resource Allocation Scheme  
In this section, we provide an offline algorithm, and in this algorithm, the channel gains are assumed to be known in advance. Obviously, the optimization problem (5) is a nonconvex mixed integer programming problem. Therefore, we cannot use traditional convex optimization tools to solve the global optimal solution. To address this problem, this paper first assumes an average allocation of power resources and adopts a heuristic algorithm to allocate time-frequency resources. Finally, when the time-frequency resource allocation result is fixed, the SCA algorithm is used for power allocation.
## 3 Results and Discussion
### Optimization Results and Analysis of Mulberry Drying Technology
#### 3.2.1 Effects of the Material Surface Temperature on Mulberry Quality  
Single drying was performed by fixing the vacuum degree at 0.07 MPa and microwave power at 350 W to investigate the effects of mulberry surface temperature (40.0 \({}^{\circ}\)C, 42.0 \({}^{\circ}\)C, 44.0 \({}^{\circ}\)C, 46.0 \({}^{\circ}\)C, 48.0 \({}^{\circ}\)C, 50.0 \({}^{\circ}\)C, 52.0 \({}^{\circ}\)C, 54.0 \({}^{\circ}\)C, 56.0 \({}^{\circ}\)C, 58.0 \({}^{\circ}\)C, and 60.0 \({}^{\circ}\)C) on its quality. The test results are listed in Table 5. With increases in the drying temperature, the chromatic aberration of mulberry first decreased and then increased; the soluble solid content and Vc content increased first and then decreased; and then the chewiness decreased gradually. The hardness increased with increases in the temperature.
# Morphological Changes in H1299 Human Lung Cancer Cells Following W-Band Millimeter-Wave Irradiation
## 2 Materials and Methods
### Exposure Conditions  
A pyramidal horn antenna, model SGH-10 (Millitech Inc.), with aperture dimensions of \(20\,\mathrm{mm}\times 25\,\mathrm{mm}\) was used to irradiate the test samples. Here, 1 mW incident power was used for all irradiation experiments, translating to a power density of 2 W/m\({}^{2}\) (0.2 mW/cm\({}^{2}\)). Every single experiment was repeated four times and involved irradiation of approximately \(2\times 10^{5}\) cells per dish when the cells reached 30% confluence. To investigate the MMW effects on morphology, cells were irradiated under specific time regimes. Cells were seeded on (15 mm) microscopic coverslips in a cell culture dish of a 30 mm diameter prior to irradiation. Samples for irradiation were prepared from cells reaching 30% confluence. The number of plates prepared corresponded to the experimental schematics presented below. Control and test samples were examined every 2-3 days in order to prevent 100% confluence. Cells reaching 60% confluence were detached and transferred to new plates in order to avoid overcrowding. Transferred cells were seeded again at 30% confluence for future growth and examination. These procedures were repeated over the 14-day observation period.  
Each experiment lasted up to 14 days. On day 1, cells were irradiated with MMW for 2 (Group a) or 4 min (Group b), respectively. Following irradiation, cells were incubated at 37 \({}^{\circ}\)C with 5% CO\({}_{2}\) for up to 14 days. Slides of cell samples were prepared at specific time points: Day 1 (90 min after irradiation), Day 7, and Day 14. Digital images of live cells were taken using a light microscope on Day 1, Day 7, and Day 14, respectively. In order to rule out morphological changes due to cell proliferation and not specific to MMW irradiation, two controls were used: 1) Cells that underwent the same procedures but were not irradiated, and 2) cells that were left untouched in the incubator.  
Control samples were assessed at the same time points as the irradiated cells (i.e., at Day 1, Day 7, and Day 14 of the experiment).