esa-sceva/satcom-chunk-collection huggingface dataset:
# Microstructure and Properties of AA6061/SiCp Composites Sintered under Ultra High-Pressure
###### Abstract  
Ultra high-pressure sintering (UHPS) was used to prepare AA6061/SiCp composites with different contents and the effect of sintering temperatures on microstructure and mechanical properties was investigated in this study. The results showed that a uniform distribution of nano-SiC particles (N-SiCp) is obtained by the UHPS method. With the increase in N-SiCp contents, the higher hardness and better wear resistance could be inspected. The interfacial reactions and Al\({}_{4}\)C\({}_{3}\) phase appeared above 550 \({}^{\circ}\)C. The relative density of composites first increased and then decreased; with the temperature raising it reached 99.58% at 600 \({}^{\circ}\)C. The hardness and wear property showed the same trend with the hardness reaching 52 HRA and wear rate being \(1.0\times 10^{-6}\) g/m at 600 \({}^{\circ}\)C. Besides, the wear mechanism of the composites is mainly composed of abrasive wear and adhesive wear.  
characterization; aluminum alloys; composites; powder methods; fracture behavior +
Footnote †: journal: Applied Sciences
# A Network Device Identification Method Based on Packet Temporal Features and Machine Learning
## 2 Data Analysis and Feature Engineering
### Feature Selection  
After extracting the initial features, to improve the efficiency of the model, we used the Xverse package to eliminate unnecessary features using a feature importance-based voting method. This method uses six different feature selection methods to calculate the importance scores of all features for each device and then uses voting to decide whether to keep the features in or not. The six scoring techniques used by this method are information value using the weight of evidence, variable importance using RF, recursive feature elimination, variable importance using extra trees classifier, Chi-square best variables, and \(L1\)-based feature selection. The result of the feature voting is shown in Figure 2. We removed the features that were voted as 0 by the six methods.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Port-Protocol** & **Class** & **Port-Protocol** & **Class** \\ \hline No port & 0 & 443-HTTS & 7 \\
0-Reserved & 1 & 1900-SSDP & 8 \\
53-DNS & 2 & 5353-mDNS & 9 \\
67-BOOTP server & 3 & 49153-ANTLR & 10 \\
68-BOOTP client & 4 & 0:1023-well-known ports & 11 \\
80-HTTP & 5 & 1023:49151-registered ports & 12 \\
123-NTP & 6 & 49151:65535-dynamic(private)ports & 13 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Source and destination port classification.  
Figure 2: The features and the average votes the UNSW dataset (2014) received.  
After removing the features with a vote of 0, we found that some of the remaining features contained information specific to a particular session, e.g., the initial values of the IP ID, TCP sequence, and acknowledgment numbers were randomly assigned, and the next values were consecutive numbers following this initial value. Therefore, these features were not valuable information for identification and we excluded them, eventually selecting 52 features.  
Finally, after eliminating redundant features, we used a genetic algorithm to decide the most appropriate set of features from those remaining. Genetic algorithms are heuristic search algorithms, inspired by the theory of biological evolution, that optimize problems by modeling natural selection and genetic mechanisms. The algorithm represents potential solutions as chromosomes, each of which consists of a series of genes that make up an initial population, which then gradually evolve to produce better solutions through an iterative process. By iterating for 100 generations and selecting the optimal result, 27 features were chosen. Using this method, we created a subset of features with higher performance and reduced the complexity of the model by reducing the number of features. The results of each stage of feature selection are shown in Table 2.
## 1 Introduction
* The design of a novel hash-based scheme that improves on the existing literature, efficiently stores integrity information for ECU software images, and addresses the various architectures of the software and vehicle markets. Our approach allows us to maintain one key security pair for signing and the same signature size to obtain the target security level. We define our scheme to accommodate interactions and compatibility between distinct software images, whether logically or physically interconnected within the vehicle. * The development of the suggested approach prototype and demonstration that our strategy fulfills the specified requirements. The remainder of this paper is organized as follows. Section 2 provides a summary of related work. Section 3 defines our system and the adversary model. Section 4 describes our scheme, including Merkle tree details and defining the interactions between entities. In Section 5, we analyze the security of our approach. Section 6 presents our performance evaluation and results. Section 7 discusses different methods to optimize the MT-SOTA approach to better address different use-cases. Finally, we conclude the paper in the last section.
## 5 Conclusions  
Based on a coupled thermal-structural model and the uncertainty propagation analysis method, the dispersions of the maximum temperature and thermal stress inside a receiver tube wall under multi-source uncertainties were presented, allowing us to provide  
Figure 12: Sensitivity analysis results of the maximum temperature inside the tube wall.  
Figure 13: Sensitivity analysis results of the maximum stress inside the tube wall.  
additional reliability in the process of the SPT molten salt receiver design. The results revealed the following findings:  
1. The maximum temperature inside the tube wall under multi-source uncertainties ranged from 847 K to 895 K with an expectation of 871 K and a standard deviation of 8 K. The results demonstrated that there was a 50% probability that the maximum temperature of the tube wall under uncertainty would be more than the deterministic value of 871 K, and the maximum deviation of the uncertainty analysis result from the deterministic value was 24 K, which may cause the overheating of the receiver.
2. The maximum thermal stress inside the tube wall under multi-source uncertainties ranged from 173 MPa to 245 MPa, with an expectation of 204 MPa and a standard deviation of 12 MPa. The results demonstrated that there was a 40% probability of exceeding the deterministic result of the maximum thermal stress inside the tube wall (204 MPa), and the maximum deviation of uncertainty analysis result from the deterministic value was 41 MPa, which may cause fatigue failure in the receiver.
3. The global sensitivity results indicated that the peak incident solar flux was the most sensitive to the maximum temperature and thermal stress inside the tube wall with contribution rates of 80.81% and 55.53%, respectively. Furthermore, the elastic modulus, the thermal expansion coefficient, and the thermal conductivity of the tube material also had great effects on the maximum thermal stress inside the tube wall, with contribution rates of 16.13%, 13.98%, and 10.05%, respectively.  
Methodology, Y.L.; software, G.L.; validation, Z.W.; investigation, Z.W.; data curation, G.L.; writing--original draft preparation, Y.L.; writing--review and editing, T.L.; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 51806009) and the State Key Laboratory of Clean Energy Utilization (No. ZJU-CEU2020019).  
The authors declare no conflict of interest.
# Integrated Fiber-FSO WDM Access System with Fiber Fault Protection
## 1 Introduction
Compared with the related recent study [24], the proposed self-protected WDM-PON architecture can not only support the fiber and FSO transmissions at the same time, but also provide self-healing fiber breakpoint protection with simple and cost-effective implement.
## 4 Results
### Progressive Damage Test in SHM Mode  
Hybrid ultrasonic-optical data was also collected in a structural health monitoring (SHM) mode. In SHM mode, data was collected on the undamaged pipe, and then after each successive growth in flaw size. Figure 6 shows enveloped waveforms from several SHM states when the second flaw, located 7 feet from the sensor, was at 0%, 0.5%, 1% and 2% CSA. As shown, the reflection amplitude increases as the flaw size increases. Additionally, the reflection amplitude from the flaw at 10 feet from the sensor remains relatively constant, because the size of this flaw remains the same. Note that the 0.5% and 1% CSA flaws represent a 0.25-inch diameter hole with 0.125-inch depth and a 0.25-inch diameter hole with 0.236-inch depth, respectively.
## 3 Results and Discussion
### Color Analysis  
One of the most straightforward characteristics of an image is color. In the following parts, the effects of the proposed method are demonstrated on the reconstruction of actual image color. The RGB color space model is chosen. The intensities of each pixel in channels R, G, and B are tallied. Taking Scene One as an example, the statistical results are given in Figure 7. The proposed method in Ref. [24] could improve the image contrast to some extent (Figure 7b), but the improvement in color is limited, since the extension of the intensity statistic distribution is limited, as exhibited in Figure 7e. In comparison, Figure 7f is considerably extended as well as the dynamic range of the image color. The color distribution of the original image ranges from 30 to 120. The clear image by our method is shown in Figure 7f, which has an extended dynamic range from 0 to 255. Additionally, the color distribution of our method is about twice that of Ref. [24].  
In addition, Figure 8 presents the results for Scene Three. Figure 8b was processed by the proposed method, displaying higher contrast and a better visual effect than Figure 8a.  
Figure 6: (**a**,**b**): The original image and the reconstructed image. (**c**,**d**): Horizontal line plot at the vertical position pixel 120 and 85 from the top for (**a**,**b**). (**e**,**f**): The enlarged tower cranes part.  
Figure 7: The R, G, and B channel intensity statistics of images in Figure 5. (**a**–**c**) are the raw image, reconstructed image by Ref. [24], and proposed method; (**d–**f**) are the channel intensity statistics of the images above.  
Additionally, the dynamic range of color is noticeably improved by approximately 2 times that of the original image, indicating much clearer details of the scene.  
All the results demonstrate the capabilities of CP imaging technology in the effective reconstruction and correction of color decay brought about by the scattering of water spray, as well as the enhancement of image quality.
# A Study of LoRa: Long Range & Low Power Networks for the Internet of Things
## 6 Conclusions  
LoRa is a long-range and low-power telecommunication systems for the \"Internet of Things\". The physical layer uses the LoRa modulation, a proprietary technology with a MAC protocol. LoRaWAN is an open standard with the specification available free of charge [23].  
This paper gives a comprehensive analysis of the LoRa modulation, including the data rate, frame format, spreading factor, receiver sensitivity, etc. A testbed has been built, to experimentally study the network performance, documented in this paper. The results show that LoRa modulation, thanks to the chirp spread spectrum modulation and high receiver sensitivity, offers good resistance to interference. Field tests show that LoRa can offer satisfactory network coverage up to 3 km in a suburban area with dense residential dwellings. The spreading factor has significant impact on the network coverage, as does the data rate. LoRa is thus well suited to low-power, low-throughput and long-range networks.  
This paper has also shown that LoRaWAN is an LPWAN protocol very similar to ALOHA. Its performance thus degrades quickly when the load on the link increases.  
This work was supported in part by the Cisco-Polytechnique Chaire \"Internet of Everything\" ([[http://www.Internet-of-everything.fr/](http://www.Internet-of-everything.fr/)]([http://www.Internet-of-everything.fr/](http://www.Internet-of-everything.fr/))). The authors would like to particularly thank [PERSON], Cisco Paris Research and Innovation Laboratory (PIRL), France, for supplying a LoRA gateway and for providing access to a LoRa network server.  
Figure 12: An example of shared gateways in LoRa. The gateways can forward the packet to different network servers.  
**Author Contributions:** Alo\(\flat\)s [PERSON], [PERSON], [PERSON] and [PERSON] conceived, designed and performed the experiments; Alo\(\flat\)s [PERSON], [PERSON], [PERSON] and [PERSON] contributed materials/analysis tools; Alo\(\flat\)s [PERSON], [PERSON], [PERSON] and [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
## 2 Materials and Methods
### Sample Preparation and Determination of VOCs  
The VOCs were analyzed using a headspace GC-MS system, as previously reported [18]. Briefly, sampling was performed by cutting the fruit flesh into 5 mm cubes using a sharp stainless-steel knife, immersing it in liquid nitrogen, and grinding it into a powder using a mortar and pestle. A total of 4 g frozen sample, together with 2 mL saturated sodium chloride solution, was transferred to a 20 mL vial and sealed with Teflon-lined septa, and then stored at \(-80\)\({}^{\circ}\)C until analysis. For the sample incubation, headspace volatile extraction and injection, an autosampler (Model MPS2; Gerstel Inc., Linthicum, MD, USA) equipped with a cooled tray holder (a cooling plate (Laird Technologies, Earth City, MO, USA) controlled using a Peltier thermostat (CTC Analytics AG, Zwingen, Switzerland)) was used. Vials with tissue homogenizer were held for 0 to 4 h at 4 \({}^{\circ}\)C in the cooled tray. The samples were incubated for 30 min at 40 \({}^{\circ}\)C. A 2 cm solid-phase microextraction (SPME) fiber (50/30 \(\upmu\)m DVB/Carboxen/PDMS; Supelco, Bellefonte, PA, USA) was exposed to the headspace for 30 min at 40 \({}^{\circ}\)C. After exposure, the SPME fiber was inserted into the injector of a GC-MS (Model 6890; Agilent Technologies, Santa Clara, CA, USA) to desorb the extract for 15 min at 250 \({}^{\circ}\)C. A 2 cm tri-phase SPME fiber (50/30 \(\upmu\)m DVB/Carboxen/PDMS, Supelco, Bellefonte, PA, USA) was exposed to the headspace for 30 min at 40 \({}^{\circ}\)C to collect and concentrate volatiles prior to insertion into the injector of a DB-5 column (60 m \(\times\) 0.25 mm i.d., 1.00 \(\upmu\)m film thickness, J&W Scientific, Folsom, CA, USA) installed in an Agilent 7890 GC coupled with a 5975 MS detector (Agilent Technologies, Palo Alto, CA, USA). Compounds were tentatively identified by matching their mass spectra to entries in the NIST 14 ([[https://www.nist.gov/srd/nist-special-database-14](https://www.nist.gov/srd/nist-special-database-14)]([https://www.nist.gov/srd/nist-special-database-14](https://www.nist.gov/srd/nist-special-database-14)), National Institute of Standards and Technology, Gaithersburg, MD, USA) library and the authentic volatile compound standards, as well as by comparing their RIs with corresponding literature data [19]. For quantification, the peak size (total ion current) was used to compare the relative abundance between samples.
## 3 Results  
The locations of finite joint CoRs among different postures for the MCP, PIP, and DIP joints of the index, middle, ring, and little fingers of each participant were estimated with the proposed method and [PERSON]'s method. For example, the estimated finite joint CoRs of the PIP joint among  
Figure 8: Determination of the finite finger joint center of rotation (CoR) between two postures by the intersection of the centerlines of the distal bone surfaces of the two postures.  
Figure 9: Determination of finite finger joint center of rotation (CoR) between two postures by [PERSON]’s method.  
different postures for the index finger are shown in Figure 10. No apparent errors in the locations of the estimated finite joint CoRs were found in the proposed method by visual inspection.  
As shown in Figure 11, the proposed method achieved mean distances (0.5 mm to 1.4 mm) among the estimated finite joint CoRs at the MCP and DIP joints of all the four fingers and the PIP joints of the index, middle, and little fingers less than [PERSON]'s method (0.6 mm to 3.9 mm); statistically significant differences were found at the MCP joint of the index finger (\(t(14)=-2.24\), \(p=0.042\)), the PIP joint of the index finger (\(t(14)=-2.46\), \(p=0.028\)), and the DIP joints of the index (\(t(14)=-2.34\), \(p=0.035\)), middle (\(t(14)=-2.26\), \(p=0.040\)), ring (\(t(14)=-2.55\), \(p=0.023\)), and little (\(t(14)=-3.90\), \(p=0.002\)) fingers. The proposed method showed a mean distance slightly higher than [PERSON]'s method at the PIP joint of the ring finger but not statistically significant (\(t(14)=0.55\), \(p=0.594\)).  
Figure 11: Mean distances and standard errors among the estimated finite joint centers of rotation by the proposed method and [PERSON]’s method at the metacarpophalangeal (left), proximal interphalangeal (upper right), and distal interphalangeal (lower right) joints of the index, middle, ring, and little fingers (dotted box: significant difference at \(\alpha=0.05\)).  
Figure 10: Estimated finite finger joint centers of rotation among different postures by the proposed method for the proximal interphalangeal joint of the index finger.  
To examine the effect of rotation angle on estimated finite joint CoRs, the mean distance between the finite joint CoR estimated under a rotation angle and other finite joint CoRs estimated under the remaining rotation angles was calculated. Rotation angles were divided into 18 groups every 5 degrees from 0 to 90 degrees. Then, the average value of the mean distances within each rotation group for all the participants was calculated and plotted. As shown in Figure 12, the average of mean distances of an estimated finite joint CoR to other estimated finite joint CoRs decreased rapidly as the rotation angle increased from 0-5-degree group to 5-10-degree group. Then, the decrease slowed down as the rotation angle increased from the 5-10-degree group to the 20-25-degree group and, lastly, became leveled off after the rotation angle reached 25 degrees. Large fluctuations were observed at the MCP and DIP joints in [PERSON]'s method, whereas no fluctuation occurred in the proposed method.
## 4 Simulation Results and Discussion  
In this section, we evaluate the performance of the three TOPSIS variants in response to rapid change in the satellite criteria values including upload speed, download speed, ping, and packet loss. The goal is to determine which of the algorithms studied is the most robust in handling variations in these performance criteria across different satellites.  
Figure 2: Block diagram of the methodology.
## 1 Introduction  
Plants adapt to the signals, such as the light quality, they perceive from the environment and accordingly modify their biological cycles [1]. Different types of photoreceptors, such as cryptochromes and phytochromes, enable plants to perceive changes in the light quality [2, 3]. Throughout their lifecycle, the growth and development of plants are influenced by the photoreceptors. Photoreceptors monitor the light environment and also help plants time key developmental transitions, such as flowering and seed germination [4]. Phytochrome is a photoreceptor that primarily absorbs red (R) and far-red (Fr) lights, while cryptochrome is a photoreceptor that primarily absorbs ultraviolet-A (UV-A) and blue (B) lights, both of which help regulate flowering [5]. Multiple cryptochrome (_CRY1_ and CRY2_) and phytochrome (_PHYA_, _PHYB_, _PHYC_, _PHYD_, and _PHYE_) varieties can exist, depending on the species [6; 7].  
Light supplementation is often utilized for enhancing the quality of seedlings and rooted cuttings [8]. Photoperiod manipulation can reduce the production time and improve the overall crop quality to reduce production costs [9]. Light supplementation may take the form of supplementary light in a background of natural light, or additional light that extends the day length [8]. Night interruption (NI) interrupts a length of dark period with lighting, thus creating modified long-day (LD) conditions [10; 11].  
Studies have reported that B light negatively affects stem elongation and leads to a reduced leaf area [12; 13; 14; 15; 16]. [PERSON] [17] found that blue light played a pivotal role in chloroplast development and formation, as well as the stomatal opening. It has been suggested that photoreceptors related to B light played a part in the flowering process [18; 19]. [PERSON] et al. [20] reported that supplementary blue light at least in part promotes the elongation of stems and internodes without inhibiting the flower bud formation. In the short-day (SD) plant chrysanthemum, NI with B light did not effectively inhibit flowering, although B light is part of visible light [21; 22]. Our previous study [11] split the traditional 4-h NI into two 2-h periods and shifted the NI light quality to examine how these changes affect the flowering and morphogenesis of chrysanthemum. They found out that B, Fr, R, and white (W) lights used in the first 2 h of the NI did not affect the morphogenesis nor flowering, while the same lights used in the last 2 h of the NI significantly impacted the morphogenesis and flowering. In addition, they discovered that flowering was induced in all NI treatments concluding with a blue light. Hence, we hypothesized that blue light at a low intensity supplemented to either LD or SD conditions may induce flowering in SD plants. Therefore, this study examined the effects of low-intensity (10 \(\upmu\)mol m\({}^{-2}\) s\({}^{-1}\) PPFD) blue light used as supplementary or NI light on the flowering, gene expression, and morphogenesis in chrysanthemum 'Gaya Yellow' (a qualitative SD plant).