esa-sceva/satcom-chunk-collection huggingface dataset:
# Generation of Pure State Photon Triplets in the C-Band
## 2 Tripartite State and Joint Spectrum
### Hamiltonian and Probability Amplitude Function
In the total cascaded process, the holistic Hamiltonian is the product of the Hamiltonian of two parts, that is \(\hat{H}=\hat{H}_{1}\hat{H}_{2}\). The expression of the last three photon states is  
\[\left|\psi_{3}\right\rangle=\int dt_{1}dt_{2}\hat{H}_{1}(t_{1})\hat{H}_{2}(t_{ 2})=B\int d\omega_{1}\int d\omega_{2}\int d\omega_{3}\hat{a}_{1}^{\dagger}( \omega_{1})\hat{a}_{2}^{\dagger}(\omega_{2})\hat{a}_{3}^{\dagger}(\omega_{3} )F(\omega_{1},\omega_{2},\omega_{3})|0\rangle+c.c. \tag{7}\]  
When we determine the frequency distribution of the down-conversion of three photons, and there is no correlation between them, then the three photons amplitude can be equivalent to  
\[F(\omega_{1},\omega_{2},\omega_{3})=\exp(-\Omega_{1}^{2}/\sigma_{1}^{2})\exp (-\Omega_{2}^{2}/\sigma_{2}^{2})\exp(-\Omega_{3}^{2}/\sigma_{3}^{2}) \tag{8}\]  
In practice, there are two sensitive parameters of the system that need to be strictly controlled: (1) the polarization stability of the light source and the optical path, and; (2) the temperature of the non-linear material. Both of them directly affect the refractive index of materials, thus affecting the phase-matching conditions.
## 4 Designed Schemes
### Effective Energy and Reliable Delivery (EERD) Routing  
Network Initialization  
The network dimensions are \(X\times Y\times Z\), where \(X\), \(Y\), and \(Z\) consider the length, depth, and width of the network, respectively. The network contains the source, relay, and sink nodes with random deployment. The position of the sink nodes is specified at the top of the ocean surface as shown in Figure 1, which are further connected to an offshore data center. In the network, each sensor node is responsible for data advancement towards the sink node. The deployed nodes communicate by acoustic links, and sink nodes correspond with the satellite by a radio link. In the network, each sensor node is energized through small battery power. The sink node communicates with sensor nodes by using acoustic links.
# Portable Raman Spectrometer for In Situ Analysis of Asbestos and Fibrous Minerals
## 3 Results and Discussion
### In Situ Identification of Asbestos and Fibrous Minerals  
Results of in situ tests realized with pRS at the mining front are summarized in Table 1. Tests performed on asbestos-bearing veins allowed the discrimination of asbestos (e.g., Valmalenco quarry, Italy), also where asbestos chrysotile and asbestos tremolite phases were intergrown (e.g., Tontouta mine, New Caledonia). Among non-asbestos fibers, Raman spectra of fibrous antigorite, saponite, and balangeroite were recorded (Table 1). When tested on peridotite matrix in Balangero mine, pRS allowed the identification of the typical mineralogical association of the site, consisting of antigorite, lizardite, chlorite, (fibrous) diopside, and olivine (Table 1 and Supplementary Material S3).  
To assess if the degree of cohesion of rock specimens may affect the performance of pRS, Raman spectra collected on differently cohesive veins, from massive to friable-powdered material, were compared. As shown in Figure 3, the progressive loss of cohesion of selected specimens did not affect data quality, and antigorite and tremolite phases were identified (Figure 2a,b, respectively). Spectra from in situ surveys with pRS were compared with literature data, and a good agreement with the main spectral features, i.e., peak position (wavenumber), shape, and relative intensity at both low- and high-wavenumber regions was observed [26; 53] (Supplementary Material S4).  
About 80% of the overall spectra collected in the three sites with pRS were of sufficient quality to allow a successful identification of the mineral phase(s) interacting with the laser beam. To quantify this important instrumental parameter, that we called \"spectral quality\", we calculated the percentage ratio between the number of analysis acquired with the sufficient quality to distinguish the mineral phases and the total number of analysis performed (n. of analyses at Valmalenco 10, Balangero 37, Tontouta 33). This allowed us to draw a first evaluation of the instrumental capabilities during in situ asbestos monitoring (Table 1). The lower spectral quality was registered during the Tontouta survey, with about 70% of the spectra resulting in assignable plots, while the quality of the spectra collected during the Valmalenco and Balangero surveys was 90% and 84%, respectively. This result may be ascribed to several factors: (i) the fluoresce phenomena, due to the host rock interference is likely higher at Tontouta mine, where Fe-rich lateritic soil may induce a higher fluorescence effect; (ii) the chances to correctly focus the laser beam on the rock is lower at Tontouta mine, where non-cohesive bundles of altered fibers were more commonly observed than at the other two sites.
# Monthly Precipitation over Northern Middle Atlas, Eastern Morocco: Homogenization and Trends
## 2 Materials and Methods
### Methodology
#### 2.4.2 Monthly Trend Analysis  
As we mentioned in the introduction, to analyze trends in monthly data series, the classic Mann-Kendall test [45; 46] is used. Statistical trend tests identify and/or estimate the existence or non-existence of a trend in a time series according to the desired degree of significance. Mann-Kendall is a nonparametric test requiring only that the data be serially independent, without assuming the normality of the distribution [47]. The Mann-Kendall statistic S is calculated as follows:  
\[S=\sum_{i=1}^{n-1}\sum_{j=i+1}^{n}\text{sgn}(x_{j}-x_{i}) \tag{1}\]  
where n is the length of the data series, xi and xj are the data values in time series i and j (j > i), respectively, and \(\text{sgn}(xj-xi)\) corresponds to the following sign function:  
\[\text{sgn}\big{(}x_{j}-x_{i}\big{)}=\left\{\begin{array}{c}+1,\text{if}x_{j }-x_{i}>0\\ 0,\text{if}x_{j-x_{i}=0}-1,\text{if}x_{j}-x_{i}<0\end{array}\right\} \tag{2}\]The variance (V) of S is calculated as:  
\[\text{V}(s)=\frac{n(n-1)(2n+5)-\sum_{k=1}^{m}t_{k}(t_{k}-1)(2t_{k}+5)}{18} \tag{3}\]  
In equation number 3, \(n\) is the number of data points, \(m\) is the number of linked groups, and \(tk\) is the number of links of extent \(k\). A related group is a set of data samples with the same value. In cases where the sample size n is greater than 10, the standard normal test statistic \(ZS\) is calculated using the following equation:  
\[Z=\left\{\begin{aligned} \frac{S-1}{\sqrt{var(s)}},ifS>0\\ 0,ifS=0\\ \frac{S+1}{\sqrt{var(s)}},ifS<0\end{aligned}\right\} \tag{4}\]  
Positive Z values indicate increasing trends, while negative values indicate decreasing trends. The trend test is performed at a specific \(\alpha\) significance level. When \(\mid Z\mid>Z_{1-\alpha/2}\), the null hypothesis is rejected and a significant trend exists in the time series. \(Z_{1-\alpha/2}\) is obtained from the standard normal distribution table [48]. In this study, the significance levels chosen are \(\alpha\) = 0.01 (or 99% confidence intervals), \(\alpha\) = 0.05 (or 95% confidence intervals), and \(\alpha\) = 0.1 (or 90% confidence intervals). At the levels of significance 1%, 5% and 10%, the null hypothesis \"absence of trend\" is rejected if \(\mid Z\mid>2.57\), \(1.96\leq\mid Z\mid<2.57\), and \(1.64\leq\mid Z\mid\) 1.96 [49], respectively. [PERSON] and [PERSON] [50] stated that in the case of long-term rainfall trend analysis, the MK test performs better than parametric tests.
# Real-Time Conveyor Belt Deviation Detection Algorithm Based on Multi-Scale Feature Fusion Network
## 3 Experiments
### Evaluations  
Formally, the evaluation metrics for the proposed strategy are pixel accuracy (PA) and testing accuracy. In this paper, PA represents the ratio of the number of edge pixels predicted correctly to the total number of edge pixels in the label, while the testing accuracy is the product of camera resolution and average pixel width. Lower testing accuracy represents a finer predicted edge. The camera resolution is measured as follows:  
When the camera is at an acute angle to the ground, as the model shown in Figure 6, \(\theta\) is the field of view of the camera, \(h\) is the camera installation height, \(\beta\) is the angle between the camera and the ground and \(L\) is the maximum distance that the camera can detect. The mathematical relationship between them is as follows:  
\[L=\frac{2h\tan\theta}{\sin\beta}. \tag{6}\]  
Assuming that the parameter of the CCD camera is vxw, the resolution is expressed as:  
\[pre=\frac{L}{\sqrt{v^{2}+w^{2}}}=\frac{2h\tan\theta}{\sin\beta\sqrt{v^{2}+w^{ 2}}}. \tag{7}\]  
Through calculation, the resolution of the camera used in this paper is 0.8 mm.  
Figure 5: Three examples from the dataset. Three conveyor images (the first line) and their ground truths (the second line).  
Figure 6: The structure and parameters when the camera is at an acute angle to the plane.
# Enabling Autonomous Agents for Mobile Wireless Sensor Networks
## 2 Related Work
### Mobile WSNs  
As it was presented in the previous section, mobile elements can be introduced to dynamically modify network topology [27]. Controlled changes [28] in the network topology can improve network balancing and therefore network lifetime. However, not only were those mobile elements introduced to improve network's operation time, but mobile WSNs can extend the characteristics and resiliency in comparison with static WSNs.  
With regard to current applications, several trends can be identified. As it was the case for static WSNs, agriculture and precision agriculture benefit from the addition of robots.  
In [29], authors use an unmanned aerial vehicle (UAV) as a mobile sink to collect agricultural monitoring data. By following predefined paths, simulations results claim data can be collected at a low energy expense. Nevertheless, most of the use cases found in the literature employ robot clusters instead of a single robot to expand network capabilities, as presented in [30]. This claim is supported by [31], where robots collectively map and collect data in a greenhouse environment. Monitoring of city pollutants using UAVs is discussed in [32], but the limited number of UAVs raises some doubts about system scalability. Multirobot systems can additionally be found in hazardous and harsh environments as in [33], where authors propose a deployment strategy for coal mine monitoring that claims to optimize the number of nodes.
# Assessing the Role of Program Suspend Operation in 3D NAND Flash Based Solid State Drives
## 4 Exploring the Program Suspend in 3D NAND Flash
### RBER in Suspended and Not Suspended Blocks  
An additional characterization to perform on the program suspend test flow is related to the reliability of the wordlines belonging either to the memory block for which the program is suspended or to other blocks that are not involved by the suspend operation. When the program operation is performed on a specific memory block, all the other blocks in the memory undergo a retention period at a certain temperature (the higher the temperature is, the higher the impact on reliability). The effect of the program suspension is to increase the overall program time of a single page; therefore, we expect that the overall programming time of a block will increase accordingly as well as the retention time experienced by non-programmed blocks.  
To better understand, we performed a test in which we programmed, exercising all the possible delay timings combinations, all the wordlines of a memory block. This test induced many program/erase cycles on the same block as, every time we chose a _Delay1_ and _Delay2_ combination, we reprogrammed the entire block. We then compared the RBER metric extracted before and after the experiment. At the same time, we extracted the RBER from a randomly chosen block in which no program operations were performed to check whether the program suspension could induce a reliability degradation in other, non-accessed portions of a 3D NAND Flash memory.  
As shown in Figure 9a, the program suspension does not alter the reliability characteristics of the wordlines in the block where the operation took place, since the RBER stays the same before and after the experiment. Concerning the wordlines in the memory block (see Figure 9b) for which no programs are executed, we actually see a slight increase of the RBER from the beginning of the experiment; however, this is likely due to the retention loss that we are experiencing at room temperature and is well below the correction limit of the many Error Correction Code engines employed in SSDs [35].  
Figure 8: (**a**) The average of the overall programming time \(t_{PGM,s}\). (**b**) The worst \(t_{PGM,s}\) extracted from the 50 wordlines involved in the experiment. Both measurements are plotted as a function of different _Delay1_ and _Delay2_ combinations.  
Based on our experiments, we reached the conclusion that the program suspension did not induce specific reliability issues on the memory with the test conditions adopted. Possible solutions to reduce the RBER in non-suspended blocks could rely on state-of-the-art techniques ranging from advanced Error Correction Code policies (e.g., read retry and soft decoding) or by implementing periodic data refresh strategies to avoid retention-induced charge loss.
# A Review: Sample Preparation and Chromatographic Technologies for Detection of Aflatoxins in Foods
## 2 Sample Preparation
### Extraction
#### 2.2.3 Immunoaffinity Column Clean-Up
Clean-up of more mycotoxins on one column means less flexibility and more stringent column conditions without comprising of clean-up efficiency for multiple mycotoxins. The complexity and cost of column production increases with the number of different types of antibodies that need to be immobilized. Furthermore, it was expected that immunoaffinity columns could provide a clean extract with marginal matrix effects on LC-MS. Unfortunately, that is not the case and making matrix-match calibration standards using immunoaffinity columns is not a practical or economical practice. Therefore, in the future, study efforts may be dedicated to address these challenges to better utilize immunoaffinity column clean-up and LC-MS for aflatoxin analysis. A noteworthy attempt associated with immunoaffinity columns is to automate the clean-up procedure using robotic devices. [PERSON] and [PERSON] [145; 146], [PERSON] et al. [147] and [PERSON] et al. [148] developed and evaluated in-house automated immunoaffinity column clean-up devices. In terms of clean-up efficiency and sample throughput, their results were comparable to manual procedures. These early devices could replace human operators to perform sample clean-up to some extent, but the degree of automation and robustness was low. These devices lacked the capability of accurately dispensing liquid extracts or troubleshooting common errors as human operators. Sample extractions still needed to be performed manually and transferred to the device. Software and hardware were not user-friendly, making training, daily operation and maintenance a burden for human operators. So far, automated sample preparation has been far from a popular option due to the complex and challenging nature of aflatoxin analysis in foods. Hopefully, researchers will continue to take advantage of the advances in computing and engineering technologies and develop automated clean-up procedures for aflatoxin analysis in the near future [41, 149].
# Blockchain Based Secure Routing and Trust Management in Wireless Sensor Networks +
## 8 Conclusions and Future Work  
A blockchained secure routing mechanism for WSNs is proposed in this paper. The SNs and ANs are authenticated using private and public blockchains, respectively. The private blockchain is deployed on ANs while the public blockchain is deployed on the BSs. The trust values of SNs are computed on the basis of DT, FR and RT after the SNs' authentication. The SNs with high trust values are considered as legitimate while others are considered as malicious. The simulation results show that when there are a large number of trusted SNs in the network, the PDR of the network is high. As the energy of SNs gradually depletes, they start to die and only a few SNs participate in the network. As a result, PDR is decreased. Furthermore, FPR and FNR are increased in the presence of malicious nodes that have a negative impact on the DA. The DA is decreased due to high FPR and FNR. Furthermore, RSA is used for the encryption and decryption of the data packets for secure routing. In the future, we will implement the proposed work in different real world networks.  
Conceptualization, S.A. and N.J.; methodology, S.A.; software, A.U.K.; validation, S.A., A.M.Q. and J.-G.C.; formal analysis, A.U.K. and A.M.Q.; writing--original draft preparation, S.A.; writing--review and editing, N.J. and J.-G.C.; Visualization, S.U. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Basic Science Research Program through the National Research Foundation (NRF) of Korea funded by the Ministry of Education under Grant 2018R1D1A1B07048948.  
Not applicable.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ANs & Aggregator Nodes \\ API & Application Programming Interface \\ BSs & Base Stations \\ DA & Detection Accuracy \\ DT & Delayed Transmission \\ FR & Forwarding Rate \\ FNR & False Negative Rate \\ FPR & False Positive Rate \\ IoT & Internet of Things \\ MAC & Media Access Control \\ NCQ & Node Communication Quality \\ PDR & Packet Delivery Ratio \\ PoA & Proof of Authority \\ PoS & Proof of Stake \\ PoW & Proof of Work \\ RT & Response Time \\ RSA & Rivest-Shamir-Adleman \\ SNs & Sensor Nodes \\ SDN & Software Defined Network \\ WSNs & Wireless Sensor Networks \\ \(AN_{ID}\) & Identity of an AN \\ \(BS_{ID}\) & Identity of a BS \\ \(SN_{ID}\) & Identity of an SN \\ \(PRB\) & Private Blockchain \\ \(PI1B\) & Public Blockchain \\ \end{tabular}
# A Methodology to Evaluate Standards and Platforms within Cyber Threat Intelligence
## 4 Standards Evaluation Results
### Standards Selected for Analysis
#### 4.1.1 Structured Threat Information eXpression  
STIX is a language created by MITRE and developed to capture, specify, characterize and communicate information in the context of cyber threat intelligence [41]. It provides mechanisms to represent structured information in different scenarios of the cyber threat ecosystem.  
The language was designed with principles such as interoperability, extensibility, focus on automation and machine readability. In the first version, STIX was modeled in the XML format and it was composed of eight cores. The second version was developed using serialization in JSON format [46].  
Its structural architecture has been significantly modified, is currently composed of twelve main objects that correspond directly to concepts embedded in the context of CTI [47]. With its holistic architecture, STIX is able to present information in a standardized, comprehensive and structured manner while allowing application in different use cases. In addition, it is directly integrable with other languages in the TI context [17].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Standard** & **Popularity** & **License Model** & **References** \\ \hline STIX & ++++ & Permissive license granted by MITRE & [9; 12; 13; 14; 16; 20; 36; 37; 41] \\ \hline TAXII & ++++ & Permissive license granted by MITRE & [9; 13; 16; 20; 32; 36; 37] \\ \hline CybOX & +++ & Permissive license granted by MITRE & [9; 16; 36; 45] \\ \hline IODEF & ++ & IETF TLP 5.0 & [9; 12; 20; 36; 37] \\ \hline RID & ++ & IETF TLP 5.0 & [32; 36; 37] \\ \hline OpenIOC & +++ & Apache 2.0 & [9; 16; 17; 36] \\ \hline VERIS & + & Attribution-ShareAlike 4.0 International & [20; 37] \\ \hline X-ARF & + & Open Source (GNU General Public License) & [35; 37] \\ \hline \hline \end{tabular} Legend: very high (++++) high (++) medium (++) low (+).  
\end{table}
Table 4: Threat Intelligence (TI) standards described by popularity and license model.  
Cyber Observable eXpression is a language created by MITRE and developed to specify, characterize and communicate information about cyber observables in a standardized way. With the release of the second version of STIX, it is no longer used as an independent language. It was integrated into the second version of STIX that defines a structured representation for observable objects in the cyber domain, called _Cyber Observable Object_[48].  
This standardization can be used to describe different types of data, from a host characterization to information about digital forensics. The objects are represented using serialization in JSON format [49].
# Evaluation of AI-Assisted Telemedicine Service Using a Mobile Pet Application
## 1 Introduction  
A global debate over telemedicine has begun due to COVID-19. The Washington Post has addressed Forrester Research's view that the number of telemedicine uses, including treatment for COVID-19, will increase to about 1 billion by the end of 2020 [1]. In addition, IHS Markit, a global information provider in the United Kingdom, forecasts that the global demand for telemedicine will reach 150 million in 2022 from 39 million in 2018 [2]. While the problem of shortage of medical institutions around the world is on the rise, the annual demand for telemedicine--particularly in North America and Asia--is increasing rapidly due to such shortage problems. According to a 2019 digital health survey conducted by Accenture, nearly half of healthy young Americans aged between 22 years old and 38 years old only use routine healthcare services. They want medical services that are more convenient and easier to access than the quality healthcare enjoyed by the previous generation. For example, they hope to have easy access to medical services through online or mobile healthcare applications (apps) [3]. In the United States, where telemedicine was common before COVID-19, plans are currently underway to implement it as an easy alternative to treating several mild diseases. In other words, by using a smartphone or a computer equipped with a webcam, patients can communicate with doctors 24 h a day, 7 days a week, and receive high-quality medical support and treatment at home through national medical communities or local medical programs, in addition to their potential in being used in preparation for the outbreak of a new virus, such as the recent SARS-CoV-2 The COVID-19 pandemic situation has provided an opportunity to avoid primary face-to-face contact between patients and medical staff [4]. This preventive action through digital telemedicine has the advantage of maximizing the treatment effect by minimizing contact between patients with mild symptoms and severe patients with underlying diseases [5]. The current status of introducing telemedicine in major developed countries that have adopted telemedicine early is summarized in Table 1.  
As shown in Table 1, telemedicine is already quite pervasive in major developed countries, with most countries making efforts to allow telemedicine through legislation after COVID-19. In contrast, many other countries, including Korea, are still opposed to telemedicine because of the safety and responsibility issues in terms of patients or the general public directly reporting their condition to doctors and receiving appropriate