esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 THz Surface Plasmon Resonance Sensing and Devices Based on ATR  
### Surface Plasmon and Surface Plasmon Resonance  
Surface plasmon (SP) is a kind of collective excitation, which usually occurs at the interface between a conductor and a dielectric. This transverse magnetic mode propagates along the interface, and the field amplitude evanesces exponentially perpendicular to the interface. The surface plasmon polariton (SPP) will be excited by the collective oscillations of free charge carriers at a metal-dielectric interface provided the dielectric constant of the two media is opposite. The most attractive property of the SPP is that the field is confined in the vicinity of the surface. This resonance, usually called surface plasmon resonance (SPR), can be extremely sensitive to the slight changes in the refractive index of the surrounding media. Herein, plenty of research has been conducted for SPR sensing. Besides, studies have shown that graphene can also support well-confined SP modes at both MIR and THz regions. [PERSON] [30] numerically investigated excitation of SP supported by doped graphene sheets at THz frequencies with ATR via Otto geometry (Figure 3a). To match the momentum between the highly confined plasmon modes and the incident radiation, the surface conductivity of graphene should be adjusted, such as varying doping levels or using few-layer graphene. Polyvinylidene fluoride (PVDF) is a kind of conducting polymer with efficient metal-like reflectance, which also supports SP modes at the THz region. [PERSON] et al. [26] realized THz plasmon-like excitation using PVDF covering a solid-core polymeric Bragg fiber via phase matching. Sensitivity of the SPR sensor using the angular interrogation method is defined as the ratio of the change in the resonance angle (\(\theta_{SPR}\)) with the change in the refractive index (\(n_{a}\)) of the sensing layer [42]:  
\[S_{n}=\frac{\delta\theta_{SPR}}{\delta n_{a}}. \tag{3}\]  
Figure of merit (FOM) estimates the performance of the sensor and is defined as the ratio of sensitivity to the full width at half maxima (FWHM) of the SPR curve [42]:  
\[FOM=\frac{S_{n}}{FWHM}. \tag{4}\]  
The following is advanced research around the SPR sensor based on Otto or Kretschmann geometry.  
Figure 3: Surface plasmon sensor structure based on Otto or Kretschmann configuration. (**a**) Surface plasmon (SP) supported by doped graphene sheets at THz frequencies with attenuated total reflection (ATR) via the Otto geometry. Reproduced with permission from [30] © 2012 AIP Publishing. (**b**) SPR-based gas sensor in THz frequency with the Otto configuration. Reproduced with permission from [42] © 2016 Elsevier. (**c**) THz SPP excited on the dielectric–Polyvinylidene fluoride (PVDF) interface with prism coupling in the Kretschmann configuration. Reproduced with permission from [29] © 2018 Elsevier. _n_: refractive index, _d_: thickness, \(\theta\): incident angle, MLG: monolayer graphene, FLG: few-layer graphene, SP: surface plasmon.
## 6 Conclusions  
In the age of wireless communication, the connectivity between devices and their seamless communication plays a pivotal role in shaping a more interconnected world. However, this interconnectedness introduces vulnerabilities in the transmission of sensitive data across shared channels as the data are susceptible to potential intruders and cyberthreats.  
This paper delves into a comprehensive survey of IoT networks integrating PUFs and examines the means of fortifying security against cyberattacks. Regarding the limitations of traditional security protocols for such networks, this study highlights the necessity of developing specifically tailored lightweight algorithms to meet the demands of IoT devices. By exploring the IoT field, its advantages, architectural applications, and associated challenges, this paper extensively researches the functionality and varied types of PUFs. It meticulously examines PUF-based authentication, communication protocols, and their diverse applications. Furthermore, it addresses eleven crucial challenges encountered in this domain along with proposed solutions.  
This survey uniquely contributes to the literature by providing a comprehensive analysis encompassing eleven studies concerning PUF-based authentication and communication protocols. Notably, MQTT and AMQP emerge as widely adopted technologies, having undergone rigorous testing across both wired and wireless platforms. Innovative solutions are proposed to tackle the challenges related to PUFs, address propagation delay, bit stability, power consumption, cost efficiency, encryption methods, and security concerns such as DDoS attacks. This study focuses on revolution around three key aspects: authentication, encryption, and PUFs within communication protocols. The aim is to fortify security within IoT systems.  
In summary, the proposed PUF-based authentication and communication protocols offer a multifaceted solution to an array of challenges faced in this domain. They strive to enhance security, reliability, and efficiency within IoT networks. By highlighting the importance of tailored algorithms and robust security measures, this study contributes to the ongoing discourse surrounding the integrity and resilience of IoT systems.  
Conceptualization, R.A.A. and M.M.S.; methodology, R.A.A. and M.M.S.; investigation, R.A.A.; resources, R.A.A.; data curation, R.A.A.; writing--original draft preparation, M.M.S.; writing--review and editing, M.M.S.; supervision, M.M.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Ministry of Higher Education Malaysia under the Fundamental Research Grant Scheme with project Code: FRGS/1/2020/ICT07/USM/02/2. The authors also would like to express sincere gratitude to AlMaarefa University, Riyadh, Saudi Arabia, for providing funding to conduct this research.  
Not applicable.  
**Conflicts of Interest:** The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 4 Evaluation
### Coffee Smart Farming Datasets  
We conduct evaluation experiments with three temperature datasets (Table 2). The first one, named \"Per_Hour\", contains data collected with the Omicron device every minute and a half from 2:00 p.m. to 2:57 p.m. on 5 November 2019. The second dataset named \"Per_Day\" was generated from an Intel device collecting data every 5 min on 6 January 2020. The third dataset called \"Per_Month\" was formed by the Libelium weather station collecting data every two minutes (approximately) from 10 th November to 10 th December of 2019. For testing purposes, we included randomly 1%, 5%, and 10% of outliers in each of the three datasets above mentioned using the InterQuartile Range (IQR) [53], generating in total nine datasets. Three datasets \"Per_Hour\" with 24 instances collected by Omicron device with 1%, 5%, and 10% of outliers, respectively. Three datasets \"Per_Day\" with 288 instances collected by Intel device with 1%, 5%, and 10% of outliers, respectively. Three datasets \"Per_Month\" with 22,532 instances collected by Libelium device with 1%, 5%, and 10% of outliers, respectively. The datasets and codes used in this paper are located in [54].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Scale** & **Number of Instances** & **Technology** \\ \hline Per\_Hour & Hour & 24 & Omicron \\ Per\_Day & Day & 288 & Intel \\ Per\_Month & Month & 22,532 & Libelium \\ \hline \hline \end{tabular}
\end{table}
Table 2: Datasets for coffee smart farming.  
Figure 3: Coffee smart farming.  
Table 3 presents the structure of datasets mentioned above. The columns represent the features and the row their format. The first two features express the date and time in the human-readable and in Unix timestamp format, respectively. The third feature (value_temp) is the original temperature data in degrees Celsius collected by the Omicron, Intel, or Libelium devices. The fourth feature (Test_1) contains the temperature data, including the outliers randomly created.
## 4 Conclusions  
With absolute alcohol, without the addition of PVP, and after reaction for 8-48 h at a temperature of 200 \({}^{\circ}\)C, lamelliform SnO\({}_{2}\) was observed in the products, but 1 DSnO\({}_{2}\) was not detected. Upon varying V from 5:3 to 9:7, the product morphology transformed from lamelliform to quasi-microsphere structures, a process facilitated by the 1 DSnO\({}_{2}\) self-assembly. Notably, at V = 3:5, the agglomeration of quasi-microspheres became severe. In the absence of templates, the time required to form 1 DSnO\({}_{2}\) decreased from 48 to 12 h with the addition of PVP and 1 DSnO\({}_{2}\) seed crystals, thereby enhancing the synthesis efficiency. The products are SnO\({}_{2}\)(NR) and SnO\({}_{2}\)(NW), with tetragonal rutile structures. The spacing between the adjacent lattice planes was 0.34 nm. This corresponds to the (110) plane of the rutile tin oxide, indicating that [001] is the preferential growth direction. Nanorods of length 422.87 \(\pm\) 110.15 nm, width 81.4 \(\pm\) 16.75 nm, and an aspect ratio of 5:1 were observed,  
Figure 7: FSEM images of SnO\({}_{2}\) (NW): (**a**) 500 nm, (**b**) 200 nm.  
in addition to nanowires of length ~200 \(\pm\) 45.24 nm, width 15 \(\pm\) 5.62 nm, and an aspect ratio of 13:1. Owing to the aggregation initiated by various interactions between nanocrystals, quasi-microsphere structures were formed by the self-assembly of 1 DSnO\({}_{2}\). The obtained 1 DSnO\({}_{2}\) nanostructures can be used to prepare gas-sensitive components.  
Conceptualization, J.Z. and H.L.; methodology, M.X.; validation, Y.C.; writing--original draft preparation, J.Z.; writing--review and editing, Y.B. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Technology Talent and Platform Project of Yunnan Province (2018 HB110), Technology Talent and Platform Project of Yunnan Province (202105 AE160027), and Yunnan Provincial General Project of Applied Basic Research (2016 FB091, 2017 FB145).  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 4 Effect of Fault Size Extension on Bearing Dynamic Characteristics
### Bearing Outer Ring Stress Change and Crack Extension Trend
#### 4.2.2 Principal Stresses
After the bearing experiences a fault, the second principal stress is compressive stress. While the rolling element is in a micro-slipping state, the compressive stress increases with the expansion of the bearing crack, effectively suppressing crack bifurcation. However, when macro-slipping occurs, the compressive stress exhibits fluctuations as the crack expands, although its value remains greater than that of normal bearings. This indicates that while macro-slipping affects the growth trend of the second principal stress, it continues to play a role in inhibiting crack bifurcation. After the bearing experiences a fault, the third principal stress is also compressive stress. The compressive stress value for the faulty bearing is lower than that of the normal  
Figure 14: Variation trends in peak values for the three principal stresses. (**a**) Material GCr15; (**b**) material AISI304. bearing and remains relatively constant with the expansion of the bearing crack. This suggests that the likelihood of crack bifurcation occurring in the vertical direction (Z direction) is relatively low after the bearing develops a fault. While the present model adopts standard assumptions to maintain generalizability, it does not incorporate system-specific factors such as lubrication conditions, assembly errors, and variable load distributions. For instance, lubrication conditions can significantly affect the slip behavior and stress distribution, potentially altering the crack propagation trends. Moreover, the assumption of smooth contact surfaces in this study may overlook the role of surface topography, which could significantly influence the dynamic characteristics and crack propagation trends. For example, increased surface roughness may lead to localized stress concentrations, altering the slip behavior and potentially accelerating crack initiation and propagation. These effects should be further investigated in future work to enhance the comprehensiveness of the findings.
# Hyperspectral Imaging for the Detection of Bitter Almonds in Sweet Almond Batches
## 2 Materials and Methods
### Hyperspectral Image Processing  
Data analysis was performed using WinISI II software package version 1.50 (Infrasoft International LLC, Port Matilda, PA, USA) [16] and Matlab v. 2018a, equipped with the PLS and the Image Processing toolboxes (The Mathworks, Inc., Natick, MA, USA).  
Initially, the reflectance value of each sample was calculated as the difference between the intensity of the sample and dark reference divided by the difference between the white and dark references [17].  
After having corrected the images, the background of each sample was removed to extract the Region of Interest (ROI). For this aim, a binary mask image was generated; to achieve this, the difference between the images at wavelengths 1009.80 and 1541.60 nm was calculated. Then, the background was removed by using a simple threshold value of 0.55 to the resultant image, which was applied to each reflectance corrected image. Subsequently, the whole of the spectra extracted from the pixels non-identified as background were averaged to obtain a mean spectrum per sample, having a total of 158 spectra.
## 5 Comparison with Other Known Methods
### Abbas Method  
A comparison of the proposed method with the [PERSON] method was performed similarly to that described in Section 5.2. The required regulator settings calculated from the relationships (23) and (24) and adopting the \(\tau_{c}\) values from the previous section should be equal to \(K_{p}=0.6987\), \(T_{i}=5\). The simulation of the operation of this system produced the results, which are illustrated in Figure 10.  
Of course, using a different desired transfer function of a closed-loop system with a longer time constant \(\tau_{c}\) using this method, we can obtain a response with overshooting within a 2% error band and then also with the shorter settling time of 15.1 s, but the achieved response and the reference process response given by the authors will still differ.  
To complete the comparison between the considered methods, integral indicators as:  
ISE (Integral of Squared Error)  
\[ISE=\int\limits_{0}^{\infty}e^{2}(t)dt\]  
Figure 10: Responses of the system with a PI controller controlling the plant from Example 1 tuned using the proposed method and the method described by [PERSON] [21].  
Figure 9: Responses of a system with a PI controller, controlling the \(G_{2}\) plant designed according to the proposed method and the method described by [PERSON] and [PERSON]. 1—the desired response of the proposed method for minimum settling time, 2—the desired response assumed in the Chen–[PERSON] method, 3—the output of the system.  
ITSE (Integral of Time-Squared Error)  
\[ITSE=\int\limits_{0}^{\infty}t\cdot e^{2}(t)dt\]  
IAE (Integral of Absolute Error)  
\[IAE=\int\limits_{0}^{\infty}|e(t)|dt\]  
ITAE (Integral of Time-Absolute Error).  
\[ITAE=\int\limits_{0}^{\infty}t\cdot|e(t)|dt\]  
where \(e(t)\) is the difference between the set-point input and controlled variable, were measured during the testing. The obtained results are summarized in Table 1.  
In general, the smallest values of integrals were measured for time series with the shortest rise times.
# Seismic Resilience in Critical Infrastructures: A Power Station Preparedness Case Study
## 4 Case Study
### Power Station Structure and Vulnerability  
The station consists of two units, each containing six gas turbines, six steam boilers, and one steam turbine. Other main components include a Gas Pressure Regulating and Metering Station (PRMS), a water treatment facility, diesel fuel tanks, water tanks, and Gas-Insulated Switchgear (GIS).  
The seismic fragility curves of the components and subcomponents are based on the fragility parameters proposed by [PERSON] [60]. Furthermore, for each component, a demonstrative damage state was selected as either moderate or complete based on the component's importance to the overall system's functionality and the potential consequences of its failure. Moreover, where necessary, a further analysis with Fault Tree Analysis (FTA) was carried out for certain systems to account for their inherent redundancy and to provide a precise representation of the system's overall seismic vulnerability.
# Fast Reaction to Sudden Concept Drift in the Absence of Class Labels
## 3 The Proposed Approach
### K-Prototype Clustering  
To label the unlabelled data, it is worth mentioning some semisupervised learning methods. Some of these are appropriate for processing data streams incrementally, namely co-training, tri-training, self-training and K-prototype clustering. Considering incoming data have both numeric and categorical values, we propose to adopt K-prototype as a solution to our semisupervised drift detector, though a number of unlabelled data are extracted from the incoming data. Then, by applying K-prototype clustering to these data, we can get labels for all these unlabelled data.  
The work in [31] proposes an algorithm based on the k-means paradigm but removes the numeric data limitation whilst preserving its efficiency. The K-prototype algorithm integrates the k-means and k-mode algorithms to deal with mixed data types. Therefore, the k-prototype algorithm is more useful practically because data collected in the real world are of mixed types. Assume a set of \(n\) objects, \(X=\{X_{1},X_{2},\dots,X_{n}\}\); \(X_{i}=\{X_{i1},X_{i2},\dots,X_{im}\}\) consists of \(m\) attributes. The goal of clustering is to partition \(n\) objects into \(k\) disjoint clusters \(C=\{C_{1},C_{2},\dots,C_{k}\}\), where \(C_{i}\) is an \(i\)-th cluster center. The distance \(d(X_{i},C_{j})\) between \(X_{i}\) and \(C_{j}\) can be calculated as follows,  
\[d(X_{i},C_{j})=d_{r}(X_{i},C_{j})+\gamma d_{c}(X_{i},C_{j}) \tag{10}\]where \(d_{r}(X_{i},C_{j})\) is the distance between numerical attributes, \(d_{c}(X_{i},C_{j})\) is the distance between categorical attributes, and \(\gamma\) is a weight for categorical attributes.  
\[d_{r}(X_{i},C_{j})=\sum_{i=1}^{p}|x_{il}-C_{jl}|^{2} \tag{11}\]  
\[d_{c}(X_{i},C_{j})=\sum_{i=p+1}^{m}\delta(x_{il-C_{jl}}) \tag{12}\]  
In Equation (11), \(d_{r}(X_{i},C_{j})\) is the squared Euclidean distance measure between cluster centres and an object on the numerical attributes; whereas, in Equation (12), \(d_{c}(X_{i},C_{j})\) is the simple matching dissimilarity measure on the categorical attributes, where \(\delta(x_{il}-C_{jl})=0\) for \(x_{il}=C_{jl}\) and \(\delta(x_{il}-C_{jl})=1\) for \(x_{il}\
eq C_{jl}\).
# Microcontroller Unit-Based Wireless Sensor Network Nodes: A Review
## 3 MCU-Based Hardware Platforms for Wireless Sensor Nodes
### Human Monitoring and Tracking
#### 3.1.2 Wireless Sensor Node with Application in Sports  
The proposed low-cost sensor node finds applicability in sports to monitor athletic abilities such as reflexes and reaction speeds [19]. However, the nodes can be configured to suit other applications. The Arduino Pro Mini 3.3 V is used for the processing of sensed data. The board incorporates an Atmel ATmega328P microcontroller consisting of 32 kB of flash memory and an 8 MHz quartz crystal. Regarding its communication capabilities, the ultra-low power nRF24L01 transceiver is used for communication. It operates at 2.4 GHz using data rates of 250 kbps, 1 Mbps and 2 Mbps. The transmission power can be set to \(-18\) dBm, \(-12\) dBm, \(-16\) dBm or 0 dBm. The transceiver is capable of achieving coverage of up to 250 m at a 250 kbps data rate and 0 dB transmission power.  
Concerning the node's capability to accommodate sensors, it is equipped with a connection interface consisting of 14 digital input/output pins and 6 analog inputs for facilitating connection to different sensors. The node can be powered by battery voltages ranging from 3.35 V to 12 V, thanks to the presence of the Arduino Pro Mini 3.3 V voltage regulator. A 3.7 V lithium ion (Li-ion) rechargeable battery, a 5 V rechargeable power bank or a 5 V, 5500 mAh Li-Po rechargeable battery can be utilized for power supply. The average current consumption in transmission and power-down modes is 5.4 mA and 0.5 mA, respectively.
# Challenges of Using Blockchain in the Education Sector: A Literature Review
## 6 Conclusions  
The role of blockchain technology in the higher education sector is promising and has expanded in recent years. The key contribution of this review is to provide a clear picture that summarizes what has already been written about the challenges of using blockchain technology in the higher-education sector. The review identified the most important and relevant studies in the field, providing details on the topics that have prompted more academic attention and detailing the blockchain adoption challenges. The methodology chosen to answer the research questions was a literature review.  
Concerning the study question, \"What are the challenges of using blockchain in the education sector?\", the challenges are classified, based on the TOE framework, into the following three perspectives: technological, organizational, and environmental. The technological challenges are as follows: poor usability, a lack of scalability, limited interoperability,and standardization (classified under the immaturity of blockchain challenge), the complexity of integration, security issues, privacy, immutability, and lack of flexibility, and data unavailability. In the organizational context, there is a lack of adequate skills, financial barriers, and a lack of management commitment and support. In the environmental context, there are also legal issues and lack of regulatory compliance, market and ecosystem readiness, and sustainability concern.  
This review showed that technological challenges received more attention from the reviewed articles than in the other two contexts. This reflects a research gap in terms of the organizational and environmental challenges that will have a negative impact and will increase resistance to the acceptance of this technology in the education sector. Therefore, more research is needed from an organizational and environmental perspective.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.