esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The present work scrutinizes the boundary layer flow coupled with heat and mass transfer of a doubly stratified nanofluid using a non-Fourier energy model, or known as the Cattaneo-Christov heat flux model. The governing model in PDEs are reduced to a system of similarity differential equations and then numerically solved using bvp4c code which is programmed in the MATLAB software. The conclusions are as follows:  
* Thermal relaxation parameter \(\gamma_{T}\) which is resulting from the application of the non-Fourier energy model, gives a different estimation of the heat and mass transfer rates as compared to the classical energy equation. The rate of heat transfer increases whereas the mass transfer rate decreases with the improvement of the thermal relaxation parameter for both stretching and shrinking cases.
* The skin friction coefficient inflates for the shrinking case (\(\varepsilon<0\)), deflates for the stretching case (\(\varepsilon>0\)) and has no change for the static plate (\(\varepsilon=0\)) when the suction parameter \(S\) is enhanced. Higher values of the suction parameter i.e., \(S>2\) also contribute to the appearance of the dual solutions in the present work.
* The stability analysis is conducted and it is mathematically proved that the first solution is the physical/real solution.
* The combined effects of buoyancy parameter (\(\lambda\) and \(N\)) with higher values of the suction parameter \(S\) in the shrinked flow gave the reverse effect of the Lorentz force to the velocity, temperature and concentration profiles.
* Both temperature and concentration slightly inclines with the imposition of the thermal \(\delta_{1}\) and solutal \(\delta_{2}\) stratification parameters, respectively.  
**Author Contributions:** Research design, N.S.K., N.M.A., E.H.H., N.W.; Formulation and methodology, N.S.K. and N.M.A.; Result Analysis, N.S.K.; Validation, N.M.A., E.H.H. and N.W.; Article preparation, N.S.K.; Review and editing, N.M.A., E.H.H., and N.W.  
**Acknowledgments:** The present work is financially supported by Universiti Putra Malaysia through the Putra Grant (9570600), Ministry of Education (Malaysia) and Universiti Teknikal Malaysia Melaka. The authors also appreciate the valuable feedbacks and recommendations by the reviewers.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# MIMO Radio Frequency Identification: A Brief Survey
## 4 Security  
RFID is set to become one of the core technologies powering the Internet of Things (IoT) networks [56]. However, with ever growing deployments and the presence of tags everywhere, the privacy concerns are growing. The reader to tag transmission is prone to eavesdropping operations exposing personal information since the backscatter channel is of broadcast nature [57; 58]. Several computationally simple cryptography algorithms have been proposed, but they have their limitations. Table 3 lists the few works we summarized associated with security in MIMO RFID systems. In case of a passive tag, eavesdropping attacks are common while active tags are prone to relay attacks. Recently, physical layer security (PLS) [59] techniques have shown themselves as a good alternative to cryptography algorithms as they can ensure proper secrecy and depends upon secrecy capacity. Little work has been done in the domain of backscatter link security employing PLS techniques. [PERSON] et al. [60] investigate the PLS of a MIMO RFID system. A noise injection precoding technique is employed that is ideal for low resourced backscatter channel. Figure 7 details the RFID reader design for EPCglobal C1 Gen-2 tags with the eavesdropper transceiver. A multiantenna tag scenarios is examined with respect to the secrecy rate maximization (SRM) issue [61] by improving both the energy as well as the precoding matrix of the artificial noise at the receiver. The results obtained from the simulations suggest that the proposed technique performs quite well in terms of computational power and enhances secrecy rate of the communication.  
In automotive domain, RFID based keyless entry [62; 63] solutions are being adopted at a rapid pace. The passive keyless entry and start (PKES) [62; 64] system enables a car to be unlocked after authentication without a physical key. However, this introduces several security challenges and security can be compromised if security of the system is weak making the system vulnerable to attacks. As discussed above, by employing multiple antennas, we can significantly improve the interrogation range of an RFID system, but it will require more sophisticated cryptographic schemes to be developed to enhance the security of the system. [PERSON] et al. [65] proposed a sophisticated security technique for PKES systems whereby the reader has multiple antennas to intercept relay attacks.  
Figure 7: MIMO RFID system with a passive eavesdropper. Reproduced from [60] with permission from IEEE.  
The proposed algorithm is based on HB (Hopper and Blum) [66; 67] protocol which is computationally a lightweight algorithm and is modified for use in the proposed MIMO RFID systems. The system employs three antennas at different proximities to deter relay attacks. It is observed through simulation results that the proposed algorithm achieves its intended tasks without compromising the performance of the system while providing protection against relay attacks.
# IoT-Enabled Smart Cities: Evolution and Outlook
## 2 IoT-Enabled Smart Cities Landscape
### Singapore  
The Smart Nation and Digital Governance Group (SNDGG), which directly reports to the Prime Minister's Office, oversees the planning and execution of smart city projects [23]. The government operates various smart city R&D projects through many subordinate organizations. Of particular note are the AI-focused ones, including the AI Singapore Project, which is being conducted in collaboration with the National University of Singapore (NUS).  
The Singapore government has established six smart city visions as follows:  
* **Smart Urban Mobility:** Uses digital technologies to enhance comfort, convenience and reliability of public transport systems, and support a vision of a car-lite Singapore.
* **Smart Nation Sensor Platform:** This is one of the anchor initiatives in Singapore that enables everyone and everything, everywhere, to be connected all the time in Singapore.
* **National Digital Identity:** A digital identity system for Singapore residents and businesses to transact digitally with the Government and private sector in a convenient and secure manner.
* **Moments of Life:** This is an initiative that aims at providing personalized and pro-active support to citizens at key junctures of their lives.  
* **E-Payments:** With this vision, citizens and businesses in Singapore are able to transact digitally in a hassle-free, seamless and secure manner.
* **Core Operations Development and eXchange (CODEX):** This is a digital platform that enables the Government to deliver better digital services to citizens faster and more cost efficiently.  
The Singaporean government has developed a policy infrastructure consisting of laws and standardized rules for smart cities, to establish an innovative smart city ecosystem. The IoT Technical Committee was organized to standardize data security and compatibility support that will be applied to various smart city services and infrastructure elements being developed.  
To solve numerous urban problems, the Singaporean government is actively helping various actors to utilize smart city data. Key smart city services include MytrransportySG that encourages public transportation usage, HealthHub that integrates personal health record management, and Moments of Life that handles birth registration, vaccination, and local event information management on a single platform.
# Secure D2D Communication for 5G IoT Network Based on Lightweight Cryptography
## 6 Conclusions  
In this paper, we propose a secure D2D communication system in a 5G IoT environment. The proposed D2D communication is designed based on an ECC-based public key cryptosystem and a lightweight AEAD cipher for efficiency in 5G use cases corresponding to IoT scenarios, mMTC and URLLC. Before the D2D communication is performed, the UE identity is verified based on the 5G-AKA provided by the 5G network, and then a token is used as the ECDSA for the D2D communication. The generated token could authenticate the legitimacy of the corresponding UE identity in the link setup process after device discovery. This can be done without connecting to the core network. In addition, by performing the encrypted communication through the lightweight AEAD cipher using the token as the associated data in the secure data communication step, the confidentiality/integrity of the data and authentication of the UE can be performed in each data transmission step. This approach can provide higher performance and energy efficiency than a general-purpose AEAD cipher-based communication system, and can also provide security against security threats such as impersonation, eavesdropping, privacy sniffing, free-riding and location spoofing.  
Conceptualization, B.S., J.H.P.; methodology, B.S. and J.C.S.S.; investigation, J.C.S., T.E. and C.X.; writing--original draft preparation, B.S.; project administration, J.H.P.; funding acquisition, J.H.P. and Y.P. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the Advanced Research Project funded by the SeoulTech (Seoul National University of Science and Technology).  
The authors declare no conflict of interest.
# Enhancements and Challenges in CoAP--A Survey
## 4 Enhancements in CoAP
### Congestion Control Mechanisms in CoAP
#### 4.1.11 CoCoA++  
CoCoA++: Delay gradient-based congestion control: Another congestion control mechanism based on the gradient of RTT measurements over time is proposed by [PERSON] et al. in [27]. The proposed method provides remedies for the issues in congestion control in default CoAP, CoCoA, and its variants, such as CoCoA+. These methods use per packet RTT measurements to predict congestion in network, but these measurements are noisy and unreliable. CoCoA++, on the other hand, relies on CAIA Delay Gradient (CDG) [40] for the purpose of predicting network congestion by obtaining a gradient of RTT over time and provides a Probabilistic Backoff Factor (PBF) for controlling congestion in network.  
With the use of delay gradient, the CoCoA++ eliminates the purpose of weak and strong RTO estimates. Also, unlike CoCoA and CoCoA+, the RTO value is not updated based on per packet RTT samples, instead the RTO is updated after receiving periodic information about the delay gradients from CDG. This enables CoCoA++ to not rely on VBF and hence it is replaced by PBF. The formula for calculating PBF is given in Equation (7).  
\[PBF=\left\{\begin{aligned} & 1.42,P[backoff]>Xandg>0\\ & 0.7,otherwise\end{aligned}\right. \tag{7}\]  
where _Plbackoff_] is the probability of backoff that CDG returns, 'X' is the uniformly distributed random value and 'g' is the delay gradient. The probability of backoff is compared with the uniformly distributed random value 'X'. The backoff is applied to RTO only in the case when we have positive rate of change of RTT. This is represented by the condition g > 0.  
In the case of congestion, the PBF increases the RTO by a factor of 1.42 whereas it decreases the RTO by a factor of 0.7 for no congestion.  
The issue with CoCoA++ is that with higher average packet sending rate, the subsequent retransmissions might occur quickly, causing the node to run out of retransmissions quickly.
# Optimization of the 3D Point Cloud Registration Algorithm Based on FPFH Features
## 2 Rough Surface Matching Based on Octree and Edge Feature Point Algorithm
### Matching Relationship of Feature Points  
After the feature points are determined, the initial matching point pairs need to be acquired. In this paper, the feature descriptor based on a histogram is used to calculate the initial matching point pair. Because the point cloud data has the invariance of rotation and translation, the features of points can be well preserved. This paper describes the geometric features by using the normal vector relationship between points and their adjacent points and then obtains the initial matching point pairs, the schematic diagram of the matching point pair is as shown in the Figure 9:  
In the registration process, only the feature elements of the target point and its neighboring points need to be calculated, which greatly reduces the complexity of the calculation. Then, the feature elements of its neighboring points are calculated, and the distance condition is used as the weight to re-determine the K-neighboring domain. The fast point feature histogram (FPFH) of the target point P is calculated as follows:  
\[FPFH(p)=SPFH(p)+\frac{1}{k}\sum_{l=1}^{k}\frac{1}{w_{k}}SPFH(d_{k}) \tag{12}\]  
where \(w_{k}\) represents the weight measured by the distance \(d_{k}\) between \(P\) and neighboring points, and \(SPFH\), the simple point feature histogram, is a simplified point feature histogram.
# BUU-LSPINE: A Thai Open Lumbar Spine Dataset for Spondylolisthesis Detection
## 3 Results  
### Lumbar Vertebrae Detection  
We evaluated lumbar vertebrae detection results by using the mean average precision (mAP) metric over various intersection over union (IoU) thresholds ranging from 0.5 to 0.9 ([EMAIL_ADDRESS]:0.95). Table 8 shows the evaluation of models. YOLOv5 achieved the highest precision of 81.93% and 83.45%, as well as the highest recall of 95.82% and 95.72% on the AP and LA views, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**View**} & \multicolumn{4}{c}{**Mean Average Precision (\%)**} & \multirow{2}{*}{**Recall**} \\ \cline{3-3} \cline{5-6}  & & **@IoU [0.5]** & & & **@IoU [0.75]** & **@IoU [0.5, 0.95]** \\ \hline \multirow{2}{*}{YOLOv5} & AP & 96.68 & 95.86 & 81.93 & 95.82 \\  & LA & 95.83 & 95.50 & 83.45 & 95.72 \\ \hline \multirow{2}{*}{MobileNetV1} & AP & 90.70 & 88.57 & 75.77 & 80.81 \\  & LA & 89.48 & 88.72 & 76.02 & 81.91 \\ \hline \multirow{2}{*}{MobileNetV2} & AP & 93.26 & 89.95 & 77.63 & 82.19 \\  & LA & 91.79 & 90.51 & 78.38 & 83.58 \\ \hline \multirow{2}{*}{ResNet50V1} & AP & 92.22 & 90.08 & 77.70 & 81.43 \\  & LA & 91.03 & 89.95 & 77.73 & 81.97 \\ \hline \multirow{2}{*}{ResNet101V1} & AP & 89.26 & 86.42 & 71.80 & 76.68 \\  & LA & 91.43 & 90.37 & 78.45 & 83.20 \\ \hline \multirow{2}{*}{ResNet152V1} & AP & 93.07 & 90.25 & 77.32 & 81.31 \\  & LA & 92.76 & 91.55 & 78.53 & 82.80 \\ \hline \multirow{2}{*}{EfficientDet D0} & AP & 93.69 & 90.46 & 75.86 & 79.90 \\  & LA & 92.39 & 89.76 & 72.60 & 77.25 \\ \hline \multirow{2}{*}{EfficientDet D1} & AP & 93.00 & 87.38 & 70.88 & 75.63 \\  & LA & 92.79 & 91.11 & 76.08 & 79.92 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Evaluation of models for detection of lumbar vertebrae.  
Figure 14: New dataset’s records: (**a**) reference point; (**b**) normal case; (**c**) disease case.  
LSTV conditions can impact the detection of lumbar vertebrae since our model was trained under the assumption that every person's spine consists of five lumbar vertebrae, as shown in Figures 15 and 16. Patients with lumbarization, characterized by the presence of six vertebrae instead of the usual five, can lead to a consistent omission of one verteb in our model's detection, as shown in Figures 15 and 16. Patients with sacralization, where there are four vertebrae instead of the usual five, cause our model to attempt to locate the missing vertebra and mistakenly mark the position on the first sacrum instead of the fifth lumbar, as shown in Figures 15 and 16. These results highlight the importance of LSTV information in the lumbar spine dataset, which is included in our BUU-LSPINE dataset.
## 3 Spectrum Management Issues
### A. Carrier Aggregation  
One of the ways to achieve the extensive bandwidth required for the next-generation 6G network is through the CA technique [78]. For the current 5G network, CA or channel bonding (between the licensed and unlicensed carriers) is considered an efficient technique, although it can be used further by effectively handling the frequency resources for designing 6G networks [79]. CA can be implemented in three different methods, which can be identified based on the patterns in which the Component Carriers (CCs) are arranged (Figure 2) [80]. These methods include (a) intra-band contiguous in which a wide contiguous bandwidth of more than 20 MHz is utilized, as in LTE-A [81, 82]. Because of the frequency allocation strategies as it is today, this may be a less likely scenario but can function in some situations, as a 3.5 GHz frequency band allocates in broadband [83]. The next method is (b) intraband noncontiguous in which the CCs work in the same spectrum as that of contiguous; this method can be used and aggregated for data transmission while adjacent resources are unavailable [84]. The last method is (c) interband noncontiguous in which communications are performed by simultaneously using two or more different operating frequency spectra, such as 800 MHz and 2 GHz bands [85]. In this manner, various wireless transmission attributes of varying frequency channels can be used to improve the robustness of mobility [86]. In the context of the physical layer, contiguous CA can be easily implemented without much modification to the LTE-A physical layer design [87]. To attain contingency in CA and support previous generation compatibility, i.e., LTE-A UE unit, a single Fast Fourier transform (FFT) module and a single Radio Frequency (RF) unit can be utilized [88]. In most cases, for both non-contiguous CA bands, multiple FFT and RF chains are mandatory. From the perspective of management and resource allocation, contiguous CA is also applicable [89]. Different CCs usually experience various Doppler shifts and path loss propagation characteristics that greatly affect network performance [90]. Several studies have been performed in CA, which helps maximize the use of the spectrum. Some of the recent studies that focus on four important areas, i.e., resource sharing, energy efficiency, capacity improvement, and transmission performance, are discussed below.
## 3 Results and Discussion
### The Effects of the Sensor Device on the Strength and Microstructure of the Concrete Columns  
In terms of compressive strength, the cubes extracted from the concrete column in the region without the devices presented an average value of 49 MPa at the age of 99 days. In contrast, the average strength of the cubes containing the two devices was 43 MPa. This represents a reduction of 12% in the compressive strength value due to the presence of the device. However, regarding ordinary applications, this reduction could be considered acceptable. In addition, the configuration of the columns with the devices as presented here also amplifies the negative effects of the devices on the strength, given that in a cross-sectional area of 320 mm \(\times\) 320 mm (referring to the tested cube), the devices occupy together an area of 12,480 mm\({}^{2}\) (12% of the total cross section).  
As for the distribution/content of air/voids, it is possible to observe that there is no change in the concrete matrix nor accumulation of air voids in the surrounding areas of the housing device. In Figure 15, one longitudinal (L1) and two perpendicular cross sections (S1, S2) show the housing device embedded in the concrete core.  
The total volume of air voids was quantified to compare both regions. The image analysis workflow (see Figure 16) using Avizo 2020.3 consisted of four main steps. First, the core was subdivided into region 1 (with the housing device) and region 2 (with no device) as shown in Figure 15. Then, the air voids, housing device and reinforcement were segmented, and then assigned geometric attributes. The housing device and reinforcement were filtered out and the total air content was calculated per region.  
Figure 15: X-ray CT reconstructed cross sections of the concrete core (UGCT CoreTOM, 180 kV, 90 W, exp. 650 ms, 90 \(\upmu\)m voxel size). L1—longitudinal section displaying the location of the housing device. S1: top section; S2: bottom section of the device. The arrows show the location of the housing device. Units are displayed in micrometers.  
In Table 4, the total volume of the concrete core and the total amount of isolated air voids per region are shown. The volume of the R1 concrete core was calculated by mathematically subtracting the volume of the housing device. R1 shows an overall higher volume of air voids in comparison to R2. Nevertheless, R2 exhibited a higher density of air voids when calculated relative to the total studied volume in each region.  
Finally, a histogram per region is displayed on Figure 17, showing a remarkably similar size distribution of the air voids by volume. The main difference relies on the slightly higher concentration of particles under \(1.0\times 10^{7}\)\(\upmu\)m\({}^{3}\) on R2 and, therefore, it is not considered that a significant difference exists between both regions due to the presence of the housing device.
## 2 Materials and Methods
### SERS Measurement  
Several amino acid molecules, including Cys, Val, and Trp, were used to assess the SERS activity of the ZIF-8@AuNPs composites. A series of amino acid solutions with varying concentrations were prepared. The tested molecules (0.8 mL) were mixed with the composite materials (0.2 mL). Subsequently, 10 mL of the mixture was extracted using a pipette and deposited onto a clean glass sheet for Raman spectroscopy measurements. The selected test parameters were as follows: laser wavelength of 532 nm, grating with a 600 mesh, 100\(\times\) objective lens, laser power of 10% (9.1 mW), integration time of 15 s, and integration two times. The low laser power and integration time produced no photothermal conversion in the analytes.