esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion and Conclusions  
In this work, a novel phase shifter based on a printed composite right/left-handed structure (CRLH) with embedded micron-sized magnetic particles is proposed. Then, a \(1\times 4\) patch antenna array operating at 5.5 GHz Wi-Fi 6 band is printed, and the patches are integrated with cascaded novel particles embedded CRLH phase shifters for the broadside and phased array applications. The proposed CRLH phase shifter is compact in size (60% size reduction) as compared to the recently published CRLH-based phase shifter. As compared to commercially available phase shifters, the proposed phase shifter does not require electronic components (resistor, inductor and capacitor) for biasing. The commercially available phase shifters require dc biasing circuitry for phase tuning, which can be coupled with the RF signal and may cause phase deviation. The proposed phase shifter does not require direct biasing arrangements. It operates on the application of a magnetic field to be applied beneath the ground plane and, therefore, isolates the coupling of the RF signal with the biasing circuitry. The proposed phase shifter can be directly integrated with the phased array in a fully planar requirement. In this study, the novel particles-CRLH phase shifters are experimentally tested with a \(1\times 4\) patch phased array for \(0^{\circ}\), \(15^{\circ}\) and \(30^{\circ}\) main beam scanning. The measured results are shown in good agreement with the simulated patterns. A possible future extension of the work is to extend the design of the proposed phase shifter for fully printing conformal array applications on a commercial router system for Wi-Fi 6.  
M.A. performed complete simulations, fabrications and measurements. I.U. supervised the research work and assisted in measurements, overall paper management, idea development and manuscript writing. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Scan Angle (\(\theta_{s}\))**} & \multicolumn{2}{c}{**Peak Gain (dB)**} & \multicolumn{2}{c}{**Sidelobe Level (dB)**} \\ \cline{2-5}  & **Simulated** & **Measured** & **Simulated** & **Measured** \\ \hline \(0^{\circ}\) & \(11\) & \(10.1\) & \(11.1\) & \(9.1\) \\ \hline \(15^{\circ}\) & \(12\) & \(10\) & \(11\) & \(12\) \\ \hline \(30^{\circ}\) & \(11\) & \(9.9\) & \(13\) & \(10\) \\ \hline \end{tabular}
\end{table}
Table 4: Comparison between simulated and measured results of a \(1\times 4\) phased array with P-CRLH excited phases.  
**Funding:** This work is jointly supported by Ignite (National Technology Fund via Project No. ICTDF/TR&D/2015/04) and NRPU-HEC (via Project No. 20-14696/NRPU/R&D/HEC/2021), Pakistan. The APC was paid by [PERSON], Server Security Integration Group, Intel Corporation, 2111 NE 25 th Ave, Hillsboro, OR 97124, USA. [PERSON] assisted in simulations and provided access to software and hardware resources.  
**Data Availability Statement:** All data have been included in study.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# General Moving Object Localization from a Single Flying Camera
## 7 Discussion
### Fast Moving Objects not Moving in a Straight Line  
Our solution works on the assumptions that the drone is moving much faster than the object, and that the drone's camera's frame rate is high. This allows us to model the object's motion as a straight line. For slow-moving objects (e.g., a walking pedestrian), the speed of the drone (11.11 m/s) and the frame rate (200 ms interval) is sufficient for these assumptions to hold true. For fast-moving objects that are not moving in a straight line (e.g., a cheetah chasing a prey, or a car driving in a zig-zag fashion), these assumptions may not be valid and there may be significant errors in the results. However, it is not the natural tendency of such objects to move at such a high speed and in an erratic fashion for long periods of time, and they are likely to revert to slower, linear motion (e.g., when the cheetah gets tired, and the driver of a car at high speed will prefer to drive in a straight line). Since we can repeat our algorithm as often as we like, we will be able to minimize the error and obtain an accurate measurement upon the repeated application of the algorithm.
# An Efficient Reliability Method with Multiple Shape Parameters Based on Radial Basis Function
## 3 The Proposed Approach: MSRBF-MCS
### The Proposed Learning Function and Stopping Criterion  
As the sequential sampling proceeds, the learning function keeps adding new samples into DoE and renews the surrogate model according to the enriched DoE. The iterations stop when the stopping criterion is satisfied. Generally, the relative error of the predicted failure probability is used to check the stopping criterion [23; 28; 34]:  
\[\frac{\left|\hat{p}_{f}^{k}-\hat{p}_{f}^{k-1}\right|}{\hat{p}_{f}^{k}}<\varepsilon \tag{18}\]  
where \(\varepsilon\) is a user defined constant, and \(\hat{p}_{f}^{k}\) and \(\hat{p}_{f}^{k-1}\) are the failure probability predicted in the \(k\)-th and \((k-1)\)-th iteration. \(\hat{p}_{f}\) can be calculated as follows:  
\[\hat{p}_{f}=\frac{1}{n_{mc}}\sum_{i=1}^{n_{mc}}I(\hat{g}(\mathbf{X})<0) \tag{19}\]  
In Equation (19), \(n_{mc}\) is the number of \(MC\) populations; \(I(\cdot)\) is an indicator: when the expression in it is real, \(I(\cdot)\) is equal to 1; else \(I(\cdot)\) equals to 0. In addition, when performing MCS for small failure probability events, the coefficient of variation (\(COV\)) of MCS needs to meet the following requirements [21]:  
\[COV=\sqrt{\frac{1-\hat{p}_{f}}{n_{mc}\hat{p}_{f}}},\;COV<5\% \tag{20}\]
## 2 AMR Signals
### Digital Modulation  
In digital modulation techniques, an analogue carrier signal is usually modulated by a binary message code, and this process can be achieved by varying the physical characteristics of the carrier, such as amplitude, frequency, or phase, or a combination of them. Therefore, when the carrier amplitude varies, the modulation is named amplitude shift keying (ASK). When the carrier frequency changes on the basis of the message signal, the modulation is called frequency shift keying (FSK). When the carrier phase varies in accordance with the signal, the modulation is called phase shift keying (PSK). In addition, QAM is a combination of PSK and ASK [17]. Lower- and higher-order modulation schemes can be used for each category, such as binary frequency shift keying (BFSK), 16-QAM, and quadrature phase shift keying (QPSK) [18; 19; 20; 21; 22; 23; 24; 25; 26].
# Security Vulnerabilities in LPWANs--An Attack Vector Analysis for the IoT Ecosystem
###### Abstract  
Due to its pervasive nature, the Internet of Things (IoT) is demanding for Low Power Wide Area Networks (LPWAN) since wirelessly connected devices need battery-efficient and long-range communications. Due to its low-cost and high availability (regional/city level scale), this type of network has been widely used in several IoT applications, such as Smart Metering, Smart Grids, Smart Buildings, Intelligent Transportation Systems (ITS), SCADA Systems. By using LPWAN technologies, the IoT devices are less dependent on common and existing infrastructure, can operate using small, inexpensive, and long-lasting batteries (up to 10 years), and can be easily deployed within wide areas, typically above 2 km in urban zones. The starting point of this work was an overview of the security vulnerabilities that exist in LPWANs, followed by a literature review with the main goal of substantiating an attack vector analysis specifically designed for the IoT ecosystem. This methodological approach resulted in three main contributions: (i) a systematic review regarding cybersecurity in LPWANs with a focus on vulnerabilities, threats, and typical defense strategies; (ii) a state-of-the-art review on the most prominent results that have been found in the systematic review, with focus on the last three years; (iii) a security analysis on the recent attack vectors regarding IoT applications using LPWANs. Results have shown that LPWANs communication technologies contain security vulnerabilities that can lead to irreversible harm in critical and non-critical IoT application domains. Also, the conception and implementation of up-to-date defenses are relevant to protect systems, networks, and data.  
LPWAN; IoT; security; attacks; responses; attack vectors; LoRa; NB-IoT; Sigfox +
Footnote †: journal: Applied Soft. 2021, 11, 3176. [[https://doi.org/10.3390/app11073176](https://doi.org/10.3390/app11073176)]([https://doi.org/10.3390/app11073176](https://doi.org/10.3390/app11073176))
# Fast Object Detection in Light Field Imaging by Integrating Deep Learning with Defocusing
## 2 Methodology
### Digital Refocusing  
Digital refocusing is essentially a process of refocusing the scene at different depth based on the light field via pure digital computation. As shown in Figure 3a,b, the ray passes through \(UV\) plane to focus on the green \(ST\) plane. The distance between the two parallel planes is the focal length \(f\) and focal point \(o\) is on the \(ST\) plane. The irradiance of point \(o\) is obtained by integrating the entire \(UV\) plane as given by Equation (1) [6]:  
\[I_{o}(s,t)=\iint L(u,v,s,t)dudv\to I_{o}(s)=\int L(u,s)du \tag{1}\]  
Assuming that the image is refocused on the blue \(S^{{}^{\prime}}T^{{}^{\prime}}\) plane which contains the focal point \(o^{{}^{\prime}}\), the irradiance of the point \(o^{{}^{\prime}}\) can be determined as follows [6].  
\[I_{o^{{}^{\prime}}}(s^{\prime},t^{\prime})=\iint L^{\prime}(u,v,s^{\prime},t^{ \prime})dudv\to I_{o^{{}^{\prime}}}(s^{\prime})=\int L^{\prime}(u,s^{\prime})du \tag{2}\]  
As shown in Figure 3b, which \(L\) and \(L^{{}^{\prime}}\) should be the same ray:  
\[L(u,s)=L^{\prime}(u,s^{\prime}) \tag{3}\]  
Figure 3: The principle of digital refocusing based on two-plane parametrization of light field. (**a**) Refocusing denoted by two-plane parametrization; (**b**) Representing the four-dimensional (4D) light field by 2D (\(u\), \(s\)); (**c**) Sampling of radiance before refocusing which focus on \(ST\) plane; and, (**d**) Sampling of radiance after refocusing which focus on \(S^{{}^{\prime}}T^{{}^{\prime}}\) plane.  
Figure 2: Two-plane parametrization of light field. (**a**) Two-plane parametrization; (**b**) two-dimensional (2D) Cartesian ray-space diagram.  
Hence, by introducing a geometric constraint:  
\[\frac{f^{{}^{\prime}}}{f}=\frac{s^{\prime}-u}{s-u}=\lambda \tag{4}\]  
where \(f^{{}^{\prime}}\) is the distance between the new image \(S^{{}^{\prime}}T^{{}^{\prime}}\) plane and \(UV\) plane, Equation (2) can be rewritten as follows:  
\[I_{f}(s^{\prime})=\int L(u,\frac{s^{\prime}}{\lambda}+u(1-\frac{1}{\lambda}))du \to I_{f}(s^{\prime},t^{\prime})=\iint L(u,v,\frac{s^{\prime}}{\lambda}+u(1- \frac{1}{\lambda}),\frac{t^{\prime}}{\lambda}+v(1-\frac{1}{\lambda}))dudv \tag{5}\]  
Equation (5) is the basis of the digital refocusing. The red 2D slice in Figure 3c,d and Equation (5) indicate that the process of digital refocus is the 2D slices shift on spatial dimension and integrate in the angular dimension. The refocusing depth is determined by a factor \(k\) defined as:  
\[k=\tan\theta=(1-\frac{1}{\lambda}) \tag{6}\]  
where \(\theta\) denotes the slope of blue streak after shift the 2D slices. Hence, the shift distance \(ku\) of each slice has a linear relationship with angular dimension \((u,v)\). Assuming that the number of focal stacks layers is fixed, then the shift distance of each slice is a constant value. Hence, the refocusing algorithm can be performed with parallel computation on GPU, or even FPGA, for real-time generation of a series of refocused image on different depths.
## 4 Simulation Analysis
### Number of control messages per delivered message  
The results of previous subsections 4.2 and 4.3 can be combined into a graph that represents number of control messages being used to deliver a message. In case of 5 m/s of node movement, as Figure 9(a) and Table 3 show, the proposed algorithm uses less control messages of 11.7% on average than AODV-DFR and 38.7%, 42.6%, and 65.1% than ZRP, LAR, and AODV respectively. And in case of 20 m/s of node movement, the proposed algorithm shows better result in reducing control messages of 9.5% on average compared with AODV-DFR and 33.9%, 43.4%, and 82.4% compared with ZRP, LAR, and AODV respectively as shown in Figure 9(b) and Table 3. These results clearly show that the suggested algorithm effectively reduces the number of control messages to deliver a message than other existing algorithms especially on the high speed of node movement.
## 3 Results and Data Analysis
### Concentration Analysis of Wind Suction near the Roof Ridge  
A notable phenomenon observed pertains to the substantial wind suction near the roof ridge of the solar greenhouse, which can exert a considerable impact on the structural integrity of the building. To investigate the relationship between solar greenhouse size and the concentration of wind suction near the roof ridge, wind pressure coefficients along the longitudinal axis of the roof were measured at intervals of 0.1 m and 0.2 m both before and after the roof ridge line. In the accompanying Figures 10 and 11, the horizontal axis represents the longitudinal distance from west to east, with data points captured at 0.5 m intervals.  
(1) The data presented in Figure 10 reveal a noteworthy disparity in wind pressure coefficients along the front roof and the rear roof near the solar greenhouse roof ridge under a 0\({}^{\circ}\) wind angle. Specifically, the absolute values of negative wind pressure coefficients at a 0.1 m distance on the front roof range from \(-1.25\) to \(-1.01\), while on the rear roof, these values range from \(-1.64\) to \(-1.22\). Furthermore, it is evident that the absolute value of negative wind pressure coefficients at the 0.1 m longitudinal line from the ridge significantly exceeds that at 0.2 m from the ridge. Interestingly, variations in size do not exert a significant influence on the distribution of negative wind pressure coefficients in the vicinity of the ridge.  
(2) As seen in Figure 11, the absolute value of the negative wind pressure coefficient near the ridge of the rear roof is greater than that of the front roof for a 180\({}^{\circ}\) wind angle. The absolute values of negative wind pressure coefficients at a 0.1 m distance from the solar greenhouse on the front roof range from \(-1.05\) to \(-0.84\), while on the rear roof, these values  
Figure 9: Cloud chart of wind pressure coefficients on the front roof of solar greenhouses with five different sizes under 180\({}^{\circ}\) wind angle.  
range from \(-1.1\) to \(-0.86\). In addition, it is evident that the absolute value of negative wind pressure coefficients at the 0.1 m longitudinal line from the ridge significantly exceeds that at 0.2 m from the ridge. Variations in size do not exert a significant influence on the distribution of negative wind pressure coefficients in the vicinity of the ridge.
# Detection and Localisation of Abnormal Parathyroid Glands: An Explainable Deep Learning Approach
## 1 Introduction
[10] investigated the precision of hyperspectral imaging (HIS) in 82 ex vivo study cases regarding tumour detection of the thyroid and salivary glands. Tissues were imaged with HSI in broadband reflectance and autofluorescence modes. For comparison, the tissues were imaged with two fluorescent dyes. Deep learning algorithms were developed for tumour detection using histological ground truths. For the classification of thyroid tumours, HSI-synthesized RGB images achieved the best performance with an AUC score of 0.90. [PERSON] [11] applied transfer learning [12] to automatically detect HTTP from ultrasound images annotated by senior radiologists. The authors employed three well-established convolutional neural networks to analyse the images and suggest potential features underlying the presence of HPPT. As a result, they achieved the best recall of 0.956 using a Single shot multibox detector (SSD) [13]. [PERSON] [14] performed a retrospective review of patients who underwent parathyroidectomy for a single adenoma between 2010-2017. SPECT/CT images were inspected by senior medical staff. The intraoperative report was used as reference for assessing the accuracy obtained by a novel method for localisation using SPECT/CT, which operates by determining the anterior-posterior relationship of the adenoma to a horizontal line in the coronal plane through the tracheoesophageal groove. The study achieved 80% accuracy and 0.706 Cohen's Kappa score, which verifies the agreement between the method and the original radiology report. Most of the studies focus on developing surgeon-assisting tools for accurately detecting PGs. Those studies' contribution to the field is beyond question. However, little has been investigated regarding the non-invasive detection of the parathyroid glands using medical image acquisition devices and before the surgery [15]. This study extends the reported results in [16], a study conducted by the same research team. The study proposes a multi-path DL pipeline to simultaneously process the MIBI early phase, MIBI late phase, and the 99 mTc04 thyroid scan. To this end, the study employs the state-of-the-art convolutional neural network (CNN) called Virtual Geometry Group (VGG) to furnish a multi-path pipeline, which performs a per-patient classification between normal and abnormal scans. Furthermore, the Grad-CAM algorithm is employed to visualise the critical local areas of each image according to the model.
## 3 Results and Discussion
### Additive Friction Stir Deposition Axial Force  
During the AFSD process, the machine's axial downward force was recorded for all the processed materials. Only the successfully produced DPs under the conditions of feed rates from 1 to 4 mm/min and 400 rpm constant rotation speed were plotted against the deposition time. In general, depending on the axial force values, the AFSD procedure has three distinct stages: material plasticization, the deposition process, and process ending. In the beginning, in the first stage, the AA7075-T6 material starts rubbing the AA2024-T4 substrate at the interface with little increase in the axial load. With increasing mechanical friction during the feeding process, the frictional heat generated rises and leads to the plasticization of the tip of the consumable rod material.  
Subsequently, in stage 2, the deposition process shows nearly stable axial forces of 976, 1020, 1144, and 1225 N at deposition feed rates of 1, 2, 3, and 4 mm/min, respectively. Thus, it can be noted that the frictional heat generated is adequate to deform and deposit continuously multilayered parts layer-by-layer with a final diameter-to-height (D/H) ratio of 2, 2.8, 3.6, and 4.7, as shown in Figure 4. In the final stage, deposition end, the axial load suddenly decreases after the deposition process ends because the rotating rod moves upwards, as observed in stage 3. From Figure 4, it can be concluded that the time needed to deposit the layers of the DP produced at 1 mm/min (Figure 5a) is longer than the time consumed in building the layers at a 4 mm/min deposition feed rate (Figure 5d).  
It is worth noting that the axial force increases with increase in the AA7075-T6 rod feed rates from 1 to 4 mm/min. This trend may be attributed to the fact that a high deposition rate faces more resistance in the deposit layers during the continuous feeding of the rotating consumable rod against the substrate, which causes more axial force to be required to achieve the deposition process than at a low deposition feed rate.  
Figure 4: AA7075 deposited part dimensions in terms of diameter (D) and height (H) and their D/H ratios processed at 400 rpm and different deposition feed rates.
# Virtualized MME Design for IoT Support in 5G Systems
## 5 Virtualized MME Design for M2M
### Proposed Schemes  
#### 5.3.1 Traffic Separated Scheme  
To mitigate the overdimensioning caused by the synchronization events of massive M2M devices, the first proposed scheme separates the processing of the traffic classes. To do that, we propose to use dedicated resources and dimension them separately for each traffic class. However, it is challenging to apply such separation at all tiers.  
Regarding the FE tier, the vMME has to be seen as a single entity by the remaining elements of the 3 GPP architecture. Additionally, the FE has to classify the traffic into the defined traffic classes. For these reasons, we do not consider the option of dividing the FE tier per traffic class. In addition, we also do not consider the option of dividing the SDB tier per traffic class. The rationale is that the SDB is considerably more expensive than the other elements of the design [34], and therefore, such a division per traffic class would yield a costly and, therefore, unattractive design.  
Consequently, we apply the separation of dedicated resources for each traffic class only at the SL tier (see Figure 7). The benefit of this scheme is that the target arrival rate of each pool of SL can be optimized using the main traffic characteristics of the traffic served. Additionally, the mean response time budget \(\overline{D}_{j}\) at the SL tier can be set differently for each traffic class.  
Figure 7: Traffic separated scheme block diagram.  
This scheme, unlike previous ones, has a different target arrival rate defined per tier. This is due to the FE tier having to convey all traffic to avoid bottlenecks at this tier. However, SL and SDB tiers can be optimized to reduce overdimensioning. Then, we set the target arrival rate in the FE tier as in the overdimensioned scheme explained above:  
\[\lambda_{FE}=\overline{\lambda}_{MBB}+\lambda_{mMZM}^{peak}+\lambda_{1 MZM}^{ intsmooth} \tag{11}\]  
Since SLs are dimensioned differently for each traffic class, we set their target arrival rate differently. For MBB traffic, the mean arrival rate, for mM2M traffic, we apply moderate smoothing of traffic peaks, \(\lambda_{mMZM}^{smoothpeak}\); and for IM2M, the traffic peak, \(\lambda_{1M2M}^{peak}\).  
Note that the mM2M pool of SL instances is not designed to support mM2M traffic peaks, but this is not expected to be a major issue as mM2M devices are assumed to be delay tolerant. Furthermore, as mentioned in Section 3, the SL tier is assumed to have enough memory resources to store all packets while they are queued. Then, we set the target arrival rate for each traffic class as:  
\[\begin{split} MBB&\lambda_{SL}=\overline{\lambda}_{MBB }\\ mM2M&\lambda_{SL}=\lambda_{mMZM}^{smootpeak}\\  IM2M&\lambda_{SL}=\lambda_{1M2M}^{peak}\end{split} \tag{12}\]  
At the SDB tier, the target arrival rate is a sum of the target arrival rate of each class of SLs, then:  
\[\lambda_{SDB}=\overline{\lambda}_{MBB}+\lambda_{mMZM}^{smoothpeak}+\lambda_{ 1M2M}^{peak} \tag{13}\]