esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theory  
### Lamb Waves  
Lamb waves are multi-modal waves propagating in thin plates, which means that, generally, more than one mode is propagating (Figure 1), with an infinite number of these modes existing in theory. Generally, these modes are classified, based on the resulting stress distribution along the cross section, into symmetric and anti-symmetric modes [65]. However, most modes comprise a cut-off frequency below which they do not propagate. The cut-off frequencies depend on the material and thickness of the plate. Two Lamb wave modes, the fundamental symmetric, \(S_{0}\), and the fundamental anti-symmetric, \(A_{0}\), modes exist, which propagate across all frequencies [66]. For most Structural Health Monitoring (SHM)-applications, the excitation is chosen so that they operate at the frequency range where only these two fundamental modes propagate [8, 21, 67]. In the presence of perturbations within the plate, such as defects [68] or in the presence of boundaries [69], mode conversion can occur. Furthermore, Lamb waves are characterized by their dispersive nature, which implies that their propagation speed is frequency-dependent (Figure 1). Since wave packets comprise multiple frequencies, rather than a single one, different propagation speeds are present within the wave packet. As a consequence, a wave packet will change its form over time, while its amplitude will drop.
## 1 Introduction
However, mmWave signals exhibit reduced diffraction and higher susceptibility to blockage compared to microwave signals; thus, mmWave channel is nearly bimodal depending on the presence or absence of line-of-sight (LoS) [21]. For this reason, if the LoS path is blocked by obstacles, an outage event may occur for high data rate applications such as multimedia data transfer that cannot be supported solely by non-LoS paths. In [29], the authors propose a stochastic model of such blockage effects in mmWave channels, where the probability of the existence of the LoS path is an exponentially decaying function of the distance between two nodes. Using this model, the authors in [30] study the coverage and rate performance in mmWave cellular networks. Moreover, in [31], the signal-to-interference-and-noise-ratio in the mmWave ad hoc network is analyzed in terms of a information theoretical metric (transmission capacity). In this paper, we also assume the blockage model in [29] and focus on the hop distance statistics under the blockage effects. We provide insights to better design multi-hop mmWave M2M communication systems using two representative and generic routing strategies with maximum and minimum possible hop distances, which can be exploited to build routing schemes and higher-layer protocols in the future. The main contributions of this paper are threefold: first, we derive closed form expressions of probability distributions of hop distances for two different routing schemes in the mmWave MWNs unlike the microwave MWNs in [13]; second, we derive the blockage-free hop distance statistics as a baseline to quantify the impact of the blockage effect; third, using the derived hop distance statistics, we estimate end-to-end performances such as outage probability, hop count, and transmit energy, which are compared with simulation results to validate our analysis. The rest of this paper is organized as follows. The system model is introduced in Section 2. In Section 3, per-hop outage probability and the distance distribution of line-of-sight (LoS) links are derived in the presence of blockage effects. In Section 4, we introduce two routing strategies targeting different system requirements and analyze their hop distance statistics. In Section 5, we propose a method to estimate the end-to-end system performances (e.g., outage probability, hop count, and transmit energy) of mmWave MWNs based on the per-hop statistics. The blockage-free performances are analyzed as baseline cases to quantify the impact of the blockage effects in Section 6. Numerical results are presented in Section 7 to validate our analysis with simulation results. Conclusions are provided in Section 8.
## 3 Methods
### Feature Engineering
\[p_{m}=0.5\times e^{(-10\times\frac{n}{n_{i}})}+0.01 \tag{9}\]  
If the iteration increases, the nonlinear function, \(p_{m}\), tends to decrease, while the maximum number of iterations is represented as \(M_{I}\). The mutation randomly selects the active features from the datasets when the nonlinear function, \(p_{m}\), is higher than the random number, which usually ranges between zero and one. The selected active features from the Rossmann and Walmart datasets are finally passed to the Bi-GRU model for retail sales forecasting. The APSO algorithm terminates when it reaches the maximum number of iterations (100). The parameters considered in the APSO algorithm are as follows: the cognitive constant, \(c_{2}\), is two; the social constant, \(c_{1}\), is three; the size of the population is 100; and the number of iterations is 100. The features selected in the Walmart dataset are the date, Consumer Price Index (CPI), fuel prices, store, weekly_sales, and holiday_flag. Correspondingly, the features selected in the Rossmann dataset are the day of the week, open promo, customers, sales, and store number. The architecture of the APSO algorithm is mentioned in Figure 2.  
The step-by-step procedure of APSO algorithm is specified as follows. Step 1: The swarm particles, size, location, objective, number of iterations, and save non-dominated solution are set into the archive. Step 2: To update the \(p_{id}\), pareto domination connection is applied. Step 3: Due to the multiplicity of solution, \(p_{gd}\) is chosen from the archives. In the beginning, crowding distance is estimated, and, formerly, binary tournament is utilized for choosing \(p_{gd}\). Step 4: At that time, the decision value is reset, which depends on \(p_{gd}\). Each value of feature vector is considered as a binary value. Figure 2: Architecture of the APSO algorithm. Step 5: Depending on step 5, the particle's position and velocity is updated. Step 6: Uniform mutation is accomplished. Step 7: Then, the external archive is updated by means of crowding distance. Step 8: Termination process: If the proposed method achieves the maximum iteration, the process is stopped; otherwise, step 2 is repeated. Therefore, the worst particles are removed by HGO. After choosing the optimal features from the Rossmann dataset (day of week, open promo, customers, sales, and store number) and Walmart dataset (date, CPI, fuel prices, store, weekly_sales, and holiday_flag) using MRMR, RFE, and APSO, forecasting is processed using Bi-GRU, which is described in the following section.
## 4 Experimental Evaluation--RIOTA's Implementation and Cork as a UNO
### Testing Procedures
#### 4.3.3 UNO Evaluation  
As mentioned in Section 2, the common evaluation methods for UNOs and PUFs are the inter- and intra-distance measures. Both distances are often represented using histograms for a given image test set and/or CRP database. Additionally, it is normal to summarize the histograms by their average and standard deviation \((\mu_{intra},\sigma_{intra})\) and \((\mu_{inter},\sigma_{inter})\). As highlighted by [PERSON] and [PERSON] in Reference [17], since these measures are frequently used in the literature, it makes, in a generic way, an objective comparison between previous work proposals. However, an important note must be pointed out. Several works use the Hamming distance as distance measure for evaluation purposes. For these systems, which use bit strings as responses, it is usual to state that the smaller the \(\mu_{intra}\), the more reliable the UNO and/or PUF. At the same time, \(\mu_{inter}\) measures the average distinguishability of two different systems based on their responses, i.e., giving them the notion of uniqueness of UNOs and/or PUFs based on the responses. On bit string responses, the best distinguishability is achieved when 50% of the bits differ. In short, it is expected to minimize both \(\mu_{intra}\) and \(|\mu_{inter}-50\%|\) for systems using bit strings as responses.  
In this work, the score is not represented by the Hamming distance but by the number of matched descriptors in two images. The higher the score, the higher the similarity and most likely is for the two sets of descriptors under test to belong to the same object. Therefore, a low value of \(\mu_{inter}\) and a high value of \(\mu_{intra}\) is desirable.
# Monitoring Land Cover Change on a Rapidly Urbanizing Island Using Google Earth Engine
## 3 Results
### The 30-Year Land Cover Change
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Accuracy Assessment**} & \multicolumn{6}{c}{**Land Cover Classes**} \\ \cline{2-9}  & **SL** & **BL** & **CL** & **FL** & **OL** & **W** & \
\begin{tabular}{c} **Overall** \\ **Accuracy** \\ \end{tabular} &
\begin{tabular}{c} **Kappa** \\ **Coefficient** \\ \end{tabular} \\ \hline \multicolumn{9}{c}{**Year: 1990**} \\ \cline{2-9}  & 83.33\% & 91.11\% & 86.05\% & 93.33\% & 88.10\% & 97.37\% & & \\ Producer’s accuracy & 93.02\% & 91.11\% & 90.24\% & 85.71\% & 80.43\% & 100.00\% & 90.09\% & 0.88 \\ F1 score & 0.88 & 0.91 & 0.88 & 0.89 & 0.84 & 0.99 & & \\ \hline \multicolumn{9}{c}{**Year: 2000**} \\ \cline{2-9}  & 82.98\% & 91.30\% & 88.37\% & 95.24\% & 88.64\% & 97.22\% & & \\ Producer’s accuracy & 92.36\% & 89.36\% & 86.36\% & 88.89\% & 86.67\% & 100.00\% & 90.31\% & 0.89 \\ F1 score & 0.87 & 0.90 & 0.87 & 0.92 & 0.88 & 0.99 & & \\ \hline \multicolumn{9}{c}{**Year: 2007**} \\ \cline{2-9}  & 82.61\% & 92.68\% & 87.18\% & 92.31\% & 88.10\% & 97.22\% & & \\ Producer’s accuracy & 92.68\% & 90.48\% & 89.47\% & 90.57\% & 86.05\% & 97.22\% & 91.08\% & 0.90 \\ F1 score & 0.87 & 0.92 & 0.88 & 0.91 & 0.87 & 0.97 & & \\ \hline \multicolumn{9}{c}{**Year: 2019**} \\ \cline{2-9}  & 83.97\% & 89.29\% & 84.41\% & 95.39\% & 82.61\% & 99.31\% & & \\ Producer’s accuracy & 80.62\% & 88.03\% & 92.39\% & 85.29\% & 82.31\% & 99.65\% & 88.43\% & 0.86 \\ F1 score & 0.82 & 0.89 & 0.88 & 0.90 & 0.82 & 0.99 & \\ \hline \multicolumn{9}{c}{NOTE: SL = sandy land, BL = built-up land, CL = cultivated land, FL = forest land, OL = other land, W = water.} \\ \end{tabular}
\end{table} 
Table 3: User’s accuracy, producer’s accuracy, overall accuracy, and Kappa coefficient on Haitan Island, China, by land cover classes. 2000 and 2007, and 9.52 km\({}^{2}\) between 2007 and 2019. The other land areas showed a significant change between 1990 and 2019, with a decrease of 27.19 km\({}^{2}\) and 59.98 km\({}^{2}\) in the first and second intervals, and an increase of 8.11 km\({}^{2}\) in the third interval.
## 4 Edge Cloud Infrastructure  
The concept of both MEC and FEC involves placing storage, computing, and network components near to the users. This approach aims to meet the extensive computing requirements by providing low latency, high bandwidth, and reliable network connectivity. As storage and computing resources are closer to the users, the edge computing paradigm is not a centralized architecture; instead, the edge data centers are distributed at the network edge. Moreover, in cloud computing, multiple applications are provisioned into a single server whereas in edge cloud one application may need to distribute on multiple edge servers. Therefore, in edge computing architecture, intra edge data centers (DCs), inter edge DCs, edge DCs to cloud DCs, and edge DCs to user devices networking is very important. As a result, to achieve the above goals, infrastructure, networking, resource virtualization, and control are the key considerations. In this section, we present key infrastructural components to build an edge cloud computing system [26, 27].
# Review of Magnetoelectric Effects on Coaxial Fibers of Ferrites and Ferroelectrics
## 5 Hexagonal Ferrite and Ferroelectric Core-Shell Fibers  
Ferrimagnets with hexagonal crystal structures with desirable magnetic parameters are of importance for use in ME composites. Hexaferrites, depending on the arrangement of spinel and hexagonal blocks, are classified into M-, W-, Y-, U-types, etc. Most of them have a very high uniaxial or planar magneto-crystalline anisotropy field, \(\rm H_{a}\)[46]. M-type ferrites such as barium (BaM) and strontium (SrM) hexaferrites, for example, have uniaxial anisotropy fields of 18 to 20 kOe. Although composites with hexaferrites have weak direct ME interactions due to smaller magnetostriction compared with CFO or NFO, they are attractive for use in ME composites due to the high \(\rm H_{a}\) values that could give rise to a strong ME coupling in the absence of an external bias magnetic field. Such zero-bias ME coupling is of importance for composite-based sensors and energy harvesters [75]. This section deals with ME coupling in composites with ferrimagnetic M-, Y-, or W-type hexaferrites and PZT or BTO.  
Figure 11: (**a**) Scattering matrix parameter \(\rm S_{21}\) vs. frequency \(\rm f\) for showing FMR for a series of in-plane static magnetic field \(\rm H\) for a rectangular sample of fibers of NZFO (x = 0.2)-PZT. Magnetostriction \(\rm\lambda_{11}\) measured along H-direction vs. H for a disc with fibers of (**b**) \(\rm Ni_{0.8}Zn_{0.2}Fe_{2}O_{4}\) core–PZT shell and (**c**) PZT core–\(\rm Ni_{0.8}Zn_{0.2}Fe_{2}O_{4}\) shell [59].  
Figure 12: (**a**) Fractional variation in \(\rm P_{r}\) vs. H for \(\rm Ni_{0.7}Zn_{0.3}Fe_{2}O_{4}\)-PZT coaxial fibers with ferrite core and PZT core. (**b**) Variation of maximum value of \(\rm\Delta P_{r}/P_{r}\) with Zn concentration for fibers of \(\rm Ni_{1.3}Zn_{3}Fe_{2}O_{4}\) (x = 0–0.5)-PZT. (**c**) DEVC vs. H in a disc of fibers of NZFO (x = 0.3) shell–PZT core [59].
# Preliminary Global NO2 Retrieval from EMI-II Onboard GF5B/DQ1 and Comparison to Tropopmi
## 3 Results and Validation  
### SCD Uncertainty  
To evaluate the uncertainty in the EMI-IIs' measured SCD, we employed a statistical analysis approach based on data from the EMI-II in the Pacific clean region (20\({}^{\circ}\)S-20\({}^{\circ}\)N; 160\({}^{\circ}\)E-180\({}^{\circ}\)E), as outlined by [31]. Initially, we segmented the region into 2\({}^{\circ}\)\(\times\) 2\({}^{\circ}\) boxes. In theory, the AMF within each box should exhibit minimal variation, resulting in negligible VCD differences among pixels. Consequently, any changes in the total NO\({}_{2}\) columns within each box are assumed to stem from slant column measurement errors, primarily due to (random) instrument noise. Thus, variations in the total detected VCD are attributed to SCD errors. We then estimated the SCD uncertainty by analyzing the deviations of the SCD values for each valid pixel from the mean SCD of the respective box. Figure 2 displays histograms of the absolute differences for valid pixels from GF5B and DQ1 for June 2022. The SCD deviations follow a Gaussian distribution, suggesting that the observed variability within the boxes is predominantly due to random error. The Gaussian distribution widths were 0.42 \(\times\) 10\({}^{15}\) molec./cm\({}^{2}\) for GF5B and 0.37 \(\times\) 10\({}^{15}\) molec./cm\({}^{2}\) for DQ1 in June 2022, respectively, which are interpreted as the average slant column errors for the EMI-II measurements. The precision of the EMI-II NO\({}_{2}\) measurement is comparable to the capabilities of GOME-2, where the estimated average SCD uncertainty was 0.45 \(\times\) 10\({}^{15}\) molec./cm\({}^{2}\), as reported by [31], which is better than the OMI NO\({}_{2}\) measurements of 0.67 \(\times\) 10\({}^{15}\), that were reported by [32] using the same method. GF-5B had a larger slant column error than DQ1, which can be partly attributed to the higher signal-to-noise ratio.
# Doppler Shift Tolerance of Typical Pseudorandom Binary Sequences in PMCW Radar
## 4 Comparative Performance Analysis  
In this section, a comparative analysis of the PACFs of typically used PRBSs in PMCW radar systems is performed based on the introduced performance metrics in Section 3. In this context, Section 4.1 briefly describes the considered PRBSs, and Section 4.2 investigates the potential of range profile distortion of those PRBSs by analyzing their PACFs under increasing Doppler shifts.  
Figure 3: Comparison between Nyquist-sampled (\(\blacksquare\)) and oversampled (\(\leftarrow\), \(t=20\)) PACFs: (**a**) PACF of m-sequence of length \(N_{\text{PRBS}}=1023\) and (**b**) ideal PACF of an hypotetical sequence of length \(N_{\text{PRBS}}=1023\).  
Figure 2: Non-oversampled range profiles obtained in a SISO-PMCW radar system adopting an m-sequence of length \(N_{\text{PRBS}}=1023\): (**a**) target at range bin \(n=300\) and (**b**) target at range bin \(n=300.5\).
## 2 Mathematical Model
### STPAP Architechture  
Consider a DPSA consisting of \(M\) pairs of crossed dipoles. A block diagram of the STPAP architecture is shown in Figure 2. The signal received by each dipole is firstly down converted to intermediate frequency in the radio frequency front end (RFFE) and then digitized by an analog to digital converter (ADC). Assume that each dipole is followed by a tapped delay line (TDL) with \(K\) taps and a delay of \(T_{0}\) seconds between taps, with which a block of \(K\) time domain samples are acquired from each dipole. After that, the received signal is processed by the proposed STPAP beamforming algorithm and then sent into the GNSS receiver for acquisition.  
Let \(\mathbf{X}(n)\in\mathbb{C}^{2 MK\times 1}\) denotes the received signal for the \(n\)-th block  
\[\mathbf{X}(n)=\left[\mathbf{X}_{1}(n);\mathbf{X}_{2}(n);\cdots;\mathbf{X}_{K}(n)\right] \tag{3}\]  
with  
\[\mathbf{X}_{k}(n)=\left[x_{1}^{\mathrm{h}}(t-(k-1)T_{0}),x_{1}^{\mathrm{v}}(t-(k-1 )T_{0}),\cdots,x_{M}^{\mathrm{h}}(t-(k-1)T_{0}),x_{M}^{\mathrm{h}}(t-(k-1)T_{0 })\right]^{\mathrm{H}}, \tag{4}\]  
where \(\mathbb{C}\) represents the set of the complex numbers, \(\mathrm{H}\) denotes the conjugate transpose, and \(x_{m}^{\mathrm{h}}(t)\), \(x_{m}^{\mathrm{v}}(t)\) are the most recent time domain samples. In addition, \(x_{m}^{\mathrm{h}}(t-(k-1)T_{0})\) and \(x_{m}^{\mathrm{v}}(t-(k-1)T_{0})\) (\(m=1,2,\cdots,M,k=1,2,\cdots K\)) denote the time domain sample of the \(m\)-th horizontal dipole and the \(m\)-th vertical dipole at the \(k\)-th tap, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Polarization Mode** & **Amplitude Ratio (\(\gamma\))** & **Phase Difference (\(\eta\))** \\ \hline HP & 0 & 0,\(\pi\) \\ VP & \(\pi/2\) & 0,\(\pi\) \\ RHCP & \(\pi/4\) & \(\pi/2\) \\ LHCP & \(\pi/4\) & \(-\pi/2\) \\ EP & [0,\(\pi/2\)] & [\(-\pi\),\(\pi\)) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Polarized parameter.  
Define the weight vector \(\mathbf{W}(n)\in\mathbb{C}^{2 MK\times 1}\) for the \(n\)-th block as  
\[\mathbf{W}(n)=[\mathbf{W}_{1}(n);\mathbf{W}_{2}(n);\cdots;\mathbf{W}_{M}(n)] \tag{5}\]  
with  
\[\mathbf{W}_{k}(n)=[w_{1}^{\rm h}(k),w_{1}^{\rm v}(k),\cdots,w_{M}^{\rm h}(k),w_{M}^ {\rm v}(k)]^{\rm H}, \tag{6}\]  
where \(w_{m}^{\rm h}(k)\) denotes the weight value for the \(m\)-th horizontal dipole at the \(k\)-th tap and \(w_{m}^{\rm v}(k)\) denotes the weight value for the \(m\)-th vertical dipole at the \(k\)-th tap.  
With (3) and (5), the output for the \(n\)-th block is presented as  
\[\begin{array}{ll}Y(n)&=\mathbf{W}^{\rm H}(n)\mathbf{X}(n)\\ &=\sum\limits_{m=1}^{M}\sum\limits_{k=1}^{K}\big{[}w_{m}^{\rm h}(k)x_{m}^{\rm h }(t-(k-1)T_{0})+w_{m}^{\rm v}(k)x_{m}^{\rm v}(t-(k-1)T_{0})\big{]}.\end{array} \tag{7}\]
## 2 Theory
### Solution Strategy
#### 2.3.2 Power Dissipation
It can be demonstrated that \(dV=\left[4\pi r^{2}-\pi\left(h_{1}{}^{2}+h_{2}{}^{2}\right)-2\pi lr\right]dr\) with \(h_{1}=R-Z_{1}\) and \(h_{2}=R-h_{0}\), where \(Z_{0}\) is the distance measured from the origin of the reference system to the top water level, and \(h_{0}\) is the depth measured from the origin of the reference system to the bottom of the flask (see Figure 1). The length of the cylinder inside the flask has a length \(l=Z_{1}+Z_{2}\) and an external radius \(R_{c}\). The power dissipation is rewritten in Equation (30), which depends on the function describing the velocity of the liquid phase. \[ \begin{split}\left|P(t)\right|=\frac{\sigma B_{0}{}^{2}}{\Phi_{ l}}\left(4\pi\int_{0}^{R}w_{l}(r,t)^{2}r^{2}dr-\pi h_{1}{}^{2}\int_{0}^{\left(R^{2 }-Z_{1}{}^{2}\right)^{1/2}}w_{l}(r,t)^{2}dr-\pi h_{2}{}^{2}\int_{0}^{\left(R^ {2}-h_{0}{}^{2}\right)^{1/2}}w_{l}(r,t)^{2}dr\\ -2\pi l\int_{0}^{R_{c}}w_{l}(r,t)^{2}r\,dr\right)\end{split}  \tag{30}\]
# An Energy-Saving Forwarding Mechanism Based on Clustering for Opportunistic Networks
## 6 Performance Evaluation
### ANOVA--Delivery Probability of Messages--Infocom Scenario  
When applying the ANOVA statistical test in the messages delivery probability, our results show a significant difference in the battery ranges used, as shown in Table 8. Analyzing the results of the ANOVA, it is observed by the \(F\) test using 5% significance that \(F>\)_Fcritical_, that is, there was at least one routing algorithm with a different message delivery performance between the battery range studied.  
So, there is a variance in means captured by each algorithm and battery range (\(F\) = 409.58 and \(F\) = 295.17). This result supports the performance difference in delivery rates between the routing algorithms previously described in Figure 3 (delivery probability graph).  
Thus, the null hypothesis H\({}_{0}\) is rejected, where all averages are equal. To specifically address where this variation is occurring, we applied the Tukey test.
# Two-Stage Optimal Task Scheduling for Smart Home Environment Using Fog Computing Infrastructures
## 1 Introduction
### Motivation  
To improve the service and operation layers in fog computing, practical scheduling algorithms are required, which comprises one of the main challenges in the fog environment. Three major scheduling issues in fog computing are resource allocation, task scheduling, and workflow scheduling [8,9]. Firstly, resource allocation deals with the optimal allocation  
Figure 1: Fog–cloud architecture for smart homes.  
of tasks \((T1,T2,\ldots,Tn)\) over fog nodes \((FN1,FN2,\ldots,FNn)\) to improve resource utilization, provide a faster response time, and reduce the energy consumption and overall cost. Secondly, task scheduling helps with the optimal resource allocation by assigning tasks to different resources considering the task's input time, wait time, deadlines, and the cost of performing the task as important parameters. Finally, workflow scheduling, which is also known as task-dependent scheduling, is based on the distribution of tasks on heterogeneous fog nodes. In order to identify an efficient technique for resource management and task scheduling, many researchers have built new algorithms. The quest to improve the efficiency of task scheduling algorithms to minimize energy consumption has led to a novel technique named the two-stage optimal task scheduling technique (2-ST), which could be one of the most-promising techniques for the IoT industry and its related applications. The IoT environment supports various real-time applications for time-sensitive data such as healthcare, smart cities, smart homes, smart traffic management, and many others, and it further deals with a large number of computing nodes or devices. However, due to the long response time taken by computing devices with their integration with the cloud environment, this technique provides the integration of fog computing and smart homes in the IoT environment. The main aim of 2-ST in smart homes is to understand the topology of the smart homes, provide optimal task allocation with less response time, make decisions, and act accordingly.