esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Working Mechanism of ESRVR
### Operation of Co-ESRVR
The destination accepts the packets only when the BER is below a certain threshold. Reception of packets by a relay from source is followed by the forwarding of the packets to the destination by the former. The decision of which nodes to select as relay and destination is carried out by the source by measuring their physical distance from itself using a control packet. The expiry time of the control packet is designed in such a manner that even the farthest nodes could communicate with the source node within the transmission range of the latter.
# TDLAS Monitoring of Carbon Dioxide with Temperature Compensation in Power Plant Exhausts
## 6 Summary and Conclusions  
Temperature compensations of CO\({}_{2}\) concentration measurements using TDLAS were reported. Absorption spectra at different temperatures were derived from HITRAN to find suitable transitions for in situ CO\({}_{2}\) measurements and recorded using DA and WMS. The temperature compensations based on temperature influence of line intensity were then applied to reduce measurement errors. Our experimental results show that there was an obvious decrease in the influence of temperature on signals after temperature compensation, and the detection limits of both methods meet the needs of the in situ CO\({}_{2}\) measurement. However, compared with WMS, DA is less sensitive to fluctuations of temperature and pressure, resulting in a superior long-term stability. The DA measurement based on temperature compensation has enough accuracy to be used for reliable determination of high concentration CO\({}_{2}\) in power plant exhausts.  
Conceptualization, X.Z. and S.Y.; Methodology, X.Z. and Z.L. ([PERSON]); Investigation, X.Z.; Writing--Original Draft Preparation, X.Z.; Writing--Review & Editing, W.R. and Z.L. ([PERSON]); Funding Acquisition, S.Y.  
This research was funded by the Guangdong Province Train High-Level Personnel Special Support Program, grant number 2014 TQ01N334, and the Guangdong Province Key Laboratory of Efficient and Clean Energy Utilization, grant number 2013A061401005.  
The authors would like to thank the Guangdong Province Train High-Level Personnel Special Support Program (2014 TQ01N334) and the Guangdong Province Key Laboratory of Efficient and Clean Energy Utilization (2013A061401005), South China University of Technology.  
The authors declare no conflict of interest.
## 2 Real-Time City-Scale Nonlinear Time-History Analysis
### City-Scale Nonlinear Time-History Analysis  
The city-scale nonlinear time-history analysis is used to perform the seismic damage simulation for the target region [11]. In general, the buildings in a city can be divided into two types: Ordinary multi-story buildings and ordinary tall buildings. Multi-story buildings often exhibit shear deformation modes under earthquakes, whereas tall buildings will deform in flexural-shear modes. Thus, the multiple-degree-of-freedom (MDOF) shear model will be used for the multi-story buildings (Figure 2a), and the MDOF flexural-shear model will be applied to tall buildings (Figure 2b). In the MDOF model, the masses of the buildings are concentrated on their corresponding stories. Specifically, for the MDOF shear model, each story is represented by a nonlinear spring, as shown in Figure 2a. For the MDOF flexural-shear model, each story is discretized into a nonlinear shear spring and a nonlinear flexural spring, which are connected to each other by rigid links, as shown in Figure 2b. The nonlinear behavior of the structure is represented by the nonlinear inter-story force-displacement relationships (Figure 2c). The tri-linear backbone curves recommended in the HAZUS report [7] are employed to model the inter-story force-displacement relationships. Note that the parameter determination of the inter-story force-displacement relationships is critical for the rationality and accuracy of the simulation results, considering the limited available information for buildings on a regional scale. The parameter determination methods for buildings in China and the United States proposed by the authors [11] are adopted in the city-scale nonlinear time-history analysis. For buildings in the other countries, the parameter determination procedure can refer to those two methods.
## 3 Principles of Operation
### Fuel Cells
#### 3.3.3 Ohmic Loss  
The power loss as a result of resistance to the electrons and ions flowing through the electrode is known as the ohmic loss. It varies over the entire range of the current density due to the nature of fuel cell resistance. The ohmic loss is represented by  
\[\eta^{ohm}=i\cdot(R_{de}+\ R_{ion}) \tag{22}\]  
where \(R_{ele}\) and \(R_{ion}\) are the electronic and ionic resistances, respectively. It has been reported that the ionic resistance is predominant in most FC cases due to the very high electronic conductivity of the graphic-based current collector. The ionic resistance of the proton exchange membrane depends on its  
Figure 3: Temperature dependent open circuit potential for the PEM fuel cell.  
water content, which is in turn a function of the temperature and current density. The ionic resistance is given by  
\[R_{ion}=\frac{I_{M}\cdot r_{M}}{A} \tag{23}\]  
where \(I_{M}\) is the membrane thickness, \(A\) is the active cell area, and \(r_{M}\) is the membrane resistivity, expressed by  
\[r_{M}=\frac{181.6\left[1+0.03\left(\frac{i}{\lambda}\right)+0.062\cdot\left( \frac{T}{03}\right)^{2}\left(\frac{i}{\lambda}\right)^{2.5}\right]}{\left[ \lambda-0.634-3\left(\frac{i}{\lambda}\right)\right]\cdot\exp\left[4.18\cdot \frac{\left(T-303\right)}{T}\right]} \tag{24}\]  
where \(\lambda\) is the water content of the membrane and \(T\) is the temperature.
## 6 Conclusions
### Applicability to Other Domains  
Beyond football, the methodologies and findings of this study have wider applicability and offer valuable contributions to various fields. In sports, the principles of data-driven decision-making highlighted here can be adapted to other team sports like basketball and hockey, where similar analytics can improve strategies and results.  
In healthcare and injury prevention, the study's focus on predictive models and fatigue management can be applied to reduce injury risks in diverse sports and physically demanding professions. These methods can help in designing injury prevention strategies not only for athletes but also for individuals in occupations that entail physical exertion.  
Additionally, the study's insights into workload management can inform strategies to optimize workforce performance in the corporate sector. By applying these principles, organizations can enhance productivity while mitigating burnout risks, paralleling the balance between training and recovery necessary for athletes.
# Two-Way Linear Probing Revisited
## 4 Lower Bounds
### Algorithms That Behave Poorly
Since there are \(\beta!\) orderings of the cells in which they can be covered (for the first time), we have  
\[\mathbb{P}\{Y_{1}=1\}=\beta!\ \mathbb{P}\{\mathfrak{B}_{1}\text{is covered in order}\}.\]  
For \(t\in\llbracket m\rrbracket\), let \(M(t)=1\) if block \(\mathfrak{B}_{1}\) is full before the insertion of the \(t\)-th key, and otherwise be the minimum \(i\in\mathfrak{B}_{1}\) such that the cell \(i\) has not been covered yet. Let \(A\) be the event that, for all \(t\in\llbracket m\rrbracket\), the first initial hashing cell of the \(t\)-th key is either cell \(M(t)\) or a cell outside \(\mathfrak{B}_{1}\). Define the random variable \(W:=\sum_{t=1}^{m}W_{t}\), where \(W_{t}\) is the indicator that the \(t\)-th key covers a cell in \(\mathfrak{B}_{1}\). Clearly, if \(A\) is true and \(W\geq\beta\), then the first block is covered in order. Thus,  
\[\mathbb{P}\{Y_{1}=1\}\geq\beta!\ \mathbb{P}\{\left\lfloor\ W\geq\beta\right\rfloor \cap A\}=\beta!\ \mathbb{P}\{A\}\mathbb{P}\{W\geq\beta\mid A\}.\]  
However, since the initial hashing cells are chosen independently and uniformly at random, then for \(n\) chosen large enough, we have  
\[\mathbb{P}\{A\}\geq\left(1-\frac{\beta}{n}\right)^{m}\geq e^{-2\beta},\]  
and for \(t\geq\lceil\ m/2\rceil\),  
\[\mathbb{P}\{W_{t}=1\mid A\}=\frac{1}{n-\beta+1}\cdot\frac{t-1}{n}\geq\frac{ \alpha}{4n}.\]  
Therefore, for \(n\) that is sufficiently large, we obtain  
\[N\mathbb{P}\{Y_{1}=1\} \geq N\beta!\ e^{-2\beta}\ \mathbb{P}\{\text{binomial}(\lceil\ m/2\ \rceil,\alpha/(4n))\geq\beta\}\] \[\geq N\beta!\ e^{-2\beta}\ \frac{(m/2-\beta)^{\beta}}{\beta!}\Big{(} \frac{\alpha}{4n}\Big{)}^{\beta}\Big{(}1-\frac{\alpha}{4n}\Big{)}^{n}\] \[\geq N\Big{(}\frac{\alpha^{2}}{8e^{2}}\Big{)}^{\beta}\bigg{(}1-\frac{ 2\beta}{m}\bigg{)}^{\beta}\bigg{(}1-\frac{1}{4n}\Big{)}^{n}\] \[\geq \frac{n}{4\beta}\bigg{(}\frac{\alpha^{2}}{8e^{2}}\bigg{)}^{\beta},\]  
which goes to infinity as \(n\) approaches infinity whenever \(a=8e^{2}/\alpha^{2}\) and \(b\) is any positive constant less than \(1\). Clearly, algorithms ShortSeq\((n,m)\) and SmallCluster\((n,m)\) satisfy the condition of Theorem 7. So this corollary follows. **Corollary 1**. : _Let \(n\in\mathbb{N}\), and \(m=\lfloor\,\alpha n\,\rfloor\), where \(\alpha\in(0,1)\) is constant. The size of the largest cluster generated by algorithm \(\textsc{ShortSeq}(n,m)\) is \(\Omega(\log n)\), w.h.p. The same result holds for algorithm \(\textsc{SmallCluster}(n,m)\)._
## 4 Discussion
### Implementation of the Machine Learning Model Developed to Map Aroma Compounds from Aerial Imagery  
Spatial distribution of LAIe and aroma profiles were possible due to high-resolution aerial images, which can be effectively used to implement the canopy cover photography algorithms described (Equations (1)-(6)). A similar approach was implemented for grapevines using \(f_{c}\), with similar results from imagery obtained using unmanned aerial vehicles [24]. From the kriging interpolated maps (Figure 7) it can be seen that some compounds are highly dependent on vigor characteristics of plants, such as isoamyl acetate, with clear differences of higher areas found for the far-right row in Block 1 (Figure 7E). These trees corresponded to almost double LAIe values (-5) compared to the average of trees in Blocks 1 and 2 (Figure 7A,B). Other compounds, such as phenethyl acetate, phenylacetaldehyde, and pyrazine tetramethyl, do not have many spatial differences among the three blocks (Figure 7C,G,F, respectively). The latter results confirm that the pattern of the machine learning models was effectively identified, and they are independent for each aroma related to canopy architecture parameters.  
The differences in the spatial distribution of growth and aroma profiles can be more clearly seen using a scattering plot, which represents the tree-by-tree scale (Figure 8). This spatial distribution map could offer growers a powerful tool to perform tailored canopy management strategies to manage aroma profile outputs that could be considered desirable or to obtain a uniform quality of pods and, finally, the chocolate produced.
## 3 Analysis of Damping Layer Parameters
### The Effect of the Damping Layer's Thickness (\(h_{d}\) Is Variable)
#### 3.3.1 Case of \(h_{d}(x_{b2})=0\)  
The thickness \(h_{d}(x)\) varies according to the following different functional patterns:  
\[\begin{split} h_{d1}(x)&=\frac{1}{150}|x-0.2|,\\ h_{d2}(x)&=-\frac{1}{36}(|x-0.2|-0.14)^{2}+\frac{49 }{90000},\\ h_{d3}(x)&=-\frac{1}{12}(|x-0.2|-0.06)^{2}+\frac{3 }{10000},\\ h_{44}(x)&=\frac{100}{27}|x-0.2|^{3},\qquad\qquad \qquad\qquad x\in[0.14,0.26]\end{split} \tag{26}\]  
The graphs of functions in Equation (26) are illustrated in Figure 6. Figure 7 demonstrates how different functions, \(h_{d}(x)=h_{d1},\cdots,h_{d4}\), respectively, for varying the thickness of the damping layer affect the modal loss factor. From Figure 7, it is observed that positioning the damping layer's maximum thickness away from the ABH tip results in a less effective enhancement of the ABH effect compared to a damping layer with uniform thickness. The damping layer that varies in thickness according to the function \(h_{d3}\) shows the most pronounced effect in enhancing the ABH effect, followed by the functions \(h_{d1}\) and \(h_{d2}\), with the least effective being the variation according to the function \(h_{d4}\). Figure 7  
Figure 5: The influence of various damping layer thicknesses (\(h_{d}=0.0002\,m\), \(0.0005\,m\), \(0.001\,m\), \(0.005\,m\)) on the modal loss factor when the position is limited between \(x_{d1}=0.14\,m\) and \(x_{d4}=0.26\,m\).  
demonstrates that the choice of damping layer thickness variation function has a very significant impact on the ABH effect.
# Predicting Human Location Using Correlated Movements
## 5 Detection of Persons with Correlated Movements
### Behavioral Similarity-Based Method
To select the second PCM, a combination of the first PCM and one of the \((m-2)\) remaining members is examined; \((m-2)\) NNs need to be learned, each of which estimates person \(p\)'s location at time slot \(t\) given the contextual information of two examined people. Similarly, to select the \(r^{\text{th}}\) PCM, \((m-r)\) NNs are used to evaluate the subset of the \((r-1)\) already selected PCMs and one of the \((m-r)\) remaining members. In the FS method, a large \(r\) value leads to a significant increase in the number of training parameters. In the BSB and FS methods, it is assumed that three selected PCMs are used for the location prediction phase, where a neural network with three hidden layers of 500, 150, and 50 neurons is considered. For a fair comparison, the number of hidden neurons for IFS is proportional to that of the BSB/FS methods in the mobility prediction phase with the ratio \((m-1):r\) because \((m-1)\) and \(r\) are the numbers of people whose location information is given to the IFS and BSB/FS methods, respectively. Table 4 compares the number of training parameters of the three methods, where \(l_{\text{BSB}}\), \(l_{\text{IFS}}\), and \(l_{\text{FS}}\) denote the number of parameters, i.e., weights and biases, in behavioral similarity-based, ignored feature selection, and forward search methods, respectively. When the total number of people, \(m\), increases, the three considered methods need to train more parameters. However, the BSB method always learns the least number of parameters. Moreover, the increase rate is quite different in each method, e.g., when \(m\) varies from 43 to 1000 people, \(l_{\text{BSB}}\) rises \(\frac{147.42}{7.0}\approx 21\) times, compared to  
\(\dfrac{1453.7}{59.02}\approx 24.6\) times with \(l_{\text{IFS}}\) and \(\dfrac{8933.1}{158.3}\approx 54.6\) times with \(l_{\text{FS}}\). In summary, because only location information of PCMs is used to predict human mobility of a given person, the proposed BSB method incurs a much lower computation cost in terms of time and space complexity than IFS and FS, which makes the BSB method more practical, particularly when there is a large number of people.
# Empirical Evaluation of Privacy Efficiency in Blockchain Networks: Review and Open Challenges
## 3 Componential Classification of Blockchain Privacy Protecting Techniques
### Effect of Blockchain's Primary Cryptography on Privacy
#### 3.1.2 Hashing  
Hashing is an integral component in the blockchain networks for maintaining the network consistency and reliability. Data is run through a hashing function to generate a kind of digital fingerprint that is essentially unique to the data file. The purpose of hashing the data is not for concealing it, rather allowing the verification that data is pure and not tampered with. This verification is convenient as modification of even a single character in the data will change the hash completely. The point of hashing is not to hide data, but to allow verification that the data has not been tampered with in any fashion. Moreover, the hash of a data cannot be \"unhashed\" or restored back into the original data. Hashing is a method used to verify the integrity of a message or file [58].  
Blockchain Solution for Data Integrity  
One of the most significant and prized features of blockchains is immutability [59]. Immutability simply refers to ensuring that the records in the chain have not been tampered with. This property of blockchain validates the integrity and truthfulness of the data in the chain.  
Blockchain transactions are grouped together and stored into blocks. The blocks consisting of various transactions are chained together. Each block in the blockchain has a unique identifier (i.e., hash) [60]. The hash of each block is generated using a hashing function based on the hash of the previous block, list of transactions and time of publication (as illustrated in Figure 3). Even a slightest change in any of these can cause the entire block hash to be refreshed, highlighting tampering of the data. This makes it very complicated for any adversary to modify the data as it must make changes on every node of the entire decentralized network, which is practically impossible [61]. Thus, the integrity of the data is kept intact.
# Accurate Needle Localization in the Image Frames of Ultrasound Videos
## 2 The Proposed Method
### Phase I: Generating the QUS Image
#### 2.1.1 Features Extraction
The GLCM can be computed using four different angles: \(0^{\circ}\), \(45^{\circ}\), \(90^{\circ}\) and \(135^{\circ}\). In the current study, the GLCM is computed along one orientation. Particularly, if the needle is inserted from right side of the imaging plane, then the angle is set to \(45^{\circ}\). Similarly, if the needle is inserted from left side of the imaging plane, then the angle is set to \(135^{\circ}\). Furthermore, the offset is set to four pixels to enable effective quantification of the pixel gray-level statistics. The GLCM is employed to calculate twenty-two texture features [36]. These features are the sum variance, sum average, entropy, difference entropy, sum entropy, energy, dissimilarity, cluster shade, correlation I and II, cluster prominence, autocorrelation, contrast, information measure of correlation I and II, homogeneity I and II, inverse difference moment normalized, inverse difference normalized, maximum probability, difference variance, and sum of squares. For example, Figure 1e,f shows the sum average and sum variance GLCM texture features that are computed for the ultrasound image in Figure 1a.
# A Framework for Constructing a Secure Domain of Sensor Nodes
## 3 The Concept of a Security Domain of Sensor Nodes
### The Concept of Protection for Sensor Nodes
#### 3.2.2 Data Stored in the Resources of the Sensor Node
The first part (in EEPROM) includes the following data:  
* N_ID (Node ID)--ID of the sensor;
* RN (Role of Node)--the role filled by the node in the domain {M, R, M+G, R+G};
* NSK (Node Security Key)--symmetric key of a registered node in the security domain; it is used to encrypt the data sent between this node and M node;
* NAD (Node ADdress in the wireless network)--64-bit address of XBee module used by the node. The second part (in RAM) consists of the following status data:  
* N_ID (Node ID)--ID of the sensor;
* Stat--the status of the node; it can take one of the values: when a node description is stored on the MASTER node {non-active, active, active non-confirmed}, when the node description is stored on REPLICA {ActiveNonConfirmedReplica};
* Time--a moment of the last and the effective transmission.
# Investigations on the Complex Band Diagram of Flexural Wave through the Fluid-Loaded Phononic Plate
## 3 Numerical Examples and Discussions  
In this section, the dynamic characteristics of flexural waves in a fluid-loaded plate with two-dimensional periodicity are investigated by the proposed \(\omega(\mathbf{k})\) and \(\mathbf{k}(\omega)\) formulations, from which the location, width, attenuation level, and directionality of the stop bands can be observed directly. Before we perform the analysis of a general two-dimensional case, the numerical model in the form of \(\omega(\mathbf{k})\) is validated by comparing to a one-dimensional case derived from Ref. [36]. As the rigid wall condition is applied in [36], the matrix \(\mathbf{C}_{f}\) is eliminated from the dynamic equations of the fluid-structure system and Equation (7) is adopted to calculate the real band diagram. The physical model is infinitely extended in the \(x\) direction and made up of the set of alternating elements of two different materials. Table 1 presents the boundary of the first and second stop bands obtained by the proposed method and Ref. [36], which is in terms of the normalized frequency \(\Omega\). The formulation of \(\Omega\) can be derived from Ref. [36] and is omitted here. A perfect agreement is clearly seen, which validates the numerical formulations given in the present work. Additionally, it is noted that the fluid loading is expected to narrow the band gap width in this one-dimensional periodic lattice.
## 4 Model Performance Analysis
### Model Validation  
Cross-validation (CV) is a resampling procedure used to evaluate machine learning models on a limited data sample. The procedure has a single parameter called k that refers to the number of groups that a given data sample is to be split into. As such, the procedure is often called k-fold CV. When a specific value for k is chosen, it may be used in place of k in the reference to the model, such as k = 5 becoming 5-fold CV (Figure 9). CV is one of the verification methods to further improve the predictive performance when new, largely untrained data is modeled as input to the training model. That is, to use a limited sample in order to estimate how the model is expected to perform in general when used to make predictions on data not used during the training of the model. This is generally more time consuming than other methods, such as simple train/test partitioning. However, it is a popular method because it prevents overfitting and does not result in biased or non-optimistic assumptions about model technology [32; 33; 34].  
Figure 8: Variable importance analysis results by random forest (%IncMSE and IncNodePurity).
###### Abstract
The work presented in this article starts with analyzing a standard single-beam metamaterial structure, its transmissibility bandgap, and enhancements. Afterwards, a simple double-layered beam-type metamaterial structure is modeled and the bandgaps of the layered-beam metamaterial structures are presented and compared with a single-beam metamaterial structure. Lastly, parametric and design studies that investigate the effects of changes in various parameters of the double-layered beam-type metamaterial structure are presented and the outcomes are compared with the results from single-beam metamaterial structures.