esa-sceva/satcom-chunk-collection huggingface dataset:
# Flow Velocity Computation in Solid-Liquid Two-Phase Flow by a Hybrid Network CNN-RKSVM
## 2 Related Work
### Reproducing Kernel-Based Support Vector Machine  
The reproducing kernel-based support vector machine (RKSVM) [24] is an improvement on the typical SVM based on the reproducing kernel function \(\varphi(\bullet)\) of Equation (5).  
If \(X\) is the sample vector in a \(d\)-dimensional space and \(y\) is the label of the sample, \(X=(x_{1},x_{2},\ldots,x_{d})\in R^{d}\), then the dataset \(S\) of all samples can be represented as \(S=\{(X_{1},y_{1}),\ldots,(X_{n},y_{n})\}\), where \(X_{k}\in R^{d}\) and \(y\in R\), and \(n\) is the total number of samples.  
RKSVM transforms any sample data \(X\) in \(S\) to a high-dimensional feature space by a nonlinear mapping \(\varphi(\bullet)\), and the linear regression model for a dataset of all samples is constructed in the high-dimensional feature space as  
\[f(X)=\varpi^{T}\varphi(X)+b,\ X\in R^{d} \tag{8}\]  
where \(\varpi\) is the multiplier of support vectors, and \(b\) is the parametrized vector.  
The optimization problem for the above regression model is  
\[f(X)=\min\frac{1}{2}\Big{(}\varpi^{T}\varpi+b^{2}\Big{)}+\frac{1}{2}\gamma\| \xi\|^{2},\ s.t._{\gamma}Y=\varphi(X)\varpi+eb+\xi \tag{9}\]  
where \(\gamma\) is the regularization parameter, \(\xi\) is the slack variable, and \(e\) is a unit vector.  
The Lagrange function is defined using Equation (9) as follows,  
\[L(\varpi,b,\xi,\alpha)=\frac{1}{2}\Big{(}\varpi^{T}\varpi+b^{2}\Big{)}+\frac{ 1}{2}\gamma\|\xi\|^{2}-\alpha^{T}(\varphi(X)\varpi+eb+\xi-Y) \tag{10}\]  
where \(\alpha\) is the vector on the Lagrange multiplier. Let these partial derivatives of \(L\) for \(\varpi\), \(b\), \(\xi\), and \(\alpha\) equal 0. Then, the above optimization problem is used to solve the following equation group,  
\[\left[\begin{array}{cc}0&e^{T}\\ e&\varphi(X)\varphi(X)+\gamma^{-1}e\end{array}\right]\left[\begin{array}{cc }b\\ \alpha\end{array}\right]=\left[\begin{array}{cc}0\\ Y\end{array}\right] \tag{11}\]  
where \(b=\alpha^{T}e\), and thus, the following equation is obtained from Equation (11):  
\[Y=\varphi(X)\varphi(X)^{T}\alpha+ee^{T}\alpha+\gamma^{-1}\alpha \tag{12}\]  
Let the kernel function be \(\varphi(X)\varphi(X)^{T}=K(X,X)\), where \(K(X,X)\) is the reproducing kernel function of Equation (5). Equation (12) is simply the regress function of RKSVM, which can be rewritten as  
\[Y=\Big{(}K(X,X)+ee^{T}+\gamma^{-1}\Big{)}\alpha \tag{13}\]  
where \(\big{(}K(X,X)+ee^{T}+\gamma^{-1}\big{)}\) is a positive definite matrix, and \(\alpha\) is solved by Equation (11).  
Compared with the typical SVM, the RKSVM has three advantages. First, the matrix in RKSVM is positive and definite, and then the optimization process is performed once to solve the RKSVM model rather than multiple iterative optimizations, which is the case with the SVM. Second, without the need to solve for \(b\), the RKSVM reduces the number of solved variables by half compared to that of the SVM. Finally, the linear regression in RKSVM has stronger generalization capabilities than those in the SVM. Hence, our design starts from RKSVM in this paper.
# Optimization of the AODV-Based Packet Forwarding Mechanism for BLE Mesh Networks
## 4 Optimized-AODV Protocol Topologies and Testbed Design
### O-AODV Topologies and Testbed Design
#### 4.1.1 Initial Findings and Proposed Optimizations for O-AODV  
In comparison to the mesh (flooding) protocol, the original AODV protocol was not performing on par in terms of the packet delivery ratio, overheads, end-to-end one-way delay, and average per-hop delay. With 2-3 nodes, the AODV protocol performed similarly to the mesh protocol, but as the number of nodes increased, the packet delivery ratio  
Figure 3: Scenario 1: linear topology with 10 nodes.  
Figure 2: Testbed setup showing the USB links used for the experiment configuration and data collection.  
declined and the overheads rose, causing real effects on protocol reliability based on initial experiments not reported in this paper. As AODV is a more dependable protocol for ad hoc networks than mesh since it forwards packets based on the route, consequently, we improved the protocol by changing the host (software) part of the BLE stack (which contains the BLE mesh architecture) to improve its performance in terms of packet delivery ratio, overheads, end-to-end one-way delay, and average per-hop delay and to bring it as close to the mesh protocol as possible. We also changed the configuration in the controller component of the stack to enhance the protocol's performance. With the aforementioned improvements, the proposed protocol produced consistent results when compared to the existing AODV protocol, as well as comparable PDR with lower overheads and average per-hop delay and end-to-end one-way delay when compared to the mesh protocol, as discussed in Section 5.7.
## 5 Experimental Results and Analysis
### Deflection Deformation Characteristics of the Pipeline  
Through statistical analysis of the settlement data of the pipeline after the uniform load was applied, curves of the pipeline settlement with the loading were drawn, as shown  
Figure 8: Scatter diagram of the force at the end of the pipeline.  
Figure 7: Schematic diagram of the force deformation and strain distribution in the pipeline.  
in Figure 9. Under the action of a uniform load, the middle part of the pipeline gradually changed shape from the initial flat-bottom shape to the funnel shape with the increase in the load.  
When the load was gradually increased from 0 to 1500 N, the middle part of the pipeline remained roughly flat, indicating that the pipeline was in the elastic stage without plastic failure. Between 1500 and 4470 N, the middle of the pipeline gradually changed shape from a flat-bottom shape to the funnel shape. Between 3550 and 4470 N, the sinking of the pipeline at A3 and A5 did not change significantly, but the A4 position in the middle of the pipeline obvious sunk, indicating that the middle of the pipeline reached its own strength limit when the load was 4470 N. Elastic-plastic failure occurred. As the load continued to increase from 4470 to 6500 N, the middle of the pipeline transformed from a smooth funnel shape to a straight triangular shape, and the deformation of the pipeline was particularly obvious when the load was 6500 N, indicating that plastic deformation at the A4 damage position in the middle of the pipeline continued to increase as the plastic failure of the pipeline continued with the application of the load. Based on this, it was concluded that the deformation of the pipeline evolved from a flat-bottom shape to the funnel shape and to a triangular shape under a uniform load.  
The strain law for the top and bottom of the middle of the pipeline under a uniform load revealed the stress characteristics and deformation behavior of the pipeline under a uniform load. As the applied load gradually increased, the overall deformation of the pipeline progressed, and the shape changed from a flat-bottom to a funnel shape and finally evolved into a triangular shape, and plastic failure occurred when the pipeline became triangular. Through an in-depth understanding of these laws, the performance and safety of pipelines can be better evaluated, which provides a scientific basis for the design, construction, and maintenance of pipelines.
# An Energy-Efficient and Obstacle-Avoiding Routing Protocol for Underwater Acoustic Sensor Networks
## 4 Design of Energy-Efficient and Obstacle-Avoiding Routing Protocol
### The Fuzzy Logic-Based Forwarding Relay Selection

Table 1: Fuzzy linguistic labels of the input variables. Figure 5: Membership functions. The output of the inference engine, the probability, is classified into five types: excellent, good, medium, bad, and weak. The membership functions of the probability can be seen in Figure 6. Lastly, the result of the above fuzzy operation \(f_{i}\Big{\{}Re\left(n_{s}^{j}\right),\ cos\theta\Big{(}n_{s}^{j}\Big{)},\ P_{d} \left(n_{s}^{j}\right)\Big{\}}\) is determined by using the centroid algorithm. Table 3 shows the examples of the result of the fuzzy operation. For example, if the input variables are \(Re\left(N_{j}\right)=0.6\), \(cos\theta\big{(}N_{j}\big{)}=0.5\), \(P_{d}\left(N_{j}\right)=0.4\), their corresponding membership are medium, medium and short, respectively. Then the fuzzy set is obtained according to the fuzzy logic rules, and the centroid algorithm is used to defuzzify to obtain a single accurate value, in this example, the accurate value is 0.7.
# Symmetric-Key-Based Security for Multicast Communication in Wireless Sensor Networks +
## 5 Implementation  
To verify the validity of the proposed security scheme, it was implemented in ContikiOS and simulated in COOJA (using COOJA motes). BMRF is used as multicast engine, with all the default values of Contiki 2.7, except for the changes listed below. Since a missing packet in the key distribution phase can result in the entire group being invalidated by the NMM, reliability is important. Therefore, BMRF is configured to use several LL unicast packets to forward IP multicast packets to its interested children. As COOJA motes do not support any RDC protocols, nullRDC is used. The following points are taken into account during the implementation.  
* To allow larger packets to be sent, the 6 LoWPAN queue buffer and the uIP/IP buffer have been increased.
* Please note that the largest variable type that can be used in ContikiOS is a double, which is 8 bytes long. To get a 16-byte key, two Lagrange polynomials need to be calculated and transmitted.
* When doing floating point calculations with potentially large numbers, the precision of the result can be limited. Since we are using LI with points that are dependent on the calculated key values, results can get very large. To assure each member works with the same result, they must be rounded.
* The calculations for the key material are performed using two temporary buffers to store intermediate values. The size of these buffers can be set in the configuration files. The minimum  
Figure 4: Summary of the unicast approach in the group key construction phase.  
required size for these buffers depends on the used approach (unicast or multicast) and on the maximum number of members of the multicast group.
* For encryption, the AES128 library, written by Texas Instruments [24] is used. For hashing, a sha256 library [25] is used.
# Comprehensive Review of RF MEMS Switches in Satellite Communications
## 3 Strategies for Optimizing RF MEMS Switch Performance
### High Reliability
#### 3.1.4 Using Appropriate Packaging
(**b**) Schematic of the ohmic RF MEMS with a zoom on vias connecting the CPW line to the probing pads outside the seal ring [135]. Reprinted/adapted with permission from [135], 2022, IEEE. (**c**) Layout and dimensions of the zero-level packaged RF MEMS switched capacitors [136]. Reprinted/adapted with permission from [136], 2020, IEEE. (**d**) Top view of a SPST switch including TFP encapsulation [72]. Reprinted/adapted with permission from [72], 2017, IEEE. minimizing the device footprint and being compatible with wire bonding and flip-chip assembly techniques. Moreover, this type of packaged switch exhibits good microwave performance and reliability, particularly in meeting redundancy requirements in satellite communications.
# A UML Activity Flow Graph-Based Regression Testing Approach
## 2 Our Testing Approach
### UML Regression Testing  
In order to address the issues that arise in regression testing of UML designs, we pose three research questions and follow the approach to regression testing as defined by [PERSON] and [PERSON]:  
1. Firstly, we try to determine if it is possible to distinguish and categorize modifications made to various iterations of a UML design.
2. Secondly, we explore the feasibility of utilizing these changes for selecting test cases in a secure and efficient manner for regression testing purposes.
3. Lastly, we investigate the feasibility of identifying the areas in a UML design that require new test cases for test generation.  
To address the aforementioned research questions, we adopt the regression testing approach proposed by [PERSON] and [PERSON]. To detect modifications made to distinct versions of a UML design, we employ the approach developed by [PERSON] et al. [18].  
In the context of regression testing for UML designs, we formulate the following problem inspired by [PERSON] and [PERSON]'s approach to retesting:  
Given software \(S_{o}\), a test set \(T\) (used to test \(S_{o}\)), and a modified version of \(S_{o}\), \(S_{m}\). Determine a strategy for utilizing the test set T to obtain a satisfactory level of confidence in the accuracy of \(S_{m}\).  
Following the approach described by [PERSON] and [PERSON], we outline our approach to solve this problem as follows:  
1. Create a mapping from \(S_{o}\) to \(S_{m}\) to identify the changes. We denote these changes by \(\rho\). Then classify the tests into mutually exclusive sets: _Reusable_ (\(U_{t}\)), _Retestable_ (\(R_{t}\)), or _Obsolete_ (\(O_{t}\))2. Using the results obtained from Step 1 construct a set of retestable tests \(R^{\prime}_{t}\subseteq R_{t}\), which may reveal changes in \(S_{m}\).
3. Test \(S_{m}\) using \(R^{\prime}_{t}\).
4. Generate a new set of tests \(T^{\prime}\) if it is identified that certain parts of the software are not tested adequately.
5. Test the software \(S_{m}\) using \(T^{\prime}\).  
Our methodology for regression testing is based on the definition of a UML test case \(T\). In UML test cases, conditions are assigned values to enable traversal of a path \(P_{T}\) consisting of vertices \(v_{1}\),..., \(v_{n}\) in the AFG. Each vertex \(v\)\(i\) is associated with a condition, \(c_{i}(vl_{1}\),..., \(vl_{m})\), which is a Boolean function taking (\(vl_{1}\),..., \(vl_{m}\)) as arguments. The function guards the edge (\(v_{i}\), \(v_{j}\)) in the AFG. The values (\(vl_{1}\),..., \(vl_{m}\)) must satisfy the requirements along the path \(P_{T}\) in order to traverse the edge. Simple conditional statements are always true for vertices without explicit conditions, such as a vertex with a single edge leaving it. The execution of the test case traverses a path in the AFG.
## Materials and Methods
### Data Analysis  
#### 2.6.1 Spectral Preprocessing  
In general, the spectral data (of the selected ROI) contain random noise and spectral variations generated by the camera or instrument; therefore, pretreatment of spectral data is essential before subjecting it to multivariate analysis [22]. The average spectra were processed independently using different preprocessing methods including moving weighted average smoothing, normalization, S-G first- and second-order derivative, multiplicative scatter correction (MSC), and standard normal variate (SNV) in order to remove any irrelevant information such as high-frequency random noise, baseline drift, signal-to-background ratio, and others [23]. The averaging technique is used to reduce the number of wavelengths or to smooth the spectrum of tomatoes. It is also used to optimize the signal-to-noise ratio [24]. A normalization method is employed to remove the scattering effect in the tomatoes spectra [25]. Derivative spectra are needed to remove baseline shift and super-imposed peaks [26]. The MSC transformation method is applied to compensate additive and multiplicative scatter effects included in the spectra [27]. SNV is another spectra correction method that removes the slope variation from the spectra generated by the scatter [28; 29].
# Review of Channel Estimation for Candidate Waveforms of Next Generation Networks
## 3 Fundamentals of Channel Estimation
### Classification of Channel Estimation Schemes  
Several CE schemes have been proposed in the literature for estimating the fading channel coefficients in different wireless communication systems. These include the single carrier communication systems such as single-input single-output CP based OFDM (SISO-OFDM), non-contiguous OFDM (NC-OFDM); the multi-carrier communication systems such as multiple input multiple output-OFDM (MIMO-OFDM) systems; and most FBMC schemes such as filter bank OFDM-OQAM and GFDM based schemes. CE schemes can broadly be classified into non-blind (pilot based), semi-blind and blind techniques [82]. In this regard, we classify the CE schemes that have been explored in literature for various wireless communication systems in Figure 12, while explaining the three major class of the CE schemes.
## 3 Methodology
### Ethics, Limitations, and Positionality
#### 3.7.2 Limitations and Positionality  
Survey recruitment faced challenges, with only 18 of 42 respondents completing the survey. Although designed to take 20 min, feedback indicated it required more time. Additionally, the sample was non-representative, highlighting the need for better participant selection in future research [71] while ensuring authenticity and anonymity [72]. To enhance validity and ensure representativeness across pilots, future studies should employ techniques such as stratified sampling.  
Roundtable discussions also had non-representative samples. At Smiths Falls Airport, twelve pilots participated, of whom only one was female, and all participants were experienced pilots or instructors. At Rockcliffe Airport, six male participants joined, of whom half were instructors and none were recent trainees. Factors that may have impacted participation include the low percentage of female pilots, and scheduling the roundtable discussions on a weekday morning, which likely excluded younger or working pilots.  
As an insider researcher, care was taken to avoid bias from personal values [73]. Assumptions or probing during discussions were avoided to prevent shaping responses with preconceptions [74]. Since this was the first study of its kind conducted in collaboration with TC, there were no prior benchmarks or comparable research to gauge expected participation levels. Consequently, participation was entirely voluntary and non-selective, with the aim of prioritising engagement by reaching as many pilots as possible who were willing to contribute.  
However, to strengthen validity and ensure broader representation in future studies, it is essential to adopt representative sampling methods that account for experience and demographic factors. Including considerations such as gender, age, and cultural background would help capture a wider range of perspectives and voices. Additionally, expanding both the survey and roundtable components to involve a larger sample will aid in identifying dominant trends whilst amplifying the contributions of groups underrepresented in this study, such as lower-hour pilots.
## 2 Radio Communication Technologies
### NB-IoT in One Glance: Why Choose NB-IoT?
This enables only a single battery charge for up to 10 years;
* Reproductivity with written permissions inside 4G and 5G mobile networking systems;
* Longer battery life of up to 10 years with a battery capacity of 5 WH;
* Achieving indoor and outdoor coverage of 20 dB compared to legacy GPRS devices;
* Supporting at least a 160 kbps data rate for both the uplink and downlink;
* Lowering the deployment complexity, which will result in a more affordable solution;
* Decreasing the data latency to 10 s or less for 99% of the devices;
* Decreasing the device costs to $5 USD per device [30; 31].  
One of the current applications of NB-IoT is its integration into the Spanish Vodafone mobile network since December 2016. Huawei plans to expand its partnerships to apply NB-IoT in many parts of the world (its first use was reported in a large number of countries in 2018). In May 2017, the Ministry of Industry and Information Technology of China also decided to increase the commercial and public employment of NB-IoT [33]. The increasing use of narrowband IoT throughout the world is one of the driving factors for the growth of the narrowband IoT market. The NB-IoT market is projected to reach $959.2 million globally by 2026, rising from $170 million in 2020 [25; 34]. Recent statistics also show the significant growth rate of the use of NB-IoT radio technology in comparison to other LPWAN technologies. In 2021, compared to 2020, the growth rate of NB-IoT was 75%, while for LoRa it was around 31% growth, for Sigfox it was 19%, and for Long-Term Evolution Machine-Type (LTE-M) communications it was 65% [35].
# The Impact of Nonlinear Mobility Models on Straight Line Conflict Detection Algorithm for UAVs
## 3 Nonlinear Mobility Model
### Gauss-Markov Mobility Model
#### 3.1.3. Extending GM to Three Dimensions  
The GM can be extended to 3D in several ways. First, the GM process can be applied to the three dimensions directly, as follows:  
\[\begin{split} x_{t}&=\alpha x_{t-1}+(1-\alpha)\mu_{x} +\sigma\sqrt{1-\alpha^{2}}w_{t-1}\\ y_{t}&=\alpha y_{t-1}+(1-\alpha)\mu_{y}+\sigma\sqrt {1-\alpha^{2}}w_{t-1}\\ z_{t}&=\alpha z_{t-1}+(1-\alpha)\mu_{z}+\sigma\sqrt {1-\alpha^{2}}w_{t-1}\\ \end{split} \tag{5}\]  
The issue with this way is that it is uncharacteristic to model the UAV flight. It is not easy to represent the UAV flight based on the UAV velocity in each direction. UAV flight can be modeled more accurately using a variable for velocity, a variable for direction, and a variable for pitch [25]. In this approach, the velocity and direction are calculated as in the two-dimensional Gauss-Markov, then we add another variable to track the vertical pitch, as follows:  
\[\begin{split} v_{t}&=\alpha v_{t-1}+(1-\alpha)\mu_ {v}+\sigma\sqrt{1-\alpha^{2}}w_{t-1}\\ \theta_{t}&=\alpha\theta_{t-1}+(1-\alpha)\mu_{ \theta}+\sigma\sqrt{1-\alpha^{2}}w_{t-1}\\ p_{t}&=\alpha p_{t-1}+(1-\alpha)\mu_{p}+\sigma\sqrt {1-\alpha^{2}}w_{t-1}\\ \end{split} \tag{6}\]  
Then, the \(x\), \(y\), and \(z\) are calculated as follows:  
\[\begin{split} x_{t}&=x_{t-1}+v_{t-1}\cos{(\theta_{t -1})}\cos{(p_{t-1})}\\ y_{t}&=y_{t-1}+v_{t-1}\sin{(\theta_{t-1})}\cos{(p_ {t-1})}\\ z_{t}&=z_{t-1}+v_{t-1}\sin{(p_{t-1})}\\ \end{split} \tag{7}\]
# Probability-Based Concrete Carbonation Prediction Using On-Site Data
## 2 Durability Prediction Approach for Carbonated Concrete Structures
### One-Dimensional Concrete Carbonation Model
#### 2.1.3 Co\({}_{2}\) Uptake, \(a\)  
The amount of CO\({}_{2}\) required to completely carbonate concretes depends on the types of cement, chemical composition, mixing conditions, and elapsed years. Given the chemical reaction of carbonation, amounts of CaO and CO\({}_{2}\) are dominant factors in carbonation and these elements are considered in this study. CO\({}_{2}\) uptake under external exposure and in the atmosphere can be calculated using Equation (5) based on the relationship between hydration and the amount of CO\({}_{2}\) in cement [22].  
Figure 1: CO\({}_{2}\) diffusion coefficient according to time.  
Figure 2: Probability distribution of CO\({}_{2}\) diffusion coefficient [22].  
\[a=0.75\text{-}C\text{-}CaO\text{-}\alpha_{H}\frac{M_{CO_{2}}}{M_{CaO}}\left(\text{ g/cm}^{3}\right) \tag{5}\]  
where C is the unit volume of cement (g/cm\({}^{3}\)); CaO is the amount of CaO in cement; \(\alpha_{H}\) is cement hydration; and M is molecular weight (CO\({}_{2}\): 44 g/M and CaO: 56 g/M).  
The amount of CaO in cement depends on the type of cement. It can be calculated based on chemical composition as shown in Table 1[23].