esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulations
### Optimization of Initial Full Width at Half Maximum  
The initial value of \(FWHM_{1}\) and \(FWHM_{2}\), \((F_{1 ini},F_{2 ini})\), had a significant effect on the measurement accuracy. Although the theoretical \(FWHM_{1}\) can be calculated according to Equation 5, the \(FWHM_{2}\) was an experimental parameter related to the characteristics of film and the environmental factors. There are even cases where not all the parameters in Equation (5) are known, failing to determine the ideal \(FWHM_{1}\). According to the intrinsic constraints, i.e., the full width at half maximum is non-negative, there are several methods to obtain the start of the full width at half maximum:  
* The smallest non-negative integer--i.e., (1, 1);
* The experimental \(FWHM\) of the overlapping-peak MDR--i.e., (934, 934);
* The theoretical \(FWHM_{1}\)--i.e., (696, 696);
* The default--i.e., (696, 800);  
To compare the performance in these four cases, we independently measured the thickness at a fixed position by the IPS-SIM 50 times. Figure 4a illustrates one of the simulated MDR and the fitting result, as the initial widths \((F_{1 ini},F_{2 ini})=\) (696, 696). The error of thickness detection, as shown in Figure 4b, indicates that the theoretical \(FWHM_{1}\) is the optimal start point because it has a detection error (purple and marked with triangles) closest to the true-value way (orange and marked with squares) and can be implemented in the application.  
Figure 4: (**a**) The simulated MDR with noise and the fitting result by the IPS-SIM as the initial widths \((F_{1 ini},F_{2 ini})=\) (696, 696) (**b**) Thickness measurement error of the different initial width values.
# Securing IoT: Mitigating Sybil Flood Attacks with Bloom Filters and Hash Chains
## 5 Performance Evaluation
### Informal Security Analysis
#### 5.3.3 Eavesdropping Attack  
While the proposed solution effectively addresses sybil flood attacks, it is essential to consider the potential threat of eavesdropping attacks. Eavesdroppers can intercept network traffic to gain unauthorized access to sensitive data, compromising the privacy and security of IoT devices. However, even if an attacker were to intercept a message and obtain its contents, they would be unable to fabricate a valid response from the same node. The hash chain mechanism ensures that each message includes a unique one-time password, making it infeasible for an attacker to generate a legitimate message without possessing the corresponding secret key.
# GPSMAP 66 sr: Assessment of Its GNSS Observations and Centimeter-Accurate Positioning
## 5 Conclusions and Outlook  
The Garmin GPSMAP 66 sr (v3.30) offers dual-frequency GNSS observations for cm-level postprocessing applications. Due to its quadrifilar helix antenna, the observation quality exceeds the quality of other consumer devices with dual-frequency capability, e.g., smartphones. Centimeter-level positioning results can be achieved in long observation sessions (several hours), with precise point positioning (PPP), or in short observation sessions (many minutes) in baseline mode.  
The main limitation of the device with respect to precise GNSS positioning is caused by the incompleteness of the tracked satellite signals. In particular, a higher priority of Galileo in the satellite selection process would considerably improve precise positioning results. If this problem is solved and with an increasing number of active Galileo satellites and more GPS satellites capable of transmitting G5 signals in the near future, the positioning performance will even exceed the level demonstrated in our trials.  
The following are available online at [[https://www.mdpi.com/article/10.3390/s22051964/s1](https://www.mdpi.com/article/10.3390/s22051964/s1)]([https://www.mdpi.com/article/10.3390/s22051964/s1](https://www.mdpi.com/article/10.3390/s22051964/s1)), File SI: Antenna calibration results in ANTEX format.  
Conceptualization, L.W. and A.H.; data curation, L.W., A.H. and V.F.; methodology, L.W., A.H. and V.F.; software, L.W.; writing--original draft preparation, L.W.; writing--review and editing, A.H. and V.F. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
A RINEX sample dataset of Garmin GPSMAP 66 sr is available from the corresponding author on request. All products of the International GNSS Service (IGS) or of one of the IGS analysis centers were obtained from CDDIS. The observation data of the virtual reference stations (VRS) were made available by Staatsbetrieb Geobasisinformation und Vermessung Sachsen (GeoSN).  
We are thankful to [PERSON], Institute of Cartography, TU Dresden, for providing a second Garmin GPSMAP 66 sr for test measurements and to [PERSON] of GeoSN for making available our Garmin antenna calibration results to the Saxonian SAPOS-GPPS-PrO service.  
The authors declare no conflict of interest.
## 4 Dynamic Load Balancing Modeling with ACO
### Moving Rules for Forward Ant
#### 4.4.1 No Backward Ant Appearing in Neighbor Nodes  
As described in Section 3.2, the generation of forward ant is triggered in diverse conditions for searching candidate nodes. Thus, we discuss the case with no backward ant appearing in the neighbor nodes from two aspects.  
(1) The forward ant triggered by overload node  
In this case, the moving probability of forward ant is calculated by:  
\[p_{\bar{q}}=\begin{cases}\begin{aligned} &[\tau_{\bar{q}}(t)]^{\text{st}}[EV_{j}^{a_{i+1}}(t)]^{ \bar{b}}[\tau_{\bar{q}}(t)]^{\text{'}}[D_{\bar{q}}(t)]^{\text{'}}\\ &\frac{\sum_{j\in N_{\text{s}}}\{[\tau_{\bar{q}}(t)]^{\text{st}}[EV_{ \bar{q}}^{a_{i+1}}(t)]^{\bar{b}}[\tau_{\bar{q}}(t)]^{\text{'}}[D_{\bar{q}}(t)] ^{\text{'}}\}}{0}\end{aligned}\end{cases}\qquad\text{if}\quad\quad j \in N_{\text{s}} \tag{16}\]  
where \(p_{\bar{q}}\) is the moving probability of the forward ant from node \(i\) to node \(j\). \(N_{\text{s}}\) is the set of unvisited neighbor nodes. \(\tau_{\bar{q}}(t)\) is the pheromone of node \(j\). \(EV_{\bar{q}}^{a_{i+1}}(t)\) is the velocity of task execution prediction in slave node \(j\). \(\tau_{\bar{q}}(t)\) is the cost from node \(i\) to node \(j\), that is, \(1/d_{\bar{q}}\). \(D_{\bar{q}}(t)\) is the proportion of the degree of node \(j\) to the sum degree of all neighbor nodes, defined as:  
\[D_{\bar{q}}=\frac{d_{j}}{\sum_{k\in NB_{i}}d_{k}} \tag{17}\]  
where \(NB_{i}\) is the neighbor set of node \(i\). This means that the node with the maximum degree ought to be more likely to be chosen as the next destination. \(\alpha\), \(\beta\), \(\gamma\), and \(\kappa\) stand for the weights of different parameters.  
(2) The forward ant triggered by underload node  
In this context, the moving probability of forward ant is calculated by:  
\[p_{\bar{q}}=\begin{cases}\begin{aligned} &[\eta_{\bar{q}}(t)]^{\text{'}}[D_{\bar{q}}(t)]^{ \text{'}}\\ &\frac{\sum_{j\in N_{\text{s}}}\{[\eta_{\bar{q}}(t)]^{\text{'}}[D_{ \bar{q}}(t)]^{\text{'}}\}}{0}\end{aligned}\end{cases}\qquad\text{if} \quad\quad j\in N_{\text{s}} \tag{18}\]where the parameters have the same meanings as in Equation (16) except for removing \(\tau_{ij}(t)\) and \(EV^{a_{i+1}}_{ij}(t)\). For the forward ant triggered by underload node, the ant's goal is not to find the nodes with idle resources in the network, but to find the overload nodes. This means that the ant selects a next destination only by the cost in the edge between node \(i\) and node \(j\).
## 5 Discussion
### Model Limitations and Future Directions
#### 5.1.1 Classification Extension  
The integration of additional roof types (new classes) into the model has been shown to enhance the system's versatility, thereby facilitating more precise identification of diverse structural forms. The augmentation of classes within the YOLO model is expected to result in a more comprehensive representation of roof typologies, encompassing less prevalent or more intricate shapes that are prevalent in real-world settings. This development is anticipated to enable more precise classification of roofs across diverse geographical regions, where a confluence of distinct building traditions and construction technologies is observed. The outcome of this research is expected to be a system that is both more flexible and resistant to errors associated with overlooking less typical constructions. This, in turn, should contribute to the system's widespread use in different geographical and industry contexts.  
Augmenting the number of classes in the YOLO model will also have a bearing on the quality of the visualization generated in AR and VR, as it will allow for a more accurate representation of the real world. By classifying roofs with greater precision, the representation of these objects in VR and AR will become more realistic (higher accuracy), thus increasing their utility.  
A significant element to consider is the incorporation of class types with associated elements and attributes. The optimal solution would be the creation of distinct classes for each additional element. Of particular importance is the material from which the roof is constructed, as this attribute will have a significant impact on the process of checking the roof for hazardous materials, such as asbestos [58]. An important aspect is the elements that cover the roof (e.g., solar panels, roof windows), the elements that modify the basic type of roof shape and these are most often the elements that light up the upper floors and attics. In most cases, roof windows (skylight) or roofibrabs are used, which do not affect the shape of the roof but the roof covering. Shape changes are influenced by the form of the dormer window or the so-called 'bull's eye'. The latter can significantly affect the recognition of roof types from orthophoto data. The construction of the roof and its basic shape can differ significantly from the photographic interpretation, the quality of which also affects the effectiveness of the classification. Most of the available and produced photographic map studies are classical orthophotos (objects close to the surface are rectified to an orthogonal position). This leads in some cases to a misrepresentation of the roof shape and a lack of coverage of the building outline [59].
# Self-Organizing Networks for 5G and Beyond: A View from the Top
## 5 Future SON Research Directions for B5G  
Currently, SON research and development is spread over many different domains. Academy, industry, and worldwide standardization bodies such as 3 GPP/ITU have evolved so that SON is fully embedded in future B5G solutions [8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 30; 56; 64; 72; 73].  
Figure 10: Environment and agent interaction.  
Interesting cases include:
* The development of Virtual SON (V-SON) platforms since NFV and SDN architectures have been suggested as one of the key drivers for SON deployment in 5G networks as well as the binding of SON with C-RAN.
* The development of new ML algorithms and the binding of SON with big data and deep learning technologies.
* The binding of SON with key 5G eMBB enablers such as mmWave.
* The application of SON in IoT network infrastructures, and massive Machine-Type Communication (mMTC).
* The backhaul network management through SON.
* The risk assessment and the development of security solutions and policies related to SON systems in 5G networks.
* The development of hybrid SON solution targeting in URLLC services.  
Figure 11 depicts the key entities of a non-roaming 5G architecture as defined by 3 GPP in [73]. The basic ones depicted in the figure are the following:  
* Network Slice Selection Function (NSSF).
* Network Exposure Function (NEF).
* Network Repository Function (NRF).
* Policy Control Function (PCF).
* Unified Data Management (UDM).
* Application Function (AF).
* Network Slice Specific Authentication and Authorization Function (NSSAAF).
* Authentication Server Function (AUSF).
* Access and Mobility Management Function (AMF).
* Session Management Function (SMF).
* Service Communication Proxy (SCP).
* User Equipment (UE).
* (Radio) Access Network ((R)AN).
* User Plane Function (UPF).
* Data Network (DN), e.g., operator services, Internet access or third party services.  
Among the key elements depicted in Figure 11, the ones directly interacting with 5G SON platforms shall be AMF, UE (Might Include UEs or IoT sensors), and RAN, including the gNBs.
# Drilling Condition Identification Method for Imbalanced Datasets
## 5 Experiment and Analysis of Drilling Condition Identification
### Analysis of the Effect of Feature Selection on Model Performance Improvement  
To validate the effect of feature selection based on weighted symmetric uncertainty on model performance, a comparison is made with the predictive results of classifiers under various feature selection methods, including no feature selection, variance threshold filtering, the chi-squared test, and extreme gradient boosting feature selection. Among them, variance threshold filtering and the chi-squared test are filtering methods in feature selection. Variance threshold filtering calculates the variance of each feature and determines whether to remove the feature from the dataset based on whether its variance is below the set threshold. The chi-squared test measures the correlation between categorical features and the target variable through statistical tests, calculating the chi-squared statistic and _p_-value using a contingency table to determine whether a significant relationship exists between the feature and the target variable. Extreme gradient boosting, belonging to the embedding methods of the three main feature selection categories, computes the contribution of each feature in building the tree model during training to assess feature importance. The feature selection process is completed based on the ranking of feature importance. Accuracy, precision, recall, and _F1_ score are selected as performance evaluation metrics for the models. A comparison of the model performance improvements under different feature selection algorithms is shown in Figure 10.  
As shown in the figure, after applying the feature selection based on weighted symmetric uncertainty to the dataset and training the model, the model's accuracy, precision, recall, and _F1_ score improved by 1.56%, 3%, 3%, and 3%, respectively, compared to the dataset without feature selection. The feature selection method based on weighted symmetric uncertainty outperformed the variance threshold filtering and chi-squared test filtering methods, as well as the embedding-based extreme gradient boosting feature selection method. Specifically, the model accuracy improved by 0.29%, 0.47%, and 0.2%, precision improved by 2%, 2%, and 2%, recall improved by 3%, 4%, and 3%, and the _F1_ score improved by 3%, 3%, and 3%, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Network Architecture** & **Parameter Setting** \\ \hline \multirow{4}{*}{BiLSTM} & Number of neurons & 128 \\  & Dropout & 0.5 \\  & Return sequences & True \\  & Number of neurons & 128 \\ GRU & Dropout & 0.5 \\  & Return sequences & True \\ Full connectivity layer & Number of neurons & (12,864) \\  & Activation function & ReLU \\  & Learning rate & 0.001 \\ Other & Batch size & 64 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyperparameter settings for the BiLSTM–GRU model.
# Characterization of Sidr (_Ziziphus_ spp.) Honey from Different Geographical Origins
## 4 Discussion
The Sidr honey from Egypt (177.8), Panjab (135.3), and Saudi Arabia (131.3) showed the highest level of DPPH radical scavenging activities (\(p<0.05\)), compared with antioxidant activities of other tested honey. These results are in accordance with the results reported by [PERSON] et al., (2008) about Buckwheat honey [60], [PERSON] et al., (2016) in Brazilian honeys [61] and [PERSON] et al., (2018) in Tunisian honeys [62]. [PERSON] et al., (2014) [63] demonstrated a positive relationship between phenolic concentration, antioxidant capacity, and the color of honey. In our study, the antibacterial activity of all honeys may be attributed to the narrow ranges of their TFC, TPC, and sugar contents. There is a positive correlation between TPC and the antibacterial activity of honey [64], which is attributed to the inhibition of virulence factors in the pathogen [65]. The antibacterial activity of honey also results from the low pH and high osmolarity along with the hydrogen peroxide activity produced by the glucose oxidase enzyme [8, 9, 34, 66, 67]. The antibacterial activity of honey may also be attributed to the presence of lysozyme, methylglyoxal, and bee peptides as well as its high sugar content [9, 34, 68, 69]. The presented results were in accordance with the research of [PERSON] (2011) [70], [PERSON] (2017) [71], [PERSON] et al., (2014) [72], [PERSON] (2011) et.al., [73], [PERSON] et al., (2018) [74], and [PERSON] (2021) [69], which also confirmed that Gram-positive bacteria were more sensitive to the honey samples than Gram-negative ones [75, 76]. Vancomycin and meropenem were used as reference antibiotics, with MIC values much lower than those obtained for honey, ranging from 0.1-64 \(\upmu\)g/mL [77]. The use of honey to treat microbial infections has been investigated previously [34, 51, 67, 76]. The honey antimicrobial properties are due to a high sugar osmolarity or the presence of other biologically active compounds [33; 77; 78; 79; 80]. The osmotic stress of honey is due to the high content of various sugars in combination with its low moisture content, which prevents the spoilage of honey by microorganisms. Hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)) is one of the antibacterial compounds of honey [81], whose presence and origin were confirmed by [PERSON] et al., (1963) [82]. In the hydrogen peroxide formation, a glucose oxidase enzyme is added by the worker bee during the collection of the nectar, allowing the conversion of glucose to gluconic acid, whereby hydrogen peroxide is produced as a side product [82]. The antimicrobial activity of honey is directly related to its botanical origin [63] and is associated with the different content of phenolic compounds, flavonoids, and phenolic acids [68; 83].
# Advances in the Study of the Behavior of Full-Depth Reclamation (FDR) with Cement
## 3 Results and Discussion
### Relationship Between Flexural Strength at Long-Term and Indirect Tensile Strength at Long-Term  
The correlation between FS and ITS values at long-term is shown in Figure 6, indicating a linear relationship between these parameters.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Materials** & \(UCS_{LT}\) (MPa) & \(FS_{LT}\) (MPa) & \(UCS_{LT}\)/\(FS_{LT}\) \\ \hline Soil-cement & 4 & 0.9 & 4–5 \\ Cement-bound granular material & 8 & 1.6 & 5–6 \\ and compacted concrete & 3.73 & 0.60 & \\ FDR with cement & (2.9 to 4.9) & (0.53 to 0.69) & 6.21 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison between average UCS values at long-term and flexural strength (FS) values at long-term for soil-cement, cement-bound granular material, and obtained values for the full-depth reclamation (FDR) with cement of the study.  
Figure 5: Relationship between unconfined compressive strength at long-term (\(UCS_{LT}\)) and flexural strength at long-term (\(FS_{LT}\)).  
A statistical analysis was performed and it was observed that the best relationship was obtained by means of a simple linear regression, expressed in Equation (2).  
\[FS_{LT\text{-}ITS}=0.187+1.063\;ITS_{LT} \tag{2}\]  
where \(FS_{LT\text{-}ITS}\) is the estimated value of the flexural strength at long-term obtained by means of \(ITS_{LT}\), and \(ITS_{LT}\) is the indirect tensile strength at long-term, both in MPa.  
The regression has a \(R^{2}\) value of 0.65 and the \(F\) of Fisher-Snedecor test indicated that the relationship was true with a significance level over 99%. The Student's \(t\)-tests for the coefficients indicated that they were true, different from 0 with a significance level over 99%.  
Once again, the average values obtained in the tests for these two parameters for the recycled material were compared to the usual values for soil-cement and cement-bound granular material [45] in Table 5. Although a direct relationship between ITS and FS is not established for cement-bound granular materials, it is indicated that the UCS value is approximately 10 times the ITS value [26; 42; 45; 56; 57; 58]. This assumption is adopted for the analysis in Table 5.  
It is observed that the ITS of the recycled material was similar to the value specified for soil-cement, while the relationship between ITS and FS was closer to a cement-bound granular material.
# A New Concept Compliant Platform with Spatial Mobility and Remote Actuation
## 2 Description of the New Concept Platform  
In several applications, the position of a mobile platform in space has to be controlled by using remote means, such as cables. Usually, the conventional mechanisms that are able to implement such a feature are rather complicated because the platform has six degrees of freedom (DoF), and moreover, they are subject to backlash and parasitic deformations. This problem is quite general, but in this paper, a specific implementation suitable for surgical applications will be presented.  
A new tendon-driven compliant mechanism is herein proposed, as a possible solution to the above-mentioned problem. The full mobility of the platform in space and, in particular, its raising motion is possible because of a new elastic joint, which combines the action of an elastic curved beam and of a portion of the conjugate surfaces. The invention consists basically of a wire-operated selective compliance mobile platform like the one represented in Figure 1. The mechanism base (k) is intended to be mounted on the end of a flexible tubular duct for endoscopic, surgical, therapeutic, or diagnostic uses.  
The platform (a) is designed for supporting surgical means, normally used during endoluminal operations, such as vision systems, forceps, scissors, cannulae, electrotomes, and so on. However, the system could be used for precision applications or in adverse environments, e.g., for operations in the aerospace environment. The base (k) and the platform (a) are connected through a plurality of legs, which provide mobility in space to the mobile surface (a), and are operated by actuating wires (i) and (f), running inside the tubular duct, to control and operate the platform remotely.  
Since the mechanical structure of the mechanism is based on selective compliance and, particularly, on lumped compliance, there is a neutral configuration that is assumed when no external force is acting on the structure.
# Mathematical Study for Chikungunya Virus with Nonlinear General Incidence Rate
## Appendix A Applied Numerical Scheme  
Let subdivide the time interval as the following: \([0,T]=\bigcup_{n=0}^{N-1}[t_{n},t_{n+1}],\quad t_{n}=n\ dt,\quad dt=T/N\). Let \(s^{n},y^{n},p^{n},x^{n},\lambda_{1}^{n},\lambda_{2}^{n},\lambda_{3}^{n}, \lambda_{4}^{n}\) and \(\Gamma^{n}\) are the approximation of the variables \(s(t),y(t),p(t),x(t)\), \(\lambda_{1}(t)\), \(\lambda_{2}(t),\lambda_{3}(t),\lambda_{4}(t)\) and the control \(\Gamma(t)\) at time \(t_{n}\). \(s^{0},y^{0},p^{0},x^{0}\), \(\lambda_{1}^{0},\lambda_{2}^{0},\lambda_{3}^{0},\lambda_{4}^{0}\) and \(\Gamma^{0}\) are the values of the variables \(s(t),y(t),p(t),x(t),\lambda_{1}(t),\lambda_{2}(t),\lambda_{3}(t),\lambda_{4}(t)\) and the control \(\Gamma(t)\) at time \(t_{0}=0\). \(s^{N},y^{N},p^{N},x^{N}\), \(\lambda_{1}^{n},\lambda_{2}^{n},\lambda_{3}^{n},\lambda_{4}^{n}\) and \(\Gamma^{n}\) are the values of the variables \(s(t),y(t),p(t),x(t),\lambda_{1}(t),\lambda_{2}(t),\lambda_{3}(t),\lambda_{4}(t)\) and the control \(\Gamma(t)\) at time \(t_{N}=T\). The state system was resolved using Gauss-Seidel-like implicit finite-difference method. The adjoint system was resolved using a backward-difference scheme as the following:  
\[\left\{\begin{array}{l}\frac{s^{n+1}-s^{n}}{dt}=\Gamma^{n}-\delta s^{n+1}-p^ {n}\frac{\eta_{1}s^{n+1}}{k+s^{n}}-y^{n}\frac{\eta_{2}s^{n+1}}{k+s^{n}},\\ \frac{y^{n+1}-y^{n}}{dt}=p^{n}\frac{\eta_{1