esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 COGNIFOG Evaluation in an IoT Connectivity Testbed
### Architecture and Main Operation  
The set of modules from the COGNIFOG framework included in the testbed spanned across the IoT-edge-cloud continuum within a single K3s-based cluster with Cilium as the network manager. Real end devices simulating the operation of smartwatches were located at the IoT level. The acquired information from these devices (i.e., traffic type C) was transmitted wirelessly to the IoT edge GW, which was located at the edge level. This element forwarded the received data to the edge server together with two other traffic flows artificially generated: a UDP background stream (i.e., traffic type A) and synthetic information from a smartwatch simulator (i.e., traffic type B). A diagram of the proposed testbed architecture is shown in Figure 7.  
At the cloud-fog level, an instance of the Slice Manager oversees the creation and management of network slices across the communication link connecting the IoT edge GW and the edge server. In the testbed setup, distinct slices were allocated for each type of traffic. Alternatively, in other potential scenarios, slices could also be utilized to segregate multi-tenant workloads within the same cluster. Other elements of the cloud-fog level are the Kentyou DataHub (responsible for retrieving, storing, and facilitating the visualization of information stored at the edge server) and the orchestrator manager (which coordinates the remote deployment and management of these elements across the infrastructure subscribed to the COGNIFOG framework by means of the corresponding orchestration agents).  
Any new module aiming to be integrated into the COGNIFOG framework should meet three basic requirements: (1) it should be IP reachable, (2) it should use K3s, and (3) it should run the COGNIFOG orchestration agent. Once these conditions are satisfied, when the orchestrator manager executes a resource discovery process, it would _see_ a new element that could be incorporated into a managed cluster and ultimately oversee the deployment and management of its services.
# Joint Location-Dependent Pricing and Request Mapping in ICN-Based Telco CDNs For 5G
## 4 Joint Pricing and Request Mapping
### The Design of PARM Module  
Taking the sites' bandwidth prices \(P\) calculated by LDP and the other parameters in Table 1 as input, in this subsection, we formulate the optimization running in the PARM module.  
To guarantee the service quality and provide economic incentives to application providers, the ICN-based telco CDN should minimize both the latency and the application providers' bandwidth payment. Therefore, the objective is set as minimizing the weighted sum of latency and total payment of all the application providers, as Equation (4a) shows. Note that \(\Psi\) is the weight factor. In practice, \(\Psi\) is set according to a CDN's actual operational objectives. For example, if the CDN focus more on guaranteeing the latency performance, \(\Psi\) should be set as a relatively small value (or even as zero). Or if the CDN wants to give users more economic incentives, \(\Psi\) should be set as a big value. In summary, the determination of the weight parameter is an engineering issue, which is out of the scope of this work. Constraint (4b) ensures that the aggregate traffic served by site \(m\) is within the site's capacity. Furthermore, we use \(\xi\) to ensure that a site's target congestion level is within a certain range of the mean value of \(\Theta\), denoted by \(\overline{\theta}\). Thus, the case that too many requests are directed to possible site with the smallest latency/price will be prevented. Constraint (4c) ensures that average latency perceived by application \(n\)'s requests is within its expected response time. Constraint (4d) ensures that the proportions that the application \(n\)'s requests generated in region \(l\) directed to different sites sum up to one. Constraint (4e) enforces the ranges of the variables, i.e., \(\alpha_{nlm}\).  
\[min\sum_{n}\sum_{l}\sum_{m}\lambda_{nl}\alpha_{nlm}t_{lm}+\Psi.\sum_{n }\sum_{l}\sum_{m}\lambda_{nl}\alpha_{nlm}p_{m} \tag{4a}\] \[s.t. \left|\frac{\sum\sum\limits_{n}\sum\limits_{l}\lambda_{nl}\alpha_{nlm }}{C_{m}}-\overline{\theta}\right|\leq\xi,\forall m\in M\] (4b) \[\frac{\sum\limits_{m}\sum\limits_{l}\alpha_{nlm}t_{lm}}{L}\leq t _{n},\forall n\in N\] (4c) \[\sum_{m}\alpha_{nlm}=1,\forall n\in N,\forall l\in L\] (4d) \[0\leq\alpha_{nlm}\leq 1,\forall n\in N,\forall l\in L,\forall m\in M\] (4e) \[var:\propto_{nlm},\forall n\in N,\forall l\in L,\forall m\in M. \tag{4f}\]  
Note that Constraint (4b) is equivalent to the following two inequalities:  
\[\frac{\sum\limits_{n}\sum\limits_{l}\lambda_{nl}\alpha_{nlm}}{C_{m}}- \overline{\theta}\geq-\xi,\forall m\in M \tag{5a}\] \[\frac{\sum\limits_{n}\sum\limits_{l}\lambda_{nl}\alpha_{nlm}}{C_{m}}- \overline{\theta}\leq\xi,\forall m\in M. \tag{5b}\]  
Therefore, the optimization is a linear programming (LP) problem, which can be solved by existing LP solvers (e.g., linprog function in Matlab).
## 2 Characteristic Analysis of the Iridium System
### The Satellite Level  
#### 2.1.1 Error of Predicted Orbit  
In LEO SOP scenarios, LEO satellite positions are typically obtained using TLE combined with orbit prediction models. However, existing orbit prediction models exhibit time-growing errors that degrade positioning performance. Doppler frequency serves as the fundamental observable element in LEO SOP positioning, which can be expressed as:  
\[f_{d}=\frac{f_{c}}{c}\cdot\frac{\overrightarrow{v}\cdot\overrightarrow{r}}{ \left|\overrightarrow{r}\right|} \tag{1}\]  
where \(f_{c}\) denotes the carrier frequency, \(c\) denotes the speed of light, \(\overrightarrow{v}\) denotes the velocity of satellite, and \(\overrightarrow{r}\) denotes the distance vector from the satellite to the receiver. The huge orbital position and velocity prediction errors will result in Doppler frequency prediction errors of up to 15 Hz [31]--far exceeding the RMSE of refined Iridium Doppler estimation algorithms [32].  
Figure 1 illustrates the simulation result of the 3D position and velocity errors of Iridium satellites predicted via the SGP4 over time. Within the first 8 h, the orbital errors remain relatively stable; beyond this period, errors increase dramatically. Therefore, the ephemeris age--defined as the time interval from the last ephemeris update to the epoch reception moment--quantifies the orbital prediction error for that specific epoch. As orbital errors constitute a significant error source in LEO SOP positioning, ephemeris age must be considered as a critical epoch characteristic.
# The Alteration of Giglio Island Granite: Relevance to the Conservation of Monumental Architecture
## 4 Results and Discussion
### Geochemical Data
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & & **SiO2** & **TiO2** & **Al2O3** & **Fe2O3** & **FeO * & **MnO** & **MgO** & **CaO** & **Na2O** & **K2O** & **P2O5** & **LOI** ****\(\mathbf{\ast}\)** \\ \hline \multirow{2}{*}{**A**} & M & 68.44 & 0.63 & 15.27 & 0.69 & 2.79 & 0.06 & 1.29 & 2.21 & 2.69 & 4.73 & 0.18 & 1.02 \\ \cline{2-13}  & s & _1.46_ & _0.08_ & _0.45_ & _0.17_ & _0.35_ & _0.01_ & _0.25_ & _0.34_ & _0.14_ & _0.32_ & _0.03_ & _0.28_ \\ \hline \multirow{2}{*}{**Aa**} & M & 68.99 & 0.59 & 15.28 & 1.15 & 2.23 & 0.06 & 1.09 & 1.88 & 2.50 & 4.88 & 0.17 & 1.12 \\ \cline{2-13}  & s & _1.09_ & _0.11_ & _0.29_ & _0.35_ & _0.31_ & _0.01_ & _0.28_ & _0.34_ & _0.20_ & _0.29_ & _0.02_ & _0.20_ \\ \hline \multirow{2}{*}{**B**} & M & 68.22 & 0.64 & 15.79 & 1.85 & 1.85 & 0.06 & 1.11 & 1.61 & 2.46 & 4.67 & 0.19 & 1.60 \\ \cline{2-13}  & \(s\) & _0.79_ & _0.09_ & _0.45_ & _0.8_ & _0.75_ & _0.01_ & _0.21_ & _0.22_ & _0.14_ & _0.27_ & _0.02_ & _0.35_ \\ \hline \multirow{2}{*}{**C**} & M & 67.25 & 0.65 & 16.16 & 2.80 & 1.30 & 0.05 & 1.11 & 1.36 & 2.27 & 4.42 & 0.17 & 2.46 \\ \cline{2-13}  & \(s\) & _1.06_ & _0.08_ & _0.51_ & _0.90_ & _0.79_ & _0.01_ & _0.18_ & _0.30_ & _0.12_ & _0.47_ & _0.03_ & _0.62_ \\ \hline \multirow{2}{*}{**Cc**} & M & 64.86 & 0.68 & 17.25 & 3.19 & 1.10 & 0.06 & 1.05 & 1.26 & 2.14 & 4.42 & 0.16 & 3.82 \\ \cline{2-13}  & s & _2.15_ & _0.11_ & _0.84_ & _1.13_ & _0.40_ & _0.01_ & _0.11_ & _0.11_ & _0.22_ & _0.27_ & _0.03_ & _1.05_ \\ \hline \hline \end{tabular}  
* M = average of 9 samples; \(\sigma\) = standard deviation;
* volumetric titration method; *
* loss of ignition.  
\end{table} 
Table 2: Chemical composition of major and minor elements from unweathered (A) to weathered samples (Aa, B, C, Cc) expressed as oxide weight %. Figure 8: Variation of the concentration of some major elements (**a**) and of the LOI (loss on ignition) (**b**) in relation to the increase in in the degree of alteration (types A, Aa, B, C, and Cc). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Chemical Weathering Index** & **Key** & **Formula** & **Reference** \\ \hline
**Parker index** & Wp & [(2 Na\({}_{2}\)O/0.35) + (MgO/0.90) + (2K\({}_{2}\)O/0.25) + (CaO/0.70)]*100 & [32] \\ \hline
**Chemical index of alteration** & CIA & [(Al\({}_{2}\)O\({}_{3}\))/(Al\({}_{2}\)O\({}_{3}\) + CaO + Na\({}_{2}\)O + K\({}_{2}\)O)]*100 & [33] \\ \hline
**Leaching coefficient** & Lc & SiO\({}_{2}\)/(K\({}_{2}\)O + Na\({}_{2}\)O + CaO + MgO) & [34] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Chemical weathering indexes selected. These indexes are in agreement with what is observed in Figure 8 regarding the alteration trend, i.e., leaching of mobile cations (K, Na, Ca) with their consequent decrease and relative increase in immobile phases (Al, Fe, Si) from samples A up to Cc.
# Enhanced Peer-to-Peer Botnet Detection Using Differential Evolution for Optimized Feature Selection
## 3 Proposed Approach
### Supervised Machine-Learning Classifiers
#### 3.3.2 K-Nearest Neighbors (KNN)  
KNN is very easily understood by beginners and very simple to implement. KNN is a supervised machine-learning algorithm based on classification as well as regression. KNN works well for complex and non-linear datasets. It is a non-parametric and instance-based algorithm, which is based on the fact that it does not impose any stringent assumptions on the data distribution and rather utilizes the similarity of the instances to make predictions [33]. KNN utilizes a distance metric such as a Euclidean distance measure to measure the similarity between data points [6]. However, distance metrics such as Manhattan or Minkowski may also be utilized. To make a prediction for a new data point, KNN identifies the K-nearest neighbors in the training set based on the chosen distance metric. It operates by computing the Euclidean distance (or any other chosen metric) between data points. The categorization of an object is determined by the majority vote among its K-nearest neighbors, considering entities from various classes [7]. The Euclidean distance between two points (A, B) in Euclidean n-space can be expressed as Equation (4):  
\[\mathrm{d}(\mathrm{A},\mathrm{B})=\sqrt{\sum_{\mathrm{x=1}}^{\mathrm{n}}( \mathrm{B_{x}}-\mathrm{A_{x}})^{2}} \tag{4}\]
## Data and Methods
### Data Preprocessing and Background Field Construction
The specific flow chart is shown in Figure 1. After data preprocessing, the global \(NmF2_{\text{models}}\) were constructed as background references, and their validity was tested by comparing global ionosphere maps (GIMs). The local F-layer anomaly in seismic effects was explored with corresponding research on RO profile fluctuations. Later, the ionosphere anomalies in different elevation and the simultaneous reaction of GIM TECs was examined successively. Figure 1: Ionosphere anomaly detection flow by FORMOSAT-3/COSMIC.
# Improved Likelihood Probability in MIMO Systems Using One-Bit ADCs
## 2 System Model
ML detection provides the optimal performance when the candidate QAM symbols are transmitted equally, which is expressed as  
\[\hat{k}[n]=\mathop{\mathrm{argmax}}_{k\in\mathcal{K}}\mathbb{P}(\mathbf{q}[n] |\mathbf{s}[n]=\mathbf{s}_{k}), \tag{4}\]  
where \(\mathbb{P}(\cdot)\) is the probability operation; \(\mathbf{s}_{k}=[\mathrm{Re}(\mathbf{s}_{k})^{T},\mathrm{Im}(\mathbf{s}_{k})^{ T}]^{T}\) is the candidate QAM symbol; and \((\cdot)^{T}\) is the transpose. Subsequently, \(\mathbf{s}_{k}\) belongs to the real-valued constellation set \(\mathcal{X}=\{\mathbf{s}_{1},\ldots,\mathbf{s}_{k}\}\). Based on [20], the ML in a one-bit ADC is computed as  
\[\mathbb{P}(\mathbf{q}[n]|\mathbf{s}[n]=\mathbf{s}_{k})=\prod_{r\in\mathcal{R }}\mathbb{P}(q_{r}[n]|\mathbf{s}[n]=\mathbf{s}_{k}), \tag{5}\]  
where the probability \(\mathbb{P}(q_{r}[n]|\mathbf{s}[n]=\mathbf{s}_{k})\) is expressed as  
\[\mathbb{P}(q_{r}[n]|\mathbf{s}[n]=\mathbf{s}_{k})=\left\{ \begin{array}{ll}p_ {r,k},&q_{r}[n]=+1\\ 1-p_{r,k},&q_{r}[n]=-1\end{array} \right.. \tag{6}\]  
Figure 1: Frame structure and MIMO receiver using one-bit ADCs considered in this study. Here, \(q_{r}[n]\) is the \(r\)-th row of \(\mathbf{q}[n]\) and \(r\in\mathcal{R}=\{1,2,\ldots,2N_{r}\}\). The probability \(p_{r,k}\) in [20] is computed as  
\[p_{r,k}=\Phi\Bigg{(}\sqrt{\frac{2}{\sigma_{n}^{2}}}\mathbf{h}_{r}^{H}\mathbf{s} _{k}\Bigg{)}. \tag{7}\]  
where \(\mathbf{h}_{r}^{H}\) is the \(r\)-th row of \(\mathbf{H}\), \((\cdot)^{H}\) is the conjugate transpose, and \(\Phi(\cdot)\) is the cumulative distribution of the standard normal random variable. In this study, \(p_{r,k}\) denotes the true likelihood probability computed based on the true channel information \(\mathbf{H}\). However, the true channel information is unavailable at the receiver. Hence, channel estimation is performed during pilot transmission. By denoting the channel estimate at the receiver \(\mathbf{\hat{H}}\), the pilot-based likelihood probability \(p_{r,k}^{pi}\) is expressed as  
\[p_{r,k}^{pi}=\Phi\Bigg{(}\sqrt{\frac{2}{\sigma_{n}^{2}}}\mathbf{\hat{h}}_{r}^ {H}\mathbf{s}_{k}\Bigg{)}. \tag{8}\]  
Using the results of (5)--(7), the ML detection is expressed as  
\[\hat{k}[n]=\underset{k\in\mathcal{K}}{\text{argmax}}\prod_{r:q_{r}[n]=1}p_{r,k}^{pi}\prod_{r:q_{r}[n]=-1}(1-p_{r,k}^{pi}). \tag{9}\]  
Here, \(\hat{\mathbf{s}}[n]\) is defined as the ML estimate, which is re-expressed as \(\mathbf{s}_{k[n]}\) using \(\hat{k}[n]\). Representative symbols in this study are summarized in Table 2.
## 1 Introduction
In these studies [20; 21; 22], coordinate transformation was involved, as the correction terms to satisfy the traction-free boundary conditions were developed using local coordinate systems, which needed to be transformed back into a global coordinate system in order to calculate the total stress fields of a dislocation segment near traction-free boundaries. Recently, a numerical solution capable of computing all stresses due to a dislocation source and the correction terms using a global coordinate system without the need for second-order tensor transformations was developed and demonstrated in [23; 24]. In this study, a numerical solution for the stress field of a dislocation line (segment/finite-length or infinite and at any spatial orientation) outside a cylindrical void (of any length and can be a non-cylindrical shape as well) is developed using the collocation point method. All the stress computations in this study are carried out in a global coordinate system, hence no tedious first-rank or second-rank tensor transformations are involved. The numerical solution developed herein is then compared with the analytical solution derived from [19] for verification purposes. Specifically, the cylinder length is increased as much as possible and the dislocation is kept parallel to the cylinder axis to match the analytical solution of the 2D problem. Additionally, the dislocation source, or the driving force in the numerical solution, needs to be an infinite dislocation in an infinite isotropic material in order to make a comparison with the 2D analytical solution. The verification process will be elaborated on in more detail in Section 4.
# Opto-Electronic Oscillators for Micro- and Millimeter Wave Signal Generation
## 2 Current Progress of the Common Topologies of the OEO  
In this section, the paper focuses on recent advances in the development of OEOs and the main challenges that they face: multimode operation, as well as short-term and long-term stability. In the first subsection, the paper focuses on multimode operation and short-term stability, with long-term stability following this subsection.
## 5 Case Study
### System Reliability Analysis of Dam Slope Stability  
Based on the reliability indices and correlation coefficients of each sliding zone, and because the reliability indices of each sliding zone are large (corresponding to a very small failure probability), the difference between the upper and lower bounds of the dam slope system failure probability determined by the Ditlevsen narrow bounds formula is small. Therefore, the average of the upper and lower bounds is used as the dam slope system failure probability, and the corresponding dam slope stability system reliability index is 6.81. Since, in this study, dam slope instability is defined as the simultaneous failure of all units in the sliding zone, according to the \"Unified Standard for Structural Reliability Design of Water Conservancy and Hydroelectric Engineering\" (GB50199-2013) [26], this type of failure is considered a Category II failure, and the target reliability index for Grade I  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sliding Pathway** & **Upstream Slope** & \
\begin{tabular}{c} **Upstream Deep** \\ **Layer** \\ \end{tabular} &
\begin{tabular}{c} **Downstream Deep** \\ **Layer** \\ \end{tabular} \\ \hline Upstream Dam Slope & 1.00 & 0.89 & 0.33 \\ Upstream Deep Layer & 0.89 & 1.00 & 0.26 \\ Downstream Deep & 0.33 & 0.26 & 1.00 \\ Layer & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Correlation coefficients between the sliding pathways.  
(important) structures is 4.2. In this example, the reliability index of the dam slope stability system is 6.81, which exceeds the target reliability index required by the standard. When the reliability of the dam slope system does not meet the requirements, relevant measures need to be taken, such as slope weight reinforcement, drainage system optimization, anti-slip structure implantation, slope geometry adjustment, etc. In practical projects, multi-measure collaborative schemes are often adopted, such as the combination of \"drainage + ballast + reinforcement\", in conjunction with safety monitoring systems (such as GNSS deformation monitoring and piezometer) to dynamically evaluate the reinforcement effect. This has also been discussed in the literature [27,28].
## 5 Evacuation Modeling Packages
### Microscale
#### 5.3.1 Macroscale Models  
EMME4.6, Evacuation Traffic Information System (ETIS), Oak Ridge Evacuation Modeling System (OREMS), and TransCAD are valuable tools for planning evacuations and simulating large-scale evacuation processes. These models are suitable for analyzing the overall evacuation process in response to wildfire threats affecting wide geographic areas. Authorities can use these macro models to make informed decisions during evacuation operations, such as selecting the appropriate evacuation style and implementing traffic strategies like contraflow or lane-reversal operations. By incorporating data on traffic flow, road networks, and potential bottlenecks, these models can help optimize evacuation strategies and enhance the overall efficiency of evacuations in wildfire-prone regions.
## 6 Limitations  
This study presents the impact of different heights and build levels on the mechanical properties of DED-manufactured specimens. However, the orientation of these specimens remains unexplored. Acknowledging the significance of build orientation in additive manufacturing processes [33], this study, focused on layer-specific effects, did not encompass the influence of specimen orientation on the mechanical attributes. Future investigations considering specimens' build orientation could offer a more comprehensive understanding of DED-manufactured part properties.  
Utilising a diverse range of materials is fundamental in evaluating DED's efficiency across various metal types. This study, conducted within an industrial site and focused on specific case studies, utilised X2 CrNiMo17-13-2 metal powder exclusively for experimentation. Expanding material variations would improve the analysis of DED's performance across different alloys and metals. However, the limited accessibility to multiple materials within the industrial context constrained the exploration to a single alloy which was compatible with the base material.  
While the study primarily emphasises tensile properties, future investigations could encompass a broader range of mechanical tests. Including tests such as flexural and compression tests would offer a more complete analysis of how layering affects the mechanical attributes of DED-manufactured parts. These additional tests could provide insights into the behaviour of DED components under various loading conditions.  
The constraints in material diversity, orientation analysis, and the need for a wider range of mechanical tests are recognised as limitations in this study. Addressing these aspects in future research could contribute significantly to a more complete understanding of DED manufacturing and its mechanical implications across diverse scenarios and materials.