esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Flying Base Station Channel Capacity and Positioning
### Flying Base Station Channel Capacity  
The flying base station channel capacity builds on the Shannon channel capacity, as described in Section 3.5. However, the flying base station channel capacity differs from the more traditional Shannon channel capacity due to the flying base station's aerial mobility and the resulting capability to position itself to improve communication performance. The flying base station is dedicated to serving the user for connectivity and, therefore, positions itself in the transmission-optimal location, as analyzed in Section 4.1. Because the transmission-optimal location depends on the relative location of the user and the stationary base station, i.e., halfway, the flying base station's channel capacity depends on its own channel and power capability as well as the channel between the user and the stationary base station, including its SNR and the transmission power of the stationary base station. We analyze the flying base station channel capacity when the base station has full-duplex capability vs. half-duplex capability.  
The channel capacity of the stationary base station depends on the SNR of the communication link between the stationary base station, as shown in Equation (1). The stationary base station's SNR to the user is the received power divided by the noise power. Building on Section 3.3 for the received power, \(\text{SNR}_{SU}=\frac{P_{\text{S}}(d_{SU})^{-\gamma}}{W\cdot N_{0}}\). Then, the channel capacity for the channel link between the stationary base station and user, denoted by \(C_{SU}\), is as follows:  
\[C_{SU}=W\ \text{log}_{2}\Bigg{(}1+\frac{P_{\text{F}}\cdot\alpha\cdot d _{SU}^{-\gamma}}{W\cdot N_{0}}\Bigg{)}=W\ \text{log}_{2}(1+\text{SNR}_{SU}) \tag{4}\]  
The channel capacity between the flying base station and the user, denoted by \(C_{FU}\), is as follows:  
\[C_{FU}=W^{\prime}\cdot\text{log}_{2}\Bigg{(}1+\frac{P_{\text{F}}\cdot\alpha \cdot d_{FU}^{-\gamma}}{W\cdot N_{0}}\Bigg{)} \tag{5}\]  
where \(W^{\prime}\) depends not only on the available channel bandwidth, \(W\), but also whether the flying base station supports full duplex or not (in the latter case with half-duplex, \(W^{\prime}=0.5W\)).
# Fog-Based CDN Framework for Minimizing Latency of Web Services Using Fog-Based HTTP Browser
## 3 Proposed Fog-Based CDN Framework
### Fog-Based CDN Framework Components  
Our proposed framework consists of five components: Fog Registry (_FR_), Fog Service Locator (_FSL_), event channel, Fog Worker (_FW_), and Fog Browser. The _FR_, _FSL_, and event channel are deployed in the cloud layer. In the fog layer, the _FW_ is installed on fog nodes to facilitate communication between the cloud and the end devices. Fog Browser is a tool installed on edge devices to take advantage of the proposed framework autonomy.  
Figure 3: Fog-based CDN architecture and components.
## 1 Introduction
### Motivation of the Research
However, the complexity of the EDNs' topologies, a high ratio between the resistance and reactance of the branches of the EDN, the lack of on-site measurements from the MV/LV EDSs, and the integration on a large scale of the distributed generation sources prevent efficient state estimation. Due to these factors, the Decision Maker cannot implement, in most cases, the weighted least square (WLS) method used in the transmission networks [6; 7]. The state estimation is essential for the efficient operation of EDNs since the data collected through the SCADA system implemented at the MV/LV EDSs level can help identify and eliminate errors. An observability assessment is performed before the start of the state estimation process to ensure that the EDN is continuously monitored. Unfortunately, the number of devices used for measuring the EDNs does not meet the requirements of the state estimation. Different pseudo-measurements could be introduced from historical load data profiles for different EDSs depending on the type of consumption structure (residential, commercial, industrial, or hybrid) [8]. However, the confidence in the state estimation process depends on the location of MV/LV EDSs with the integrated RTUs to meet the observability requirements, which ensures the location of the data to the central concentrator of the SCADA system from the HV/MV EDS. Following best practises is essential for DNOs to improve their SCADA system integration. These practises help it identify the various components of the system that need to be integrated and establish a clear understanding of the EDNs' requirements. In addition to  
Figure 2: The share of usual control types integrated into a SCADA system: (**a**) main control types, (**b**) functions integrated inside each control type. conducting a comprehensive analysis of the current infrastructure, these pracises can also help with implementation in the optimal locations (EDSs) for its integration. But, without the proper mathematical tools, DNOs often face issues regarding the state estimate process of the EDNs. Instead, the solution is to develop new state estimation methods to establish the link between the control areas and the RTU devices. Today, various algorithms and methods are available to satisfy these requirements.
# A High-Resolution Ultrasonic Ranging System Using Laser Sensing and a Cross-Correlation Method
## 5 Results and Discussion
### Ranging Results  
The distance of each target was measured by a coordinate measuring machine, which was viewed as the reference distance. The coordinate measuring machine was a Global Classic SR07.10.07 from Hexagon. The measuring error in space is less than 1.9 \(\upmu\)m. The positioning repeatability is less than 5 \(\upmu\)m. For each target, we measured the spatial position of 12 points and fitted a plane. The distance of each target was calculated by all the measuring points.  
The ranging results of the five targets using the laser sensing system and cross-correlation method are shown in Figure 12. For each target, we took 10 repeated measurements. The average values are shown in Figure 12. The ultrasonic transducer used here was the 1P28.  
A comparison of the results for the coordinate measuring machine and the proposed method is shown in Table 2. We use the distance variations measured by our system to compare to values measured by the coordinate measuring machine. The accuracy was better than 0.3 mm. The average relative error was nearly less than 1% for all targets. The proposed method showed good accuracy compared to the coordinate measuring machine.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Target** & **Results by the Coordinate Measuring Machine/mm** & **Results by the Proposed Method /mm** & **Relative Error** \\ \hline
1-2 & 25,351 & 25.545 & 0.77\% \\
2-3 & 25,033 & 25.219 & 0.74\% \\
3-4 & 25.174 & 25.343 & 0.67\% \\
4-5 & 24.602 & 24.439 & \(-0.66\%\) \\
1-3 & 50.496 & 50.764 & 0.53\% \\
1-4 & 75.858 & 76.108 & 0.33\% \\
1-5 & 100.447 & 100.734 & 0.29\% \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of ranging results.  
Figure 11: Ranging results with a step distance of 0.01 mm.  
Figure 12: The ranging results of the five targets using the proposed method.
Future Internet_**2010**, \(2\), 662-669; doi:10.3390/fi2040662  
_future internet_  
**ISSN 1999-5903**  
www.mdpi.com/journal/futureinternet  
_Article_  
**Improving Anomaly Detection for Text-Based Protocols by Exploiting Message Structures**  
**[PERSON] \({}^{1}\), [PERSON] \({}^{1,\star}\), [PERSON] \({}^{2}\), [PERSON] \({}^{1}\) and [PERSON] \({}^{1}\)**  
\({}^{1}\) Institute of Communication Networks and Computer Engineering (IKR), University of Stuttgart, Stuttgart, Germany; E-Mails: [EMAIL_ADDRESS] (M.G.);  
[EMAIL_ADDRESS] (M.K.); [EMAIL_ADDRESS] (C.M.M.)  
\({}^{2}\) Bell-Labs Germany, Alcatel-Lucent Deutschland AG, Stuttgart, Germany;  
E-Mail: [PERSON]ESS]  
\({}^{\star}\) Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +49-711-685-69012.  
_Received: 23 October 2010; in revised form: 16 December 2010 / Accepted: 17 December 2010 / Published: 21 December 2010_  
**Abstract:** Service platforms using text-based protocols need to be protected against attacks. Machine-learning algorithms with pattern matching can be used to detect even previously unknown attacks. In this paper, we present an extension to known Support Vector Machine (SVM) based anomaly detection algorithms for the Session Initiation Protocol (SIP). Our contribution is to extend the amount of different features used for classification (feature space) by exploiting the structure of SIP messages, which reduces the false positive rate. Additionally, we show how combining our approach with attribute reduction significantly improves throughput.  
**Keywords:** anomaly detection; classification; text-based protocols; SIP; SVM
## 2 Underwater Wireless Sensor Network
### Acoustic Link and Its Constraints
#### 2.1.3 High Path Loss  
In UWSNs, attenuation is the most important factor that has a direct impact on path loss. The acoustic channel has been absorbed because of converting sound energy to heat energy [2; 20; 22]. In comparison to the radio frequency, absorption loss increases in underwater acoustics. The reason behind this increase is that the radio frequency in the space depends on distance and frequency [17; 18; 23]. Based on the distance and frequency, the equation \(A(l,f)=l^{k}\times(10^{\frac{a(f)}{100}})^{l}\) shows signal attenuation model in UWSNs, where \(A(l,f)\) represents the average signal attenuation, and \(l\), \(f\), and \(k\) denote the distance, frequency, and spread, respectively. Moreover, based on [PERSON]'s empirical equation [24; 25; 26], the absorption coefficient \(\alpha(f)\) is calculated using the equation \(\alpha(f)=(\frac{0.11\times f^{2}}{1+f^{2}}+\frac{44.0\times f^{2}}{4100+f^{2} }+\frac{2.75\times f^{2}}{10000}+0.0003)\). It is calculated based on [PERSON]'s equation, where \(f\) is the frequency in KHz.
## 3 Finite Element Analysis (FEA)
### Outlines of FEA
In this study, initial imperfections of \(L_{b}/500\) are applied at the bracing locations of target segments of the FEA models that have the maximum bending moments. Furthermore, initial imperfections of \(L_{b}/2000\) and \(L/2000\) are applied at both target segments between the brace points and mid-span, as suggested in the previous study [8]. Thus, the three different imperfections obtained from three different static analyses were adopted in the FEA models for the geometrically nonlinear FEA simultaneously (see Figure 4). Moreover, it was applied at the upper flange locations near the target segments. The verification analysis for this study is provided in Appendix A.  
Figure 3: Applied loading conditions considering various moment gradient effects when the number of bracings is (**a**) 1; (**b**) 3; (**c**) 5; (**d**) 7; (**e**) 9; (**f**) 11; and (**g**) 13. Figure 2: Boundary conditions adopted in the FEA models: (**a**) longitudinal (y-axis) constraints at midpoints; (**b**) vertical (z-axis) constraints at each end; and (**c**) lateral (x-axis) constraints at each end. Meanwhile, [PERSON] [5] investigated total torsional bracings considering various effects produced by bracings, girders, and stiffeners for estimating the effective torsional bracing stiffness. Based on their research, the total bracing stiffness can be expressed as a series connection of each bracing stiffness, as shown in Equation (8). \[\frac{1}{\beta_{T}}=\frac{1}{\beta_{b}}+\frac{1}{\beta_{g}}+\frac{1}{\beta_{ssc}} \tag{8}\]  
where \(\beta_{b}\) is the torsional stiffness of attached bracing members, \(\beta_{g}\) is the strong axis stiffness of girder systems, and \(\beta_{ssc}\) is the cross-section web stiffness. For tension bracing systems, such as z-type bracing, \(\beta_{b}\) can be determined using Equation (9) as follows:  
\[\beta_{b}=\frac{ES^{2}h_{b}^{2}}{\frac{2L_{x}^{2}}{A_{x}}+\frac{S^{3}}{A_{h}}} \tag{9}\]  
Torsional stiffness induced by the contribution of the in-plane girder flexibility to the brace system is shown in Equation (10) as follows:  
\[\beta_{g}=\frac{12S^{2}EI_{x}}{L^{3}} \tag{10}\]  
For special cases, when the torsional bracing is attached near both flanges or a vertical diaphragm element is approximately the same depth as the girder, then web distortion will be insignificant, and \(\beta_{ssc}\) may be considered infinite.
# A Robust Mutual Authentication with a Key Agreement Scheme for Session Initiation Protocol
## 2 Review of [PERSON] et al.'s Scheme
### Registration Phase  
A client registers on a remote server via a secure channel by following the steps listed below:  
**Step 1:**: The client selects an identity \(ID_{i}\), a password \(PW_{i}\), a random number \(N_{c}\), and computes \(V_{i}=h(ID_{i}||PW_{i}||N_{c})\). Then, the client submits a registration message \(\{ID_{i},V_{i}\}\) to the server.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Notation** & **Description** \\ \hline \(ID_{i}\) & Client’s identity \\ \(PW_{i}\) & Client’s password \\ \(p\) & Base point on ECC \\ \(k_{s}\) & Server’s secret key \\ \(K_{s}\) & Server’s public key (\(K_{s}=k_{s}\dot{P}\)) \\ \(||\) & Concatenatio operation \\ \(\oplus\) & Exclusive-or operation \\ \(E_{s}(\cdot)\) & Symmetric key encryption under the key \(s\) \\ \(D_{s}(\cdot)\) & Symmetric key decryption under the key \(s\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notations used in this paper.  
**Step 2**: When the registration message is received, the server first checks the validity of \(1D_{i}\). Then, it computes \(A_{i}=h(ID_{i}||k_{s})\), \(B_{i}=h(A_{i}||k_{s})\), and \(C_{i}=E_{B_{i}}(V_{i})\). Next, it stores \(\{A_{i},C_{i},E_{s}(\cdot),D_{s}(\cdot)\}\) into the memory device and issues it to the client.
**Step 3**: On receiving the memory device, the client stores \(N_{c}\) into it.
## 4 Experimental Results and Discussion
### Running Time  
To evaluate the computational complexity, the running times of all methods were compared for the ten testing stereoscopic images listed in Table 1, as shown in Table 4. All results were acquired from the corresponding authors' MATLAB code, and ours were likewise obtained on MATLAB software. All the algorithms were run on the same machine with an Intel 2.30-GHz CPU and 16 GB of RAM. It was apparent that the proposed method had a faster processing speed than the SC method [13]. This is because the proposed method does not need to solve a complex optimization problem as the SC method does [13]; thus, the running time of the proposed method was less than that of the SC method. Moreover, the proposed method's speed was close to that of SRCNN [15]. As soon as the training of our modified CNN was complete, the SR results were quickly obtained by the proposed feed-forward method.
## 5 Assessing the Impact of Welding Parameters on Ultrasonic Images
### Mechanical Deformation  
The final assessed feature of the weld formation model is the inclusion of mechanical deformation in solidification modelling. This effect is well agreed to be fundamental for simulations aimed at calculating, e.g., residual stress, but its importance for supporting ultrasonic imaging is still discussed in the community. With mechanical deformation, the weld formation model is considerably more complex, and it represents a coupled phenomenon. Hence, the solution is costly, and only a small number of 2D simulations can be performed in a reasonable time. The 3D model is too computationally demanding to be a viable option for supporting TFM imaging. In this context, the team at BZN proposed a pragmatic approach, where at the cost of ignoring the coupled effects, notable time gains are attained. The present comparison shows the impact of this assumption on TFM images.  
Figure 31 depicts grain orientation comparison for the 2D thermal-only (Case 1) and coupled thermo-mechanical (Case 12) FE simulation. We observe notable differences, above 30\({}^{\circ}\), both along the chamfer and in the upper left region of the weld. The coupled simulation produces a smoother map, visually more similar to the metallography image in Figure 2 than that coming from the thermal simulation only.  
How this difference manifests itself in the TFM image is shown in Figure 32. The SNR for most defects is notably reduced by over 7 dB and the legibility of signatures decreased, especially for the SDHs close to the backwall and the  
Figure 30: TFM image from the acquisition simulated at the arc finish location based on 3D orientations from the 3D weld formation model.  
longer propagation paths; hence, the delay law mismatch has a more prominent effect). The detrimental effect is moderate, indicating that neglecting mechanical deformation may be a promising pragmatic solution. While the SNR for most troublesome defects is reduced with the support of simplified thermal simulations, the CPU cost of the latter is notably decreased. Being a practically relevant factor, it contributes to the trade-off between solidification model complexity and the enhancement of TFM images.
## 4 Results and Discussions
### Results for Guwahati Link  
The maximum power, in morning time 0530 IST, was received from elevated beams (elevation angles 2 to 11) for 5% of the days in the year, mostly from beam sets 2 to 4. For the evening time 1730 IST, the occasions when the maximum power was received from elevated beams increased to 16.5% of days in the year. As in the case of the Ahmedabad link and the Guwahati link as well, the maximum power during the evening time was received from almost all of the elevated beams (elevation angles 2 to 10) on different occasions, except from the beam with an elevation angle of 11. The maximum difference between the maximum power received and the lowest elevation angle beam set received power, when considered for all of the available days, was found to be 1.54 dB in the month of May for the morning time and 4.19 dB in the month of March for the evening time. Comparing the results obtained for the summer and winter seasons, the average received power was higher in the summer seasons, but the difference was not as prominent as for the Ahmedabad link. The maximum powers in the summer were again received mostly from elevated beams, especially in the evening.
# Preliminary Analysis of the Water Quality Status in an Urban Mediterranean River
## 3 Results  
### Correlations between the Examined Water Quality Parameters along Kifisos River  
Due to of the small size of the sample data, we used the Shapiro--Wilk test to test for normality of distributions of the following water quality parameters (Table 1). The \(p\) values were considered statistically significant at a 95% confidence interval. According to the results of the Shapiro-Wilk test for normal distribution, most of the examined water quality parameters are not normally distributed. As the majority of the data did not fit a normal distribution, [PERSON]'s correlation analysis was then applied to check for covariances. [PERSON]'s correlation analysis showed that temperature has significant (\(p<0.05\)) correlations with nutrients, particularly with phosphorus (Table 2). Regression analysis for orthophosphates and total phosphorous as \begin{table}
\begin{tabular}{c c c c} \hline \hline
**No** & **Parameter (Unit)** & **Statistic** & \(p\) **Value \({}^{*}\)** \\ \hline
1 & pH & 0.972 & 0.182 \\
2 & DO (mg/L) & 0.971 & 0.160 \\
3 & DIN (mg/L) & 0.961 & 0.054 \\
**4** & **Salinity (ppt)** & **0.524** & **-0.001** \\
**5** & **TN (mg/L)** & **0.953** & **0.027** \\
**6** & **TP (mg/L)** & **0.917** & **-0.001** \\
**7** & **T (\({}^{\circ}\)C)** & **0.952** & **0.019** \\
**8** & EC (\(\mu\)S/cm) & **0.539** & **-0.001** \\
**9** & **NO\({}_{3}\)**\({}^{-}\)N (mg/L)** & **0.944** & **0.006** \\
**10** & **NO\({}_{2}\)**\({}^{-}\)N (mg/L)** & **0.508** & **-0.001** \\
**11** & **NH\({}_{4}\)**\({}^{+}\)**-N (mg/L)** & **0.551** & **-0.001** \\
**12** & **PO\({}_{4}\)**\({}^{-}\)P (mg/L)** & **0.460** & **-0.001** \\
**13** & _E. coli_ (cfu/100 mL)** & **0.604** & **-0.001** \\ \hline \hline \end{tabular} \({}^{*}\)\(p\) values in bold were considered statistically significant at a 95% confidence interval.  
\end{table} 
Table 1: Test of normality (Shapiro–Wilk). function of temperature yielded values (R2) 0.347 and 0.346, respectively. Figure 2 also indicates a strong seasonal trend among temperature and high nutrient values, especially in the summer season (upward-sloping power curve; June-July-August). HWQI and temperature are also strongly correlated, confirming that during warm periods, the physicochemical quality gets worse. The DO concentration is also strongly correlated with all nutrients, HWQI and _E. coli_, which implies that when these substances' levels increase, dissolved oxygen is decreased. This is expected since organic pollution, which also consumes dissolved oxygen due to intense microbial activity, is the main factor affecting these parameters. HWQI and _E. coli_ are more strongly correlated with NO2-N and NH4+N than with the rest of nutrients, likely implying the frequent presence of
## 1 Introduction
[26] employed potential flow theory to design curvilinear fibre paths for variable-stiffness composite plates and analyzed their buckling performance using the discrete Ritz method. Their results demonstrated substantial improvements in load capacity compared to constant stiffness composites. Further, [PERSON] et al. [27] investigated sinusoidal corrugated web girders with stiffened circular openings, revealing the impact of geometric imperfections on shear capacity and noting that stiffeners enhance both strength and stiffness, although their effect diminishes beyond a certain thickness. Finally, [PERSON] et al. [28] examined the shear resistance of stainless-steel plate girders with perforated webs, finding a 52% reduction in shear strength due to perforations and evaluating the accuracy of several design guidelines. Together, these studies highlight the complex interplay of material properties, geometric configurations, and design considerations in optimizing shear resistance and buckling performance across different structural components. The structure of this paper is as follows: The theoretical underpinnings of linear eigenvalue analysis for buckling are presented in Section 2. The case study is covered in Section 3, together with geometric configurations and an explanation of the testing arrangement. The finite element method and the validation procedure, which compares the simulation results to experimental data, are described in Section 4. Based on the analysis of different geometric characteristics and load scenarios, Section 5 presents the results pertaining to stress patterns of distribution, buckling strength, and fatigue life. Finally, Section 6 wraps up the research with conclusions derived from the data.