esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Assessment of the Selected Models  
The study used data obtained in 3 years from the city of Cuenca through the Salesian Polytechnic University's meteorological station (real-data were taken every hour for three years). To minimize the errors, data from the city of Cuenca were used in a TMY file. Its source is an estimate based on data collected by satellite of previous years.  
A comparison and post evaluation of this type of file is carried out together with the data or values measured locally by Ecuador's meteorological stations. It will be essential to know what percentages of effectiveness have this type of file and present a comparison based on the models proposed for various Ecuador cities.  
Figure 8 shows the difference in insolation and inclination between the measured data and a TMY, with a margin of error in the insolation of 19.11% and in the inclination of 22.2%. These values are obtained from the error percentage formula, with a \(-1\%\) margin of the maximum insolation value in each case. These results are in concordance with Reference [35], where it is indicated that the difference between measured data and TMY data is between 8.89% and 23.75%.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{6}{c}{**Irradiance, Inclination, and Orientation Values**} \\ \hline
**Models** & \multicolumn{2}{c}{**[PERSON]**} & \multicolumn{3}{c}{**[PERSON]**} \\ \hline Cities & (Wh/m\({}^{2}\)) & I (\({}^{\circ}\)) & O (\({}^{\circ}\)) & (Wh/m\({}^{2}\)) & I (\({}^{\circ}\)) & O (\({}^{\circ}\)) \\ Guayaquil & 1878.34 & 20 & 51 & 1777.555 & 23 & 52 \\ Quito & 2142.09 & 18 & 35 & 2456.078 & 17 & 38 \\ Cuenca & 2240.651 & 19 & 36 & 2627.494 & 18 & 37 \\ Santo Domingo & 1671.294 & 18 & 47 & 1551.653 & 22 & 49 \\ Machala & 1783.287 & 20 & 52 & 1644.508 & 25 & 53 \\ Portoviejo & 1952.73 & 21 & 53 & 1895.283 & 24 & 54 \\ Loja & 2217.426 & 21 & 40 & 2561.746 & 22 & 38 \\ Ambato & 2175.057 & 19 & 38 & 2521.848 & 18 & 39 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Obtained values for [PERSON] and [PERSON] models.  
Figure 8: Solar insolation as a function of the tilt of a north facing flat surface in Cuenca (measured data vs. TMY data).
## 6 Examination of a Real Lighting Scene
Firstly, in most conditions, to transform the _S/P_ ratio calculated using the 2- and 10-degree SSC outcomes, a conversion coefficient equal to 0.916 should be used. Secondly, in most practical applications, the difference of the calculated mesopic luminance using 2- and 10-degree SSCs was approximately in the range of 3%-5%, and the conversion coefficients ranged from 1.03 to 1.05. Thirdly, for applications with CCTs \(>\) 10,000 K, and for luminance values \(>\)1 cd/m\({}^{2}\), the differences may be \(>\)10%, and mesopic luminance should be calculated using suitable SSCs. Most urban lighting scenes, such as road lighting and tunnel lighting, are in the mesopic vision range, and in the nightscape lighting. Most of these lighting scenes require large visual field, while others may require small visual fields to observe. Applying the suitable SSC to reach more precise assessments could help improve the lighting design, yield a smaller energy consumption, and reduce the risk of potential light pollutions. There are some limitations associated with this study. Firstly, it is difficult to determine a very precise photopic adaptation luminance for complex luminance distributions because adaptation luminance may be related to luminance distributions, eye movements, and surrounding luminance effects [21]. Secondly, the adaptation luminance determined from 2-degree and 10-degree visual fields should be different, because the visual angles and adaptation fields [12] are different. The CIE TN 007:2017 recommends the adaptation field to be taken as the design area, and the adaptation luminance as the average luminance of the adaptation field. This is not applicable to designing suitable adaptation fields for each observation point when assessing a large-scale urban lighting scene. There is no feasible way at present to apply a suitable adaptation luminance for the entire road. Therefore, in this work, we only consider the SSC as the factor influencing photopic adaptation luminance and adopt a local photopic adaptation luminance for each observation point. **Author Contributions:** Conceptualisation, [PERSON] and [PERSON]; methodology, [PERSON]; validation, [PERSON]; formal analysis, [PERSON]; investigation, [PERSON]; writing--original draft preparation, [PERSON] and [PERSON]; writing--review and editing, [PERSON]; funding acquisition, [PERSON]. **Funding:** This research was funded by the National Natural Science Foundation of China (NSFC) (grant number 61605125) and Beijing Advanced Innovation Center for Future Urban Design, Beijing University of Civil Engineering and Architecture (Project No. UDC2018010411). **Conflicts of Interest:** The authors declare no conflict of interest.
## 3 System Throughput and Packet Loss Rate
### Throughput
Thus, the system throughput \(\mathcal{T}(G)\), with different erasure probabilities at the satellites, is\[\mathcal{T}(G) =\lim_{N\to\infty}\frac{1}{N}\sum_{\mathbb{J}\
eq\varnothing,\, \mathbb{J}\subseteq\{1,\ldots,K\}}(-1)^{|\mathbb{J}|+1}\left|\bigcap_{j\in \mathbb{J}}D_{j}\right|\] \[=\sum_{\mathbb{J}\
eq\varnothing,\,\mathbb{J}\subseteq\{1,\ldots, K\}}(-1)^{|\mathbb{J}|+1}\sum_{u=0}^{\infty}\left[\frac{G^{u}e^{-G}}{u!}u\prod_{k\in \mathbb{J}}(1-\varepsilon_{k})\varepsilon_{k}^{u-1}\right]\] \[=\sum_{\mathbb{J}\
eq\varnothing,\,\mathbb{J}\subseteq\{1,\ldots, K\}}(-1)^{|\mathbb{J}|+1}e^{-G}\prod_{k\in\mathbb{J}}(1-\varepsilon_{k}) \,\sum_{u=0}^{\infty}\left[\frac{uG^{u}}{u!}\left(\prod_{k\in\mathbb{J}} \varepsilon_{k}\right)^{u-1}\right]\] \[=\sum_{\mathbb{J}\
eq\varnothing,\,\mathbb{J}\subseteq\{1,\ldots, K\}}(-1)^{|\mathbb{J}|+1}\,G\prod_{k\in\mathbb{J}}(1-\varepsilon_{k})\,\,e^{-G \left(1-\prod_{k\in\mathbb{J}}\varepsilon_{k}\right)}. \tag{6}\]  
For instance, in the particular and practical case of \(K=2\), we have that \(\mathbb{J}=\left\{\{1\},\{2\},\{1,2\}\right\}\) and \(\mathcal{T}_{2}(G)=G(1-\varepsilon_{1})e^{-G(1-\varepsilon_{1})}+G(1- \varepsilon_{2})e^{-G(1-\varepsilon_{2})}-G(1-\varepsilon_{1})(1-\varepsilon _{2})e^{-G(1-\varepsilon_{1}\varepsilon_{2})}\), but when \(\varepsilon_{1}=\varepsilon_{2}=\varepsilon\) the system throughput becomes \(\mathcal{T}_{2}(G)=2G(1-\varepsilon)e^{-G(1-\varepsilon)}-G(1-\varepsilon)^ {2}e^{-G\left(1-\varepsilon^{2}\right)}\), the same as in ([36], Equation (6)). Finally, it is important to remark that in the case of equal erasure probabilities, \(\varepsilon_{k}=\varepsilon\), \(\forall k\), the cardinality of the union in (4) becomes \(\sum_{k=1}^{K}\binom{K}{k}(-1)^{k-1}\big{|}\bigcap_{j\in\mathbb{J}}D_{j}\big{|}\) as in ([36], Equation (5)). Then, after some manipulations, the throughput in (6) can be written as in ([36], Equation (2)), and therefore the model of equal erasure probabilities is a particular case of the formulation presented in this subsection.
## 4 Experiment and Security Analysis
### Statistical Analysis  
Statistical analysis is a conventional method used in steganalysis. The idea is analyzing the statistical changes after message embedding and measuring changes by statistical metrics, such as standard deviation and correlation coefficients in three directions: horizontal,  
Figure 11: The RS diagrams for dual “Lena” (**top row**) and “Goldhill” (**bottom row**) stego images.  
Figure 12: The PVD histograms for the test image “Lena” (**top row**) and those for “Kodim07”.  
s&p attacks, the BERs are small when concealing messages using the single base (SB) approach, but they are larger if we conceal messages using the weighted modulus (WM). These results are reasonable because the message embedding scheme, as shown in Equation (6), is vulnerable to any changes in the Red, Green, or Blue channels due to the fact that messages must be concealed via the vector dot operation. In contrast, the SB scheme, as shown in Equation (7), is more robust to any changes in the Red, Green, or Blue channels, as messages are concealed independently in each channel. Nevertheless, the overall results for the BER evaluation are satisfactory.  
Table 15 also exhibits the image quality results. The reference image is the restored image derived from a stego image _without_ any attacks, while the test image is the restored image from the one under various types of attacks. The PSNR values are greater than 20 dB; the lower the attack ratios, the larger the PSNR values. In addition, the experimental results, revealing a high SSIM, larger than 0.941, a high Q-index, larger than 0.952, indicates that the restored image continues to possess high image quality, even when the stego images are under various attacks. The experimental results confirm that our scheme is robust, able to resist the salt-and-pepper noise and image cropping attacks.  
Figure 13 exhibits visual inspection of the attacked stego images. Note that the attacked areas in the restored image, \(Rl_{\text{correct}}^{\prime\prime}\), are not all black because pixel values are still correct in some of the Red, Green, and Blue channels. Nevertheless, the details of the image contents can still remain visible, and the effects of attacks are limited, thereby indicating the ability to resist the noise and cropping attacks.
## 2 Results and Discussion
### Multiple Regression Modelling of the Total Phenol Content and Analysis of Variance
The flattening of the curvature at 200 \({}^{\circ}\)C potentially suggests that beyond this temperature, the TPC is reduced. Compared to the respective graph for the extraction yield for the aboveground part (Figure 1a), the observation is the same at low temperatures but differentiates as the temperature is raised. Given that the extraction yield includes several classes of compounds and not only phenols, it can be hypothesized that the influence of time increases for classes of compounds that are not as readily soluble in SWE as phenols. Therefore, if the extraction target includes other components of interest, the effect of time should not be overlooked. The most striking feature of Figure 2b is the different effect of temperature between the lowest and highest water/plant ratio. Notably, at a ratio of 0.1, raising the temperature \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**TPC (Aboveground)** & **Sum of Squares** & **df** & **Mean Square** & **\(F\)-Value** & **\(p\)-Value** \\ \hline AB & 36.55 & 1 & 36.55 & 0.43 & 0.5428 \\ \hline AC & 880.66 & 1 & 880.66 & 10.26 & 0.0239 \\ \hline BC & 1399.17 & 1 & 1399.17 & 16.31 & 0.0099 \\ \hline A\({}^{2}\) & 845.86 & 1 & 845.86 & 9.86 & 0.0257 \\ \hline B\({}^{2}\) & 2.54 & 1 & 2.54 & 0.030 & 0.8701 \\ \hline C\({}^{2}\) & 7319.23 & 1 & 7319.23 & 85.30 & 0.0002 \\ \hline Residual & 429.04 & 5 & 85.81 & & \\ \hline Cor Total & 42,285.47 & 14 & & & \\ \hline R\({}^{2}\) & 0.9899 & & & & \\ \hline CV \% & 12.68 & & & & \\ \hline TPC (Root) & & & & & \\ \hline Model & 35,735.85 & 9 & 3970.65 & 75.22 & \(<\) 0.0001 \\ \hline A—temperature & 825.62 & 1 & 825.62 & 15.64 & 0.0108 \\ \hline B—extraction time & 24.96 & 1 & 24.96 & 0.47 & 0.5222 \\ \hline C—water/plant ratio & 29,995.72 & 1 & 29,995.72 & 568.26 & \(<\)0.0001 \\ \hline AB & 33.75 & 1 & 33.75 & 0.64 & 0.4602 \\ \hline AC & 160.83 & 1 & 160.83 & 3.05 & 0.1413 \\ \hline BC & 5.75 & 1 & 5.75 & 0.11 & 0.7548 \\ \hline A\({}^{2}\) & 400.28 & 1 & 400.28 & 7.58 & 0.0401 \\ \hline B\({}^{2}\) & 4.19 & 1 & 4.19 & 0.079 & 0.7894 \\ \hline C\({}^{2}\) & 4063.80 & 1 & 4063.80 & 76.99 & 0.0003 \\ \hline Residual & 263.93 & 5 & 52.79 & & \\ \hline Cor Total & 35,999.78 & 14 & & & \\ \hline R\({}^{2}\) & 0.9927 & & & & \\ \hline CV \% & 8.54 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._increases the TPC, whereas when more water is added in the system (ratio of 0.4), the TPC remains practically unchanged. This is an indication that phenol degradation occurs after extraction with the participation of water molecules, to support the hypothesis proposed in Section 2.1 (Figure 1b). Figure 2: _Cont._  
Figure 2c confirms the minimal role of extraction time within the studied range, whereas it becomes clear that increasing the water/plant ratio has a negative effect on TPC, which comes in good agreement with the degradation hypothesis proposed above. Figure 2d-f, which correspond to the responses obtained by the SWE of the roots, closely follow the patterns observed in Figure 2a-c.
# A Mobility Management Using Follow-Me Cloud-Cloudlet in Fog-Computing-Based RANs for Smart Cities
## 4 Mobility Management Using Follow-Me Cloud-Cloudlet Approach
### Follow-Me Cloud Phase  
This phase mainly implements the cooperation of cloud-cloudlet \(CL_{d\to d}\). After UE is moving to a different region, a data migration operation is executed from \(c_{s}\) to \(c_{d}\), it also means that CN is migrated from \(c_{s}\) to \(c_{d}\). After performing _follow-me cloudlet_ phase, assume that we have \(TIM=(\underbrace{1,1,\cdots,1}_{m^{\prime}}\underbrace{0,0,\cdots,0}_{n-m^{ \prime}})\). Thus, \(m^{\prime\prime}-m^{\prime}\) packets are then transmitted from the new serving data center of \(c_{d}\) to the previous cloudlet \(l_{s}\), and then be re-forward to the new cloudlet \(l_{d}\) to UE, before the new route path from \(c_{d}\) to \(l_{d}\) is not re-calculated in FMCC. Then, we have \(TIM=(\underbrace{1,1,\cdots,1}_{m^{\prime\prime}}\underbrace{0,0,\cdots,0}_{n -m^{\prime\prime}})\), where \(m^{\prime}\leq m^{\prime\prime}\). That is, there are \(m^{\prime\prime}-m^{\prime}\) packets are transmitted in this phase. The detailed procedure is given.  
1. The FMCC, DMAM and MIGW decide to execute the data migration procedure
2. DMAM sends a session-migration-approved message, \(SMA\_message\), to FMCC. DMAM instructs FMCC to generate the essential traffic of the control plane by ensuring the seamless service migration procedure below. After FMCC receiving \(SMA\_message\) from DMAM, it enables OpenFlow rules of the FMCC. FMCC sends out _session-migration-request_ message, \(SMR\_message\) to notify \(c_{s}\) and \(c_{d}\) to execute the data migration. Based on information of TIM message, all un-transmitted packets, for all \(b_{k}=0\) and \(m+1\leq k\leq n\), including TIM message are migrated from DC of \(c_{s}\) to DC of \(c_{d}\).
3. After DC of cloud \(c_{d}\) verifying the received TIM message, the DC of cloud \(c_{d}\) randomly pre-transmits some \(k^{\prime}\)-th un-transmitted packets, where \(b^{\prime}_{k}=0\) and \(m+1\leq k^{\prime}\leq n\). The corresponding bits are set 1, for \(b^{\prime}_{k}=1\), in the TIM message if the packet are successfully pre-transmit toward to cloudlet \(l_{s}\) and keep the pre-transmitted packets in cache of \(l_{s}\). This pre-transmission operation is done until the new route path is determined in _follow-me cloud-cloudlet_ phase.  
As shown in Figure 9, 8-th packet is transmitted if TIM = (1, 1, 1, 1, 1, 0, 1, 0, 0), and \(b_{7}=0\), from the last phase. Finally, TIM = (1, 1, 1, 1, 1, 1, 1, 0, 0), and the above operation is done before the new route path from \(c_{d}\) to \(l_{d}\).  
Figure 8: An example of _follow me cloudlet_ phase.
###### Abstract  
Due to unpredictable and fluctuating conditions in real-world control system applications, disturbance rejection is a substantial factor in robust control performance. The inherent disturbance rejection capacity of classical closed loop control systems is limited, and an increase in disturbance rejection performance of single-loop control systems affects the set-point control performance. Multi-loop control structures, which involve model reference control loops, can enhance the inherent disturbance rejection capacity of classical control loops without degrading set-point control performance; while the classical closed Proportional Integral Derivative (PID) control loop deals with stability and set-point control, the additional model reference control loop performs disturbance rejection control. This adaptive disturbance rejection, which does not influence set-point control performance, is achieved by selecting reference models as transfer functions of real control systems. This study investigates six types of multi-loop model reference (ML-MR) control structures for PID control loops and presents straightforward design schemes to enhance the disturbance rejection control performance of existing PID control loops. For this purpose, linear and non-linear ML-MR control structures are introduced, and their control performance improvements and certain inherent drawbacks of these structures are discussed. Design examples demonstrate the benefits of the ML-MR control structures for disturbance rejection performance improvement of PID control loops without severely deteriorating their set-point performance.  
multi-loop model reference control; PID controllers; disturbance rejection control
# Evaluating Ensemble Learning Mechanisms for Predicting Advanced Cyber Attacks
## 1 Introduction
### Problem Statement  
Current state-of-the-art network intrusion detection systems (NIDS) still face significant challenges in accurately detecting cyber threats whether new or evolving. Traditional NIDS approaches rely on pattern recognition and are not capable of detecting sophisticated attacks such as zero-day attacks and advanced persistent threats (APT). On the other hand, NIDS that use individual ML algorithms to generate detection models are criticized for numerous drawbacks such as overfitting, bias, and poor generalization of new data. These limitations can cause false negatives, where attacks go undetected, or false positives, where legitimate traffic is misclassified as an attack. Ensemble machine learning has emerged as a promising approach to address these limitations and improve the accuracy of attack detection. However, there is a need to explore the effectiveness of different ensemble methods and algorithm combinations for NIDS, as well as their potential limitations in terms of interpretability, complexity, and computational resources.
## 3 Results
### Elevation Estimation at a Point  
The challenge is to estimate as well as possible the elevation z of a point Q. Assuming that zI < z2 (see Figure 3) and since QQ' is parallel to P2P'2, in the triangle P1P2P'2 we have:  
\[\frac{z_{Q}-z_{1}}{z_{2}-z_{1}}~{}=~{}\frac{\mathrm{dist}\big{(}P_{1},Q^{ \prime}\big{)}}{\mathrm{dist}\big{(}P_{1},P_{2}^{\prime}\big{)}} \tag{25}\]  
From (25) we obtain the elevation of Q:  
Figure 6: Output of the Visual C++ program.  
\[\text{z}_{\text{Q}}\;=\;\text{z}_{1}+\sqrt{\frac{\left(\text{x}_{\text{Q}}-\text{x}_ {1}\right)^{2}+\left(\text{y}_{\text{Q}}-\text{y}_{1}\right)^{2}}{\left(\text{x }_{2}-\text{x}_{1}\right)^{2}+\left(\text{y}_{2}-\text{y}_{1}\right)^{2}}}( \text{z}_{2}-\text{z}_{1})\]  
It is immediate that, without the assumption of z1 < z2, the following formula can be used to estimate the elevation of Q:  
\[\text{z}_{\text{Q}}\;=\;\min\{\text{z}_{1},\text{z}_{2}\}+\sqrt{\frac{\left( \text{x}_{\text{Q}}-\text{x}_{1}\right)^{2}+\left(\text{y}_{\text{Q}}-\text{y} _{1}\right)^{2}}{\left(\text{x}_{2}-\text{x}_{1}\right)^{2}+\left(\text{y}_{2} -\text{y}_{1}\right)^{2}}}\left|\text{z}_{2}-\text{z}_{1}\right|} \tag{26}\]
## 1 Introduction  
Research trends within technological innovation are leading to the appearance of the Internet of Things (IoT)-aided robotics application. An IoT-aided robotic comprising an IoT device connected to a mobile platform plays a major role in tomorrow's society, continuing to help persons in accomplishing many duties, spanning from precision farming to industrial warehouses, from wildlife monitoring to infrastructure or asset management. Within the robot localization itself, there is a taxonomy of problems to be solved based on how one looks at the problem [1]. One such category is based on type of the initial knowledge that a robot may possess initially and/or at run-time. There are three problems in this category; position tracking, global localization [2, 3], and kidnapped problem [4, 5]. Position tracking is a problem to localize a mobile robot based on the information from the environment and previous knowledge of the pose. Global localization is similar to pose tracking, however there is no previous pose knowledge available as discussed in [6] and [7]. Kidnapped Robot Problem (KRP) is more difficult than the two. This problem is defined as a condition when the robot is being transported somewhere in the map while doing localization or pose tracking, without knowing where the destination is and without even realizing it has been moved.  
KRP rarely happens in real life, however the ability to solve KRP is usually considered as a measure of good localization technique. Another importance of KRP study is the concern on robot's safety. An undetected and/or unsolved KRP leads to incorrect map and incorrect pose estimate, thus may hinder the robot from performing its task. A more dangerous problem is when the robot wanders around an undefined/incomplete map while believing that it is still performing localization well. This condition is dangerous when there is a potential hazard in the undefined/incomplete map. The kidnapping event may happen at any time during indoor robot's exploration [8]. Therefore, an accurate online kidnapping detector is preferable in this case, such that there is immediate and reliable information that the robot is kidnapped at any points in time. This type of detector will allow precautionary action to be taken right after kidnapping, and determine whether a global localization process is required.  
This paper proposes an augmented online approach for a mobile robot to effectively detect the kidnapping event in range-finder-based indoor localization, based on shape similarity of environment scans between two consecutive time instances. The shape similarity measure used in the approach is a variant of Two-Dimensional Dynamic Time Warping. The rest of the paper is organized as follows. In Section 2, a literature review on existing detection methods is presented. Section 3 lays the groundwork and details of the proposed detection method while the details of benchmarks are presented in Section 4. Section 5 discusses the results of proposed online detection approach and lastly Section 6 concludes the paper.
## 2 Performance of IWP with Steel Anchor Beam
### Experimental Results
#### 2.2.2 Stress of the Rebar  
Due to the bond between rebars and surrounding concrete, before cracking, the strains in the rebars and the concrete were consistent. However, when the concrete cracks, the tensile forces were transferred to the rebars. Based on this principle, strain gauges were placed on the rebars at locations where the concrete tensile stress was high to determine whether the concrete at those positions was cracked, as shown in Figure 10. Figure 10a shows the relationship of axial stress with load at certain measurement points on the rebar between the plates. When the load is below 1.2P, the slope of the curves at each measurement point remains almost unchanged, indicating that the local area near the measurement points is in the elastic stage. When the load exceeds 1.2P, the slope of the curve corresponding to measurement point V-3 starts to increase significantly, indicating that the concrete near this measurement point has entered the plastic stage, and the proportion of load borne by the rebar begins to increase. For the other three measurement points, the slope of their corresponding curves begins to increase when the load exceeds approximately 1.7P, but the increase is not significant. Figure 10b shows the relationship of axial stress with load at the measurement points on the transverse perforating rebar. The overall stress level on the transverse rebar is relatively low, and it mainly exhibits a compressed state. The maximum tensile stress is 13.4 MPa, and the maximum compressive stress is 60.2 MPa.