esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 SensorNC Applications
### Optimal Sink Placement
#### 7.1.1 Small-Scale WSNs: Comparison between OSP and GASP  
In this set of experiments, we analyze the worst-case delay for GASP and OSP for different, but relatively small network sizes of 30, 50 and 100 nodes. The number of candidate locations for the sinks were about 450, 950 and 2200 for the 30-, 50- and 100-node network, respectively. The number of sinks was restricted to two, since for the 100-node network, this already meant that the OSP strategy had to evaluate \({2200\choose 2}=\) 2,418,900 different combinations of sink placements. Each evaluation consists of 100 per-flow delay analyses, resulting in a total run-time of the OSP of several days. For the GASP, on the other hand, we chose a population size of 40 individuals, and the number of generations was set to 200, resulting in only 8000 different sink placements that where evaluated in only a few minutes. The worst-case delay results for the best sink placements found by the GASP and OSP are shown in Figure 11.  
As can be observed, the GASP performs very well in comparison to the OSP: for the 30- and 50-node network, it actually finds the global optimum; only for the 100-node case, the best sink placement found by the GASP lies slightly above the one found by the OSP (8.02 s vs. 7.70 s). That should be considered a success of the GASP since with a computational effort that is several orders of magnitude lower than for the OSP, it achieves almost the same quality of solutions. Whether this holds true for larger-scale scenarios is difficult to assess as the OSP is prohibitively computationally expensive. Therefore, in the next subsection, we compare the GASP against the MCP in larger-scale networks.
## 7 Performance Analysis
### Security Features  
A comparative analysis of the security properties was conducted between our scheme and existing works [15; 32; 34; 35; 36; 37; 39]. We conducted a comparative analysis of the following security features: \(S_{1}\): \"Resistance to stolen mobile device attacks\", \(S_{2}\): \"Resistance to user impersonation attacks\", \(S_{3}\): \"Resistance to sensor capture attacks\", \(S_{4}\): \"Resistance to sensor impersonation attacks\", \(S_{5}\): \"Resistance to off-line guessing attacks\", \(S_{6}\): \"Resistance to insider attacks\", \(S_{7}\): \"Resistance to MITM attacks\", \(S_{8}\): \"Resistance to replay attacks\", \(S_{9}\): \"Resistance to stolen verifier attacks\", \(S_{10}\): \"Resistance to privileged insider attacks\", \(S_{11}\): \"Resistance to ESL attacks\", \(S_{12}\): \"Resistance to DoS attacks\", \(S_{13}\): \"Support user anonymity and untraceability\", \(S_{14}\): \"Support mutual authentication\", and \(S_{15}\): \"Support perfect forward secrecy\". A comparative analysis is summarized in Table 8. The related works have limitations in defending against security attacks, including stolen mobile device, sensor capture, and insider attacks. Thus, our scheme offers enhanced security.
# AI-Enabled Interference Mitigation for Autonomous Aerial Vehicles in Urban 5G Networks +
## 8 Simulation Set-Up and Results
### Outcomes  
In this section, the outcomes of the performance measures are presented:  
1. _Convergence:_ We observe normalised convergence under (17), where \(\delta_{0}^{thresh}=5\) dB. Each time step is equal to one radio sub-frame, with a duration of 1 ms. With an increase in the number of \"h\", the number of episodes necessary to achieve convergence also increases, with little to no impact on the threshold. \(\delta_{0}^{thresh}\) since \(h>>\delta_{0}^{thresh}\).
2. _Runtime:_ The normalised runtime is observed. As h increases, the complexity of runtime complexity also increases for the proposed algorithm. This is justified because of an increased number of interfering elements.
3. _Coverage:_ Coverage is defined by \(1-F_{r}(\delta)\), which evidently improves everywhere. Coverage improves where \(\delta\) increases monotonically.  
Additionally, the proposed solution is compared against some of the state-of-the-art methods implemented in the environment used in our work. The following Table 9 summarises the results. It is observed that the proposed DQL is considerably better than the other methods. SINR sees a 41.66% increase while using DQL as compared to mean-field game theory, the next best algorithm.  
In Figure 21, we plot the average SINR values of different methods as well as the proposed method and observe that the proposed method performs better than the existing methods in effectively mitigating interference. This can be explained by the following reasons: convolutional neural networks (CNNs) often require large memory spaces to store the weights and activations of the convolutional layers; this results in large overhead and complex design as opposed to the proposed DQL. Mean-field Q-learning has scalability limitations, unable to cope with a large number of agents and the consequent large growth of the action space. Particle swarm optimization can also be used for power allocation to mitigate interference. However, this method is designed to exploit the best solutions in the search spaces to a global optimum, which may not be the best trade-off between exploration-exploitation. Mean-field game theory for interference mitigation is also one of the methods amongst the existing works. However, this method is computationally complex, expensive, and challenging.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Reference** & **Algorithm** & **Average SINR Value** \\ \hline
[29] & CNN & 6 dB \\
[48] & Mean-field Q-Learning & 0.30 dB \\
[49] & PSO-based Power Allocation & 4 dB \\
[37] & Q-Learning & 6.5 dB \\
[38] & Mean-Field Game Theory & 6.42 dB \\ Proposed & Proposed & 9.095 dB \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison of results with state-of-the-art method.
## 3 Results  
### Case Study Application  
The Shanmei Reservoir, situated midstream of the Dongxi River, a tributary of the Jinjiang River in Nan'an County, Fujian Province, is a large-scale, multi-functional water conservancy project primarily designed for water supply. It also serves additional purposes, including flood control, power generation, irrigation, and ecological regulation. The controlled catchment area above the dam site spans 1023 km\({}^{2}\), with a total reservoir capacity of 578 million m\({}^{3}\). The basin's average annual precipitation ranges from 1200 to 1900 mm. The reservoir's main dam is an earth-rock embankment with a clay core wall, featuring a crest elevation of 105.48 m, a maximum dam height of 75.5 m, a crest width of 8.0 m, and a crest length of 305 m. Since its construction in 1972, the Shanmei Reservoir has experienced 47 small-to-moderate floods with peak flows ranging from 1000 to 2000 m\({}^{3}\)/s, and 7 major floods with peak flows exceeding this range. Note that all the data used in this study are sourced from the Fujian Provincial Water Resources Management Center.
# Autonomous Pointing Control of a Large Satellite Antenna Subject to Parametric Uncertainty
## 5 Conclusions  
The autonomous pointing problem of a large satellite antenna, which is achieved through satellite attitude maneuvers, is addressed in this paper. The LSA pointing dynamics are firstly proposed. The LSM-based adaptive controllers and active disturbance rejection filter are then respectively implemented in the presence of modal parameter uncertainty and pointing errors. It should be noted that the modal parameter uncertainty could lead to poorer performance for LSA pointing control. This is because a particular SF can only remove particular bending vibration signals. The LSM can effectively estimate the actual bending frequency, and then the LSM-based adaptive controller can increase pointing accuracy and stability. The active disturbance rejection filter is designed in the modified controller, which can further reduce periodic pointing errors. To deal with constant error, the integrator provides a realizable solution for a closed-loop system. Numerical results are finally presented to show that the proposed autonomous controllers are effective and simple, which makes them easier to implement in real-time applications. For the future work, the modified least squares methods, to further improve the estimation performance and efficiency of actual frequency, could be investigated for autonomous controller design.  
This work is supported by the National Natural Science Foundation of China (11502040 & 11572069), and the Fundamental Research Funds for the Central Universities (DUT15 LK31 & DUT15 RC(438).  
[PERSON], [PERSON] and [PERSON] conceived and designed the control algorithms; [PERSON] and [PERSON] performed the numerical simulation and analyzed the data; and [PERSON] and [PERSON] wrote the paper. All the authors reviewed the manuscript.  
The authors declare no conflict of interest.
## 3 Results
### Pollution Level in Groundwater Due to Localization of Piezometers
#### 3.2.2 Chemical Oxygen Demand (COD) in Groundwater  
In this study, the chemical oxygen demand (COD) was used for assessing organic pollution levels in groundwater (Figure 7). Based on the monitoring results from 1998, it should be noted that the highest COD level was observed near piezometer P-7A (COD value at 700 mg O\({}_{2}\)/L). Lower COD values were obtained for groundwater taken from piezometers located in the northern part of the study area, beyond the area of the MBT installation and the composting plant. In piezometers located in the area of the biological processing of wastes, average values of COD were in the range of 100-150 mg O\({}_{2}\)/L. In the eastern and western part of the study area (vicinity of piezometer P-2A and P-9, respectively), the COD level was almost equal to 400 mg O\({}_{2}\)/L. The lowest level of COD was detected in the northern part of the study area (COD at the level of 10 mg O\({}_{2}\)/L). Monitoring data from 2004 and 2016 clearly indicate a decrease of COD concentrations in the piezometers. The most significant change had occurred near piezometer P-7A (COD level decreased from 703 mg O\({}_{2}\)/L in 1998 to 132 mg O\({}_{2}\)/L in 2004 and 100 mg O\({}_{2}\)/L in 2016).  
Figure 5: Temporal changes of the BOD level in piezometers located in the area of composting plant.  
Figure 6: Temporal changes of the BOD level in piezometers located in the area of adjacent facilities.  
Figure 7: Distribution of the mean values of the chemical oxygen demand (COD) in groundwater in 1998, 2004 and 2016.  
Figures 8-10 show stabilization in the COD level after November 2000 in almost all piezometers with the exception of piezometers P-9, P-11 and P-10. In piezometer P-9, located in the area of landfill, the most visible change in COD level has been observed after 2006. It depends on the putrefraction of vegetal remnants along the railway siding. In piezometer P-11, located in the area of composting plant, significant increase of COD level was observed in 2005, 2009 and 2012. Apart from these changes, monitoring data show stabilization. There is no doubt that development of an adjacent area is the main cause of COD changes in groundwater in piezometer P-10.  
In Poland, there are no standards established for the COD level in groundwater or water intended for human consumption, but general standards for discharge of environmental pollutants mention the value of COD equal to 250 mg O\({}_{2}\)/L as the highest acceptable limit. For instance, according to the National Agency for Food and Drugs Administration and Control (NAFDAC), the recommended maximum permissible level of COD is given as 294 mg O\({}_{2}\)/L [42].
# Fracturing Fluids and Their Application in the Republic of Croatia
## 6 Hydraulic Fracturing in Croatia
### Case Study  
For this study, four wells (marked as W-1 well to W-4 well) were considered as case studies and they have been fractured in the last five years. Two of them were fractured by a lower volume of gel and proppant (the W-1 well and the W-2 well), while the other two were fractured by a higher volume of gel and proppant (the W-3 well and the W-4 well) depending on the reservoir permeability, porosity, well design and well depth. All wells produce gas with condensate. The W-1 well is a cased hole completion and is finished with a liner, while the W-2 well is an open hole completion, and gas is produced from lithotamniam limestone of Baden age in both wells. Well W-3 and well W-4 are cased hole completions and they are deeper in comparison with the previous two. Their lithology is quartzite of lower Triassic age in well W-3 and siltstone of Mesozoic age in the W-4 well. Formations opened by the W-3 well and the W-4 well have very low permeability (0.021 \(\times\) 10\({}^{-3}\) um\({}^{2}\) and 0.1 \(\times\) 10\({}^{-3}\) um\({}^{2}\), respectively) and HF is required to enable gas production. W-1, W-2 and W-3 were in production before HF, while the W-4 well was a new exploration well. The main reasons for HF were: to remove skin (near wellbore damage) and to increase the productivity index and wellhead pressure to enable gas production and to enable production with lower drawdown due to water cut decreasing. Geological and well design data for the selected wells are given in Table 14, while the locations of the wells are given in Figure 4.
_Analysis of results._ The S/N ratio is a quality indicator by which the effect produced on a particular parameter can be evaluated. The variation in the response obtained in dynamic characteristics, the S/N ratio, is shown below in the following equation: \[S/N=10\cdot log_{10}\left(\frac{\beta_{i}}{MSE_{i}}\right),\] (1) where \(\beta_{i}\) is the square of the largest value of the signal, and \(MSE_{i}\) represents the root mean square deviation in the performance of the neural network, or in other words, the mean square of the distance between the measured response and the best fit line. A valid robustness measure is related to obtaining the highest values in the S/N ratio, because the configurations of control factors that minimize the effects on noise factors can be identified. 4. _Execution and confirmation of tests in optimal conditions._ In this stage, a confirmation experiment is carried out by performing training with optimal design conditions in order to calculate the performance robustness measure and verify if this value is close to the predicted value.
# A Proposal for IoT Dynamic Routes Selection Based on Contextual Information
## 1 Introduction
This is an optimization problem, which seeks to maximize the reliability of data packet delivery, minimize the delay and increase QoS and network lifetime by using one or more of the objective functions proposed in this work. The objective functions meet a set of constraints (traditionally, constraints are expressed by algebraic equations). In this way, the objective function is used as a means of quantifying and qualifying the potential solutions of the problem in question. Then, the main contributions of this work are the following: (i) a proposal of creation of four new Objective Functions (OFs) that are used dynamically and at run time and (ii) a Fuzzy System-Based Route Classifier that enable the devices to establish routes high quality order to meet the context of the applications specific. The rest of the paper is organized as follows. Section 2 sets out the related work on the topic and Section 3 describes the proposed improvement on the RPL protocol. The performance assessment of the proposed approach is discussed in Section 4, followed by the conclusions and research directions in Section 5.
## 3 Photodetector Electrical Structures
### PIN Detectors  
PN junctions are one of the most commonly used configurations for semiconductor photodetectors. The PIN diode where \"I\" stands for intrinsic, includes an intrinsic region in between the P and N regions. Due to the built-in potential or external reverse bias, the intrinsic region is depleted and has high resistivity, so that the voltage drop takes place mainly in this region, giving rise to high electric fields for effective collection of photo-generated electron-hole pairs (EHP). In this configuration, the thickness of the intrinsic region is always many times larger than the highly-doped regions so that most of the EHP's are generated within the intrinsic region where the strong electric field helps to sweep the EHP to the adjacent p+/n+ region faster than diffusion. Another advantage of the PIN structure is that the depletion-region thickness (the intrinsic layer) can be tailored to optimize both the quantum  
Figure 2: **(a)** HR-TEM image of epitaxial Ge layer using two-step Ge growth method combining with an intermediate SiGe buffer layer. **(b)** Zoom-in image of the heterostructure epitaxial layers of Si/ Si\({}_{0.75}\)Ge\({}_{0.25}\) /Ge. From [31].  
efficiency and response bandwidth. In Ge PIN photodetectors, while the photoabsorption intrinsic layer is usually Ge for effective absorption around 1.55 um, the p\({}^{+}\) and n\({}^{+}\) region can be formed either by implantation [32] or _in-situ_ doping to form p\({}^{+}\) and n\({}^{+}\) regions for the PIN structure [14]. Another way is to use p\({}^{+}\)/n\({}^{+}\) single crystalline Si substrates or deposited polycrystalline Si heterojunctions [33].
## 4 Energy and Power Optimization Allocation
### Step-by-Step Optimization Scheme
#### 4.2.1 Optimization for Synchronous Networks  
For the single-slot synchronous ISAL network, there are two steps in the step-by-step scheme, and the power optimization allocation strategy is as follows:  
Step 1 : Radar Localization  
\[\mathfrak{A}_{5}^{\rm SDP}: \min \quad\operatorname{tr}(\mathbf{H}_{\mathrm{r}})_{\mathbf{p}_{t}}\] (54) s.t. \[\quad\mathbf{H}_{\mathrm{r}}\succeq 0 \tag{55}\] \[\left[\begin{array}{cc}\mathbf{H}_{\mathrm{r}}&\mathbf{I}\\ \mathbf{I}&\mathbf{I}_{\mathbf{e}}^{\mathrm{R}}\end{array}\right]\succeq 0\] (56) \[\sum_{j\in\mathcal{N}_{\mathrm{a}}\cup\mathcal{N}_{\mathrm{r}}}P_{ j}\leq E_{\rm step1}/1\] (57) \[0\leq P_{j}\leq P_{\rm r,max},\forall j\in\mathcal{N}_{\mathrm{r}}\] (58) \[0\leq P_{j}\leq P_{\rm a,max},\forall j\in\mathcal{N}_{\mathrm{a}}; \tag{59}\]  
Step 2: Target Sensing  
\[\mathfrak{A}_{6}^{\rm SDP}: \min \quad\operatorname{tr}(\mathbf{H}_{\mathrm{r},t})_{\mathbf{p}_{t}}\] (60) s.t. \[\quad\mathbf{H}_{\mathrm{r},t}\succeq 0 \tag{61}\] \[\left[\begin{array}{cc}\mathbf{H}_{\mathrm{r},t}&\mathbf{I}\\ \mathbf{I}&\mathbf{I}_{\boldsymbol{\theta}}^{\rm syn}\end{array}\right] \succeq 0\] (62) \[\sum_{j\in\mathcal{N}_{\mathrm{a}}\cup\mathcal{N}_{\mathrm{r}}}P_{ j}\leq E_{\rm step2}/1\] (63) \[0\leq P_{j}\leq P_{\rm r,max},\forall j\in\mathcal{N}_{\mathrm{r}}\] (64) \[0\leq P_{j}\leq P_{\rm a,max},\forall j\in\mathcal{N}_{\mathrm{a}}, \tag{65}\]  
where \(\mathbf{H}_{\mathrm{r}}\) and \(\mathbf{H}_{\mathrm{r},t}\) are the auxiliary matrices, the total energy \(E_{\rm total}=E_{\rm step1}+E_{\rm step2}\), and there is prior knowledge of \(\mathbf{I}_{\mathrm{e}}^{\mathrm{R}}\) obtained from step 1 in \(\mathbf{I}_{\boldsymbol{\theta}}^{\rm syn}\) in (62).
# Closed Form Solutions for Thermal Buckling of Functionally Graded Rectangular Thin Plates
## 5 Conclusions  
In this study, the closed form analytical solutions were obtained via the separation-of-variable method to determine the thermal buckling of FGM thin plates with simply supported, clamped and guided edges. A novel method to find the critical buckling temperature was given; the explicit expressions of the critical buckling temperature were presented for three special cases.  
Two types of FGM thin plates and uniform, linear and nonlinear temperature fields were taken into account in this work. The present results coincide well with those in the literature, which illustrates the correctness of this present method. It follows that the critical buckling temperature of FGM thin plates decreases as the length-thickness ratio and power law index increase, and increases as the  
Figure 5: The relation between the critical buckling temperature \(\Delta T_{\rm cr}\) and the length–width ratio \(a/b\) of type-I FGM, ceramic and metal rectangular thin plate (\(a/h=100\), \(n=2\), \(T_{0}=300\) K).  
Figure 6: (**a**) The relation between \(\Delta T_{\rm cr}\) and the initial temperature \(T_{0}\) of type-I FGM rectangular thin plate (\(a/h=100\), \(a/b=2\), \(n=2\)); (**b**) The relation between \(T_{\rm cr}\) and the initial temperature \(T_{0}\) of type-I FGM rectangular thin plate (\(a/h=100\), \(a/b=2\), \(n=2\)).  
length-width ratio increases, and the initial temperature affects the critical buckling temperature of temperature-dependent materials. The uniform temperature field has the greatest influence on the critical buckling temperature, and the critical buckling temperatures become very close for both linear and nonlinear temperature fields.  
The work is supported by the National Natural Science Foundation of China (11372021, 11672019).  
[PERSON] conceived the research ideas; [PERSON] derived equations, calculated and analyzed numerical results; both authors participated in the writing of this article.  
The authors declare no conflict of interest.
## 1 Introduction  
Packet level information entropy can reveal useful insights into the types of content being transported across data networks, and whether that content type is consistent with the communication channels and service types being used [1, 2]. By comparing payload entropy with baseline values, we can ascertain--for example--whether security policy is being violated (e.g., an encrypted channel is being used covertly). To the best of our knowledge, there are no published baseline information entropy values for common network services, and, therefore, no way to easily compare deviations from 'normal'. In this paper, we analyse several large packet datasets to establish baseline entropy for a broad range of network services. We also present an efficient method for recovering entropy during flow analysis on live or offline packet data, the results of which, when included as part of a broader feature subset, will considerably enhance the feature set for subsequent analysis and machine learning applications.