esa-sceva/satcom-chunk-collection huggingface dataset:
# IoT Sensor Challenges for Geothermal Energy Installations Monitoring: A Survey
## 2 Sensor Technologies
### Measurement of Mechanical Parameters
#### 2.3.1 Optical Sensors  
Fibre-optic cables are used to measure pressure in geothermal wells [48]. Distributed acoustic sensing systems use fibre-optic cables to measure oscillatory strain rates caused by oscillatory pressure changes. These cables have significant advantages in being able to withstand harsh environments; they can be deployed for geomechanical, seismic and temperature monitoring [53]. Fibre-optic cables are capable of operating at temperatures up to 315\({}^{\circ}\)C and 20 MPa [51].
## 4 Findings and Results
### Comparative Analysis of Existing and Proposed STS Framework  
The sociotechnical systems (STS) framework offers a comprehensive and holistic approach to investigating and exploring cybersecurity challenges, particularly in the context of the current study, as it requires integration across multiple dimensions, such as political, technical, social, economic, and environmental subsystems. Table 7 highlights the significance of the STS cybersecurity framework when compared with other existing frameworks from the literature. The proposed STS framework integrates risk considerations within its technical subsystem but goes further to explore how risks emerge from and influence social behaviors, political decisions, and economic pressures. Moreover, the framework's inclusion of environmental factors allows for the evaluation of external shocks (e.g., crises, natural disasters, climate change) that conventional risk frameworks often neglect. \begin{table}
\begin{tabular}{p{14.2 pt} p{142.3 pt} p{142.3 pt}} \hline \hline  & **STS Framework by Challenger and Challenger (2010)** & **[PERSON] (2007)** & **Proposed STS Cybersecurity Framework in Current Research** \\ \hline
1 & The framework proposed involves the elements from the social and technical subsystem. & The study extends and includes elements from five organizational subsystems: social, technical, political, economic, and environment. \\ \hline
2 & The framework focuses on disaster and fire events. & The study has classified cybersecurity challenges that emerged amidst major crises considering HERS exclusively. \\ \hline
3 & The framework is based on three crowd-related disasters: the Hillsborough football stadium disaster (1989), the King’s Cross underground fire (1987), and the Bradford City stadium fire (1985). & The proposed framework classifies security issues considering the recent major crisis that is COVID-19 pandemic. \\ \hline
4 & The study itself recommends using the STS framework in other areas, including security, and resilience. & The proposed framework has empirically explored and classified cybersecurity challenges using the STS dimensions. \\ \hline
5 & The framework is general for achieving joint optimization in organizations by interrelating only social and technical elements. & The framework explores emerging cybersecurity challenges by studying social, technical, political, economic, and environmental interrelations. \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Identified gaps between the current and proposed framework.
## 3 Results  
### Spatiotemporal Dynamic Variation Pattern in LUEmax  
#### 3.1.1 Temporal Dynamics of LUEmax for the Same Vegetation in Different Climate Types  
The LUEmax values for the same vegetation type exhibited significant spatiotemporal heterogeneity in different climate zones (Figure 3). Over the study period, the annual average LUEmax values were 0.084 \(\pm\) 0.025 \(\upmu\)mol CO\({}_{2}\)/\(\upmu\)mol PPFD for ENF, 0.078 \(\pm\) 0.05 \(\upmu\)mol CO\({}_{2}\)/\(\upmu\)mol PPFD for DBF, 0.08 \(\pm\) 0.05 \(\upmu\)mol CO\({}_{2}\)/\(\upmu\)mol PPFD for GRA, 0.06 \(\pm\) 0.04 \(\upmu\)mol CO\({}_{2}\)/\(\upmu\)mol PPFD for WET, and 0.05 \(\pm\) 0.03 \(\upmu\)mol CO\({}_{2}\)/\(\upmu\)mol PPFD for CORN. The LUEmax values of ENF showed notable variations across temperate, continental, and polar and alpine climate types, with lower average values for polar and alpine types but higher fluctuations and peaks. DBF displayed higher LUEmax values with pronounced peaks in tropical areas, while temperate and continental aeras exhibited more stable, moderate values. Significant interannual variability was observed for GRA, with polar and alpine climates exhibiting the highest peaks and greatest variability. CORN showed consistently lower LUEmax values, with slightly higher peaks in continental compared to temperate climate types. WET exhibited relatively stable and low LUEmax values across temperate, continental, and polar and alpine climates, with polar and alpine zones showing slightly more significant variability and peaks.  
This may be due to photosynthesis in ENF being limited by low temperatures and short growing seasons in polar and alpine regions, resulting in a low average LUEmax. DBF undergo intense photosynthesis in tropical areas due to the abundant light and heat, leading to high LUEmax values with peaks. The stable climate conditions in temperate and continental regions result in moderate LUEmax values. GRA are sensitive to the environment and show significant peaks and inter-annual variations in polar and alpine climates. CORN have little photosynthetic potential and poor adaptability, leading to a low overall LUEmax value. WET prefer a mild and stable environment, and their LUEmax values were low and stable across various climates, with slightly greater variability and peaks in polar and alpine regions.  
These results highlight that the LUEmax values for the same vegetation type vary for different climate types, reflecting the influence of climatic conditions on photosynthetic efficiency. Generally, wetter or more variable climates (e.g., polar and alpine climates) are associated with greater fluctuations and peaks in the LUEmax. Conversely, more stable climates (e.g., temperate climates) tend to show lower and more consistent LUEmax values. These patterns underscore the importance of considering climatic variability when understanding ecosystem productivity and carbon cycling.  
Figure 3: Dynamic patterns in LUEmax values for the same vegetation type across different climatic conditions.
## 3 Results and Discussion
Further processing of the distillation straws by collecting and extracting only flower tissues (FWE) resulted in a different chromatographic profile showing an even proportion of some of the compounds, mostly flavonoids, obtained in the TWE (Figure 3B,C). Among the identified flavonoids, the aglycones luteolin and apigenin were already reported in flower distilled straws from _L. angustifolia_[1]. The presence of these two compounds agreed with the observed accumulation of flavone 7-O-glycos derived from luteolin and apigenin, as a typical biochemical trait of _L. angustifolia_[49]. The ability of PUAE to extract a wide range of phenolic and flavonoid compounds that were just partially released from the plant material during the essential oil distillation process confirmed that distilled straws constitute a rich source of secondary metabolites. These compounds can be used, e.g., as flavour or as antioxidants [50], or even as a potential synergistic of insecticides [51]. Since phenolic compounds and flavonoids show different biological activities depending on their structure [52], observed differences in the relative abundances of these compounds in the studied extracts likely resulted in overall differences in extract properties. By comparing the proportional abundance of the most abundant compound in each type of extract, it was possible to clearly characterize these hypothesized differences. As mentioned before, TWE extracts were distinguished by the abundance of gentisic acid (43.84% of the total concentration), while the most abundant compounds in LEACH and FWE samples were rutin (21.33%) and syringic acid (11.09%), respectively. Maximum percentage abundances are equivalent to the Berger-Parker index of dominance [53]. Therefore, an increase of the reciprocal form of this index, in this case 2.28 for TWE, 4.69 for LEACH and 9.02 for FWE indicated an increase in chemical diversity associated with a reduction in compound dominance [54]. These results reinforced the view that the two extracts from distilled straws (TW and FW) were significantly different between them (Figure 3B,C), pointing to the potential importance of post-processing of distillation by-products.
## 3 Results and Discussion
### Evolution of Frozen Soil Distribution in the Study Area  
#### 3.3.1 Forward Modeling  
Forward modeling was conducted using four electrode configurations, with the resulting resistivity profiles presented in Figure 9. The profiles reveal that all configurations effectively delineate the resistivity transition zone between the surface thawed layer and the freezing transition layer, indicating that impedance contrasts due to ice-soil structural changes at this interface are readily identifiable. For the upper boundary between the freezing transition layer and the deeper permafrost layer, each profile exhibits high-resistivity anomalies closely matching the model-defined depths, demonstrating the forward-inversion process's strong resolution capability for this interface. However, at the lower boundary of the permafrost layer, all configurations show a downward extension of low-resistivity zones slightly beyond the actual model boundaries. This deviation primarily results from the least-squares inversion process overfitting the shallow high-resistivity features, leading to a \"stretching\" effect in the deeper low-resistivity regions.  
Figure 9: Forward modeling resistivity profiles obtained using different resistivity configurations: (**a**) Wenner-\(\alpha\) array, (**b**) Wenner-\(\beta\) array, (**c**) Wenner-\(\gamma\) array, (**d**) Schlumberger array.  
Comparative analysis of the anomaly responses from the four configurations indicates that the Wenner-\(\alpha\) and Schlumberger arrays produce profiles with the most concentrated and continuous high-resistivity anomalies in the permafrost layer, with anomaly amplitudes closely aligning with theoretical model values. Notably, the Schlumberger array achieves the highest congruence between the resistivity gradient zones and the actual interface positions, with inversion bandwidth errors less than 5% relative to model thicknesses. In contrast, while the Wenner-\(\beta\) and Wenner-\(\gamma\) arrays can identify the primary interfaces, they exhibit slightly lower anomaly amplitudes and broader transition zones, suggesting comparatively reduced resolution for the permafrost layer [56]. Consequently, the Wenner-\(\alpha\) array offers robust anti-interference and stability in field environments, whereas the Schlumberger array provides higher interface localization accuracy under similar conditions. Considering the challenging geological conditions of the study area, the Wenner-\(\alpha\) array is recommended for field electrical resistivity surveys of frozen soil layers [57].
# Predicting the Aggregate Mobility of a Vehicle Fleet within a City Graph
## 4 A Markov Chain Model
### Markov Chains and Transition Matrices  
Let \(\{X_{t}\}_{t=0,1,\ldots,T}\) be a Markov Chain (MC) [9; 16], which represents a discrete-time stochastic process with finite state space \(X_{t}\in S=\{s_{1},\ldots,s_{N}\}\), whose associated probability distribution satisfies the _Markov property_:  
\[P(X_{t+1}\text{=}s_{j}\mid X_{t}\text{=}s_{i},X_{t-1}\text{=}s_{k},\ldots,X_{0 }\text{=}s_{l})=P(X_{t+1}\text{=}s_{j}\mid X_{t}\text{=}s_{i}). \tag{4}\]  
Let \(\Pi\) be the Markov chain transition matrix so that given two states \(s_{i},s_{j}\), the term \((j,i)\) of such matrix is denoted as \(\Pi_{ji}=\Pi(s_{j}\gets s_{i})=P(X_{t+1}\text{=}s_{j}\mid X_{t}\text{=}s _{i})\) and represents the transition probability from state \(i\) to state \(j\). Defined this way, the columns of \(\Pi\) are stochastic vectors so that \(\forall i=1,\ldots,N\) we have \(\Pi_{.i}=[\Pi_{1i},\ldots,\Pi_{Ni}]\) satisfies \(\Pi_{ji}\geq 0,\forall j\in\{1,\ldots,N\}\) and \(\sum_{j=1}^{N}\Pi_{ji}=1\).  
For each \(t\), let us denote \(P_{t}\) as the (discrete) probability distribution corresponding to \(X_{t}\). \(P_{t}\) is defined as a stochastic vector \(P_{t}=[p_{t}^{(1)},\ldots,p_{t}^{(N)}]\) with \(p_{t}^{(i)}\geq 0,\forall i\in\{1,\ldots,N\}\) and \(\sum_{i=1}^{N}p_{t}^{(i)}=1\). Note that \(P_{0}\) corresponds to the distribution of the initial condition or initial state \(X_{0}\); in case such an initial state is known to take a deterministic value \(X_{0}=s_{j}\), for some \(j\in\{1,\ldots,N\}\), then we have that \(P_{0}=\delta_{j}=[\delta_{1,j},\ldots,\delta_{j,j},\ldots,\delta_{N,j}]\) meaning that \(p_{0}^{(j)}=1\) and \(p_{0}^{(i)}=0,\;\forall i\
eq j\).  
According to (4), the distributions \(P_{t},\;t=0,\ldots,T-1\) for \(T\in\mathbb{N},\;T>1\) satisfy the condition  
\[P_{t+1}=\Pi\cdot P_{t},\;t=0,\ldots,T-1. \tag{5}\]  
Note that (5) defines a dynamical system that--starting from \(P_{0}\)--allows to recursively compute for each time, \(t\), the probability distribution, \(P_{t}\), corresponding to \(X_{t}\).  
According to (4), \(\{X_{t}\},t\in\{0,\ldots,T\}\) are not independent variables, but their joint distribution can be characterized stepwise making use of the corresponding conditional distributions. Hence, we can take a sample of the Markov chain by relying on such conditional distributions, which are completely defined with \(P_{0}\) and matrix \(\Pi\).
## 3 Dual-Fusion Active Contour Model
### Saliency Image Fitting Energy  
This paper uses the pyramid feature attention network [28] to acquire the saliency images. However, due to the low contrast of underwater images, there were some errors in the saliency detection results, such as a local inhomogeneous intensity, background noise, and missing contour information. In view of the local inhomogeneous intensity of the saliency images, we preliminarily employ the local binary fitting to construct the energy functional \(E_{\text{sal}}\):  
\[E_{\text{sal}}\ (C,f_{1}(x),f_{2}(x))=\lambda_{1}\int_{\text{in}(C)}(S-f_{1}(x ))^{2}dx+\lambda_{2}\int_{\text{out}\ (C)}(S-f_{2}(x))^{2}dx, \tag{9}\]  
where \(S\) is the saliency images, \(C\) is a contour in the image domain \(\Omega\), and \(f_{1}\) and \(f_{2}\) are the image local fitting intensities near the point \(x\). The local fitting intensities \(f_{1}\), \(f_{2}\) can be expressed as follows [29; 30]:  
\[f_{1}(x)=\frac{K_{\sigma}(x)\cdot[H_{\varepsilon}(\phi(x))S]}{K_{\sigma}(x) \cdot H_{\varepsilon}(\phi(x))} \tag{10}\]\[f_{2}(x)=\frac{K_{\sigma}(x)\cdot\left[(1-H_{\varepsilon}(\phi(x)))S\right]}{K_{ \sigma}(x)\cdot\left[1-H_{\varepsilon}(\phi(x))\right]} \tag{11}\]  
where \(K_{\sigma}(x)\) is the Gaussian kernel, \(S\) is the saliency images, and \(H_{\varepsilon}\) is the Heaviside function \(H(\cdot)\) can be expressed as:  
\[H_{\varepsilon}(x)=\frac{1}{2}\bigg{[}1+\frac{2}{\pi}\text{arctan}\Big{(} \frac{x}{\varepsilon}\Big{)}\bigg{]}, \tag{12}\]  
However, the local binary fitting may introduce some local minimums and is sensitive to noise. Affected by the accuracy of saliency detection, the saliency map of underwater images will inevitably have background noise. Moreover, the initialization curve greatly affects the segmentation results. To solve the aforesaid problems, we introduce the global fitting term from the CV model into the energy functional \(E_{\text{sal}}\). The local-global fitting intensities can be expressed as follows:  
\[\left\{\begin{array}{l}I_{1}=\omega c_{1}+(1-\omega)f_{1}\\ I_{2}=\omega c_{2}+(1-\omega)f_{2}\end{array}\right. \tag{13}\]  
where \(I_{1}\) and \(I_{2}\) are the mixed intensity, \(c_{1}\) and \(c_{2}\) are two constants derived from Equation (3), and \(\omega\) is a weight coefficient (\(0\leq\omega\leq 1\)). According to the test images in this paper, the value of \(\omega\) can be taken from 0.5 to 0.9. Moreover, the more inhomogeneous the image intensity, the smaller the value of \(\omega\).  
With the level set representation, the energy functional can be expressed as follows:  
\[E_{\text{sal}}\left(\phi,I_{1}(x),I_{2}(x)\right)=\lambda_{1}\int_{\Omega}(S- I_{1}(x))^{2}H(\phi(x))dx+\lambda_{2}\int_{\Omega}(S-I_{2}(x))^{2}(1-H(\phi(x)))dx \tag{14}\]  
The improved fitting energy \(E_{\text{sal}}\) not only takes the local intensity information into account but also avoids the local minimization. Therefore, for the saliency images of underwater images, the improved energy functional can extract the contour of the inhomogeneous images more accurately.
## 5 Experiments and Results
### People Perception  
The People Perception method presented in Section 3.2 was implemented on our Mbot robot, and the People perception FBM was used to test and evaluate the method. As mentioned before, in the People Perception FBM, the benchmarking system automatically records the true and estimated position of every person, computes the position error and checks to see if the robot has correctly recognized the person. An average position error and a percentage of correct identifications is computed at the end of a trial. Table 1 shows results from a single benchmark trial. Figure 14 illustrates the perception results from six different trials of this FBM.
## 4 Simulation Results and Analysis
### System Performance Comparison
In conclusion, the channel estimation algorithm proposed in this paper can guarantee good performance in different scenarios and greatly reduce the computational complexity, which proves the superiority of the proposed algorithm.
## 4 Results and Discussion
### Splitting Tensile Strength  
Table 8 and Figure 16 show the influence of fibers on the splitting tensile strength of geopolymer concrete. In the PFRGC, alike compressive strengths, there are two effects of splitting tensile strength. From 0% to 0.5% of the volume fraction of fiber, the splitting tensile strength increased gradually from 0.5% to 1.5%, this strength has a tendency of decrement. At the volume fraction 0.5%, PFRGC specimens had the highest splitting tensile strength than GC specimens without fiber. Compared to the splitting tensile strength (3.70 MPa) of GC specimens, the strength of specimens using polypropylene fiber P1-05 and P2-05 increased to 11.9% and 10%, along with 4.20 MPa and 4.11 MPa respectively.  
In terms of specimens using steel fiber HFRGC, the splitting tensile strength increase with the increment of volume fraction except at 1.5%. At 0.5% and 1% of volume fraction, compared to the GC  
Figure 16: Splitting tensile strength.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Volume Fraction of Fiber (\%)**} & \multicolumn{4}{c}{**Splitting Tensile Strength (MPa)**} \\ \cline{2-5}  & **P1** & **P2** & **H1** & **H2** \\ \hline
0.0 & 3.70 & 3.70 & 3.70 & 3.70 \\
0.5 & 4.20 & 4.11 & 4.29 & 4.18 \\
1.0 & 4.11 & 3.87 & 4.98 & 5.15 \\
1.5 & 3.95 & 3.88 & 4.25 & 4.58 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Splitting tensile strength.  
Figure 15: Poisson’s ratio.  
specimens without fiber, the strength increases strongly from 11.48% to 28.16%, correspondingly to 4.18 MPa to 5.15 MPa. The highest values were achieved by adding 1.0% hooked end steel fiber to the mixture, with a value of 5.15 MPa of the H2-10 specimen series.  
The highest splitting tensile strength of PFRGC and HFRGC were 5.15 MPa and 4.20 MPa with disparity was 18.45%. The difference between the strength of PFRGC and HFRGC was small when adding 0.5% fiber into the geopolymer concrete mixture. However, at volume fractions 1% and 1.5%, the strength of specimens using hooked end steel fiber was much higher than specimens using polypropylene fiber.  
Australia Standard AS 3600 [29] revealed Equation (4) for evaluating the compressive strength and splitting tensile strength relationship of ordinary Portland cement concrete, where fct is splitting tensile strength and fcm is compressive strength:  
\[\mathrm{f_{ct}}=0.4\sqrt{\mathrm{f_{cm}}}\ (\mathrm{MPa}) \tag{4}\]  
For geopolymer concrete, [PERSON] recommended Equation (5) to determine the splitting tensile strength:  
\[\mathrm{f_{ct}}=0.7\sqrt{\mathrm{f_{cm}}}\ (\mathrm{MPa}) \tag{5}\]  
Figure 17 shows compressive strength and splitting tensile strength relationship by using Equations (4) and (5). For fly ash fiber-reinforced geopolymer concrete, the splitting tensile strength had a greater value than those of ordinary concrete. Compared with non-fiber geopolymer concrete, this value was also small--approximately under 10%. Therefore, compressive strength and splitting tensile strength could be evaluated by Equation (5).
# DRLLA: Deep Reinforcement Learning for Link Adaptation
## 3 Background
### Link Adaptation  
LA aims to find the optimal modulation and coding scheme (MCS) for a given channel quality. Usually, each communication standard has its own MCS table, containing different combinations of signal modulations and signal codings. They are sorted in an ascending order based on their theoretical data rate and can therefore be referred to by their MCS index.  
In traditional communication systems, the channel quality indicator (CQI) is used as a metric for choosing an appropriate MCS. The user equipment (UE) estimates a CQI value based on the signal-to-interference-plus-noise-ratio (SINR) of the downlink signal [8]. After communicating the CQI to the base station, an MCS can be chosen and used for the following wireless data transmission. To enhance this typical LA approach, we propose using reinforcement learning.