esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Polymer Types on the Mechanical Properties of Polymer-Modified Cement Mortars
## 4 Test Results
### Effect of Polymer Type on Flexural Strength  
Table 6 and Figure 3 illustrate the averaged flexural strengths for both PCMs and the control specimen. PCM samples ranked EVA, PVA, SBR, and AC in flexural strength from high to low. The PCM containing EVA exhibited the highest strength, and the value was 63% higher than that of the control mortar. The flexural strength of the PCM containing SBR was 21% higher than that of the PCM with AC. [PERSON] et al. [28] pointed out that the effects of the polymer incorporated in the PCM can be more clearly expressed in terms of flexural strength rather than the compressive strength. The effect of the polymer investigated in this study was clear in the measured flexural strength test. Specifically, 28-day flexural strength values of the PCMs containing EVA, PVA, SBR, and AC were 63%, 46%, 41%, and 16% higher, respectively, than that of the control specimen. [PERSON] et al. [31] recommended that the repair material should have 10% higher flexural strength compared to that of control mortar. Thus, it can be concluded that all the PCMs adopted in this study satisfy the recommendation. The increase in flexural strength between seven and 28 days varied greatly depending on the polymer type. The rates of strength increase of the PCMs ranged between 25% and 41%, while the control had only a 10% increase between seven and 28 days. In more detail, the PCMs containing EVA and SBR showed the same 41% increase in measured flexural strength. In conclusion, the PCM sample produced with EVA exhibited the best performance in flexural strength.
## 2 Preliminaries
### HR+SN and Max-HR+SN
The next proposition describes the interaction between the preference lists in \(I\) and the edges in \(G\). **Proposition 4**. : _Let \((I,G)\) be an HR instance. Suppose two workers \(w_{1}\) and \(w_{2}\) do not have a firm in common in their preference list or, equivalently, there is no firm that has \(w_{1}\) and \(w_{2}\) in its preference list. Let \(e=\{w_{1},w_{2}\}\). Then \((I,G-e)\) and \((I,G+e)\) have the same set of locally stable matchings as \((I,G)\)._  
Proof. : Without loss of generality, assume \(e\) is an edge of \(G\). It is easy to verify that when \(G^{\prime}\) is a subgraph of \(G\), every locally stable matching of \((I,G)\) is also a locally stable matching of \((I,G^{\prime})\). Thus, to prove the proposition, we simply have to show that every locally stable matching of \((I,G-e)\) is also a locally stable matching of \((I,G)\). Suppose \(\mu\) is a locally stable matching of \((I,G-e)\) but has a local blocking pair \((f,w)\) in \((I,G)\). Let \(f\) and \(w\)'s point of contact be \(w^{\prime}\). Hence, both \(w\) and \(w^{\prime}\) have \(f\) in their preference lists; that is, \(\{w,w^{\prime}\}\
eq\{w_{1},w_{2}\}\). Thus, the edge \(\{w,w^{\prime}\}\) is in \(G-e\) so that \((f,w)\) is also a local blocking pair of \(\mu\) in \((I,G-e)\), a contradiction. It follows that \(\mu\) has no blocking pairs in \((I,G)\). We have shown that \(\mu\) is a locally stable matching of \((I,G)\) if and only if it is also a locally stable matching of \((I,G-e)\). The same argument holds for the locally stable matchings of \((I,G)\) and \((I,G+e)\) when \(e\) is not an edge of \(G\). In Section 4, we shall consider max-HR+SN. Given an HR+SN instance \((I,G)\), let \(A\) be an algorithm that outputs a locally stable matching of \((I,G)\), which we denote as \(A(I,G)\). Then \(A\) is an \(f(N)\)_-approximation algorithm of max-HR+SN_ if for all instances \((I,G)\) of size \(N\), \(|\mu^{*}(I,G)|/|A(I,G)|\leq f(N)\) where \(\mu^{*}(I,G)\) is a maximum locally stable matching of \((I,G)\). Thus, according to Proposition 3, the Gale-Shapley algorithm is a \(2\)-approximation algorithm of max-HR+SN. The problem max-HR+SN is _NP-hard to approximate within \(f(N)\)_ if the existence of an efficient \(f(N)\)-approximation algorithm implies P = NP.
# State of the Art and Trends Review of _Smart Metering_ in Electricity Grids
## 2 Trends of the _Smart Metering_ Systems
### Architecture of a Smart Metering System
#### 2.1.3 Communication System  
The transmission of data must be guaranteed in terms of quality, time, and security. Therefore, the communication technologies play a key role, as they have to be cost-efficient and should provide  
\begin{table}
\begin{tabular}{l} \hline \hline
**2012/148/EU Recommendation** \\ \hline \hline
**Consumer** \\ Provide reading directly to the consumer and/or any third party. \\ Update the readings frequently enough to use energy saving schemes. \\ \hline
**Metering Service Operator** \\ Allow remote reading by the operator. \\ Provide bidirectional communication for maintenance and control. \\ Allow frequent enough readings to be used for networking planning. \\ \hline
**Connerential Service Issues** \\ Support advanced tariff system. \\ Allow remote ON/OFF control supply and/or flow or power limitation. \\ \hline
**Security and Data Protection** \\ Provide secure data communications. \\ Fraud prevention and detection. \\ \hline
**Distributed Generation** \\ Provide consumed, generated, and reactive metering data. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Minimum requirements for electricity _SM_s according to 2012/148/EU recommendations.  
good coverage, security features, bandwidth, and power quality with the least possible number of repetitions [10]. Section 5 addresses the main challenges that the _Smart Metering_ communication system must face. Communications in power grids have evolved from one-way communication systems and radial topology to bidirectional systems with network topology. A wide review of communication features, requirements, and technologies for _SG_ and _Smart Metering_ communication networks can be consulted in [11].
# Feasible Trajectories Generation for Autonomous Driving Vehicles
## 6 Symmetric Polynomial Trajectory Generation  
This part presents a new method of symmetric trajectory generation for smoother, safer, and easier to control for autonomous vehicle tracking on its trajectory. This method is based on the optimal motion planning in [23]. The vehicle moving from the start point to the destination point with the travelling time \(t=0\div T\) can be now reformulated based on a symmetric polynomial function:  
\[x(t)=-(\frac{t}{T}-1)^{3}x_{0}+\left(\frac{t}{T}\right)^{3}x_{T}+a_{x}\bigg{(} \frac{t}{T}\bigg{)}^{2}(\frac{t}{T}-1)+b_{x}\frac{t}{T}(\frac{t}{T}-1)^{2} \tag{42}\]  
and the position of \(y\) is:  
\[y(t)=-(\frac{t}{T}-1)^{3}y_{0}+\bigg{(}\frac{t}{T}\bigg{)}^{3}x_{T}+a_{y} \bigg{(}\frac{t}{T}\bigg{)}^{2}(\frac{t}{T}-1)+b_{y}\frac{t}{T}(\frac{t}{T}-1) ^{2} \tag{43}\]  
The velocity of \(x(t)\):  
\[\dot{x}(t)=-3(\frac{t}{T}-1)^{2}x_{0}+3\bigg{(}\frac{t}{T}\bigg{)}^{2}x_{T}+a_ {x}2\frac{t}{T}(\frac{t}{T}-1)+a_{x}\bigg{(}\frac{t}{T}\bigg{)}^{2}+b_{x}( \frac{t}{T}-1)^{2}+b_{x}2\frac{t}{T}(\frac{t}{T}-1) \tag{44}\]  
Figure 9: Steering and the body angle. and the velocity of \(y(t)\):  
\[\dot{y}(t)=-3(\frac{t}{T}-1)^{2}y_{0}+3\bigg{(}\frac{t}{T}\bigg{)}^{2}y_{T}+a_{y} 2\frac{t}{T}(\frac{t}{T}-1)+a_{y}\bigg{(}\frac{t}{T}\bigg{)}^{2}+b_{y}(\frac{t} {T}-1)^{2}+b_{y}2\frac{t}{T}(\frac{t}{T}-1) \tag{45}\]  
Then, the acceleration of \(x(t)\)  
\[\ddot{x}(t)=-6(\frac{t}{T}-1)x_{0}+6\frac{t}{T}x_{T}+a_{x}2(2\frac{t}{T}-1)+a_ {x}2\frac{t}{T}+b_{x}2(\frac{t}{T}-1)+b_{x}2(2\frac{t}{T}-1) \tag{46}\]  
and the acceleration of \(y(t)\)  
\[\ddot{y}(t)=-6(\frac{t}{T}-1)y_{0}+6\frac{t}{T}y_{T}+a_{y}2(2\frac{t}{T}-1)+a_ {y}2\frac{t}{T}+b_{y}2(\frac{t}{T}-1)+b_{y}2(2\frac{t}{T}-1) \tag{47}\]  
The vehicle velocity:  
\[rv_{1}=\frac{\dot{x}}{cos\theta}=\frac{\dot{y}}{sin\theta} \tag{48}\]  
At the starting point \(t=0\):  
\[\dot{x}(0)=k_{0}cos\theta_{0},\text{ and }\dot{y}(0)=k_{0}sin\theta_{0} \tag{49}\]  
At the destination point \(t=T\):  
\[\dot{x}(T)=k_{T}cos\theta_{T},\text{ and }\dot{y}(T)=k_{T}sin\theta_{T} \tag{50}\]  
From Equations (49) and (50), for simplicity, we assumed that the coefficients of velocity at the start and destination points, \(k_{0}=k_{T}=k\), then  
\[y(t)=-(\frac{t}{T}-1)^{3}y_{0}+\bigg{(}\frac{t}{T}\bigg{)}^{3}x_{T}+a_{y} \bigg{(}\frac{t}{T}\bigg{)}^{2}(\frac{t}{T}-1)+b_{y}\frac{t}{T}(\frac{t}{T}-1) ^{2} \tag{43}\]0  
and similarly  
\[y(t)=-(\frac{t}{T}-1)^{3}y_{0}+\bigg{(}\frac{t}{T}\bigg{)}^{3}x_{T}+a_{y} \bigg{(}\frac{t}{T}\bigg{)}^{2}(\frac{t}{T}-1)+b_{y}\frac{t}{T}(\frac{t}{T}-1) ^{2} \tag{43}\]1  
Simulation of this method is conducted with the same vehicle parameters in previous examples and shown in Figures 10 and 11. The velocity coefficients are set at \(k_{0}=k_{T}=k=1\).
## 2 Materials and Methods
### Measurement of Nutrients and Phytoplankton Community  
Subsamples (10 mL) were taken from the incubation container six times between the beginning and the end of the incubation period to measure dissolved inorganic nutrients by the method described earlier. Phytoplankton in the subsamples (10 mL) were filtered through a 20 \(\upmu\)m Nytex(r) mesh and classified into two size classes: net (\(>\)20 \(\upmu\)m) and nano (\(<\)20 \(\upmu\)m). To measure the chlorophyll \(a\) (chl _a_) concentration, 10 mL of whole water and 10 mL of 20 \(\upmu\)m filtrate were filtered through [PERSON](r) 25 mm GF/F glass microfiber filters (with a 0.7 \(\upmu\)m nominal pore size). The filters were placed in dark test tubes prefilled with 8 mL extraction solution (90% acetone and 10% distilled water). After storage for 12 h at 4 \({}^{\circ}\)C, chl \(a\) concentration was measured using a Turner Designs(r) 10 AU fluorometer. The concentration of chl \(a\) in each size fraction was estimated by subtracting the \(<\)20 \(\upmu\)m fractions from the chl \(a\) concentration of the entire water sample. It was assumed that humic acids were not retained in the filters to interfere with the chlorophyll \(a\) measurement.  
To identify phytoplankton species, subsamples (10 mL) were collected and fixed with [PERSON]'s solution (final I2 concentration: 250 \(\upmu\)g mL\({}^{-1}\)). After mixing, 1 mL of the sample was dropped into a Sedgewick-Rafter counting chamber (50 \(\times\) 20 \(\times\) 1 mm), and phytoplankton species were identified and counted using a ZEIS(r) Axioskop 2 MAT microscope. Phytoplankton species were categorized into five distinct taxonomic groups: Bacillariophyta (diatoms), Cyanobacteria (blue-green algae), Cryptisa/Cryptophyceae (cryptophytes), Chlorophyta (green algae), and others. Ambient concentrations of dissolved inorganic nutrients and phytoplankton communities in situ at the sampling stations were analyzed using the methods described by [PERSON] et al. [15].
# The Role of Environmental Assumptions in Shaping Requirements Technical Debt
## 3 Methodology
### Analytical Procedure  
Building on the case context described earlier, we conducted a structured, theory-informed analysis of the requirements and their environmental assumptions. The steps below outline how we examined the potential for assumption-driven RTD:  
1. Document Review: A thorough examination of the DroneResponse case study presented by [PERSON] et al. [34] was conducted, with particular attention to how environmental assumptions were linked to derived safety requirements.
2. Scenario Manipulation: For each requirement-assumption pair, hypothetical scenarios were explored in which assumptions were omitted, altered, or allowed to evolve. These variations reflect plausible conditions such as increased communication latency, sensor inaccuracies, or changes in wind dynamics.
3. RTD Impact Evaluation: Using well-established concepts from the RTD literature [4, 6, 7], the potential consequences of each assumption breakdown were analyzed. The evaluation focused on how these assumption failures could introduce issues into the requirement specifications, such as ambiguity, misalignment, or degradation over time.
4. Interpretive Analysis: The analysis leveraged domain knowledge in cyber-physical systems and requirements engineering to interpret how assumption failures contribute to RTD formation.  
This methodology supports a structured investigation into the influence of environmental assumptions on RTD formation. By grounding the analysis in an existing case study and synthesizing theoretical and practical knowledge, this study offers a foundation for deeper empirical and automated techniques in future work.
## 4 Pilot Protection Based on Jensen-Shannon Distance  
### Protection Principle  
The grid-forming PV system shown in Figure 2 serves as the analytical model, where the PV plant connects to the M-terminal of the 220 kV transmission line, interfacing with the main grid through the line MN. Five fault points (F\({}_{1}\)-F\({}_{5}\)) are established along the line: F\({}_{1}\) and F\({}_{5}\) represent external faults, while F\({}_{2}\), F\({}_{3}\), and F\({}_{4}\) are internal faults.  
With the positive current direction conventionally defined from bus to line, external faults produce currents \(I_{\text{M}}\), \(I_{\text{N}}\) measured at both ends that satisfy \(I_{\text{M}}=-I_{\text{N}}\). This indicates near-identical waveforms between \(I_{\text{M}}\) and \(-I_{\text{N}}\). Conversely, after the internal fault occurs, the PV-side fault current \(I_{\text{M}}\) exhibits frequency and phase distortion, leading to the deviation from power-frequency sinusoidal characteristics. Furthermore, since virtual impedance control restricts the fault current magnitude at the PV side, the PV-side current \(I_{\text{M}}\) is substantially below the grid-side current \(I_{\text{N}}\). Consequently, waveforms at both ends exhibit markedly distinct features, which enable reliable external fault discrimination without requiring strict phase synchronization.
## 3 Prediction Hybrid Method
### Hybrid Method Testing  
In the hybrid method, the decision tree model was used as _AI Method 1_, and the artificial neural networks as _AI Method 2_. The hybrid method was tested on data obtained from monthly reviews at Level 2 technical reviews. About 900 training cases were used for this purpose, 80% of which were used for the model training process, while 20% were used for the simulation process. Only the input data were given then, and the results were compared with the actual values obtained during technical inspections. The _AI Method 1_ uses the decision tree method, which consists of decision nodes and leaf nodes. Each decision node represents the optimal splitting decision based on xi values. The decision tree is constructed by recursively splitting the root nodes to the left and right until the maximum depth is reached. In each prediction step, the value of one predictor (variable \(\times 1\) to \(\times 10\)) is checked, and the Gini diversity index is calculated. In the proposed model, the maximum number of splits was 100, the Gini diversity index was used as the splitting criterion, and there were no surrogate decision splits. The Gini diversity index calculates the probability of a specific feature being misclassified when randomly selected. If all elements are assigned to a single class, it is considered pure. The Gini diversity index  
Figure 11: Stage VI, recommended maintenance during review at level 1.  
ranges from 0 to 1, where 0 indicates classification purity, meaning all elements belong to a specified class or only one class exists. Additionally, a value of 1 indicates a random distribution of elements across different classes. This model was presented and tested on the predictive model described in the article by [38]. The _AI Method 2_ uses the artificial neural network method. Selected predictive models have been presented and tested in the article by [39]. Table 4 presents a summary of results of selected ANN predictive models. As a loss function, a MSE function was used as follows:  
\[loss=\frac{1}{2N}\sum_{i=1}^{M}(X_{i}-T_{i})^{2} \tag{10}\]  
where \(\rm X_{i}\) is the network prediction, \(\rm T_{i}\) is the target value, \(\rm M\) is the total number of responses in \(\rm X\) (across all observations), and \(\rm N\) is the total number of observations in \(\rm X\).
# Evaluation of Land Subsidence during Groundwater Recovery
## 2 Geological Conditions in Bangkok
### Groundwater Level Situation  
The characteristics of the groundwater level in the Bangkok area depend on the groundwater pumping. Since 1997, the groundwater level in the Bangkok area has tended to recover to the ground surface level after groundwater pumping started to be controlled. Figure 1 shows the changes in the groundwater level and land subsidence during the past and future (predicted) years of groundwater consumption [11]. The graph shows an increase in the groundwater level since 1997, and a trend for the level reaching the ground surface in the future. In contrast, land subsidence is still occurring but has gradually become stable since 2008.  
At present, the DGR measures the groundwater level using piezometers. Table 1 shows that the groundwater level recovery rate is increasing in each province. All of the provinces located along the Chao Phraya River basin have a groundwater level recovery rate of about 0.57 to 2.74 m/year.  
Figure 1: Changes in groundwater level and land subsidence due to groundwater pumping.
# Detection of Cracks in Arch Dams Using Monitoring Data and Numerical Models
## 1 Introduction  
### Dam Safety  
During the early 20 th century, particularly, a significant development of hydropower began in many developed countries, leading to the construction of numerous dams. For the most part, these dams remain in operation, and, although aged, they are expected to remain operational for many years. Looking specifically at high arch dams, it can be seen that most of them suffered unexpected cracks during the construction stage or in service operation [1, 2, 3, 4] and therefore, it is important to evaluate the structural safety of these dams [5].  
Providing safe and reliable operation of dams throughout their service life is a major interest all over the world. To this end, the significance of monitoring and analyzing the behavior of large concrete dams, especially by using experimental data along with numerical modeling, has been well recognized for decades [6, 7]. Hence, it is a major concern to provide accurate analysis methods to evaluate the present condition of dams as well as to assess the safety of these structures, especially the partially damaged ones. However, due to the inherent complexity of the dam foundation system, no method is available nowadays that automatically detects the cracks in an arch dam based on monitoring data.
# Service for Deploying Digital Twins of QKD Networks
## 5 Validation and Results  
Three different kinds of experiments have been conducted to validate the developed solution. All tests have been carried out in the 5G Telefonica Open Network Innovation Centre (5 TONIC) [51], which counts with an NFV infrastructure based in OpenStack and OSM [52].  
In order to test the two possible uses of the QKD network digital twin orchestrator discussed in Section 4.1, we have conducted two of the experiments over a pre-deployed machine pool, while the third one was performed using our service OSM feature.
### Contributions  
Based on the previous research works in this area, the research gaps can be summarized as follows:  
* None of the previous studies on CNEP consider the flexibility index of power systems. However, it is important in a power system in the presence of RESs to modify the generation injection and/or consumption patterns that are not suitable from a power balancing viewpoint due to the uncertainties of RESs. This index can be improved in the power system using the optimal planning of energy storage systems such as batteries.
* Many research works reviewed above take into account DC power flow constraints in their CNEP models. This simplification results in the elimination of reactive power studies in an expansion planning problem. It should be noted that the capacity of lines and power sources also depends on the reactive power as well as the active power, and without considering the reactive power, the CNEP solution may lead to impractical results.
* Some works use evolutionary methods to solve the CNEP problem. These methods search for different directions of the solution space, and their progress is usually slow. Therefore, the computation time of evolutionary methods is high. In addition, their convergence to the global optimum for the CNEP problem cannot be guaranteed.  
To address the above issues, this paper presents a CNEP method to coordinate the optimal expansion planning of transmission systems with the size and the placement of flexible and renewable sources, i.e., ESSs and wind farms, considering the network's flexibility. In the proposed model, the difference between investment and operation costs and the flexibility benefit is minimized. The linearized AC optimal power flow equations, ESS and RES operational constraints, and the flexibility limit are the constraints of the proposed model. Hence, this paper presents a mixed-integer linear programming (MILP) model to guarantee the global optimal solution with a low computational burden. In addition, the uncertain parameters of the problem are modeled based on the scenario-based stochastic programming (SBSP) approach. Therefore, the roulette wheel mechanism (RWM) is implemented for scenario generation, and the Kantorovich method is used for the scenario reduction to model the forecasted error uncertainty of the load, the energy price, and the RES power. The main contributions of this paper can be summarized as follows:  
* Modeling the coordinated network expansion planning in the power system according to a hybrid method of optimal planning of renewable and flexible sources and TEP to improve the operation and the flexibility indices.
* Obtaining the linear model of the proposed strategy, considering the least calculation error concerning the original method using linearized AC optimal power flow equations.
* Using scenario-based stochastic programming based on a hybrid approach with the roulette wheel mechanism and Kantorovich method to model the uncertainty of the load, the energy price, and the RES power.
## 3 Topological Classification
### Hybrid FCS and Battery System
#### 3.1.2 Direct Connection of FCS  
The FC is directly connected to the motor's DC bus in Figure 8, while the battery is linked to the DC bus through a DC/DC bi-directional converter (BDC). The BDC's basic function is to maintain a stable output voltage [44]. One of the most attractive BDC topologies is the dual active bridge [45]. The simplicity of connection and straightforwardness of implementation are the benefits of this connection approach. Additionally, the BDC enables the adoption of batteries with low voltage level and enhances the battery's consumption [46]. Nevertheless, using a DC/DC converter decreases the system efficiency. As a result of the direct connection of the FCS to the DC bus, the FCS terminal voltage determines the voltage level of the DC bus. This means that variations in FCS power can cause significant fluctuations in the bus voltage level, particularly in urban environments characterized by frequent and heavy load variations. The misalignment between the voltage levels of the FCS and bus voltage can cause a reduction in FCHEV performance.