esa-sceva/satcom-chunk-collection huggingface dataset:
# Autonomous Intersection Management by Using Reinforcement Learning
## 3 Methodology
### Reinforcement Learning for Traffic Control
#### 3.1.1 State Definition  
The state space \(S=\{s_{1},\,s_{2}\,s_{3}\,\,,\ldots,\,s_{|S|}\}\) represents the state of traffic environment. A state space is a collection of every state that can be reached from the starting point. The red signal is state space. There are two main definitions for state space.  
**Queue size** is the number of vehicles whose speed is less than a certain number, e.g., 5 km per hour, waiting in front of the intersection. We will introduce three main approaches here to define queue size. First, each lane \(i\) has queue size \(s_{t}^{i}\) at time \(t\) which can be classified into different level: less (\(s_{t}^{i}<\theta_{1}\)), moderate (\(\theta_{1}<s_{t}^{i}<\theta_{2}\)), and maximum (\(s_{t}^{i}>\theta_{2}\)), where \(\theta_{1}<\theta_{2}\) are thresholds ([PERSON] et al., 2018). Secondly, the state can be defined as a comparison of queue size among lanes. An example of a four-lane intersection is \(s_{t}^{1}\geq s_{t}^{4}\geq s_{t}^{3}\geq s_{t}^{2}\), which represents the fact that the largest queue size is of lane 1 and the lowest one is lane 2. Thirdly, the state can be defined as the maximum queue size among all lanes.  
**Timing of red signal** is the time elapsed since the signal of the \(i\)th lane turned red. The purpose of using this metric is to prevent the signal of a lane from remaining in red for too long so that we can guarantee fairness [20].
## 4 Findings  
Comparing the graphs in Figure 2a,b, it can be seen that the indicators of the pre-COVID start-ups obtained lower average scores at the start of the programmes (between 1.6 and 3.1) compared to the COVID start-ups (2.0 and 3.7). The averages for the six categories considered were between 2.1 and 2.4 for pre-COVID programmes and between 2.8 and 3.2 for COVID programmes. This can be attributed to the fact that the maturity of the start-ups that participated in the Space technology accelerators was higher, as subsequently demonstrated throughout the acceleration programme.  
There was also greater variability in the progress of the pre-COVID start-ups compared to the COVID start-ups. Thus, the delta of the pre-COVID categories varied between 0.1 and 0.8, while the delta of the COVID categories remained between 0.4 and 0.5. This behaviour is justified by the difference in maturity of the start-ups observed across the four programmes. While more mature start-ups may need to progress across in all categories and may start from a more advanced position, the less mature start-ups must focus on product development, market analysis and acquiring funding, while branding and marketing as less determinant of their survival. On the other hand, the progress of pre-COVID start-ups in the other categories (Technology, Product, PR & Comms and Financing) was higher in all of them and in two of them almost twice as high (PR & Comms and Financing). This was because the progress of the COVID start-ups was lower than expected under normal circumstances, not reaching 0.8 points in any of the categories.  
This can be attributed to a wide variety of factors stemming from the constraints imposed by the pandemic, including:  
* The intensity of the acceleration programmes, where founders are not fully immersed in an accelerator environment and cannot as easily draw on the accelerator team and resources as needed.
* The lack of face-to-face workshops limits the acquisition of knowledge as it is more difficult to interact to clarify or expand on themes of particular interest.
* On-line environments hindering the informal learning, socialising and support that accompany the founders who are part of the face-to-face cohort.
* The difficulty of effective networking where people engage in conversations that allow relationships to develop and where contacts made are better contextualised and more memorable than their virtual equivalents.
* The temporary moratorium on corporate budgets that emerged during the early stages of the pandemic, in which non-essential spending was frozen in many companies, essentially delaying any possibility of piloting new technologies.
* Zoom fatigue [49], a recognised phenomenon that has confirmed that burnout occurs after exposure to long periods of videoconferencing.
* The following sections describe the findings in each of the categories.
## 3 Numerical Example
### Uncertain Parameters  
Analysis of the influence of many uncertain items is one of the valuable properties of seismic demand analysis. The modeling parameters are divided into two categories: the uncertainties of structural properties and the uncertainty of material properties. The selection strategy and the distribution of two kinds of uncertainties is provided in this subsection [26; 27].  
The uncertainties of structural properties mainly affect the global dynamic characteristics of the bridge structures. According to the previous works [28], the mass per length of the superstructure \(m_{g}\) is often assumed following a normal distribution with mean value as its designed value and 10% of the coefficient of variation (COV). According to [29], the post-yield stiffness \(K_{db}\) of LRB is often uncertain and assumed to follow the normal distribution with 14% of the COV.  
Figure 2: Nonlinear finite element model of the continuous girder bridge finite element model.  
For some existing bridges, the deterioration of material properties may causes material uncertainties. According to [30; 31], the relevant corrosion-induced deterioration of reinforced concrete structures manifests in the form of the degradation in the mechanical properties of steel rebars and the loss of concrete strength. In this regard, the initial yield stress (\(f_{y}\)) _Steel02_ and the peak compressive strength (\(f_{c}\)) of _Concrete02_ are taken as uncertainties. Besides the deterioration affect, the uncertainties involved in bridge seismic fragility analysis should be considered, since they may involve the nonlinearity of the structural seismic response [32]. According to [32; 33], the peak compressive strength (\(f_{c}\)), the strain at the peak strength (\(\varepsilon_{c}\)) and the strain at the crushing strength (\(\varepsilon_{u}\)) of _Concrete02_ are considered to be uncertain parameters following lognormal distribution. For _Steel02_, the elastic Young's modulus (\(E_{s}\)), the initial yield stress (\(f_{y}\)) and the strain hardening ratio (\(b_{s}\)) are considered to be uncertain parameters following lognormal distribution since they have a greater influence on the nonlinear behavior of longitudinal reinforcement [32; 34].  
All the aforementioned uncertain parameters are taken as the independent uncertain inputs in the following global sensitivity analysis. For clear demonstration, their abbreviations and statistical information details are listed in Table 1.
# Secure Outsourced Blockchain-Based Medical Data Sharing System Using Proxy Re-Encryption +
## 4 The Proposed Scheme
### The Re-Encryption Processes
#### 4.3.3 Data Storage  
When user \(u_{i}\) creates an EHR \(M\) and attempts to store it in the EHR management system, the processes involved are as follows:  
User \(u_{i}\) signs \(M\) with one's private key. Let \(S\) be \(u_{i}\)'s digital signature on \(M\);  
To encrypt both the message and the signature, user \(u_{i}\) randomly creates a symmetric key \(K\) for the AES and encryppts \((M\|S)\) with \(K\). Let \(\mathcal{C}\) be the encrypted data, i.e., \(\mathcal{C}=Enc(K,(M\|S))\). In the next step, the second-level encryption of the PRE begins;  
To encrypt \(K\), user \(u_{i}\) selects \(u\in\mathds{Z}_{q}^{*}\) and \(\omega\in\{0,\ 1\}^{\ell_{1}}\) randomly, and computes \(r=H_{1}(K,\omega)\);  
User \(u_{i}\) calculates the following:  
\[D = \Big{(}pk_{M,1}^{H_{4}(pk_{M,2})}\cdot pk_{M,2}\Big{)}^{u},\] \[E = \Big{(}pk_{M,1}^{H_{4}(pk_{M,2})}\cdot pk_{M,2}\Big{)}^{r},\] \[F = H_{2}(g^{r})\oplus(K\|\omega);\]  
The user computes \(s=(u+r\cdot H_{3}(D,E,F))\mod q\), outputs the ciphertext \(\mathcal{E}=(D,\ E,\ F,\ s)\), and sends \(\mathcal{C}\) and \(\mathcal{E}\) to the server;  
After the server receives \(\mathcal{C}\) and \(\mathcal{E}\), it verifies if the following equation holds:  
\[\Big{(}pk_{M,1}^{H_{4}(pk_{M,2})}\cdot pk_{M,2}\Big{)}^{s}=D\cdot E^{H_{3}(D,E,F)}.\]  
If it does not hold, then the server rejects \(\mathcal{C}\);  
The server finally stores \(\mathcal{C}\), and writes the access information on the blockchain.
# Short-Text Semantic Similarity (STSS): Techniques, Challenges and Future Perspectives
## 4 What Are the Existing Deep Learning Techniques Used in Short-Text Semantic Similarity (STSS)?
### Model Selection for Contextual Word Embeddings (CWE)  
To achieve the second objective, we studied three models for contextual word embeddings (CWE). These models (BERT, ELMO, GPT-2) present the text features from long as well as short sentences. Traditional methods such as Glove, Word2 Vec, FastText, count vector, TF-IDF, and co-occurrence matrix are context-independent [107]. Unlike these methods, transformer learning models generate similarity based on the context. As we have noted in Figure 7, the ELMO captures the context bidirectionally. The model is more suitable for domain-specific tasks. However, the GPT-2 is a domain-independent model, also referred to as a task-agnostic model. It identifies the context unidirectionally [108]. The BERT predicts the context bidirectionally; it requires additional changes (addition of different model architectures such as LSTM, CNN, semantic fusion, etc.) for the improvement of many NLP tasks [109]. The BERT model has the ability to represent the word tokens in both directions. When it comes to supervised learning, the BERT is quite similar to the GPT-2. First, the added output layer of the BERT model uses the representations with additional changes to predict the words or sequence of words. Second, the model fine-tunes the parameters for specific tasks. For the newly customized dataset, the model needs to train the parameters from scratch [110]. Moreover, the BERT model achieved tremendous performance on the majority of NLP tasks, such as question-answering systems (QAS), short-text similarity (STS), single-text classification, text tagging, and so on. Moreover, Table 10 illustrates the summary of all three models. Based on the current state-of-the-art models, we have noted that the BERT model produces maximum similarity. The model has vocabulary for the English language by default, but it outperformed in various other languages as well.  
\begin{table}
\begin{tabular}{c} Masked \\ language \\ modeling \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 10: Summary of BERT, ELMO, and GPT-2 Language Models.
## 3 Results
### AI for Environmental Monitoring and Mapping of Land Use, Vegetation, and Soils
Land degradation mapping using machine learning algorithms and Landsat 8 satellite imagery enabled the classification of areas according to their degradation risk levels. The resulting degradation likelihood distribution (DLD) maps serve as valuable decision-support tools for regional natural resource management policies [24]. AI is widely used in digital soil mapping, significantly improving the accuracy of assessing physical and chemical properties. In Iran, the analysis of soil-forming factor similarity using Gower's index helped to fill gaps in existing soil maps and enhanced resource management efficiency [1]. In India, algorithms such as Random Forest, support vector machines, decision trees, and logistic regression were employed to develop high-precision models of soil properties, contributing to more sustainable agricultural management practices [25]. Spatial mapping of available nitrogen and phosphorus in the alpine meadow soils of the Qingzang Plateau, using a Random Forest model, identified ecological drivers influencing nutrient dynamics [26]. This information is critical for pasture management and maintaining ecosystem productivity. Ensemble learning techniques have also been applied to estimate soil organic carbon (SOC) at multiple depths within the Heihe River Basin [27]. These models incorporate climate and landscape variables affecting SOC levels and support the development of agroecological strategies tailored to local environmental conditions. In addition, recent studies incorporate hyperspectral remote sensing, proximal soil sensors, and AI-based methods--such as Random Forest, SVM, and neural networks--to account for spatial uncertainty in digital soil mapping [28]. This is particularly important for assessing the impacts of climate change and soil degradation, as well as for developing sustainable environmental protection strategies. The body of research shows that integrating AI with remote sensing data enables the development of new, effective tools for monitoring and mapping key ecosystem components--such as wetlands, vegetation, soils, and land use. These technologies not only allow for detailed spatial-temporal assessments but also help identify causal relationships between anthropogenic pressures, climatic variables, and ecosystem degradation. AI applications support the development of more accurate forecasts, inform science-based decision making in natural resource management, and contribute to strengthening the resilience of local ecosystems. These approaches are especially valuable for early detection of degradation processes, optimizing agricultural policy, protecting biodiversity, and enhancing climate adaptation strategies at the regional scale. In total, according to the regional classification, the following references pertain to Asia [1; 16; 18; 19; 20; 21; 22; 23; 25; 26; 27] and Africa [10; 17].
## 6 Evaluation and Analysis
### Simulation Results
#### 6.3.5 Comparison between Context-Aware and Non-Context Aware DR-MAC/DAAM Protocol Sets  
This section evaluates the performance of the context-aware DR-MAC/DAAM protocol set (Set 9), and the original DR-MAC/DAAM protocol set without context awareness (Set 5). Similar to previous evaluations, results are shown for both normal and high traffic scenarios. In order to achieve further insights, additional results are shown in this section for a light traffic scenario. The traffic parameters for all three scenarios are summarized in Table 8.  
Figure 21a,d present the results of the two protocol sets in terms of throughput, PDR, end-to-end delay, and energy cost per packet delivered. Clearly, context awareness can enhance the protocol performances particularly under high traffic. Protocol set 9 can achieve up to 74%, 68%, 46%, and 14% improvement over protocol set 5 in terms of throughput, PDR, end-to-end delay, and energy cost, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{2}{c}{**Description/Value**} \\ \cline{2-3}  & **Normal** & **High** \\ \hline Node density & 100 nodes/ 200 \(\times\) 200 m\({}^{2}\) & 100 nodes/ 100 \(\times\) 100 m\({}^{2}\) \\ Node mobility (Aml users) & 1.2 m/s (walk) & 4.4 m/s (run) [28] \\ \hline \hline \end{tabular}
\end{table}
Table 7: Node density and mobility parameter settings of protocol set 9.  
Figure 20: _Cont._  
The improvement can be attributed to the AmI context information which is utilized: (1) for adapting the backoff behavior of the MAC protocol to enhance the success of frame delivery between the two protocols.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Scenario/Parameter** & **Description/Value** \\ \hline Scenario 1 & Light traffic \\ D\({}_{\text{freq}}\) & 1 frame every 2 s \\ D\({}_{\text{size}}\) & 512 bits \\ Scenario 3 & High traffic \\ D\({}_{\text{freq}}\) & 2 frames per second \\ D\({}_{\text{size}}\) & 512 bits \\ Scenario 3 & High traffic \\ D\({}_{\text{freq}}\) & 10 frames per second \\ D\({}_{\text{size}}\) & 1024 bits \\ \hline \hline \end{tabular}
\end{table}
Table 8: Parameter settings for context-aware and non-context aware DR-MAC/DAAM protocol sets.  
Figure 21: **(a)** Throughput; **(b)** PDR; **(c)** End-to-end delay; and **(d)** Energy cost comparison between context-aware and non-context aware DR-MAC/DAAM protocol sets.  
neighboring node pairs; and (2) for prioritizing packets and selecting data paths with delays corresponding to the packet priority by the network routing protocol. This is evidenced from the higher proportion of high-priority data packets delivered by protocol Set 9 when AmI context information is applied, as shown in Figure 21b. This results in improved throughput, PDR, and end-to-end delay. With more sensor data packets delivered, the energy efficiency is also improved, _i.e._, lower energy cost per packet delivered.
## 2 Description of the Calculable Resistors
### Bifilar and Cottofilar Frequency Evaluation  
During 2017, the bifilar and one of the octofilar CRs were sent to METAS, which is the National Metrology Institute (NMI) of Switzerland [24]. To not damage the integrity of the wires and their connections to their terminals, before sending the CRs to Switzerland, their wire strains were wholly removed moving the PTFE support, one centimeter down. At METAS, the strain in the wires was re-adjusted through the strain adjustment systems displayed in Figure 4, and after one month, the two standards of CENAM had stabilities better than 0.2 \(\upmu\Omega/\Omega/\mathrm{day}\). The CRs of CENAM are contained in individual temperature-controlled enclosures to avoid any temperature effect in the resistance value. The enclosures ensure temperature stability of \(\pm\)0.015 \({}^{\circ}\)C, which implies variations of the resistance value due to temperature changes of less than 0.02 \(\upmu\Omega/\Omega\).  
Because the METAS 1 k\(\Omega\) coaxial CR frequency dependence has been validated through different comparisons [23; 33], it was chosen as the reference standard for the evaluation of CENAM CRs. Table 3 listed the three CRs involved in the performed measurements.  
Figure 4: CR’s wire supports with strain adjustment system, for: (**a**) the bifilar CR, and (**b**) the octofilars CRs.  
To perform the measurements between CENAM and METAS CRs, a Coaxial Bridge developed at METAS was utilized [33]. This bridge is able to perform automatic measurements of four-terminal-pair impedance standards with 1:1 and 10:1 ratios. To provide the voltage ratio, the bridge uses a double-screened ratio transformer (RT). Using a reversal measurement technique in the 1:1 ratio measurements, the effect of the RT error, and the effect of the cables used to connect the standards to the bridge are eliminated. For the 10:1 ratio, the frequency dependence of the RT error was measured using, as a reference, coaxial CRs with nominal values of 12,906 \(\Omega\) and 1290.6 \(\Omega\). Because the time required for a frequency sweep from 50 to 20 kHz does not take more than 2.5 hours, including the reversal measurements for the 1:1 ratio, the short term stability of the standards have an impact on the results of less than 0.025 \(\upmu\Omega/\Omega\).
# Reputation-Driven Dynamic Node Consensus and Reliability Sharding Model in IoT Blockchain
###### Abstract
Its advantages are (1) improving the transaction processing speed of blockchain by dividing sorting sharding and consensus sharding; (2) NCRS ensures the reliability of segmentation. The contributions of this paper are as follows:  
1. To quantify node behavior, we propose a reputation evaluation scheme for node behavior, and design RDSCM based on this scheme;
2. RE-PBFT is proposed, based on the evaluation scheme, including the primary node election scheme and abnormal node determination and elimination scheme, which not only reduces the abnormal nodes but also reduces the possibility of an abnormal node becoming the main node;
3. The necessary conditions that sharding should meet are proposed and proved. Based on the evaluation scheme, NCRS is proposed to make the existing malicious nodes evenly distributed among all shardings and reduce the situation that the sharding is taken over by malicious nodes. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Name** & **Scalability** & **Throughput** & **Latency** & **IoT Suitability** \\ \hline PoET & high & high & low & no \\ PoA & high & low & high & no \\ IBFT & low & high & low & yes \\ PBFT & low & high & low & yes \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of consensus algorithms. * The idea of sorted sharding and consensus sharding is proposed. Each consensus sharding produces a prepared block separately, which is processed by sorted sharding and linked to the blockchain.
# LMI Pole Regions for a Robust Discrete-Time Pole Placement Controller Design
## 4 Discrete-Time Robust Pole Placement Control for Magnetic Levitation (ML) Laboratory Plant

Table 1: State feedback PI controller parameters designed using Equation (8) for the considered \(D_{R}\) regions. The designed controllers were then verified on a nonlinear simulation model. The obtained results are shown in Figures 10 and 11. In Figure 10, the prescribed pole regions are depicted for all considered \(D_{R}\) region variants, as well as for the corresponding closed loop poles. In all cases the poles are inside the prescribed region. Note that in the case of stabilization only (green), the poles are concentrated close to the stability border with almost no damping, which causes a response overshoot and a relatively longer settling time. The elliptic region, on the other hand, guarantees the damping. However, the poles are not allowed to keep closer to the point [1, 0], which indicates a too demanding control variable. The latter observation was approved by simulations for the nonlinear ML model. The results for 3 working points (3 different positions of levitating ball) are shown in Figure 11. Rapid significant changes of the control variable for elliptic approximation are outperformed by the ellipse-cone controller with both output and control variables smooth. This feature is important for the practical implementation of the designed controller. For comparison we tried to design the continuous-time PI controller for the prescribed damping angle \(60^{\circ}\)--the corresponding convex cone region is shown in Figure 3a. Then we recalculated the continuous to the discrete-time PI controller (for the used sampling period 1 ms). We verified the resulting controller on the nonlinear simulation model. We can conclude that, though the continuous-time controller design requires only \(2\times 2\,D_{R}\) region matrices (the ellipse-cone approximation  
Figure 11: Simulation results for the nonlinear ML model in 3 working points (WP) for the designed robust pole placement controller for unit circle (green), elliptic approximation for damping angle \(87^{\circ}\) (magenta), and ellipse-cone (AE: blue dots): (**a**) Output variable—ball position; (**b**) control variable—current in the coil. Figure 10: Prescribed pole regions and corresponding closed loop poles for tested variants: (**a**) The overall regions and (**b**) the detail on pole location near the right-hand side border of \(D_{R}\) regions. uses \(4\times 4\)\(D_{R}\) region matrices), the recalculated continuous time PI controller provided significantly slower response than the originally designed discrete-time PI controller based on the ellipse-cone. Generally, the discrete-time controller recalculated from the continuous-time one provides a closed loop system performance that is not better (usually worse) than the continuous-time closed loop one [17].
## 3 Results
### Filters  
It was observed that the fourth-order Butterworth low-pass filter was reported more frequently (N = 6) [14; 15; 16; 17; 18; 21], followed by the low-pass filters (without specification) (N = 2) [20; 22] and the Kalman filter (N = 1) [19] and the cutoff frequency adopted varied from 1.25 Hz to 20 Hz. The most reported frequency was 4 Hz (N = 2) [16; 17], then 20 Hz (N = 1) [21], 1.25 Hz (N = 1) [18], 10 Hz (N = 1) [15], 0.25 Hz (N = 1) [21] and not reported (N = 4) [14; 19; 20; 22]. In general, signal processing occurred after data collection (N = 8), through analysis software [14; 15; 17; 18; 20; 21] or cloud [19; 22]. Only one study had signal processing in the application [16].