esa-sceva/satcom-chunk-collection huggingface dataset:
# Asymmetric Measurement-Device-Independent Quantum Key Distribution through Advantage Distillation
## 2 Methods
### Asymmetric MDI-QKD
\begin{split}& Y_{11}^{X,L}=Y_{11}^{X,\varepsilon}=\frac{P_{v_{A}}^{ 1}P_{v_{B}}^{2}Q_{w_{A}w_{B}}+P_{1A}^{1}P_{w_{B}}^{2}P_{v_{A}}^{0}Q_{w_{B}}+P_{ w_{A}}^{1}P_{w_{B}}^{2}P_{v_{B}}^{0}Q_{v_{A}0}}{P_{w_{A}0}^{1}P_{v_{A}}^{0}(P_{w_{B}} ^{1}P_{v_{B}}^{2}-P_{w_{B}}^{2}P_{v_{B}}^{1})}\\ &-\frac{P_{w_{A}}^{1}P_{w_{B}}^{2}Q_{v_{A}v_{B}}+P_{w_{A}}^{1}P_{ w_{B}}^{2}P_{v_{A}}^{0}P_{w_{B}}^{0}Q_{w_{B}}}{P_{w_{A}}^{1}P_{v_{A}}^{1}(P_{w_{B}} ^{1}P_{v_{B}}^{2}-P_{w_{B}}^{2}P_{v_{B}}^{1})}-\frac{P_{v_{A}}^{1}P_{v_{B}}^{2} P_{w_{B}}^{2}\mathbb{H}}{P_{w_{A}}^{1}P_{v_{A}}^{1}(P_{w_{B}}^{1}P_{v_{B}}^{2}-P_{w_{ B}}^{2}P_{v_{B}}^{1})},\\ & Y_{11}^{X,L}=Y_{11}^{X,f}=\frac{P_{v_{B}}^{1}P_{v_{A}}^{2}Q_{w_{ A}w_{B}}+P_{w_{B}}^{1}P_{w_{B}}^{2}P_{v_{A}}^{0}Q_{w_{B}}+P_{w_{B}}^{1}P_{w_{B}}^{ 2}P_{w_{A}}^{0}Q_{w_{B}}+P_{w_{B}}^{1}P_{w_{B}}^{2}P_{w_{B}}^{0}Q_{v_{A}0}}{P_{ w_{B}}^{1}P_{v_{B}}^{1}(P_{w_{A}}^{1}P_{v_{A}}^{2}-P_{w_{B}}^{2}P_{v_{A}}^{1})}\\ &-\frac{P_{w_{B}}^{1}P_{w_{B}}^{2}P_{A}^{2}Q_{v_{A}v_{B}}+P_{w_{B }}^{1}P_{w_{A}}^{2}P_{w_{A}}^{0}P_{w_{B}}^{0}Q_{w_{0}}}{P_{w_{B}}^{1}P_{v_{B}}^ {1}(P_{w_{A}}^{1}P_{v_{A}}^{2}-P_{w_{B}}^{2}P_{v_{A}}^{1}P_{v_{A}}^{2})}\\ &\qquad\qquad\qquad e_{11}^{X,U}=\frac{T_{w_{A}w_{B}}(1+\gamma \sqrt{1/(N_{xw_{A}w_{B}}T_{w_{A}w_{B}})})-\mathbb{H}/2}{P_{w_{A}}^{1}P_{w_{B}} ^{1}Y_{11}^{X,L}},\end{split}  \tag{4}\]  
\[ \begin{split}& e_{11}^{Z,U}=\frac{T_{s_{A}s_{B}}+P_{s_{A}}^{0}P_{ s_{B}}^{0}T_{oo}-[P_{s_{A}}^{0}T_{s_{B}}+P_{s_{B}}^{0}T_{s_{A}0}]}{P_{s_{A}}^{1}P_{s_{B}} ^{1}Y_{11}^{Z,L}},\end{split}  \tag{5}\]  
\[ \begin{split}\mathbb{H}=P_{w_{A}}^{0}Q_{ow_{B}}+P_{w_{B}}^{0}Q_{w_{ A}o}-P_{w_{A}}^{0}P_{w_{B}}^{0}Q_{oo},\end{split}  \tag{6}\]  
where \(P_{l_{A}}^{n}(P_{l_{B}}^{m})\) denotes the photon-number distribution of the source at [PERSON]'s ([PERSON]'s) side, \(Q_{l_{A}l_{B}}\) and \(T_{l_{A}l_{B}}\) are the gain and the total quantum bit errors [25], and \(\mathbb{H}\) is the combination of the gain of the decoy state and the vacuum state. \(\gamma\) is the standard error, and its value is set to 5.3 here. The expression for \(Y_{11}^{X,L}\) is equal to \(Y_{11}^{X,\varepsilon}\) when \(P_{v_{A}}^{1}P_{w_{A}}^{2}P_{w_{B}}^{1}P_{w_{B}}^{2}<P_{w_{A}}^{1}P_{v_{A}}^{2} P_{v_{A}}^{1}P_{v_{B}}^{2}P_{w_{B}}^{2}\), otherwise the expression equals \(Y_{11}^{X,f}\)[19; 24]. Considering the effect of statistical fluctuations on multiple observations, the method of collective constraints can provide tighter constraint conditions between different sources(\(s_{A},w_{A},v_{A},s_{B},w_{B},v_{B},o\)) than independent bounds. Thus, these parameters \(Y_{11}^{X,L},e_{11}^{Z,U},\mathbb{H}\) can be further optimized to achieve a higher key rate by the joint constraints method [8]. By the above formulas, we can calculate the final secret key rate of the asymmetric seven-intensity MDI-QKD protocol.
# Research on Medical Text Parsing Method Based on BiGRU-BiLSTM Multi-Task Learning
## 5 Conclusions  
In order to improve the accuracy of entity recognition and intention classification in medical question processing, this paper proposes a medical text parsing method based on BiGRU-BiLSTM multi-task learning, which consists of a sharing layer and specific task layer. The sharing layer uses the pre-trained BERT model to generate the input sequence, captures the longer-distance context information, generates a more fine-grained representation of the above and the following through the BiGRU-BiLSTM hybrid neural network, and constructs a multi-task learning sharing layer. For the intention recognition task, DPCNN is used on the output of the BiGRU-BiLSTM layer, which predicts the category of intent for each input text. For entity recognition tasks, in order to better understand the needs of users, CRF is used for sequence tagging, and the output of the BiLSTM layer is used as the input of CRF to obtain the tags of entities in the sequence, which improves the performance of intelligent man-machine dialogue. According to the method proposed in this paper, a hybrid neural network model based on multi-task learning is constructedand experiments are carried out on CBLUE and CMID datasets. The experimental results show that the accuracy of named entity recognition and intention recognition is 86% and 89%, respectively, which effectively improves the accuracy of semantic understanding, the generalization ability of the model, and the understanding of long and difficult sentences. Through multi-task learning, the processing ability of fine-grained information is improved, the meaning of input text can be better understood, the context information in text can be captured more easily, and the processing of complex text and long sentences is improved to some extent.  
However, the method proposed in this paper also has some shortcomings. Large-scale and high-quality tagged data are needed in the process of deep learning model training, but the data labeling in the medical field is relatively complex and requires a professional background. Therefore, the scale of the data used in this paper is not large enough; as a result, in this method there is still some deviation in understanding in text parsing.  
In our future work, we will introduce deeper context understanding to enhance the model's capture of long-distance dependency relationships, and because data from some specific diseases or areas in the medical field may be scarce, we will study the optimization model in a low-resource environment, and explore ways to introduce a large language model to achieve higher accuracy in the case of balancing resources and efficiency.
# Physical, Thermal, and Mechanical Characterization of PMMA Foils Fabricated by Solution Casting
## 3 Results
### Thermal Characterization of PMMA Powder, Commercial Foil, and Prepared As-Cast Foils  
The glass transition temperatures (T\({}_{\mathrm{g}}\)) for the PMMA powder, commercial foil, and other two solution casting foils were investigated. Figure 4a-d represents the glass transition temperature, and corresponding values are presented in Table 1.  
Figure 3: (**a**) Photographic image of solution cast PMMA foils (possibly air voids in sample). (**b–d**) 3D image of commercial foil (purchased from Konig film) and as-prepared foils.  
Figure 2: (**a**) Surface image of PMMA powder and (**b**) Commercial PMMA foil (**c,d**) prepared PMMA foils from 18 and 24 mL concentrations.  
The glass transition temperature of PMMA powder is 117 \({}^{\circ}\)C, which may correspond to the volume of various sizes of powder. However, the values of Tg are lower in commercial foil, and the value decreases significantly in solution-cast samples. However, PMMA foils obtained from an S/L ratio of 18 and 24 mL show very similar values of Tg that arise from the combined reaction of solvent with solute. This may correspond to the solvent toluene remaining in the polymer foil that arises from different states of entanglement. The lower value arises from the weaker interaction of toluene and PMMA during the heating cycle.  
Based on the application of composite in sensors and actuators it is crucial to integrate both shape memory effects of NiTi alloy with PMMA foil. As the transformation temperatures of NiTi can be adjusted between room temperature and 100 \({}^{\circ}\)C, a very interesting composite can be designed. The glass transition temperature of polymer foil is desired to be at a lower temperature than the austenite finish temperature of NiTi alloy. At a temperature above 100 \({}^{\circ}\)C, NiTi alloy will be in the austenite phase; however, the polymer will be above glass transition temperature with a viscous stage (zero storage modulus). In this stage, the polymer will adhere to the engraving valley of the NiTi surface. However, during the cooling stage, the alloy will soften while the polymer will stiffen. That will create a lag force with a reverse one-way effect in the composite structure. In this aspect, a polymer with lower Tg is more suitable for application as a composite in various sectors for actuators with a two-way shape memory effect
# Research on a Rail Defect Location Method Based on a Single Mode Extraction Algorithm
## 2 An Accurate Modal Identification Method
### Basic Characteristics of Ultrasonic Guided Waves in Rails
stress vectors of the element can also be expressed by the displacement of the nodes. According to the Hamilton principle, the dynamic equation of guided waves propagating in a CHN60 rail can be obtained by calculating the strain energy and potential energy at any point simultaneously [20]:  
\[\left[K_{1}+i\xi K_{2}+\xi^{2}K_{3}-\omega^{2}M\right]_{M}U=0 \tag{5}\]  
In this form, \(U\) contains the displacements of the nodes in \(x\), \(y\), and \(z\) directions. \(M\) is the mass matrix of the nodes. \(\xi\) and \(\omega\) are the wave number and angular frequency, respectively, and \(K_{1}\), \(K_{2}\), and \(K_{3}\) are the stiffness matrices. Given the frequency \(\omega\), the eigenvalue of Equation (5) is the wave number \(\xi\) of the guided waves, and the eigenvector contains the mode shapes of the rail. Normalized processing can be used to plot the corresponding mode shapes of the guided waves. The modes with pure imaginary or complex wave numbers are not considered here because these modes will exponentially decay as the distance increases and cannot propagate. The dispersion curves of the phase velocity and group velocity of ultrasonic guided waves in the CHN60 rail are plotted, as shown in Figure 3.  
The dispersion curves of the phase velocity and group velocity are shown in the figure. At the same frequency, the rail has several propagable guided wave modes, and at the higher the frequency, it has more guided wave modes.
## 3 Materials and Methods
### Question Answering Method
Additionally, TQAgentcollaborates with the Retriever to fetch historical examples, thereby minimizing errors and refining reasoning paths. Experimental results indicate that compared to general large-scale models, small language models (SLM Reasoning) exhibit significant performance gaps without dynamic planning chains. As shown in Table 3, the zero-shot reasoning accuracy of Qwen2-7b and LLama3-8b on TabFact is 60.21% and 61.34%, respectively, nearly 10 percentage points lower than GPT-3.5's 70.45%, primarily due to parameter size constraints on implicit structured reasoning. Through multi-path sampling of dynamic operation trees and knowledge graph injection, TQAgent empowers the instruction-tuned LLama3-8b-Instruct to reach 73.60% accuracy, exceeding the original GPT-3.5's 70.45%. This validates that explicit reasoning chain modeling successfully transfers planning capabilities from large to small architectures. Cross-domain analysis further reveals that models without knowledge injection attain a BLEU score of 27.47 on FeTaQA, which rises to 28.62 with knowledge augmentation. The improvement rate (4.2%) substantially surpasses Dater's cross-domain performance gap on WikiTQ (11.5%), confirming the synergistic effect of dynamic planning and knowledge injection. This agent framework can be seamlessly adapted to other large models. While it primarily leverages the planning capabilities of large-scale models, its planning logic is implemented locally. When substituting the base model, GPT still outperforms the 8B small-scale variant, yet given the scaling advantages of large models, our method remains effective. Notably, the fine-tuned model exhibits markedly superior performance to its non-fine-tuned counterpart, proving successful migration of Instruct models to dynamic-planning agent capabilities. **Impact of Sample:** This section investigates the impact of varying sample counts (n) on model performance under a fixed probability threshold \(P_{threshold}=0.25\). As shown in Table 4,

* **Low-sample (\(n\leq 3\)):** Limited sampling efficiently captures critical decision paths, with each new sample providing novel information (e.g., WikiTQ accuracy increases from 46.23% to 50.78% as n grows from 1 to 3). * **High-sample (\(n\geq 4\)):** Redundant samples emerge due to overlapping information, reducing marginal benefits (e.g., FeTaQA BLEU score plateaus at 28.62 despite n increasing from 4 to 6). The system achieves 95% of peak performance at n = 4, suggesting practical sampling limits to balance computational cost and model enhancement. As shown in Table 5, the integration of knowledge graphs significantly enhances model performance across multiple table-based reasoning tasks. On the TabFact dataset, knowledge injection improves accuracy by 3.89%, demonstrating enhanced factual verification capabilities through external knowledge supplementation. For WikiTQ, the 4.13% accuracy gain highlights the effectiveness of Knowledge Graphs in resolving complex entity relationships within heterogeneous tables. In the FeTaQA generation task, the
## 3 Related Theories and Technologies  
### Multi-Party Secure Computation Security Model  
The mathematical concept of Multi-Party Computation (MPC) involves several participants (such as \(P_{1},P_{2},\ldots,P_{n}\)), each holding private input data (\(x_{i}\)). These participants collaboratively execute a computation of the function \(f(x_{1},x_{2},\ldots,x_{n})\) with the goal of ensuring that each participant can only access their own computational results, while being unable to ascertain the inputs and results of others. There are generally two security models employed in secure multi-party computation protocols [37; 38]:  
1. **Semi-honest model:** In this model, participants adhere to the protocol's execution rules but may attempt to gather other participants' inputs, outputs, and any accessible information during the execution of the protocol. This model assumes that the participants do not deviate from the established procedural rules but will use all available information to deduce the private data of others.
2. **Malicious adversary model:** Unlike the semi-honest model, the malicious adversary model accounts for the possibility that attackers may manipulate a subset of the participants to perform illicit actions, such as submitting incorrect input data or maliciously altering data to steal the private information of honest participants. Malicious adversaries might also disrupt the protocol by intentionally terminating its execution or by refusing to participate, thus preventing the protocol's completion.  
The security model considered in this paper is the semi-honest security model.
# Energy Harvested and Cooperative Enabled Efficient Routing Protocol (EHCRP) for IoT-WBAN
## 5 Energy Harvested and Cooperative-Enabled Routing Protocol (EHCRP) Scheme
### Forwarder Node Selection  
The priority of a sender node is to send data by involving lesser number of hops counts to the CNC. However, for the sake of energy balancing the path with more number of hops counts may also be considered.  
**6. Data Transmission through Decision-Based Cooperative Effort Number Already Used**  
When the sensor senses data, it needs to be forwarded to sink node. While transmitting the data first the SN checks its total current energy. If the available energy is found to be lower than the  
Figure 4: Hello Packet block diagram.  
pre-defined threshold, then the sensor has to wait for the next round of data transmission. Meanwhile it harvests energy for the upcoming round.  
Sensors may sense the same data in a WBAN in repetitive data sensing rounds. Sensing this duplicate data consumes valuable sensor energy. Much of the sensor's energy can be saved by avoiding duplicate data during sensing or transmission. This can be achieved by using a supportive decision-based data transmission scheme as shown in Algorithm 3. In the EHCRP protocol, the duplicate sensed data in consecutive rounds is eliminated from the transmission pool. Before deciding to send the data to a sink node, a minimal process is done to check whether to send the data or not. Data transmission takes place if either of the following conditions is fulfilled:  
1. The data which is transmitted to sink node is not similar to the data stored in former round.
2. If the data is of an emergency/critical nature.
# Short-Term Power Prediction of Wind Turbine Applying Machine Learning and Digital Filter
## 3 Time Series Forecasting Experiments
### Data Description
#### 3.1.3 Effect of Wind Direction  
The default ideal case for calculating the power output of a wind turbine is to assume that the wind is coming in the direction of the impeller. However, even if the turbine can follow the wind direction in the actual state, the adjustment of the direction needs to overcome the inertia due to the huge size of the turbine equipment and takes some time to achieve. Therefore, the wind direction and turbine orientation are not always consistent, and only if the wind speed consistency is taken into account without considering the wind direction shift will the power prediction in advance lose accuracy.  
Further, we can draw some inferences from Figure 8 that winds from another direction will also have different characteristics in terms of spatial velocity and turbulence. This has a non-negligible impact on the short-term power forecasts we are going to make, and wind  
Figure 7: Power–wind speed distribution.  
changes should be used as important input information for the forecasts. It is also evident from Figure 9 that the distribution of significant wind speed intervals at all sample points is clearly correlated with the specific wind direction, and thus the concentrated distribution segments of the power output intervals also show consistency with it. In this study, the self-learning mechanism of neural networks is used for the time series prediction to explore the potential impact of wind speed and direction on the synergy of future short-term power output.
# Simulation of Achievable Data Rates of Broadband Power Line Communication for Smart Metering
## 6 Simulation--Basic Topologies
### Influence of Direct Line Length from Transmitter to Receiver on Throughput  
In this scenario the NAYY150 SE underground power line cable was considered (cable used in the real installation for Smart Metering issue). Background noise was set up for ideal conditions at the value of \(-\)90 dBm/Hz. This value represents an ideal environment without branches, discontinuities, and transitions.  
The peer to peer topology was considered and Figure 4 shows the results. It can be seen from the trend in this figure that the capacity of the cable channel decreases almost logarithmically with increasing length of the cable between transmitter and receiver. The decreasing capacity is caused by higher attenuation with increasing length. The channel capacity with overhead shows the decrease of capacity due to the header of the Ethernet frame (a data unit of 256 B was considered).  
Figure 5 shows comparison of throughput for NAYY150 SE and CYKY 3 \(\times\) 2.5 cables. The NAYY150 SE cable shows lower attenuation than CYKY 3 \(\times\) 2.5, therefore it achieves higher throughput. For NAYY150 SE cable, the theoretical communication distance was also longer.
## 3 Results  
### Content of Bioactive Compounds  
One of the ways to improve bioactive compound extraction from willowerh leaves is to use SPF in various conditions. In this experiment, SPF changed the amounts of polyphenols and carotenoids in organic fermented willowerh leaves under aerobic and anaerobic conditions. The content of total phenolic acids, polyphenols, and flavonoids was highest under the aerobic fermentation conditions, but the total quantity of carotenoids was higher in anaerobic fermentation (Table 2). The amount of individual bioactive compounds differed according to fermentation conditions. According to [PERSON] et al. (2016), ellagitannins, such as oenothein B, are among the compounds considered to be the primary biologically active components in willowerh extracts [2]. The quantity of tannin oenothein B in aerobic fermentation (24 h) was 19.25% higher compared to control (not-fermented), and 53.88% higher than under anaerobic conditions (24 h) (Table 3). In willowerh leaves fermented for 48 h, oenothein B quantity decreased by 13.07%, and in fermented leaves for 72 h--33.08%. Flavonoids were found as the prime group of naturally-occurring phenolic compounds [14]. It was established that the higher amount of flavonoid quercetin-3-\(O\)-rutinoside was determined in not-fermented willowerh leaves (control), and after 48 h under the anaerobic fermentation. The highest quantities of myricetin, luteolin, quercetin, and kaempfer were determined after fermentation in aerobic conditions, but the highest amount of quercetin-3-\(O\)-glucose was identified after 24 h under anaerobic fermentation conditions (Table 3). Phenolic acids can be used to treat oxidative damage illnesses (cancer and heart diseases), and they are the plant's secondary metabolites [15]. Our results show that the highest amount of gallic and benzoic acids occurred after 72 h anaerobic fermentation. The highest quantity of chlorogenic acid (9.26 mg 100 g\({}^{-1}\)DW) was determined in not-fermented organic willowerh leaves (control), for ellagic acid (1630.68 mg 100 g\({}^{-1}\)DW) after 24 h aerobic fermentation, and \(p\)-coumaric after 24 h anaerobic fermentation (345.85 mg 100 g\({}^{-1}\) DW) (Table 4). Carotenoids are very important for human health, and it is advised that they have to be a regular part of daily food. Carotenoids are found in many plant products [16]. According to our results in willowerh leaves samples, all individual carotenoids levels were significantly higher after 24 and 72 h anaerobic fermentation: lutein 27.83 mg 100 g\({}^{-1}\) DW (24 h) and 29.75 mg 100 g\({}^{-1}\) DW (72 h), zeaxanthin 13.96 mg 100 g\({}^{-1}\) DW (24 h) and 14.32 mg 100 g\({}^{-1}\) DW (72 h), and beta-carotene 6.31 mg 100 g\({}^{-1}\) DW (24 h) and 6.30 mg 100 g\({}^{-1}\) DW (72 h) (Table 5).
## 6 Results and Discussion  
### Statistical and Machine Learning Models  
This subsection shows how statistical and machine learning models were used to predict the fare of the Uber service in Madrid with a one-hour prediction horizon (using scikit-learn's open-source machine learning library). Several input features were used, which include Uber API data and the new exogenous variables imposed on the model (as shown in Section 4).  
The Uber fare variable (FARE) was used as the target variable explained by all the other variables listed in Table 2 as features (e.g., trip distance, travel time, delay, etc.). In this study, the default hyperparameters for the three machine learning models were used. It is noteworthy that the same random data split of training and testing sets was used for all models (i.e., Train/Test equal to 80/20) for comparison purposes. Predictive accuracy is evaluated and compared across all models (see Table 4).  
The results show that the Random Forest model has the best average performance according to the RMSE (EUR3.40) and MAPE (6.32%) metrics. Although the RMSE results do not fluctuate as much, the MAPE results show that the Random Forest model performs better than the Decision Tree and Linear Regression models. Figure 6 shows the scatterplots of real fare values versus predicted ones.  
The Linear Regression model has difficulty in predicting higher fare values (mainly values above EUR 50), while the Decision Tree and Random Forest models show similar trends, and both predict better high fares (but it is the Random Forest that best predicts fares overall). However, it is still possible to observe some fare peaks in which the predicted values differ greatly. Thus, the next subsection presents the application of cluster analysis to highlight the differences in predicting Uber fares across the alternative models.