esa-sceva/satcom-chunk-collection huggingface dataset:
# Algorithm Providing Ordered Integer Sequences for Sampling with Replacement Confidence Intervals
## 1 Introduction  
The sampling strategy is foundational when dealing with finite populations. In \"sampling with replacement,\" after extracting an individual (or a case) from a population, it can be selected for insertion in the population, having a non-zero probability of being extracted again in the future. In medical studies, sampling a dichotomous outcome emulates a Bernoulli trial [1], and the ratio between the desired (positive) outcome (\(x\)) and the number of trials (or sample size, \(m\)) leads to a proportion (\(0\leq x/m\leq 1\)).  
Fundamental in this context is the probability associated with the reproduction of the observed outcome. Imposing the size of a second sample to have the value of the first one, the distribution function for the probability of having \(y\) as the outcome is binomial (referring to the \((^{m}_{y})\) notation; see [2]), as in Equation (1):  
\[f_{B}(y;x,m)=\binom{m}{y}\Big{(}\frac{x}{m}\Big{)}^{y}\Big{(}1-\frac{x}{m} \Big{)}^{m-y} \tag{1}\]  
In Equation (1), the true proportion rate from the population has been replaced by the observed value of it from the sample. In the absence of any other observation, all the information from the sampling has been used. The question is \"How far away from \(x\) can \(y\) be?\". The result can be expressed using a confidence interval (CI) assuming a success rate (usually 95%).  
Calculating the CI (with a conventional risk of 5% or otherwise) for a discrete probability distribution is a problem of combinatorics, which is a fact that has been recognized for quite a long time [3], but the complexity of the problem denied the availability of an exact method for its calculation until much later [4].  
A series of approximations have been proposed, speculating the normal asymptotic behavior of the binomial distribution [5]. The construction of the exact CI has been proposed as well [4; 6]. Feeding an optimization algorithm with the values obtained from a pool of variants is yet another explored alternative [7].  
Turning to the fields of survey sampling and robust estimation, readers can find very useful results, including [PERSON]'s, communicated in [8] (reporting a novel class of estimators and several new member estimators, combining the ratio and product forms, within the framework of adaptive cluster sampling), as well as those in [9] (reporting new estimators utilizing ranked set sampling to assess the population mean when faced with both correlated and uncorrelated measurement errors), and those in [10] (reporting a case study on a Poisson distribution).  
An algorithm balancing the actual error around the imposed level of error is proposed here for variables taken from sampling with replacement and their proportions.
# Analysis and Design of Infrared Search and Track System with Afocal Zoom Telescope
## 4 Design of the IRST Optics  
### Design and Optimization Process  
Based on the analysis in Section 3, the specified IRST in Section 3.1 consists of a 6\(\times\) afocal zoom telescope and imaging objective with a focal length of 60 mm. Both the individual and combined optical systems are carried out with the Code V 10.6 software. The merit functions with constraints such as the linearity of the \(m_{\text{tele}}\), pupil location and transverse ray aberrations are built, and the manufacturable limitations for the lens data and glass materials are also added gradually. After both parts met good performance, we turned to the further optimization of the combined IRST. With the addition of more configurations, the maximum image wandering is found and further optimized until the result meets the performance requirements.
## 2 Literature Review
### IDEA Project 52--USA  
The IDEA Project 52 [25] for a track maintenance system was designed to localize vehicles with the use of a GPS system and so-called navigation by calculation. The main localization principle is to identify position by means of differential GPS and with Carrier-Phase Differential GPS (CDGPS) algorithm level 2 [26]. This principle works on the basis of raw GPS L-band carrier signals, enabling the determination of the position to an accuracy within centimetres (10-30 cm). The continuously extended High Accuracy--National Differential GPS (HA-NDGPS) equipment is used at stationary differential GPS stations. The position data is then transferred via the communication line to a dispatcher's site, which allows effective transport control within the maintained track.  
The original solution proposed the use of one standard frequency. However, a study [26] demonstrated that, to achieve the required frequency, it was necessary to maintain maximum intervals between the stationary stations of up to 30 km. A dual-frequency (L1/L2) GPS receiver must be used to reach greater intervals between the stationary stations (up to 150 km). A further step to increase the algorithm calculation accuracy was the introduction of the Kalman filter. Distances between the stationary stations, thus, eventually increased to 200 km. However, the required accuracy at these distances (150-200 km) has not been proved.  
For the identification of a rail vehicle within infrastructure, a digital track map is used. The identification of a rail on a single track is trivial. The problem turns up in the identification of rail on multirail tracks. This problem is solved by means of a calculation that determines the most presumable rail. The accuracy is 40 cm with a probability of 97%.
# Design of Chaotic Interleaver Based on Duffing Map for Turbo Code
## 2 Parallel Concatenated Convolutional Codes
### Quality Scale for Bit Error Rate  
The performance of turbo codes is first analyzed and then compared using BER based on the SNR. Four types of interleavers are employed in turbo codes with different short blockchains. BER is assessed through a comparison with the quality scale of the International Telecommunication Union (ITU), shown in Table 1[30].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**ITU Quality Scale** & **BER** \\ \hline Excellent & \textless{}1 \(\times\) 10\({}^{-4}\) \\ \hline Good & \(1\times 10^{-4}\) to 4 \(\times\) 10\({}^{-4}\) \\ \hline Satisfactory & \(4\times 10^{-4}\) to 8 \(\times\) 10\({}^{-4}\) \\ \hline Poor & \(8\times 10^{-4}\) to \(1\times 10^{-3}\) \\ \hline Very Poor & \textgreater{}1\(\times\) 10\({}^{-3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: ITU quality scale in terms of the BER.  
Figure 2: Selected turbo encoder scheme for one-third rate.
# Development of Pretreatment Approaches for Authentic Representation of Tea Infusion Aroma
## 1 Introduction
[16] identified these 10 compounds (linalool, nonanal, safranal, \(\beta\)-farnesene, mycrene, (Z)-3-hexenyl hexanoate, hexyl hexanoate, geranyl acetone, \(\beta\)-ionone, and an unknown compound) as making significant contributions to Longjing tea's aroma profile, particularly linalool, nonanal, (Z)-3-hexenyl hexanoate, and \(\beta\)-ionone. In contrast, [PERSON] et al. [17] reported a different top-10 set of aroma contributors for Longjing tea infusion, comprising dimethyl sulfide, 3-methylbutanal, 2-methylbutanal, hexanal, octanal, linalool, nonanal, decanal, 3-methyllnonate-2,4-dione, and \(\beta\)-ionone. It is evident that the aroma compounds 3-methylbutanal, 2-methylbutanal, linalool, nonanal, decanal, and \(\beta\)-ionone represent particularly crucial components of Longjing tea's characteristic flavor profile. Water is a critical medium for tea quality formation and transformation, with different water qualities yielding distinct tea liquor characteristics and nutritional profile [18; 19; 20; 21]. While numerous studies highlight the impact of water quality on aroma based on sensory evaluations, few have investigated its influence on aroma composition [17], and optimized extraction conditions for aroma analysis remain scarce. Notably, no study has explored whether high-temperature, prolonged extraction affects the true aroma profile of tea infusion brewed with different water quality. However, establishing an appropriate aroma extraction method is essential for efficient and accurate preparation of aroma compounds, enabling precise analysis of water quality's impact on tea aroma. Research indicates that different tea categories exhibit varying sensitivities to water quality [22]. Green tea demonstrates particularly high sensitivity to ionized water [23], and premium-grade green teas generally exhibit higher sensitivity to water quality variations compared to conventional-grade green teas [24]. Green tea tends to develop a cooked aroma character when brewedwith high-ion-content water, and this effect is markedly exacerbated under prolonged high-temperature conditions [23]. In other words, the aromatic profile of green tea may undergo transformations during extraction when brewed with different water quality. Consequently, investigating optimal aroma extraction conditions for green tea infusions prepared with varying water quality becomes particularly imperative. This study employs HS-SPME/GC-MS to compare extraction conditions for aroma analysis of Longjing tea brewed with different water qualities, aiming to identify parameters that authentically represent green tea soup aroma and maximize the enrichment of aroma components. The findings are expected to provide theoretical and technical guidance for future research on water quality's influence on tea aroma substances.
## 4 Robustness Analysis of the ADN against the CCF
### Robustness Analysis
#### 4.1.4 Network Topology and Robustness of the ADN  
The relationship between the final expected proportions \(R_{PF}(\infty)\) and \(R_{IF}(\infty)\) of remaining normal operational nodes and the network topology is mainly reflected in the probability of occurrence of the event \(Evt_{P3}\) and the event \(Evt_{I3}\), then in \(f_{P}(\lambda)\) and \(f_{\rm I}(\gamma)\) (in Appendix B). \(Evt_{P3}\) is an event which is defined to represent the failure of a node \(n\) of the PN due to the load redistribution caused by the failure of its neighbor nodes. \(Evt_{I3}\) is an event which represents the failure of a node \(n\) in the CN due to the load (data packets) redistribution caused by the failure of its neighbor nodes. Under the same average node degree, \(f_{P}(\lambda)\) and \(f_{\rm I}(\gamma)\) of a BA network is smaller than that of an ER network (see Appendix B analysis). Therefore, the fourth conclusion of our analysis is that the robustness of the ADN with ER network topological structure is greater than that with BA network topological structure under the same average node degree.
## 1 Introduction
The experimental validation of the QWSA-HDLAR model is tested using two datasets, namely KTH and UCF Sports datasets. In short, the major contributions are listed as follows:  
* An automated QWSA-HDLAR technique encompassing NASNet-based feature extraction, QWSA-based hyperparameter tuning, and HCNN-BiRNN-based classification is presented for the identification and classification of human activities on HCI. To the best of our knowledge, the presented QWSA-HDLAR technique does not exist in the literature. * Employ QWSA-based NASNet model to extract feature vectors, where the QWSA helps in accomplishing enhanced classification results due to the hyperparameter tuning process. * Validate the performance of the QWSA-HDLAR technique using two datasets, such as KTH and UCF Sports datasets.
# Reinforcement Learning Based Relay Selection for Underwater Acoustic Cooperative Networks
## 1 Introduction  
Over the last two decades, the development of underwater acoustic (UWA) systems for ocean monitoring has grown sharply. As a matter of fact, the demand of UWA data collection has become more and more important for numerous research and industries fields, such as renewable energies, underwater mining, offshore oil and gas [1, 2, 3]. However, the ocean environment presents unprecedented challenges for UWA data collection including, but not restricted to, limited communication distance, limited bandwidth, limited service energy and dynamic channel conditions. In the complex and dynamic ocean environment, the platforms or sensing nodes with intelligent learning capabilities have the potential to learning optimum strategy for performance improvement.  
The unique characteristics of UWA channel bring many limitations to data transmissions. For example, the large frequency and distance dependent transmission attenuation [4, 5] will lead to the limited communication distances. At the same time, the temporal and spatial variation characteristics [6, 7, 8] will make the data link lose the connectivity. During the long-term deployment of UWA network, the link disconnection is highly likelyto occur which results in transmission failure. Cooperative communication is a key technology that can achieve reliable transmission, where the relay node can assist the failure nodes to forward data to destination. One of the crucial issues in cooperative communication is effective relay selection. This paper will explore how intelligent reinforcement learning helps to enhance the performance of relay selection in UWA communication networks.
## 6 Application to Experimental Data
### Results  
As mentioned above, for 1000 simulations, the simulated data that are classified as target signals are utilized as target feature vectors while the remaining simulated data and the Rock 2 measurements are done as clutter feature vectors. Before training the logistic regression model, PCA is applied to the training vectors to obtain 31 principal components with explained variance of 0.99. In the simulations, the test data consists of 2520 cylinder feature vectors and 3600 feature vectors of the Rock 2 measurements. For 1000 simulations, the mean of AUCs is 0.9325 with its standard deviation of  
Figure 14: 2D plots of the matched filtered backscattering time series of the three objects at the orientation of 0 degrees (broadside). The dB scale is relative to the brightest pixel in each measurement group. The red lines represent the cross range from \(-2.5\) to \(2.5\) m, where 400 time series are located.  
0.0038. One of the 1000 ROC curves is shown in Figure 15. As mentioned above, the AUC of 0.9325 indicates that the model achieves outstanding discrimination performance [14].  
The classification table for the test data, which are averaged over 1000 simulations, is shown in Table 4, where the numbers in parentheses indicate the standard deviation. According to the classification table in Table 4, the average detection rate is 0.7743 with its standard deviation of 0.0115, while the average false alarm rate is 0.058 with its standard deviation of 0.0091.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Predicted Class**} \\ \cline{3-5}  & & **No Target** & **Target** & **Total** \\ \hline \multirow{3}{*}{**True class**} & **No target** & 3479.773 (19.8751) & 120.227 (19.8751) & 3600 \\  & **Target** & 568.718 (29.0805) & 1951.282 (29.0805) & 2520 \\ \cline{1-1}  & **Total** & 4048.491 & 2071.509 & 6120 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Classification table of the model with the test data for 1000 simulations.The numbers in parentheses indicate the standard deviation.  
Figure 15: One example of 1000 ROC curves with average AUC = 0.9325 and its standard deviation = 0.0038.
# Protocol for Energy-Efficiency in Networked Control Systems Based on WSN
## 6 Example of Application of the Energy-Efficiency Protocol
### Results
#### 6.3.1 Energy Saving Protocol--Trade-Off Approach--\(L\) Unique  
In this section, a dynamic output-feedback controller is designed considering the case where \(L\) is fixed \(\in[1,\ldots,30]\). The maximum value of \(L\) was chosen equal to 30 because the probability of successful transmission between the source and sink (\(P_{S}\)) for each fragment of \(y(k)\) and \(u(k)\) tends to 1 for \(L>30\), which provides the same performance of the classic control design. The minimum value of \(L\) can not be zero, since in this case the system would operate in open-loop (null control signal). Thus, the minimum value of \(L\) was set to \(L=1\), ensuring a closed-loop operation. Using these assumptions  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(\mathcal{R}\) & \(p\) & \(N\) & \(\mathcal{S}\) \\ \hline
1 & 0.5 & 6 & \(\tilde{g}_{1}\) \\
2 & 0.5 & 4 & \(\tilde{g}_{2}\) \\
3 & 0.45 & 5 & \(\tilde{n}_{1}\) \\
4 & 0.4 & 5 & \(\tilde{n}_{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Network parameters used in the experiment.  
49.25% greater than the classical case. Although \(L=5\) provides greater energy savings, the trade-off relationship between the norm and energy savings is negative. Even so, this choice can be attractive depending on the criterion adopted by the designer.  
Knowing that the node with highest energy consumption is the node closest to the source belonging to the route with the lowest probability per hop (\(p=0.4\)), one has that the critical node is \(\mathcal{N}_{16}^{[4,\bar{u}_{2}]}\). Another information obtained from Table 5 is that the variation in the global energy consumption (\(E_{GE}\)) in function of \(L\) does not present a linear relationship with the variation in the energy consumption of the critical node (\(E_{GE}(\mathcal{N}_{16}^{[4,\bar{u}_{2}]})\)).
## 2 Material and Methods
### Radiometric (Emanation) Measurement Method  
The measurement of radon radiation using \"Alpharad Plus\" is based on the electrostatic deposition of charged Po-218 (RaA) ions from the air sample to the surface of the semiconductor detector. The electrical impulses generated under the influence of alpha particles on the detector were amplified with a preamplifier, fed to the input of an analogue-to-digital converter, and then processed with a computer. The measurement results were displayed on a colour LCD screen and stored in non-volatile memory. The radon radiation was determined by the number of registered alpha particles during the decay of RaA atoms deposited on the detector [46].  
To install the samplers, boreholes were drilled at key points with a diameter of 3 to 5 cm and a depth of 0.7 to 1 m. The number of key points was determined by the need to study variations in radon radiation within the kimberlite body, at contacts with the host environment, and also considering the background radiation outside the pipe. The installation of the samplers was carried out in a suspended state with the subsequent sealing of the borehole neck with soil. The exposure time of the sampler in the borehole, required to equalise the radon radiation in the soil air and in the sampler, ranged from 12 to 18 h. After the end of the exposure, the sampler was removed and sealed with plugs. Measurements using a radon radiometer were carried out no more than one hour after removing the sampler from the borehole.  
Figure 2: Spatial distribution of radon radiation and gas permeability of soils in the territory of the Chidvinskaya pipe.
## 4 Experiments and Analysis
### Experimental Results and Analysis
#### 4.5.7 Broader Applicability and Generalization  
TempoQR [6] is a temporal knowledge reasoning model that extracts textual features of questions, associates entity information from knowledge graphs, and parses time ranges through three core modules. It ultimately integrates these three types of information within a Transformer architecture for joint reasoning, enabling precise answers to complex questions involving temporal changes. TempoQR has developed two methods to recover time-related information specific to a given question. The first method, referred to as hard supervision, retrieves relevant entities and facts from the knowledge graph (TKG) to obtain temporal information. The second method, known as soft supervision, infers temporal information by analyzing the representation of the question, utilizing operations in the embedding space to recover missing temporal facts. Since the time granularity processed is in years, the multi-granularity time aggregation module in the MTQADC model was not utilized when handling the CronQuestions dataset. However, our model employs both soft and hard supervision from TempoQR to recover time-related information specific to the question. Table 9 presents the experimental results on the CronQuestions dataset.  
The experimental results on the CronQuestions dataset indicate that the hard supervision method of MTQADC achieved a Hits@1 score of 0.875 and a Hits@10 score of 0.977, demonstrating its effectiveness in retrieving relevant answers. While the performances of both TempoQR and MTQADC's hard supervision methods are commendable, they show limitations in handling complex questions, with Hits@1 scores of 0.864 and 0.789, respectively, suggesting that they may not fully meet the demands of certain complex queries. The soft supervision method of MTQADC achieved a Hits@1 score of 0.771 and a Hits@10 score of 0.958, which, although slightly lower, still showcases the potential of soft supervision in recovering time-related information.  
In comparison with other models, MTQADC's hard supervision method demonstrated strong performance in retrieving entity answers, with Hits@1 at 0.903 and Hits@10 at 0.980, reflecting its capability in entity recognition. Other models, such as TempoQR, also warrant attention for their performance in handling temporal answers; TempoQR's hard supervision method achieved a Hits@1 score of 0.918 and a Hits@10 score of 0.978. Overall, the MTQADC model, by combining the soft and hard supervision methods from TempoQR, effectively recovers information related to specific time ranges. However, further optimization is needed to enhance its overall performance and adaptability, particularly in addressing complex questions and certain types of answers.
# Superpixel Segmentation Using Weighted Coplanar Feature Clustering on RGBD Images
## 4 Weighted Coplanar Feature Clustering
### Convergence of Clustering  
The convergence rate of linear iterative clustering has a significant influence on the speed of the superpixel algorithm; therefore, the influence of different designs of distance measure on the convergence rate was evaluated. First, we used the distance measure in Equation (3), a Euclidean distance, because the convergence of the linear iterative clustering frameworks can be proven when using Euclidean distances mathematically. The results of many images demonstrated that the clustering method converged within 10 iterations. Second, we used the dynamic distance measure in Equation (8). After experimenting on several RGBD images, it was confirmed that 20 iterations were sufficient for convergence in practice.