esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Fuzzy Harmony Search Algorithm Using Fuzzy Logic for Dynamic Parameter Adaptation
## 4 Experiment Results Comparison of the Simple Harmony Search Algorithm with Fuzzy Harmony Search
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multicolumn{6}{c}{**FHS2 Rules Increase in Range 0.7 to 1**} \\ \hline
**Function** & **Best** & **Worst** & **Average** & **Function** & **Best** & **Worst** & **Average** \\ \hline
**Spherical** & \(2.36\mathrm{E}-05\) & \(1.49\mathrm{E}-04\) & \(6.57\mathrm{E}-05\) & **Spherical** & \(1.54\mathrm{E}-09\) & \(2.64\mathrm{E}-04\) & \(1.38\mathrm{E}-05\) \\
**Rosenbrock** & \(1.47\mathrm{E}-11\) & \(1.31\mathrm{E}-02\) & \(4.88\mathrm{E}-04\) & **Rosenbrock** & \(5.44\mathrm{E}-14\) & \(2.80\mathrm{E}-10\) & \(1.76\mathrm{E}-11\) \\
**Rastrigin** & \(9.31\mathrm{E}-12\) & \(2.24\mathrm{E}-07\) & \(2.26\mathrm{E}-08\) & **[PERSON]** & \(1.20\mathrm{E}-11\) & \(1.28\mathrm{E}-07\) & \(1.08\mathrm{E}-08\) \\
**[PERSON]** & \(8.72\mathrm{E}-06\) & \(4.54\mathrm{E}-04\) & \(1.18\mathrm{E}-04\) & **[PERSON]** & \(1.07\mathrm{E}-08\) & \(4.28\mathrm{E}-04\) & \(9.55\mathrm{E}-06\) \\
**[PERSON]** & \(7.33\mathrm{E}-05\) & \(1.80\mathrm{E}-03\) & \(2.71\mathrm{E}-04\) & **[PERSON]** & \(1.10\mathrm{E}-10\) & \(7.55\mathrm{E}-04\) & \(4.72\mathrm{E}-05\) \\
**Sum Square** & \(4.82\mathrm{E}-06\) & \(1.05\mathrm{E}-04\) & \(3.59\mathrm{E}-05\) & **Sum Square** & \(2.06\mathrm{E}-08\) & \(5.50\mathrm{E}-06\) & \(2.53\mathrm{E}-06\) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison results obtained with FHS2 and the harmony search algorithm. the best fuzzy system, in this case FHS2 with rules and increase in the output range from 0.7 to 1. The best results obtained are of the proposed method. The third proposal was proposed to make dynamic the _HMR_ and _PArate_ parameters in the range from 0.7 to 1, both for increasing and decreasing the parameters. In Table 9, the overall averages, as well as best and worst obtained for each mathematical function and method, are presented. The results were obtained with the FHS3 using the range of 0 to 1 and rules increment or decrement are shown. In Table 10, the averages of the 30 experiments for each mathematical function are shown; also the best and worst results for each mathematical function and the overall average in each function are shown. The results of these experiments use the range of 0.7 to 1 and rules in increment with the proposed method. In Table 11, the averages of the 30 experiments for each mathematical function are shown. The best and worst results for each mathematical function and the overall average obtained in each function for each method are also shown. The results are with the original algorithm and the best fuzzy system, in this case FHS3 with rules in increase in the output range from 0.7 to 1. The results obtained in this case are better only in two benchmark functions; other evidence is not significant since similar results are obtained.
## 3 Scheme Design
### Scheme Process
#### 3.2.1 Identity Registration: One-Time-Use Accounts
Individuals in possession of the private keys are empowered to decrypt and scrutinize the status of logistics. To reduce malicious behavior, every change to the sending or receiving address must be signed using the private keys of both parties. Therefore, employing one-time-use accounts for transactions can avoid privacy issues caused by repeated transactions with a single account. The one-time-use account generated is securely associated with the user's identity through digital signatures or encrypted tokens. To ensure the validity of the one-time-use account, the system can employ several mechanisms, including time-based validation methods, digital signatures, or asymmetric key verification. These techniques play a crucial role in guaranteeing that the account remains valid for a specified duration or until the intended purpose is accomplished. Once the predetermined period expires or the purpose is fulfilled, the account is either invalidated or rendered unusable. Users maintain only one account, simplifying management, and the process of generating DID for each transaction is concealed from the user through the client's access to the logistics company's interface. When transferring funds to another person's public address, the funds are actually dispatched to a randomly generated, one-time destination address known as the stealth address, as shown in Figure 2. This one-time address is instantly generated for the transaction and is public. However, the sender's public key and the recipient's public key remain private, and the sender's public address does not appear in public transaction records. Only the sender and receiver are aware of the concealed address of the order funds. Recipients who wish to inquire about funds need to possess a view private key to scan the blockchain to verify whether the funds have arrived. Only the recipients of the funds know their viewing private keys. Importantly, these addresses are not associated with the sender's or recipient's real identity to reduce identity leaks and prevent insider intrusions.
## 3 Experimental Results
### Results
#### 3.4.2 SumMe Dataset  
Regardless of the temporal subsampling method used, all results in the SumMe dataset in Table 2-B are without stepwise regression. Across all learning architectures, the best model uses HEVC-based temporal subsampling, and the second-best model uses PCA-based temporal subsampling. The highest overall scores appear with the use of the random forest architecture.
## 4 Results
### Comparison between Post-Treatment (T2) and Final ClinCheck (T2 ClinCheck) Results  
The predictability of ClinCheck of the Invasilign software was determined at the end of the treatment (Table 2) by establishing comparisons between the final molar rotations achieved in the digital models (T2) and the planned one (T2 ClinCheck). A statistically significant difference was detected, meaning that the amount of change planned was not associated with the obtained outcomes. The mean difference between T2 ClinCheck-T2 was \(-4.2^{\circ}\) (mean HA T2: \(11.2^{\circ}\)\(\pm\) 5.7\({}^{\circ}\); mean HA T2 ClinCheck: 7.0\({}^{\circ}\)\(\pm\) 5.4\({}^{\circ}\); \(p\) < 0.001). Comparable results were obtained for the measurements on the sagittal plane, showing a mean difference of 0.9 mm for the mesiobuccal cusps (mean MBS T2: 30.3 mm \(\pm\) 2.5 mm; mean MBS T2 ClinCheck: 31.2 mm \(\pm\) 2.3 mm; \(p\) < 0.001) and 0.7 mm for the distobuccal cusps (mean DBS T2: 25.2 \(\pm\) 2.5 mm; mean DBS T2 ClinCheck: 25.9 \(\pm\) 2.2 mm; \(p\) < 0.001). On the contrary, the expansion movements showed high predictability for both the mesiobuccal and distobuccal cusps. The mean difference between T2 ClinCheck-T2 was 0.4 mm for MBE and 0.3 mm for DBE, meaning no statistically significant changes. As a percentage, predictability was 60% for Henry's angle, 52.6% for mesiobuccal sagittal movement, and 56.30% for distobuccal sagittal movement (Table 3).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Measurements** & **Pre-Treatment T1 (_n_ = 53)** & \multicolumn{4}{c}{**Post-Treatment T2 (_n_ = 53)**} \\ \hline  & **Mean** & **SD** & **Mean** & **SD** & **Diff** & **95\% CI** & \(p\) **Value** \\ \hline Henry’s angle (HA) & 17.5 & 4.2 & 11.2 & 5.7 & \(-6.3\) & \(-8.526\) to & *** \\ Mesiobuccal & & & & & & \(-4.165\) & \\ Expansion & 24.3 & 1.0 & 26.5 & 0.8 & 2.2 & 1.905 to 2.511 & *** \\ (MBE) & & & & & & & \\ Distobuccal & & & & & & & \\ Expansion & 26.1 & 0.9 & 27.6 & 0.8 & 1.5 & 1.255 to 1.779 & *** \\ (DBE) & & & & & & & \\ Mesiobuccal & & & & & & & \\ Sagittal (MBS) & & 2.2 & 30.3 & 2.5 & 1.0 & 1.431 to 0.636 & *** \\ Distobuccal & & & & & & & \\ Sagittal (DBS) & & 24.3 & 2.1 & 25.2 & 2.5 & 0.9 & \(-0.475\) & *** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Descriptive statistics and statistical comparisons of T2-T1 changes (Paired _t_-test).
# Effects of Fineness and Dosage of Fly Ash on the Fracture Properties and Strength of Concrete
## 3 Experimental Details  
Concrete beam specimens were tested to determine the relationship between compressive strength, fracture energy, and stress intensity factor as a function of the cement replacement ratio of fly ash and the fineness of fly ash. The water-cemetitious ratio (w/cm) used was 0.35 for all concrete mixtures. Three cement replacement ratios of 0%, 10%, and 20%, and three particle sizes of fly ash that pass through no. 175 (90 \(\upmu\)m), no. 250 (63 \(\upmu\)m), and no. 325 (45 \(\upmu\)m) sieves were selected for the mixtures. The three-point bend test on notched beams was used to determine the load-deflection relations, and the maximum load for calculating the fracture energy (\(\mathrm{G_{F}}\)) and the critical stress intensity factor (\(\mathrm{K^{S}}_{\mathrm{IC}}\)).
# Temporal Multimodal Data-Processing Algorithms Based on Algebraic System of Aggregates
## 3 Approach and Requirements for Temporal Multimodal Data Processing  
In our research, we consider an approach to temporal multimodal data processing based on the formal specification of the objects under study. For this purpose, we need a theoretical apparatus to provide the logic of presentation and processing of temporal multimodal data at the level of mathematics, algorithms, and software, taking into account such features of this data:  
* Multimodality means that the object is determined by a collection of data of a different nature; the logic of presenting and processing data about the object depends on the qualitative and quantitative composition of the data set.  
* Temporality means that the elements of data collection are ordered by the time of their receiving; the order of following individual elements of the data sequence affects the result of processing the entire set of data of the object.  
The description of the object and the effective processing of temporal multimodal data require specific mathematical abstractions and mechanisms to operate them. The basic requirements for a mathematical apparatus to describe and process data include the possibility of the following:  
1. Presenting a data set as a structure of semantically interconnected elements.
2. Considering the sequence of data set elements when performing logical operations on them.
3. Reordering elements of the data set.  
Let us consider several candidates for a mathematical concept to meet these requirements. As the data are temporal, the first possible candidate is the theory of time series (TTS) [13; 14]. The TTS is one of the branches of mathematical statistics that deals with the analysis of stochastic processes. The TTS enables the analysis of data sequences, as well as the study of trends, predictions, and other similar data processing. However, time series do not allow any logical operations of the data elements. Therefore, the TTS cannot be employed for logical processing of temporal multimodal data.  
Two other candidates are the theory of sets (TS) [15] and the theory of multisets (TMS) [16; 17]. Both of them provide flexible mechanisms for the logical processing of data sets; however, they do not consider the sequence of elements and, accordingly, do not provide the possibility of ordering the elements in a set of temporal multimodal data.  
Thus, neither of the considered theories fully correspond to the needs of temporal multimodal data processing based on the formal specification of the objects under study. However, the abovementioned requirements are satisfied by the Algebraic System of Aggregates (ASA) [18; 19]. This mathematical concept enables data processing with consideration of both required data features, namely multimodality and temporality (Table 1).  
Let us consider the main provisions and possibilities of the ASA for the presentation and processing of temporal multimodal data describing an object.
# Data Quality Measures and Efficient Evaluation Algorithms for Large-Scale High-Dimensional Data
## 2 Related Work
### Descriptor-Based Approaches  
[PERSON] and [PERSON] [1] proposed simple but powerful quality measures based on descriptors. They proposed 12 quality measures, namely \(F1\), \(F2\), \(F3\), \(L1\), \(L2\), \(L3\), \(N1\), \(N2\), \(N3\), \(N4\), \(T1\), and \(T2\). The \(F\) measures represent the amount of feature overlap. In particular, \(F1\) measures the maximum Fisher's ratio which represents the maximum discriminant power of between features. \(F2\) represents the volume of overlap region in the two-class conditional distributions. \(F3\) captures the ratio of overlapping features using the maximum and minimum value. The \(L\) measures are for the linear separability of classes. \(L1\) is a minimum error of linear programming (LP), \(L2\) is an error rate of a linear classifier by LP, and \(L3\) is an error rate of linear classifier after feature interpolation. The \(N\) measures represent mixture identifiability, the distinguishability of the data points belonging to two different classes. \(N1\) represents the ratio of nodes connected to the different classes using the minimum spanning tree of all data points. \(N2\) is the ratio of the average intra-class distance and average inter-class distance. \(N3\) is the leave-one-out error rate of the nearest neighbor (1 NN). \(N4\) is an error rate of 1 NN after feature interpolation. The \(T\) measure represents the topological characteristic of a dataset. \(T1\) represents the number of hyperspheres adjacent to other class features. \(T2\) is the average number of data points per dimension. These quality measures can capture various aspects of data quality; however, they are fixed for binary classification and not applicable for multi-class problems. Furthermore, quality measures, such as \(N1\), \(N2\), and \(N3\), require a large amount of computation time on large-scale high-dimensional data.  
[PERSON] and [PERSON] [2] proposed a quality measure for high-dimensional but small biomedical datasets. They used singular value decomposition (SVD) with time complexity \(\mathcal{O}(min(m^{2}n,mn^{2}))\), where \(m\) is the number of data points, and \(n\) is the number of features. Thus, it is computationally demanding to calculate their measures for the datasets with large \(m\) and \(n\), such as recent image datasets.  
There are other descriptor-based approaches, for example for meta learning [8; 9], for classifier recommendation [10], and for synthetic data generation [11]. However, only a small number of data points in a low dimensional space have been considered in these works.
# Unknown Traffic Recognition Based on Multi-Feature Fusion and Incremental Learning
## 4 Evaluation
### Selection of Feature Number and Threshold  
The number of features extracted per neural network channel in the feature extraction and fusion module and the threshold value in the unknown traffic classification algorithm are significant parameters that affect the performance of our model. In this section, we discuss how to select the values of the two parameters.  
We first considered the effect of the number of features extracted by the mRMR algorithm on the recognition effect, and the results are shown in Figure 4. We set the number of features extracted from each neural network channel to be the same, so the number of features after fusion is a multiple of 3. As can be seen from Figure 4, the accuracy results are better when each neural network extracts and selects the optimal first 100 features with the mRMR algorithm, that is, when the number of fused features is 300. When the number of fused features is over 360, the accuracy of model detection gradually decreases, which we analyze may be caused by the problem of feature redundancy in encrypted traffic, and the excessive number of features might cause interference in traffic classification.  
Figure 3: Loss function of the model during pretraining.  
The threshold value in the unknown traffic detection algorithm also has an important impact on model performance. As for the threshold selection problem, we conducted the following experiments.  
We calculated the distribution of the distance between the features extracted from all classes of traffic and the average known features, as shown in Figure 5, where the red bars represent known features, the blue bars represent unknown features, the vertical coordinates show the number of features, and the horizontal coordinates indicate the Euclidean distance from the average known features. It can be seen that the known features are close, whereas most of the unknown features are farther away, and the two features can be better distinguished when the distance is between 1.0 and 1.5. Therefore, we chose a threshold value of 1.3.
# Dynamic Voltage Optimization Based on In-Band Sensors and Machine Learning
## 6 Machine Learning in Voltage Optimization
### Unsupervised Learning  
Unsupervised Learning is a method that does not require the labeling of the data or the target it needs to reach. Also, the unsupervised learning method does not require to obtain a feedback mechanism or know the structure of the data. This method enables the exploration of the data to extract meaningful information without the guidance of known outcomes or reward functions [42]. One of the most common applications of unsupervised learning is clustering and dimension reduction. Clustering is a data science technique that help to organize data into sub-clusters depending in how the machine correlates data membership to categories of groups. This has shown in how data can be classified into unknown or unforeseen groups that exists in the data. Dimension reduction is a technique that receives data with high-dimensions or each data element contains several parameters and reduces the dimensions or number of parameters for each data element. This is also used for visualization purposes to \"simplify\" the number of dimensions that are display and not lose the interpretation of the overall data. A characteristic example of supervised and unsupervised learning is the clustering of energy data and energy usage analysis from a large dataset of smart meter data [42].
## 4 Experiments
### Performance of the Final Networks  
The DSC Dense Block performs feature extraction and representation in the generator network. The activation function affects the convergence speed, stability, and quality of the generated image. Therefore, different activation functions can be chosen to build the DSCSRGAN to train and test on the DIV2K dataset, including PReLU, LeakyReLU, Tanhshrink, and Softsign. Experimental results showed that a network model using LeakyReLU as the activation function had faster convergence and better network stability in the early stage. The results are shown in Figure 3.  
The number of algorithm parameters generated in the network reported in this paper was reduced by 557064 compared with SRGAN, a drop of 58.2% (Table 1). This reduction in the number of parameters helps to reduce the complexity of the algorithm, improve its robustness, and enhance the real-time performance of the algorithm.  
Five reconstruction algorithms based on learning and DSCSRGAN were selected to perform image reconstruction tests on the Set5 and Set14 datasets. The objective evaluation index average PSNR and average SSIM were calculated. The objective indices were better than those generated by the other algorithms. In Set5, PSNR and SSIM enhancements reached 3.3 dB and 15.97%, respectively. For the Set14 dataset, the PSNR increased by 3.1 dB, and the SSIM increased by 16.46%, as shown in Table 2.  
Figure 4 shows the reconstruction effects of the different algorithms. In the experiments, each picture of the image data set to be trained was first cut into a \(128\times 128\) sized picture, and then the 64 pieces were sent as a batch to the DSCSRGAN to train the generator and the discriminator, with the learning rate set to \(10^{-5}\). The number of iterations was 1000, and the Adam algorithm was used for optimization. The up-sampling factor was \(\times 4\). The generator was pre-trained to avoid falling into a local optimum. At the end of each training batch, the average PSNR and SSIM of the image generated  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Network Model** & **DSCSRGAN** & **SRGAN** & **WSRGAN** & **SRCNN** & **FSRCNN** & **ESPCNN** \\ \hline Parameters & **399367** & 956431 & 956431 & 148608 & 13697 & 61680 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of different network models.  
Figure 3: Changes of loss function with the number of iterations.  
by the batch were recorded. The subjective visual effects of images super-resolution using DSCSRGAN were clearer, and details such as faces, hats, textures, text, and shadows were well restored.
# Analyzing Data Reduction Techniques: An Experimental Perspective
## 3 Related Work
It was possible to conclude that data dimensionality reduction techniques make it much easier and faster to process and analyze data by machine learning algorithms and human input. [PERSON] et al. [12] proposed an energy-efficient approach for IoT data collection and analysis. The proposed technique consists of a data compression technique based on a fast error-bounded lossy compressor conducted on the collected data before sending them to the edge layer. Then, after sending the data to the edge, a supervised machine learning technique is applied. The authors' results proved that the data can be reduced up to 103 times without affecting the data quality. Energy savings are up to 27% after 4 h, and the data accuracy is 98%. The algorithm is better suited for multisensory reading compression. It remains open to try to increase the energy reduction to increase the lifetime of the IoT network. [PERSON], [PERSON], and [PERSON] [13] created a solution that addresses the two main bottlenecks of the analyzed solutions. The first bottleneck is the inability of time series network-reduction data reduction techniques to make decisions on an item-by-item basis. The other bottleneck is the lack of systems that can apply any data reduction method without heavy reconfiguration or significant delays. Despite the ability of the proposed solution to handle streamed data, it has inferior results when compared to other a posteriori data reduction techniques. Its accuracy may depend on the domain in which it is applied. It remains to further investigate the \"streamification\" of data reduction techniques and adaptation of data reduction techniques to specific use cases. In addition, the authors do not present the original size of the studied data in the paper. [PERSON] et al. [14] gather information about compression techniques and design a solution within a node that compresses data using two lossless compression techniques. The author's concern in designing the method was to create a system that can compress data at a minimal energy cost in low-power devices. The system achieved a compression ratio of 52.67% and a maximum space saving (free space after compression) of 46.1%. However, the paper does not provide detailed information on the type, size, and accuracy of the data examined. Table 1 summarizes the comparison between the different data reduction algorithms according to the references.