esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### Machine Learning-Based Techniques
However, the data replication problem is expressed as a special Bin Packing Problem that requires a standard solver for integer linear programs, potentially leading to increased computational complexity. The authors of ref. [12] proposed a KR Rough Clustering Technique (K-Means Rough), which applies the rough set approach to knowledge mining and clustering in large datasets. The method allows objects in the dataset to belong to multiple clusters, providing a more flexible and nuanced clustering result. The method better handles datasets with diverse types of data and capture the inherent uncertainty and vagueness in the data by combining distance, similarity, and approximations. However, the method leads to increased computational complexity and time compared with traditional clustering algorithms. The authors of ref. [13] proposed an approach for optimizing query processing in DDBS through vertical fragmentation. The method aims to achieve better results in terms of query processing optimization compared with existing methods. The authors conduct experiments and compare their solution with two alternatives: VFAR and the K-means algorithm with the hamming distance. However, the performance gains achieved by the proposed approach may vary based on the dataset and the query workload, also there lacks a method to assess the generalizability and robustness of the proposed approach. The authors of ref. [14] developed a new model by combining vertical fragmentation, replication, and allocation techniques for DDBS. The method aims to reduce communication costs and query response times in DDBS by implementing the model and significantly enhancing the performance of distributed database environments. However, the approach lacks an assessment of the general applicability and robustness of the proposed model in large and diverse environments. In summary, machine learning-based techniques offer innovative solutions for data fragmentation in DDBMS, and allow us to overcome many challenges related to the efficiency of query processing, communication costs, response times, and replication problems. However, the existing techniques based on a machine learning (ML) approach suffer from several drawbacks: (1) Data quality, where the severe lack of values inside the dataset could highly affect the training phase of the ML models. In addition, applying traditional methods of handling missing data does not ensure an improvement of data quality. (2) Model parameter adaptation, where most of the proposed techniques consider default parameter values or, in the best case, a hyper-parameter process is applied. Unfortunately, without proposing new methods for parameter optimization, the existing technique will not guarantee high accuracy. (3) Complexity, especially when ML models are combined with heuristic algorithms. Therefore, designing less complex and highly accurate techniques is becoming essential for data fragmentation.
## 7 Results and Discussion
### Validation Using UK-DMC Data  
To drive the development and exploitation of GNSS-R on-board, an experimental GPS reflectometry receiver was equipped with UK-DMC satellite [33]. The UK-DMC satellite was launched into a 680 km sun synchronous orbit as part of the disaster monitoring constellation in October 2003 and the first successful data collection was on 22 October 2005 [20]. Here, the data collected on 16 November 2004 over the ocean surface is used to verify the proposed approach. The red lines in Figure 9(a),b are the averaged waveform with the tracking refresh period of 1 s and 3 s, respectively. It is clear that compared to the waveform with the tracking refresh period of 1 ms, which are shown by the blue curves in Figure 10, those waveforms without proper delay compensation are distorted. The deformation is more serious when tracking refresh period is longer as mentioned above. In [22], according to the observation geometry at the time of data collection, the delay difference change rate is computed as 5.97 chips/s, therefore, \(\Delta\tau\) in this paper is 0.00597. The retracked waveform using Equation (21) are displayed by the block curves in Figure 10 from which it is noted that the deformed waveforms are recovered well. Compared the mean squared errors (MSEs) of 0.110 and 0.156 between the non-aligned waveform with the refresh period of 1 s and 3 s and the aligned waveform, respectively, the MSEs between retracked and aligned waveform reduce to 0.026 and 0.044. In addition, comparing to the results of retracking for the distorted waveform with the TPR of 1 s and 3 s, it is could be seen that the retracking performance for the waveform of 1 s is better than the one of 3 s. The one reason is that the waveform of 3 s have more serious deformation than the one of 1 s; the other is that, as shown in Figure 4, the longer TPR results in the larger disagreement of delay difference change when \(\Delta\tau\) in Model (18) is assumed as the constant.
## 1 Introduction
[40] found that for Polish learners of Mandarin at the A1 level, improvement in monosyllabic words could be achieved by training T4 to start at the same high pitch as T1, resulting in a production similar to native speakers. In contrast, ref. [41] found T2 and T3 particularly problematic for six Polish adult learners (three M and three F) in their mid-twenties with B2/C1 proficiency levels. In terms of perception, ref. [42] examined how Polish learners perceive Mandarin monosyllabic tones and found that T3 had the lowest correct perception rate among Polish learners. Further studies by [43; 44] continued to investigate the tonal performance of Polish learners in continuous speech. Evaluations by three native Mandarin speakers using statistical analysis also found that T3 was the most challenging tone for Polish learners. These studies revealed that in continuous speech, the ending point of each tone has a greater impact on the overall understanding of the tone than the starting point. The findings can be categorized into two areas of investigation: differences in overall mean values and differences in overall slope. Firstly, there is a significant difference in the overall mean values between Polish learners and Taiwanese Mandarin speakers, especially for those advanced Polish learners, who scored 4-5 in tonal performance evaluations. Secondly, the overall slopes of all tonal combinations for Polish learners differed significantly from those of Taiwanese speakers across various tonal combinations, indicating inconsistency in pitch variation patterns. In summary, these studies highlight the specific difficulties Polish learners face with certain tones in Mandarin and suggest that focusing on the starting and ending points of tones, as well as overall tonal patterns, can aid in improving their production and perception. This study aims to provide a detailed analysis of tonal acquisition in Mandarin Chinese disyllabic words by Polish learners of Mandarin using several statistical methods, including a mixture random effects model and pairwise comparisons, to offer a realistic observation. Additionally, the study will present pedagogical suggestions to enhance future Chinese tone instruction.
## 3 Results and Discussion
### Statistical Evaluation of Model Performance  
The evaluation results presented in Table 7 demonstrate that the performance of the linear models varies across the four SCS climate zones. Overall, the models show acceptable error levels, with RMSE values ranging from 0.79 \({}^{\circ}\)C to 2.19 \({}^{\circ}\)C, and MAPE values between 2.40% and 9.80%, depending on the city and climatic context.  
Among the zones, the model for SCS 4 achieves the lowest average error, with a mean RMSE of 1.40 \({}^{\circ}\)C, MAE of 1.14 \({}^{\circ}\)C, and MAPE of 4.48%, indicating a robust predictive capacity in warmer and drier climates. Similarly, SCS 3 shows comparable accuracy, with a mean RMSE of 1.45 \({}^{\circ}\)C and the lowest average MAPE, at 4.59%.  
In contrast, the model for SCS 1 exhibits the highest mean error values (RMSE = 1.59 \({}^{\circ}\)C; MAPE = 6.99%), suggesting that predicting LST in cooler and more variable climates poses greater challenges due to potentially higher spatial heterogeneity or microclimatic effects not fully captured by the selected parameters.  
Despite these variations, all four models maintain error levels well within acceptable thresholds for urban temperature modeling, confirming the effectiveness of the zone-specific approach. Since the aim is to characterize average summer spatial patterns rather than maximum UHI peaks, the obtained RMSE and MAE are considered appropriate for representing relative spatial variations. These results also validate the relevance of tailoring models to distinct climatic contexts, as a global model would likely mask important local dynamics.
# Malware Detection Using Memory Analysis Data in Big Data Environment
## 4 Material and Methods  
In this section, firstly, information about Apache Spark big data platform is given. Then, the dataset used in the study is introduced. The preprocessing steps performed are mentioned. Finally, information about machine learning and deep learning methods used in classification is given.  
Apache Spark [42] is an open-source project designed to process big data in parallel. Apache Spark is developed in the Scala language. Its basic structure is Resilient Distributed Dataset (RDD). RDDs are distributed, flexible, and fault-tolerant structures. Apache Sparkdoes in-memory data processing. Thanks to this feature, it can process faster than MapReduce running on disk. Apache Spark consists of Spark Core, Spark SQL, Spark Streaming, MLlib, and GraphX components. Spark Core is the structure on which all components are built. Spark SQL, Spark Streaming, MLlib, and GraphX are the most important libraries of Apache Spark. Spark SQL processes structured data while Spark Streaming is used for the analysis of real-time data. MLlib is Spark's machine learning library. Graph and network analyses are performed with the GraphX library. These libraries can be used together in a single project. Spark has multi-language support for realizing projects. Applications can be made on Spark using Scala, Java, Python, and R languages. Apache Spark can use Hadoop Distributed File System (HDFS) for storage and can be integrated with many big data technologies.
## 4 Results
### SEA Analysis  
During the total monitoring period (three years), the instrumented joints and micro-fractures did not show significant variation of the strain rates, which signifies that no changes of the rheological behaviour occurred that could lead the rock block to failure closely in time. However, the collected data demonstrated that cyclical temperature changes influence the strain rate measured in the rock mass, as they are responsible for the slight and regular accumulation of irreversible strain (plastic vs. elastic strain aliquots). In fact, part of the deformation due to thermal input occurs under elastic conditions, and it will be completely recovered at the end of the thermal cycle, while a very residual part of the deformation occurs under inelastic conditions; therefore, it will not be recovered at the end of the thermal cycle. For this reason, after each daily thermal cycle, the monitored rock mass could accumulates a slight net strain when two consecutive thermal cycles that have the same thermal excursion imply two consecutive strain cycles that present a very marked different amplitudes (i.e., the strain does not return to the starting value unlike the temperature). Such a net strain can be cumulated after a \"critical\" thermal cycle in the rock mass, in which the deformation cannot be recovered. The total cumulative strains recorded so far, due to inelastic effects, range from 55 up to 500 strain depending on the considered sensors (Figure 11).  
Figure 11: Net deformation value (\(\varepsilon_{\mathrm{P}}\)), daily cumulated for each strain device.  
Figure 10: Correlograms indicating examples of cross-correlations between: (**a**) the rate series of the rock temperature and the strain; (**b**) the rate series of the rainfall and the strain; (**c**) the rate series of storm wind and the strain. The LAG value is expressed as a multiple of the sampling step of R\({}^{2}\) (equal to 10 min in our case).  
Considering the cumulative value of net strains cumulated in the rock mass over a large (pluri-seasonal) time window, which are due to inelastic joint deformations, a steady creep-like behaviour results for the rock mass. Nevertheless, by decreasing the analysed time windows (i.e., tending to daily time window or, at most, pluri-diurnal), the observed time-dependent effect properly expresses a thermal fatigue phenomena, known as thermomechanical creep, acting on the monitored rock mass over long time windows; thermomechanical creep can lead towards failure conditions, as it could occur after accelerating creep. The preliminary SEA analysis, carried out in order to obtain the long period daily net deformation rate series on which will apply the FFMs methods, show that the maximum amplitude of this value and the higher frequency of peaks are concentrated in Spring and Autumn seasons. During these seasons, the following two main factors affected the amplitude of the net strain response: (i) the daily thermal excursion is higher than other seasons and (ii) the maximum and minimum thermal variations between two consecutive days is often not negligible.
## 5 Evaluation and Performance
### Simulation Environment  
The simulation of the proposed multi-tier clustering framework for energy efficient routing was performed using MATLAB R2015a. The parameters used for evaluation has been listed in Table 2. The whole network is divided into four zones. The bottom-most zone has only one cluster, while the uppermost zone has four clusters. The upper zone aggregates the traffic from lower zones and hence traffic at zone near BS is higher compared to the lower zone. The efficiency of the proposed approach has been analyzed and results have been compared against state-of-the-art clustering techniques such as LEACH, M-LEACH, EACRP, and EEMAC.
# Extraction of Bioactive Compounds from _Ulva lactuca_
## 3 Results and Discussion
### Carotenoids Profile  
Figure 6 presents the HPLC chromatogram of _U. lactuca_ extract E12, in which seven carotenoids were identified. According to the data presented by [PERSON] et al., 2020 [30] whose HPLC protocol for carotenoids separation and detection was followed by the present work, peaks 2 and 7 correspond to 9-cis neoxanthin and 9-cis lutein, respectively. All the other peaks were identified by using external standards. According to these standards, peak 1 corresponds to _all-trans_-neoxanthin, peak 3 to _all-trans_-violaxanthin, peak 4 to _all-trans_-astaxanthin, peak 5 to _all-trans_-lutein, and peak 6 to _9-cis_-astaxanthin. In general, the carotenoids reported for _Ulva_ spp. in the published literature are lutein, violaxanthin, fucoxanthin, zeaxanthin, neoxanthin, astaxanthin, \(\alpha\)- and \(\beta\)-carotene and their isomers, which are in accordance with the carotenoids commonly found in Chlorophyta [21, 43].  
In conclusion, the extract that exhibited the most potent antioxidant activity was found to contain important carotenoid compounds [43]. Particularly, _all-trans_-violaxanthin possesses anti-inflammatory effects and _all-trans_-lutein can help in the prevention of cataracts, coronary syndromes, strokes, and retinitis, as well as in the treatment of macular disintegration [44].  
Figure 6: HPLC chromatogram of the E12 _U. lactuca_ extract. Peak 1: _all-trans_-neoxanthin; Peak 2: _9-cis_-neoxanthin; Peak 3: _all-trans_-violaxanthin; Peak 4: _all-trans_-astaxanthin; Peak 5: _all-trans_-lutein; Peak 6: _9-cis_ astaxanthin; Peak 7: _9-cis_-lutein.
## 4 Experimental Validation
### CASE 1: Performance Verification of IMFE Algorithm
#### 4.1.2 Performance Experiment  
As illustrated in Figure 6, IMFE exhibits smaller entropy fluctuations across varying subsequence lengths (1024 to 6656), demonstrating greater stability. As the signal length increases, the FE values show a general upward trend with substantial fluctuations. For sequence 1, the FE value rises from 1.741 to 1.923, an increase of approximately 10.4%. In contrast, the IMFE for sequence 1 increases only from 1.240 to 1.293, a mere 4.3% increase, which is significantly smaller than the fluctuation range seen in FE, confirming that IMFE displays notably reduced variability across different lengths.  
Additionally, for sequence 2, MFE rises from 0.189 at a length of 1024 to 0.376 at 6656, an increase of 98.9%. However, the IMFE for the same sequence increases only from 0.271 to 0.374, an increase of 37.9%. This indicates that MFE fluctuates significantly with increasing signals length, while IMFE demonstrates lower sensitivity to scale variations, allowing it to capture sequence features with greater stability.  
The results across all sequence data indicate that IMFE has a smaller fluctuation range, with an average increase kept within 5%. In contrast, FE and MFE exhibit increases of over 10% as length grows. Table 1 shows that IMFE demonstrates superior uncorrelated performance in sequences \(x_{1}\) and \(x_{3}\), while the values for \(x_{2}\) and \(x_{4}\) are also close to those of FE, showing that IMFE maintains significant robustness under varying signal lengths. This robustness makes it particularly advantageous in complex signal analysis. Tables 2 and 3 and Figure 7 present the standard deviation and variance results for IMFE, both of which are consistently low across the four signals. These results indicate that IMFE possesses higher stability and interference resistance across various signal lengths, making it more  
Figure 5: The specific forms of the four signals. (**a**) \(x_{1}\); (**b**) \(x_{2}\); (**c**) \(x_{3}\); (**d**) \(x_{4}\).  
Figure 6: The results using FE, MFE, and IMFE methods for different lengths of four experimental signals.
###### Abstract  
The air void content is one of the most important volumetric properties of asphalt matrixes, such as asphalt mixtures or mortars, because it can greatly affect the performance of the matrix. At the mixture level, there are standardized methods for measuring the air void content, which is an important design parameter of a mixture. However, at the mortar scale, no unified method has been proposed to determine the air void content corresponding to the asphalt mixture. Therefore, this research aims to, first, characterize the air void distribution within the mortar of an asphalt mixture and then develop an updated theoretical method for calculating the air void content of asphalt mortar. The internal structures of air voids from three commonly used asphalt mixtures with different gradations were captured using an industrial CT scanning technique and then morphologically characterized using the image analysis method. Three-dimensional models of the air voids were reconstructed, and the air void content calculated from the CT images was also verified by density tests. The scanning results show that the air void content and air void size fit the cumulative Weibull curve. The results show that the nominal maximum particle size (NMPS) of mortar and the mixture air void content were the main parameters that affected the mortar's air voids. The mortar air void content had linear relationships with both the mixture air void content and the mixture asphalt aggregate ratio. The mortar air void content was more sensitive to the NMPS of the asphalt mixture compared with the air void content or asphalt aggregate ratio of the mixture. This research provides a quantitative method to calculate the air void content of asphalt mortar within a mixture, which may help in the matrix design of mortar.  
sphalt mortar; air void calculation; CT scanning; image analysis Article
## 4 Discussion
### Research Constraints  
This study's scope is restricted to Taichung City, Taiwan. Due to the difficulty in obtaining accurate park project budgets, only six parks were included in the analysis. Future research could gather data from more parks for a more encompassing investigation. Fur thermore, as parks were established at different times, comparing newer and older parks will be feasible with a broader dataset. In the calculation of park carbon footprint, this study focuses solely on plant types (tree canopy and shrub/grass coverage). For computational convenience, differences among species, plant cultivation, and related calculations are not considered. Another limitation lies in the seasonal assessment of NDVI, which was only conducted during spring. Data for other seasons is unavailable in this study. This study only included the carbon emissions associated with the construction phase of six newly constructed parks in Taichung City due to the unavailability of data on park maintenance and daily operations, which are typically documented separately. Consequently, the formula for \"Landscape Engineering Total Carbon Footprint\" includes components like CFrm (maintenance emissions) and CFeu (daily use emissions) that cannot be quantified. Future research will benefit from acquiring detailed data on maintenance and daily operations to provide a more comprehensive assessment of the parks' carbon footprints.
# Two-Stage Deep Learning Method for Breast Cancer Detection Using High-Resolution Mammogram Images
## 3 Methodology  
The overall process of the proposed two-stage DL method is shown in Figure 1. Stage 1 includes breast region extraction and square patches' generation steps. In stage 2, a trained model locates breast masses and classifies them into BI-RADS categories. Finally, using bounding box mapping and duplication removal algorithms, detected bounding boxes from patches are mapped into the original image.
## 2 Fiber Parameters
### Nonlinear Coefficient  
The nonlinear coefficient of a fiber typically depends on the doping concentrations of the core and the cladding. In a DOF, due to the variation of the core radius along the fiber length, the effective mode area changes. This in principle should also change the nonlinear coefficient \(\gamma\). In fact, the mode area can be computed from the mode solver straight forwardly. From the standard literature, we know however, that the variation of \(\gamma\) in a DOF with \(\bar{\beta}_{2}\) of the order of a few tens of ps\({}^{2}\)/km is small and can be neglected [14]. The nominal value of nonlinear parameter for this is typically of the order of 5 \((\text{W}\ \text{km})^{-1}\).
## 2 Design and Analysis of the Device
### Mechanical and Thermal Design  
Other aspects like thermal management, sensors, and cooling systems were also critical for the design of the prototype. The prototype operational temperature was aimed to be 60 K. In order to get such a low temperature, two independent cooling systems were considered: two liquid nitrogen (LN2) reservoirs and a cryocooler of 48 W at 77 K.  
The critical temperatire of YBaCuO is 90 K. This means that all the superconductors have to be below 90 K to behave as superconducting material. Therefore, all superconducting bulks were placed in the static part, directly in conduction to the cryocooler or LN2 reservoirs.  
The two floating axles, as shown in Figure 4, were held by a launch-lock system until the system reached the target temperature. Once the temperature was low enough, the launch-lock would open and they would be free to float in the vacuum. Similar concepts for launch-lock systems have been used in several devices [50]. With the axles floating in a high vacuum, the only possible heating source would be radiation. Since the radiated areas of the floating axles were small, radiation was considered  
Figure 4: Mechanical model of the prototype including magnetic harmonic drive and SMB.  
negligible and, therefore, the temperature of the floating axles in operation was expected to be close to the one just before release while testing.