esa-sceva/satcom-chunk-collection huggingface dataset:
# A Study on Data Profiling: Focusing on Attribute Value Quality Index
## Materials and Methods
### Data Quality Diagnosis  
Profiling is the estimation of potential error data through the analysis of data statistics and patterns [8; 13; 14]. Data profiling techniques are largely classified into column profiling, single-table profiling, and cross-table profiling techniques [8]. This study mainly uses column and single-table profiling techniques. Data profiling is a core function of data quality diagnosis and allows performing  
\begin{table}
\begin{tabular}{c l l} \hline \hline \multicolumn{2}{c}{**Target Data Type**} & \multicolumn{1}{c}{**Contents**} \\ \hline \multirow{4}{*}{Text} & \multirow{2}{*}{Metadata} & DB form constructed from data having various information \\  & & about contents \\ \hline \multirow{3}{*}{Text} & Direct input method & DB type constructed by direct input of text \\  & OCR conversion method & DB form constructed by OCR conversion of characters \\  & Chinese character data & DB form constructed by inputting the data written only in Chinese \\  & & characters such as old documents and old books \\ \hline \multirow{4}{*}{Video} & Image & DB form constructed through scanning or camera shooting \\  & Sound & DB form built by editing recording or holding tape \\  & & DB form built by editing shooting or holding data (reel tape, beta \\  & & tape, video tape) \\  & & DB forms constructed from 3D data through image-based modeling \\  & & and rendering methods and 3D scanning for building images from \\  & & digital filming into 3D data \\  & GIS & DB format constructed by inputting scanning and attribute \\  & & information of a map that was already produced \\  & & DB format constructed by recording filming information and spatial \\ Aerial photograph & & information on film and photo data and aerial photos stored \\  & & DB form constructed by converting past satellite raw data and earth \\  & & observation satellite binary data into standard format \\  & & DB type constructed with numerical orthphotographic image data \\ Cartographic satellite pictures & & by inputting attribute information to satellite photographs \\ \hline \hline \end{tabular}
\end{table}
Table 1: Example of content classification.  
technical analysis of data values and structure. The profiling function includes analysis functions such as column analysis, date analysis, code analysis, referential integrity analysis, and pattern analysis [8; 15; 16; 17]. The scope of the profiling defines the detailed functions in consideration of the type of data, the level of data quality control, and the goals [18]. The quality diagnosis method is divided into profiling, checklist (interview or questionnaire), business rule diagnosis, and unstructured survey, and Table 2 shows the methods of quality diagnosis of data value and unstructured data [8].  
As big data are increasing explosively, it is hard for humans to diagnose data quality directly through actual measurement. This study focuses on the improvement of data value diagnostic profiling and the unstructured test method.
## 4 Conclusions  
The results presented in this research suggest that the industrial application of Continuous Flow Ohmic Heating for tomato basil sauce can be a more sustainable and viable alternative to conventional heating methods. The heating experiments were conducted using tomato basil sauce prepared with pure tomato puree, basil, olive oil, and other spices proprietary to the recipe. A classical PID controller and advanced controllers (MPC and AMPC) were developed using a validated mathematical model, and were uniquely implemented using an OPC server via MATLAB/Simulink for effective control in real time. This study presents the first instance of obtaining and reporting such experimental results.  
The experimental results established that the CFOH system has an energy conversion efficiency of at least 88.72%. Additionally, the analysis showed that this efficiency can be further increased by applying advanced controllers, such as MPC and AMPC. It was also observed that there were no significant differences in electrical power consumption when comparing PID, MPC, and AMPC controllers, indicating that the observed energy efficiency was largely a function of the controller performance.  
Future work will focus on enhancing the CFOH system to include pasteurization and sterilization capabilities. Plans include adding holding tubes to assess the degree of lethality in comparison with conventional heating methods. Furthermore, the potential for heat recovery during the cooling phase of the food materials will also be explored.  
O.O.-o.: Methodology, Software, Validation, Formal analysis, Writing. T.J.: Writing--Review and Editing. M.H.: Supervision, Validation, Writing--Review and Editing. X.X.: Supervision, Validation, Writing--Review and Editing. M.R.: Writing--Review and Editing. H.Z.: Supervision, Conceptualization, Formal analysis, Resources, Writing--Review and Editing. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the EU Horizon 2020 SUSFOOD 2 MEFPROC project, under grant agreement No. 727473.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Time (s)**} & **PID Power** & **MPC** & **AMPC** & & & \\  & **(KWh)** & **Power** & **Power** & **PID EF (\%)** & **MPC EF** & **AMPC EF** \\  & **(KWh)** & **(KWh)** & **(KWh)** & & **(\%)** & **(\%)** \\ \hline
0–60 & 4.41 & 4.70 & 4.70 & 35.14 & 33.57 & 33.98 \\
60–300 & 71.47 & 71.89 & 70.76 & 88.72 & 88.96 & 89.81 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Energy consumption and energy efficiency of the implemented controllers.
## 4 Discussion
Our model performs well in many respects, yet it has limitations. It is designed for marine areas that have previously been surveyed and now require remeasurement due to changes over time or new needs. These areas typically have existing single-beam survey data. Thus, the optimization algorithm partly depends on prior knowledge of seabed characteristics, which may limit its application in uncharted or sparsely mapped terrains. Additionally, while the model is generally stable, it may encounter challenges in highly complex terrains or where substantial data noise is present. Future research could focus on improving the model's adaptability and accuracy in unknown or data-sparse regions by incorporating machine learning-based predictive algorithms that can infer seabed features from minimal initial data. Another potential area for development involves integrating a comprehensive path-planning system, combining software and hardware, which uses real-time seabed topography data from current scans to adjust survey routes dynamically. This system could optimize survey efficiency, reduce reliance on prior data, and ensure the model's robustness across various complex marine environments. These advancements would contribute to more flexible and precise seabed mapping techniques in increasingly challenging terrains.
# Improved Selective Deep-Learning-Based Clustering Ensemble
## 3 Improved Selective Deep Clustering Ensemble  
The framework of the ISDCE method is illustrated in Figure 1. As can be seen in Figure 1, similar to the classical clustering ensemble step, ISDCE contains two phases: a deep clustering generation step and selective clustering ensemble step. In the deep clustering generation step, the blue and green network structures in Figure 1 represent the network framework for deep autoencoder clustering. We firstly trained \(S\) deep autoencoder clusterings on the original input data, such as blue network structures \(DAE_{i}\) to \(DAE_{S}\) in Figure 1. The selective clustering ensemble step consists of two main modules: selective strategy and consensus ensemble. Considering that partial ensemble may outperform full ensemble, we incorporated the selective strategy into the \(DAE\) and constructed the ensemble quality and diversity evaluation metrics of base clusterings to select \(M\) higher-quality and rich-diversity candidate clusterings (such as green network structures \(DAE_{j_{1}}\) to \(DAE_{j_{M}}\) in Figure 1). In the consensus ensemble step, we considered the local diversity of clusters within the same base clusterings. An entropy-based criterion was utilized to estimate the local uncertainty of different ensemble members. Considering that graph-based consensus functions can be applied to larger-scale data, we constructed a weighted graph partition consensus function to efficiently aggregate the candidate clusterings. The final consensus clustering result was obtained by using Tcut [18] for graph partitioning. The details of each module are described below.
## 3 Model Application in a Simulated River Network
### Impact of Initial Bed Roughness as Input into the Developed Model  
A stable mathematical or numerical-solution model should be able to produce similar or even the same results under different initial conditions with disturbances. The solution is unique rather than invalid or scattered due to the selection of initial values with disturbance. To stabilize the developed model in this paper, the model was comprehensively tested with the inputs of different initial conditions, i.e., the initial roughness in this paper.  
The initial roughness of each river section was the same and set at 0.02, 0.025, and 0.03 for the comparative analysis of the roughness inversion results. We used 3 stations at Nodes 3, 7, and 26 with 30 observational data points each. We then set \(C_{d}\)\({}^{1/2}\) = 0.0001 m and \(C_{n}\)\({}^{1/2}\) = 0.01. Results in Table 3 show that the model calculation cost and roughness RMSE were both insensitive to the variation in initial roughness. The radar map (Figure 8) also shows that the inversed individual section roughness generally overlapped in circumstances with different initial roughness values. Due to the sensitivity to the water level, the tiny variations in roughness caused gentle fluctuations in the water-level RMSE values at different nodes. Thus, sensitivity analysis indicates that our model had good stability capacity when 3 measurement stations and 30 observational data for each were used.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Initial** & **Calculation Cost (s)** & **Roughness RMSE** & **Water-Level RMSE** & **Water-Level RMSE** \\
**Roughness** & & & **at Node 3 (m)** & **at Node 7 (m)** & **at Node 26 (m)** \\ \hline
0.02 & 40.2 & 0.00104467 & 7.46 \(\times\) 10\({}^{6}\) & 3.04 \(\times\) 10\({}^{6}\) & 1.08 \(\times\) 10\({}^{5}\) \\
0.025 & 39.2 & 0.00104360 & 4.90 \(\times\) 10\({}^{6}\) & 4.40 \(\times\) 10\({}^{6}\) & 5.68 \(\times\) 10\({}^{6}\) \\
0.03 & 44.6 & 0.00104886 & 5.92 \(\times\) 10\({}^{6}\) & 5.40 \(\times\) 10\({}^{6}\) & 6.36 \(\times\) 10\({}^{6}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: RMSE of roughness and water level at Nodes 3, 7, and 26 with different initial roughness values.  
Figure 7: Roughness inversion results at all section nodes with different observational data points.
# An Interactive Method for Detection of Process Activity Executions from IoT Data +
## 4 Method to Identify Activity Executions
### Overview First: Visualize the Entire IoT Data Set  
The first natural step is to get an overview of the entire data set that should be analyzed. Thus, finding an appropriate visualization for the type of data is the first challenge. Since activity executions can be associated with a specific time frame (i.e., they have a duration) and IoT data is available as time series [49], line graphs plotted for each sensor and actuator over time provide a suitable visualization to get an overview of the data (cf. Figure 5).  
The line graph plots of all the 77 sensors and actuators from our data sets result in a rather complex visualization. Filtering and zooming are necessary actions to derive specific correlations among sensors and actuators that may indicate the execution of process activities. However, in the figure, we are already able to see some first recurring patterns that are worth to be further investigated. On the one hand, there are time frames with high frequencies of sensor and actuator changes, which might be an indication of activity execution, and there are time frames with almost no changes, which might be an indication of inactivity. On the other hand, CPS components are active at different times or the entire time, which might be an indication of their relevance and the type of activity being executed. We use these two criteria--CPS components and time frames--as the basis for filtering in the next step.
## 4 System Model, Algorithms, and Security Model
### Algorithms
#### 4.2.4 Decryption  
Decrypt(CT, GPP, \(\mathrm{FK_{gid}}\)) \(\rightarrow\) (K): The decryption algorithm uses a global public parameter, the ciphertext denoted as CT, and the final secret keys' set \(\mathrm{FK_{gid}}\) as the inputs. The decryption is complete when the user's attributes satisfy the policy.
## 3 Methodology
### Complex Network Analysis and Girvan-Newman Method
#### 3.2.3 Efficiency  
The efficiency of a network represents a measure of information exchange in the network. Efficiency uses the inverse of the shortest path. That is, shortening the path length increases the efficiency. There are two types of network efficiency: global efficiency and local efficiency. The global efficiency is the efficiency of the entire network.  
\[E^{\text{global}}=\frac{2}{N(N-1)}\sum_{i=1}^{N}\sum_{j=1}^{i-1}\frac{1}{d_{i \to j}} \tag{8}\]  
\[E^{\text{local}}=\frac{1}{N}\sum_{i=1}^{N}E^{\text{global}}_{i} \tag{9}\]  
\(E^{\text{global}}_{i}\) is the global efficiency of the subgraph of nodes that are directly connected to node \(i\) and \(E^{\text{local}}\) is the average of them.
# UCMAC: A Cooperative MAC Protocol for Underwater Wireless Sensor Networks
## 4 Operation of Proposed Protocol
### Waiting Times
#### 4.3.3 Waiting Times at Destination  
After sending CTS or NACK, the destination waits for DATA arrival. As shown in Figure 8, the waiting time varies according to the packet type (CTS or NACK) by:  
\[d_{\text{w\_DATA}}^{\text{D}}=\left\{\begin{array}{ll}2\times\tau_{\text{S,D}}+ d_{\text{delay}}^{\text{S}}+T_{\text{DATA}},&\text{after sending CTS to source}\\ \Delta_{\text{C}_{j}}-T_{\text{NACK}},&\text{after sending NACK to cooperator }j.\end{array}\right. \tag{10}\]  
Figure 8: Decision of \(d_{\text{w\_DATA}}^{\text{D}}\) at destination.  
If no cooperators exist, the destination does not request any cooperation and returns to an idle state.
## 2 Materials and Methods
### Test Schemes and Test Methods
#### 2.2.2 Methods  
1. Sample preparation methods;  
According to the preparation scheme in Table 3, the soil samples were mixed with the binder, and the corresponding mass of deionized water was added and mixed uniformly. The mixture was then placed into molds with a diameter of 50 mm and a height of 100 mm. The molds were placed in a curing chamber for standard curing (20 \(\pm\) 2 \({}^{\circ}\)C, humidity \(\geq\) 95%). After 3 days of curing, the samples were de-molded and further cured until 28 days, after which subsequent tests were conducted.  
Refer to ASTM D560-03 [29] for freeze-thaw cycles. The specimen of standard maintenance for 28 d is put into the automatic freeze-thaw cycles test equipment for maintenance, and the design of freeze-thaw cycles temperature is \(-\)20 \({}^{\circ}\)C\(\sim\)20 \({}^{\circ}\)C. Firstly, the specimens are put into the freezing chamber of \(-\)20 \({}^{\circ}\)C for 24 h, and then they are put into the thawing chamber of 20 \({}^{\circ}\)C to melt for 24 h as one freeze-thaw cycle. Then, 0, 2, 4, 6, 8, 10 freeze-thaw cycles are carried out. The unconfined compressive strength test and microscopic test were performed after reaching a set number of cycles.  
2. Unconfined compressive strength test  
For the specimens that reached the designated curing age, unconfined compressive strength tests were conducted using the WCY-1 unconfined compression tester manufactured by Nanjing Soil Instrument Company. The test was performed under strain-controlled loading at a rate of 1%/min, and the test was terminated upon specimen failure.  
3. Microscopic examination  
An XRD analysis was performed using a Rigaku Miniflex600 X-ray diffractometer to determine the mineral phase composition. The samples were dried at 105 \({}^{\circ}\)C to constant weight, powdered through a 0.075 mm sieve, and a copper rotary anode target was used. Scanning method: 20 goniometer, accuracy 0.002\({}^{\circ}\); scanning angle 5-75\({}^{\circ}\).  
An SEM-EDS analysis was conducted using a German Zeiss Gemini500 field-emission scanning electron microscope in combination with an X-ray energy spectrometer from Oxford Instruments ULTIM MAX100 to analyze the microscopic morphology and elemental composition. The samples were lyophilized and treated with gold spraying on the surface, and the magnification was set at 20,000\(\times\).  
An MIP analysis was performed using a Micro Active AutoPoreV9600 porosimeter, with a measurable pore size range of 0.006 to 300 \(\upmu\)m.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Soil Type** & **Binder** & **Mixing Ratio of Binder (\%)** & **Curing Age (d)** & **Freeze–Thaw Cycle Times (Cycles)** & **Water Admixture (\%)** \\ \hline CSS011 & & & & & \\ CSS122 & SSGM & & & & \\ CSS111 & P.O 42.5 & & & & \\ CSS100 & & & & & \\ \hline \end{tabular}
\end{table}
Table 3: Sample preparation scheme for solidified soil.
## 1 Introduction
To empirically validate the efficiency of the proposed PPS algorithm, we simulated a selective assembly scenario that mirrors real-world conditions. The simulation was based on data collected from an actual bearing assembly facility, where three critical components are assembled. The facility layout, which features slot rows for two out of the three components, provides a complex but highly relevant environment for applying the PPS algorithm. Our objective is to meticulously assess the impact of the PPS algorithm on two key performance indicators: surplus part ratio and process capability.
# Failure of Rock Slope with Heterogeneous Locked Patches: Insights from Numerical Modelling
## 3 Numerical Results
### Length Effect  
The failure pattern of the rock slope with different length of locked patches resembles the numerical models with locked patches of different strength where cracks are mainly distributed along the interface of the locked patch. It also yields similar shear stress-shear displacement-acoustic emission relationships. Figure 6 summarises the variation of AE, shear strength, and peak shear displacement with length of right locked patch. With the increased length of right locked patch, accumulated AE counts and energy of two locked patches increase while the peak shear displacement and strength of two locked patches initially increase and then decrease. The effect of right locked patch length on the left one can be divided into two stages. When the length of right locked patch increases from 0.1 to 0.25 m, the strength of the left locked patch increases by 61%. When the length of right locked patch increases from 0.
## Appendix B Modelling Tightly Focused Ultrafast Laser Beams in Vacuum  
The work of [PERSON] _et al._ was one of the first to propose a systematic method to deal with the nonparaxial propagation of laser beams [32]. Following this seminal work, similar perturbative methods have also been developed for spatial corrections of various types of beams [33; 96; 97]. The same approach was used in the temporal domain to solve the paraxial wave equation for ultrafast pulses [98]. Ultimately, a unification of these perturbative methods was proposed to deal with tightly focused ultrafast laser beams in a complete spatio-temporal treatment [30]. Despite its historical importance, the use of the perturbative approach is limited as it gives open-form solutions in terms of infinite series whose convergence must be ensured.  
Closed-form solutions can generally be obtained _via_ integral (spectral) methods: by direct Fourier and Hankel transforms [55, 99, 100] or through angular spectrum decomposition [47, 57, 101]. Very elegant solutions were also obtained using the complex source-point model [25, 31]. Extending those formalisms to include both ultrashort duration and strong focusing requires extra care [77]. Final solutions should be compared against other methods, or submitted to extensive numerical tests (see, for example [102]).
## 3 Data and Model Configuration
### Data Preprocessing and Feature Extraction  
For our training data and test data, we performed the following data preprocessing. First, we sampled all the speech from 8 kHz to 16 kHz and then used audio activity detection (VAD) to remove the silence from the speech. Since too long training data will lead to insufficient video memory space in the training process, we cut the data of the training set into fragments of the longest 30 s, and then send the data of the training set and the test set into the pre-trained wav2 vec2.0 model [10,11] for feature extraction. The pre-trained model is trained on 56,000 h of speech data in 53-language Multilingual LibriSpeech, CommonVoice, and Babel [41, 42, 43]. The preliminary training model can be obtained from huggingface [[https://huggingface.co/s3](https://huggingface.co/s3) prl/converted_ckpts/resolve/main/xlsr_53_56k.pt]([https://huggingface.co/s3](https://huggingface.co/s3) prl/converted_ckpts/resolve/main/xlsr_53_56k.pt) (accessed on 10 June 2023). The dimension of input feature X of our model is 1024, and all speech frames are divided into segments, each segment contains 20 frames, so as to ensure that each small segment of phoneme unit is 400 milliseconds long and can contain at least two phonemes.
# Efficient Top-\(k\) Spatial Dataset Search Processing
## 3 Notations, System Model and Problem Description
### Notations  
The notations used in this paper are listed as follows:  
* \(G\): the two-dimensional spatial space which are equally divided into \(2^{u}\times 2^{u}\) grids, \(G=\{g_{x,y}|x,y\in\{1,2,\cdots,2^{u}\}\}\), where \(u\) is the space-dividing threshold.
* \(\mathcal{S}\): the spatial data repository contains a set of spatial datasets, \(\mathcal{S}=\{S_{1},S_{2},\cdots,S_{n}\}\).
* \(S_{i}\): the spatial dataset in \(\mathcal{S}\) contains \(m_{i}\) location points, \(S_{i}=\{p_{i,1},p_{i,2},\cdots,p_{i,m_{i}}\}\), where \(p_{i,j}\in S_{i}\) is a location point.
* \(S_{\epsilon}\): the exemplar spatial dataset of a top-\(k\) spatial dataset search processing.
* \(D_{i}\): the grid distribution representation of the spatial dataset \(S_{i}\), which consists of a set of triples.
* \(E_{i}\): the grid-based minimum bounding rectangle (GMBR) of the spatial dataset \(S_{i}\), which is composed of the minimum grids just covering all locations of \(S_{i}\).
* \(\mathcal{I}^{\epsilon}\): the \(\epsilon\)-pooling index for the spatial data repository, which consists of a two-layer indexing structure.