esa-sceva/satcom-chunk-collection huggingface dataset:
# Predicting Employee Absence from Historical Absence Profiles with Machine Learning
## 2 Background and Related Work
### The Problem of Predicting Employee Absence  
In this paper, we define the prediction of employee absence as the process of **predicting when and how often employees might be absent from work in the future**. Our main goal in this paper is to predict employee absenteeism based on historical timesheet data. We focus on predicting two types of absences: sick leave and vacation leave absence. For this purpose, we use the historical absence profiles of employees together with demographic data.  
In our view, predicting vacation leave and sick leave represents two distinct analytical tasks, each with unique characteristics and implications. **Vacation leave** is usually planned and predictable, with trends possibly correlated with school holidays, summer months, or the holiday season, making it somewhat easier to predict using historical data. In contrast, **sick leave** is often unplanned and can be influenced by a variety of unpredictable factors such as personal health problems or epidemics within the community. Therefore, modelling techniques and the type of data used for prediction differ significantly. For vacation leave, the data could include the timing of past holidays, length of service, and company-wide vacation policies. In contrast, predicting sickness absence could focus on local health trends, historical sickness absence data, and individual health predispositions.  
The development of separate forecasting models for vacation and sick leave is crucial, as each type of absence is of fundamental importance. Separate models allow for customization of features, such as the inclusion of public health data for sickness absence predictions and school calendar data for vacation predictions, improving the accuracy and relevance of predictions. In addition, the consequences of each type of leave can be different. For example, an unplanned sick leave may require an immediate temporary replacement, while a planned leave may give managers time to prepare. Precise models are therefore required to optimize personnel management and maintain productivity.
## 2 Context of the Experiments
### Control Algorithm
#### 2.7.2 Charging Power Models  
In the considered set-up, controlling the charge of an EV involves the control of its SOC by modifying over time the power that the station can deliver. The set of these maximum power values over time is called \"SetPoints\". The function that links the SetPoints and the SOC is very complex. It depends on the maximum power accepted by the car when it is charging with an AC connection. This parameter depends on the car model, on the size of its onboard charger, on the type of connection with the charging station and on the cable that is used to connect the car to the station. It also depends on the externaltemperature and on the traction battery temperature. Besides, the user may use a software (generally an application on a smartphone) that may also limit the value of \(\mathrm{P_{Max}}\). In the experiments, the participants are encouraged to deactivate such external control. In the following, we consider that \(\mathrm{P_{Max}}\) is constant and that it is equal to the value measured during the \"discovering phase\". The energy that can be stored in the traction battery, further called \"Capacity\" and noted \(\mathrm{E_{Max}}\), is also a key parameter that depends on the vehicle model and on options. For example, a Tesla Model 3 may have four sizes of battery: 55, 62, 75 and 82 kWh. In our case, we consider that Capacity is constant and equal to the value declared by the participants. For the cars of people not participating in the experiments, Capacity is considered as the maximum size of the battery for the EV model. Other parameters exist, such as the number of phases that are used to charge the car. We consider public information to obtain these data. In general, if the onboard charger is less than 11 kW, it is one phase, otherwise, it is a three-phase charger. However, there are exceptions: for example, the Seat Mii charge at 7.4 kW with a two-phase charger.
## 2 Materials and Methods
### Biofilm Assay on Polystyrene Microplates  
The biofilm production assay on polystyrene microtiter plates was used to evaluate the ability of BAC to prevent or reduce biofilm formation. BAC was added to individual wells of flat-bottomed, sterile 96-well polystyrene microtiter plates (Greiner Bio-One, Frickenhausen, Germany). BAC was tested at 1.25, 2.5 and 5.0 mg/L, the concentrations of BAC that showed different effects on biofilm formation, according to our previous results [19]. The concentrations of overnight cultures of S2-1 and S2\({}^{\text{BAC}}\) were adjusted with TSB-YE to approximately \(5\times 10^{7}\) colony-forming units (CFU)/mL. A volume of 0.1 mL of the diluted cell suspension was transferred to the wells of a microtiter plate. For each strain, eight wells (rows) of different BAC concentrations (columns) were inoculated. Two independent experiments were conducted (\(n=16\)). Eight wells filled with sterile medium served as blank controls. Subsequently, the plates were incubated at 37 \({}^{\circ}\)C for 24 h, to allow for biofilm formation.  
To quantitatively measure the living cells present in the biofilm, it was stained with RSZ, which is a redox indicator that allows the metabolic activity of the cells to be monitored [23; 26]. The RSZ sodium salt (Sigma) was dissolved in Milli-Q water at a concentration of 200 mg/L and sterilized by filtration. RSZ staining was performed as described by [PERSON] et al. [23], with small changes. In summary, after removing the culture medium, the plates were washed twice with distilled water to remove non-adhering bacterial cells and dried with paper towels. Thereafter, 0.1 mL of TSB-YE, diluted 1:10 in distilled water [26] and containing RSZ at 40 mg/L [23] was distributed in each well, and the plate was incubated in the dark for 1-2 h at 37 \({}^{\circ}\)C, without stirring. The color of the wells was observed visually. The blue color reflects the absence of metabolic activity (without biofilm or with dead cells in the biofilm) and the pink color (presence of resorufin, formed by the reduction of RSZ) reflects the cellular metabolic activity. When an intense pink color of the growth control was observed, \(A_{570}\) and \(A_{600}\) was monitored using the Thermo Multiskan Plate Reader. The metabolic activities (\(A_{570}\)-\(A_{600}\)) of _L. monocytogenes_ biofilms were quantified in the absence or presence of BAC. Results from RSZ staining are expressed as means \(\pm\) standard deviation of the metabolic activity (\(A_{570}\)-\(A_{600}\)) of _L. monocytogenes_ biofilm.
# GNSS-Based Monitoring Methods for Mining Headframes
## 3 Monitoring Data Processing and Analysis
### 3D Deformation Spatiotemporal Analysis
#### 3.2.2 Spatiotemporal Data Model  
Given the changes in deformation data over time and space, the use of spatiotemporal big data models for a thorough analysis of deformation patterns is particularly considered. Taking into account cost, data redundancy, and intuitiveness, this work employs the base state with amendments model [32] to process the aforementioned monitoring data. According to the theory of the baseline correction model, when analyzing the change characteristics of time-series data relative to a specified reference point, it is only necessary to store the positional information of that reference point and the relative positional changes of other points with respect to it. This approach eliminates the need to store complete positional data for all other points. Thanks to the flexibility in selecting baseline data points, the model also allows for the autonomous choice of different starting points, enabling the observation of motion trajectories relative to comparison points over subsequent time periods. This highlights the spatiotemporal visualization capabilities of deformation monitoring.  
A model is established to quantitatively study the displacement of each monitoring station relative to the base state data (assuming that the position does not change and setting the first epoch data received after installation on 16 April 2024 as the base state), that is, the amendment term, and to visually analyze the degree of headframe deformation on the basis of the clustering property of the amendment term. Figure 6 shows the base state with amendments plots of some monitoring stations drawn using Statistical Package for the Social Sciences (SPSS) software (version 27). The positions labeled with the serial number \"GS\" are the base state, and the rest of the points in different colors represent the monitoring quantities of different epochs.  
Figure 5: Three-dimensional deformation surface of some monitoring stations.  
The results of the spatiotemporal modeling of the deformation data over 56 consecutive days indicate that the deformation of the headframe's surrounding points, JC01 and JC03, is concentrated near the base state, with a greater clustering property for the planar displacement quantities (\(\Delta\)X, \(\Delta\)Y). The displacements are roughly in the same direction, not exceeding \(\pm\)6 mm. Their settlement quantity \(\Delta\)H exhibits symmetry about the base state, with the maximum settlement being around \(\pm\)8 mm. By contrast, the deformation of the monitoring stations near the sheave wheels, STL-L and XTL-L, is more pronounced than that of the first two monitoring stations. Relative to the base state, the distribution of the correction terms is more dispersed, and the deformation regarding the settlement quantity \(\Delta\)H is more significant, with most being around \(\pm\)10 mm.
## 3 Data Collection and Processing  
The objective of feature parameter selection is to reduce the number of parameters, enhancing the model's generalizability and mitigating the risk of overfitting. The correlation analysis between the target parameter blast furnace air permeability and characteristic parameters is carried out, and the characteristic parameters strongly related to blast furnace air permeability are used as input variables of the data-driven model. In this study, the collected blast furnace production data are analyzed by the nonlinear correlation analysis method. First, the analysis of the interplay between blast furnace permeability and gas flow is used, and second, Spearman correlation analysis and MIC (maximum information coefficient) analysis are selected to correlate the characteristic parameters. Finally, the merged set of analysis results is selected as the input variable.
## 3 Database and Methodology  
### Database  
This study entails the collection of data pertaining to ancient earthquakes across different segments of 22 active fault zones in the northeastern Tibetan Plateau (refer to Appendix A Research data Table A1). Data selection adheres to three primary criteria: (1) Collection, sorting, and comparative analysis of data to scrutinize ancient earthquake events exposed by various trench profiles within the same fault segment, while excluding repetitive events. (2) Exclusion of ancient earthquake events that transpired an excessive length of time ago. (3) Computation of the recurrence interval (\(T\)) for each fault segment to establish the average recurrence interval of ancient earthquakes (\(T_{ave}\)). Subsequently, the ancient earthquake data (\(T/T_{ave}\)) for each segment are normalized to mitigate data dispersion. Finally, a total of 119 data points (\(T/T_{ave}\)) are available for statistical analysis based on the aforementioned principles.
# Non-Iterative Cluster Routing: Analysis and Implementation Strategies
## 3 Methods
### Proposed Routing Variations
#### 3.4.2 Our Second Routing Variation  
While conducting extensive experiments with various hyperparameters in cluster routing, we observed that increasing the number of votes per cluster (denoted as \(\mathbf{K}\)) in both single- and multiple-channel versions can lead to a decrease in performance, as demonstrated in Figures 3 and 4, respectively. Therefore, we introduce a second variation of the routing mechanism to mitigate the impact of increasing \(\mathbf{K}\) while enhancing its performance.  
In Figure 5, the \(\mathbf{K}\) and \(\mathbf{N}\) dimensions are split only once in the original cluster routing, using the.view() method in PyTorch. This second variation suggests that the breakdown is not limited to only two times (as depicted in Figure 5) but, rather, depends on the dimension of the tensor, which must satisfy the condition \(K1\times N1\times K2\times N2=K\times N\).  
Figure 4: Performance of the multiple-channel version of cluster routing with \(\mathbf{C}=4\) and \(\mathbf{D}=6\).  
Figure 3: Performance of the single-channel version of cluster routing with \(\mathbf{N}=8\) and \(\mathbf{D}=32\).  
The reference CNN employed for comparison was composed of five ReLU convolutional layers. Each layer incorporates ReLU activation, 256 filters, and layer normalization, resulting in a total of 3.38 million parameters.
## 5 Conclusions  
This paper proposes a SOGTV algorithm for EIT-based lung imaging. The algorithm was evaluated by comparisons with the SVD, TV, Accelerated TV, and TVAL3 algorithms using experimental simulation models with different sizes and shapes. We developed the physical model of the circular water tank to validate our algorithm. The experimental results demonstrated that the SOGTV algorithm achieved both stable solutions and  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **PSNR** & **SSIM** & **LPIPS** \\ \hline SVD & 33.7745 & 0.8142 & 0.2416 \\ TV & 33.6126 & 0.8083 & 0.2254 \\ Accelerated TV & 33.5852 & 0.7946 & 0.2287 \\ TVAL3 & 33.4770 & 0.7930 & 0.2313 \\ Ours & 33.7061 & 0.8194 & 0.2235 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Indexes for evaluating tank experiments.  
Figure 7: Convergence rate of the three algorithms (Accelerated TV, TVAL3, SOGTV).  
improved image boundary contrast and sharpness levels compared to those of full-variance-regularized images. The proposed algorithm presents a viable solution for the advancement of electrical impedance imaging in lung research. The effectiveness of our algorithm was verified through simulation experiments and tank experiments. However, the algorithm still has some shortcomings that need to be addressed due to limited experimental conditions. Further optimization is required to improve its performance. Additionally, the algorithm needs improvement in terms of generalization, as it currently struggles to accurately image multi-objective water tank models with complex target conditions. To enhance the generalization ability, it is recommended to increase the dataset or further refine the algorithm.  
Conceptualization, R.Z. and C.X.; methodology, R.Z.; software, R.Z. and W.M.; validation, R.Z.; investigation, Z.Z.; resources, Z.Z. and W.M.; data curation, R.Z.; writing--original draft preparation, R.Z.; writing--review and editing, R.Z.; visualization, R.Z.; supervision, C.X. and W.M.; project administration, C.X.; funding acquisition, R.Z., C.X., Z.Z., and W.M. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
The data that support the findings of this study will be available from the corresponding author upon reasonable request. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest and the funders had no role in the design of the study.
## 1 Introduction
Applications of fuzzy information systems can be found in the fields of decision-making [25; 26] and rule extraction [26; 27]. Recently, new decision-making concepts were introduced in [28] by combining regret theory with three-way decisions in fuzzy incomplete information systems, or in [29], by multi-attribute predictive analysis using fuzzy rough sets. In this research, a fuzzy information system to induce rules was applied with respect to classification attribute values. Next, a transformation into fuzzy rules was proposed, and finally, the fuzzy sets used were expanded into type-2 fuzzy sets. To provide classification, the Takagi-Sugeno model was applied in order to simplify our previous concept, which was related to the Mamdani fuzzy model [30]. The Takagi-Sugeno model does not require rule consequents defined as fuzzy sets, but introduces additional parameters. For that reason, in this research, an optimization procedure was applied to determine the best fuzzy classifier for the benchmark data considered. The research experiments were provided for binary classification problems defined with well-known benchmark data. Therefore, our research goal was to provide a classification method, by replacing the Mamdani model used in our previous research with the first-order Takagi-Sugeno fuzzy inference. This simplified the induction of type-2 fuzzy rules in the classification process, as fuzzy sets were not required to be defined as rule consequents. The main research advantage of this work states the proposal of a fuzzy information system used to induce type-2 fuzzy rules, incorporated with the Takagi-Sugeno inference to provide classification. The rest of the paper is organized as follows: in Section 2, the benchmark data used and some theoretical background are introduced; in Section 3, the introduced classification procedure is explained; in Section 4, the classification results are presented; and finally, Sections 5 and 6 draw corresponding discussion, possible further developments and conclusions.
## 3 Results and Discussion
### Effect of DB and LTGN on the SI Physical-Mechanical Characteristics  
#### 3.2.1 Microhardness  
Figure 6a shows the microhardness profiles before LTGN. The microhardness of the bulk material is approximately 420 HV. Turning slightly increases the microhardness at a depth of approximately 0.07 mm. The subsequent DB significantly increases the microhardness, reaching 575 HV at a depth of approximately 0.01 mm in both variants (smoothing or hardening). However, while the depth of the hardened layer due to smoothing DB is below 0.1 mm, hardening DB achieves a significantly greater depth. Figure 6b shows the microhardness profiles after LTGN, with the prior treatments being, respectively, turning, smoothing DB, and hardening DB. The highest microhardness is provided by LTGN after turning--approximately 2000 HV at a depth of several micrometers--and the depth of the hardened layer is only 0.03 mm. When the prior treatment is DB (either smoothing or hardening), the effect of LTGN on the microhardness decreases with respect to the near-surface layers: the maximum microhardness at a depth of 5 um is 1400 HV and 1700 HV, respectively. At the same time, the depth of the hardened layer is greater, a consequence of the cold work introduced through DB. The explanation of this phenomenon is that the presence of severe surface plastic deformation introduced through DB prevents the  
Figure 5: Microstructure near the surface layer after LTGN and hardening DB: (**a**) affected layer; (**b**) EDX outcomes.  
formation of a quality S-phase, which is the hardest compared to the other established phases in the ALs.
## 3 Results and Discussions
### Durability
#### 3.6.2 Freeze and Thaw Resistance  
Figure 11 is a graphical presentation of the freeze-thaw resistance results of calcined clay-silica fume blended cement mortar mixes after 120 cycles. The control mix, before the freeze-thaw cycles recorded a compressive strength of 52 MPa, which steadily declined as the number of cycles increased. At the end of the 120 cycles, the control mix had retained about 62.9% of its strength. It is clear from the results that the control mix offered the least resistance to the cycles at all levels. This observation is consistent with earlier reports [52]. Generally, the freeze-thaw effect on blended cement mixes was observed to be marginal within the first 30 cycles and became more severe beyond 60 cycles. Even though all samples suffered a reduction in strength as a result of the freeze-thaw cycles, the blended cement samples, irrespective of pozzolan content and dosages, demonstrated a better  
Figure 9: Relative compressive strength of blended cement mortar.  
resistance compared to the control mix. Among the blended cement mixes, the specimen containing 30% silica fume (30 SF) obtained the highest compressive strengths values.  
Strength retention values for 20 CC10 SF, 10 CC20 SF, 15 CC15 SF, 30 CC and 30 SF were 79.6%, 77.6%, 76.8%, 72.9% and 86.2%. This implies that the best-performing blended cement mix (30 SF) outperformed the least (Control) by 23%. The deterioration of cementitious materials by freeze and thaw occurs within the pore structure of the material via the release of expansive forces during the freezing of bound water. Thus, the amount of ice present  
Figure 11: Effect of freeze–thaw cycles on blended cements.  
Figure 10: Sorptivity and permeability porosity of blended cements.  
determines the severity of freeze-thaw damage, which is directly related to the porosity of the cementitious material [52]. Superior freeze andthaw resistance in the blended cement mixes is attributed to finer pore structure and lower porosity compared to the control mix. This leads to a relatively lower internal expansive force and a higher resistance to freeze andthaw attack. This explains the reason for the better freeze-thaw resistance in SF-rich samples. Similar trend of results was reported by earlier researchers [52; 53; 54].
# An Improved Anti-Interference Precoding of Large-Scale Fading System Based on Channel Inversion
## 2 System Model
### OFDM Duplex Setting  
In this article, a multi-user MIMO system that used the OFDMA (Orthogonal Frequency Division Multiple Access) technology is studied. In the OFDMA, each user is assigned to a group of subcarriers, and the number of subcarriers for each user in each cell is defined by the system scheduling. For the sake of simplicity, it is assumed that the system model uniformly sets K users in each cell, that is, K subcarriers. The communication link between a base station and a user is divided into downlink and uplink duplex modes. We consider LSAS (large scale antenna system) with the TDD (Time Division Duplexing) protocol. The TDD protocol was set up with three steps: Tu channel was used for uplink training; Tpchannel was used for processing precoding; Td channel was used for downlink training.  
In TDD, the uplink and downlink transmissions were allocated to different communication time slots and could not be performed simultaneously; thus, the transmission and reception of a user signal was completed through the assigned subcarriers. However, they were performed at different times, and the corresponding time slots could be dynamically and flexibly allocated. Due to the reciprocity of uplink transmission and downlink transmission, the MIMO technology and antenna technology could be combined with time division duplexing.