esa-sceva/satcom-chunk-collection huggingface dataset:
# Decision Support Software for Forecasting Patient's Length of Stay
## 3 Research Methodology
### Dataset  
The data set consisted of patients hospitalized in the General Hospital of Kalamata, Greece during the period between 2008 and 2012. We identified 2702 patients, of both genders and different kind of diagnoses, limited to age over 65 years. It must be mentioned that despite our efforts, we have not been able to gain access to any other age groups. Data cleansing and preprocessing operations involved the deletion of duplicated records, irregularities, and irrelevancies and manipulation of records with missing and outlier data. Moreover, it is worth noticing that records with the same admission and discharge date (i.e., resulting in 0 LoS) were excluded from our research.  
Table 1 presents the set of the thirteen (13) attributes used in our study concerning demographic, clinical, geographical and administrative factors. The first three (3) attributes are related with patient's personal information such as gender, age, and insurance type. Notice that each patient in Greece belongs to a specific insurance fund based on his occupation such as IKA, OGA, NAT, TAYT, or he/she has a private health insurance. The following four (4) geographical and demographic attributes concern the patient's residence altitude, urbanity, and distance from the hospital as well as the medical cover of the residence. The last five (5) attributes are related with patients' pathological and clinical characteristics. These attributes concern the day and month of the patient's admission in the hospital and the number of patients which have been admitted that day. Additionally, the hospital ward in which the patient was admitted and the ICD-10 diagnosis code according to the World Health Organization [18] are usually the main reasons of patient's LoS [19].  
Finally, the patients were classified according to the number of days in the hospital: \"1\", \"2\", \"3\", \"4\", \"5\" and \"5+\" days. Figure 1 presents the class distribution which depicts the number of patients who hospitalized for \"1 day\" (758 instances), \"2 days\" (598 instances), \"3 days\" (321 instances), \"4 days\" (205 instances), \"5 days\" (156 instances) and \"5+ days\" (664 instances). Clearly, from the number of instances of each class, we can observe the imbalance of the dataset's class distribution.
# An Estimation of Speech Privacy Class Based on ISO Parameter
## 2 Background
### Measurements of Sound Insulation in Buildings  
The ISO 16283-1 standard defines measurement procedures, measuring devices, and the method for calculating the frequency-dependent parameter \(R\)[50]. These procedures are intended for rooms with volumes ranging from 10 m\({}^{3}\) to 250 m\({}^{3}\) in the frequency range of 50 Hz to 5000 Hz. The process of measuring the sound reduction index of a partition can be explained using the example shown in Figure 1. The partition being tested divides two rooms and has an area \(S_{12}\). In the source room, a non-directional sound source is placed. The sound levels measured in the source room are averaged to obtain the value \(L_{1}\). Similarly, in the receiving room, the level \(L_{2}\) is calculated. The level difference (\(D\)) is defined as the difference between the level values in 1/3-octave frequency bands between the source and receiving rooms. In the receiving room, it is necessary to measure the ambient noise level at several positions, and the average value is denoted as \(L_{b}\). If the differences in the level values \(L_{2}\) and \(L_{b}\) are lower than 6 dB, a level correction must be performed according to [49]. Additionally, it is necessary to measure the reverberation time at several positions in the receiving room \(T\) according to ISO 3382-2 [51]. The sound reduction index of the partition is then calculated using the following equation:  
\[R^{\prime}=D+10\log\!\left(\frac{S_{12}}{A}\right)=L_{1}-L_{2}+10\log\!\left( \frac{T\,S_{12}}{0.16\,V}\right), \tag{1}\]  
where \(V\) represents the volume of the receiving room. The apostrophe indicates an in situ value. The measured sound reduction index result is expressed as a single-number quantity for airborne sound insulation, \(R^{\prime}{}_{w}\), calculated according to ISO 717-1 [44].
## 5 Model Formulation and Description
### Model Description
#### 5.2.2 Step Two: RoI-Boundary Sensor Optimization  
The objective of this step is to move nodes placed on the boundary, in the right direction to collect more interest. We decided to use the gradient method. Two classes are defined and represent the class matrix. Class {1} represents points with interest, class {\(-1\)} represents points without interest. After we use the clustering technique and creating the reference matrix, we apply the gradient function so we can detect nodes on the boundary, and secondly decide the movement direction to seek more interest. Figure 9a,b show an example of this process where blue sensor nodes are the boundary nodes that should be relocated.  
Figure 8: Topology with selected sensor nodes.  
Figure 6: Triangular deployment: (**a**) Iteration I where \(\mathrm{d}_{\mathrm{min}}<2\times\mathrm{R}\mathrm{s}\); (**b**) Iteration I + k where \(\mathrm{d}_{\mathrm{min}}>2\times\mathrm{R}\mathrm{s}\).  
Figure 7: Triangular grid deployment.  
At each step, the gradient of the reference matrix is calculated and the sensor location is updated to make a small step in the right direction to gather more interest. The joint interest will be recalculated for the new solution, and this step is repeated until the maximum number of iterations is reached. When the distance between two nodes is less than the sensing range, in our case \(Rs/4\), the sensor on the RoI boundary is deleted. In this case, the maximum number of iterations needed for a node to move from its position on the Roi boundary until approximating its neighbor by Rs/4, should be at least \(t=\left(d-\frac{Rs}{4}\right)/\mu\), as already described in the pseudo-code. Figure 10 shows that better sensor positions were found after applying this method to the design presented in Figure 8.Distance is an important factor for sensing. When the distance between the node and the event increases, the sensing power decreases and battery consumption increases [30; 31]. One of the most important requirements of WSNs is to reduce the energy consumption [32; 33]. Hence, this technique allows not only seeking better positions for nodes to monitor more events but also getting close to the RoI so energy saving can be achieved.
# Research on Short-Term Prediction Methods for Small-Scale Three-Dimensional Wind Fields
## 5 Discussion  
To achieve short-term predictions of small-scale three-dimensional wind fields, this study proposed a prediction method based on the multi-scale coupling of WRF and CFD. In response to the characteristics of the historical inflow boundary dataset, this study proposed a hybrid neural network model, ConvLSTM-DSConvGRU, to predict CFD boundary conditions. After training, this network is capable of accurately predicting the wind speed and temperature as boundary conditions for the next five time steps, enabling predictions of small-scale three-dimensional wind fields up to the next 50 min. The experimental results indicate that compared with the nearest neighbour interpolation method, the inverse distance weighted interpolation method is more suitable for the simulation and prediction of three-dimensional wind fields in the simulated domain of this study. In addition, the proposed hybrid network prediction model exhibits excellent spatiotemporal feature extraction capabilities, with comprehensive advantages in terms of the number of parameters and prediction accuracy. In the next steps, the configuration and parameters of the prediction model will be further optimised to enhance the wind field prediction accuracy, particularly for different locations and meteorological conditions.  
Figure 12: Comparison of wind field component images obtained from WRF with predicted images. (**a**) Visualisation of WRF output wind speed components from 21:20 to 22:50 on 23 March 2022; (**b**) hybrid network predicted images from 22:10 to 22:50; (**c**) absolute errors between WRF output images and predicted images.  
Figure 13: Comparison of wind field component images obtained from WRF with predicted images. (**a**) Visualisation of WRF output wind speed components from 07:20 to 08:50 on 21 March 2022; (**b**) hybrid network predicted images from 08:10 to 08:50; (**c**) absolute errors between WRF output images and predicted images.  
**Author Contributions:** Conceptualisation, Y.M.; methodology, Y.M. and H.H.; software, H.H. and X.T.; supervision, P.-W.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** National Natural Science Foundation of China under Grant U1833111.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data used to support the findings of the study are available from the corresponding author upon request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Optimization of Organic Content Removal from Aqueous Solutions by Fenton-Ozonation
## 1 Introduction
This molecule is chosen for its resistance to degradation by conventional processes (such as chlorination or biological processes) and its relative ease of analysis, making it an ideal target for assessing the effectiveness of AOPs on these materials. Various studies have shown various methods of processing 2,4-D, including adsorption on activated carbon [5; 16; 20], microbial degradation [21], electrochemical oxidation [22], photocatalytic oxidation [18], and non-thermal plasma oxidation [16]. This study focuses on the degradation and mineralization of 2,4-D by the Fenton process, which relies on the reaction between hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)) and ferrous ions (Fe\({}^{2+}\)) to generate highly reactive hydroxyl radicals capable of breaking down pollutants. This work shows that it is possible to optimize and evaluate the performance of each process, not only in terms of pollutant concentration reduction, but also in terms of mineralization, i.e., the complete conversion of organic molecules into inorganic products such as water and carbon dioxide. For this, iron-enriched carbon materials are synthesized for use as catalyst carriers in the Fenton process. An experimental design was employed to determine the optimal conditions for degradation and mineralization of pollutants in water. Modeling and optimization of the 2,4-dichlorophenoxyoxyacetic degradation process were performed by means of a central composite design (CCD). Its characterization is performed by dinitrogen adsorption at 77 K, thermogravimetric analysis (TGA), and scanning electron microscopy (SEM).
# Power Control and Link Selection for Wireless Relay Networks with Hybrid Energy Sources
## 3 Optimal Control for Expected Total Rewards  
In this section, we assume \(h_{SR}^{i}\), \(h_{SD}^{i}\) and \(h_{RD}^{i}\) are causally known. Consequently, we aim to adapt the transmission power and link selection to maximize the expected total rewards. Thus, the problem can be formulated as:  
\[\text{P}_{1}:\max\limits_{I_{j}^{i}p_{SR}^{i}p_{SD}^{j}p_{R}^{i}}E\left\{\sum \limits_{i=1}^{N}{[\omega_{t}(I_{SD}^{i}R_{SD}^{i}+I_{SRD}^{i}R_{RD}^{i})- \omega_{g}(I_{SD}^{i}Tp_{SD}^{i}+I_{SRD}^{i}\frac{T}{2}p_{SR}^{i})]}\right\} \tag{10}\]  
s.t. Equations (1) and (7)  
\[C_{R}^{i}\leq E(i) \tag{12}\]  
\[R_{SR}^{i}=R_{RD}^{i} \tag{13}\]  
\[p_{j}^{i}\leq p_{j}^{\max}\forall i\in\mathbb{N},j\in\{S,R\} \tag{14}\]  
\[I_{j}^{i}\in\{0,1\},\forall i\in N,j\in\{SD,SRD\}. \tag{15}\]  
In \(\text{P}_{1}\), the objective is the expected total rewards over N time slots. Equation (12) is the energy constraint that the energy consumed by relay should not exceed that of the battery. Equation (13) is the throughput constraint that the bits delivered in the two stages of relay link should be Equations (14) and (15) are the power constraint and the link selection constraint.
## 3 Results and Discussion
### Comparative Analysis of Volatiles Between Fresh and Frozen PJs
These two aldehydes were also described as having green, citrus, or fatty odors [32]. Increases in hexanal, octanal, and nonanal were expected to result in stronger grassy notes in the frozen samples (FPJ\({}_{--}\)20 \({}^{\circ}\)C, FPJ\({}_{--}\)40 \({}^{\circ}\)C, and FPJ\({}_{--}\)80 \({}^{\circ}\)C). However, the sensory results (Figure 1) contradicted this prediction, with FPJ0 showing the highest grassy intensity. This discrepancy might be due to the presence of other grassy contributors in FPJ0, i.e., (Z)-3-hexen-1-ol. On the contrary, other aldehydes in fresh FPJ0 ((_E_)-2-hexenal, decanal, benzaldehyde, and undecanal) decreased significantly or became undetectable after freezing. Notably, the reduction pattern of (_E_)-2-hexenal mirrored that observed in pasteurized guava puree during frozen storage, suggesting a similar degradation behavior under frozen storage [33]. Figure 2: Concentrations of various compound types in FPJ and frozen pomegranate juice, measured by GC-MS. ***: significant at \(p\) < 0.001 level. Terpene compounds were also identified as key factors in describing the fragrance of pomergnate fruit ([PERSON] et al., 2016). This group exhibited a similar decreased tendency to that of alcohols following the freezing treatment. Table 2 showed that seven terpenes were detected in the fresh control sample FFJ0, compared only with one, two, and four terpenes in FFJ\({}_{--}20\)\({}^{\circ}\)C, FPJ\({}_{--}40\)\({}^{\circ}\)C, and FPJ\({}_{--}80\)\({}^{\circ}\)C, respectively. The total terpene content in FPJ0 was 3.32 ug/kg, significantly higher than in frozen-treated samples. Similar results were found by [PERSON] et al. (2015). Limonene, the most abundant terpene in PJ, constituted up to 44.56% of the total terpene content in FPJ0. This compound was related to fruity characteristics (i.e., lemon, orange, and citrus odors) ([PERSON] et al., 2016). In contrast, the frozen-treated groups (\(-20\), \(-40\), and \(-80\)\({}^{\circ}\)C) exhibited reductions in limonene content of 81.08%, 72.97.56%, and 71.62%, respectively, compared to FPJ0. In FPJ0, the other seven terpenes, including \(\beta\)  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**No.
# Application of Molecularly Imprinted Polymers in the Analysis of Waters and Wastewaters
## 5 Application of MIPs for the Detection of Different Classes of Contaminants
### Endocrine Disrupting Chemicals (EDCs)
#### 5.4.2 Bisphenol S  
After the prohibition of BPA use, bisphenol S (BPS) emerged as a key substitute for BPA in consumer products. Bisphenol S, with a much similar chemical structure to that of BPA, shown in Figure 8, contains a sulfone group showing enhanced electron-absorbing abilities and two hydroxyl groups, which makes Bisphenol S more acidic than other bisphenol and more stable than BPA[167]. As an intermediate in the manufacturing of epoxy resins and polycarbonate plastics, bisphenol S can be found in many of our human products today. As a result, individuals are also exposed to BPS in the same way that they are to BPA, though several studies have shown that BPS may be as toxic as BPA in some cases. While BPS had showed similar effects on estrogen and androgen receptor activities as BPA[168], it was found to affect the efficacy on 17\(\alpha\)-hydroxy progesterone of all bisphenol analogues[169].  
A BPS electrochemical sensor is reported based on modified GCE using MIP (polypyrrole membrane) as a recognition element and the electron-conducting layer was composed of three-doped carbon quantum dots B, N, and F-CQDs and silver nanoparticles (AgNPs), as shown in Figure 10[148]. The synergy of B-N and F-CQDs and AgNPs significantly increased sensitivity and resulted in an increase in the electrical signal. In addition, the MIP significantly enhanced the sensor's selectivity by allowing it to recognize BPS through the imprinted cavities. There was a linear response range of 1 \(\times\) 10\({}^{-8}\) to 5 \(\times\) 10\({}^{-5}\) M,  
Figure 9: Scheme of the preparation process of BPA electrochemical sensor based on glassy carbon electrode (GCE) and used a mixture of carbon diimide (EDC) and succinimide (NHS) in the preparation of cadmium telluride carboxylate[135]. (Reproduced under permission from Elsevier, doi:10.1016/j.microc.2021.106737).  
with a detection limit of 1.12 \(\times\) 10\({}^{-8}\) M. This electrode proposed in this article can be considered to satisfy the need for trace-level measurement of BPS in biological and environmental samples.
# A Low-Cost IoT-Based System to Monitor the Location of a Whole Herd
## 2 Materials and Methods
### Data Analysis  
Different collar/tag ratios, from 1/25 to 25/25, have been simulated in order to study the changes in the following key performance indicators (KPIs) of the system: average number of BLE tag messages received per day, average number of BLE tags detected by all the GPS collars over a day, average time between two consecutive messages from the same BLE tag, average number of BLE tags included in each message sent by GPS collars, and the percentage of collar messages received without BLE tag information.  
Figure 1: Architecture diagram of the proposed solution.  
The average values of KPIs over the experiment have been calculated for each collar/tag ratio by randomly selecting the correspondent number of collars. The random selection process has been repeated 5000 times, following the recommendations of [29]. Both average and standard deviation have been calculated for each KPI. The same procedure has been used to study the evolution of KPIs over the experiment and within an average day.  
For the graphics included in the present paper, a trend line was calculated and drawn to make charts more easily understandable. Generalized additive models [30] were used with a basis of 10 for daily charts and 15 for hourly ones.  
R Project [31] has been used for data handling, calculations, and graphics.
## 5 Discussion  
### Scientific Findings  
From the results of the substudies analysed in Section 4.2, conclusions can be extracted as scientific findings for the basic research of a hybrid, flow-oriented organisational structure for assembly. These findings provide essential insights into the dimensioning and efficiency of such a hybrid, flow-oriented organisation structure.  
One fundamental aspect is the assignment of assembly operations within the hybrid structure.  
**General finding 1:** The hybrid value of the production universality degree \(PUD_{hy}\) indicates which assembly operations are assigned to which area (according to the flow or workshop principle) as a function of their relative frequency within the forecast production programme. However, the existing assembly precedence restrictions can lead to deviations, quantified with the suffering target variable \(Z_{i}\).  
Beyond operational assignment, determining the appropriate capacity allocation for a hybrid structure is crucial.  
**General finding 2:** To size a hybrid organisation structure, knowledge of the required assembly process times for both flow and job shop implementation is required. Based on this, the capacities of the individual areas are planned.  
In addition to structuring and sizing, efficiency considerations play a vital role in decision-making.  
**General finding 3:** The hybrid organisation structure is more efficient than a pure flow or job shop organisation structure from a certain level of production programme complexity.  
Finally, the selection of the appropriate organisational structure depends on a thorough evaluation of the production program itself.  
**General finding 4:** The selection of a suitable organisation structure depends on the production programme. The complexity of a production programme must be evaluated, which serves as a basis for deciding whether to choose a pure or hybrid organisation structure.  
These scientific findings underline the importance of balancing flexibility and efficiency in hybrid, flow-oriented organisational structure for assembly, ensuring that organisational structures are tailored to production requirements.
###### Abstract  
In order to study the residual strength of buried pipelines with internal corrosion defects in seasonally frozen soil regions, we established a thermo-mechanical coupling model of a buried pipeline under differential frost heave by using the finite element elastoplastic analysis method. The material nonlinearity and geometric nonlinearity were considered as the basis of analysis. Firstly, the location of the maximum Mises equivalent stress in the inner wall of the buried non-corroded pipeline was determined. Furthermore, the residual strength of the buried pipeline with corrosion defects and the stress state of internal corrosion area in the pipeline under different defect parameters was analyzed by the orthogonal design method. Based on the data results of the finite element simulation calculation, the prediction formula of residual strength of buried pipelines with internal corrosion defects was obtained by SPSS (Statistical Product and Service Solutions) fitting. The prediction results were analyzed in comparison with the evaluation results of B31G, DNV RP-FI01 and the experimental data of hydraulic blasting. The rationality of the finite element model and the accuracy of the fitting formula were verified. The results show that the effect degree of main factors on residual strength was in order of corrosion depth, corrosion length, and corrosion width. when the corrosion length exceeds 600 mm, which affects the influence degree of residual strength will gradually decrease. the prediction error of the fitting formula is small and the distribution is uniform, it can meet the prediction requirements of failure pressure of buried pipelines with internal corrosion defects in seasonally frozen soil regions. This method may provide some useful theoretical reference for the simulation real-time monitoring and safety analysis in the pipeline operation stage.  
internal corrosion; differential frost heave; orthogonal design; residual strength; fitting formula +
Footnote †: journal: Applied Sciences