esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction of Blast Crushing Lumpiness Based on CPO-BP Modeling
## 4 Engineering Applications
### Parameter Selection  
The key variables related to blasting parameters are listed in Table 1. Each variable is labeled with its name and unit in the first row, including hole distance (m), resistance line (m), unit explosive consumption (kg/m\({}^{3}\)), drilling extra depth (m), fracture development (T), and the resulting fragmentation size represented by p50 (m). These parameters are selected based on prior field experiments and blasting design practices.  
Table 1 provides the field data used for model input analysis. To reduce dimensionality and improve model performance, the Mean Impact Value (\(MIV\)) method was applied to assess the influence of each variable on blasting fragmentation. The variables with lower MIV scores were excluded from the model input, while the most impactful features were retained and used as inputs to the CPO-BP model for block size prediction.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Hole** & **Resistance** & **Unit Consumption of** & **Drilling Extra** & **Fracture** & **p50 (m)** \\
**Distance (m)** & **Line (m)** & **Explosives (kg-m\({}^{-3}\))** & **Deep (m)** & **Development (T)** & **p50 (m)** \\ \hline
5.80 & 4.30 & 0.45 & 0.80 & 1 & 0.357 \\
5.80 & 4.30 & 0.49 & 1.20 & 1 & 0.314 \\
5.80 & 4.30 & 0.49 & 2.20 & 1 &  
According to the related research, among the neural networks evaluating the correlation of variables, the Mean Influence Value method (\(MIV\)) [27] is one of the best indicators. The steps for the application of the \(MIV\) method, and its specific implementation are generally to screen the key influencing factors based on the relative magnitude of the MIV value, to process one of the input parameters by increasing or decreasing it by a magnitude of 10%, while leaving the other covariates untouched, and finally to feed this adjusted data into the prediction model to calculate the average degree of change of the results [28]. This adjusted data is fed into the forecasting model to calculate the average degree of change in the results. The formula is as follows:  
\[MIV_{k}=\frac{1}{M}\sum_{j=1}^{M}\left|y_{j}^{(k+)}-y_{j}^{(k-)}\right| \tag{22}\]  
where \(k\) is the first k input variable, \(M\) is the overall sample size, \(y_{j}^{(k+)}\) is the predicted value of the j sample after a 10% increase in the k variable, and \(y_{j}^{(k-)}\) is the predicted value of the j sample after a decrease of the same proportion in the k variable.  
As shown in Figure 5, unit consumption of explosives, hole distance, resistance line, and fracture development have a strong influence on the prediction of blasting block size and are selected as input parameters.
## 4 Simulation, Implementation, and Results  
In this, we employed two distinct methods to validate our proposed approach: simulation using IBM SPSS Modeler and fog layer implementation. Here is a breakdown of each method.  
Simulation using IBM SPSS Modeler:  
As illustrated in Figure 9, the Excel file is accessed by the modeler for data processing. Prior to importing the file into IBM SPSS Modeler, a new row labeled \"Online Blood Glucose\" was appended to include input data for another week, while any aberrant data points in the \"Blood Glucose\" column were filtered out. Within the Excel node configuration, time serves as the input, code functions serves as the classifier, and the blood glucose level is designated as the input data. Notably, the target variable is denoted as the online glucose level, as depicted in Figure 10.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Time** & **Code** & **Blood Glucose** & **Blood Pressure** & **Heart Rate** \\ \hline
8:30 & 58 & 135 & 115.51 & 89 \\ \hline
10:30 & 62 & 231 & 100.54 & 83 \\ \hline
12:30 & 60 & 216 & 133.75 & 83 \\ \hline
14:30 & 62 & 177 & 133.69 & 85 \\ \hline
16:30 & 48 & 180 & 149.66 & 83 \\ \hline
18:30 & 48 & 170 & 149.72 &As depicted in Figure 11, the KNN algorithm was operationalized through the utilization of the type and KNN nodes. The system was configured to classify the data from the \"Blood Glucose\" column based on the corresponding values in the \"Code\" column. The overarching objective is to enable a comparative analysis between \"Online Glucose\" data and the classes derived from \"Blood Glucose\" to detect abnormalities effectively.  
Figure 12 illustrates the classification of data based on the condition codes.  
Figure 11: Implementation of KNN algorithm.  
Figure 10: Different inputs of the modeler.  
Figure 9: Simulation process.  
Figure 13 demonstrates the comparison of a blood sugar of 197 mg/dL, which exceeds the normal range, with three neighboring data points.  
Figure 14 illustrates that the blood sugar level of 197 mg/dL deviates significantly from its neighboring data points, indicating that it does not belong to the correct class.  
Figure 14: Distance from neighbors.  
Figure 12: Data classification.  
Figure 13: KNN results.  
Hence, the blood sugar level of 197 mg/dL does not align with the class of neighboring points. Consequently, the system categorizes these data as abnormal and may trigger alerts to caregivers as deemed necessary.
## 2 Antenna Design
### Effect of Decoupling Structure  
The Figure 6 illustrates how integrating a decoupling structure significantly enhanced the S-parameters of the MIMO antenna system. Notably, the \(S_{2,1}\) parameter, which represents mutual coupling between antenna elements, showed a significant reduction when the decoupling structure was applied (gcl1 = 20 mm), indicating improved isolation. This enhancement is crucial for MIMO performance, as lower coupling leads to better signal quality and reduced interference between antenna elements. Additionally, the improved \(S_{1,1}\) parameter suggests better impedance matching, leading to more efficient signal transmission and reception. Overall, incorporating a decoupling structure helps optimize signal integrity, boost diversity performance, and enhance system efficiency, making it an essential design choice for next-generation wireless communication systems like 5G and beyond.
## 1 Introduction  
Peri-implant diseases are defined as inflammatory lesions of the surrounding peri-implant tissues and include mucositis and peri-implantitis [1]. Peri-implant mucositis (PiM) is a reversible inflammatory lesion that affects the soft tissues around a dental im-plant, and the main clinical feature is bleeding on probing (BOP) [2]; radiographically, bone loss is absent [1]. An increase in probing depth is often observed due to swelling or decreased resistance to probing [2]. Mucositis can cause discomfort due to bleeding and implant failure if left untreated [1].  
The therapy for peri-implant mucositis involves mechanical debridement of the implant surface using curettes, ultrasonic devices, and air abrasives [3]. However, when mechanical debridement fails to control inflammatory changes [3], adjunctive treatments such as antibiotics and local antiseptics [1] have been used, and their efficacy has been demonstrated for mucositis, showing an improvement in clinical parameters, especially bleeding on probing [4]. However, there is a growing interest in exploring alternative treatments that can reduce inflammation and improve immune function [5].  
The World Health Organization (WHO) describes probiotics as living microorganisms that can provide health benefits when consumed in adequate amounts. They have been extensively studied for their potential to improve intestinal health and stimulate the immune system [6]. However, there is a growing body of evidence [7; 8; 9; 10] suggesting that probiotics may also be beneficial in the treatment of peri-implant mucositis. Probiotics containing _Lactobacillus_ spp. have been shown to reduce inflammation and bleeding in patients with peri-implant mucositis [11]. Additionally, it has been found that probiotic lozenges containing _Lactobacillus reuteri_ prevent the development of peri-implant mucositis and mucositis related to chemotherapy and radiation therapy [12].  
In this context, systematic reviews have emerged to evaluate the efficacy of different non-surgical protocols for the treatment of peri-implant mucositis [13], and at the same time, other systematic reviews have evaluated the effectiveness of probiotics in the treatment of peri-implant diseases [14; 15]. However, these reviews included patients with and without mechanical instrumentation and also included patients who had been diagnosed not only with PiM and peri-implantitis, or they included patients treated with other adjunctive therapies besides probiotic therapy.  
This systematic review evaluated the effects of adjunctive probiotic therapy on mechanical debridement in peri-implant mucositis. This article aims to inform healthcare professionals and patients about the benefits and limitations of probiotics for peri-implant mucositis through the PICOS question addressed: \"What are the clinical and microbiological effects of adjunctive probiotic use in the treatment of peri-implant mucositis?\".
## Materials and Methods
### Depolarization Fading System Model Based on Probe with Built-in Faraday Rotator
\begin{array}{cc}a&-b\\ b^{*}&a^{*}\end{array} \right]. \tag{2}\]  
Note that the collimator and achromatic lens in the self-designed probe are made of isotropic material, whose impact on the polarization of the beam is small enough to be ignored. In addition, the cavities generated between the collimator and Faraday rotator, Faraday rotator and achromatic lens, and achromatic lens and object under detection are full of air, so the polarization of light through this free space can be considered unchanged. The Jones matrix, corresponding to a Faraday rotator, for the axis of propagation by an angle \(\theta\), is given by:  
\[F=\left[ \begin{array}{cc}\cos\theta&\sin\theta\\ -\sin\theta&\cos\theta\end{array} \right] \tag{3}\]  
Figure 2: The self-designed probe with a built-in Faraday rotator: (**a**) the schematic diagram; (**b**) a picture of the probe and Faraday rotator details. There is no mirror in the self-designed probe; instead, the surface of the object under detection reflects the focused light back into the probe. Thus, the Jones matrix for the reflection at the surface of an object can be described as:  
\[M=\varepsilon\left[ \begin{array}{cc}-1&0\\ 0&1\end{array} \right], \tag{4}\]  
where \(\varepsilon\) denotes the loss coefficient for receiving the return light (less than 1%). The net Jones matrix corresponding to the self-designed probe, including the surface of an object, is given by:  
\[T=\left[ \begin{array}{cc}\cos\theta&\sin\theta\\ -\sin\theta&\cos\theta\end{array} \right]_{\theta=-45^{\circ}}\varepsilon\left[ \begin{array}{cc}-1&0\\ 0&1\end{array} \right]\left[ \begin{array}{cc}\cos\theta&\sin\theta\\ -\sin\theta&\cos\theta\end{array} \right]_{\theta=45^{\circ}}=\varepsilon \left[ \begin{array}{cc}0&-1\\ -1&0\end{array} \right]. \tag{5}\]  
When considering the whole process of transmitting light along a length of fiber through the self-designed probe and back along the same fiber, the Jones matrix is thus described by:  
\[R=\overset{\leftarrow}{R}\overset{\rightarrow}{R}=\alpha^{2}\varepsilon \left[ \begin{array}{cc}0&-1\\ -1&0\end{array} \right]. \tag{6}\]  
It is found that when the light travels forwards and backwards through the self-designed probe, the resulting polarization becomes constant and orthogonal to the original polarization, which is independent of the birefringence properties of the fiber. Through the above analysis, it is clear that the self-designed probe has the same effect as FRM, and both should be added separately in the two interference arms of a Michelson interferometer. No matter what the distribution of the birefringence is along the separate arms of the Michelson interferometer, the evolution of their respective polarization will always end up at the same point. As a consequence, the interference fringe visibility is kept stable with respect to the variance of the polarization of the two arms.
## 3 Methodology
### Dataset Description and Analysis
#### 3.2.2 Dataset Composition  
The primary dataset comprises three key components. First, the occupancy data covers a total building population of 2606 occupants, with a gender distribution of 54% male (1403) and 46% female (1203). The floor-wise distribution shows progressive increases in occupancy from level 2 through level 6, with level 2 housing 274 occupants (11%), level 3 with 473 occupants (18%), level 4 containing 493 occupants (19%), level 5 supporting 650 occupants (25%), and level 6 accommodating 716 occupants (27%).  
The facility inventory provides detailed information about sanitary fixture distribution across all floors. Table 1 provides a comprehensive overview of current fixture allocation for each gender on floors 2 through 6, revealing potential imbalances in distribution. Femalefacilities total 58 water closets, 44 wash hand basins, and 6 showers, while male facilities comprise 37 water closets, 38 urinals, 35 wash hand basins, and 5 showers. Figure 1 illustrates a typical floor layout using level 3 as an example, providing insight into restroom locations and accessibility considerations.  
The temporal usage patterns encompass 525,600 time points, representing minute-by-minute data over 365 days. These data were aggregated into 8760 hourly records, 365 daily summaries, and 52 weekly patterns for analysis.  
The fixture inventory includes comprehensive facilities for both genders. Female facilities consist of 58 water closets (WC), 44 wash hand basins (WHB), and 6 showers. Male facilities include 37 water closets (WC), 38 urinals (UR), 35 wash hand basins (WHB), and 5 showers.
## 1 Introduction  
The fat-to-muscle ratio (FMR) has been defined as the ratio of fat mass to muscle mass, and has recently emerged as a simple and useful ratio to assess cardiovascular diseases [1] and metabolic syndrome [2] in adults as well as in children [3]. FMR considers not only the accumulation of fat mass but also skeletal muscle mass. In children, the negative effects of an excessive accumulation of fat mass on physical fitness (PF) and motor competence (MC) parameters are well known thanks to studies on BMI, the sum of skin-folds, and waist circumference [4; 5; 6]. However, less attention has been paid to the positive role of muscle mass related to fat mass in MC and PF [7].  
PF is used as a set of measurable health- and skill-related attributes which refer to good health or physical condition as a result of physical exercise [8]. Motor competence can be defined as the collection of skills, abilities, and areas of knowledge that enable individuals to tackle movement challenges across their lifespan [9], and involves the capacity to adjust actions to different surroundings [10].  
It is currently accepted that a correlation exists between PF (especially cardiorespiratory fitness and strength), and health [11; 12; 13] in childhood and adolescence; strong evidence of there being a positive association between PF and MC [5; 10; 14] and PF and MC with fat mass accumulation also exists [4; 5; 6]. Improvements in both PF and MC were observed to substantially influence weight trajectories over time, although the direction of these relationships is not clear [14]. On the other hand, little data exist regarding the role of the fat-to-muscle ratio in body composition, MC and PF in children. The primary objectives of this study were (1) to examine the relationship between body composition (FMR, WC, and BMI) and MC and PF in 7-year-old children and (2) to determine if FMR influences the relationship between MC and PF.
# Privacy-Preserving E-Voting System Supporting Score Voting Using Blockchain
## 7 Security and Privacy Analysis  
In this section, we will discuss the security and privacy concerns and how the proposed approach mitigates them.  
**Proposition 1**.: _Our proposed scheme preserves the privacy of voters by masking their votes._  
Proof.: A voter in the proposed scheme interacts with a randomly generated _one-request-only_ Blockchain address, i.e., the corresponding public key. Then, the voter shall prove their knowledge to the public key in a zero-knowledge before submitting their votes to the Blockchain. The proposed scheme preserves the voters' privacy by masking their votes before it is submitted to the Blockchain.  
**Proposition 2**.: _The proposed scheme ensures election fairness by mitigating malicious voters who may manipulate the voting scores._  
Proof.: The yes/no voting approach requires an eligible voter to submit a yes/no for a candidate, while the score voting requires the voter to submit a score in a predefined range for each candidate. To preserve privacy, votes shall be encrypted before it is submitted to the Blockchain, however, a malicious voter may falsely submit a score that is not in the range to manipulate the election result in the favor of a certain candidate. The proposed approach ensures the security against malicious behavior of these voters by leveraging the zero-knowledge set membership proof such that the voter shall prove first in a zero-knowledge that the score selected lies in the predefined set without allowing the Blockchain  
Figure 5: Impact of increasing the transaction rate and number of transactions on latency.  
Figure 6: The impact of increasing the number of transactions on latency and throughput.  
and/or eavesdroppers to know the score value per candidate. In the proposed scheme, the Blockchain will accept the authorized voters' vote if and only if the voter has correctly selected a score value that is in the set defined by the election authority. This is due to the _Soundness_ and _Completeness_ properties of ZKSM [35]. This ensures the fairness of the elections.  
Our proposed scheme does not suffer from a single point of failure.  
Proof.: The underlying Blockchain is used to execute the proposed scheme and even if 51% of nodes are malicious, they cannot collude to obtain the election results because the designed scheme requires the knowledge of all masked values shared by the election authority. This is because the S is divided among all the voters so our tallying scheme is secure against the 51% attack.  
Passive eavesdroppers can not infer any private information about the Ballot secrecy.  
Proof.: One of the threats is to protect the score voting scheme from passive adversaries who are not actively engaged in the process of voting, but instead are trying to gain knowledge of the election proceedings by eavesdropping on communication channels and the Blockchain. This is done by masking the votes as well as using the ZKSM to prove that the submitted score lies in the set in a zero-knowledge manner. Therefore, a passive adversary cannot infer any sensitive information about the elections.
# Evaluation of AEP Predictions for Commercial Wind Farms in Sweden
## 4 Results  
For each wind turbine, by normalizing and re-calculating the monthly wind power production data according to Equations (1)-(3) and comparing them to the given P50 in Equation (4), the errors of the pre-construction AEP predictions compared to production-based WCP were calculated. The mean error was 7.1%, which means that, overall, the predicted AEP from the planning phases of the studied wind turbines was overestimated. The mean overestimation is larger for Sweden than that previously shown for the British Isles and South Africa [6]. Dividing the results by terrain type, the error becomes 9.5% for open terrain, 6.3% for forest areas, and 2.0% for offshore turbines. This indicates that the higher complexity of the forest terrain is not the main reason for the error.  
Figure 4 presents the error divided by the installation year of the wind turbines. The predicted AEP was most accurate for wind turbines installed from 2011 to 2013, whereas it was slightly underestimated for wind turbines installed in 2013 and 2016 (only four wind turbines installed in 2016 were used in the analysis). In summary, accuracy has not improved over time. This contradicts improvements such as access to better long-term corrected wind data, improved flow models (including improved wind-shear calculation models), more detailed terrain data, improved tools for sector-based zero-plane  
Figure 3: Monthly production plotted against the ERA5T correlation index for the corresponding months with the closest index site to one of the wind turbines. The red values have been excluded by the WCP filter.  
displacement, refined measurement methods, improved standards, and a better understanding of cold-climate effects.  
To gain an indication of whether the wind-farm blockage effect is the main contributor to the overestimations of AEP predictions in this work, the results could be divided depending on whether evaluating a single wind turbine or a wind farm (and by the wind farm size). This is a possible continuation of this work. The assumption that the predicted AEP reported by the owners to Vindstat comes from the project planning phase adds some uncertainty to the results. However, this is likely to be the case for most multi-megawatt wind turbines installed after 2005. A more thorough study, although based on fewer data, could be performed by collecting individual reports of the predicted AEP from the project planning phase of several projects.
## 3 Results and Discussion
### Antitrypanosomal Study  
African trypanosomiasis is a disease with devastating adverse effects, which is difficult to control, thereby imposing a high risk to public health [55]. Studies have shown that trypanosomes are developing high resistance to the existing antitrypanosomal agents and, remarkably, plants have been used in time immemorial for the treatment of diseases because of the enormous bioactive compounds they possess [56]. In the present study, the antitrypanosomal activity of chloroform, ethyl acetate and ethanol extracts of _O. genistifolia_ was investigated and results obtained from this experiment showed their exceptional antitrypanosomal potential (Table 2). The extracts of the studied plant (chloroform extract IC\({}_{50}\) = 0.5 \(\upmu\)g\(\cdot\)mL\({}^{-1}\), ethyl acetate extract IC\({}_{50}\) = 0.5 \(\upmu\)g\(\cdot\)mL\({}^{-1}\) and ethanol extract IC\({}_{50}\) = 0.4 \(\upmu\)g\(\cdot\)mL\({}^{-1}\)) were more effective against the parasites as compared to those reported from previous studies (Figure 3). Similarly, crude hexane and ethyl acetate extracts of _Crateva adansonii_ displayed good antitrypanosomal potential against _T. b. brucei_ (IC\({}_{50}\) 12.5 \(\upmu\)g\(\cdot\)mL\({}^{-1}\)) [57]. On the other hand, [PERSON] et al. [58] reported a very good antitrypanosomal activity (IC\({}_{50}\) = 2.08 \(\upmu\)g\(\cdot\)mL\({}^{-1}\)) from the ethyl acetate extract of _Ocimum gratissimum_. Other researchers have also reported the antitrypanosomal potentials of some medicinal plants such as _Moringa oleifera_, _Terminalia ivorensis_, _Waltheria indica_, _Artemisia maciverae_, _Eucalyptus canaldulensis_ and _Psidium guajava_[56; 59; 60; 61; 62; 63].  
However, the mechanism by which chloroform, ethyl acetate and ethanol leaf extracts of _O. genistifolia_ demonstrated exceptional antitrypanosomal effects against the parasite is vague and we are yet to find ways to describe the biological activity shown. However, facts highlighted in the study of [PERSON] et al. [56] proposed that the bioactive compounds in some plants have the potential by generating free radicals that can cause peroxididative damage to trypanothione reductase which consequently alters the redox balance of the parasites. The report of [PERSON] et al. [64] highlighted that some bioactive compounds from _Q. borealis_ can bind with the DNA of the plasmodium parasite and subsequently activate events that could lead to the parasite's death.  
Figure 3: Dose-dependent curves of the antitrypanosomal activity of chloroform (14), ethyl acetate (15) and ethanol (16) extracts of _O. genistifolia_.