esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Range of Allowable Values  
In the following discussion, the range of allowable values is specified. As for the rail, the range of possible values is quite narrow; basically, there are two guide sets of values for the 54E1 and 60E1 rails, determining the limits on \(EI\) and \(m\). More variability can be attributed to other data. They are listed in Table 1 and correspond to the values given in [46; 47; 48].  
The range of allowable parameters is defined with respect to their dimensionless counterparts, for which a reference Winkler beam is selected. First, the dimensionless moving spatial coordinate is introduced as  
\[\xi=\chi r\text{ where }\chi=\sqrt[4]{\frac{k_{f}}{4 EI}} \tag{9}\]  
The other values are related to the reference Winkler beam by  
\[\mu_{s}=\frac{M_{s}}{m},\ \kappa_{p}=\frac{k_{p}}{k_{f}} \tag{10}\]  
\[\eta_{p}=\frac{\text{c}_{p}}{2\sqrt{mk_{f}}},\ \eta_{f}=\frac{\text{c}_{f}}{2 \sqrt{mk_{f}}},\ \eta_{N}=\frac{N}{2\sqrt{k_{f}EI}},\ \eta_{s}=\frac{k_{s}}{2\sqrt{k_{f}EI}} \tag{11}\]  
where for simplicity, the previous designations are kept, but all values (except for \(N\)) are assumed as longitudinally distributed.  
Therefore, the decisive parameters are \(\mu_{s}\) and \(\kappa_{p}\). Assuming that the sleepers are spaced from \(l_{s}\) = 0.545 m [49; 50] to \(l_{s}\) = 0.711 m [50], this together with values in Table 1 identifies a relatively narrow interval for \(\mu_{s}\in(1;5.5)\), but it gives a very large variability for \(\kappa_{p}\in(0.03;40000)\). A typical spacing in Portugal [51], in most European and some other countries, e.g., India, is 0.6 m. According to [50], \(l_{s}\) should be related to the sleeper  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Approximate Range** \\ \hline \(EI\) (MNm\({}^{2}\)) & 4.9–6.4 \\ \hline \(m\) (kg/m) distributed & 54–60 \\ \hline \(M_{s}\) (kg) concentrated & 40–160 [46] \\ \hline \(k_{p}\) (MN/m) concentrated & 20–5000 [46] \\ \hline \(k_{f}\) (MN/m\({}^{2}\)) distributed & 0.22–1000 [47; 48] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Range of typical values of the main components of the two-layer model.  
type and ballast height. There are studies analyzing how significant savings could be achieved by increasing \(l_{s}\)[52]; nevertheless, these are mainly theoretical studies, and larger spacings than specified above are not yet common in practice. For practical reasons, a shorter distance is inadequate, having in mind the typical sleeper base.  
There is little data on shear stiffness, so it will be set to zero in most examples. Non-zero values would make the model generally stiffer. Similarly, a compressive normal force would make the model softer and a tensile one stiffer. However, since this is not the main concern, it will also be set to zero in most examples. Regarding the viscous damping coefficients, very different values can be found in the literature, so it was decided to directly vary the dimensionless parameters.
# All-in-Focused Image Combination in the Frequency Domain Using Light Field Images
## 2 All-in-Focused Image Combination
### Harmonic Mean (HM)  
HM measures the information of the eSUML results and is used for confirming the reliable focus measure. The HM value at block \(B\)(\(x\),\(y\)) is calculated based on the eSUML values in the window around \(B\)(\(x\),\(y\)), which is \(N\times N\). HM is defined by  
\[\begin{array}{l}H(x,y)\ =\ \left[\frac{1}{M}\sum_{m=1}^{M}\frac{1}{\mu_{m}(x,y) }\right]^{-1}\end{array} \tag{5}\]  
where \(M\) determines the size of the window, (\(x\),\(y\)) represents the block position, and \(\mu_{m}\) is the average value of the eSUML results at block \(B\)(\(x\),\(y\)). High values of HM will be deemed as in-focused regions and the out-focused regions will have low HM values.  
HM has two advantages. First, arithmetic mean estimate can be distorted significantly by the large variances of the out-focused regions, while the harmonic mean is robust. Second, the harmonic mean considers reciprocals, hence it assists the small variances and increases their influence in the overall estimation. Although most variances of the out-focused regions may have small values, one large variance value can make the arithmetic mean value in those regions larger than the value in the in-focused regions. It causes the out-focused regions to be falsely considered as in-focused regions.
## 3 Methodology  
### Data Collection and Preprocessing  
This study's dataset includes measurements from many modules of diverse photovoltaic technologies, concentrating on 11 internal module features, as detailed in Section 4. The input features comprise Open Circuit Voltage (Uoc), Short Circuit Current (Isc), Maximum Power (Pmpp), Solar Irradiation Spread (SIS), Maximum Voltage (Umpp), Maximum Current (Impp), Fill Factor (FF), Parallel Resistance (Rp), Series Resistance (Rs), and Module Temperature (Tm). The output feature is the Efficiency of the photovoltaic system, deter mined as the ratio of electrical power output to solar power input. Data were originally collected every 5 min in the 6 th month of 2022 from the Utrecht University Photovoltaic Outdoor Test Facility (UPOT). These data were then aggregated to hourly averages for analysis, resulting in a final dataset of 468 data points after cleaning and preprocessing. The eleven input parameters were selected based on their established influence on PV module efficiency, as reported in the literature [19]. These parameters represent key electrical characteristics, material properties, and operating conditions that directly affect the conversion of solar energy into electricity. Prior to model training, a correlation analysis was performed to confirm the influence of these parameters on the output variable (Efficiency). The correlation matrix indicated strong relationships between most of the selected features and Efficiency, supporting their inclusion in the model. However, the Record Time (RecTime) parameter was found to have a very low correlation with Efficiency and was excluded from the final feature set to prevent unnecessary complexity.  
Data pretreatment encompassed common protocols to guarantee data quality and appropriateness for modeling. Missing values were adequately addressed, and outliers were recognized and managed. Normalization techniques were employed for feature scaling to enhance model convergence. The dataset was partitioned into training (80%) and testing (20%) sets to assess model performance. While a separate validation set is common in machine learning workflows, we opted for a simpler train/test split due to the relatively small size of our dataset (_n_ = 468). With such a limited number of data points, creating a separate validation set would further reduce the size of the training set, potentially hindering the models' ability to learn effectively. To mitigate the risk of overfitting, we employed regularization techniques within each model and carefully monitored performance on the test set.
## 2 Methods
### Data Preprocessing and Normalization
#### 2.3.2 Data Cleaning, Filtering, and Dimensionality Reduction  
The original data include a lot of useless data, which will impact the precision accuracy. Data dimensionality reduction helps in obtaining the core structure of the data and enhances the accuracy of applications. In the data collected from the geological core drilling test bench, all the experiments used the same rotational speed. Due to the good drillability of the concrete blocks and the shallow drilling depth, it was observed and concluded that the influence of pump flow on the drilling speed in this experiment is extremely small and can be considered negligible. To improve the training accuracy of the model and reduce the computational complexity of the artificial neural network, the pump flow rate was removed from the drilling parameters as part of the dimensionality reduction process.  
For the geological core drilling test rig, sensor measurement errors and initial calibration errors can cause a large amount of interfering noise, as well as frequent tripping of the drill string and situations such as sticking and balling during drilling. Additionally, secondary cutting of cuttings that are not promptly evacuated can lead to increased errors in drilling parameters and an increase in abnormal data points, thus requiring preprocessing of the drilling parameter data. A digital filter is an algorithm formed by a certain data logic that can filter out specific noise in the input signal. The weighted median filtering algorithm is a nonlinear signal processing technology based on order statistics theory that can effectively suppress noise. The filter window size can be set to L(L is odd), and the sample pixel in the filter window is \(\{x_{1},x_{2},\cdots,x_{\underline{1}\underline{2}},\cdots,x_{L}\}\). Then the output of a center-weighted median filter (CWMF) whose central weight is \(\omega_{C}\) (\(\omega_{C}\) is an integer) is shown in Equation (6).  
\[y_{CWMF}=median\Big{\{}x_{1},x_{2},\cdots,x_{\underline{1}\underline{2}}, \omega_{C}ox_{\underline{1}\underline{2}},x_{1}+\underline{1},\cdots,x_{L} \Big{\}} \tag{6}\]  
Figure 6: Correlation analysis of drilling parameters.  
Here, \(\omega_{C}\alpha\chi_{\frac{L+1}{2}}\) indicates data replication, so \(\omega_{C}\alpha\chi_{\frac{L+1}{2}}=\chi_{\frac{L+1}{2}}\), \(\chi_{\frac{L+1}{2}},\dots,\chi_{\frac{L+1}{2}}\) (there are \(\omega_{C}\) of \(x_{\frac{L+1}{2}}\)); median \(\{\cdot\}\) represents the median operation.
## 3 Methods
### Feature Selectors
#### 3.4.2 Maximum-Likelihood Feature Selector  
MLFS ranks input features based on their mutual information (MI) with the target variable, using a maximum likelihood estimator. MI quantifies the dependency between variables and is defined as  
\[I(X,Y)=\iint p(x,y)\text{log}\frac{p(x,y)}{p(x)p(y)}dxdy \tag{19}\]where \(p(x,y)\) is the joint probability density of the input variable \(x\) and the target variable \(y\), and \(p(x)\) and \(p(y)\) are their respective marginal densities. MLFS estimates MI through the density ratio \(w(x,y)\), which represents the relationship between the joint and marginal densities [37], as follows:  
\[w(x,y)=\frac{p(x,y)}{p(x)p(y)} \tag{20}\]  
Given \(n\) paired samples \((x_{i},y_{i})\), the MI can be approximated as  
\[I(X,Y)\approx\frac{1}{n}\underset{i=1}{\overset{n}{\sum}}\log w(x_{i},y_{i}) \tag{21}\]  
where \(w(x,y)\) is estimated by maximizing the log-likelihood function \(L(\alpha)\). In Equation (22), \(\alpha\) is a parameter vector used to scale the feature values, and \(\phi(x,y)\) is a basis function representing the feature transformations:  
\[L(\alpha)=\underset{i=1}{\overset{n}{\sum}}\log\left(\alpha^{\top}\phi(x_{i},y_{i})\right) \tag{22}\]  
Normalization constraints ensure that the estimated density ratio \(w(x,y)\) sums to 1 and remains non-negative, providing an accurate approximation of the relationship between the variables. This optimization helps identify the most relevant features by focusing on those that maximize MI, improving model performance while reducing redundancy.
# Feasibility of a Patient-Specific Bolus Using the Life-Casting Method for Radiation Therapy
## 5 Conclusions  
We applied the LC method for the first time to fabricate patient-specific boluses and evaluated their performance. Compared to commercial boluses, the LC method allowed for a reduction in the air gap, increased the accuracy of dose delivery, and improved the reproducibility of the bolus setup. In addition, the patient-specific boluses created using the LC method exhibited a similar performance to that of boluses fabricated in other studies using 3D printing techniques. This suggests that patient-specific boluses can be produced without the existing limitations, such as the cost of purchasing 3D printing and design software, spatial constraints, and long production times. In particular, the LC method offers high accessibility, as it does not require advanced 3D design skills and employs easily obtainable materials, such as plaster. Furthermore, this approach prevents unnecessary radiation exposure for the patients by reducing the number of CT scans required. Therefore, the method proposed in this study is expected to be useful for creating patient-specific boluses for radiation therapy applications.  
Conceptualization: J.K., T.-G.K. and B.P. (Byungdo Park); methodology: J.K.; validation: B.P. (Beomjun Park); formal analysis: J.K. and B.P. (Beomjun Park); writing--original draft preparation: J.K.; writing--review and editing: [PERSON], B.P. (Beomjun Park), T.-G.K. and B.P. (Byungdo Park); supervision: T.-G.K. and B.P. (Byungdo Park). All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean Government (MIST) (No. 2022R1F1A105955412).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Measurement Conditions
### Vibration Measurements  
Measurements were made using the Kistler LabAmp 5165A data acquisition system and Kistler 8702B500 accelerometers. Positions of accelerometers and measurement directions are shown in Figure 12. The measurements have been done on the left side of the return pulley's shaft, in two directions. The sampling frequency of signals was 50 kHz. The raw signal collected on the machine and RMS vectors are presented in Section 4.1.
## 5 Case Study
### Case Study Presentation
At this point, the company began exploring technological solutions that could improve production efficiency and machine reliability, leading them to investigate options such as real-time monitoring systems, predictive maintenance, and the use of modern tools to assist in early fault detection and maintenance optimization. This process of adaptation and technological modernization marked the first step for Alpha in overcoming its maintenance challenges and ensuring a more efficient and sustainable future for its operations. The implementation of SIRPM in the case study was based on RPA processes optimized for the collection, processing, and analysis of operational data from industrial equipment. These automated processes were implemented in virtual machines (VMs) configured in a hybrid cloud environment. RPA software (Uipath 2022.4.1) licenses were installed directly on the VMs, where the robots executed the automations necessary to interact with platforms to access the databases. IoT sensors were strategically positioned on the machines to monitor critical variables such as temperature, vibration, and pressure. The data collected by the sensors were transmitted via LTE and Wi-Fi-based telecommunications networks to IoT gateways. These gateways played a crucial role in efficiently aggregating and transmitting this information to the central operations system. This connectivity architecture was designed to ensure low latency and high reliability in real-time data flow, essential characteristics for predictive maintenance and industrial automation systems. Similar IoT architectures using LTE and Wi-Fi networks have been widely used in industrial and automation systems. For example, studies such as [63; 64] discuss the use of IoT gateways for efficient communication in high-data-density scenarios, while [65] explores the advantages of LTE in real-time applications, especially in critical environments. Furthermore, hybrid cloud solutions, as described in [66], were considered in the system design, enabling effective integration between IoT gateways and centralized databases. The adoption of such an approach has shown promising results in balancing the scalability, security, and reliability of the system, being essential for the continuous transmission and processing of the collected data. The data collected by the RPA robots were processed, which guaranteed consistency and suitability of the data for analysis. The information was then sent to the predictive maintenance model, which used machine learning-based algorithms to identify patterns and predict possible failures.
# Zero-Shot Recognition Enhancement by Distance-Weighted Contextual Inference
## 2 Related Work  
### Instance-Level ZSL  
Existing ZSL models differ in their use of semantic embedding spaces or other external knowledge sources. Early models used manually constructed attribute spaces [11; 12; 13; 14; 15; 16; 17] to represent categories as binary vectors that implied the presence of attributes. In general, the use of attributes has seemed promising [18; 19; 20; 21] in various research fields, including ZSL, but it has limited scalability due to domain dependency and the cost of manual construction. To relax those limitations, semantic word-vector spaces that are automatically trained on a textual dataset have been used in more recent ZSL works [6; 22; 23; 24; 25]. The word-vector spaces from text corpora, such as word2 vec [26] and GloVe [27], motivated the use of large-scale ZSR with many unseen categories because they are unrestricted and less costly than manually annotated attributes. Some ZSL works has used knowledge graphs instead [9; 28; 29; 30]. In particular, some recent works [9; 30] based on a graph convolutional network (GCN) [31] have used the WordNet [32] taxonomy to propagate classifier weights from seen to unseen categories.  
Most existing ZSL works make instance-level inferences about an individual unseen object by transferring external knowledge from seen categories based on visual similarities, and they have shown promising prediction results in test sets with a specific type of categories such as Caltech-UCSD Birds (CUB) [33], Stanford Dogs (Dogs) [34], and Animals With Attributes (AWA) [35]. However, despite many attempts of ZSR with large-scale datasets such as ImageNet [36], which contain various types of categories for generic objects, instance-level ZSL works have produced comparatively poor performance, which implies that different types of categories have irrelevant semantic characteristics despite their visual similarities. Therefore, we consider not only visual prediction about an unseen object but also its surrounding information to determine the most likely category.
## 3 Results  
### Data Validation from Wearable Device  
The patient's body temperature was measured with an AHT20 sensor and was compared with a commercial non-contact sensor. The maximum relative error was computed as 0.97%, whose value depends upon environmental conditions, such as humidity as well as the accurate placement of the sensor.  
When we compared the results from wearable devices with medical devices, we found that there was a strong correlation between them. The performance evaluation of the sensors in the wearable device was performed by experimenting with measuring body temperature (\({}^{\circ}\)C), blood oxygen (in %), and heart rate (BPM), to be compared with readings from medical devices, namely thermometers, pulse oximeters, and blood pressure monitors. Thirty sets of sample data records were collected and analyzed to find the correlations  
\begin{table}
\begin{tabular}{c c} \hline
**Emotion** & **Class** \\ \hline anger & Risky \\ contempt & Risky \\ disgust & Risky \\ fear & Risky \\ sadness & Risky \\ happy & Good \\ neutral & Good \\ surprised & Good \\ \hline \end{tabular}
\end{table}
Table 1: Emotion matching with patient symptoms.  
between readings from the sensors and those from the medical devices. This was to assess the effectiveness of sensors in the wearable device and ensure accurate readings, desired to be as similar to medical devices as possible.  
Figure 4a shows the correlation of body temperature from the AHT20 sensor of the wearable device to body temperature from the medical device (R = 0.942, r2 = 0.887).  
\[y=0.986x+0.460\]  
The oxygen levels of the COVID-19 patients measured by MAX30102 sensors were compared with a medical device (Figure 4b). The maximum relative error was computed as 1.32% which indicates the high accuracy of the SpO\({}_{2}\) measurement system.  
Figure 4b shows the correlation of blood oxygen (SPO2) from the MAX30102 sensor in a wearable device with that from a medical device (R = 0.773, r2 = 0.598).  
\[y=0.870x+12.808\]  
Heart rates of COVID-19 patients were measured by the MAX30102 sensor and compared with a blood pressure gauge (Figure 4c). The maximum relative error calculated was 1.13%, indicating high accuracy of the heart rate measurement.  
Figure 4c shows the correlation of heart rate from the MAX30102 sensor in a wearable device with that from a medical device (R = 0.955, r2 = 0.913).  
\[y=0.9226x+2.5072\]  
However, the readings from the sensors depend on the surface area in contact with the individual wearable device, pressure, external environmental conditions such as humidity, and the position in which the device is worn.  
IoT wearable devices display the data received from the sensor using an Arduino program, which here displays the heart rate and the blood oxygen level. Each reading is checked for reliability, and the correct data will be used in calculations or analysis.
# Venus Magnetotail Long-Term Sensing Using Solar Sails
## 1 Introduction
Note that, in Figure 3, the orbit of Venus around the Sun is assumed to be circular with a radius \(r_{SV}\simeq 0.7233\,\mathrm{AU}\) so that the planet moves in its heliocentric orbit with a constant angular velocity equal to \(\sqrt{\mu_{S}/r_{SV}^{3}}\simeq 1.6\deg/\mathrm{day}\), where \(\mu_{S}\) is the Sun's gravitational parameter. In this study, the orientation of the reflective solar sail with respect to a classical orbital reference frame is assumed to be fixed. In particular, a Sun-facing configuration is assumed, while the required propulsion system's performance is evaluated as a function of the characteristics of the elliptic science orbit, that is, the value of the pericytherion radius \(r_{p}\) and the apocythering radius \(r_{a}>r_{p}\)[42; 43]. In this context, as discussed in detail in Section 2, the required thruster performance parameters (in terms of the value of the reference, characteristic, and propulsive acceleration magnitude) are obtained by using both a simplified mathematical model of the spacecraft's planetocentric dynamics and a semi-analytical approach, which allows the reference propulsive acceleration to be easily written, in a closed compact form, as a function of \(r_{p}\) and \(r_{a}\). The numerical results illustrated in Section 3 show that a medium-low-performance sail is able to artificially precess the apse line of a medium-high-elliptic orbit around Venus, in order to ensure the long-term sensing of the planet's induced magnetotail. The performance analysis in Section 3 is conducted parametrically using the two geometric terms \(\{r_{p}\), \(r_{a}\}\) as design parameters, while a set of curves is obtained to quickly determine the required value of the sail-induced (reference) propulsive acceleration magnitude. The last part of Section 3 briefly describes the analysis of the effects of the eclipse period on the mission performance, by using a simplified (cylindrical) model of the Venus-induced shadow. Finally, as usual, the last section includes the conclusions of this study. Figure 3: Conceptual scheme of a GeoSail-type mission scenario applied to a Venus-centered case. The solar sail-induced thrust rotates the apse line of the spacecraft (elliptic) science orbit in order to maintain the apocythering inside Venus’s induced magnetotail. The spacecraft and Venus move around the Sun along the same plane \(\mathcal{P}\). The artificial precession of the apse line can be maintained, theoretically, for a very long period of time.