esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Data Preparation for Neural Networks
### Outliers Removal  
A technique based on median absolute deviation (MAD) was used for identifying and dealing with outliers in the dataset. MAD is a robust statistical measure, based on calculating the distance between the median of each point in the univariate distribution (single channel in the dataset) and the median of the distribution itself, as described in Equation (5).  
\[MAD=med(|x_{i}-med(X)|) \tag{5}\]  
Instead of using the mean and standard deviation (standard z-score method for outliers imputation), this method uses the median as the measure of central tendency and the median absolute deviation to quantify the spread or dispersion of the data, as can be seen in Equation (6). The median is robust to outliers and represents the center of the data without being heavily influenced by extreme values. The MAD is also robust to outliers and characterizes the variability in the data without being distorted by extreme values [55].  
\[Z_{MAD}=\frac{x_{i}-med(X)}{MAD} \tag{6}\]  
where if \(Z_{MAD}>Threshold\), then \(x_{i}\) is considered an outlier. The threshold value, as in the standard z-score method, is chosen according to the application.  
In summary, the MAD-based z-score method was preferred over the standard z-score for identifying and handling outliers because it is less influenced by extreme values, interpretable, and robust in various data scenarios.
## 3 Results and Discussion
### OPLS-DA Analysis of Soluble Compounds

Table 4: Soluble compounds screened by variable importance in projection (VIP \(>\) 1.5), fold change (FC \(>\) 2 or FC \(<\) 0.5), and \(p\) value (\(p\) \(<\) 0.01) of OPLS-DA analysis. Among the differentially expressed sugars, D-glucose had the highest VIP value of 14.70, followed by sucrose with a value of 12.22 and D-tagatose with 7.27. D-tagatose also had the highest fold change, with a ratio of 95.46 (IBW/FBW), followed by Alpha-D-glucose at 59.36 and sucrose at 12.75. Sucrose, D-glucose, and D-tagatose can be considered as the most important differentially expressed sugars as they are the main contributors to the sweetness of bayberry juice. During fermentation, yeast typically metabolizes sugars to produce alcohol and acids; thus, most carbohydrate substances in IBW are higher in concentration compared to those in FBW, with the exception of raffinose.
# Effects of Doped Hole-Transporting Layers on Perovskite Solar Cell Performances
## 2 Materials and Methods
### Characterization  
Current density versus voltage (J-V) characteristics were measured using a Keithley (Solon, OH, USA) 2400 Source Measure Unit and McScience K201 LAB55 solar simulator under an AM 1.5 (100 mW/cm) condition. Measurements were not corrected for reflection loss or light absorption in the ITO electrode. The surface morphologies of the average and root mean square roughness of the perovskite film were evaluated using an atomic force microscope (AFM: S.I.S. GmbH, Hamburg, Germany). The information on surface topography and the composition of the perovskite films were analyzed using a scanning electron microscope (SEM: Hitach, TM3000, Tokyo, Japan). The perovskite crystalline structure was analyzed using an X-ray diffractometer (XRD: Rigaku MiniFlex 300, Tokyo, Japan).
## 3 Materials and Methods
### Road Structure Delineation  
As shown in Figure 8, for the trajectory segment set of one turning mode \(TS=\{T_{i}\,|\,T_{i}\) = \(\{p_{1}{}^{(0)}\,...\,p_{t}{}^{(0)}\}\), \(i=1\ldots n\}\), the road central line of the turning mode is delineated by the following iterative piecewise fitting method:  
**Step1:** Select the longest trajectory segment in \(TS\) as the initial central line \(TC=\{C_{1}\ldots C_{m}\}\).  
**Step2:** Build the buffer regions \(G_{1}\ldots G_{m}\) for all vertices \(C_{1}\ldots C_{m}\) in \(TC\) by setting the buffering distance as \(\,|\,C_{1}C_{2}\,|\,/2\ldots\,|\,C_{m-1}C_{m}\,|\,/2\) and \(\,|\,C_{m}C_{m-1}\,|\,/2\), respectively.  
**Step3:** Search the trajectory points falling in each buffer region, and calculate their average geometric centers as the new positions of \(C_{1}\ldots C_{m}\) to update the current central line.  
**Step4:** Repeat Step2 and Step3 until the Hausdorff distance of the fitted central lines between two iterations is less than a given threshold \(\varepsilon\).
# Numerical Simulation-Based Study on the Arching Effect in Subsequent Backfill
## 1 Introduction
These findings provide a theoretical basis for the optimal design of the filling body under complex geometric conditions, particularly emphasizing that the roughness treatment of the interface should be emphasized in practical engineering. These advancements indicate that the precise characterization of the arch effect is crucial for the optimal design of the filling body. Therefore, in this study, a three-dimensional model of the filling body that conforms to the engineering site was established. Through FLAC3D [20; 21; 22] numerical simulation, the influence of various parameters on the arching effect was systematically studied. This research aimed to deepen the theoretical understanding of the arch-forming effect of filling bodies and provide a theoretical basis for practical engineering design and construction applications.
# The Impact of GCP Chip Distribution on Kompsat-3A RPC Bias Compensation
## 4 Discussion  
Detailed error pattern analysis in satellite image bias modeling necessitates abundant and well-distributed GCPs across the target image. Manual acquisition of numerous GCPs is impractical due to time and cost constraints, as highlighted in previous studies emphasizing the need for efficiency in GCP acquisition. Consequently, most prior research relied on a limited number of GCPs, resulting in sparse data. An automated process becomes pivotal to overcoming this acquisition limitation. This process involves extracting GCP chips from aerial orthoimages and determining image coordinates using robust image-matching techniques, yielding satisfactory outcomes in this study. Robust image matching allowed for the generation of a substantial amount of GCP information up to 100-200 points per tested Kompsat-3A image space. Obtaining dense GCPs over geographically uniform areas might prove challenging such that test images over the urban area were selected in the study.  
Figure 18: Error histogram for sub-cases of the three GCP zone.  
An added advantage of dense GCP information lies in their categorization into control and check points. Check points serve solely in independent error checks, not for bias modeling, but this division is limited if the GCP count is insufficient. The biases observed in the tested Kompsat-3A data ranged from \(-28\) to \(31\) pixels, corresponding to approximately \(18\)-\(20\) m, highlighting the necessity for improvement in large-scale mapping and GIS analysis. Additionally, dense GCPs facilitated the graphical plotting of skewed error changes across the tested Kompsat-3A data. While each dataset exhibited distinct bias patterns, noticeable fluctuations in bias along the image line were evident. This non-uniform bias underscores the significance of well-distributed GCPs, aligning with insights from prior studies.  
In the error analysis across diverse GCP zone cases, maximum errors reached \(10\) pixels for the single zone case, while errors were confined within \(3\) pixels for cases involving four or five zones. The three-zone case mostly yielded accuracy similar to that of the four- and five-zone cases in RMSE, yet occasional maximum errors of up to \(4.5\) pixels were observed. Hence, it is recommended to utilize GCPs covering at least four or five zones. In scenarios with limited GCP acquisition, such as three zones, it is advisable to avoid a straight-line zone distribution.  
It is expected that more and more high-resolution satellite images including constellation satellites will be used in the fields of remote sensing and geospatial analysis. The experimental results put an emphasis on bias pattern analysis before they decide on the efficient and economic geometric processing approach for the satellite images. In addition, future research should include the exploration of alternative methods or strategies for bias compensation in scenarios where dense GCP information is not feasible or available.
# Enhancement of Metabolite Production in High-Altitude Microalgal Strains by Optimized C/N/P Ratio
## 2 Materials and Methods  
### Strains  
_Chlorella_ sp. (CHLO_UFPS019) and two strains of _Desmodesm_ sp. (DESM_UFPS020 and UFPS021) were isolated from lakes located at 3300-3900 M.A.S.L. in the region of Norte de Santander (Colombia) and have shown an interesting capability to produce large concentrations of carbohydrates, proteins, lipids, and carotenoids (data not shown).  
The identities of the strains were determined by internal transcribed spacer (ITS) gene analysis. DNA was extracted according to the CTAB-NaCl protocol [99]. The ITS gene was amplified by polymerase chain reaction (PCR) using ITS1F ITS4R primers [100] following the conditions described by [PERSON] et al. [101]. The sequences were compared with ITS gene sequences available in GenBank databases, showing that all the strains had a high percentage of identity with their respective genera (99.2% for _Desmodesm_s sp. UFPS16, 99.46% for _Desmodesm_s sp. UFPS18, and 98.85% for _Chlorella_ sp. UFPS14).  
The strains are kept in the INNOValgae collection (Universidad Francisco de Paula Santander, Colombia), and were cultured in a 2 L tubular glass flask with 1.3 L of [PERSON]'s basal medium [102] at pH 7.0. Each flask was mixed through the injection of filtered air with 1% (\(\
u/v\)) CO\({}_{2}\) at a flow rate of 0.78 L min\({}^{-1}\), with a light:dark cycle of 12:12 h at 100 \(\upmu\)mol m\({}^{-2}\) s\({}^{-1}\), at 27 \({}^{\circ}\)C for 30 days.
# A Secure and Lightweight Group Mobility Authentication Scheme for 6 LoWPAN Networks
## 4 System Models
### Aggregated Message Authentication Codes (Agg-MAC)  
**Definition 1**.: _MAC aggregates [23] are a set of probabilistic polynomial times (PPTs) and are explained as follows._* _MAC: Let \(\kappa\in\{0,1\}\lambda\) be a key with a length equal to the security parameter, \(\lambda\), and let \(msg\in\{0,1\}*\) be any arbitrary length message; MAC is constructed from the standard keyed pseudo-random function, tag \(\leftarrow\) MACx(msg) = Fx(msg), where F is a pseudo-random function._
* _Agg-MAC: Let (msgi, di) and tagi be a message/identifier pair and its corresponding tag for node \(i\), respectively. The new message (M, tag) is the aggregation of all messages, i.e., M = {(msg1, id1), (msg2, id2), (msg3, id3), \(\ldots\), (msg1, id1)}, and the corresponding tag is constructed by simply XOR-ing all the messages: tags, tag = tag1 \(\oplus\) tag2 \(\oplus\) tag3 \(\oplus\)\(\cdots\)\(\oplus\) tag1._
* _Verify: Given the set of all identifier keys {K1, K2, K3, \(\ldots\), K1}, and the message tag pair, (M, tag), the verify algorithm, verify (K1, ID1), (K2, ID2), \(\ldots\), (KI, ID1), (M, tag), outputs upon successful authentication; and it is zero otherwise._
## 6 Results and Discussion
### Entropy Generation  
Figure 19 displays the impact of magnetic parameter \(M\) on \(N_{G}(\eta)\). The higher estimations of magnetic parameter \(M\) escalate the entropy generation rate. The higher magnetic parameter effect, the greater the Lorentz force which increases the resistance of thin film fluid motion, and consequently the temperature increases. This heightens the thermal transfer rate at the wall as shown in Figure 19. Since entropy optimization \(N_{G}(\eta)\) is a function of temperature gradient, so the robust magnetic field escalates entropy optimization \(N_{G}(\eta)\). Figure 20 depicts the impact of Brinkman number \(Br\) on entropy optimization \(N_{G}(\eta)\). Here, entropy rate upsures against higher estimations of Brinkman number. Actually \(Br\) is a heat created source within the fluid moving region. Heat generated together with the heat transmission from the wall increases the entropy optimization. The higher Brinkman number allows less thermal conductivity to the fluid flow and therefore \(N_{G}(\eta)\) boosted.  
Figure 19: Variation in \(N_{G}(\eta)\) via \(M\).  
Figure 21 displays the variation in entropy profile via Reynolds number. The higher Reynolds number escalates \(N_{G}(\eta)\). Physically, with a higher Reynolds number, the inertial force in the system increases the viscous force which enhances the disturbance in the fluid movement and promotes an increase in entropy generation. Therefore, entropy optimization increases due to the contribution of heat transfer. Figure 22 indicates the variation in entropy generation via temperature difference parameter \(\alpha\). A decreasing impact is depicted here.  
Figure 21: Variation in \(N_{G}(\eta)\) via Re.  
Figure 20: Variation in \(N_{G}(\eta)\) via \(Br\).
## 3 Problem Formulation  
Sales route optimization is a combinatorial optimization problem that can be mathematically modeled as a variation of the _Traveling Salesman Problem_ (TSP). Given a set of customers with known geographical locations, the objective is to determine the optimal route that minimizes both total travel distance and the estimated travel time based on real-time conditions.
## 3 Materials and Methods
### Architecture
#### 3.2.1 Feature Fusion  
The feature fusion module of the deep learning-based elevator fault time series prediction model is shown in Figure 2. The input data were categorized into three types: dense features, sparse features, and static features. Feature extraction uses these three types of features as hidden layer features, and the feature fusion module is employed to process temporal hidden layer features to obtain temporal fusion features. Finally, the static hidden layer features, temporal hidden layer features, and temporal fusion features are collectively fed into the time series prediction model to extract temporal abstract features and predict elevator fault probabilities.  
Feature transformation is divided into two parts: feature mapping and feature fusion, allowing hidden layer features to better represent data characteristics.  
Feature mapping can be divided into the following three steps:  
First, raw data are divided into discrete and continuous data based on data type. For discrete data, similar to the vocabulary approach used in natural language processing, hidden layer features are stored for each discrete code. For dense features, a linear layer is used for feature mapping, and after obtaining continuous features, normalization is applied to prevent poor model training results due to large differences in feature dimensions after concatenation. Second, raw data are divided into categories based on their meaning, including geographical location, performance parameters, related entity information, fault information, maintenance information, and weather temperature. Since these static feature data have no explicit connections, a multilayer perceptron (MLP) is used for feature abstraction for each data category.  
Finally, time series data are organized on a monthly basis. Weather and temperature data occupy excessive space in the time series and often convey redundant information, while fault and maintenance records are sparsely distributed in the time series. As sparse features, MLPs and convolutional blocks are used to process these features, making the representations more compact and enhancing the model's generalization ability for these types of features.  
Feature fusion integrates features with different data meanings and time sequences using convolutional blocks to obtain temporal fusion features that can represent the global time series.
## 5 System-Level Performance Evaluation
 \tag{5}\]  
where \(Ex_{compress}\) is the energy required to compress \(X\) Bytes and successfully transmit the compressed data. \(Ec_{XBytes}\) is the energy required for compressing \(X\) Bytes. \(E_{tx\_{X}\times y\%Bytes}\) is the energy required to successfully transmit the compressed data. \(P_{compr}\) is the power consumption (static + dynamic) of the compression module, when active. \(Tc_{XBytes}\) is time interval required to compress \(X\) Bytes, which can be accurately calculated for a specific processing clock frequency. \(V_{tx\_radio}\) is the voltage supply required for a specific radio transceiver (provided by respective datasheets). \(I_{tx\_radio}\) is the current draw during the transmission of specific radio transceiver (provided by respective datasheets). \(Tx_{X\times y\%Bytes}\) is the time required to successfully transmit \(X\times Y\%\) Bytes; it is measured using a specific platform and a specific WSN communication technology. It is noted that energy consumption is not considered for the decompression phase, under the assumption that the receiver (i.e., the home gateway) is always plugged in the main power supply. Furthermore, two assumptions are made so as to extract the results presented in following section. First, it is assumed that when the compression module is not used it is shut down and does not consume energy; second, it is assumed that when the radio does not transmit it is tuned off and thus there is no energy consumption.
## 2 Materials and Methods
### Auger Electron Microscopy (AES) and X-ray Photoelectron Spectroscopy (XPS) Characterization  
The FDTS SAM characterization process is illustrated in Figure 1b. Auger electron microscopy (AES) was conducted in an ultrahigh vacuum (UHV) chamber at room temperature with an Auger electron microscope (ULVAC-PHI 710, Chigasaki-shi, Japan). The vacuum pressure of the chamber was set at 10-8 Pa. AES spectra were acquired with a primary electron beam of 5.0 keV with a current of 10 nA. The e-beam incident angle was zero degrees from the normal surface plane. For element mapping, the intensity of each element's AES peak was measured corresponding to the background of the secondary electrons at each pixel.  
Figure 1: Schematic diagram of microfabrication and characterization of FDTS SAM film patterns based on SAM vapor deposition technique. **(a)** Fabrication process by several semiconductor fabrication techniques combined, including photolithography, SAM vapor deposition, and lift-off technique; **(b)** characterization process by AES and XPS.  
The X-ray photoelectron spectroscopy (XPS) analysis was performed in a PHI 5000 Versa Probe III spectrometer (ULVAC-PHI, Chigasaki-shi, Japan). The spectrometer is characterized by a monochromatized Al k\(\alpha\) primary X-ray beam (1486.6 eV); a photoelectron take-off angle of 45 degrees was chosen against the sample. Analysis quantification was based on sensitivity coefficients proposed by the equipment manufacturer. Depth profiles were obtained by alternating 4.0 keV ion sputtering steps and analysis sequences by characterizing the FDTS SAM film patterns in a three-dimensional (3D) manner.
## 4 Simulation Results  
### Impedance Voltage Tests  
In this section, we present the simulation results using a real-time digital simulator (RTDS) to verify the effectiveness of the proposed model. To begin with, we tried to verify the model's accuracy through an impedance voltage test. The equivalent circuit for the impedance voltage test of the proposed transformer was organized as shown in Figure 10. The parameters in Table 2 are identical to those of the transformer certified by the Korea Electrotechnology Research Institute (KERI). After we set the input parameter as Table 2, the impedance voltage test was performed.  
Figure 10: An equivalent circuit diagram for impedance voltage test.  
Figure 9: Comparison between computational loads of (**a**) Scott connection using the Substep library models (processor assignment for Scott connection using a single-phase transformer and 3-winding transformer from the Substep library) and (**b**) the proposed Scott transformer (processor assignment for Scott connection based on the proposed model).  
Note that the currents in M- and T-phases became unbalanced in the impedance voltage test of the RSCAD Library model as shown in Figure 5. By comparison, in the results shown in Figure 11, both sides of the proposed model are in a balanced state. As a transformer parameter, the measured impedance voltage in Table 3 is identical to the leakage value (12.5%) input. Thus, the effectiveness of the model is demonstrated by the fact that the three-phase structure was accurately converted to two single phases (i.e., M- and T-phases), and the balance of each phase was maintained.
###### Abstract  
Investigating deformation and failure mechanisms in shafts and roadways due to rock subsidence is crucial for preventing structural failures in underground construction. This study employs FLAC\({}^{\text{3D}}\) software (vision 5.00) to develop a mechanical coupling model representing the geological and structural configuration of a stratum-shaft-roadway system. The model sets maximum subsidence displacements (MSDs) of the horsehead roadway's roof at 0.5 m, 1.0 m, and 1.5 m to simulate secondary soil consolidation from hydrophobic water at the shaft's base. By analyzing Mises stress and plastic zone distributions, this study characterizes stress failure patterns and elucidates instability mechanisms through stress and displacement responses. The results indicate the following: (1) Increasing MSD intensifies tensile stress on overlying strata results in vertical displacement about one-fifth of the MSD at 100 m above the roadway. (2) As subsidence increases, the disturbance range of the overlying rock, shaft failure extent, and number of tensile failure units rise. MSD transitions expand the shaft failure range and evolve tensile failure from sporadic to large-scale uniformity. (3) Shaft failure arises from the combined effects of instability and deformation in the horsehead and connecting roadways, compounded by geological conditions. Excitation-induced disturbances cause bending of thin bedrock, affecting the bedrock-loose layer interface and leading to shaft rupture. (4) Measures including establishing protective coal pillars and enhancing support strength are recommended to prevent shaft damage from mining subsidence and water drainage.  
horsehead roadway; thin bedrock; shaft wall; failure characteristics; instability mechanism Article