esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Isolation and Identification Methods  
A dominance of culture-based methods associated with morphological identification (58%) and target regions sequencing (31%) was recorded, the latter has become progressively dominant since the 2000s. The culture media used varied depending on the research purposes and included Czapek Dox agar (CZ), malt extract agar (MEA), malt agar (MA), potato dextrose agar (PDA), Sabouraud dextrose agar (SAB), and oatmeal agar (OA) among the most frequently used. The use of mycological agar (MYC), Cook's Rose Bengal (CRB), and yeast peptone dextrose (YPD) was sporadic. Data on growth temperature and incubation time were frequently missing, accounting for 31.25% (Figure 4A, green) and 54.12% (Figure 4B, green) of all papers, respectively. This trend was particularly evident in the  
Figure 2: Taxonomic composition of the fungal diversity recorded on wall paintings on the total dataset and in relation to the different environments, at (**A**) phylum and (**B**) order level.  
earliest papers where more attention was paid to fungal species than to the conditions used to isolate them (e.g., [40; 47; 84; 88]). The most frequent temperature settings were 25 \({}^{\circ}\)C (35%) and within the range 27-32 \({}^{\circ}\)C (21.25%). When reported, the incubation frequently corresponded to 7 days (31.76%) (Figure 4B). When isolated strains were identified by molecular approaches, the identification was performed targeting different barcoding regions, such as the complete internal transcribed spacer (ITS), a part of it (ITS1), portions of 18S (SSU), 26S (LSU), and \(\beta\)-tubulin.
## 2 Background
### Previous Work
#### 2.3.4 Post-Quantum Setting  
There are several papers considering cold boot attacks on post-quantum cryptographic schemes. In particular, [PERSON] et al. [15] studied NTRU in the cold boot attack setting by reviewing two existing NTRU implementations, the ntru-crypto implementation and the tbuktu/Bouncy Castle JAVA implementation, and developed key recovery algorithms for various private key formats found in these implementations. One of their key recovery algorithms was able to tolerate a noise level with \(\alpha=0.001\) and \(\beta=0.09\) for one of the formats when performing a \(2^{40}\) enumeration. This work is followed upon by [PERSON] [5] who evaluated BLISS in the cold boot attack setting. This paper particularly introduces a general key strategy via key enumeration whose results for various parameters found in BLISS are comparable to the results found in the NTRU case.  
Furthermore, [PERSON] et al. [16] explored cold boot attacks on cryptographic schemes based on the ring and module variants of the learning with errors (LWE) problem. They particularly focused on two encodings to store LWE keys for two cryptographic schemes: the Kyber key encapsulation mechanism (KEM) and new hope KEM. When the first encoding is used, the polynomial coefficients are stored directly in memory. When the second encoding is used, a number theoretic transform (NTT) on the key is applied just before storing it. The authors showed that, at a 1% bit-flip rate, their algorithmic techniques are able to recover the keys of Kyber KEM with a cost of \(2^{43}\) operations when the second encoding is used for key storage, compared to \(2^{70}\) operations when the first encoding is employed.
# BDS/IMU Integrated Auto-Navigation System of Orchard Spraying Robot
## 3 Results
### Dynamic Positioning  
The performance of the navigation system was tested under the straight-line driving state of the spraying robot, and the BDS positioning and BDS/IMU integrated positioning were compared. In the test, the spraying robot ran along the preset straight line at a speed of 0.4 m/s, both BDS and BDS/IMU integrated positioning data were output. The heading  
Figure 10: The positioning results of the spraying robot in the static experiments. (**a**) Positioning measurements of BDS in static state; (**b**) Heading angle measurements of BDS in static state; (**c**) Positioning measurements of BDS/IMU in static state; (**d**) Heading angle measurements of BDS/IMU in static state.  
angle and path data of the spraying robot for the BDS positioning and the BDS/IMU integrated positioning are shown in Figure 11. In BDS positioning, the position error was less than 5.29 cm, the mean errors in X and Y directions were 1.42 and 1.18 cm, and the variances were 1.95 and 1.35, respectively. The heading angle error was less than 3\({}^{\circ}\), the mean value was 40.179\({}^{\circ}\), and the variance was 0.5543. In BDS/IMU integrated positioning, the position error was less than 2.49 cm, the mean errors in X and Y directions were \(-\)0.37 and 0.31 cm, and the variances were 0.87 and 0.60, respectively. The heading angle error was less than 2\({}^{\circ}\), the mean value was 40.430\({}^{\circ}\), and the variance was 0.237.  
It can be seen that in the moving state, the results of BDS/IMU integrated positioning are more accurate than that of BDS.
## 4 Conclusions  
The design, integration, and analysis of two millimeter-wave 5G antenna arrays at 28/38 GHz and a WLAN antenna at 2.45 GHz have been demonstrated for a perfect full-screen display smartphone. The novelty of the proposed antennas is threefold. First, while few studies have designed millimeter-wave antenna arrays with a consideration for a conductive touchscreen, we have developed three antenna modules for a handheld device where the front and back panels are fully coated with copper. Second, although the design space for parallel conductive plates adds some complexity, the 28 GHz and 38 GHz antenna arrays still deployed 12 and 8 elements, respectively, with broad impedance bandwidths (26.5-32.0 GHz and 35.2-42.0 GHz) and sufficient realized peak gains (16.7 dBi and 16.4 dBi). Third, the relatively large antenna design for the WLAN module was integrated into the proposed design, and this antenna provided the desired performance. It is important to emphasize that the proposed design neglects fourth generation (4G) transceiver modules.  
Figure 11: Beam scanning performance of (**a**) the 28.0 GHz and (**b**) 38.0 GHz antenna arrays.  
Our design is only suited for future mobile handsets that are deployed for millimeter-wave 5G mobile communications. It is expected that the proposed antennas will contribute to these scenarios with a maximized screen size.  
Conceptualization, Y.-S.C.; methodology, S.-Y.M.; software, S.-Y.M. and F.-P.L.; validation, S.-Y.M. and F.-P.L.; formal analysis, S.-Y.M.; investigation, S.-Y.M. and F.-P.L.; resources, Y.-S.C.; data curation, S.-Y.M. and F.-P.L.; writing--original draft preparation, F.-P.L.; writing--review and editing, F.-P.L. and Y.-S.C.; visualization, F.-P.L.; supervision, Y.-S.C.; project administration, Y.-S.C.; funding acquisition, Y.-S.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Ministry of Science and Technology, Taiwan, under Contract MOST 110-2636-E-027-003.  
Not applicable.  
The simulated and measured data used to support the findings of this study are included within the article.  
The authors declare no conflict of interest.
# Energy-Aware Control of Error Correction Rate for Solar-Powered Wireless Sensor Networks
## 2 Related Work  
### Solar Energy as a Power Source for WSNs  
In the case of battery-based WSNs, most studies have attempted to overcome the limited battery problem by minimizing the energy consumption. However, even with this effort, the lifetime of a battery-based sensor node is usually under 30 days. Therefore, a periodic cost is required for network maintenance. Assuming that a WSN is used for applications that must be long-lasting, such as ecosystem management, forest fire detection or disaster monitoring, sensor nodes are supposed to be installed in dangerous or isolated locations that are difficult for a person to access. In these cases, the cost of maintaining the network would be significant. However, this problem can be resolved to some extent by using an energy harvesting sensor node, which collects energy from the environment, such as sun, wind or vibrations. Among these, solar energy is widely employed in WSNs, because of its higher harvesting rate (15 mW/cm\({}^{2}\)) compared with other resources, as shown in Table 1. In addition, solar energy has the two following special properties:  
* Periodicity: The Sun rises and sets once a day. This is the duration of a charging or harvesting cycle. A new supply of solar energy can be expected during every harvesting cycle.
* Dynamics: Solar energy varies throughout the day. Commonly, it increases in the morning, decreases in the afternoon and is absent during the night. In addition, it varies from day to day depending on the weather and season.  
These characteristics necessitate a new energy optimization scheme that is suitable for solar-powered sensor nodes, which will be completely different from those appropriate for battery-based nodes. While a battery-based sensor node aims to minimize energy consumption, a solar-powered sensor node must attempt to fully utilize the periodically-harvested energy. In this study, we attempt to determine the best use of harvested energy, especially for data correction with the FEC scheme.
# Detection and Tracking of Weak Exoatmospheric Target with Elliptical Hough Transform
## 5 Simulations
### Raw Measurements  
The radar measurements are available in radar spherical CS at discrete time instants. Figure 4 shows the true target and radar raw measurements in the preconditioned surveillance space \(\mathbf{D}\), with Figure 4a the measurements in the radar raw PPI display and Figure 4b the converted measurements in THIS-CS defined in this paper. It can be seen that, while the trajectory of the target in the PPI display is a continuous and complicated curve, in the THIS-CS it indeed is a part of the elliptical curve and symmetrical with the Earth center.  
Note that in Figure 4 we do not consider the SNR. The gray-scale plot of all 200-frame primary threshold detections is shown in Figure 5, with darker points denoting higher power. Actually, the threshold \(\eta=2.3026\) means an average SNR of 10 dB. It can be seen that the target is more visible as an elliptical curve, but it scatters in a cluttered environment. Conventional recursive trackers usually need an average SNR of 20 dB sufficient to maintain the track, and in this low SNR case, they may lose tracks due to frequent misdetections of the target erroneously associated with unwanted clutter points.  
Figure 4: Raw measurements in surveillance space. (**a**) Measurements in radar PPI display. (**b**) Measurements in THIS-CS.  
Figure 5: Gray-scale plot of converted data in THIS-CS during the interval \([150,250]\) s. The SNR of the target is 10 dB.
## 5 Fair Training  
### Fair Training Methods  
In response to the above bias analysis, we hope to be able to develop an AI system without bias by conducting fair training so that we can avoid perpetuating inequalities due to discriminatory appearances caused by biases. Fairness training aims to reduce these biases and promote fair decision making. There are several fair training methods that are currently in common use, including pre-processing fairness [136], in-processing fairness [137], post-processing fairness [46], regularization-based fairness [43], counterfactual fairness [41; 45; 138].  
Pre-processing, in-processing, and post-processing fairness techniques address bias in AI systems from different angles. Pre-processing involves modifying training data to balance group representation. In-processing modifies learning algorithms to integrate fairness. Post-processing adjusts model predictions to align with fairness goals. Additionally, regularization-based methods introduce fairness constraints in optimization, aiming to minimize disparities, while counterfactual fairness measures fairness by assessing outcome consistency for similar individuals across sensitive attributes.  
Fair training techniques, as depicted in Table 5, strive to mitigate biases in AI systems by integrating fairness considerations into the training process. Through the incorporation of sensitive attributes and fairness constraints, these methods aim to diminish the impact of such attributes on model predictions, guarding against biased outcomes that could disproportionately affect marginalized groups. The challenge lies in striking a balance between fairness and accuracy, avoiding the compromise of model performance while enhancing fairness.
###### Abstract  
Due to their independent structure, piping, and access, townhouses offer great flexibility in floor plan changes and high spatial autonomy, making them the mainstream housing type in Taiwan. This study focuses on row houses and examines 2022 completion cases in Kaohsiung City. It collects floor plans from 14 newly constructed buildings, totaling 227 units, and analyzes spatial dimensions including the facade width, depth, habitable room, staircases, bathrooms, and total floor area for each case. The objective of this study is to examine the spatial layout of row houses in Kaohsiung City, with the aim of providing a reference for the future planning and design of such structures. The study results showed that 81.8% of the row houses analyzed have a total floor area per unit ranging from 136 to 192 m\({}^{2}\), a facade width between 4.1 and 6.38 m, and a building depth from 7.67 to 12.68 m. In addition, they showed a low negative correlation between the facade width and total floor area, a high positive correlation between the building depth and total floor area, and a moderate negative correlation between the facade width and building depth. The spatial distribution within these houses includes 78% allocated to habitable room, 13.1% to staircases, and 8.7% to bathrooms. The total area of habitable rooms and bathrooms increases with the total floor area of the building. However, the total area of staircases remains almost constant as the area of habitable rooms increases. Furthermore, the most common location for staircase planning is the rear-right (RB) position, while the least common is the rear-left (LB) position. Among various staircase types, the C-shaped staircase has the largest average area per floor and the highest average proportion. Conversely, the I-shaped staircase has the smallest average area and the lowest average proportion. The U-shaped staircase is the most frequently planned, whereas the C-shaped staircase is the least frequently used. This means that planning of staircase location and type significantly affects the size and layout of habitable rooms and bathrooms in townhouses.  
Keywords:townhouses; principal spaces; spatial layout; relevance +
Footnote †: journal: Applied
# COMS Visible Channel Calibration Using Moon Observation Data
## Appendix A
### Defining on-Moon Pixels  
The sensitivity test used to select only Moon pixels in the Moon image area was conducted using Moon observation data obtained from April 2011 to December 2016. The mean and the standard deviation (\(\sigma\)) of the space area were calculated by selecting the upper and the lower 10 lines of the Moon image in this period. The mean of the selected space area was approximately 795.18 and the standard deviation was approximately 22.33. Using the mean and the standard deviation and relating with the confidence interval, the thresholds were defined as 800, 820, 840, and 860. Figure 11 shows the Moon pixel area according to the observed Moon image taken on 19 August 2016, 03:28:45 UTC, and 12 July 2016, 22:43:35 UTC, respectively.  
As shown in Figure A1, the Moon pixel area differed according to the Moon pixel threshold selected. In particular, it demonstrates that, when the phase angle was 70\({}^{\circ}\) or more (as on 12 July 2016, when the moon was a half-Moon), there were significant differences among the selection of moon pixels according to boundary values. Differences in Moon pixel areas according to threshold values affected the irradiance of the Moon and affected the degradation ratio (P(t)) of the visible channel.  
Figure A2 shows the irradiance ratio (P(t)) obtained from threshold values of (800, 820, 840, and 860) and total pixels in selected Moon image areas. From this, it is evident that the degradation ratio differed according to the threshold value. In particular, the radiance of Moon observations with phase angles up to 70\({}^{\circ}\) appeared sharp or decayed.  
To eliminate the phenomenon whereby the radiance ratio changed according to the threshold, and to prevent occurrence of the threshold changing as a result of performance degradation of the visible channel with the passing of time, it was necessary to monitor the performance of the COMS MIvisible channel using entire pixels. In this test, the threshold was defined as 860 digital counts and the Moon region was selected to minimize the effect from both space and Earth pixels as well as to calculate Moon radiance for all the pixels in the selected Moon region.
# Nonlinear Analysis and Closed-Form Solution for Overhead Line Magnetic Energy Harvester Behavior
## 6 Continuous-Discontinuous Mode Boundary  
The discontinuous current mode (DCM) regime described above is the result of a relatively high volt per turn applied across the harvester's winding that brings the core to saturation before the end of a half-cycle. In DCM, the instant the rapidly rising magnetizing current intercepts the secondary current the rectifiers are cut off at zero current at \(t_{2}<T/2\); see Figure 5. The rectifiers resume conduction at the instant the voltage induced in the winding (due to the varying magnetic field generated by the line current) equals the CVL voltage; see (8). However, for lower values of the applied voltage, the build-up of the magnetizing current is slower and the interception point occurs at the instant, \(t_{2}\), at which the induced voltage is already of a sufficient magnitude to initiate the immediate rectifier's conduction (yet, in the reversed polarity). Thus, the dead time in the rectifiers' input current, \(i_{in}\), see Figure 5, vanishes. As the result, for lower CVL voltages the rectifier can conduct for the entire half-cycle. Therefore, this operational regime is dubbed the continuous current mode (CCM). Since in CCM the rectifiers' conduction lasts for the entire half-cycle and the operation is symmetrical, the magnetic flux density in the core becomes a true triangular waveform. Simulated waveforms of an OLMEH at the CCM-DCM boundary are shown in Figure 7c. Here, the applied CVL voltage, \(V_{b}^{*}\), brings the positive peak value of the magnetic flux density infinitesimally close to \(-B_{1}\) (recall that B1 is negative). Applying the CCM condition \(t_{2}-t_{1}=T/2\) to (11) yields the DCM-CCM boundary condition:  
\[\frac{V_{b}^{*}}{NA_{c}}\left(\frac{T}{2}\right)+2B_{1}=0 \tag{19}\]  
Substitution of (9) and (10) into (19) and further manipulation gives:  
\[V_{b}^{*}=\frac{2\omega aNA_{c}}{\pi^{2}}\Bigg{[}\pi+\frac{bl_{c}}{I_{1m}}- \sqrt{2\pi\frac{bl_{c}}{I_{1m}}-\left(\frac{bl_{c}}{I_{1m}}\right)^{2}}\Bigg{]} \tag{20}\]  
However, under a constant voltage load condition the CCM-DCM mode transition is determined by the line current, which can be derived from (20):  
\[I_{1m}^{*}=\frac{4\omega abNA_{c}l_{c}V_{b}}{\left(\pi V_{b}-2\omega aNA_{c} \right)^{2}} \tag{21}\]  
Figure 6: Comparison plot of the \(sin(x)\) function vs. the linear approximation, vs. the parabolic approximation, and vs. the “mirrored Froelich equation” (15) (for \(K_{1}=3.232\), \(K_{2}=2.861\)).  
The mode changes under varying CVL voltage and line current conditions are illustrated in Figure 7. In the given simulation example, the calculated DCM-CCM boundary occurs at \(V_{b}^{*}=23.16\)\(V\) for \(I_{1}^{*}=100\)\(A\) rms; see Figure 7c. DCM prevails in the low-line current range, \(I_{1}<I_{1}^{*}\), as in Figure 7a or for a high voltage range \(V_{b}>V_{b}^{*}\) as in Figure 7b, whereas CCM prevails in the high line current range, \(I_{1}>I_{1}^{*}\), as in Figure 7d or for a low voltage range \(V_{b}<V_{b}^{*}\) as in Figure 7e.
# Current Applications and Development of Radiomics in Osteoporosis: A Narrative Review
## 1 Introduction
On one hand, bone-quality-related information from widely used clinical imaging modalities was efficiently extracted by radiomics, including radiographs, CT images, and magnetic resonance imaging (MRI) images, thus supporting opportunistic osteoporosis screening [20]. On the other hand, the radiomics-derived features, which differed from traditional bone quality parameters, hold potential as novel biomarkers for bone quality [21]. For instance, the radiomics feature wavelet-LLL_firstorder_10 percentile employed wavelet transform to decompose complex signals, enabling effective quantification of the intricate structure of vertebrae [22; 23]. This feature demonstrated high performance in detecting osteopororotic vertebral fractures, with an area under the curve (AUC) of 0.76 [23]. Additionally, entropy within the texture features was considered a potential sign for bone damage [24]. Therefore, radiomics offers a promising method for enhancing opportunistic osteoporosis screening, improving fracture risk prediction, and providing therapeutic efficacy assessment. The general workflow for applying radiomics to osteoporosis is depicted in Figure 1. It consists of six steps: image acquisition, image segmentation, image processing, radiomics feature extraction, radiomics feature selection, and modeling and analysis. In this review, current applications and the development of radiomics in osteoporosis were summarized. It was critical to conduct an accurate assessment of bone strength for predicting fracture risk. Thus, the utilization of radiomics for assessing bone strength was reviewed. Additionally, the application of radiomics derived from CT images, radiographs, and MRI images for diagnosing osteoporosis and predicting fracture risk was provided. Finally, the advantages, limitations, and potential clinical applications of radiomics in bone strength assessment, osteoporosis diagnosis, and fracture risk prediction were analyzed and summarized. The outline of this review is shown in Figure 2.