esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 A2G Channel Measurement Campaign
### Typical UAV Flight Phases  
The fixed-wing VTOL UAV is featured with the characteristics of both fixed- and rotary-wing UAV, which is capable of taking-off and landing without long runway. As a result, it is suitable for in complex and irregular terrains such as urban operations or rural areas. This kind of UAV can carry heavier payloads and has the capability of high-speed flight; therefore, it can accommodate more sensors and communication equipment at high UAV altitudes.  
Figure 1: (**a**) Measurement scenarios, with the UAV’s take off point and the ground reception point marked in the figure. (**b**) The UAV is equipped with transmitter system and antenna as the air terminal. (**c**) Ground receiver system and antenna.  
Thus, as shown in Figure 2, we consider different flight operational stages of the UAV to analyze the propagation characteristics of the A2G channel, which are specifically divided into the four stages:  
* Vertical takeoff and landing: the UAV activates its rotors and increases the rotary speed, enabling the UAV take-off or land vertically.
* Departure and approach at low altitude: the UAV switches to fix-wing flight mode, where the speed and altitude steady increase.
* Hovering preparation: the UAV ascends in a circular path with a radius of 300 m up to 300 m height, flying at a constant speed about 27 m/s.
* Steady flight at various altitudes: The UAV maintains straight-line flight at heights of 300 m, 500 m and 800 m. Noting that, the UAV always flight along the same route at all three altitudes.  
Considering that UAVs have similar channel propagation characteristics for vertical takeoff or landing and for low departure or approaches, we only analyze one of these conditions. Finally, by combining different UAV heights and Tx-Rx distances, we consider six flight scenarios, marked as FS1 to FS6. Detailed measurement parameters of different flight stages are listed in Table 2.
## 2 Methodology
### Fuzzy-AHP Method  
Combining MCDM and fuzzy methods is an effective approach to solving decision-making problems in situations where information and preferences are unclear or uncertain. In other words, the ability to make logical and justifiable decisions in complex and ambiguous contexts is enhanced through the fuzzification of data [7; 30; 52; 71; 72; 73; 74]. As shown in Figure 1, multiple steps are involved in addressing the problem using the fuzzy-AHP method:  
Initially, questionnaires are designed and distributed to specialists based on the criteria and alternatives of the problem. The questions should effectively compare all criteria individually while keeping the objectives of the problem in mind. Following this, the triangular fuzzy matrix corresponding to the experts' responses is constructed using [PERSON]'s development analysis [72], which is a traditional method similar to the classical AHP approach.  
Figure 1: Step-by-step flowchart for implementing the hybrid FAT method [30; 49; 56; 74; 75].  
Step 1: Formation of the pairwise comparison matrix, following established guidelines in the literature [72].  
Step 2: Calculation of the relative weight of criteria using fuzzy expansion relationships.  
Step 3: Creation of a relative weight matrix of criteria, adhering to fuzzy rules, based on the findings from the previous step. This matrix consists of one row, with the number of columns corresponding to the total number of criteria in the problem, and it is used in subsequent phases.
## 3 Method  
### Microtremor Array Method  
Microtremor array method [21] is developed on the basis of background noise imaging, and it refers to the way to extract surface waves from a group of weak-vibration signals (number of stations \(\geq 2\)) without a specific source, analyze their dispersion characteristics, and obtain the velocity structure of the underground strata through an inversion of the dispersion curves. Microtremor signal is a complex one composed of body wave (longitudinal wave and transverse wave) and surface wave (Rayleigh wave and Love wave). Surface wave energy accounts for more than 70% of the total signal energy [22]. Microtremor signal is from both natural phenomena, such as air pressure, wind speed, waves, and tidal changes, and vehicle movement, machine operation, and people's daily production and life. Therefore, this method is not interfered by urban electromagnetic environment and cultural and industrial activities and is a new way for a detailed detection of urban underground space.  
The commonly used array types are generally regular and irregular ones. The regular type includes linear array (such as linear array, \"T\" array, and \"L\" array) and circular array (including triangle array and \"cross\" array which can be regarded as special circular array). The phase velocity obtained by triangular and linear arrays are in good agreement with each other [23]. Circular array is conducive to receiving random microtremor information from different directions, which can ensure the accuracy of dispersion curves to a certain extent. However, in densely populated urban areas, the existing infrastructure will limit the distribution of two-dimensional array. Although noise resistance of linear array is weak due to its azimuth directivity, in urban geophysical exploration with limited sites, rich environmental noise can basically meet the condition that surface wave covers all directions, providing a theoretical basis for the long-spread linear microtremor array method.
# Influence of Pile Foundation Stiffness on Column Design in One-Story Reinforced Concrete Frames
## Appendix B  
The imperfections according to Section 5.2 of Eurocode 2 [1] are as follows:  
\[\alpha_{h}=\frac{2}{\sqrt{l}}=\frac{2}{\sqrt{8.0}}=0.707,\ \ \alpha_{m}=\sqrt{0.5(1+1/m)}=\sqrt{0.5(1+1/3)}=0.816.\]  
The pillar angle \(\theta_{i}=\theta_{0}\alpha_{h}\alpha_{m}=\frac{1}{200}0.707\cdot 0.816=0.00288\).  
Horizontal forces caused by imperfections:  
* in the end columns \(H_{1}=\theta_{i}P_{1}=0.00288\cdot 400=1.152\) kN;
* in the center columns \(H_{2}=\theta_{i}P_{2}=0.00288\cdot 1000=2.88\) kN. The calculations were performed as in Example 1.  
The coefficient \(k_{1}\) according to Section 5.8.7.2 of Eurocode 2 [1] is as follows:  
\[k_{1}=\sqrt{f_{\rm{ck}}/20}=\sqrt{30/20}=1.225\]  
For \(l_{0}=2l_{cd}=2\cdot 8=16.0\) m, the radius of inertia \(i=\frac{h}{2\sqrt{3}}=\frac{0.45}{2\sqrt{3}}=0.1299\) m.  
The slenderness \(\lambda=l_{0}/i=16.0/0.1299=123.2\).  
The effective creep coefficient was assumed to be \(\varphi_{\it{cf}}=2.4\).  
The coefficient \(k_{2}\) for the end columns is as follows:  
\[n=\frac{N_{Ed}}{(A_{c}f_{cd})}=\frac{400}{0.4\cdot 0.45\cdot 20.0\cdot 10^{3}}=0.111\]  
\[k_{2}=n\cdot\frac{\lambda}{170}=0.111\frac{123.2}{170}=0.08\ \leq\ 0.20.\]  
The moment of inertia of the reinforcement is as follows:  
\[I_{s}=2A_{s}\left(\frac{h}{2}-a\right)^{2}=2\cdot 12.56\cdot 10^{-4}\left(\frac{0.45 }{2}-0.040\right)^{2}=8.60\cdot 10^{-5}\ \text{m}^{4}.\]  
The coefficient \(K_{c}=k_{1}\ k_{2}/(1+\varphi_{\text{ef}})=1.225\cdot 0.08/(1+2.4)=0.029\) and \(K_{s}=1\).  
The nominal stiffness of the end columns is as follows:  
\[EI=K_{c}E_{\alpha l}I_{c}+K_{s}E_{s}I_{s}=0.029\cdot 26,670\cdot 3.038\cdot 10^{-3 }+1.0\cdot 200\cdot 10^{3}\cdot 8.60\cdot 10^{-5}=19.55\ \text{MNm}^{2}.\]  
The coefficient \(k_{2}\) for the inner columns is as follows:  
\[n=\frac{N_{Ed}}{(A_{c}f_{cd})}=\frac{1000}{0.4\cdot 0.45\cdot 20.0\cdot 10^{3}}=0.278\]  
\[k_{2}=n\cdot\frac{\lambda}{170}=0.278\frac{123.2}{170}=0.20\]  
\(K_{c}=k_{1}\ k_{2}/(1+\varphi_{\text{ef}})=1.225\cdot 0.08/(1+2.4)=0.029\) and \(K_{s}=1\).  
The moment of inertia of the reinforcement is as follows:  
\[I_{s}=2A_{s}\left(\frac{h}{2}-a\right)^{2}=2\cdot 21.98\cdot 10^{-4}\left( \frac{0.45}{2}-0.040\right)^{2}=15.04\cdot 10^{-5}\ \text{m}^{4}.\]  
The nominal stiffness of the internal column is as follows:  
\[EI=K_{c}E_{cd}I_{c}+K_{s}E_{s}I_{s}=0.072\cdot 26,670\cdot 3.038\cdot 10^{-3 }+1.0\cdot 200\cdot 10^{3}\cdot 15.04\cdot 10^{-5}=35.91\ \text{MNm}^{2}.\]
# FPN-SE-ResNet Model for Accurate Diagnosis of Kidney Tumors Using CT Images
## 2 Materials and Methods
### Preprocessing  
Indeed, preprocessing is essential to the classification of CT images. Typically, CT images are stained with various dyes to emphasize various structures and characteristics, and they can vary considerably in terms of staining intensity, colour, and texture. Therefore, preprocessing is required to improve image quality and consistency and eliminate noise and artefacts that could interfere with the classification process [68]. In contrast, Convolutional neural networks (CNNs) are designed to handle smaller inputs, requiring reduced image resolution to maintain essential details. Preprocessing processes and resizing or downsampling CT images are crucial for CNNs to process them effectively. The input image size significantly impacts the network's efficacy, so reducing the resolution while maintaining essential characteristics and structures is essential. Downsampling methods, such as bilinear or nearest-neighbor interpolation, or more complex methods, such as wavelet-based or deep learning-based approaches, can achieve this. Within this range, The initial step in processing these images is to convert them from DICOM to PNG.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Properties** & **Values** \\ \hline Number of all Image & 7899 \\ Number of Training Images & 5841 \\ Number of Validation Images & 1027 \\ Number of Testing Images & 1031 \\ Image format & Png \\ Modality & CT \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset Properties Used for the Experimentation are cited.  
DICOM images are converted to PNG format during preprocessing through a standardized transformation process. This conversion maintains image quality. It is located on Kaggle at \"[[https://github.com/hwanseung2/kidney-tumor-segmentation/blob/main/2D_segmentation-fastai.ipynb](https://github.com/hwanseung2/kidney-tumor-segmentation/blob/main/2D_segmentation-fastai.ipynb)]([https://github.com/hwanseung2/kidney-tumor-segmentation/blob/main/2D_segmentation-fastai.ipynb](https://github.com/hwanseung2/kidney-tumor-segmentation/blob/main/2D_segmentation-fastai.ipynb))\" (accessed on 5 March 2023). Moreover, put images in size 128 \(\times\) 128.
## 2 Results
### Identification of Terpene Synthes Genes in Z. _candida_  
To investigate terpene biosynthesis in Z. _candida_, transcriptome sequencing was performed; a total of 42,679,098 raw reads were generated and subsequently processed using Fastp for the removal of adapters, low-quality sequences, and unknown sequences. A total of 42,416,162 clean reads were obtained, which were assembled into 186,527 unigenes and 297,622 transcripts with an N50 of 750 and an average length of 571 bp. After redundancy elimination, TransDecoder software (version 5.5.0) predicted 42,322 open reading frames (ORFs) from the transcripts. The Q20 (%) and Q30 (%) quality scores were 97.92 and 93.77, respectively.  
To identify terpene synthase genes in Z. _candida_, the BLASTP algorithm was used with Arabidopsis _TPS_ genes as queries. A total of 17 unigenes were identified as TPSs, and five the unigenes appeared to be in full length. Two genes, TRINITY_DN524_c0_g1_i4_p1 and TRINITY_DN1469_c0_g1_i2_p1, which were designated as _ZcTPS01_ and _ZcTPS02_, respectively, were selected as candidate genes because of their high expression levels. The fragments per kilobase of the exon model per million mapped fragments (FPKM) of ZcTPS01 and _ZcTPS02_ were 2141.42 and 1214.48, respectively. The FPKMs of the other three genes (TRINITY_DN16452_c2_g1_i4_p1, TRINITY_DN12218_c0_g1_i2_p1, and TRINITY_DN64768_c0_g2_i7_p1) were quite low (<10). Phylogenetic analysis showed that the two TPSs belong to the TPS-b subfamily (Figure 2), indicating that these two TPSs may be responsible for monoterpene formation in Z. _candida_. The length of ZcTPS01 was 1719 bp with 573 amino acids encoded, while ZcTPS02 had a total length of 1767 bp and encoded 589 amino acids. Sequence alignment analysis showed that ZcTPS02 features the RRxsW motif whereas ZcTPS01 does not (Figure 3). The proteins encoded by ZcTPS01 and ZcTPS02 contained the aspartate-rich region DDoxD and the NSE/DTE motif, which coordinate a trinuclear Mg\({}^{2+}\) cluster that binds to the diphosphate prior to ionization [24; 25]. Both ZcTPS01 and ZcTPS02 have another conserved sequence, known as the RxR motif, which is responsible for binding the diphosphate group after substrate ionization [26].  
Figure 1: GC-MS analysis of volatiles from Z. _candida_ flowers, showing the gas chromatographs of volatile compounds emitted from Z. _candida_ flowers. The numbers (ordered by retention time) represent the peaks of volatiles. 1, \(\alpha\)-thujene; 2, \(\beta\)-thujene; 3, \(\beta\)-myrcene; 4, D-limonene; 5, \(\beta\)-terpinyl acetate; 6, \(\alpha\)-pinenee; 7, \(\beta\)-ocimene; 8, (+)-4-carenel; 9, allo-ocimene; 10, limonene oxide; 11, (+)-(E)-limonene oxide; 12, myxroide; 13, (E)-9-octadecene; 14, linalool; 15, humulene; 16, \(\alpha\)-terpineol; 17, \(\alpha\)-farmesene; 18, (E)-\(\beta\)-ocimene. *, impurities.
# Age Prediction from Korean Speech Data Using Neural Networks with Diverse Voice Features
## 4 Results and Analyses
### Comparative Analyses
#### 4.3.2 Gender-Based Performance Analysis  
The results indicated a noticeable performance difference between male and female voices. This section presents a comparative analysis of the results. For the 16 combinations, the average prediction performance for males was 81.52%, whereas that for females was 71.33%. In all combinations, male performance surpassed female performance. This suggests that Korean male voices exhibit more distinct characteristics across five-year age intervals than female voices.  
This performance disparity between male and female voices can be attributed to several physiological and acoustic factors [45]. Males typically exhibit more pronounced anatomical changes in their vocal apparatus with age, particularly in laryngeal structure and vocal fold characteristics. These changes result in more distinct and progressive alterations in fundamental frequency, harmonic structure, and overall voice quality across age groups [46]. Furthermore, male voices generally show more consistent patterns of age-related changes in terms of pitch lowering and resonance modifications, making it easier for the models to learn and classify age-specific characteristics. The relatively stable progression of these changes in males contributes to the higher prediction accuracy.  
In contrast, female voices present more complex patterns of age-related changes, influenced by both physiological and sociocultural factors. The effects of hormonal changes throughout life stages, particularly during and after menopause, create more variable patterns in voice characteristics [47]. Additionally, female speakers often demonstrate greater variability in speaking styles, intonation patterns, and voice modulation, which can obscure age-specific features [48]. This variability is further complicated by social and professional factors, as women may consciously or unconsciously modify their speaking patterns to conform to various social contexts. These combined factors result in more overlapping voice characteristics between different age groups in females, especially in the middle-age ranges (30-39 years), making accurate age classification more challenging for the models. The lower prediction accuracy for females thus reflects the inherent complexity and greater variability in female voice aging patterns.  
Analyzing performance variations across age groups revealed that, for both sexes, the accuracy tended to be higher in younger groups (e.g., 11-14 and 15-19) and older groups (e.g., 55-59), while middle-aged groups, such as 30-39, showed slightly lower accuracy. This may suggest that vocal changes in middle-aged speakers are less distinct, potentially due to overlapping pitch and tone characteristics during this period of life. Additionally, the difference in performance between male and female speakers was particularly pronounced in these middle-aged groups, possibly indicating more gradual or subtle vocal changes in females than in males as they aged.
# Removal of Volatile Solids from Greywater Using Sand Filters
###### Abstract  
Sand filtration is a primary stage of treatment for reuse of greywater. This study aimed at assessing the volatile solid removal efficiency of a sand filter and imitating its performance using analytical simulation. This study used artificial greywater, medium sand as a filtering material, and nine PVC cylinders as filter columns. Samples of the sand were collected after 6, 14 and 21 days, with the aim of specific deposit determination. The vertical distribution of specific deposit (volatile solids) in the sand filters was typical for gravitationally operated sand filters. Relatively high removal efficiency of volatile solids (51-60%) was achieved at relatively low cumulative hydraulic load. The average removal efficiency of organic compounds (detected as chemical oxygen demand) was 26.8%. Maximum specific deposit was achieved for a cumulative hydraulic load of 363.6 m. The filter coefficient was identified empirically for application of the Iwasaki formula. The filter coefficient \(\lambda\) was corrected for a better fit of the modelled values with measured values.  
greywater; filter coefficient; modelling; sand filter; specific deposit Article
# Visual Tracking of Small Unmanned Aerial Vehicles Based on Object Proposal Voting
## 3 Object Proposal Baseline  
The object is characterized by a closed boundary when its edge component is extracted. Subsequently, if the closed curve can be accurately found in the image, candidate object regions can be determined precisely. Therefore, it is necessary to find a closed curve more accurately to propose an object region candidate group. Hence, in this study, EdgeField [1] is adopted, which smoothens the energy distribution of the objective function by blurring the image. This blurring process does not cause images to lose their details because we change a single channel-based image (i.e., gray image) to multiple channel-based images (i.e., thresholded binary images at multiple intensity levels), and each channel preserves its own information even after blurring. To create multiple channels, we set the threshold of an image at the \(l\)-th intensity level at time \(t\), \(I_{t}^{l}\), which yields multiple thresholded images, as follows:  
\[I_{t}^{l}(\cdot)=\begin{cases}255&\text{if }\;I_{t}(\cdot)\geq l\\ 0&\text{if }\;\text{otherwise}\end{cases}. \tag{1}\]  
Subsequently, we apply the Gaussian blurring to each thresholded image, as follows:  
\[I_{b}^{l,t}=I_{t}^{l}\otimes G_{\sigma}, \tag{2}\]  
where \(G_{\sigma}\) denotes a Gaussian kernel with variance \(\sigma\) and \(\otimes\) is a convolution operation. Note that blurring smoothens the energy landscape for edge extraction, which indicates a geometric shape of the objective function for edge extraction. As the last step, the closed boundaries in \(I_{b}^{l,t}\) are found by conventional contour detection functions \(Contour\) (e.g., [36]).  
\[\mathcal{O}^{l,t}=\left\{O_{j}^{l,t}\right\}_{j=1}^{|\mathcal{O}^{l,t}|}\sim Contour \left(I_{b}^{l,t}\right), \tag{3}\]  
where \(\mathcal{O}^{l,t}\) denotes a set of object proposals obtained for an input image \(I_{b}^{l,t}\) and \(|\mathcal{O}^{l,t}|\) denotes the number of object proposals. The contour detection functions \(Contour\) in (3) produce binary images using border-following algorithms, in which connected pixels for object regions (background regions or holes) have values of 1 (0). From this binary image, the function extracts borders that split 1 and 0 regions, which yield border description images. Subsequently, each border in border description images are converted into bounding boxes that compactly fit the corresponding borders.
###### Abstract  
Solar-induced chlorophyll fluorescence (SIF) measured from space has shed light on the diagnosis of gross primary production (GPP) and has emerged as a promising way to quantify plant photosynthesis. The SCOPE model can explicitly simulate SIF and GPP, while the uncertainty in key model parameters can lead to significant uncertainty in simulations. Previous work has constrained uncertain parameters in the SCOPE model using coarse-resolution SIF observations from satellites, while few studies have used finer resolution SIF measured from the Orbiting Carbon Observatory-2 (OCO-2) to improve the model. Here, we identified the sensitive parameters to SIF and GPP estimation, and improved the performance of SCOPE in simulating SIF and GPP for temperate forests by constraining the physiological parameters relating to SIF and GPP by combining satellite-based SIF measurements (e.g., OCO-2) with flux tower GPP data. Our study showed that SIF had weak capability in constraining maximum carboxylation capacity (V\({}_{\text{cmax}}\)), while GPP could constrain this parameter well. The OCO-2 SIF data constrained fluorescence quantum efficiency (fqe) well and improved the performance of SCOPE in SIF simulation. However, the use of the OCO-2 SIF alone cannot significantly improve the GPP simulation. The use of both satellite SIF and flux tower GPP data as constraints improved the performance of the model for simulating SIF and GPP simultaneously. This analysis is useful for improving the capability of the SCOPE model, understanding the relationships between GPP and SIF, and improving the estimation of both SIF and GPP by incorporating satellite SIF products and flux tower data.  
solar-induced chlorophyll fluorescence; photosynthesis; Orbiting Carbon Observatory; parameter optimization; model-data fusion; data assimilation; ecosystem model; carbon cycle; eddy covariance; radiative transfer +
Footnote †: journal: _Fronte sensing_
# An Approach to Predicting Sediment Microbial Fuel Cell Performance in Shallow and Deep Water
## 5 Conclusions  
We offer the following conclusions from this work:  
1. Shallow sediment MFCs are predicted to yield significantly greater power densities than deep sediment MFCs. We predict shallow sediment MFC power density to range from 5 to 10 mW/m\({}^{2}\) off the Texas coast and 4 to 7 mW/m\({}^{2}\) off the South Korean coast, using a 1 m\({}^{2}\) anode. We predict deep sediment power density < 1 mW/m\({}^{2}\) for the same anode size.
2. Near-bottom water temperatures off Texas and South Korea warm \(\sim\)10 \({}^{\circ}\)C seasonally from February to August. This temperature increase is predicted to increase MFC power densities by 1 to 2 mW/m\({}^{2}\).
3. Observations connected with this study suggest that testing MFCs with sediments collected at depth (\(\geq\)1000 m) and not pressurized during retrieval or in the laboratory does not unduly impact power density estimates.  
Conceptualization, K.R.; Formal analysis, K.R.; Funding acquisition, J.A.; Project administration, J.A.; Writing--original draft, K.R.; Writing--review & editing, K.R. and J.A.  
This project was funded by Office of Naval Research, Naval Biosciences Program (code 342) and the Office of Naval Research's ILIR program through SPAWAR.  
This work is part of larger team at SPAWAR who delve into numerous disparate MFC studies but who cross-pollinate each other with ideas and results. In particular, [PERSON] in ONR's Warfighters Protection and Applications Division has supported and continues to support and encourage our work. We note that Figures 1 and 2 were previously published in the Oceans '11 proceedings [13], Figures 12 and 17 in the Oceans '16 proceedings [19], and Figures 6 and 8 in the Oceans '15 proceedings [20]. We also thank two anonymous reviewers for their comments which improved this manuscript.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.