esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
The paper proposes a multi-UAV network where each UAV, acting as a relay, carries out the NOMA method for users experiencing severe blocking to the BS in mmWave communications. Hybrid beamforming architecture is commonly combined with mmWave transmission. We have investigated how the angle of arrival information can be estimated by the MUSIC technique performed after the analog part and the analog-to-digital conversion in the hybrid beamformer to derive the DoA of each UAV. The estimation of DoAs  
Figure 4: Comparison between multi-UAV-aided NOMA and terrestrial NOMA cellular systems.  
Figure 3: Achievable rate for multiple UAVs and a single UAV in LOS environment.  
allows the specific antenna multi-beam targeting to be formed to each UAV. This provides an intrinsic mitigation of the UAV fluctuation effects, enhancing the UL signal reception, and leads to the substantial improvement of the system sum rate, as confirmed by simulation results. We have investigated the problem of how to maximize the sum rate of the multiple-UAV-aided NOMA mmWave system, where each pair of users is served by an UAV and where we need to find the hybrid beamforming vector to steer towards each UAV. MUSIC allows DoAs and received power to be estimated more accurately. This results in the enhanced overall sum rate (close to 50%) of the multiple-UAV-aided NOMA network with respect to the NOMA cellular system without the UAV network. In the future, research should investigate minimizing the energy consumption of a multi-UAV network by discovering efficient power associations of ground users with UAV trajectories.  
This research received no external funding.  
Not applicable.
## 4 Conclusions  
This work assessed three hydrochars (H-180, H-215, and H-250) from Co-HTC of sawdust and non-dewatered sewage sludge as potential methylene blue adsorbents, and it also evaluated the ecotoxicological effects of the resulting PW. H-180 and H-215 had better adsorption performance than H-250. H-180 and H-215 presented a \(q_{max}\) value of around 70 mg\(\cdot\)g\({}^{-1}\), which was superior compared with the adsorption of methylene blue by other hydrochars in the literature. The Sips isotherm model was better than the Langmuir and Freundlich models for describing the adsorption with H-180 and H-215. The Sips model indicated a \(q_{max}\) value of around 80 mg\(\cdot\)g\({}^{-1}\). It is noteworthy that H-180 and H-215 were not subjected to modification by physical or chemical activation, which could enhance their adsorption capacity. However, an activation step would also raise the production cost of the adsorbent. Compared with the PFO and PSO models, the Elovich model presented a superior fit for describing the kinetics of methylene blue adsorption by H-180. Regarding the toxicological effects of the resulting PWs on _Daphnia magna_, the high toxicity, especially at higher Co-HTC temperatures, underscores the need for careful management and further study to assess their environmental impact and potential valorization approaches.  
Conceptualization, M.C.; methodology, M.C., T.B.H., N.L.J. and B.K.; formal analysis, M.C., T.B.H., N.L.J., B.K. and V.G.; investigation, M.C., T.B.H., N.L.J., B.K. and V.G.; resources, N.L.J., B.K., V.G., H.B., R.B., A.L.W., W.G.M. and A.B.d.C.J.; data curation, M.C., T.B.H., N.L.J. and B.K.; writing--original draft preparation, M.C. and T.B.H.; writing--review and editing, M.C., T.B.H., N.L.J., B.K., V.G., H.B., R.B., A.L.W., W.G.M. and A.B.d.C.J.; visualization, M.C. and T.B.H.; supervision, N.L.J., B.K., H.B., R.B., A.L.W., W.G.M. and A.B.d.C.J.; project administration, A.B.d.C.J.; funding acquisition, R.B., W.G.M., A.L.W. and A.B.d.C.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Brazilian Foundation Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES)--Finance Code 001.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors thank the Brazilian foundation Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES), the laboratory of the graduate program in Bioprocess Engineering and Biotechnology of the Federal University of Parana, and the DEEP (Dechets Eaux Environmentes Pollutions) Laboratory of the Lyon National Institute of Applied Sciences (INSA Lyon) for the supervision and the analyses carried out. This work was performed within the framework of the EUR H2O'Lyon (ANR-17-EURE-0018) of Universite de Lyon (UdL), within the program \"Investissements d'Avenir\" operated by the French National Research Agency (ANR).  
The authors declare no conflicts of interest.
## 3 Experiment and Result
### Analysis of Experimental Results
In the face of network attack data, the oblivion gate discards outdated or irrelevant information, the input gate filters and adds current important information, and the output gate outputs information that is useful for the identification of the attacks based on the internal state, thus effectively capturing the characteristics of the attack behavior and accurately identifying attacks.
# Hierarchical Phoneme Classification for Improved Speech Recognition
## 2 Phoneme Clustering
### Phonemes  
Phonemes are the set of units representing the distinctive sounds of a spoken language. In most languages, there are about 20 to 60 units. In this paper, we adopt ARPAbet [21], developed by the Advanced Research Projects Agency (ARPA), which exploits standard English alphabet symbols to represent IPA (International Phonetic Alphabet) symbols [22]. Table 1 shows the mapping from ARPAbet to IPA symbols, with 11 more symbols [23] that are used in the TIMIT dataset [24; 25] for a more detailed phoneme analysis. The symbol [h#] is not an actual phoneme but a special indicator symbol for the beginning and ending of a sentence, so the number of additional TIMIT phonemes is 11.  
The phonemes are broadly classified into consonants and vowels [26], and they are shown in the first and second columns of Table 1. The number of vowels and consonants used in ARPAbet are 19 and 31, so the total number of phonemes in this paper is 61. The consonants and vowels are further divided into more detailed categories according to the method of vocalization [27; 28]. The most common types of consonants are stops, fricatives, affricates, nasals, and semivowels [29]. Stops are generated by the constriction and release of air in the mouth (closing-compression-release) [30], so there exists a certain duration called \"stop gap\" when the air is compressed inside the mouth. The stop sounds usually have large energies in low frequencies, roughly below 200 Hz. Fricatives have a looser constriction in the mouth, which allows producing some amount of friction at the point of contact. Most of their acoustic energy occurs at higher frequencies. Fricatives are divided into sibilants versus non-sibilants [31]. Sibilants tend to be louder than non-sibilants, and turbulence noise is stronger in sibilants than non-sibilants. Nasals have the same constriction as plosives except that air is allowed to pass through the nose but not through the mouth [32]. Nasals show the abrupt loss of overall energy because the nose is less efficient than the mouth in radiating the energy to the outside. Nasals involve a voiced feature in the frequency domain and in addition, they have formant frequencies like vowels.  
The set of 61 phonemes are mapped to a smaller set of 39 labels [33] are shown in Table 2. In the new phoneme classes with multiple phonemes (classes 2, 5, 6, 8, 14, 15, 19, 20, 21, 28, 38, and 39), similar phonemes are grouped together because they are known to be hard to distinguish with acoustic features. All the closure sounds, pause, and glottal plosive ([q], equivalent to IPA [2]) are all grouped together to mapped phoneme 39. They are relatively weak, are often accompanies by other sounds, and are very hard to distinguish by only sound signals without surrounding phonemes.
# CNN-Based Pill Image Recognition for Retrieval Systems
## 3 Methodology
### Preprocessing and Features Extraction  
An input pill image (query image) undergoes a series of preprocessing processes in order to make up for the color distortion and identify relevant information. The overall preprocess shows 3 main steps for the detection and extraction of color, shape, and imprint.  
Before a pill image undergoes the segmentation steps in Figure 1, the image is converted to a grayscale format. This preprocessing step is used to regulate the intensity of the red, green, and blue (RGB) components in the image. Therefore, it is essential to denote a single intensity value for each pixel. Figure 2 shows an example of a raw input pill image (Figure 2a) and its grayscale version (Figure 2b). We note that all the pill images shown in the various figures in this paper are from the National Library of Medicine (NLM) pill image dataset [46].  
Figure 1: Proposed Approach.  
For color detection, a Gaussian filter is applied to the greyscale image to blur the image, thus removing unwanted details and noise. After that, a mean filter is applied to the output of the Gaussian filter to smooth the image. Next, histogram equalization is used to enhance the color contrast and to extract the colors. Figure 3 visualizes the color detection process of the same pill image in Figure 2b. For shape detection and extraction, we use Sobel filtering on the greyscale image to refine the image, which helps reveal the edges and the boundary lines of the drug pill. Figure 4 visualizes the shape detection and extraction process of the same pill image in Figure 2b. Lastly, for imprint extraction, we apply a Canny edge detector to determine all the edges in the image, followed by a dilution operation to soften the image. Clear imprint is finally revealed after applying Scale Invariant Feature Transform (SIFT) and Multi-Scale Local Binary Pattern (MLBP) descriptors. Figure 5 visualizes the imprint extraction process of the same pill image in Figure 2b.  
Figure 4: Fill image after applying various filters for shape detection: (**a**) Local histogram equalization filter. (**b**) Sobel filter. (**c**) Segmented shape.  
Figure 3: Pill image after applying various filters for color detection: (**a**) Gaussian filter. (**b**) Mean filter. (**c**) Local histogram equalization filter.  
Figure 2: Colored vs. grayscale intensity pill image: (**a**) Raw input pill image; (**b**) pill image after grayscale conversion.
# Burst Channel Error Reduction Based on Interleaving for Efficient High-Speed Wireless Communication
## 2 Burst Error, OFDM, and Interleaving
### Interleavers
#### 2.3.2 Other Interleavers  
There are many other interleavers based on the different standards summarized in Figure 3 [32]. In addition, Table 1 summarizes the interleaver algorithms.  
\begin{table}
\begin{tabular}{c|c} \hline \hline
**Interleaver Type** & \multicolumn{2}{c}{**Algorithm**} \\ \hline BTC &
\begin{tabular}{c} Multi-Step computation including intra-row permutation computation \\ \(S(j)=(\
u\times S(j-1))\) \%\(p\); \(r(i)=T(q(i))\): \\ \(U(i,j)=S((i/\times r(i)))\) \%\((p-1)\); \(q\) mod\((i)=r(i)\%(p-1)\); \\ \(RA(i,j)=(RA(i,j-1)+q\) mod\((i))\%(p-1)\); \\ \(l_{l_{l}}=\{C\times r(i)\}+U(i,j)\) \\ \end{tabular} \\ \hline
1 st, 2 nd, and HS-DSCH & \
\begin{tabular}{c} Standard block interleaving with different column permutations \\ \(\pi(k)=\left(p\left[\frac{1}{k}\right]+C\times(k\%R)\right)\%\)\%\(K_{\pi}\) \\ \end{tabular} \\ \hline QPP for BTC &
\begin{tabular}{c} \(l_{l_{l}}=\left(f_{1}\times f_{2}\cdot x^{2}\right)\%\) \\ \end{tabular} \\ \hline Sub-Blk. Int. & \multicolumn{2}{c}{Standard block interleaving with given column permutations} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Most of the current interleaver standards [32].  
Figure 3: Other interleavers [32].  
Figure 2: Generation of 4 \(\times\) 6 helical interleavers. (**a**) Master interleaver written in matrix form, \(\pi\) = [20, 6, 7,..., 8, 16]. (**b**) First helical interleaver written in matrix form, \(\pi_{1}\) = [20, 17, 2,..., 4, 16]. (**c**) Second helical interleaver written in matrix form, \(\pi_{2}\) = [20, 19, 23,..., 13, 14] [28, 29].
# FFESSD: An Accurate and Efficient Single-Shot Detector for Target Detection
## 4 Experiments  
In order to evaluate the detection performance of the proposed FFESSD, we trained the proposed method on the union of PASCAL VOC2007 trainval and PASCAL VOC2012 trainval [16], which includes 16,551 images with 40,058 targets and we evaluated the results on the PASCAL VOC2007 test, which contains 4952 images with 12,032 targets. In VOC 2007 and VOC 2012, a predicted bounding box is positive if its match with the ground truth is higher than a threshold (0.5). The backbone of FFESSD is the reduced VGG16 [14], which is pre-trained on the ILSVRC CLS-LOC [28] dataset. We conducted all the experiments on a machine with a single NVIDIA GeForce GTX 1080 Ti graphics processing unit (GPU) and our code was based on PyTorch [29]. The metric to evaluate target detection accuracy was the mean Average Precision (mAP).
## 1 Introduction
### Preliminary Work
In the field of coil design, the workgroup published a design of a data transfer interface with two figure-8 coils [17] to minimize interaction and interference; its relatively complex shape and the full assignment of two conductive layers of the substrate might hinder its general application. [PERSON] et al. refined the PHM concept by incorporating an additional negative pulse to decrease the duration of the ringing oscillation, achieving 50 MBit/s with structures being resonant at 205 MHz at an impressive bit error rate of \(4.5\times 10^{-10}\)[18]. The compatibility with wireless power transfer remains unknown, even though non-overlapping spectral components of data and energy signals were proven.
## 4 Parametric Study of SHSS Evaluation Methods  
For the detailed investigation of SHSS methods, their performance was examined considering different influential parameters such as element type, mesh size, nodal averaging, and stress types present at the weld-toe. To study the effect of these variables, the FEA stress results for IIW QSE and LSE 515 are presented in Figure 8a-f. In the QSE method, there are stress results at three reference points (0.4, 0.9, and 1.4t), while in the LSE 515 method, there are stress results at two reference points (0.5t, 1.5t) to study the variation in stresses for different mesh types. The LSE 515 method is also applicable to coarser mesh (4/8-CQ8/4). Furthermore, a detailed comparison of SHSS results concerning each mesh type is given in Figure 9a,b for both the cases (i.e., deck and rib-toe). The graphs are subdivided into four parts, and each part represents SHSS results for different element types used in this study. Moreover, weld-toe stresses (WTS) are also presented for the comparison with SHSS results (Figure 8e,f and Figure 9a,b). The WTS is a definite nodal stress determined at the weld-toe in the deck and the rib-plate. The detailed analysis of the results for each method is discussed in the following section.
## 3 RS Applications in Ocean
### Sea Level (SL)  
SL is an important oceanographic variable that should be measured precisely for long-term trend assessments and climate studies [205]. SL has a pivotal role in studies related to OSC, mesoscale eddies, and marine gravity fields [206; 207; 208]. In recent decades, anthropogenic activities and global warming have mainly resulted in SL change. For instance, the Intergovernmental Panel for Climate Change (IPCC) reported a Global Mean SL (GMSL) rise of 3.6 mm/yr between 2006 and 2015 [209; 210]. However, the relative rate of SL change is not globally identical because it depends on different spatial and temporal parameters (see Figure 7).  
Conventionally, SL estimation was based on coastal monitoring stations, tide gauges, buoys, and ship surveys [212]. However, the high cost and sparse observations of these approaches make them inappropriate for SL measurements in most cases. Moreover, in situ measurements contain significant interannual and decadal effects and do not perfectly manifest the SL change [213]. However, with the advancement of RS technology, satellites provide valuable datasets to study SL at different local to global scales. In addition to studies that only focused on SL measurements using RS systems, many studies related SL observations to different environmental variables. Through these analyses, it was widely argued that the main contributors to SL rise are thermal expansion of seawater [214], Antarctic and Greenland ice sheet melting [215], and land-water storage change due to the groundwater depletion [216]. Consequently, SL rise has many environmental and economic impacts, including reef island destabilization [217], wave resource alteration [218], coastal erosion [219], saltwater intrusion into aquifers [220], sea turtle nesting threatening [221], lowland and delta vulnerability [222], coastal flooding [223], seaport infrastructure susceptibility [224], wetland inundation and displacement [225], island and offshore baseline loss [226], tidal dynamics [227], and length-of-day changes [228].  
Among different RS systems, GNSS-R, altimeters, and gravimeters have been widely employed for SL studies [229, 230, 231, 232, 233]. The advantages and disadvantages of these systems applied for SL mapping are provided in Table 4. The following subsections discuss the applications of each system.  
Figure 7: (a) Global SL change between 1993 and 2022. Regional mean SL changes and trends of (b) the Atlantic Ocean and (c) the Pacific Ocean calculated from a combination of TOPEX/Poseidon, Jason-1, Jason-2, and Jason-3 satellite altimetry datasets. Satellite altimetry data were downloaded from [211].
# Development and Field Testing of a Wireless Data Relay System for Amphibious Drones
## 3 Results
### Wireless Data Relay by Introducing Additional (Relayer) Drone  
The water quality monitoring amphibous drone system was assessed at Ogata Pond. The pond had surrounding shoreline vegetation, which obstructed direct wireless communication between the transmitter drone (on water) and the receiver (with the drone controller). We introduced an additional drone system for the wireless data relay. By choosing appropriate test conditions, we conducted successful tests, although the system was not available in some situations. Troublesome situations included a site with large metallic fences along the shoreline (wireless communication failure), pond water with frozen surfaces/dense aquatic vegetation (direct landing failure), and prolonged testing with low-temperature water (battery exhaustion failure). The limitations identified from the field tests were assessed afterwards to establish a confirmed routine for reproducible water quality monitoring. In the wireless data relay tests, the relayer drone was able to monitor water quality data during the landing time on water. The series of water quality data in the repeated flights and landings on water show trends of the sensor responses (Figure 13).  
The membrane-electrode DO sensor consumed oxygen to decrease the readings (Figure 13). Therefore, we chose the first reading after each landing on water. Manual measurements of the nearshore water with an optical DO sensor showed oversaturation with oxygen (up to 12.03 mg L\({}^{-1}\) at 24.5 \({}^{\circ}\)C water temperature, 1004 hPa). In this case, we were unable to wait until a sufficient equilibration time for the DO sensor. The other indicators (pH, EC, temperature) were stabilized after each landing on water (Figure 13).  
Figure 12: Summarized result of the data stability test of the system for the SplashDroneTM 4 with rechargeable batteries. In the figure, tc represents temperature correction; std represents calculated standard data by the equations from [PERSON] (1980). Also, EC_tc data are standardized values at 298 K (25 \({}^{\circ}\)C).  
The selected readings showed stable water temperature and electrical conductivity, but the pH value increased during repeated landings on water (Table 2).
# Graphene-Based Functional Hybrid Membranes for Antimicrobial Applications: A Review
## 4 Functional Tailoring of Graphene-Based Membranes  
Graphene has a stable structure and is not easy to react with other substances. Oxygen-containing functional groups make graphene oxide more functional and endow many excellent properties. GO can be very stably dispersed in water without dispersants and stabilizers, which is beneficial to its subsequent functional regulation. Since oxygen-containing groups are prone to chemical reactions, the surface of GO can undergo various chemical modifications to form graphene nanocomposites [33]. Graphene membranes have a nanoporous structure and functional feasibility, which make them ideal for achieving a higher permeation flux, higher selectivity, and better stability through control of the pore size and shape of membranes [60].  
Graphene nanocomposites are promising antibacterial materials. Polymer matrix composites can give full play to the advantages of graphene and polymers on the basis of controllable graphene content [61]. Nanocomposites can overcome the limitations of individual components, and their structures have good properties, such as excellent electrical conductivity, easy modification, good dispersion and thermal stability, high specific surface area, high mechanical strength, and good flexibility [62]. The following sections describe graphene-based bicomponent and multicomponent nanocomposites for antibacterial activity. These nanocomposites include metals, metal oxides, polymers, biopolymers, and other composite materials.
## 4 Experiments and Results
### Evaluation of the Discrimination Ability of IACS  
In this section, we evaluated the differences between adversarial examples and normal examples based on IACS values. Figure 2 shows the IACS values (from the penultimate layer of Resnet for CIFAR10) of 100 randomly selected adversarial examples (green) generated by CW\({}_{2}\)[24] and those of 100 random normal examples (red) from the CIFAR10 test dataset. We found that the IACS values for the normal examples were significantly larger than the IACS values for the adversarial examples. This met our expectation that the similarity between a normal example and a normal example was greater than that between an adversarial example and a normal example.  
We also studied the cosine similarity as a basic metric. We evaluated the AUC score with just a single layer detector with IACS values and ICS values.  
In Figure 3, we show the AUC score of each layer from the start layer to the end layer. We found that the overall performance of the IACS was better than that of the ICS. Notice that we only output one IACS or ICS value for each Resnet block for convenience.