esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Cytotoxicity  
The cell viability and cytotoxicity were assessed by the LDH assay, and the results are presented in histogram Figure 7, as relative growth rate (RGR) calculated according to Equation (1).  
Lactate dehydrogenase (LDH) is an enzyme present in the cytoplasm of all cells. It is rapidly released into the media of cell culture when plasma membrane lysis occurs. Thus, LDH detection and quantification method in the culture media is correlated to the amount of formazan product formed during a specific time period and it can be used as a quantitative test for the toxicity of materials and molecules [85].  
According to ISO 10993-5 standard, if the percentage of the mean RGR is greater than 75%, the tested group will be considered non-cytotoxic. Therefore, from Figure 7, it can be observed that all tested groups exhibited results above this threshold. Regarding the titanium alloy Ti-6 Al-4V without the EDM treatment, the results obtained were similar to those found by other studies [86; 87]. Statistical analysis showed no significant difference between the groups (\(p\) > 0.05). Thus, none of the samples were considered cytotoxic to the cells used in the assay. To build upon these results, additional biological studies, such as cell adhesion, proliferation, gene expression analysis, and in vivo evaluations are recommended to better understand the biological behavior of the modified surfaces [81].  
Considering the surface characteristics observed for each experimental condition as a whole, it is possible to identify distinct biomedical applications. The DF1-F(-) condition, which exhibited the highest surface roughness, good wettability, and non-cytotoxic behavior, appears particularly suitable for implants requiring robust initial osseointegration. This includes dental implants and orthopedic screws used in low-density bone regions, where mechanical interlocking and enhanced cellular anchorage are essential [88; 89].  
Figure 7: Relative growth rate obtained in LDH assay for samples machined with DF1-F or DF2-Ca dielectric performed in each polarity.  
On the other hand, the DF2-Ca(+) condition demonstrated exceptionally high surface hardness (up to 1520 HV) and superhydrophilic behavior, while also maintaining cytocomputability. These characteristics are highly desirable for articulating prostheses and fixation plates, especially in load-bearing applications, such as hip or knee prostheses, where wear resistance and rapid biological interaction are critical [90,91].
## 4 Proposal
### Database Matching Function  
We implemented a _database matching function_ to determine the room coordinates. It compares the cleaned text against the list of room numbers in the room database using the _Levenshtein distance_ algorithm [50]. The _Levenshtein distance_ measures the similarity between two strings of text based on the edit distance. Equation (1) formulates the calculation of the _Levenshtein distance_. We employed the _RapidFuzz_ Python library to implement the _Levenshtein distance_[51]. \[\text{lev}_{a,b}(s,t)= \begin{cases}\max(|s|,|t|)&\text{if }\min(|s|,|t|)=0\\ \min\begin{cases}\text{lev}_{a,b}(s[1:],t)+1\\ \text{lev}_{a,b}(s,t[1:])+1\\ \text{lev}_{a,b}(s[1:],t[1:])+1_{(s_{i}\
eq t_{j})}\end{cases}&\text{otherwise} \end{cases}  \tag{1}\]  
where \(lev_{a,b}(s,t)\) represent the _Levenshtein distance_ between strings \(s\) and \(t\). The \(|s|\) and \(|t|\) refer to the length of string \(s\) and \(t\). The smaller the _Levenshtein distance_, the more similar the strings are. It selects the room data with the smallest _Levenshtein distance_ values. To conclude the system's workflow, the types of data processed in each function are visualized in Table 1.  
In Table 1, the source represents the image captured by the phone camera. The _object detection function_ visualizes the _YOLOv8_ output and the _isolated sign image_. Then, the _text extraction function_'s results consist of the _PaddleOCR_ output and the cleaned text. The output of _PaddleOCR_ contains some incorrect characters due to recognition errors, which are caused by the visual similarity of characters in the _isolated sign image_ to other letters or numbers.
# A Study of Adversarial Attacks and Detection on Deep Learning-Based Plant Disease Identification
## 3 Experiments and Results
### Efficacy of Adversarial Attacks  
To investigate the efficacy of adversarial attacks, we perturb the testing set used in Section 3.2. Each of the four adversarial attacks of Section 2.3 is applied to generate adversarial samples from randomly selected 50% of the test samples. The generated adversarial samples are combined with another half of normal samples as the testing set for adversarial attacks. Please note that all four attacks are bounded by a pre-defined maximum perturbation size \(\epsilon\) with respect to the \(L_{\infty}\) norm. We generate different testing sets by varying \(\epsilon\) from 0.2/255 to 4/255. Examples of adversarial images generated by the four attacks and their corresponding normal images under \(\epsilon=1/255\) for fine-tuned 2-class and multi-class VGG-16 models are shown in Figures 2 and 3, respectively. It can be seen  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Class** & **Number of Images** \\ \hline Scab & 630 \\ Black Rot & 621 \\ Cedar Apple Rust & 275 \\ Healthy & 1645 \\ \hline
**Total** & 3171 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Apple leaf disease dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**DNN Model** & **Dataset** & **Test Accuracy** \\ \hline VGG-16 & 2-class & 100\% \\ VGG-16 & multi-class & 99.67\% \\ ResNet-101 & 2-class & 99.84\% \\ ResNet-101 & multi-class & 100\% \\ Inception V3 & 2-class & 100\% \\ Inception V3 & multi-class & 100\% \\ DenseNet-121 & 2-class & 100\% \\ DenseNet-121 & multi-class & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance of fine-tuned DNN models without adversarial attacks.  
Figure 3: Examples of normal images and adversarial images generated by different attacks (VGG-16, multi-class, \(\epsilon=1/255\)).  
Figure 4: Performance comparison of four adversarial attacks on 2-class DNN models under different \(\epsilon\).  
Figure 5: Performance comparison of four adversarial attacks on multi-class DNN models under different \(\epsilon\).
# Mean Shift Segmentation Assessment for Individual Forest Tree Delineation from Airborne Lidar Data
## Materials and Methods
### Methods
#### 3.2.3 Other Segmentation Methods  
A canopy height model (CHM)-based method, marker-controlled watershed [15], was implemented to have an independent reference of the mean shift's performance. Watershed is also a classical image segmentation method as mean shift. A typical procedure of watershed segmentation of a CHM image is first detecting local maxima in the image, and then segmenting the image by watershed transform. However, the local maxima can be erroneous. To improve the detection, an allometric function of tree height and crown size was introduced to adapt the search window to detect canopy maxima, which were then smoothed by a Gaussian filter. The refined tree tops were used as markers to control the watershed segmentation. Parameters, including search radius and merge radius, as implemented by [48], were fine-tuned for each dataset to produce the best results.  
A more recent method from [PERSON] et al. [49] was also implemented for comparison. Instead of using watershed, a moving window is used to locate local maxima, which then serve as the 'initial region' for region growing, considering the vertical height difference of neighboring pixels. The final regions are approximated by convex hulls, and are treated as tree crowns. In addition, the point-basedmethod proposed for the benchmark data in [22] are tested for further comparison. The point clouds are first voxelized, and some structure elements are proposed for tree top detection, which are constrained by certain rules based on tree morphological characteristics.
# Cm-Level Photonic-Crystal-Like Subwavelength Waveguide Platform with High Integration Density
## 4 Conclusions  
We numerically investigated and optimized a cm-level photonic-crystal-like subwavelength waveguide platform, and proposed a T-shaped power splitter based on this waveguide platform. The structural degree of freedom, i.e., arrangement of air holes in PhCs, the isolation on the propagation loss and integration density of the waveguides are discussed. The center wavelength of reflection band has a linear relation with the period of air holes, which could be applied in wavelength filtering. Finally, a T-shaped power splitter was designed. By optimizing the radii and positions of air holes and the size of triangular metal at the junction, the splitting efficiency is up to 46.5%. These results reveal that the cm-level photonic-crystal-like subwavelength waveguides are a promising candidate for the large-scale photonic integration.  
Conceptualization, [PERSON] ([PERSON]); methodology, [PERSON] ([PERSON] ) and [PERSON] ([PERSON]); specimen design and testing, [PERSON] ([PERSON]) and [PERSON]; data analysis, [PERSON] ([PERSON]) and [PERSON]; writing--original draft preparation, [PERSON] ([PERSON]), [PERSON] ([PERSON]) and [PERSON]; and writing--review and editing, [PERSON] ([PERSON]) and [PERSON] ([PERSON]).  
This research was supported by the Science and Technology Plan of Guangzhou in 2017 (Grant No. 201707010173)  
The authors declare no conflict of interest.
## 4 Discussion  
### Performance of GNSS-RTK Availability Assessment  
The GNSS-RTK Availability Assessment method proposed in the paper functions as selecting available GNSS-RTK solutions under urban canyons for further fusion with LIO. The exclusion of unavailable GNSS-RTK solutions with potential large errors guarantees the convergence and the accuracy of the optimization-based fusion. The availability assessment is implemented by evaluating the mean elevation angle mask of the buildings surrounding the GNSS-RTK solution as Equation (3). It benefits from the 3D range information perceived by LiDAR and the accurate point cloud map produced by LiDAR/inertial odometry. A large mean elevation angle mask indicates tall objects around. While a small one indicates open areas.  
Ideally, under urban scenarios, a large mean elevation angle mask implies deep urban canyons and thus the ATE of the GNSS-RTK measurements should be large due to signal blockage or reflection. Such as the three places denoted as \"()\" in Figures 6, 8 and 10. Oppositely, a small mean elevation angle mask denotes unblocked areas and the ATE of the GNSS-RTK measurements is expected to be small. Such as the three places denoted as \"()\" in Figures 6, 8 and 10. Most cases shown in the experiments on the three datasets are in line with the above law. Hence, available GNSS-RTK solutions could be selected according to the mean elevation angle mask. The correctness of the GNSS-RTK availability assessment via calculating the mean elevation angle mask of the point cloud map is validated.  
However, false GNSS-RTK availability assessment under relatively open areas would occur affected by tall and dense trees, etc. Such as the two places denoted as \"()\" in Figures 6 and 10. These trees detected by the LiDAR over the vehicle would derive a large mean elevation angle mask while their influence on GNSS positioning accuracy is not that great. Such false assessment would miss really available GNSS-RTK solutions occasionally. To avoid this, semantic annotation of the 3D point map would be conducive to eliminate the influence of the tall objects nearby which will be one of our future works.
## 2 Background: Identification of Dynamic Systems by Unscented Kalman Filter
### Applications of Filter Methods and Practical Issues
The applications using three different approaches, i.e., joint state-parameter estimation, input-state estimation, and parameter estimation confirmed that the state-space model must be developed appropriately based on target estimations, and it is essential to use a proper filtering method to obtain optimal estimations. It is also noted that, in all filter-related research, it is critical to tune the filter by selecting covariance matrices \(\mathbf{Q}_{k}\) and \(\mathbf{R}_{k}\) which describe uncertainties of the artificial noises in system-process and measurement-output model respectively. Although the tuning process of those covariance matrices is vital for good filter performance, there is no general technique available. In efforts to handle this, many studies assumed that the artificial measurement noise can be prescribed using the available specifications of the sensors. Then, the artificial process noise is determined by minimizing the rootmean square (RMS) of time-history \"innovations,\" which are defined as follows, to quantify the differences between measurements and responses corresponding to the system parameters predicted during the time-update part [13; 16]:  
\[f_{\rm{I}}(\mathbf{Q}_{k})=\sqrt{\frac{1}{n_{T}}\biggl{(}\sum_{k=0}^{T}\| \boldsymbol{v}_{k}(\mathbf{Q}_{k})\|^{2}\biggr{)}} \tag{7}\]  
where \(\boldsymbol{v}_{k}(\mathbf{Q}_{k})\) is an innovation vector defined for the time duration \(k=0,\ldots,T\); and \(n_{T}=T+1\). In several references [25; 26], it has been shown that \(f_{\rm{I}}(\mathbf{Q}_{k})\) in Equation (7) tends to decrease following an \(L\)-shaped curve as \(\mathbf{Q}_{k}\) increases. Generally, the inflection point of the \(L\)-shaped curve is recommended as a near-optimal solution in terms of the filter performance. The opposite case, in which the artificial process noise is known, and the corresponding artificial measurement noise is to be determined, can be solved in a similar manner. If both types of artificial noises are unknown, the objective function can feature both \(\mathbf{Q}_{k}\) and \(\mathbf{R}_{k}\). To address this, for instance, [PERSON] et al. (2018) utilized particle swarm optimization (PSO) [14]. In this paper, PSO is used to obtain the best tuning parameters of the proposed dual adaptive filtering.
## 4 Conclusions  
In conclusion, our study provides a comprehensive analysis of the spatiotemporal characteristics and driving mechanisms of storm surges induced by Typhoon In-fa (2021) in the East China Sea. We propose an innovative approach integrating a parametric wind field model (Fujita-Takahashi) and reanalysis dataset (ERA5) to simulate typhoon wind fields. Comparative analysis showed the Fujita-Takahashi model demonstrated higher accuracy during the migration phase while ERA5 remained more precise around landfall. By selectively applying the Fujita-Takahashi model for prelandfall and ERA5 data for postlandfall, we aimed to improve the reliability of surge simulations. This hybrid wind field modeling approach allowed for the capturing of dynamic wind-surge interactions during different typhoon stages. The findings provide new insights into the optimized utilization of diverse wind field models for enhanced storm surge prediction.  
Furthermore, our study highlights the significant impact of Typhoon In-fa (2021) on the coastal and estuarine regions of the East China Sea. The results showed that the southern sea area of Jiangsu and the Yangtze River Estuary were the most affected areas, followed by the central sea area, and the northern sea area had the least impact. The insights gained from this study provide a scientific basis for disaster prevention and mitigation in coastal areas. The findings could be used to inform the design of marine buildings and infrastructure, as well as to improve the accuracy of storm surge forecasting and warning systems. Overall, the proposed storm surge model stands as a robust tool for predicting the potential impacts of storms on coastal areas. The utilization of ERA5 wind field data, in conjunction with the Fujita-Takahashi parameterized wind fields, enhances the precision of storm surge height predictions. This advanced modeling approach equips decision-makers with more accurate information for devising evacuation plans, implementing protective measures, and allocating resources in the event of impending storms.  
Conceptualization, K.D.; funding acquisition, Z.S. and H.Z.; methodology, Z.L., K.D. and F.C; project administration, Z.S. and H.Z.; visualization, F.C.; writing--original draft, F.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Major Project of Science and Technology in Zhejiang Province (No. 2023C03119) and the Science and Technology Program of Zhoushan City of China (No. 2022C31046).  
Not applicable.  
The data presented in the present study are available on request from the corresponding author.  
We thank the European Centre for Medium-Range Weather Forecasts (ECMWF) for providing the ERA5 dataset. We appreciate the insightful discussions with the researchers from Ningbo Hangzhou Bay Bridge Development Co., Ltd. We are grateful to the editors and the anonymous reviewers for their constructive comments and suggestions, which have significantly enhanced the quality of this manuscript.  
The authors declare no conflict of interest.
# An Evaluation of Orbital Angular Momentum Multiplexing Technology
## 4 Experimental Demonstration Wireless OAM Multiplexing Technology using 28 GHz
### Experimental OAM-MIMO Multiplexing Results Using Multiple UCAs
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & **Mode -2** & **Mode -1** & **Mode 0** & **Mode 1** & **Mode 2** \\ \hline Tx & Modulation (QAM) & & & & 64 & \\ UCA No. 0 & Channel coding rate & & & & 3/4 & \\  & Trans. rate (Gbps) & & & & 9 & \\ \hline Tx & Modulation (QAM) & 64 & 64 & 16 & 64 & 64 \\ UCA No. 1 & Channel coding rate & 9/10 & 3/4 & 9/10 & 9/10 & 5/6 \\  & Trans. rate (Gbps) & 10.8 & 9 & 7.2 & 10.8 & 10 \\ \hline Tx & Modulation (QAM) & 64 & 64 & 16 & 64 & 64 \\ UCA No. 4 & Channel coding rate & 3/4 & 3/4 & 9/10 & 3/4 & 3/4 \\  & Trans. rate (Gbps) & 9 & 9 & 7.2 & 9 & 9 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Experimental results (OAM-MIMO multiplexing).
## 5 Implementation and Evaluation
### SignFi Dataset  
The authors of SignFi dataset [29] have captured the raw CSI measurements for 276 sign gesture with 20 and 10 instances for each gesture in the laboratory (lab) and home environments. The SignFi [29] dataset is chosen as the reference dataset for the present work, as this is the only dataset containing CSI traces with 276 gestures and a larger number of gesture samples with greater clarity in data acquisition. Hence, it is considered as the most promising dataset to implement our proposed methodology. There are 8280 gesture instances, 5520 from the lab and 2760 from the home, for 276 sign gestures in total from 1 user. Experiments also performed to capture CSI traces from 5 different users in the lab environment for 7500 instances of 150 sign gestures.  
The setting of the lab and home environment used for the measurement as reported in [29] is shown in Figure 6, and the description of the lab and home environments is reproduced here for immediate reference. The lab has more surrounding objects, leading to a more complex multi-path environment than the home. The distance between the Wi-Fi transmitter (AP) and receiver (STA) is 230 cm and 130 cm respectively, for the lab and home environment. For the home environment, the transmit antenna array is orthogonal to the direction from the transmitter to receiver. For the lab environment, the angle between the transmitter antenna array and the direct path is about 40 degrees. The major differences of these two environments are (1) dimension of the room, (2) distance between the transmitter and receiver, (3) angle between the transmit antenna array and the direct path, and (4) multi-path environments.  
Table 1 shows a part of the full gesture labels representation of the SignFi dataset [29]. The raw CSI traces of the gestures were extracted using 802.11n CSI tool [59] in openrf [60] environment, with two Intel 5300 network interface card (NIC) equipped laptops. One laptop (AP) with three external antennas works as a transmitter (\(N_{t}=3\)), and another laptop with one internal antenna acts as a receiver (\(N_{r}=1\)) for data acquisition. Both transmitter and receiver operate at 5 GHz, with the channel width of 20 MHz in the Non-Line of Sight (NLoS) scenario. However, the 802.11n CSI tool [59] extracts the signal information for first 30 subcarriers (\(N_{S}=30\)) only, out of 52 subcarriers available, with 200 CSI samples per gesture. Interested readers are recommended to refer the SignFi [29] paper for more insight about data acquisition and experimental setup.  
Figure 6: Schematic representation of testing environments as reported in SignFi [29].  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Gesture** & **Count of** & \multicolumn{10}{c}{**Description**} \\
**Class** & **Samples** & & & & & & & & & \\ \hline \multirow{3}{*}{Common} & \multirow{3}{*}{16} & \multirow{3}{*}{
\begin{tabular}{c} ‘FINSHF’ \\ 'PLASE’ \\ 'ANDIMAL’ \\ ‘MOME’ \\
## 3 Results and Discussion
### Multivariate Statistical Analysis
A relatively high Q2 value close to R2 was obtained, suggesting that the predictive ability was very good (Figure S3). The compounds found to be the most useful for varietal differentiation in this study in the most part did not match those highlighted in previous reports. Diethyl succinate, 2,3-butanediol, nerol, 3-penten-2-one, diethyl malonate, \(\beta\)-santalol, ethyl 9-decenoate, alcohol-C\({}_{9}\), 4-carene, tetrahydro-2(2H)-pyranone, dihydro-2(3H)-thiophenone, and \(\alpha\)-methyl-\(\gamma\)-crotonolactone were the major differentiators between Chardonnay, Sauvignon Blanc, Pinot Noir, Merlot, and Cabernet Sauvignon wines based on GC\(\times\)GC-TOF-MS results [28]. Ethyl hexanoate, ethyl octanoate, 1-hexanol, benzyl alcohol, and isoamyl acetate differentiated well Syrah, Malbec, and Bonarda red wines [13], whereas in another study, Cabernet Sauvignon and Merlot wines were successfully discriminated based on the levels of 1-hexanol, ethyl decanoate, and 2-phenylethanol [16]. Such different outcomes of the various studies that had a similar goal indicate the complexity of the interaction of the effects of many different factors, from the varietal and geographical origin inextricably linked to local pedoclimatic conditions, across grape growing conditions, to the parameters applied in wine production and finishing. Figure 1: **(a)** Separation of Plavac mali and Teran monovarietal red wines based on variety along principal components PC1 and PC2; **(b)** factor loadings of selected volatile aroma compounds, as determined by gas chromatography\(-\)mass spectrometry (GC\(-\)MS) and two-dimensional gas chromatography with time\(-\)of\(-\)flight mass spectrometry (GC\(\times\)GC\(-\)TOF\(-\)MS) analysis, on PC1 and PC2. Numbers indicating compounds correspond to those in Table 2 (GC, i.e., GC\(-\)MS) or in Table 3 (GCGC, i.e., GC\(\times\)GC\(-\)TOF\(-\)MS). Figure 2: Clustering of Plavac mali and Tera monovarietal red wines by hierarchical cluster analysis (HCA) based on the composition of volatile compounds obtained by gas chromatography—mass spectrometry (GC\(-\)MS) and two-dimensional gas chromatography with time-of-flight mass spectrometry (GC\(\times\)GC\(-\)TOF\(-\)MS) analysis. The rows in the heatmap diagram represent compounds and the columns represent wines. Numbers indicating compounds correspond to those in Table 2 (GC, i.e., GC\(-\)MS) or in Table 3 (GCGC, i.e., GC\(\times\)GC\(-\)TOF\(-\)MS). Colors of the heatmap cells indicate low (dark blue), medium (white), and high (dark red) abundance of a particular compound.