esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulation and Analysis  
In this section, we simulate and analyze the performance of the designed ICRS in the distributed spacecraft networking algorithm proposed in Section 2, measuring relative distance and exchanging effective messages between the two satellites. All models and simulations were implemented using MATLAB software, and the main components of the receiver include the acquisition, tracking, data demodulation, and symbol timing synchronization part. The acquisition component is similar to signal acquisition in a GPS receiver, which is introduced in [26; 27]. Then, the tracking part is also similar to the GPS receiver with a carrier tracking loop and an early-late code tracking loop. Lastly, the data demodulation component is proposed in Section 2.  
The ranging error and BER in the two links are two factors that are evaluated in our algorithm, and are related closely to the CNR or SNR in each branch. In the uplink, because of the adoption of QPSK modulation, the transmitting power is distributed to the two branches equally. Therefore, the equivalent SNRs in both of the branches are 3 dB less than that of the total. In the downlink, due to UQPSK modulation, the transmitting power is distributed to the two branches according to the PWDF, resulting in the changes of SNR in two branches. In addition, it is necessary to simulate the clock error between the two satellites, which is determined by DOWR as described in Section 2.
## Comparative Analysis of Flower Volatiles from Nine Citrus at Three Blooming Stages  
**[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] * and [PERSON]**  
Laboratory of Fruit Quality Biology/The State Agriculture Ministry Laboratory of Horticultural Plant Growth, Development and Quality Improvement, Zhejiang University, Zijingang Campus,  
Hangzhou 310058, China; E-Mails: [EMAIL_ADDRESS] (M.A.); [EMAIL_ADDRESS] (M.S.);  
[EMAIL_ADDRESS] (F.F.); [EMAIL_ADDRESS] (B.Z.); [EMAIL_ADDRESS] (Y.X.);  
[EMAIL_ADDRESS] (K.C.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +86-571-8898-2289; Fax: +86-571-8898-2224.  
_Received: 14 October 2013; in revised form: 31 October 2013 / Accepted: 1 November 2013 / Published: 13 November 2013_  
**Abstract:** Volatiles from flowers at three blooming stages of nine citrus cultivars were analyzed by headspace-solid phase microextraction (HS-SPME)-GC-MS. Up to 110 volatiles were detected, with 42 tentatively identified from citrus flowers for the first time. Highest amounts of volatiles were present in fully opened flowers of most citrus, except for pomelos. All cultivars were characterized by a high percentage of either oxygenated monoterenes or monoterene hydrocarbons, and the presence of a high percentage of nitrogen containing compounds was also observed. Flower volatiles varied qualitatively and quantitatively among citrus types during blooming. Limonene was the most abundant flower volatile only in citrus; \(a\)-cital and \(\beta\)-cital ranked 2 nd and 3 rd only for Bergamot, and unopened flowers of Ponkan had a higher amount of linalool and \(\beta\)-pinene while much lower amount of \(\gamma\)-terpinene and \(p\)-cymene than Satsuma. Taking the average of all cultivars, linalool and limonene were the top two volatiles for all blooming stages; \(\beta\)-pinene ranked 3 rd in unopened flowers, while indole ranked 3 rd for half opened and fully opened flower volatiles. As flowers bloomed, methyl anthranilate increased while 2-hexenal and \(p\)-cymene decreased. In some cases, a volatile could be high in both unopened and fully opened flowers but low in half opened ones. Through multivariate analysis, the nine citrus cultivars were clustered into three groups, consistent with the three true citrus types. Furthermore, an influence of blooming stages on clustering was observed, especially with hybrids Satsuma and Huyou. Altogether, it was suggested that flower volatiles can be suitable markers for revealing the genetic relationships between citrus cultivars but the same blooming stage needs to be strictly controlled.  
citrus types; volatiles; unopened flower; half opened flower; fully opened flower; HS-SPME; GC-MS
# In-Orbit Attitude Determination of the UVSQ-SAT CubeSat Using TRIAD and MEKF Methods
## 3 Attitude Determination Methods
### Multiplicative Extended Kalman Method
#### 3.2.1 Formulation  
At each iteration, MEKF [29] uses the quaternion as the attitude representation and the state vector \(\delta\mathbf{\theta}\) for the representation of the attitude errors. The true quaternion can then be defined as follows (Equation (15)):  
\[\mathbf{q^{true}}=\delta\mathbf{q}(\delta\mathbf{\theta})\otimes\mathbf{\hat{q}} \tag{15}\]  
where \(\mathbf{q^{true}}\) is the true quaternion that represents the real orientation of the object that is defined from a product. \(\mathbf{\hat{q}}\) is the estimate quaternion giving an estimate of the object orientation. \(\delta\mathbf{q}(\delta\mathbf{\theta})\) is the error quaternion defined by \(\delta\mathbf{\theta}\), with the three components representing the attitude error. \(\otimes\) is the quaternion product symbol first used in [30] and defined in [29] as follows (Equation (16)):  
\[\mathbf{\bar{q}}\otimes\mathbf{q}=\begin{bmatrix}q_{4}\mathbf{\bar{q}_{1:3}}+ \mathbf{\bar{q}_{4}q_{1:3}}+\mathbf{\bar{q}_{1:3}}\times\mathbf{q}_{1:3}\\ \mathbf{\bar{q}_{4}q_{4}}-\mathbf{\bar{q}_{1:3}}\mathbf{q}_{1:3}\end{bmatrix} \tag{16}\]  
where \(\mathbf{\hat{x}}\) represents the estimate of the quantity \(x\) (for example, the state vector). \(\mathbf{\hat{x}}^{+}\) represents the updated quantity of \(\mathbf{\hat{x}}^{-}\), before being updated. The local attitude error is the true linearized variable of interest to compute the attitude at each iteration. However, the gyrometer which is required to predict the motion of the satellite has several calibration parameters that need to be computed, such as the misalignments, scale factors, and time-dependent drift biases. Therefore, those quantities must be computed at each iteration. So, they must be considered as state variables.
## 3 Results and Analysis of Influencing Factors
### The Impact of Drill String Properties on Signal Attenuation
#### 3.5.1 The Impact of Drill String Conductivity on Signal Attenuation  
The model is consistent with the previous model. Since drill pipes are generally made of steel, the range of drill string conductivity is set between \(1\times 10^{6}\) S/m and \(1\times 10^{8}\) S/m, with the relative magnetic permeability set to 100.  
Figure 7 reveals the correlation between drill string conductivity, signal attenuation, and reception strength. As the conductivity increases, the signal attenuation gradually decreases, and the strength of the received signal correspondingly increases. When the transmission distance is 100 m, the signal attenuation for a drill string with conductivity of \(1\times 10^{6}\) S/m is 195.01 dB, while for a drill string with conductivity of \(1\times 10^{7}\) S/m, the signal attenuation is 135.4 dB, with a difference of 62.65 dB in the received magnetic field amplitude at 100 m. This phenomenon highlights the importance of the drill string as the main transmission channel for electromagnetic waves. With the increase in conductivity, the amplitude of the received magnetic field tends to stabilize. However, it is worth noting that the reliability of the connections at the drill pipe joints has a significant impact on conductivity and consequently affects the transmission efficiency of the signal. Therefore, in engineering practice, ensuring the stability of the drill pipe joint connections is vitally important to maintain the overall transmission efficiency of the system.  
Figure 6: The impact of formation resistivity on signal attenuation.  
Figure 7: The impact of drill string conductivity on signal attenuation.
# Contrast Analysis of Polarization in Three-Beam Interference Lithography
## 4 Discussion  
There is a contradiction between narrower line width and higher contrast in LIL. The smaller feature size of the pattern means a larger spatial incident angle, which also brings about the enhancement of the standing wave effect and the sharp decrease in the contrast of the light field. When the coherence of the light source is not considered, the contrast of MIL depends on the polarization state of the beam, the angle of incidence, the deviation of the polarization angle, and the stability of the beam pointing at the same time. In this paper, by constructing a MIL model, we obtained the optimal solution for the high-contrast light field of three-beam interference lithography under different pattern line widths. In the case of a small incident angle interference, the co-directional TE polarization incident combination is adopted, which can reduce the exposure time while obtaining a high-contrast light field. When the incident is at a large angle, the azimuthal TE polarization incident combination  
Figure 9: The TE-TE-TE polarization mode is used for three-beam interference, and the contrast at different incident angles is obtained when the polarization direction of the three beams has a deviation of \(\pm 5^{\circ}\).  
is used to obtain a high-contrast light field while allowing higher beam pointing stability deviations. This conclusion is also applicable to MIL, which provides a theoretical reference for the generation of narrow linewidth patterns and equipment development in interference lithography.
# An Improved Method for Ship Target Detection Based on YOLOv4
## 5 Conclusions  
In this paper, we proposed an improved model. Firstly, we used the RFB_s structure instead of the SPP structure to enhance the receptive field and enhance the detection performance on small ships. Secondly, we introduced the CBAM module, which adaptively adjusts the weights of different feature maps before feature fusion to effectively highlight salient features of objects and detect small ships more effectively while improving the model's resistance to background interference. The mAP of the improved model was increased from 77.66% to 91.40%. The experimental results showed that the improved model can accurately detect small ships and effectively resist background interference. Additionally, the problem of wrong and missed detection was greatly addressed.  
Although the detection effect of the method proposed in this paper was significantly improved, this paper only classifies the ships into four categories for coarse identification, and there is room for further improvement in detection accuracy. In future work, on the one hand, the dataset will be expanded, focusing on images containing small ships and images with complex backgrounds, and the ships will be divided into fine-grained categories. On the other hand, the training ship detection model for fine recognition will be explored, and the ability of the model to resist complex background interference will be further improved.  
Conceptualization, X.J. and F.W.; methodology, Z.H. and F.W.; software, Z.H. and Y.F.; validation, Z.H., Y.Z., T.F. and J.P.; formal analysis, Z.H. and F.W.; resources, X.J.; data curation, Z.H. and T.F.; writing--original draft preparation, Z.H. and X.J.; writing--review and editing, Z.H. and F.W.; project administration, X.J.; funding acquisition, X.J. and F.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China, grant number 2022 YFB3902300, and was funded by the National Natural Science Foundation of China, grant number 42001345.  
Not applicable.  
Figure 11: Comparison of ablation experiment test results. (**a**) Detection result of YOLOv4 model. (**b**) Detection result of YOLOv4-CBM model. (**c**) Detection result of YOLOv4-RFB_s model. (**d**) Detection result of our improved model.  
Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflict of interest.
# Optimizing Geospatial Data for ML/CV Applications:
## 3 Methods
### Automated Data Retrieval from LP-Portal
#### 3.1.3 Retrieval of Road Nodes  
The script called for each road the 'getInteractiveLegendInfoByLayerIdAndOptionId' endpoint, providing the road name as a parameter. The JSON response contained road path nodes, represented by coordinates in the EPSG:3857 coordinate reference system (CRS). The 'Transformer' class from the 'pyproj' library converted these coordinates into EPSG:2180, ensuring consistency with other datasets.
## 3 Proposed Fault Diagnosis Scheme
### Hybrid Deep Recognition Scheme
#### 3.3.1 Underlying Feature Extraction  
The architecture of DCNN provides a technique for the reduction of input dimension, retaining important features to be learned easily. In this part, a set of DCNNs are built where each DCNN corresponds to one type of sample. The DCNNs are pre-trained until they meet the expected accuracy, which is regarded as an indicator for the feature extraction performance. Differently, achieving the same diagnosis performance, the requirement of accuracy of DCNNs in the proposed scheme is lower than that in traditional single DCNN-based diagnosis as the final performance of diagnosis depends  
Figure 3: The proposed hybrid deep recognition scheme (hybrid Ada-DCNN).  
on the assemble effect of DCNNs, which will be demonstrated by following works. In other words, the proposed diagnosis scheme can reduce the work of DCNN construction.  
After the training of DCNNs, they perform as extractors over corresponding examples. The activations of the last FC layer are picked up and fused to produce the integrated feature space as below,  
\[Fea(i)=\Big{\{}[\text{S}_{1,1},\dots,\text{S}_{1,m1}],\ [\text{S}_{2,1},\dots, \text{S}_{2,m2}],\dots,[\text{S}_{n,1},\dots,\text{S}_{n,m3}]\Big{\}}, \tag{13}\]  
where \(Fea\) (\(i\)) is the integrated feature space corresponding to the \(i^{\text{th}}\) signal segment, and \(\text{S}_{p,q}\) donates the \(q^{\text{th}}\) element of features obtained from the FC layer of the \(p^{\text{th}}\) DCNN.
## 3 Materials and Methods
### Identification and Quantification of Phenolic Compounds and Chlorophyll a by HPLC-DAD  
Leaf samples of green and red perilla were filtrated through a 0.20 um membrane and analyzed by HPLC in order to quantify target phenolic compounds and chlorophyll a content, according to [PERSON] and [PERSON] [5]. The analysis was performed with an 1100 Series HPLC system (Agilent Technologies, Palo Alto, CA, USA) equipped with a quaternary pump (G13111A) and a diode array detector (DAD; G13114B) using a 20 \(\upmu\)L sample injection loop. A reversed phase C18 column (150 \(\times\) 4.6 mm i.d., particle size 5 um; Agilent Eclipse XDB-C18) was employed. The eleuents were 5% (\(v/v\)) formic acid in water (eluent A) and 5% (\(v/v\)) formic acid in acetonitrile (eluent B). The gradient program was as follows: 2-7% B (5 min), 7-12% B (10 min), 12-17% B (5 min), 17-25% B (5 min), 25-35% B (10 min) at a constant flow rate of 1.3 mL min\({}^{-1}\). Total runtime was 35 min. Detection was performed at 320 nm for rosmarinic, caffeic and sinapic acids and at 666 nm for chlorophyll a. Identification was made by comparing retention times to rosmarinic, caffeic, sinapic and chlorophyll a standards purchased from Sigma Aldrich (Milan, Italy), by spiking samples with internal standards and by calculating theirrecovery. Calibration curves of high linearity (R\({}^{2}\) > 0.999) were derived for each standard using five concentration levels (1, 10, 20, 50 and 100 mg\(\cdot\)L\({}^{-1}\)). All data are presented as mean \(\pm\) standard error (\(n\) = 3), and expressed as mg g\({}^{-1}\) dry matter. In addition, as suggested by the reviewer representative HPLC-DAD chromatograms of polyphenic (rosmarinic, caffeic and sinapic acids) and chlorophyll a extracts of _Perilla_ leaves monitored at 320 nm and 666 nm, respectively are shown in Figure S1.
## 3 Materials and Methods
### Data Augmentation and Preprocessing  
In order to simulate the sugarcane harvesting scenarios in complex environments and further improve the generalization performance of the sugarcane tail tip detection model, we perform data augmentation on the sugarcane tail tip dataset. In this paper, data augmentation is performed on the original dataset by rotating, cropping, mirroring, adjusting brightness, adjusting saturation, and adding noise in random combinations with each other. Among these techniques, the rotation operation can simulate the multi-angle tilting of sugarcane plants due to the difference in growth patterns or wind force; the cropping process can simulate the mutual occlusion between sugarcane tail tips, allowing the model to better adapt to scenarios where only part of the target is visible; the mirror flip enhances the model's adaptability to the left-right symmetry of sugarcane's growth characteristics; the luminance adjustment simulates the effect of different light intensities (e.g., strong light in the noon and low light at dusk) on the target's phenological appearance; and the change in saturation corresponds to the color distortion scene caused by rainy weather or soil reflection; the addition of noise can be equivalent to the equipment imaging noise or field noise interference. Color distortion scenarios are caused by rainy weather or soil reflections, and added noise can be equated to equipment imaging noise or field dust interference. Samples of the original and data-enhanced images are shown in Figure 2.  
Figure 1: Examples of images acquired from sugarcane tails under different environments: (**a**) Ishiyama background; (**b**) strong light in the noon; (**c**) low light at dusk; (**d**) front lighting; (**e**) backlighting; (**f**) mutual occlusion.  
On the basis of the original 2300 collected images of sugarcane tails, each image was generated into 2 different images by the above data augmentation method, and 4600 enhanced images were obtained, and the original image and the enhanced image totaled 6900 images. The images are randomly divided in the ratio of 8:1:1 to produce the training set, validation set, and test set with the numbers 5520, 690, and 690, respectively. We manually annotated the augmented dataset using the open-source tool Labellmg, using rectangular bounding boxes to mark the entire tail tip region of each sugarcane. The bounding boxes provide the top-left and bottom-right coordinates of the target object for precise localization. The annotation information for each sugarcane is stored in a corresponding TXT file, which is uniformly saved in the Labels folder. An example of labeling is shown in Figure 3.
## 4 AVs Critical Issues: Aspects Related to Specific Infrastructures Components
### AVs Effects on Roadway Pavement  
Once treated themes related to AVs' advantages and expected consequences, it is necessary to focus on infrastructural design features [42; 43]. One aspect can regard the change of load layout applied on road pavement. As previously argued, AVs would be able to keep with high precision in lane position. It is possible to consider that each vehicle's wheels would stress road pavement always in the same areas and with almost constant speed in a context aimed at AVs exclusive transition. Ultimately, it is expected that road pavement would be stressed by vehicle loads not homogeneously, like for vehicles driven by a human driver (since each driver has its way of driving), but in a cross-section area that could be almost constant [44; 45]. Table 4 reports an overview of AVs' effects on roadway pavement.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Effect** & **Consequence** \\ \hline \multirow{2}{*}{**Channelized traffic flow**} & Easier forecasting process related to cracking and \\  & deformation detection. \\  & Pavement surface optimization due to higher frequency \\  & of load application. \\
**Acceleration/deceleration** & Reduction of longitudinal pavement surface stress. \\
**optimization** & Incidents reduction, traffic management facilitates. \\
**Stop/start maneuvers reduction** & Increased pavement life-cycle due to lower temperature. \\ \hline \hline \end{tabular}
\end{table}
Table 4: Overview of AVs effects on pavement [46].  
Figure 9: Computer output from a Minimum Jerk model—comparison between several traffic parameters for various exit speeds [37].  
With the introduction of autonomous vehicles, ordinary traffic will be less subjected to vehicles' wandering phenomena than channelized traffic. Such conversion can have several consequences, such as greater rutting, significantly increasing longitudinal fatigue cracks. In this case, heavy vehicles would have to be mainly analyzed because tires could cause an elastic deflection bowl. This phenomenon is attributed to the possibility that AVs provide to reduce heavy vehicle headways. It was demonstrated by [PERSON] (1992) that, considering loads cycle application applied quicker, due to high frequency related to headway reduction, road pavement keep a slightly deflected condition that is, in turn, stressed again by following load axis application.  
So, the subsequent deflection will be higher. Such issues can be managed with a technique named \"strip road concept,\" consisting of installing two parallel strips made of asphalt, concrete, or improved soil positioned in correspondence to specific vehicles' wheels that will transit on that roadway. Significant localized stress pavement is subjected to AVs. It was detected during accelerated pavement testing (APT). It is noted, in Figure 10, different pavement responses under expected AVs and conventional vehicle loading respectively channelized and wandering traffic conditions standard hot mix asphalt surfaced.
## 3 Results
### Contextual Stage  
In the contextual stage, the relevance assessment ensures that the system under study is suitable for the implementation of a DT, considering its complexity, available data, and the problems to be addressed. Clear objectives are defined, such as simulations, predictions, or predictive maintenance, along with key performance indicators (KPIs). Additionally, the scope of the physical system is delineated by identifying its boundaries, critical variables, and necessary technological resources, such as sensors, actuators, and communication systems.
# Graphs from Features: Tree-Based Graph Layout for Feature Analysis
## 4 Methodology
### Graph Construction  
A complete undirected graph \(G\) is constructed having one vertex for each feature and with edge weights \(w(i,j)\) that represent dissimilarity between each pair of features \(i\) and \(j\).  
Different dissimilarity measures may be used in the analysis. GFF includes Euclidean distance (Equation (1)), cosine distance (Equation (2)), Manhattan distance (Equation (3)), Chebyshev distance (Equation (4)) and Pearson correlation (Equation (5)). In the equations, \(x\) and \(y\) are two feature vectors, \(x_{i}\) and \(y_{i}\) denote their \(i\)-th component, and \(\overline{x}\) and \(\overline{y}\) denote the mean of \(x\) and \(y\) respectively.  
\[Euc(x,y)=\sqrt{\sum_{i=1}^{n}{(x_{i}-y_{i})}^{2}} \tag{1}\]  
\[Cos(x,y)=\frac{\left|\sum_{i=1}^{n}{(x_{i}y_{i})}\right|}{\sqrt{\sum_{i=1}^{n} x_{i}^{2}\sum_{i=1}^{n}{y_{i}^{2}}}} \tag{2}\]  
\[Mht(x,y)=\sum_{i=1}^{n}{\left|x_{i}-y_{i}\right|} \tag{3}\]  
\[Che(x,y)=\max_{1\leq i\leq n}{(\left|x_{i}-y_{i}\right|)} \tag{4}\]  
\[Per(x,y)=\frac{\sum_{i=1}^{n}(x_{i}-\overline{x})(y_{i}-\overline{y})}{\sqrt{ \sum_{i=1}^{n}(x_{i}-\overline{x})^{2}\sum_{i=1}^{n}(y_{i}-\overline{y})^{2}}} \tag{5}\]  
Regardless of the dissimilarity measure, edge weights are scaled to the range \([0,1]\), by evaluating \((dis(x,y)-\min/(\max-\min))\), where \(\min\) and \(\max\) are the minimum and maximum dissimilarity values. \(Cos\) and \(Per\) are turned into a dissimilarity measure by simply subtracting from 1.