esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Test Sites and Varieties  
This experiment took place in the Venlo glass greenhouse at the Key Laboratory of Modern Agricultural Equipment under the auspices of the Ministry of Education at Jiangsu University (Zhenjiang, China). Due to its significant representativeness, short growth cycle, susceptibility to diseases, and accessibility, this study selected Chinese cabbage (rape variety) as the experimental subject. Chinese cabbage was cultivated in PP5 plastic cultivation soft pots with an outer diameter of 120 mm, an inner diameter of 111 mm, a bottom diameter of 85 mm, and a height of 108 mm, using nutrient soil as the culture substrate, whose main components included organic peat, mature coconut bran, and perlite. During the experiment, the vegetables were grown naturally under greenhouse conditions, receiving equal volumes of tap water and nutrient solution every 2-3 days.
# Feature Deep Continuous Aggregation for 3D Vehicle Detection
## 1 Introduction
Apart from classification and bounding box regression, the output target of the 3D object detection method has one more orientation estimation; meanwhile, there is also one more dimension in the bounding box regression. The diversity of loss categories and calculation methods makes it difficult to optimize the network. Being able to balance the loss weight of each optimization target is equally important to the final detection results. In this paper, a feature deep continuous aggregation (FDCA) network is proposed to solve the above problems (Figure 1). The contributions of this work are as follows:  
* A seven-channel BEV map was generated, which contains one global height channel, five local height channels, and one density channel to preserve the local and global features. The five local height channels preserve the local location correlation among layers by global normalization. * The feature deep continuous aggregation (FDCA) architecture is proposed to improve the generalization ability of the network. The abstract features were fused continuously and deeply by concatenating different feature layers in the feature extractors and the refinement network. * RoIAlign was exploited to improve the cropping accuracy of features for proposal regions. * A light fully convolutional network (FCN) was adopted to reduce the computation load and memory requirements in the 3D proposal generator. * A loss category weight mask was used to balance the proportions of different loss categories in the total loss, and two refined regression loss weight masks were exploited to improve the regression accuracy in high dimensions. The structure of this paper is as follows. The related works are introduced in Section 2. The proposed network of this paper is illustrated in Section 3. The results of comparison experiments and ablation experiments are discussed in Section 4. Finally, the conclusions are given in Section 5.
# Intelligent Detection of Parcels Based on Improved Faster R-CNN
## 2 Method  
### Overall Framework  
The network structure of the Faster R-CNN is shown in Figure 3, which consists of three main stages: (1) First, the extraction of the shared feature map is achieved through the feature extraction network; (2) the object candidate boxes are generated using the RPN network, and the corresponding feature matrices are obtained by projecting the RPN-generated candidate boxes onto the feature map in (1); and (3) each feature matrix is scaled to a 7 \(\times\) 7 size feature map by the ROI Pooling layer in Fast R-CNN, and then the feature map is flattened and passed through a series of fully connected layers to obtain the detection results.  
Faster R-CNN achieves remarkable results in the VOC2012 [28] and COCO2017 datasets. However, the parcel dataset examined here, where the parcels have various sizes and are obscured, prohibited Faster R-CNN from accurately detecting all the parcels within the images, i.e., false detection and missed detection cases occur (see Figure 4).  
Figure 4: Missing and false detection in Faster R-CNN.  
Figure 3: The network structure of Faster R-CNN.  
We improved the RPN and ROI Pooling in Faster R-CNN to improve the parcel detection accuracy. The overall framework is shown in Figure 5. First, to address the problem of dense stacking and mutual occlusion leading to missed and false detection of box artefacts, the proposed method adds an edge detection module to the RPN according to the HED [29], using edge features to generate more accurately positioned parcel candidate boxes; then, to address the problem of feature misalignment and loss due to rounding quantization in ROI Pooling, the self-attention ROI Align module is built to solve the problem of feature misalignment and loss while using the attention mechanism to filter and enhance features. Finally, the edge detection loss is added to the entire loss function to accelerate the convergence of the training process, reduce the net loss and further improve the detection accuracy.
## 3 Results
### Quantification of Aroma-Active Compounds by SPME-GC-TOF-MS  
Among the 72 aroma-active compounds in four samples, 70 compounds were successfully quantified using SPME-GC-TOF-MS with authentic standards (Tables 4 and S1). 2,6-Diethyl pyrazine and tetramethyl pyrazine, although tentatively identified in GC-O, could not be detected by SPME-GC-TOF-MS due to their extremely low concentration, thus were not included in the further study. We also found that the repeatability of a few compounds in the sample matrix was worse than that in the synthetic matrix. This may be due to the complex composition of baijiu, which affects the adsorption stability of SPME fiber. OAVs were also calculated according to the quantification data (Table 4). Excepting the compounds without published odor thresholds, a total of 49 aroma compounds with OAV > 1 were obtained. In the strong-aroma baijiu samples, the OAV of ethyl hexanoate was the highest, followed by ethyl butyrate, ethyl valerate and ethyl caprylate. 1-Butanol and its OAV were in the forefront among alcohols, followed by 1-hexanol. The OAV of butyric acid and hexanoic acid were significantly higher than those of other acids. Furfural was the aldehyde with highest concentration and OAV. Due to the low threshold [18], the OAV of 2-pentanone was significantly higher than that of other ketones. Comparing the volatile compounds in strong-aroma baijiu samples from different regions (Figure 1), it could be found that samples from the Sichuan region exhibited generally higher total concentration of esters, while those from the Jianghuai region contained more alcohols compared to Sichuan samples. In contrast, concentrations of other compound categories showed no significant geographical variations and were primarily brand-dependent. Figure 1: Total concentrations of volatile compounds by category in strong-aroma baijiu samples. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \multirow{2}{*}{**No.
# Protection of Digital Elevation Model--One Approach
## 3 Results
### Testing of the Proposed Model
#### 3.2.2 Testing of Integrity, Authenticity, and Non-Repudiation  
Using the e-Doc Encryptor application we simulated electronic signing on the LiDAR side of the associated certificate for electronic signing and the means for creating a qualified electronic signature (smart card). The resulting digitally signed file was sent to the end user who verified the electronic signature, Figure 6.  
The verification of the electronic signature was performed successfully, which means that there were no changes in the content of the original file generated by the LiDAR radar. In this way, the integrity of the data is preserved. Figure 7a shows the content of the original file. The public key contained in the signer's certificate is used in the verification process. Based on the messages received during verification, we see that the signer's certificate (LiDAR radar) is valid and that the chain of trust trusted by the signer is valid. Since both parties trust the Certification Authority of the MoD and SAF, which issued the certificate, the end user can be sure that the received file is generated on the side of the real LiDAR radar. In this way, the authenticity of the sender or the creator of the LiDAR file received by the recipient was demonstrated.  
The sender uses a private key corresponding to the public key in his certificate during the signing. The sender cannot deny the generation of the content received by the end user, because only he owns the private key that corresponds to the public key in the certificate,  
Figure 6: Digital signature verification. (**a**) Successful verification; (**b**) Failed Verification.  
Figure 7: Display of DEM data in GIS. (**a**) Original; (**b**) After integrity violation.  
and he used it to digitally sign. In this way, the non-repudiation of the generated data was proven, that is, it cannot be claimed that the observed LiDAR radar did not generate data.  
An attack that changed the content of the file during transmission was simulated, Figure 7b. The digital signature was verified on the recipient's side. However, the verification was unsuccessful and the received message indicates that the content has been violated, Figure 6b. This means that the data has been changed and does not match the original data that was generated on the LiDAR radar.
## 3 Experiments and Results
### Study of the Applied Classifiers
#### 3.1.4 Binary Decision Tree  
Decision trees (DTs) are a non-parametric supervised learning method which creates a model that predicts the value of a target variable by learning simple decision rules inferred from the data features [37]. The premier advantages of DTs are: they require little data preparation, they are able to handle both numerical and categorical data, the cost of predicting data is logarithmic in the number of data points used to train the tree model, and they can handle multi-output problems [37]. However, there are a few disadvantages of DTs: they can create over-complex trees that do not generalize the data effectively, they can be unstable due to small variations in the data, and they create biased trees if some classes dominate [37].  
Binary DT classifier is capable of multi-class classification on a sample distribution. The detailed mathematical formulation of DT learning method is articulated in [37]. To characterize the classification criteria, if the outcome takes on values \(0,1,...K-1\), for node \(m\) representing a region \(R_{m}\) with \(N_{m}\) observations and given training vectors \(x_{i}\in R_{m}\) and a label vector \(y\), let us consider  
\[P_{mk}=1/N_{m}\sum_{x_{i}\in R_{m}}I(y_{i}=k) \tag{13}\]  
be the proportion of class \(k\) observations in node \(m\). Common measures of impurity are defined as--  
Gini:  
\[H(X_{m})=\sum_{k}P_{mk}(1-P_{mk}) \tag{14}\]Entropy:  
\[H(X_{m})=-\sum_{k}P_{mk}log(P_{mk}) \tag{15}\]  
and Misclassification:  
\[H(X_{m})=1-max(P_{mk}) \tag{16}\]  
where \(H(\cdot)\) is an impunity function and \(X_{m}\) is the training data in node \(m\)[37].
## 4 Results
### Classification of Refractory Spent Materials
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c} \hline
**Weight** & **Components** & **S0818** & **S0826** & **S0283** & **RFF91** & **RFP26** & **RFP36** & **RF94** & **RFP58** & **RF96** & **RFP70** & **RF98** & **RFP99** & **Insert** & **Hardwood** \\ \hline
0.07284 & As & 0.98871 & 0.98871 & 0.98871 & 0.93133 & 0.95088 & 0.94051 & 0.79673 & 0.85266 & 0.66924 & 0.0340 & 0.08482 & 0.01011 & 0.89526 & 0.63463 \\
0.0375 & But & 0.99684 & 0.99684 & 0.99684 & 0.98672 & 0.95664 & 0.94429 & 0.85052 & 0.62000 & 0.86799 & 0.03429 & 0.44882 & 0.84437 & 0.87942 & 0.49871 \\
0.10447 & Cd & 0.99874 & 0.99874 & 0.99852 & 0.99032 & 0.98414 & 0.71542 & 0.89910 & 0.84035 & 0.49633 & 0.57346 & 0.49943 & 0.95738 & 0.65890 \\
0.07285 & Cr\({}_{\alpha}\) & 0.95614 & 0.95634 & 0.95634 & 0.99292 & 0.98624 & 0.97201 & 0.75266 & 0.95811 & 0.95631 & 0.45683 & 0.01236 & 0.00644 & 0.96364 & 0.40701 \\
0.0460 & Cui & 0.97031 & 0.97030 & 0.97030 & 0.97030 & 0.9984 & 0.99670 & 0.64217 & 0.99396 & 0.57036 & 0.00000 & 0.51696 & 0.43044 & 0.92026 & 0.59200 \\
0.1047 & He & 0.97030 & 0.97030 & 0.97030 & 0.99698 & 0.97034 & 0.99099 & 0.72499 & 0.83444 & 0.74186 & 0.62332 & 0.46502 & 0.56461 & 0.97080 & 0.62030 \\
0.07285 & No & 0.96479 & 0.96479 & 0.96469 & 0.96463 & 0.97935 & 0.97175 & 0.56251 & 0.96899 & 0.02372 & 0.235416 & 0.12269 & 0.04479 & 0.41218 \\
0.07285 & Ni & 0.97965 & 0.99786 & 0.97965 & 0.99792 & 0.99614 & 0.98564 & 0.86975 & 0.86975 & 0.84927 & 0.84246 & 0.83024 & 0.8529 & 0.86797 & 0.50813 \\
0.07285 & By & 0.97965 & 0.97965 & 0.97928 & 0.99614 & 0.98035 & 0.97566 & 0.98406 & 0.92214 & 0.87056 & 0.97594 & 0.98257 & 0.97920 & 0.50796 \\
0.10447 & Se & 0.97368 & 0.97366 & 0.97366 & 0.97988 & 0.98631 & 0.99627 & 0.81604 & 0.82036 & 0.89728 & 0.98402 & 0.98124 & 0.72491 & 0.98400 & 0.76031 \\
0.0460 & Zn & 0.99338 & 0.97383 & 0.98835 & 0.97395 & 0.98833 & 0.81266 & 0.86246 & 0.880807 & 0.01182 & 0.12426 & 0.12236 & 0.95796 & 0.56813 \\
0.0375 & & 0.98927 & 0.99858 & 0.99852 & 0.99712 & 0.97940 & 0.84169 & 0.95513 & 0.72804 & 0.14385 & 0.18690 & 0.31595 & 0.99511 & 0.51149 \\
0.02823 & Cr\({}_{\alpha}\) & 0.99211 & 0.99211 & 0.99211 & 0.951646 & 0.99892 & 0.92167 & 0.99955 & 0.98365 & 0.52329 & 0.99944 & 0.03659 & 0.53012 & 0.99182 & 0.58417 \\
0.02823 & SO\({}_{2}\)\({}^{2}\) & 0.99396 & 0.99497 & 0.98503 & 0.99064 & 0.99945 & 0.99971 & 0.99955 & 0.62936 & 0.81457 & 0.97799 & 0.00551 & 0.28179 & 0.9062 & 0.56986 \\  & EAI & **0.98009** & **0.98010** & **0.97991** & **0.98128** & **0.98128** & **0.97884** & **0.9813** & **0.97317** & **0.91392** & **0.2450** & **0.36459** & **0.95644** & **0.64910** \\ \hline \end{tabular}
\end{table} 
Table 6: Results of the EAI for the studied refractory samples and inert sample.
## 8 Application Challenges and Strategic Directions for Digital Twin Integration
### Barriers to Deployment: Organizational, Technical, and Cultural Factors  
#### 8.2.1 Skills Gap and Organizational Resistance  
A significant obstacle to the deployment of DTs is the shortage of interdisciplinary expertise. Effective DT integration requires the convergence of systems engineering, embedded software, real-time simulation, and cybersecurity--disciplines that are often separated within traditional automotive organizations.  
Moreover, development teams often show inertia toward replacing well-established test procedures with simulation-driven workflows, especially for safety-critical systems. This highlights the need to establish dedicated DT specialists with cross-domain proficiency in model integration, physical-to-virtual synchronization, and secure data handling. Organizational resistance may also stem from perceived risks related to the reliance on virtual models over physical validation. Overcoming this challenge requires the establishment of robust test traceability, quantitative validation metrics, and continuous alignment between DT predictions and empirical outcomes.
## 2 Experimental Procedures  
Cylindrical samples used in these experiments were drilled from the same block of anthracite, a block collected from an open-pit mine located in Hazle, Pennsylvania, USA. The drilling direction was kept perpendicular to the beddings. The desired diameter to height ratio for the cores was 1:2. However, several trial drillings demonstrated that the extremely cataclastic texture of the anthracite block did not make this possible. By reducing the ratio to 1:45, 11 cores were obtained with nominal dimensions of \(\Phi\)45 \(\times\) 65 mm. Subsequently, the ends of the sample were ground and polished according to ISRM standards [41]. Typical samples prepared for the test are shown in Figure 2. In order to eliminate the influence of water, the samples were oven-dried until their weight remained constant.  
The experiment was conducted in a basement with a constant temperature of 16 \(\pm\) 2 \({}^{\circ}\)C. A series of creep tests under uniaxial compression were performed on a servo-controlled hydraulic testing machine (shown in Figure 3). The machine had a load frame of stiffness 6000 kN/mm and a compression load capacity of 1000 kN with a resolution of 0.5%. Its stroke was \(\pm\)50 mm with a resolution of 1 \(\upmu\)m. The tests were performed using 11 pre-set stress levels ranging from 8 MPa to 18 MPa, each level being increased by 1 MPa. However, because of the inevitable variations in the diameters of the cores produced during drilling, the actual stress values applied in the tests (shown in Table 1) differed slightly. For the tests, creep stress was applied by increasing the loading force to the prescribed level at a constant rate 100 N/s. The pre-set duration of each stress level is 6 h. Commercially available piezoelectric transducers with a 200 kHz main frequency were used for both transmitting and receiving the ultrasonic signals. As illustrated in Figure 3, one piezoelectric transducer, the transmitter, was placed between the upper face of specimen and the fixed end of the hydraulic platen, and the receiving transducer was located between the bottom face of the specimen and the lower (loading) end of the platen. The ultrasonic measurement was acquired and recorded using a digital system consisting of a pulse generator, time measuring circuit, receiver amplifier, and time-display unit. The sampling interval of ultrasonic signal was 3 min.  
Figure 2: Typical samples prepared for the test.
# A Novel STDM Watermarking Using Visual Saliency-Based JND Model
###### Abstract
The visual saliency modulated JND model guided watermarking scheme, where the JND model combined with visual saliency's modulatory effect is fully used to determine image-dependent upper bounds on watermark insertion, allows us to provide the maximum strength transparent watermark. It is widely accepted that incorporating VS features appropriately can benefit JND metric. [PERSON] et al. [18] proposed a simple information hiding algorithm based on Stentiford visual saliency model [19]. [PERSON] et al. [20] proposed visual saliency to modulate just noticeable distortion (JND) profile and guide watermark embedding. [PERSON] et al. [21], presented a watermarking algorithm based on the logarithmic spread transform dither modulation framework (STDM). The authors use the [PERSON]'s model [8] to get the JND threshold, and the AC coefficients are used to compute visual saliency map. However, the simple visual saliency model used can not achieve better prediction performance, and the existing VS-based JND models cannot provide maximum performance in terms of robustness while maintaining the fidelity constraint for a practical watermarking framework. In this paper, we present a novel STDM watermarking scheme based on an effective visual saliency-based JND model. Inspired by our previous watermarking method [14], a new luminance-based JND model [22], which is better correlated with the real visual perception characteristics of HVS, is applied within the VS-based JND model. We claim that the VS map can be used as a feature map to characterize the JND model. The underlying reason is that perceptible JND thresholds can lead to measurable changes of images' VS maps. Consequently, a simple yet very effective VS model is used for the JND task. In our work, the new proposed VS-based JND model is implemented in the STDM watermarking framework. Experimental results show that the proposed watermarking scheme has a superior performance in comparison with existing schemes. The rest of this paper is organized as follows. Section 2 provides a brief introduction of the conventional perceptual STDM schemes. The robust VS-based JND models for the STDM watermarking framework are proposed in Section 3. Section 4 incorporates the proposed model to design an improved STDM-based watermarking scheme. In Section 5, experimental results are provided to demonstrate the superior performance of the proposed scheme. Finally, Section 6 summarizes the paper.
# Volterra Black-Box Models Identification Methods: Direct Collocation vs. Least Squares
## 4 Least-Square Method  
Let us denote \(N>m+m_{1}m_{2}-1\). We have the situation where number of equalities is larger than number of unknown coefficients \(A_{i}\) and \(C_{ij}\). Thus we have the overdetermined system of linear equations with respect to the unknown coefficients \(A_{i},\ i=0,1,\ldots,m-1\) and \(C_{ij},\ i=0,1,\ldots,m_{1}-1,\ j=0,1,\ldots,m_{2}-1\):  
\[y_{k}=\sum\limits_{i=0}^{m-1}A_{i}\beta_{ik}+\sum\limits_{i=0}^{m_{1}-1}\sum \limits_{j=0}^{m_{2}-1}C_{ij}\gamma_{ijk}, \tag{13}\]  
where  
\[\beta_{ik}=\int\limits_{0}^{t_{k}}T_{i}(s)x(t_{k}-s)\,ds,\quad\gamma_{ijk}= \int\limits_{0}^{t_{k}}\int\limits_{0}^{t_{k}}T_{i}(s_{1})T_{j}(s_{2})\,x(t_{ k}-s_{1})\,x(t_{k}-s_{2})\,ds_{1}ds_{2}. \tag{14}\]  
The system is inconsistent. The least-square method is used to find the approximate solution of the system. The point of the method is to find such coefficients \(A_{i}\) and \(C_{ij}\) such that the following criteria is minimized:  
\[\sum\limits_{k=0}^{N-1}\left(y_{k}-\sum\limits_{i=0}^{m-1}A_{i}\beta_{ik}- \sum\limits_{i=0}^{m_{1}-1}\sum\limits_{j=0}^{m_{2}-1}C_{ij}\gamma_{ijk}\right) ^{2}\longrightarrow\min. \tag{15}\]