esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results and Discussions
### Results Discussions
#### 3.2.1 Twitter US Airline Dataset Results  
The confusion matrix of our model based on the Twitter US Airline dataset is presented in Figure 3, together with the mutuality matrix of the \(10\) feature segments.  
The results presented in Figure 3a show that the proposed model classifies negative labels better than both positive and neutral labels. To explain this behavior of the model, we generate the mutuality matrix for the 10 feature segments, as presented in Figure 3b.  
The mutuality between every two feature segments is defined using (20), applying a level 1 Markov property assumption and Axiom 1. This results in the diagonal values next to the leading diagonal values in Figure 3b. The mutuality matrix at this level gives information about the interrelationship between the feature segments. As shown in Figure 3b, most of these relationships are decreasing mutual relationships because, for every two feature segments, \(X_{i}\) and \(X_{j}\), \(M(X_{i},X_{j})<1\).  
We further examined the mutuality of the features in each segment and found that the segment \(X_{10}\) with the highest combined mutuality contained features with semantically negative sentiments and whose combined mutuality was amongst the highest in  
Figure 3: Results with the Twitter US Airline dataset. (**a**) Confusion matrix. (**b**) Mutuality matrix for the 10 feature segments. (**c**) Combined mutuality of each feature in the segment with the highest mutuality.  
all the segments, such as the words \"worst\" and \"wait\" in Figure 3c. This implies that mutuality (and heterogeneity) between the features or feature segments plays an essential role in the classification process of this model; hence, they can be used to explain its classification results.  
The type of semantic distinctions (classification) of features with respect to a class label during label classification is considered in this study to represent the semantic intelligence of the model on the labels, i.e., the ability to understand the meanings of the labels. This implies that training the predictive (causal) intelligence of this model will imply training its semantic intelligence and vice versa. However, one should not expect the semantic logic of the model on the text to always be similar to human semantic logic applied to the same text, since the model may use a different semantic logic from humans, although it is formally trained for human semantic awareness.
# DCAGS-IoT: Dynamic Cross-Domain Authentication Scheme Using Group Signature in IoT
## 4 Analysis of Proposed Protocol
### Efficiency Analysis  
We first analyzed the efficiency of the scheme described in Section 4 in terms of the security parameters. The time complexity of the group public key gpk was \(\mathcal{O}\big{(}\lambda\cdot log^{2}\lambda\big{)}=\widetilde{\mathcal{O}}(\lambda)\), the time complexity of the signature key gsk was \(\mathcal{O}\big{(}\lambda\cdot log^{2}\lambda\big{)}=\widetilde{\mathcal{O}}(\lambda)\), and the size of the signature was \(\mathcal{O}\big{(}\lambda\cdot log^{3}\lambda\big{)}\cdot\omega(log\lambda)= \widetilde{\mathcal{O}}(\lambda)\). Table 3 shows the efficiency comparison between this scheme and other group signature schemes.
## 4 Design and Optimization of Hardware Prototype System
### Hardware Prototype Design  
A hardware platform [26] with the processing core of a digital signal processor (DSP) plus a field programmable gates array (FPGA) is shown in Figure 10. While the DSP specializes in complex calculations and task scheduling, the coprocessor FPGA is good at high-speed digital signal processing in parallel and the interface control. The GNSS RF unit and the MEMS IMU unit are used to receive GNSS signals and inertial data, which are connected to the processor by the I/O of FPGA. The MEMS IMU on the prototype consists of a tri-axis accelerometer (LIS344 ALH), a single-axis gyroscope (LPR510 AL) and a double-axis gyroscope (LY510 ALH). In addition, all units on the platform share the same clock, which can be TCXO, OCXO or other external clocks.  
The scalar-based deeply-coupled system described in Figure 3 is implemented on the hardware platform, and its task assignment is shown in Figure 11. While the FPGA is responsible for GNSS IF data sampling, GNSS baseband correlators, IMU data sampling and preprocessing, _etc._, DSP is used for GNSS baseband control, satellite positioning, INS mechanization, Kalman filter algorithm, the LOS Doppler estimation and tracking loop aiding. Using the sampling clock generated by the FPGA, the down-converted GNSS IF signal is digitized in the RF unit and sent to the baseband for digital signal processing. The correlators with high speed and multi-channels (including carrier NCO, code NCO, code generator, mixer and accumulator) are completed in the FPGA, and the timing module needs to be realized in the FPGA. The accumulating results are passed to DSP via the data bus in every interrupt, and the acquisition and tracking control of all channels are realized by the flexible DSP program. High-speed data exchange between the FPGA and DSP is carried out through the external memory interface (EMIF) of the DSP.  
There are several advantages to implementing the deeply-coupled system on this hardware platform.  
1. All of the function units are triggered by the same clock, which is beneficial for time synchronization.
2. Different types of oscillators, IMUs and GNSS RF units can be chosen for a series of comparative experiments.
3. The high-speed interface is helpful for frequent data exchanging in the deeply-coupled system.  
Figure 11: Integrated GNSS/INS deeply-coupled software architecture. PR represents pseudo-range, CP represents carrier-phase.  
Figure 10: Hardware platform of a GNSS/INS deeply-coupled system. DSP means digital signal processor.  
* All of the software algorithms are realized on the same DSP, which is consistent with the integration feature of the deeply-coupled system.
## 4 Dataset Generation and Simulation Environment
### Experimental Procedure and Stress-Testing Scenarios  
All models were evaluated using a standardized procedure with a participant-level 70/30 train-test split to ensure generalizability. Model performance was assessed not only on clean data but also across four stress-test scenarios designed to simulate realistic data challenges: a Baseline Scenario (unaltered data), a Noise Robustness Scenario (15% of data points corrupted by a multiplicative noise factor), a Missing Data Scenario (15% of data points randomly set to NaN), and an Imbalanced Data Scenario (highly skewed proficiency distribution, e.g., 80% Novice). Performance was quantified using Accuracy and Weighted F1-Score, with the find_state_mapping function ensuring correct alignment of unsupervised model labels with true proficiency states.
# Potential and Limitations of Open Satellite Data for Flood Mapping
## 4 Results
### Manual Mapping of the Flooded Area  
We tested the possibility of mapping the flooded areas of all our case studies manually. In some cases, this mapping was possible, mainly if a co-flood image was available. In general, areas covered by water are clearly visible on multispectral or SAR images, and the manual drawing can be done. In particular, false color images (Figure 4) made by using SWIR-NIR-BLUE bands of multispectral data allow better definition of the inundated areas. The main advantage of visual mapping is the possibility of minimizing errors (e.g., false positives and false negatives) of the semi-automatic classification methods, using geomorphological criteria during drawing. Conversely, the manual mapping is more time-consuming and requires the operator to possess experience in geomorphology and the dynamics of floods.  
Figure 4 shows examples in our study areas of visual interpretation using remote sensing data.  
* In the Ebro valley near Zaragoza (Figure 4A), we used a Landsat-8 co-flood image acquired on 2 March 2018; in this case, the false-color image allowed mapping the entire flooded area.
* In the Po River basin, around Alessandria town (Figure 4C), we used the Proba-V image acquired in the morning of 26 November 2016. In this case, it was possible to map most of the area flooded by Tanaro River, but the area flooded by Bormida River was not clearly detectable.
* In the Po River basin, at the South of Turin city (Figure 4B), the Sentinel-2 post-flood image shows only weak traces, and the semi-automatic classifications better identified the inundated area.
* In the Arahal area (Figure 4D), the Sentinel-2 post-flood image shows flood traces that are very difficult to detect, and the manual mapping fails. Here, we needed to use the automatic extraction of the flooded area for more reliable results.  
In most of the considered cases, the polygon of flooded areas drowned manually needs refinement via DEM and other ancillary data.  
Figure 4: Examples of satellite images characterized by different degrees of difficulty for visual interpretation. (**A**) Ebro valley, Landsat-8 false-color image (SWIR-NIR-BLUE) acquired on 2 March 2015; (**B**) Alessandria area, Proba-V data false-color image (SWIR-NIR-BLUE) acquired on 26 November 2016; (**C**) South Turin area, Sentinel-2 false-color image (SWIR-NIR-RE) acquired on 1 December 2016; (**D**) Arahal area, Sentinel-2 acquired on 8 December 2017 (SWIR-NIR-RE).
## 3 Results and Discussion
### Part Classification Results
#### 3.1.4 Classification Results of Local Features Using SVM and BP Algorithms  
Fruit and background were differentiated by back-projection of the histogram of color and saturation of the average image of the fruit area. In the case of stems and leaves, the canopy and the background of sweet pepper were differentiated using the NDVI operation of images at 735 and 660 nm band wavelengths. After segmenting the image, local features of fruits, stems, leaves, and suckers were extracted using SURF from skeletonized images and differentiated using the SVM algorithm and the BP algorithm. Performances of these two algorithms were then compared.  
A total of 100 images of each region were used for features of the SURF region of fruits, stems, leaves, and suckers. As a result of classifying a total of 400 images through the SVM algorithm using the RBF kernel, the accuracy was 63.75%. Table 5 shows results of classification using SVM for fruits, stems, leaves, and suckers. The precision for fruit was 75% and the recall rate was 73.5%. The precision for stems was 68% and the recall rate was 61.26%. The precision for leaves was 60% and the recall rate was 57.69%. The precision for suckers was 52% and the recall rate was 62.65%. F-measure results for fruits, stems, leaves, and suckers were 74.26, 64.46, 58.82, and 56.83%, respectively.  
Figure 14: Numbers of SIFT features (**a**) and SURF features (**b**).  
Table 6 shows results of 178,750 features collected from 400 images of each class of fruit, stem, leaf, and sucker into a training set and a validation set used to train the BP algorithm. SURF region features were extracted from 400 images including fruits, stems, leaves, and suckers. Of a total of 178,750 features, 10% were used for training. As a result of repeating the learning for 500 generations (epoch) with the learning rate set to be 0.001, the final learning result was 98.46%.  
The accuracy was 95.95% as a result of classifying through the BP algorithm using a total of 400 images with 100 images for each class targeting SURF local features of fruits, stems, leaves, and suckers. Table 7 shows results of classification using the BP algorithm for local features of fruits, stems, leaves, and suckers. The accuracy for local features of fruits was 95.27% and the recall rate was 96.18%. The accuracy for local features of stems was 96.26% and the recall rate was 97.60%. The accuracy for local features of leaves was 95.57% and the recall rate was 93.42%. The accuracy for local features of suckers was 94.47% and the recall rate was 90.94%. F-measure results for local features of fruits, stems, leaves, and suckers were 95.72, 96.93, 94.48, and 92.67%, respectively.  
The accuracy of the BP algorithm was 95.95%, which was higher than that of the SVM at 63.75%. Accuracy, recall, and F-measure were all higher with the BP algorithm than those with the SVM. When the BP algorithm was used to classify SURF local features of fruits, stems, leaves, and suckers, it showed a higher performance than the SVM.
# Multi-Vehicle Tracking Based on Monocular Camera in Driver View
## 3 Proposed Algorithm
### Motion Model
Kalman Filter Update The measurement residual \(\tilde{y}_{k}\) is \[\tilde{y}_{k}=Z_{k}-H_{k}\hat{X}_{k|k-1}.\] (18) The residual covariance \(S_{k}\) is \[S_{k}=H_{k}P_{k|k-1}H_{k}^{T}+R_{k}.\] (19) The Kalman gain \(K_{k}\) is \[K_{k}=P_{k|k-1}H_{k}^{T}S_{k}^{-1}.\] (20) The updated state estimate \(\hat{X}_{k|k}\) is \[\hat{X}_{k|k}=\hat{X}_{k|k-1}+K_{k}\tilde{y}_{k}.\] (21) The updated estimate covariance \(P_{k|k}\) is \[P_{k|k}=(I-K_{k}H_{k})P_{k|k-1}.\] (22)
## 3 Underwater Communication Experiment and Discussion
### Calculation of System Transmission Distance  
Due to limitations in the experimental conditions, the present study employed theoretical calculations to assess the transmission distance, incorporating factors such as system link loss, system loss, and the geometric loss of the optical antenna. Taking clear seawater as an example, with a loss coefficient of 0.151 m\({}^{-1}\) and assuming a transmission distance of 100 m, the underwater channel loss is calculated to be \(-\)65.58 dBm by Equation (6). Due to the size limitation of the receiving aperture that prevents the entire laser beam from entering the detector, there is an additional loss of optical energy known as the geometric loss of the optical antenna. This loss can be calculated as follows [51]:  
\[\tau_{geo}=10\lg\left(\frac{D}{2\times Z\times\tan(\frac{\theta}{2})+D}\right) ^{2}, \tag{7}\]  
where \(\tau_{geo}\) represents the geometric loss; \(D\) is the spot exit diameter; \(Z\) is the transmission distance; and \(\theta\) is the divergence angle. The geometric loss is calculated to be \(-\)3.98 dB for a laser spot diameter of 54 mm and a beam divergence angle of 0.315 mrad under 100 m underwater transmission conditions. The additional loss of the system due to spectral attenuation is \(-\)6 dBm. The total loss is calculated to be \(-\)75.56 dBm. The semiconductor laser provides a power of 5 W, equivalent to 37 dBm. Therefore, after 100 m of underwater transmission, the total power of the detector is \(-\)38.56 dBm. The experimental results demonstrate that system in this study maintains reliable communication when the received light intensity approaches the APD sensitivity threshold of \(-\)46 dBm. As a result, the received power still meets the test criteria for reliable transmission after a distance of 100 m underwater. Subsequently, utilizing the reception threshold of the APD detector as a reference, the maximum transmission distances for the system under various water quality conditions were calculated. The results indicated distances of 111.35 m for clear seawater, 49.60 m for coastal seawater, and 7.66 m for turbid harbor water, respectively.
# Dynamic Simulation Model and Performance Optimization of a Pressurized Pulsed Water Jet Device
## 5 Performance Optimization of the PPWJ Device
### Optimization Process
#### 5.1.4 Optimization Criteria  
Firstly, the peak value of pulse pressure is the most important. Only when the peak value of pulse pressure reaches the threshold pressure of rock breaking, the stress wave effect of pulsed water jet will cause the macroscopic damage of rock.  
Secondly, the nozzle diameter determines the energy contained in the pulse jet, and the magnitude of the jet energy determines the degree of rock breaking. The higher the energy applied to the rock per unit time during rock breaking, the greater the degree of rock breaking. Under the condition of not changing the peak pressure of the pulse, there exists an optimal value for the nozzle diameter, which maximizes the output power of a single pulse.  
Finally, the pulse frequency reflects the number of jet impacts per unit time, and a high pulse frequency is more likely to aggravate the rock breaking.
## 3 Evaluation
### Scenario 1  
In this scenario, the BS is in a corner, which implies that some CHs could be far away from the BS, at the other end of the diagonal of the deployment field. Those further nodes will spend more energy when sending aggregated data to BS. In Figure 6, the plotted data show that CHEF has better values in terms of FND and HND but not in terms of LND. DCAGBS is the best distributed method, and its results are similar to CHEF. In short, DCAGBS adapts very well to this location of the BS, especially for those applications that require a very long lifetime because DCAGBS has the largest LND.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline \(E_{elec}\) & 50 nJ/bit \\ \(E_{DA}\) & 5 nJ/bit \\ \(E_{fs}\) & 10 pJ/bit/m\({}^{2}\) \\ \(E_{mp}\) & 0.0013 pJ/bit/m\({}^{4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Energy consumption coefficients for the first order radio model and for data aggregation.
## 4 Experimental Environment, Dataset, and Parameters
### Dataset
The complete dataset comprises a total of 3000 verified bone stick pairs, corresponding to 6000 high-resolution images, 6000 transcribed inscriptions, and 6000 sets of corresponding archeological metadata. All sample pairings were rigorously validated by expert archeologists to ensure both scientific accuracy and annotation reliability. To enhance the effectiveness of model training and the objectivity of performance evaluation, the dataset was partitioned into training, validation, and test subsets using a standard 7:2:1 split ratio. During the training phase, to improve the model's generalization capability and robustness, the original image data were augmented through a series of data enhancement strategies. These include random translation and scaling operations, designed to increase sample diversity and mitigate the risk of overfitting to specific visual patterns. Moreover, to comprehensively assess the model's performance under different scenarios, two distinct test sets were constructed. Test Set 1 contains bone stick images in which each image has a corresponding matched counterpart, thereby evaluating the model's performance under ideal matching conditions. In contrast, Test Set 2 builds upon Test Set 1 by incorporating unmatched bone stick images, allowing for the evaluation of the model's ability to generalize in the presence of non-matching samples. The detailed image data distribution across the subsets is summarized in Table 2.
## 2 Power Flow Model for Hybrid AC/DC Distribution Network
### Voltage Source Converter Model  
The schematic diagram of the voltage source converter (VSC) is shown in Figure 2.  
As shown in Figure 1, \(r_{i}^{\text{VSC}}\) and \(x_{i}^{\text{VSC}}\) represent the equivalent resistance and reactance of the VSC connected with node \(j\) in the AC network. The branch from node \(j\) to AC side node \(i\) of the VSC can be regarded as the AC branch in the AC network. The VSC model can be described as [20,21]:  
\[P_{i,t}^{\text{VSC(AC)}}=P_{k,t}^{\text{AC}}-(I_{k,t}^{\text{AC}})^{2}r_{i}^{ \text{VSC}}\ \forall i\in\mathcal{N}_{\text{SVC}}, \tag{12}\]  
\[Q_{i,t}^{\text{SVC(AC)}}=Q_{k,t}^{\text{AC}}-(I_{k,t}^{\text{AC}})^{2}x_{i}^{ \text{VSC}}\ \forall i\in\mathcal{N}_{\text{SVC}}, \tag{13}\]  
\[(U_{i,t}^{\text{VSC(AC)}})^{2}=\left(U_{j,t}^{\text{AC}}\right)^{2}-2(r_{i}^{ \text{VSC}}P_{k,t}^{\text{AC}}+x_{i}^{\text{VSC}}Q_{k,t}^{\text{AC}})+\left(I_ {k,t}^{\text{AC}}\right)^{2}\!\!\left[(r_{i}^{\text{VSC}}\right)^{2}+\left(x_{ i}^{\text{VSC}}\right)^{2}\!\right], \tag{14}\]  
\[3P_{i,t}^{\text{VSC(AC)}}=P_{i,t}^{\text{VSC(DC)}}\ \forall i\in\mathcal{N}_{ \text{SVC}}, \tag{15}\]  
\[U_{i,t}^{\text{VSC(AC)}}=\frac{\sqrt{3}}{3}\mu M_{i,t}U_{i,t}^{\text{VSC(DC)}} \ \forall i\in\mathcal{N}_{\text{SVC}}, \tag{16}\]  
where \(P_{i,t}^{\text{VSC(AC)}}\) and \(Q_{i,t}^{\text{SVC(AC)}}\) are the input active and reactive power at the AC side of the VSC while \(P_{i,t}^{\text{VSC(DC)}}\) is the output active power at the DC side of the VSC; \(U_{i,t}^{\text{VSC(AC)}}\) and \(U_{i,t}^{\text{VSC(DC)}}\) are the voltage  
Figure 2: Schematic diagram of the voltage source converter.  
at the AC and DC sides of VSC \(i\); \(\mu\) is the utilization rate of the DC voltage; and \(M_{i,t}\) is the modulation degree of SVC \(i\) within the range of 0 to 1.
## 10 Quantum Dots  
Quantum dots (QDs) are nanoscale semiconductor particles that possess unique optical and electronic properties due to their size, typically ranging from 2 to 10 nanometers. These properties arise from quantum mechanics, specifically the quantum confinement effect, which occurs when the particle size is smaller than the exciton Bohr radius. This confinement leads to discrete energy levels and results in the emission of light at specific wavelengths when excited. The size of the quantum dots determines the color of the light they emit, allowing them to be tuned for various applications by simply changing their size [156].  
QDs hold significant promise in biofilm elimination. QDs can be used in photodynamic therapy (PDT), a treatment that involves the use of light-sensitive compounds (photosensitizers) and light to produce reactive oxygen species (ROS) that can kill bacteria and disrupt biofilms. QDs are excited by light, and transfer energy to oxygen molecules, creating ROS that can damage the biofilm structure and kill the embedded microorganisms. Moreover, quantum dots can be functionalized with various molecules, including antibiotics, peptides, or antibodies, to target specific components of the biofilm or the bacteria within it. This targeted approach ensures that the therapeutic agents are delivered precisely where needed, increasing their efficacy and reducing the likelihood of off-target effects. The small size and surface properties of quantum dots enable them to penetrate the dense extracellular matrix of biofilms. This penetration is crucial for delivering antimicrobial agents directly to the bacteria and for the physical disruption of the biofilm structure. Additionally, the interaction of QDs with biofilms can lead to the release of encapsulated drugs in a controlled manner, ensuring a sustained attack on the biofilm [157; 158].  
QDs are nanoscale semiconductor particles with unique optical properties, making them valuable for imaging, diagnostics, and drug delivery. However, their toxicity and biocompatibility are significant concerns, primarily due to the heavy metals (like cadmium) often used in their core. These metals can leach out, leading to oxidative stress, inflammation, and cytotoxicity. Additionally, the surface coatings used to stabilize QDs can influence their interaction with biological systems, potentially triggering immune responses. To enhance biocompatibility, researchers focus on developing QDs with safer materials, improving surface modifications, and ensuring rapid clearance from the body to minimize potential toxic effects. Comprehensive in vivo studies are essential to assess their long-term safety [180].