esa-sceva/satcom-chunk-collection huggingface dataset:
# P D N: A Priori Dictionary Network for Fashion Parsing
## 4 Conclusions  
In this paper, we present a priori attention module-based CNN method for fashion parsing. First, we extracted features from training images and built a priori dictionary to store the features. Then, we attached the priori dictionary to a multi-scale backbone derived from VGG-16 and ResNet. Furthermore, a joint loss function that is composed of L2 loss and cross-entropy loss was proposed to guide the model training. We conducted our model on four public datasets, and the experiment results show that the CNN model can effectively annotate different types of targets by introducing our latent features, with mIOU values of 73.99, 79.91, 69.88 and 45.12 on the four datasets, respectively. Through a fair comparison with other State-of-the-Art models, we demonstrate that the PDN can achieve competitive results for fashion parsing. Additionally, the ablation study shows that the priori attention module and joint loss function improve the performance of the model. Although the PDN model can obtain precise parsing results on high-quality annotated datasets, the feature templates cannot lead to a significant improvement in the performance of predicting pixel labels for small targets. In future work, we will explore the use of priori knowledge introduction to improve parsing accuracy in more complex backbone models.  
Conceptualization, J.H. and Y.L.; methodology, J.H.; software, J.H. and Y.L.; validation, Y.Y.; formal analysis, Y.Y.; investigation, J.H. and Y.L.; resources, Z.L.; data curation, Y.L.; writing--original draft preparation, J.H.; writing--review and editing, J.H. and Y.L.; visualization, Y.Y.; supervision, Z.L.; project administration, Z.L.; funding acquisition, Y.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Zhejiang Sci-Tech University of Technology Research Initiation Fund, grant number 21072325-Y.  
Not applicable.  
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 conflicts of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Priori Attention** & **Joint Loss Function** & **Accuracy** & **mIoU** \\ \hline  & & 48.32 & 36.27 \\ \(\surd\) & & 98.81 & 67.98 \\  & \(\surd\) & 59.57 & 25.39 \\ \(\surd\) & \(\surd\) & **99.26** & **73.99** \\ \hline \end{tabular}
\end{table}
Table 6: Ablation experiments of each module. \(\surd\) indicates that the module was used.
## 2 Physical Modelling
### Research Material  
The model trench has dimensions of a rigid container measuring 0.3 m \(\times\) 0.3 m \(\times\) 0.5 m. The composition of SCP is as follows: sand with particle sizes greater than 0.25 mm accounts for 23% of the total weight, sand with particle sizes between 0.18 mm and 0.25 mm constitutes 70% of the total weight, and sand with particle sizes smaller than 0.18 mm makes up 7% of the total weight. Sand compaction is performed in six layers using the sand compaction pile (SCP) method, with a compaction density (_DL_) of 90%. The selection of the foundation materials takes into account the pore water pressure and consolidation state in the long-term seabed environment. Polystyrene particles (EPS) are employed, mixed with clay at the optimal moisture content, to replicate the high compressibility and low permeability characteristics of marine clay [45]. The polystyrene particle (EPS) foam has a particle size of 0.3 mm, with specific characteristics as detailed in Table 1. The  
Figure 1: Experimental equipment: (**a**) model scale; (**b**) schematic diagram of the equipment.  
Figure 2: Arrangement angle of the SCP.  
physical properties of the artificially deposited seabed soil in the experiment are presented in Figure 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Foundation**} & \multicolumn{2}{c}{**Sand**} \\ \hline
**Properties** & **Value** & **Property** & **Value** \\ \hline Particle size \(d_{\text{EFS}}\) (mm) & 0.3 & \(\gamma_{1}\) (\(d>\) 0.25 mm) & 0.23 \\ \hline Volume ratio \(V_{\text{soil}}\):\(V_{\text{EFS}}\) & 8/1 & \(\gamma_{2}\) (\(d\) = 0.18–0.25 mm) & 0.7 \\ \hline Permeability (m/d) & 0.0016 & \(\gamma_{3}\) (\(d<\) 0.18 mm) & 0.07 \\ \hline Void ratio \(e\) & 1.07 & Permeability (m/d) & 2.592 \\ \hline Moisture content \(W_{\text{s}}\) & 0.216 & Pile depth \(L\) (m) & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of sand piles and foundations.  
Figure 3: Basic physical properties: (**a**) gradation curve; (**b**) shear strength.  
At the pile-soil interface shown in Figure 1b, soil pressure box sensors, displacement sensors, and strain gauges are evenly distributed along the depth direction, as depicted in Figure 2. The stress and deformation within the plastic regions of the piles are measured under different water pressures and SCP arrangement angles. The seismic input for the model is selected from the PEER Ground Motion Database-NGA west2, and time-history analysis is performed using data from the Hexi Lin Earthquake Observatory in Greece. The earthquake has an intensity of 7.5, a characteristic period of 0.40 s, a damping ratio of 0.05, and a peak acceleration of 0.05 g, categorizing it as a major seismic event. This information is illustrated in Figure 4.
# Vitamin C in Plants: From Functions to Biofortification
## 6 Vitamin C Biofortification
### Manipulation of Regulatory Networks  
Despite the many results obtained by overexpressing vitamin C-related genes, limited success has been reported in most species. In light of this, attention has shifted to the manipulation of components of the regulatory network, such as transcription and regulator factors. The overexpression of ERF98, which is a positive regulator of GMP, GGP, and GLDH genes, enhanced vitamin C content and increased tolerance to salt stress in _Arabidopsis_[223]. Similarly, tomato plants overexpressing HZ24, a transcriptional factor that binds the promoters of GMP, GME2, and GGP, showed increased vitamin C levels and reduced sensitivity to oxidative stress [196]. Overexpression of the regulator factors KONJAC1 and 2, which are two nucleotide sugar pyrophospholase-like proteins that modulate GMP activity, led to an increase in vitamin C content in _Arabidopsis_[224]. _Arabidopsis_ and tomato plants, overexpressing the regulator factor SIZF3, showed inhibition of GMP degradation by COP9 signalosome, with a consequent enhancement of vitamin C content and tolerance to salt stress [225]. A new transcription factor bHLH59, which can activate the transcription of PMI, PMM, and GMP 2-4 and colocalize with the vitamin C QTL TFA9, has been identified in tomato. The overexpression of bHLH59 causes vitamin C accumulation and increases oxidative stress tolerance. The differences in vitamin C accumulation within different tomato accessions is ascribed to nucleotide differences in the promoter region ofHLH59. This finding could be used to plan breeding strategies for vitamin C improvement [226].
# New Method to Estimate Rock Mass Deformation Modulus Based on BQ System
## 4 Estimation of Deformation Modulus Based on BQ
\begin{table}
\begin{tabular}{c c} \hline \hline
**Correlations (E\({}_{\text{m}}\) in GPa)** & **Estimation Formulas** \\ \hline \(\text{E}_{\text{m}}=2\text{RMR}-100\) & \(\text{E}_{\text{m}}=0.3282\text{BQ}-126.51\) & (7) \\ \(\text{E}_{\text{m}}=10^{(\text{RMR}-10)/40}\) & \(\text{E}_{\text{m}}=10^{(0.0041\text{BQ}-0.5814)}\) & (8) \\ \(\text{E}_{\text{m}}=300\text{Exp}\left(0.07\text{RMR}\right)\times 10^{-3}\) & \(\text{E}_{\text{m}}=0.3\text{exp}\left(0.0115\text{BQ}-0.9279\right)\) & (9) \\ \(\text{E}_{\text{m}}=0.1(\text{RMR}/10)^{3}\) & \(\text{E}_{\text{m}}=0.1(0.0164\text{BQ}-1.3256)^{3}\) & (10) \\ \(\text{E}_{\text{m}}=0.0097\text{RMR}^{3.54}\times 10^{-3}\) & \(\text{E}_{\text{m}}=97(0.1641\text{BQ}-13.256)^{3.54}\times 10^{-7}\) & (11) \\ \(\text{E}_{\text{m}}=0.0736\text{Exp}(0.0758\text{RMR})\) & \(\text{E}_{\text{m}}=0.0736\text{Exp}(0.0124\text{BQ}-1)\) & (12) \\ \(\text{E}_{\text{m}}=100\text{Q}_{\text{c}}^{1/3}\) & \(\text{E}_{\text{m}}=10\text{Q}_{\text{c}}^{1/3}\) & \(\text{E}_{\text{m}}(0.118\text{Q}-4.217)(\sigma_{\text{c}}/100)\) & (13) \\ \(\text{E}_{\text{m}}=100\left(\frac{1-\text{D}/2}{1+e^{(\text{i}+250-(\text{i} +250-(\text{i}+164\text{BQ})/11)}}\right)\) & \(\text{E}_{\text{m}}=100\left(\frac{1-\text{D}/2}{1+e^{(\text{i}+250-(\text{i} +164\text{BQ})/11)}}\right)\) & (14) \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Estimation formulas of deformation modulus based on BQ. Figure 4: Empirical equations for predicting rock mass deformation modulus compared with both measured mean values (Code 1) and suggested values (Code 2). * In Formula (13), \(\sigma_{\text{c}}=100\) MPa; in Formula (14), D = 0.  
and (13) provide a relatively low predicted value; and the other formulas have different limitations or deficiencies. For the engineering of high dam foundations where the load is heavy, only Formula (3) (Figures 3 and 4) provides a good fit for the values suggested by Code 2; most of the other formulas predict an excessively high value. Based on the above, it is proposed that Formulas (8) and (11), and especially Formula (10) (given its exceptionally good fit to the mean measured Code 1 values), should be used to predict the deformation modulus of rock masses in general rock mass engineering; for rock projects where there is a large load, Formula (3), which was established in this paper, is recommended. The above established formulas originate from different engineering projects and involve the transformation of different related relationships. Therefore, although the aforementioned formulas can predict the deformation modulus of rock masses well, there may still be certain inaccuracies in certain situations. In view of this, further analysis and verification using some numerical methods may help to correctly understand the deformation characteristics of rock masses.
# Adjacency-Information-Entropy-Based Cooperative Name Resolution Approach in ICN
## 4 Proposed Approach
### Latency Neighbor Discovery  
To perform cooperative name resolution, each name resolution node must first discover its latency neighbors accurately. Since the centralized management structure of the distributed system has limitations in scalability, the nested tree structure in LNMRS is constructed through the autonomy of nodes, and ACNR uses the same distributed conception for the discovery of latency neighbors. At each name resolution node, the node is responsible for providing services such as name mapping registration, resolution, and deregistration. In addition, each node also maintains a structure management module that has the functionality to perform latency measurements and structure construction algorithms. The discovery function of latency neighbor is also carried through this structure management module, where latency neighbors are maintained in the form of a list. After each name resolution node completes the construction of the main structure of the nested tree, it will perform the following initialization process and discovery process in order.
# Microwave Staring Correlated Imaging Based on Unsteady Aerostat Platform
## 2 MSCI Method Based on Unsteady Aerostat Platform  
### Imaging Scene  
MSCI can be realized by using a multi-transmitter configuration to transmit time-independent and group-orthogonal waveforms. To realize observation of targets on ground, MSCI radar can be raised to the air by a tethered aerostat. As shown in Figure 1, the antenna array with \(N\) transmitters and one receiver at its array center is carried by AVS which is able to control the antenna beam orientation, and POS is placed at the center of the array to dynamically measure its position and the attitude during the imaging process.  
To illustrate the geometry of the imaging scene, as the earth-surface inertial reference frame, the coordinate system \(O_{t}X_{t}Y_{t}Z_{t}\) is established, with its origin \(O_{t}\) located in the projection point of the array center on the ground on \(O_{t}X_{t}Y_{t}\) plane at the beginning imaging time, its \(X_{t}\) axis pointing to the east along local latitude line, its \(Y_{t}\) axis pointing to the north along local meridian and its \(Z_{t}\) axis pointing upward along the local geographic vertical line.  
The independent signal of random frequency hopping transmitted synchronously by all transmitters and the signal transmitted by the \(n\)-th transmitter is denoted as  
\[s_{n}(t)=\sum_{l=1}^{L}rect[\frac{t-(l-1)T_{p}}{T}]\exp\{j2\pi f_{nl}[t-(l-1)T _{p}]\}, \tag{1}\]  
where \(f_{nl}\) is the frequency of the \(l\)-th pulse emitted by the \(n\)-th transmitter and randomly selected within the system bandwidth. \(rect(t)\) is rectangular function. \(L\) is the total number of pulses. \(T_{p}\) denotes pulse repetition interval and \(T\) is pulse width.  
Figure 1: Imaging geometry of MSCI based on unsteady aerostat platform.  
During the imaging process, POS will dynamically record the position and the attitude of the antenna array. The attitude of the array Euler angles includes yaw angle, pitch angle, and roll angle. To give definition of these angles, the aerostat coordinate system \(O_{b}X_{b}Y_{b}Z_{b}\) is established on the array with its origin \(O_{b}\) located at its array center, its \(X_{b}\) axis pointing to the right along the horizontal axis of the array, its \(Y_{b}\) axis pointing forward along the longitudinal axis of the array and its \(Z_{b}\) axis perpendicular to the radar array plane. The yaw angle \(\theta\) is defined as the angle between the projection of \(Y_{b}\) on the \(O_{t}X_{t}Y_{t}\) plane and the \(Y_{t}\) axis, with the \(Y_{b}\) axis right side being positive. The pitch angle \(\varphi\) is defined as the angle between the \(Y_{b}\) axis and the \(O_{t}X_{t}Y_{t}\) plane, with \(Y_{b}\) axis up side being positive. The roll angle \(\phi\) is defined as the angle between the \(Z_{b}\) axis and the vertical plane containing the \(Y_{b}\) axis, with \(Z_{b}\) axis right side being positive. The graphical diagram for the altitude angles is shown in Figure 2. \(Y^{\prime}{}_{b}\) is the projection of \(Y_{b}\) on the \(O_{t}X_{t}Y_{t}\) plane and \(Z^{\prime}{}_{b}\) is the projection of \(Z_{b}\) on the \(O_{t}Z_{t}Y_{b}\) plane.
## 2 Classical Analytical Formulation for Frequency Calculations
### Restraints and Delaminations for In-Service LG Systems  
A relevant influencing parameter for the vibrational analysis of LG beams is certainly represented by the effect of real restraints, with respect to the ideal supports (Figure 2c).  
It was shown in [12], for example, that the restraints characterized by a certain flexibility (i.e., axial (\(K_{\mathrm{s}}\)) and rotational (\(K_{r}\)) stiffness due to the presence of soft layers, gaskets, etc., see Figure 5) should be properly taken into account for the dynamic analysis of even monolithic glass members with cantilever or beam behaviour, affecting both frequency and damping calculations.  
Figure 4: Example of analytical frequency estimations for double laminated glass (LG) beams, as a function of the restraint type (S-S or C-C) and shear stiffness of the interlayer. The grey region denotes the sensitivity of analytical frequency estimates to the restraints and interlayer stiffnesses.  
The presence of delaminations and their effects on the flexural stiffness of the resisting sandwich section can represent another crucial aspect to account for dynamic analysis of LG members. Critical regions for delaminations are commonly represented by restraints and edges (see, for example, Figure 6 and [36, 37]). However, research studies on the structural performance of delaminated LG sections are still limited (i.e., [38, 39, 40, 41]). In addition, literature investigations are focused on the stress response of simple LG members under laboratory conditions, which is with artificially imposed delaminations.  
In this regard, Equation (2) is expected to provide only approximate estimates of the actual dynamic performance of in-service LG members, thus suggesting the need of more refined methods of analysis.
## 4 Wireless Communication Protocol  
The bandwidth for both on-chip as well as off-chip communication is limited by the transceiver performance and on-chip antenna design. However, adopting efficient wireless medium access mechanism, suitable flow control and routing protocols can significantly improve the bandwidth utilization and hence, performance. In this section, we discuss various flow control, routing, and MAC mechanisms for contention free, reliable, and energy-efficient wireless communication. The goal of the MAC should also be a seamless integration of the intra-chip and inter-chip wireless data transfer in order to reduce overheads of protocol transfer across different MACs.
## 3 Design of the Stress Wave-Based Communication System
### Hardware Design
#### 3.2.2 PPM Signal Generator Design
The capacitor C36 has a withstand voltage of 600 V.  
Power Converter Circuit Design  
As shown in Figures 7 and 8, the input AC voltage (220 V) is reduced to \(\pm\)5 V DC or \(\pm\)12 V DC by the AC/DC switching power supply module. The +5 V DC is stepped down to +3.3 V via DC-DC to work as the power supply for MCU, and the +12 V DC is used as the power supply for the low-voltage pulse control circuit. On the other hand, another 150 V DC from the AC/DC module serves as the power supply for the charge-discharge circuit. To satisfy the requirement of power supply for all the components in the device, MEW15-S5D12B was selected for AC-DC switching-mode power supply. It has a broad input voltage range (85\(\sim\)265 VAC, 165\(\sim\)265 VDC), and advantages of low cost, high efficiency, high reliability, and safety. The regulator LM117 produced by Texas Instruments (TI) was selected for DC-DC buck module to convert +5 V DC to +3.3 V DC. It has current limiting and thermal protection with the input voltage range of 6.2 V\(\sim\)12 V, and the maximum output current of 800 mA.
# Improvement of 5G Transportation Services with SDN-Based Security Solutions and beyond 5G
## 4 Traffic Measurement with SDN
### Generic Measurements of the Framework
\[\boldsymbol{Error}(e)=\frac{\left|\boldsymbol{D_{del}}(\boldsymbol{t})- \boldsymbol{D_{pre}}(\boldsymbol{t})\right|}{\Delta\boldsymbol{t}} \tag{1}\]  
To maintain the QoS level dependent on the delivered (\(\boldsymbol{D_{del}}\)) and predicted (\(\boldsymbol{D_{pre}}\)) data, the ITS will continue to rely on ubiquitous broadband connectivity, an efficient 5G network, and reliable infrastructure. Additionally, QoS depends on bandwidth, packet loss, and the rate of request data (frequent, confidential, etc.). For instance, intelligent content delivery over wireless via SDN depends on variable or dynamic bandwidth. It investigates thenetwork and packets by sending and receiving a point. Measuring error with a dynamic bandwidth is a challenge that enhances the intelligence in transportation systems. \[\mathbf{D_{pre}}(\mathbf{t})=\mathbf{F}\times\mathbf{D_{pre}}(\mathbf{t})+\mathbf{n} \tag{2}\]  
Here, \(\mathbf{F}\) is the transition matrix from \((\mathbf{t}-1)\) to \(\mathbf{t}\), \(\mathbf{n}\) is the added noise that varies with the communication channel. In the prediction stage, the state of the ITS with uncertainty at the time, \(\mathbf{t}\), is estimated from its previous state at the time \((\mathbf{t}-1)\).
## 6 Discussion and Conclusions
In summary, the enhanced convergence speed of GSO (validated by both objective-value curves and CPU-time comparisons) supports its recommendation as the first choice for structural-weight minimization when both accuracy and run-time efficiency are critical. Beyond raw performance, the native APDL integration itself proved to be a key contribution. By embedding the optimization logic within APDL, file-based data exchange was eliminated, Python-to-ANSYS calling overhead was avoided, and the ANSYS solver state was preserved between iterations. In conclusion, this comparative study highlights two main takeaways. First, among the three metaheuristics, GSO with adaptive ranger movements offers the best trade-off between convergence speed and final weight, followed by FA and then GA. Second, implementing these algorithms directly in APDL not only simplifies workflow integration but also yields measurable gains in computational efficiency--benefits that are especially evident in large-scale, high-fidelity structural models. Future work will explore extending the APDL-based framework to multi-objective formulations (e.g., incorporating both weight and stiffness) and to other structural domains, such as shell or solid-element topologies. Conceptualization, G.M., A.M. and T.I. ; methodology, G.M., V.R. and T.I. ; software, G.M., A.I.M. and A.C.; validation, A.I.M. and A.C.; formal analysis, G.M., V.R., A.I.M. and A.C.; investigation, A.M. and T.I. ; writing--original draft preparation, G.M., A.I.M., A.C., T.I., V.R. and A.M.; writing--review and editing, G.M. ; supervision, T.I. and A.M. All authors have read and agreed to the published version of the manuscript. This research received no external funding. This research received no external funding. Not applicable. Not applicable. Not applicable. **Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author. **Conflicts of Interest:** The authors declare no conflicts of interest.
## 4 Data Collection  
Phantom 3 Pro was used for the data collection. The characteristics of this UAV are as follows: weight, 1216 g, four motors, max. flight time, 25 min. The flight with UAV is performed under suitable weather conditions. The drone contains a built-in ultra HD camera. The camera has an F2.8 lens with a 20 mm focal length and a viewing angle of 94 degrees. The built-in camera is attached to the drone by a three-axis gimbal. The drone itself costs approx. 700-800 Eur; this can be taken as a low-cost solution [43].  
The planned flight was used for comprehensive data collection. The planned flight ensures low data redundancy, selects the optimal points for capturing images (waypoints), and ensures a smooth flight of the desired area. The flight was planned in DJI software, DJI GS PRO for iPad (version 2.0.16), and according to the rules for obtaining high-quality data [43]. The data from the planned flight are collected in the form of individual images with a 60% overlap with each other, a 60 m altitude, and a 90-degree angle of view (perpendicular to the drone); see Figure 5. In addition, the flight itself is subject to legislation on the operation of unmanned aerial vehicles. The rules on flying are administered by the CAA, called UCL Czechia [44].  
Figure 4: Reservation Baroch, data source: [42]. The red box bounds the area of interest.  
Figure 5: Screen from the flight planning software, source: authors.