esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Atmospheric Pollutants and Weather Conditions on Agricultural Productivity in Italy
## 2 Materials and Methods
### Crops Analysis  
Crop yield production values are reported in the following figures (Figures 6-8) to analyze the production behavior during the years considered.  
Figure 4: Annual particulate matter (black solid line), ozone (black dashed line), nitrogen dioxide (grey solid line), and sulfur dioxide (grey dashed line) concentrations measured in Padua during 2012–2021. Sulfur dioxide is reported on the secondary axis.  
Figure 5: Annual particulate matter (black solid line), ozone (black dashed line), nitrogen dioxide (grey solid line), and sulfur dioxide (grey dashed line) concentrations measured in Venice during 2012–2021. Sulfur dioxide is reported on the secondary axis.  
Figure 6: Yield production of durum wheat in Palermo and Bari during the period 2012–2021.  
The duration wheat production in Palermo has remained almost constant in the years considered (2012-2021), while in Bari, there is a decrease between 2012 and 2015 and a peak in 2016 which, however, already shows a decline from the following year which remains constant until 2021. Crop yields appear to be higher in Palermo, as this ratio translates into a higher production of durum wheat per hectare of land in Palermo than in Bari, even though the hectares of land in Bari increased from 2017 to 2021. It is only in 2016 that the crop yields in Bari are higher than those in Palermo (Figure 6).  
The production of common wheat in Padua increased in the years 2013-2016 with a slight decrease until 2019. In 2020-2021, the production in Padua increases again. The city of Ferrara has had an increase in hectares which has led to a linear increase in production. Instead, the city of Padua had an increase in cultivated hectares in 2018, before recording a low in 2019 and a drop in production. Over the years, the production in the city of Ferrara has been high except for 2012-2014 when the crop yields are comparable (Figure 7).  
Corn production in Padua and Venice appears to be similar over the years. The crops of both cities had an increase from 2012 to 2014 and were almost constant over the years (Figure 8). Padua and Venice are close, so the production is similar.
###### Abstract  
During humanitarian crises, social media generates over 30 million multimodal tweets daily, but 20% textual noise, 40% cross-modal misalignment, and severe class imbalance (4.1% rare classes) hinder effective classification. This study presents CLIP-BCA-Gated, a dynamic multimodal framework that integrates bidirectional cross-attention (Bi-Cross-Attention) and adaptive gating within the CLIP architecture to address these challenges. The Bi-Cross-Attention module enables fine-grained cross-modal semantic alignment, while the adaptive gating mechanism dynamically weights modalities to suppress noise. Hierarchical learning rate scheduling and multidimensional data augmentation further optimize feature fusion for real-time multiclass classification. On the CrisisMMD benchmark, CLIP-BCA-Gated achieves 91.77% classification accuracy (1.55% higher than baseline CLIP and 2.33% over state-of-the-art ALIGN), with exceptional recall for critical categories: infrastructure damage (93.42%) and rescue efforts (92.15%). The model processes tweets at 0.083 s per instance, meeting real-time deployment requirements for emergency response systems. Ablation studies show Bi-Cross-Attention contributes 2.54% accuracy improvement, and adaptive gating contributes 1.12%. This work demonstrates that dynamic multimodal fusion enhances resilience to noisy social media data, directly supporting SDG 11 through scalable real-time disaster information triage. The framework's noise-robust design and sub-second inference make it a practical solution for humanitarian organizations requiring rapid crisis categorization.  
humanitarian crisis classification; contrastive learning; bidirectional cross-attention; adaptive gating; multimodal fusion; cross-modal alignment; CrisisMMD dataset; real-time inference
# A Study on DNN-Based Practical Model for Predicting Spot Color
## 1 Introduction
### Related Works
The diversity of packaging formats has led companies to use a variety of inks to achieve accurate color reproduction on different types of substrates, some with the same CMYK but different properties, and some with no pigments and only additives. Spot color inks, which are blends of these inks, may contain different spot colors in addition to the basic CMYK and additives. There are also slight differences between inks of the same CMYK, depending on the manufacturer, so decisions in the color mixing process need to be able to cover a wide range of inks, but there is a lack of research in this area. In painting, another field where color reproduction is important, [PERSON] et al. [35] proposed a neural network-based model to predict the reflectance and transmittance of a mixture of 18 pigments for water-based painting, and [PERSON] et al. [36] built a deep neural network (DNN)-based smart palette system with 13 pigments for novice painters who find it difficult to create the desired color, but they differ from the color matching process in printing in that only two colors are used in each mixture. [PERSON] et al. [37] modeled a DNN that predicts the color of mixed materials by considering the mixing of multiple glazes and pigments in the ceramic industry and proposed an optimization strategy to improve the prediction performance, but it is difficult to assume the same situation because the mixture is applied to a non-paper substrate. Therefore, this study aims to present a practical prediction model that uses data collected during the color mixing process for actual printing without making assumptions about specific situations. The prediction is achieved by exclusively using the usage information of the inks involved in the color mixing process. This approach simplifies the prediction process and provides an efficient way to predict the results of color mixing process. Rather than a comprehensive consideration of the different environments of all packaging printers, we present an approach based on the company's data as a starting point that can be applied in the future by any company with data in a similar field.
# Computational Vibro-Acoustic Time Reversal for Source and Novelty Localization
## 5 Examples
### Novelty Localization Examples
#### 5.2.3 Novelty Case C  
In this example, we are trying to see whether it is possible to detect novelties at a structure by exciting the medium with forces resulting from the recorded structural kinematic responses (accelerations, velocities or displacements) on another one. Thus, in this section, we are actually examining the possibility of detecting a novelty at one beam by emitting signals recorded at the other beam. All five emitters of the horizontal beam, which had been recording displacements, are the only ones made active, while the defect to be detected is at the top end boundary condition of the vertical beam, which is made free in the backward step of the experiment.  
The pressure time history for a simulation duration of 4k steps at the top point of the vertical beam, is shown in Figure 15. The absolute pressure of the entire domain at time step 3632 (time instance = 1.210 ms), is depicted in Figure 16. Again, pressure activity is evident at the top end of the vertical beam. We should mention that if the kinematic signals recorded at the beams (or at any other structure), are going to be used solely, or in combination with other emitters in the domain, for an ample excitation of the medium at the backward stage, an adequate level of signal amplification should definitely be considered. The amount of amplification should depend on the radiation efficiency of the structure under consideration.  
**Remark 4**.: _Our numerical results illustrate that the proposed methodology allows for novelty detection and localization. Receivers may be placed in the acoustic part and/or the structural component. Qualitatively different refocusing is observed for different types of defects. Two defect types were considered here a support failure modeled by modifying the BC at the end of a beam and a beam that is damaged in the interior modeled by modifying the Young modulus. Future work  
Figure 16: The absolute pressure on the whole domain at time 1.210 ms. Beams are plotted with light blue color. Pressure activity is evident at the top end of the vertical beam.  
Figure 15: Pressure time history at the top end of the vertical beam.  
may exploit these different qualitative focusing properties to distinguish between different types of defects._
# DroidDetectMW: A Hybrid Intelligent Model for Android Malware Detection
## 5 Experiments
### Classification Results Based on Hybrid Features
\begin{table}
\begin{tabular}{r c c c c c c c c} \hline \hline
**Algorithm** & **K-NN** & **SMO** & **SVM** & **RF** & **DT** & **NB** & **MLP** & **Proposed** \\ \hline Accuracy (\%) & 0.946 & 0.915 & 0.919 & 0.946 & 0.942 & 0.923 & 0.931 & 0.969 \\ FPR (\%) & 0.068 & 0.096 & 0.101 & 0.063 & 0.063 & 0.095 & 0.082 & 0.042 \\ TPR (\%) & 0.965 & 0.930 & 0.946 & 0.957 & 0.949 & 0.946 & 0.947 & 0.983 \\ Precision (\%) & 0.917 & 0.883 & 0.875 & 0.925 & 0.925 & 0.883 & 0.900 & 0.950 \\ F-measure (\%) & 0.940 & 0.906 & 0.909 & 0.941 & 0.937 & 0.914 & 0.923 & 0.966 \\ MCC (\%) & - & - & - & - & - & - & - & - \\ AUC (\%) & 0.924 & 0.894 & 0.887 & 0.931 & 0.931 & 0.894 & 0.909 & 0.954 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: The effectiveness of integrated feature selection for malware category classification. Figure 5: Classifiers for binary malware classification were compared based on (**a**) Accuracy and (**b**) MCC with dynamic, static, and integrated features. It demonstrates that the integrated approach outperforms the dynamic and static features separately for all classifiers.
## 3 Results and Discussion
### Effect of Chloride Ions on Electrical Resisitvity
The ratio was 1.0 when the carbonation depth was about 5 to 8 mm. This means that whether carbonation or chloride ions has a greater effect on the electrical resistivity of concrete should be determined at a carbonation depth of 5 to 8 mm. The ratio of the electrical resistivity of the carbonated concrete (\(\rho_{comb}\)) to that of non-carbonated concrete (\(\rho_{water}\)) was derived by linear regression, as follows:  
\[\frac{\rho_{comb}}{\rho_{water}}=a\cdot x_{c}+b \tag{4}\]  
where \(x_{c}\) is the carbonation depth (mm), \(a\) and \(b\) are constants (\(a\) = 0.0187, 0.0348 for chloride 1.0%, 2.0%, respectively; \(b\) = 0.8547, 0.8177 for chloride 1.0%, 2.0%, respectively). As shown in this study, a trend of the electrical resistivity of concrete under the combined deterioration of carbonation and chloride ions was not clear. Carbonation increases the electrical resistivity, while the chloride ions reduce the electrical resistivity. Therefore, when concrete is damaged by the combined deterioration of carbonation and chloride, the mutual interference effect causes collision, which depends on the carbonation depth, degree of contamination of the chloride, and the degree of hydration. In the future, it is necessary to investigate the mutual interference effect of the combined deterioration as hydration progresses.
# Wire-Grid and Sparse MoM Antennas: Past Evolution, Present Implementation, and Future Possibilities
## 5 Wire-Grid Sparse Antennas
### Occa Method  
#### 5.4.1 General OCGA  
Using the general OCGA method described above, the sparse antenna was obtained for the modeled antenna from [301]. Two different sparse antennas were obtained only to demonstrate the difference between the resulting sparse structure at each frequency. The current matrix elements were normalized with respect to the maximum current magnitude in structure wires. These sparse structures were obtained taking into account the GEET value equal to 10%. All the elements in which the current value was smaller than 10% of the maximum one were deleted. The resulting structures are presented in Figure 9. The antenna gain calculated using the wire grid (full structure) and those obtained using the general OCGA (sparse structure) are compared and presented in Figure 9 at 5.1 GHz and 5.9 GHz. The results obtained using the sparse antenna comparing to the measured ones and those obtained using the wire grid are consistent. The differences between them can be controlled by the GEET value. Moreover, the difference might arise with the frequency increase as shown in Figure 9. By comparing the full wire-grid structure and the sparse one, we found that the antenna mass reduced by 1.51 times at 5.1 GHz and by 1.42 times at 5.9 GHz. Meanwhile, the computational time spent on antenna modeling also reduced by 3.44 times at 5.1 GHz and by 2.88 times at 5.9 GHz. The required memory also reduced by 2.28 times at 5.1 GHz and by 2.02 times at 5.9 GHz.  
Figure 7: Isometric view of the parabolic antenna structure (**a**); and the wire-grid modeled structure of the parabolic modeled antenna (**b**).
## 4 Discussion
The detectable mode range is proportional to \(l_{0}/n^{2}\), while the resolving power is inversely proportional to \(l_{0}/n^{2}\). With \(l_{0}/n^{2}\) decreasing, the mode resolving power for superimposed vortices increases, but it reduces the range of detectable modes. Therefore, the coefficients \(l_{0}\) and n can be selected in practical applications according to the requirements of detection range and resolution. For broadband PMTs and vortex sorting, we carried out simple simulation verifications for four wavelengths of 480, 532, 633, and 780 nm, although the focal length is wavelength dependent, and the focal depth is long enough that we can choose a transverse plane across all the focal planes. The results are not significantly different from the spin-decoupled metasurface [37], and it is possible to simultaneously detect wideband vortices covering the entire visible band, which greatly increases its potential application value. For example, there are two main ways to use vortex light in optical communication systems: one is to use vortex beams of the different OAM states as different carriers to transmit information (OAM division multiplexing); another method is to use different OAM states in the vortex as symbols of information transmission for data transmission (OAM coding). Therefore, the system that can easily and quickly identify the large capacity of the vortex with different OAM states has important potential application value for increasing the capacity of optical communication systems.
## 2 Mathematical Formulation
### Constraints
Equation (14) introduces a constraint to ensure that the voltage magnitude at each node remains within predefined operational limits, a necessary condition for maintaining the stability and reliable operation of the system. These voltage limits are established in accordance with the national regulations applicable to the distribution system operator, ensuring compliance with local technical standards and regulatory frameworks. In this equation, \(V_{i}^{\min}\) and \(V_{i}^{\max}\) denote the minimum and maximum permissible voltage levels at node \(i\), respectively. \[P_{i}^{gc,\text{min}}\leq P_{i,h}^{gc}\leq P_{i}^{gc,\text{max}} \tag{6}\]1  
As the final constraint, Equation (15) ensures that the current flowing through each line of the MG does not exceed its nominal capacity, thus preventing overloading and contributing to the secure operation of the system. This current limit is determined by the constructive characteristics of the MG, as defined during its design and construction phases, and reflects the thermal and operational capacity of the distribution infrastructure. \[P_{i}^{gc,\text{min}}\leq P_{i,h}^{gc}\leq P_{i}^{gc,\text{max}} \tag{6}\]2  
Here, \(I_{ij,h}\) refers to the current in the line between nodes \(i\) and \(j\) at hour \(h\), while \(I_{ij}^{\max}\) denotes the maximum current capacity of the line, beyond which its integrity may be compromised. These constraints, together with the objective functions designed to minimize operating costs, energy losses, and \(CO_{2}\) emissions, constitute the core of the proposed optimization framework. This mathematical model allows for a realistic and technically sound representation of the operation of wind-based MGs under both grid-connected and islanded conditions. The formulation captures the essential dynamics and operational decisions associated with active and reactive power dispatch. Moreover, the inclusion of detailed technical constraints, such as power balance, voltage and current limits, and generation capabilities, ensures that all optimization results remain within the real physical and regulatory boundaries that govern the operation of practical AC MGs. As such, this framework constitutes a robust tool for analyzing and improving the performance of MGs while respecting their actual operational limitations.
## Appendix B Calculating Translational Velocity, Momentum, and Rotational Energy  
All translational kinetic and kinematic calculations were based on the trajectory of the whole-body CoM. The three-dimensional velocity vector (\(\overrightarrow{v}\)) expressed the time-normalized frame-to-frame changes in CoM coordinates:  
\[\overrightarrow{v}=\left(\frac{dx}{dt},\frac{dy}{dt},\frac{dz}{dt}\right)\]  
Scalar velocity (\(v\)) was also calculated frame-by-frame as  
\[v=\sqrt{\overrightarrow{v}x^{2}+\overrightarrow{v}y^{2}+\overrightarrow{v} z^{2}}\]  
Translational momentum (\(\overrightarrow{p}\)) was obtained from body mass (\(m_{b}\)) and vector velocity:  
\[\overrightarrow{p}=m_{b}\cdot\overrightarrow{v}\]  
Translational kinetic energy (\(E_{t}\)) was calculated from body mass and scalar velocity as  
\[E_{t}=\frac{1}{2}.m_{b}\cdot v^{2}\]
# Congestion Control Mechanism Based on Backpressure Feedback in Data Center Networks
## 4 Bfcc
### Rate Reduction Module
#### 4.2.1 Admission Control  
Traffic priority scheduling is a network traffic management method used to prioritize different flows in order to correctly allocate bandwidth and resources in the network. By setting flow priorities, the network can cater to critical applications, provide a better user experience, and ensure that network services are fair and efficient. The priority queue is a simple and straightforward traffic scheduling method that divides packets into different queues based on their priorities. When transmitting packets, the device first queues out the packets in the high-priority queue and then processes the packets in the low-priority queue. This approach ensures a fast transmission of high-priority packets.  
With the concept of priority scheduling, when receiving the backpressure feedback information from the downstream, the switch immediately reacts and starts the scheduling strategy. According to the idea of priority scheduling, the congested flows are classified as low priority while the non-congested flows go to high priority. As illustrated in Figure 7, a delayed sending of congested flows is achieved using high and low priority scheduling. When the scheduling is enabled for later incoming traffic, the ingress packet is inserted into a field called \"state\", which is used to keep track of whether or not a slowdown has been enabled, with 0 being \"False\" and 1 being \"True\". In the switch traffic manager (TM), the priority queue is equivalent to a black box, and the low-priority pair is the speed-down queue. Traffic that contributes significantly to downstream congestion is classified upstream into the speed-down queue, while other traffic is sent quickly into the high-priority queue at the previous speed.  
Assigning high priority ensures the faster forwarding of non-congested traffic, providing them with a higher bandwidth allocation, while low-priority traffic can be restricted or adjusted according to network congestion to prevent congested flows from placing a greater burden on the downstream traffic. With the help of this method, access control is introduced in the ingress pipeline to perform the first speed reduction of congested flows, with the specific algorithm outlined in Algorithm 1.  
Figure 7: Virtual queues and admission control at the data plane. This scheme achieves the first speed reduction.  
```
1:Packet p, Backpressure\(-\)Feedback\(-\)Packet bfp, Virtual\(-\)Queue vq;
2:None
3:if\(bfp.congestion\_degree\)!= 0 and \(bfp.vq\)!= 0 then
4: Set \(packet\_state\);
5:if\(p.vq\)==\(bfp.vq\)then
6: Set high priority;
7:else
8: Set low priority;
9:endif
10: Set high priority;
11:endif
12: Update register of the virtual queue to count packets.
```  
**Algorithm 1** Pseudo-code of the BFCC algorithm implemented at the ingress pipeline of the PDP
# Experimental Measurements of a Joint 5G-VLC Communication for Future Vehicular Networks
## 2 VLC-5G Integration System Model  
Before describing the 5G field trial, in this section we present the system model of the VLC-5G integration used in the field-trial that will be detailed later. The model is presented in Figure 1. In particular, the VLC segment is provided by the traffic lights of a (smart) city, while the 5G network provides the connectivity to the traffic lights.  
Let us assume that a set {1,..., \(s\),..., _S_} of traffic lights are serving a set {1,..., \(u\),..., _U_} of vehicular users. Each traffic light is connected to 5G network and it is capable to generate a VLC signal towards the incoming vehicles. Each traffic light is assumed to be within the coverage of a 5G cell. Vehicles are assumed to be equipped with a VLC receiver. Since each traffic light has a directive beam, we assume that traffic lights do not interfere each other, i.e., the VLC bandwidth can be reused.  
The signal-to-noise ratio (SNR) for the _u_th user served by the _s_th traffic light through a VLC link can be written as [14].  
\[\mathbf{\gamma_{u,s}}=\frac{\mathbf{R_{PD}H_{u,s}P_{s}}}{\xi} \tag{1}\]  
where \(\mathbf{R_{PD}}\) denotes the responsitivity of the photodiode, \(\mathbf{H_{u,s}}\) is the line-of-sight (LOS) VLC channel gain between the _s_th traffic light and _u_th vehicle, \(\mathbf{P_{s}}\) is the transmission power, and \(\xi\) denotes the cumulative noise power. The channel gain \(\mathbf{H_{u,s}}\) depends on several variables: photodetector area, angle of irradiance, angle of incidence, signal transmission coefficient of the optical filter, refractive index of the optical concentrator, user's field of view (FOV), and the distance \(d\) between the traffic light and the user vehicle.