esa-sceva/satcom-chunk-collection huggingface dataset:
# Post-Quantum Biometric Authentication Based on Homomorphic Encryption and Classic McEliece
## 1 Introduction  
In electronic transactions, people must be able to prove who they are online. Typically, the person proves that they: (a) know a unique secret ('what the person knows'), (b) have a unique possession ('what the person has') and/or (c) are a physical entity ('who the person is'). The physical entity of a person is defined by his/her biometric characteristics [1], which provide an intrinsic link between the person and the electronic entity without having to know or remember secrets and without the need for possessing a device or card (which can be stolen).  
A biometric recognition scheme usually includes an enrollment phase and a verification phase [2]. At the enrollment phase, biometric characteristics of a person are acquired and discriminative features are extracted and stored as a reference. At the verification phase, features are extracted from biometric queries, which are matched to the stored reference. If during matching, the comparison result (typically a distance measurement) satisfies a threshold, the recognition decision determines that the person is successfully authenticated.
## 2 Improvements and Comparative Analysis of IEEE 802.11 bd
### Key Improvements in Data Transfer Rate  
Compared with the IEEE 802.11p standard, the significant increase in the data transfer rate of the IEEE 802.11 bd standard mainly stems from the wider bandwidth allocation. Alongside continuing the use of the 5.9 GHz frequency band as in the 802.11p standard, the 802.11 bd standard has been allocated channels in the 60 GHz frequency band with a bandwidth of 20 MHz, which is double that of the 802.11p standard. As indicated in Table 1, the impact of doubling the bandwidth on the data transfer rate is evident. When using the same modulation and coding scheme (MCS) and coding rate (R), the 802.11 bd standard achieves an approximate 125% increase in the data transfer rate on the NGV channel with a 20 MHz bandwidth. Even on a compatible communication channel with a 10 MHz bandwidth, an improvement of 8-10% in the data transfer rate can still be achieved due to the advancements in the coding scheme and other fundamental technologies.  
From Table 1, it is evident that the 802.11 bd standard introduces three enhanced data transmission modes (i.e., 64-QAM 5/6, 256-QAM 3/4, and 256-QAM 5/6), providing a wider range of data transfer rate options. This enhancement empowers the 802.11 bd standard to substantially improve the data transfer rate, with a maximum potential increase of up to 3.3 times compared to the 802.11p standard. Additionally, the introduction of MIMO technology provides the option for dual spatial streams (\(N_{SS}=2\)), resulting in nearly a twofold increase in data transfer rates.  
Regarding the length of a single data packet, the wireless channel's data packet length is limited by the maximum allowable duration of the data packet. In specific fields, regulators impose a limit of 10,968 \(\upmu\)s or less on the maximum transmission duration of a single data packet. This implies that higher data transfer rates also raise the maximum packet length. A single data packet transmitted by the IEEE 802.11p standard cannot exceed 4095 bytes, whereas the IEEE 802.11 bd standard increases it to nearly twice this size, at 7991 bytes. The advancements in the single data packet length can lead to significant performance gains, particularly in small-security-class packet applications.
# Image-Based Dedicated Methods of Night Traffic Visibility Estimation
###### Abstract  
Traffic visibility is an essential reference for safe driving. Nighttime conditions add to the difficulty of estimating traffic visibility. To estimate the visibility in nighttime traffic images, we propose a Traffic Sensibility Visibility Estimation (TSVE) algorithm that combines laser transmission and image processing and needs no reference to the corresponding fog-free images and camera calibration. The information required is first obtained via the roadside equipment which collects environmental data and captures road images and then analyzed locally or remotely. The proposed analysis includes calculating the current atmospheric transmissivity with the laser atmospheric transmission theory and acquiring image features by using the cameras and the adjustable brightness target. Image analysis is performed using two image processing algorithms, namely, dark channel prior (DCP) and image brightness contrast. Finally, to improve the accuracy of visibility estimation, multiple nonlinear regression (MNLR) is performed on the various visibility indicators obtained by the two methods. Extensive on-site measurements analysis confirms the advantages of TSVE. Compared with other visibility estimation methods, such as the laser atmospheric transmission theory and image analysis method, TSVE significantly decreases the estimation errors.  
traffic visibility; laser atmospheric transmission theory; image processing algorithms; dark channel prior; multiple nonlinear regression +
Footnote †: journal: Applied Sciences
# Design of Road-Side Barriers to Mitigate Air Pollution near Roads
## 3 Results and Discussion
### Effect of the Barrier Location on Ground Pollutant Concentration  
Then, the effects of varying the barrier location on its ability to disperse air pollutants were studied. Figure 9a shows \(f_{r}\) as a function of barrier location (\(d^{\star}\)) and size (\(h^{\star}\)) for the quarter ellipse barrier oriented toward the road. Figure 9b reproduces Figure 9a in its two-dimensional form. These figures show that \(f_{r}\) increases with \(h^{\star}\) but decreases monotonically with \(d^{\star}\). Again, the base case is when there is not any barrier (\(h^{\star}\) = 0) and therefore \(f_{r}\) = 1. Similar results (Figure 9c,d) were obtained for the case of the inclined and vertical straight barriers.  
Then, it can be concluded that, regardless of the barrier geometry, the greatest reduction occurs when the barrier is located at the edge of the road (\(d^{\star}\) = 0). In the situations that the barrier should be located down downwind the road, for example, to include a safety  
Figure 9: Concentration reduction factor (\(f_{r}\)) in the region downwind the barrier, as a function of barrier height (\(h^{\star}\)) and location (\(d^{\star}\)) for a quarter ellipse barrier in **(a)** a 3D plot and **(b)** a 2D plot; and 2D plots **(c)** for the vertical and **(d)** 45\({}^{\circ}\) inclined barrier. Barriers are oriented toward the road.  
sideline for the vehicles, the quarter ellipse inclined toward the road continues being the best barrier geometry, independently of how far downwind the road it should be located.  
Previous results are difficult to compare with the reported by other authors (Table 1) since the reductions in pollutant concentrations obtained with the barriers depend on barrier geometry (height, distance to the road, and shape), meteorological conditions (wind speed, atmospheric stability), and emission rates, and in most of the cases these variables have not been reported. These dependence on multiple variables explain the differences in the results reported in the works listed in Table 1.
## 2 Design and Implementation of On-Line PD Monitoring System
### Multi-Channel Data Concentrator Module  
Measurement data is sent to the multi-channel data concentrator (labeled as MPD-001 module) using protocol elaborated based on the so-called Process Bus (PB) according to the guidelines of the international substation automation standard IEC 61850-9-2 [103]. As it was mentioned before, the fiber-optic cables allow to effectively reduce the influence of external electromagnetic interferences occurring on the substation. The schematic diagram and photograph of the MPD-001 module are demonstrated in Figure 22.  
The data into the MPD-001 module are entered through an integrated, specialized MSO-001 network card containing up to eight fiber-optic interfaces (ISO). The MSO-001 network card transmits the measurement data via the PCX/1 bus to the PJC-900 module based on the E660T ATOM processor. Individual data streams are decoded by the FPGA type CYCLONE V implemented in the PJC-900 module. The FPGA also supports the following external network transmission channels:  
* the basic channel of the Ethernet network in standard FX1000 used for data transmission, warning signals, download the parameterization from the server,
* configuration channel in TX100 standard dedicated to programming, local parameterization, and service,
* synchronizing channel in 1 PPS or IRIGB standard.  
The PCX/2 bus is used to transfer data streams from FPGA to the embedded PC (EPC) computer module. As with the signal processing module, MPD-001 can work in the monitoring or diagnostic mode. In the monitoring mode, based on the data sent from the analog-to-digital signal processing module, the embedded PC calculates for three different time intervals (one minute, fifteen minutes, and one hour) parameters such as the number of PD pulses, maximum and average pulse amplitude, as well as maximum and average pulse energy. In diagnostic mode, the computer prepares data for PRPD patterns. For this purpose, it aggregates 1-s data frames with a duration of 55.5 \(\upmu\)s. The data prepared in this way is sent to the server. If necessary, data from the MPD-001 module can be transferred to other local or remote SCADA systems using the DNP-3.0 or IEC 61850 protocol.  
The module MPD-001 is powered with the use of the 220 V AC/DC power supply type MZA-502, in which the voltage level is supervised by the PJC-900 module.  
After manufacturing the complete prototype of MPD-001 module, the EMC properties of the housing ports and power ports were tested in accordance with IEC 60255-26 standard [104].
# DGANet: Dynamic Gradient Adjustment Anchor-Free Object Detection in Optical Remote Sensing Images
## 2 Related Work  
### Object Detection in Remote Sensing Images  
With the continuous development of deep learning techniques, some of the latest networks take object detection as a classification problem and prediction performance is constantly being improved. Simultaneously, many approaches have been introduced into the optical remote sensing field, where detection faces more complex challenges. [PERSON] et al. proposed a hybrid CNN for vehicle detection in satellite images [41]. This method was capable of extracting multi-scale features at its highest convolutional layer, which could improve the separability of the space feature. But the method cannot be used for multi-target recognition because it selected a time-consuming sliding-windows search paradigm to locate targets. Singular Value Decompensation Networks (SVDNet) was proposed [42] to generate candidate objects through convolution layers and nonlinear mapping layers. Then it further verified each candidate by using feature pooling and the linear SVM classifier. But the dependence on prior knowledge limited its application expansion. [PERSON] et al. proposed an object detection method based on the classical paradigm Faster R-CNN, which improved the effectiveness and robustness by sharing features between the region proposal generation stage and the object detection stage [43]. In order to improve the accuracy of Faster R-CNN, authors [44] introduced dilated convolution, multi-scale combination and Online Hard Example Mining (OHEM) [45] into the network. Besides, this method used a fully convolution neural network instead of the fully connected layer to realize the lightweight of model. In order to detect small objects under complex background conditions, scholars proposed an object detection method based on multi-attention, which identified a global spatial attention module to pay attention to the location of objects and reduced the loss of small items [46]. However, the performance and the real-time function of those methods need to be further improved.
## 3 Materials and Methods
### Prioritized Hindsight Experience Replay
The integration of EMAC with PHER results in the overall algorithm, detailed in Algorithm 1. ```
Initialize Replay Memory \(\mathcal{D}\)  Initialize Actor-Critic Network \(Q_{\theta_{1}},Q_{\theta_{2}},\pi_{\phi}\)  Parameters set \(\theta_{1},\theta_{2},\phi\)  Initialize target network \(\theta^{\prime}_{1}\leftarrow\theta_{1},\theta^{\prime}_{2}\leftarrow\theta_{2},\phi^{\prime}\leftarrow\phi\)  Initialize Max Network \(\theta^{\max}_{1}\leftarrow\theta_{1},\theta^{\max}_{2}\leftarrow\theta_{2},\phi^{\max}\leftarrow\phi\) for each Episode do for each environment step do \(a_{t}\sim\pi_{\phi}(a_{t}|s_{t})\) \(s_{t+1}\sim p(s_{t+1}|s_{t},a_{t})\) \(\mathcal{D}\leftarrow\mathcal{D}\cup\{(s_{t},a_{t},r_{t},a_{g},g,p_{t},\text{episode}_{N})\}\) endfor for each \(k\)do  Sample Batch of \(N\) transitions \((s_{t},a_{t},r_{t},a_{g},g,p_{t},\text{episode}_{N})\) from \(\mathcal{D}\)  Choosing Batch Size Data: 1. Sample Episode transition \(j\sim^{\text{episode}}P(j)=\frac{\pi^{\text{episode}}_{j}}{\sum_{i}^{\text{episode}}p_{i}^{\text{episode}}}\) 2. Sample Step transition \(j^{\prime}\sim^{\text{step}}P(j^{\prime})=\frac{\pi^{\text{step}}_{j}}{\sum_{i} ^{\text{step}}p_{i}^{\text{episode}}}\) (Using HER and PER Proccess)  Compute \(\overline{a}\): \[\overline{a}\leftarrow \begin{cases}\pi_{\phi}(s^{\prime})+\epsilon,&\text{if not Max Reward}\\ \pi_{\phi^{\max}}(s^{\prime})+\epsilon,&\text{if Max Reward}\end{cases} , \epsilon\sim\text{clip}(\text{Noise}(0,\overline{\sigma}),-c,c)\]  Compute \(y\): \[y\leftarrow \begin{cases}r+\gamma\min_{i=1,2}Q_{\theta_{i}}(s^{\prime}, \overline{a}),&\text{if not Max Reward}\\ r+\gamma\max_{i=1,2}Q_{\theta^{\max}_{i}}(s^{\prime},\overline{a}),&\text{if Max Reward}\end{cases} \]  Update critic: \[\theta_{i}\leftarrow\arg\min_{\theta_{i}}N\sum(y-Q_{\theta_{i}}(s,a))^{2}\]  Update \(\phi\): \[\
abla_{\phi}J(\phi)=N\sum\
abla_{a}Q(s,a)\big{|}_{a=\pi_{\phi}(s)}\
abla_{\phi }\pi_{\phi}(s)\]  Update target network:  if Max Reward then \(\theta^{\prime}_{i}\leftarrow\theta_{i},\quad\phi^{\prime}\leftarrow\phi\) else \(\theta^{\prime}_{i}\leftarrow\tau\theta_{i}+(1-\tau)\theta^{\prime}_{i}\) \(\phi^{\prime}\leftarrow\tau\phi+(1-\tau)\phi^{\prime}\) endif endfor endfor
```  
**Algorithm 1** Extended Maximum Actor-Critic (EMAC) with PHER.
# OpticalTrust: A Sensor-to-Blockchain Framework Using Free-Space Optical Communication
## 6 Conclusions  
In the evolving landscape of digital signatures, the integration of blockchain technology with distributed sensor networks represents a significant improvement in tamper-proof data transmission systems. Through the implementation of smart contracts and IPFS, this study provides a robust framework for tamper-proof data validation and storage. The blockchain architecture ensures unprecedented levels of data integrity and transparency while enabling automated verification processes essential for real-time applications. The sensor network implementation showcases the adaptability and efficiency of the system in environmental monitoring and system optimization. Performance metrics from experimental outcomes demonstrate strong capabilities in real-time data processing, establishing a foundation for smart city applications. To facilitate high-speed data transmission within this integrated system, FSOs technology emerges as a complementary solution. The performance analysis encompasses a comprehensive range of operational parameters, examining bit rates of 8, 12, 16, and 20 Gbps across transmission distances from 5 to 25 km, particularly when enhanced with EDFA technology. These extensive testing parameters enable the identification of optimal configurations for various deployment scenarios, ensuring reliable connectivity for the blockchain-validated, sensor-driven network architecture. This synergy between blockchain security, sensor network intelligence, and FSOs transmission presents compelling opportunities for future telecommunications infrastructure. While the simulation results demonstrate promising performance metrics under controlled conditions (simulation and test network), real-world implementations may experience variations due to environmental factors, network congestion, and atmospheric conditions affecting FSOs transmission quality. Future work may focus on extensive field
# Three-Dimensional Convolutional Vehicle Black Smoke Detection Model with Fused Temporal Features
###### Abstract
To this end, we have assembled and publicly released a dataset for black smoke detection, which has been sourced from roadway CCTV cameras within China. Despite the development of this novel dataset, in this paper, we also present a model for black smoke detection. Unlike the existing approaches that rely solely on static features from single images, our method leverages the temporal continuity in video data to enhance detection accuracy. By considering the motion and evolution of black smoke over multiple frames, we can more effectively distinguish it from other visual artifacts. Specifically, our model is based on the ResNet50 network and designs a dual-branch structure to extract black smoke feature information at different resolutions effectively. A 3D convolution module is introduced into the backbone network to construct a dual branch structure based on the 3D ResNet50 network, which fully preserves short-term dynamic and semantic features between continuous black smoke video frames. Additionally, a cross-scale feature fusion module is proposed to concatenate the features of two sub-segments to fuse black smoke feature information at different scales. Finally, a self-attention mechanism module is introduced to enrich the black smoke feature information while mitigating the influence of noise and other factors, and the obtained mixed features are used for black smoke classification and recognition. We summarize our contributions as follows:  
* Dataset: We assembled and publicly released a dataset for black smoke detection, which has been sourced from roadway CCTV cameras within China. We hope our dataset will facilitate the advancement of black smoke detection models and support their practical applications. * Model: Our model features a dual-branch architecture and 3D convolutions for the concurrent analysis of temporal features, capturing black smoke movement, alongside a feature fusion network that integrates cross-scale and self-attention mechanisms to refine detection by focusing on key features. * Experiments: The proposed method is trained and validated on our novel dataset. The results demonstrate a substantial improvement in vehicle black smoke detection accuracy for video streaming data compared to most current methods, validating the effectiveness of our approach in real-world scenarios. The rest of this paper is organized as follows: In Section 2 we briefly analyze the existing object detection methods and the black smoke detection methods. Section 3 presents the dataset we collected. The techniques of our models are given in Section 4. In Section 5 we discuss the experiments, including the basic settings, the compared methods, and the experimental results. Finally, we conclude the paper in Section 6.
# Energy Efficiency Policies to Face Buildings' Climate Change Effects in Paraguay
## 3 Review of Paraguayan Actions
### Energy Efficiency Overview of the Situation in the Building Sector
The residential sector accounted for 43.5% share of the total electricity consumption structure, which implies a 4% increase in the residential electricity consumption per capita, a slightly higher value than the previous year (3.5%) [35]. Within the BEN it is highlighted that the residential sector is the most influential in total electricity consumption. This steadily increasing rate in electricity consumption will lead to less availability of hydroelectric energy for export. Figure 4: Net Energy consumption by sector and Electricity end use in the residential sector [34; 38]. Another important point to consider is the country's tariff structure, which has subsidies assumed by the State that caused a high waste of energy, mainly in the residential sector, since 25% of users consume more than 250 Kwh/month, a value which exceeds the normal range compared to other countries of the region. It is even high when compared with European countries, since in Spain for example, the percentage of families that exceed this level of consumption is 23% [63]. The government are reducing energy subsidies aiming to reverse this situation in the medium term. Thus, in March 2017 tariffs were updated and consumers' energy costs were raised. Accordingly, three monthly consumption ranges were established: users who use up to 100 Kwh/month will pay 25% of the normal residential rate; those who use between 101 and 200 kWh/month will pay 50% of the normal residential rate and those who use between 201 and 300 kWh/month will pay 75% of the normal residential rate [64]. Given this, consumers need to understand that the application of EE strategies will impact their economy, reducing the amount of their monthly electric bills independently the energy costs variation [63; 64]. According to [PERSON] and [PERSON] [65] for the National Electricity Administration, the state institution in charge for the generation, transmission, distribution and commercialization of electric energy in the country, the incorporation of EE measures represents an advantage since they can reduce the peak demands thus incorporating more users with existing facilities without needing for large infrastructure investments. The National Energy Efficiency Plan (PNEE) includes a building section, where the actions to be implemented are focused on decreasing the energy requirement and user consumption. It is also pointed out that the same actions apply to the Commercial, Public and Services sectors (see Figure 6). Through the actions set, it can be said that the CNEE recognises the savings potential of this sector, where increasing the thermal performance of buildings, as well as increasing the use of efficient materials, represent the main strategies proposed. Nevertheless, since PNEE was launched in 2014, the achievement of these goals still represents a challenge. Figure 5: Appliances possessions and Electricity consumption in the Residential sector [52].
## 3 Methods
### Data Processing and Feature Extraction
#### 3.8.3 Electrodermal Activity Feature  
Similarly to the facial expression feature, the raw EDA time-series data, from the onset of the voice user interface speech until the onset of participant response, were averaged and used as a value for EDA features. This calculation was performed for both the experimental and calibration time windows. Once this calculation was performed, the experimental values were standardized by subtracting the overall average of the values calculated for time windows during the calibration time period.  
EDA features were processed in order to obtain phasic and z-score time series data. Often referred to as EDA \"peaks\", phasic changes are abrupt increases in the skin conductance [11]. In other words, phasic EDA stems from faster changing elements of the signal, known as the Skin Conductance Response (SCR) [11]. As for the z-score, it requires the mean and standard deviation to be used in place of a hypothetical maximum [11].  
The phasic component of the EDA time-series was extracted. In parallel, the conversion of the entire raw EDA time-series into a z-score was performed. The phasic EDA and z-score EDA time-series data were further processed to derive phasic and z-score features, serving as targets for an arousal assessment, for each interaction tested within the statistical analyses.