esa-sceva/satcom-chunk-collection huggingface dataset:
# Weather-Aware Long-Range Traffic Forecast Using Multi-Module Deep Neural Network
###### Abstract  
This study proposes a novel multi-module deep neural network framework which aims at improving intelligent long-term traffic forecasting. Following our previous system, the internal architecture of the new system adds deep learning modules that enable data separation during computation. Thus, prediction becomes more accurate in many sections of the road network and gives dependable results even under possible changes in weather conditions during driving. The performance of the framework is then evaluated for different cases, which include all plausible cases of driving, i.e., regular days, holidays, and days involving severe weather conditions. Compared with other traffic predicting systems that employ the convolutional neural networks, \(k\)-nearest neighbor algorithm, and the time series model, it is concluded that the system proposed herein achieves better performance and helps drivers schedule their trips well in advance.  
traffic forecasting; deep learning; neural network; transportation network; weather aware prediction +
Footnote †: journal: Applied Sciences  
_Article_
# A Low-Complexity Security Scheme for Drone Communication Based on PUF and LDPC
## 3 Security Architecture
### System Model
#### 3.1.2 Authentication Model
\begin{table}
\begin{tabular}{p{227.6 pt}} \hline \hline
**Registration Procedure** \\ \hline
**Step 1:** Analyze the usage of the on-chip memory of the target drone and select an uninitialized RAM segment that is as long as possible. \\ \hline
**Step 2:** Repeat power-up 100 times to detect the distribution of unstable bits in this segment and select a more stable 64-byte contiguous segment as the \(P_{fp}\). Choose a more stable segment to extract the 32-byte ID and record all bit changes in this segment. \\ \hline
**Step 3:** Starting with the first bit of the segment used to extract the ID, if 90\% of the records in that bit are “\(1\)” or “\(0\)”, then record it in the \(ID\), otherwise record it as an unstable set \(S_{us}\), until the length of the \(ID\) reaches 32 bytes. Record the starting address \(start\) of the segment and use the total length \(len\) for registration. \\ \hline
**Step 4:** Record the starting position of the \(P_{fp}\) and its length in the drone’s program, along with a static secret key \(key_{i}\). \\ \hline
**Step 5:** Since the stability of the bits of the ID is classified according to a threshold, the bits that are still unstable need to be powered up again several times to test them and recorded in the set \(S_{pus}\) of predicted unstable bits. \\ \hline
**Step 6:** Record the registration information in the database in the format \\ \(\left\{IP,key_{i},P_{fp},start,len,S_{us},ID,S_{pus}\right\}\). \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Registration procedure. The network access permission for a drone is determined by second-stage authentication, while first-stage authentication ensures that the extraction information during second-stage authentication is difficult to steal, and if stolen, it can ensure that the network formation of the other drones remains secure. If an authorized situation occurs during authentication, the GS should not send the broadcast key to the drone, and the ground station needs to report to the database to permanently blacklist all its information. If the drone wants to continue to be secure, the network can be secure, and the network  
using the network, it needs to re-register with completely new authentication information in the database. After successful authentication, the ground station informs the drone of the current broadcast key using the generated temporary key. The latest communication key seed set is then broadcasted across the entire network using the broadcast key, which is related to the ground station and all the authorized drones of the current mission. After flood broadcasting, the newly authorized drone can access the network normally. For the recalled drone with abnormal work, if it does not return within the specified time, the GS regards it as lost and informs the database to block its registration information.
## 3 Results and Discussions
### Electrical Characterization
#### 3.3.2 Light Weight Module  
The addition of thin glass between the PMMA and SC can help the surface mechanical integrity and the reduce moisture in a light weight PV module. It improves the reliability of module. The performance of the coating in the light weight module model was analyzed by placing the CaF\({}_{2}\)-CG between the PMMA and SC. Table 2 shows a comparison of the different PV parameters by placing the Un CG and CG between the PMMA and SC having average and corresponding standard deviation values. The data were analyzed after recording the performance data of five different samples.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Measurement Conditions** & \(V_{ac}\) **(V)** & \(J_{sc}\) **(mA/cm\({}^{2}\))** & **FF** & \(\eta\) **(\%)** & \(\Delta\eta\) **(\%)** \\ \hline SC & \(0.7072\pm 0.0008\) & \(40.58\pm 0.28\) & \(0.7406\pm 0.0017\) & \(21.25\pm 0.16\) & \(0.00\) \\ CG/SC & \(0.7057\pm 0.0007\) & \(39.03\pm 0.45\) & \(0.7402\pm 0.0020\) & \(20.39\pm 0.24\) & \(-4.05\) \\ Un CG/SC & \(0.7045\pm 0.0011\) & \(38.10\pm 0.43\) & \(0.7406\pm 0.0030\) & \(19.88\pm 0.24\) & \(-6.45\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the different electrical parameters of a solar cell (SC), CaF\({}_{2}\)-coated glass (CG)/SC and uncoated glass (Un CG)/SC measured under 1 sun illumination (100 mW/cm\({}^{2}\)).  
Figure 4: CaF\({}_{2}\)-coated BSG on solar cell to represent the conventional photovoltaic (PV) module model.  
Statistical Analysis  
The analysis showed that there is a reasonable improvement being observed in \(J_{sc}\) and \(\eta\) as a result of putting CG between PMMA and SC. The PMMA/CG/SC have an average \(J_{sc}\) and standard deviation of \(36.43\pm 0.43\) with an effective \(\eta\) of \(19.64\pm 0.24\) as compared to PMMA/Un CG/SC, which have \(J_{sc}\) and a standard deviation of \(35.65\pm 0.36\) with an effective \(\eta\) of \(19.17\pm 0.20\), respectively, as shown in Figure 5(a). The relation between the current density-voltage (\(J\)-\(V\)) curve for the Un CG and CG between the PMMA and SC, measured under 1 sun illumination (100 mW/cm\({}^{2}\)), is shown in Figure 5(b).  
Supporting Documents show the \(J\)-\(V\) curve for five different light weight PV module structures.
## 3 Results and Discussion  
### Spatiotemporal to One-Year Operations  
Table 4 (a,b) shows the comparison data for the two-time horizons between different amounts of inflow on the supply side (looking from the biosphere and the technosphere) and the amount of outflow on the sink capacity (the products and environmental impacts). \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **(a) FU Model (Time Scale: Seconds)** & & & \\ \hline  & **Identifications** & **Unit** & **C1** & **C2** & **C3** \\ \hline Supply-side & \multicolumn{4}{c}{External requirement inputs (LCI) (Biosphere)} & \\ \hline Water & Water & m\({}^{3}\) & 20.5 & 8.73 & 8.73 \\ \hline Energy (PES) & Natural gas & Nm\({}^{3}\) & 1165.0 & 669.7 & 669.7 \\ \hline  & \multicolumn{4}{c}{Internal requirement inputs (LCI) (Technosphere)} & \\ \hline  & Caustic soda (50\%) & kg & 6.3 & 0.01 & 0.01 \\ \hline  & Sulphuric acid (98\%) & kg & 5.6 & 0.01 & 0.01 \\ \hline  & Aluminium sulphate & kg & 0.7 & 0.001 & 0.001 \\ \hline Materials & Chlorine & kg & 0.69 & 0.0007 & 0.001 \\ \hline  & Chemical lime & kg & 0 & 0.00 & 0.00012 \\ \hline  & Condensed of comeback & t & 0 & 3.1 & 0 \\ \hline MEA make-up (30\%) & t & 0 & 0 & 0.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the internal and external flow matrix of the formal process (CHP plant) for the FU (Flows/MWh) model (a) vs. the scaled CHP to 1 year (b) for the flows-EFs nexus (Adapted from [PERSON] et al. [3]). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type of Forest in Tuzandepetl** & **Forest Land (ha)** & **Area Biomass** & **Capture** & **Capture by** & **Average by Park** & **Total Capture** \\ (**Tr2 Pak** & **(**C/ha)** & **(**HC/ha)** & **Average 60 Years** & **Coverage (**HCO\_y/yr)** & **(HCO\_Ha/yr)** & **(HCO\_y/yr)** \\ \hline Secondary tree vegetation & 297.5 & 72.3 & 2.0 & 599.8 & \\ Secondary shrub vegetation & 62.0 & 4.6 & 0.9 & 53.6 & \\ Mangrove & 55.4 & 74.0 & 4.6 & 255.4 & \\ Grassland & 304.2 & 2.2 & 0.2 & 60.6 & 1.9 & 909.4 \\ Total & 719.0 & & & 969.4 & \\ \hline
**Jayaound (ja Park)** & & & & & \\ \hline Secondary tree vegetation & 166.0 & 106.0 & 2.4 & 401.8 & \\ Secondary shrub vegetation & 113.0 & 27.0 & 0.8 & 90.6 & \\ High evergreen lowland forest & 139.0 & 158.0 & 1.7 & 223.2 & \\ Grassland & 383.0 & 1.5 & 0.4 & 145.4 & 1.3 & 870.0 \\ Total & 801.0 & & 870.0 & & 189.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The baseline of carbon content and capacity of carbon sequestration by forest type in Tuzandepetl and Jaguaroundi Park.
## 3 Sensing Per Crop
### Sensing Mango and Avocado Diseases  
Anthracnose, the most important mango disease, is caused by the fungus _Colletotrichum gleosporoides_. Flower blight, fruit rot, and leaf spots are among the symptoms of this disease [129,130]. Fruit infection commonly occurs and can result in serious decay problems in the orchard, in transit, at the market, and after sale [131]. The fungus invades the skin of fruit and remains in a \"latent\" state until fruit ripening begins. Ripe fruit, both before or after picking, can then develop prominent dark-brown to black decay spots and can result in extensive fruit rotting. Anthracnose is usually more serious in years when rain and heavy dews are frequent, from the onset of flowering until fruit are about half size.  
Stem-end-rot causing fungi in mango are endophytic, and such pathogens are activated when fruits begin to ripe and continue until complete degradation of the tissues [132]. The long time taken for natural ripening allows fungi to multiply rapidly producing stem-end-rot before ripening process is completed [133-135]. The major fungus responsible for stem-end-rot _Lasiodiplodia theobromae_, cannot directly penetrate into the plant tissue and hence requires wounds to facilitate penetration.
## 3 Proposed Methodology  
Figure 2 represents the overall framework of the Blind Image Quality Assessment system used in this research paper. The framework consists of three main stages: Feature Extraction, Feature Selection, and Quality Prediction.  
Figure 1: Sample images from the dataset.  
In the Feature Extraction stage, the reference and distorted images are preprocessed using several well-known image processing techniques to extract relevant features. The extracted features include Haar wavelet coefficients, Prewitt and Sobel edge detection, and Log filters.  
In the Feature Selection stage, the extracted features are passed through the WEKA machine learning tool to select the most relevant features using the Information Gain attribute algorithm aiming to reduce the in dimensionality of the feature map and improve the efficiency of the proposed model. The selected characteristics are supplied into the Support Vector Machine (SVM) Regression model in the Quality Prediction step to forecast the quality of the distorted images.
# Coordinated Multi-Platooning Planning for Resolving Sudden Congestion on Multi-Lane Freeways
## 4 Proposed Platoon-Based Coordination Scheme to Minimize Traffic Congestion
### Mechanisms for Platoon Control
#### 4.4.2 Cooperative Adaptive Cruise Control (CACC)  
Through CACC, each platoon member adjusts its velocity from its preceding vehicle. According to the study [40], vehicle \(j\)'s velocity (\(v_{j}\)) and acceleration (\(a_{j}\)) in the platoon \(p_{n}\) can be given by  
\[\begin{cases}v_{j}=\frac{dx_{j}}{dt}=\dot{x}_{j}\\ a_{j}=\frac{dv_{j}}{dt}=\dot{v}_{j}=k_{1}*a_{j-1}+k_{2}a_{d}+k_{3}\dot{\epsilon }_{j}+\\ k_{4}(v_{j}-\sigma_{d})+k_{5}*\epsilon_{j}\\ \varepsilon_{j}=x_{j}-x_{j-1}+L_{j-1}+d_{pgap}\\ \dot{\epsilon}_{j}=v_{j}-v_{j-1},\end{cases} \tag{10}\]  
where \(x_{j}\) and \(a_{j}\) are the current position and acceleration of the vehicle \(j\); \(a_{d}\) and \(v_{d}\) are the acceleration and velocity of the platoon leader; \(a_{j-1}\) is the acceleration of the preceding vehicle; \(L_{j-1}\) is the length of the preceding vehicle; \(\varepsilon_{j}\) is the distance error for \(d_{pgap}\). In addition, the detailed definitions of \(k_{1},k_{2},k_{3},k_{4},k_{5}\) can be found in [40].  
\[\begin{cases}k_{1}=1-C_{1};k_{2}=C_{1};k_{5}=-\omega^{2}\\ k_{3}=-(2\xi-C_{1}(\xi+\sqrt{\xi^{2}-1}))\omega\\ k_{4}=-C_{1}(\xi+\sqrt{\xi^{2}-1}),\end{cases} \tag{11}\]  
where \(C_{1}\) is a weighting factor between the accelerations of the leader and the preceding vehicle (default set to 0.5), \(\xi\) is the damping ratio (default set to 1), and \(\omega\) is the bandwidth of the controller (default set to 0.2 Hz).
# SR-Inpaint: A General Deep Learning Framework for High Resolution Image Inpainting
## 5 Experimental Results and Discussion
### Quantitative Evaluation  
For a more objective comparison between our method and state-of-the-art method in terms of high-resolution inpainting, we tested our method against state-of-the-art method on 2K and 4K image testsets and calculated the numerical metrics. The quality of our results are evaluated using the following metrics: Peak Signal-to-Noise Ratio (PSNR) [47], Structural SIMilarity (SSIM) [48], Normalized Root Mean Square Error (NRMSE), and Frechet Inception Distance (FID) [49]. Among them, PSNR is used to measure the degree of deformation and noise; SSIM is used to describe the degree of similarity of the graphics structure; NRMSE is used to measure the pixel error; FID is used to measure the perceptual error based on deep features, using a pre-trained Inception-V3 model [50].  
Table 1 presents the numerical results of our model and current state-of-the-art model on the 2K and 4K image testsets. It can be seen that our model performs better on both 2K and 4K testsets for all numerical metrics. It indicates that our framework is ahead of the state-of-the-art method in the quality of inpainting at 2K and 4K resolutions, better adding pixel-level details, better recovering the global structure, and obtaining more realistic results on perception.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**2K Inpainting**} & \multicolumn{4}{c}{**4K Inpainting**} \\ \hline
**Method** & **PSNR\(\uparrow\)** & **SSIM\(\uparrow\)** & **NRMSE\(\downarrow\)** & **FID\(\downarrow\)** & **PSNR\(\uparrow\)** & **SSIM\(\uparrow\)** & **NRMSE\(\downarrow\)** & **FID\(\downarrow\)** \\ \hline HiFill & 21.386 & 0.810 & 0.175 & 1.193 & 20.503 & 0.813 & 0.239 & 2.082 \\ SR-Inpaint & **27.364** & **0.923** & **0.092** & **0.097** & **26.065** & **0.910** & **0.130** & **0.138** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quantitative comparison of our model and the state-of-the-art model.  
Figure 6: Zoomed-in comparison of 2K and 4K inpainted details: (**left** to **right**) input image, local details of ground-truth, local details of LR inpainting with bicubicly upsampling, local details of LR inpainting upsampled on Gaussian pyramid, local details of LR inpainting with wavelet enhancement, local details of LR inpainting with SR enhancement, local details of state-of-the-art technology.
## 4 Results and Discussion: Statistical Approach
### Multiple-Regression Analysis
#### 4.3.2 Model Development
In fact, their relevance in the estimation was low and their forced introduction led to an appreciable decrease in the estimation accuracy. It was therefore decided to work with no other values than compressive strength and modulus of elasticity, properties mainly related to compressive behavior, which showed a very strong relationship with the UPV readings, both from the correlation approach, and in terms of simple regression. Thus, reconstructing the linear combination model, but only considering those two mechanical properties, yielded Equation (6), which had a coefficient R\({}^{2}\) of 89.80%. If the weights of this model are analyzed, it can be noted that they were practically identical to those obtained in Equation (4), which showed that the properties related to bending-tensile behavior were hardly relevant in the statistical approach. \[UPV_{S}=1.246\times CS_{S}-0.344\times ME_{S} \tag{6}\]  
Similarly, it was decided to work with the mean value of the compressive-behavior-related properties to eliminate the effect of the signs, resulting in Equation (7). This model presented a very similar coefficient R\({}^{2}\) to the previous one, underlining that the mean value of the properties related to compressive behavior explained around 90% of the value of the UPV. Since the introduction of the properties related to bending-tensile behavior had reduced the precision of the estimation, the remaining 10% of the UPV value can be explained in terms of the experimental variability of the UPV itself, rather than in terms of these properties, which is known to be very large, as shown in other studies [16; 24]. \[UPV_{S}=0.894\times\left(\frac{CS_{S}+ME_{S}}{2}\right) \tag{7}\]
# PUF and Chaotic Map-Based Authentication Protocol for Underwater Acoustic Networks
## 5 Informal Security Analysis
### Known Session Key Attack  
If the implementation of the authentication scheme can generate a unique session key, and the compromise of the key has no effect on other session keys, the authentication scheme can provide known session key security. In the proposed scheme, the session key, \(SK\), is unique to each session run because the random numbers \(ru\) and \(rs\) are generated randomly and independently by the user and the sensor node. Therefore, our scheme can provide known session key security.
# Protecting Hybrid ITS Networks: A Comprehensive Security Approach
## 3 Proposed Approach
### Implementation  
In this research, the CA service (Figure 7) was not implemented, as in this phase, the priority was to exchange messages with security guarantees, and as seen (Section 3.1.2), the CA is not utilised in that procedure. Consequently, when evaluating the protocols and system's performance regarding secure message exchanges and hybrid networking, the absence of the CA does not impact the contemplated use cases or objectives. Nevertheless, it is to emphasise the importance of its implementation in future iterations of this work to test the complete system.  
Regarding the broker technology, message queuing telemetry transport (MQTT) was used in this implementation. As stated in [40], MQTT is lightweight and suitable for constrained environments. Thus, it is an approach for building event-driven solutions across edge and fog layers. In addition, its choice prevailed over other message-oriented middleware because when considering essential criteria such as latency, bandwidth/overhead and standardisation, MQTT stands out over other technologies, such as the advanced message queuing protocol (AMQP), Kafka and ZeroMQ [41]. In particular, compared to Kafka [42], MQTT is simpler in terms of implementation complexity [41]. Due to the greater complexity of Kafka, e.g., the ability to store events (which is not relevant to this scenario, as the goal is to process messages timely), there are occasions that Kafka demands a more resource-intensive and slower process [43]. These traits make Kafka less ideal than MQTT for our environment, where there is resource-constrained equipment in EC and FC.  
Each node application implements and can be configured with one of three security approaches: No security, DLAPP [13] or MFSPV [14]. The software was modularised to be independent of the security approach in use. It expects an object representing the security protocol, with two methods: \"protection\" and \"deprotection\". Protection involves applying a security protocol to a message and encapsulating it with the protocol. Deprotection entails verifying a message according to the configured protocol. If the message is valid, the security bytes are then removed. The \"no security\" approach was added so that it is possible to assess the security impact on the performance. As the CA was not implemented, each application has the cryptographic material configured locally. Empirically, for each node application (OBU, RSU and smartphone), the DLAPP protocol was implemented with a secret system key \(k_{s}\) of 32 bytes. Each element of the hash chain was obtained using the SHA-256 hash function. Therefore, each key is 256 bits long. In MFSPV, 32-byte keys were also used, such as the \(V_{sk}\) and the \(R_{k}\). Each entity's application implementation will be briefly described, highlighting important considerations related to their development.
## 3 The Proposed Conversational System: CORES
### Workflow of CORES
#### 3.5.1 Item Embedding  
The first phase of CORES focuses on creating vector representations of items based on their textual descriptions in the dataset to identify retrieval for the user. Using the GPT-4 model and sentence transformers, the system computes embedding weights for each item's description, encapsulating its key features. This process involves the following:  
1. _Feature extraction:_ decomposing each item description \(d_{i}\) into words or sub-words \(f_{j,d_{i}}\), where \(j\) is the index of each feature within the description and \(i\) is the number of items varied from 1 to \(N\).
2. _Feature embedding:_ assigning a unique embedding vector \(E_{f_{j,d_{i}}}\) to each feature \(f_{j,d_{i}}\) using GPT-4, capturing its contextual meaning of the whole sequence.
3. _Concatenating embeddings:_ combining all feature embedding vectors of an item into a single vector \(E_{d_{i}}\), as defined by: \[E_{d_{i}}=Concat\Big{(}E_{f_{1,d_{i}}},E_{f_{2,d_{i}}},\dots,E_{f_{M,d_{i}}} \Big{)}\] (1) where \(M\) is the total number of features in the item description \(d_{i}\) (with \(i\in[1..N]\)).
4. Repeat all steps 1-3 for the next item description \(d_{i}\).