esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Examples and Simulation
### Monthly Average Accuracy Comparison  
To compare the accuracy of the three models in different seasons, we choose the data in Spring (from April to June), Summer (from July to September), Autumn (from October to December) and Winter (from January to March) for testing. The results are shown in Table 1.  
We can see from Table 1 that, in terms of \(nRMSE\), the prediction accuracy is higher in Winter and Summer than in Autumn and Spring; among the three models, Model 1 has the highest accuracy while Model 3 is the lowest. In terms of \(MAPE\), the prediction accuracy is higher in Summer than in Winter; again, the accuracy of Model 1 is the highest and that of Model 3 the lowest. In a nutshell, the FOS-ELM method proves to have the best performance.
## 3 Experimental Results and Analysis
### Performance Metrics
#### 3.2.1 Peak-to-Signal-Noise-Ratio Measure (PSNR)  
PSNR is a well-known measure to assess quantitatively the quality of a recovered image after processing, either by compression or encryption. It quantifies the difference between the original and recovered image pixels. The mathematical equations of this metric are  
\[PSNR=10\times\log\left(\frac{255^{2}}{MSE}\right) \tag{12}\]  
Figure 5: Example images: (1) CASIA V 1.0 dataset (**a–d**); (2) SIPI dataset (**e–h**).  
\[MSE=\frac{1}{MN}\sum_{i=0}^{M-1}\sum_{j=0}^{N-1}[I(i,j)-K(i,j)]^{2} \tag{13}\]  
where the mean square error (\(MSE\)) quantifies the error difference between the original image \(I\left(i,j\right)\) and the recovered image \(K\left(i,j\right)\). Furthermore, the variables \(M\) and \(N\) represent the dimensions of the images \(I\) and \(K\), and \(\left(i,j\right)\) represents the indices of these images.
# Research on Dynamic Path Planning of Multi-AGVs Based on Reinforcement Learning
## 5 Experimental Results and Discussion
### Experiment 1: Experiments to Verify the Effectiveness of the K-L Algorithm
Experiment 2: Comparison of the Convergence Time of K-L Algorithm and the Improved Q-Learning Algorithm  
With the same raster maps and the same start point and end point, we compare the K-L algorithm with two representative improved Q-learning algorithms: the path-integral-based reinforcement learning algorithm (PIRL) [23] and a hybrid A* algorithm with reinforcement learning (HARL) [24]. The maps used for the experiments are \(30\times 30\) and \(40\times 40\) raster maps, respectively. In the experiment, a dynamic obstacle is randomly generated in order to demonstrate that the K-L algorithm converges well in different environments. As shown in Tables 6 and 7, \(30\times 30\) and \(40\times 40\) raster maps are used in the experiment, with randomly placed obstacles. Based on the number of states that, although the map has changed, the K-L algorithm still reduces the number of states by adaptive clustering, leading to faster convergence compared to PIRL and HARL. Because PIRL achieves path optimization according to an a priori knowledge base, its convergence slows down because the larger the map, the more states and less prior knowledge. HARL is a length-first best path algorithm based on the A* algorithm and prior knowledge RL strategy, so similarly, it converged slowly due to a lack of prior knowledge. Experiment 3: Comparison of the Convergence Time of K-L Algorithm and Q-Learning Algorithm Based on Clustering Network Improvement  
To further verify the advantages of the K-L algorithm with respect to convergence speed, we compared the K-L algorithm with two reinforcement learning algorithms based on the clustering network: AHC [13] and the K-means clustering-based reinforcement learning algorithm (KML) [25]. The convergence curve of the path length on a \(20\times 20\) raster map is shown in Figure 9. \begin{table}
\begin{tabular}{c c c} \hline _Algorithm_ & _Convergence Time (s)_ & _Number of States_ \\ \hline _K-L_ & **116.58** & **1500** \\ PIRL & **178.52** & **9863** \\ HARL & **282.35** & **10,085** \\ \hline \end{tabular}
\end{table} 
Table 6: Time of convergence and the number of states in a \(30\times 30\) raster map. Figure 8: Q-value curve. Figure 9 shows the difference in convergence speed of the three reinforcement learning algorithms. K-L converges after approximately 90 episodes, whereas AHC converges after approximately 122 episodes, and KML begins to converge after approximately 115 episodes. The number of states of K-L is 1500, whereas the number of states of AHC is 1631, and the number of states of KML is as high as 2074, mainly because rather than using hierarchical clustering like AHC and KML, K-L accomplishes clustering by self-competition, which speeds up the convergence by dynamically adjusting parameters. Furthermore, the K-L algorithm does not send any fluctuations after 90 iterations, which proves that the K-L algorithm has good stability.
# High-Temperature Mechanical Properties of 4.5%Al \(\delta\)-TRIP Steel
###### Abstract  
The high-temperature mechanical properties of a 4.5% Al-containing \(\delta\)-transformation-induced plasticity (TRIP) steel were studied by using the Gleeble 3500 thermomechanical simulator. The zero ductility temperature (ZDT) and the zero strength temperature (ZST) were measured, and the brittle zones were divided. The phase transformation zone was determined by differential scanning calorimetry (DSC). The temperature of the phase transformation and the proportion of the phase were calculated by the Thermo-Calc software. The ZDT and the ZST of the 4.5% Al-containing \(\delta\)-TRIP steel are 1355 and 1405 \({}^{\circ}\)C, respectively. The first brittle zone and the third brittle zone of the steel are 1300-1350 \({}^{\circ}\)C and 800-975 \({}^{\circ}\)C, respectively. The reason for the embrittlement of the third brittle zone of the 4.5% Al-containing \(\delta\)-TRIP steel is that the \(\alpha\)-ferrite formed at the austenite grain boundary causes the sample to crack along the grain boundary under stress. The ductility of the 4.5% Al-containing \(\delta\)-TRIP steel decreases first and then increases with the increase of the \(\alpha\)-ferrite. When the proportion of the \(\alpha\)-ferrite reaches 37%, the reduction of area (RA) of the 4.5% Al-containing \(\delta\)-TRIP steel is reduced to 44%. The 4.5% Al-containing \(\delta\)-TRIP steel has good resistance to the high-temperature cracking.  
high-temperature mechanical properties; crack-sensitive zone; brittle zones; phase transformation; equilibrium phase diagram Article High-Temperature Mechanical Properties of 4.5%Al \(\delta\)-TRIP Steel
## 3 Probabilistic Numerical Model of SFRC Beam Flexural Behavior  
Nonlinear flexural behavior of SFRC beams is simulated using a distributed plasticity displacement-based fiber finite element [24] as implemented in OpenSEES [25; 26]. Each finite element consists of several cross-sections that serve as integration points, where each cross-section is further discretized into fibers.  
To the best of the authors' knowledge, no previous models of SFRC's flexural behavior employed displacement-based fiber finite elements. As this is a distributed plasticity model, the inelastic flexural behavior of the beam is captured along the entire beam length, since each of the fiber cross-sections can simulate the nonlinearities in the SFRC's flexural behavior. Similarly to the analytical models, SFRC is modeled as a single-phase material. However, the uncertainties related to its behavior are considered by defining its material parameters as probability distributions. In terms of the computational cost vs. model accuracy trade-off, the proposed FEM model presents a compromise between simplified computationally inexpensive analytical models that usually consider a single cross-section, and computationally more expensive and complex, but usually more accurate and comprehensive, 3D multi-phase finite element models. Furthermore, fiber-level discretization of cross-sections allows access to stresses and strains in any part of the cross-section along the beam span at any loading level, thus enabling a more detailed insight into the beam behavior without incurring a high computational cost. Due to the small computational cost of such finite element models, probabilistic analysis, such as Monte Carlo sampling and variance-based sensitivity analysis, are feasible.  
However, the proposed FEM model has several important limitations. The interaction between shear and flexure is not captured. Shear failure is not considered, hence, only beams that fail in flexure can be simulated using the proposed FEM model. Furthermore, the proposed FEM model may not be able to simulate the deflection-softening behavior of certain beams, as the concrete stress-strain model (Figure 1) does not account for strain-softening in compression.
# Optimal Route Planning for Truck-Drone Delivery Using Variable Neighborhood Tabu Search Algorithm
## 5 Numerical Experiment
### Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Route Code** & & & & & & **Optimal Delivery Route** & & & \(\mathbf{\tau}\) \\ \hline \multirow{4}{*}{A} & \(i\), \(z_{i}^{n}\) & 0 & 7 & 1 & 8 & 16 & 9 & 17 & 10 & 2 & 0 \\ \cline{2-11}  & \(u_{j}^{n}\) & 5 & - & 28 & - & 76 & - & 127 & - & 153 & 192 \\ \cline{2-11}  & \(u_{j}^{n^{\prime}}\) & 5 & 26 & 41 & 59 & 77 & 95 & 117 & 146 & 166 & 192 \\ \cline{2-11}  & \(\pi_{i}^{n}\) & - & - & 13 & - & 1 & - & 0 & - & 13 & - \\ \hline \multirow{4}{*}{B} & \(i\), \(z_{i}^{n}\) & 0 & 3 & 11 & 12 & 18 & 19 & 20 & 21 & 13 & 0 \\ \cline{2-11}  & \(u_{j}^{n}\) & 5 & - & 34 & - & 59 & - & 103 & - & 137 & 172 \\ \cline{2-11}  & \(u_{j}^{n^{\prime}}\) & 5 & 18 & 34 & 51 & 68 & 86 & 103 & 121 & 136 & 172 \\ \cline{2-11}  & \(\pi_{i}^{n}\) & - & - & 0 & - & 10 & - & 0 & - & 0 & - \\ \hline \multirow{4}{*}{C} & \(i\), \(z_{i}^{n}\) & 0 & 5 & 4 & 22 & 23 & 14 & 15 & 6 & 0 & - \\ \cline{2-11}  & \(u_{j}^{n}\) & 5 & - & 29 & - & 87 & - & 140 & - & 177 & - \\ \cline{1-1} \cline{2-11}  & \(u_{j}^{n^{\prime}}\) & 5 & 19 & 37 & 53 & 80 & 101 & 119 & 154 & 176 & - \\ \cline{1-1} \cline{2-11}  & \(\pi_{i}^{n}\) & - & - & 8 & - & 0 & - & 0 & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Optimal delivery route and total delivery time of each route. Figure 4: Optimal truck–drone delivery routes. Figure 5: Comparison of truck–drone delivery and pure-truck delivery.
## 1 Introduction  
Low Power Wide Area Networks (LPWANs) have been widely employed in the last years in the most disparate fields whenever a high coverage, low cost and low power pervasive monitoring infrastructure is required. Those characteristics are mandatory any time that such networks have to be deployed in harsh environments so as to limit maintenance interventions which may be too time consuming and additionally entailing high running costs. LPWANs have then proved to be crucial for the realization of Internet of Things (IoT) architectures, where a large quantity of devices are required to transmit and receive small amounts of data to Internet cloud infrastructures. LPWAN-based IoT architectures can be found in the most disparate application fields: while the Smart City scenario [1; 2; 3] is probably the most studied, several other LPWAN-based systems can be found in the literature. In particular, LPWAN networks have been used in healthcare scenarios for remote patient monitoring [4] as well as in smart energy management [5], they have been deployed on monumental historical buildings [6] and in industrial plants [7; 8; 9], analyzing their possible placement even in critical use-cases [10; 11; 12] or at remote sites [13; 14; 15].  
The work standing behind this paper essentially falls within this last application scenario. Indeed, a tailor-made LPWAN based on the Long Range (LoRa) modulation implementing the LoRa Wide Area Network (LoRaWAN) protocol was set up in a marine environment in order to fulfill the objectives of a research and development project, which will be described later on (see Section 3), named _SeaFactory_.  
In particular, a sensor node was offshore installed testing two elevations (i.e., 3.5 m and 2.1 m above sea level) and transmitting data towards land where two LoRaWAN Gateways forwarded such information to a remote server via the Internet.  
This paper is structured as follows: Section 2 exposes some related works on long distance LoRa links as well as on LoRa based networks deployed in harsh environments (e.g., water basins) while in Section 3 the proposed application scenario is described. Section 4 treats some theoretical concepts on wireless transmissions. Within Section 5 the validating field tests for the LoRaWAN network are presented and in Section 6 their results are highlighted and discussed. Finally, in Section 7 remarks and conclusions are brought up.
## 4 Proposal for Data Transformation to Resolve Incompatibilities between Medical Formats
### Missing Values and Their Replacement  
While adding entries from a differently organized table dataset to our own, it is inevitable that we are going to have to deal with missing values, due to the tables having different features. To avoid throwing away entire features (and potentially important components of our model), we have to assign the missing fields some default value instead.  
As it was mentioned above, such default values are \"0\" for Boolean values and the mean of all entries for numerical (or otherwise represented by numerical values) features. This is based on the assumption that this should have the least effect on the variance value of a particular feature compared to other values. Unfortunately, the same cannot be achieved for Boolean values. Since not all systems may support uncertainties, one cannot make any assumptions about Boolean traits. As a result, it may be better to assume the trait is not present until there is proof that it is, which lead to \"0\" being the proposed value for the experiments.  
Figure 3: Scheme for an experimental unit conversion approach.  
The machine learning algorithms for missing data imputation, such as Mean/mode replacement, k-nearest neighbours, multiple imputation, linear regression, MICE (Multivariate Imputation by Chained Equations), decision tree regression, Bayesian network, k-means clustering, could be used here. These algorithms use statistical methods to fill in missing data in a dataset. This is achieved by using the existing data to learn the patterns and trends within the dataset, and then using those patterns and trends to estimate the missing values.
###### Abstract  
With the rapid development of wireless sensor networks (WSNs) technology, a growing number of applications and services need to acquire the states of channels or sensors, especially in order to use these states for monitoring, object tracking, motion detection, etc. A critical issue in WSNs is the ability to estimate the source parameters from the readings of a distributed sensor network. Although there are several studies on channel estimation (CE) algorithms, existing algorithms are all flawed with their high complexity, inability to scale, inability to ensure the convergence to a local optimum, low speed of convergence, etc. In this work, we turn to variational inference (VI) with tempering to solve the channel estimation problem due to its ability to reduce complexity, ability to generalize and scale, and guarantee of local optimum. To the best of our knowledge we are the first to use VI with tempering for advanced channel estimation. The parameters that we consider in the channel estimation problem include pilot signal and channel coefficients, assuming there is orthogonal access between different sensors (or users) and the data fusion center (or receiving center). By formulating the channel estimation problem into a probabilistic graphical model, the proposed Channel Estimation Variational Tempering Inference (CEVTI) approach can estimate the channel coefficient and the transmitted signal in a low-complexity manner while guaranteeing convergence. CEVTI can find out the optimal hyper-parameters of channels with fast convergence rate, and can be applied to the case of code division multiple access (CDMA) and uplink massive multi-input-multi-output (MIMO) easily. Simulations show that CEVTI has higher accuracy than state-of-the-art algorithms under different noise variance and signal-to-noise ratio. Furthermore, the results show that the more parameters are considered in each iteration, the faster the convergence rate and the lower the non-degenerate bit error rate with CEVTI. Analysis shows that CEVTI has satisfying computational complexity, and guarantees a better local optimum. Therefore, the main contribution of the paper is the development of a new efficient, simple and reliable algorithm for channel estimation in WSNs.  
artificial intelligence algorithm; truth inference; channel estimation; message passing 2020205939 +
Footnote †: journal: Sensors  
0202020593902020593902020593902020593902020593902020593902020205939
## 2 Novel Contributions  
In this section, the paper's unique contributions are discussed, and the proposed work is highlighted. Data accuracy, instant responses, security, consistency, fault tolerance and privacy are all required for such an ITS-V2X system. The accuracy of any ITS relies on the huge data accumulation and training through an Artificial Intelligence (AI) agent which requires correct information and integrity to produce a useful feedback and directions. Security and privacy are required to keep the operations running smoothly with no fails or undesired events to keep peoples' and nodes' identities secured and private. Consistency is required to ensure that the operations are always on and will not encounter any issues even during an attack such as Distributed Denial of Service (DDoS). Low latency is a need, since all operations in ITS require the lowest time to execute. In addition, power consumption is a critical factor which should be minimized for sustainable and reliable operations.
# Searching Open-Source Vulnerability Function Based on Software Modularization
## 1 Introduction
Module-level function matching greatly reduces the workload of open-source vulnerability function detection and finds vulnerability functions reused in the binary program. Overall, this paper makes the following major contributions. * We propose a binary function clustering method for directed graphs, named binary modularization based on directed graph (DBM), which divides binary modules based on overlapping community detection ideas of OSLOM. Then we carry out modular software component analysis through matching features between binary modules and source code. * We accurately locate specific source vulnerability functions in open-source components through function signature technology, then match vulnerability functions in binary modules reusing components, which narrows the scope of open-source vulnerability detection. * We implement the open-source vulnerability detection prototype BMVul. The results show that BMVul is superior to the current detection method, which can detect open-source vulnerability functions in binary modules and be of great significance to software security work. The rest of this paper is organized as follows. Section 2 describes the research status in related fields. We introduce the proposed model BMVul in Section 3. In Section 4, we evaluate the performance of BMVul in open-source component detection and vulnerability function detection compared with the state of the art work. It is concluded in Section 5.
# Investigation of Data Quality Assurance across IoT Protocol Stack for V2I Interactions
## 1 Introduction
### Timeliness  
Timeliness refers to the prompt and timely delivery of data within a specified time, ensuring its relevance and currency for the intended use. Timeliness underscores the significance of transmitting and receiving data within predefined time boundaries, catering to the demands of real-time or time-sensitive applications. Timeliness measures the currency of data. That is when data was generated and arrived at the destination [39]. The timeliness dimension has received a lot of attention in IoT protocol research and is one of the most important dimensions in V2I interactions to facilitate communications for various use cases, such as emergency warning messages, pedestrian safety messages and traffic signal pre-emption, which are safety-critical V2I communications.
# Elastic Computing in the Fog on Internet of Things to Improve the Performance of Low Cost Nodes
## 4 Design and Implementation
### Hardware, Operating System, and Network Configuration of the Nodes  
We used four RPi version 3B [8] as the hardware base of the cluster, with its respective power supplies and microSD cards. RPi version 3B uses an ARM Cortex-A53 @ 1.2 GHz and 1 GB LPDDR2 ofRAM. Figure 2 shows an example of a cluster using four RPis. In this figure, we see also that the cluster is performing a soundscape monitoring and this information is visualized on a map. This scenario is described later in Section 5. In the cluster, the assigned names for the nodes in the cluster are RPiC1 as master, and as slaves--RPiC2, RPiC3, and RPiC4. We used the default operating system provided by the manufacturer, called Raspbian [20]. Raspbian was developed by Raspberry Pi and based on a Linux Debian distribution. We used version 9, named \"Stretch\".  
For simplicity and scalability within the whole architecture, we relied on the wireless mesh infrastructure placing the nodes within a range lower than 50 m. In this scenario, a multihop routing protocol is required to interconnect the different nodes within the network and/or the cluster.  
There are many multihop routing protocols. From all of them, based on our experience and our requirements, better approach to mobile ad hoc networking (BATMAN) [21] is very reliable, stable, well-known, and has little in the way of an overhead.  
BATMAN is a proactive routing protocol based on distance-vector algorithm, that builds a loop free network. BATMAN does not try to estimate the whole path to each destination, but only the best next-hop to a given destination, avoiding unnecessary routing-information exchanges among nodes. If the origin and destination nodes are close, it creates direct routes. This is especially relevant in a cluster, since it allows direct communication among the nodes, without any additional intermediate node (relying nodes), speeding up the transmission of packets and minimizing errors. It must be stressed that BATMAN works in Layer 2; thus, the whole multihop network is a LAN and each node is identified by its own MAC address, simplifying the nodal configuration.  
Finally, to allow Internet connection for the whole system, the master acts as a router (default gateway) for the nodes. The master must use another interface for this purpose, such as an Ethernet card, a second wireless card, or any telco adapter.
## 2 Materials and Methods
### Sequencing Protocol  
Three beads from the cryopreservation tube were washed with extraction buffer containing lysozyme (or lysostaphin for _Staphylococcus_ sp.) and RNase A, incubated for 25 min at 37 \({}^{\circ}\)C. Proteinase K and RNaseA were added and incubated for 5 min at 65 \({}^{\circ}\)C. Genomic DNA was purified using an equal volume of SPRI beads and resuspended in EB buffer. DNA was quantified in triplicate using the Quantit dsDNA HS assay in an Eppendorf AF2200 plate reader. Genomic DNA libraries were prepared using Nextera XT Library Prep Kit (Illumina, San Diego, CA, USA) following the manufacturer's protocol with the following modifications: two nanograms of DNA instead of one were used as input, and PCR elongation time was increased to 1 min from 30 s. DNA quantification and library preparation were carried out on a Hamilton Microlab STAR automated liquid handling system. Pooled libraries were quantified using the Kapa Biosystems Library Quantification Kit for Illumina on a Roche light cycler 96 qPCR machine. Libraries were sequenced on the Illumina HiSeq using a 250 bp paired end protocol. Reads were adapter-trimmed using Trimmomatic 0.30 with a sliding window quality cut off of Q15 [22]. De novo assembly was performed on samples using SPAdes version 3.7 [23] and contigs were annotated using Prokka 1.11 [24].