esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Background
### Smart Gates
#### 2.1.1 Technologies Applied in Smart Gates  
As noted above, smart gates in seaports apply various technologies to automate and optimize entry and exit processes. According to the literature, some of the main technologies commonly used in smart gates are as follows:  
* Sensors: These are used to detect the presence of vehicles and containers and monitor traffic flow at the gates. They help identify when a vehicle is approaching the entrance or exit, triggering the start of the verification and registration process [12; 39].
* Automated identification: This is performed using technologies such as RFID (radio-frequency identification) and NFC (near-field communication). Vehicles, containers, and drivers can have tags or cards equipped with these technologies, allowing for quick, contactless identification when passing through the gate. This helps to control entry and exit operations at the gate and to make the gate itself more available [12; 21; 22].
* Biometric recognition: Using techniques such as fingerprint reading or facial recognition, this is used to verify the identity of drivers and other individuals entering the terminal, and this information is later stored in a database using big data technology. In this way, these systems contribute to the security of the terminal [39; 40; 43].
* Cameras and OCR: High-resolution cameras and optical character recognition (OCR) systems are employed to capture images of vehicles, license plates, damage inspection, and other relevant information. These cameras can automatically read and interpret information, speeding up the registration and verification process. So, OCR can be used for the collection and verification of trucks and their driver information and other port users' information [13; 21; 22]. (Although RFID reduces throughput time just as OCR does, the latter has the advantage that vehicles and containers do not need specific technologies to reduce this time [22].)
* Data integration: Automated gate systems are connected to different port information systems, such as the port community system (PCS) and the terminal operating system (TOS), and technologies, such as OCR and RFID, to handle the flow of cargo. This integration allows the exchange of information in real-time and the automation of processes. Also, these systems could be integrated with external parties to improve the flow of information [22].
* Data analysis and artificial intelligence: The data collected by the smart gates are analyzed using artificial intelligence and machine learning algorithms. This makes it possible to identify patterns, optimize traffic flow, predict waiting times and truck arrivals, and make informed decisions to improve operational efficiency [44; 45].
## 3 Outage Probabilities
### Scenario 2: Repeated Transmission through a Relay  
In the second scenario, as shown in Figure 4, the source transmits data to the receivers in the group, and the relay passes the data to the receivers if the transmission from the source to the relay is successful. With given source and relay positions \((x_{S},y_{S})\) and \((x_{R},y_{R})\), the outage probability for a receiver at location \((x_{D},y_{D})\), denoted as \(P_{Outage(2)}(x_{D},y_{D})\), can be represented as:  
\[\begin{array}{l}P_{Outage(2)}(x_{D},y_{D})=1-\left\{\left(1-P_{SD}^{Outage} \right)+P_{SD}^{Outage}\Big{(}1-P_{SR}^{Outage}\Big{)}\left(1-P_{RD}^{Outage} \right)\right\}\\ =P_{SD}^{Outage}P_{RD}^{Outage}+P_{SD}^{Outage}P_{SR}^{Outage}-P_{SD}^{Outage}P_ {SR}^{Outage}P_{RD}^{Outage}\end{array} \tag{30}\]  
where \(P_{RD}^{Outage}\) and \(P_{SD}^{Outage}\) are functions of \((x_{D},y_{D})\). The outage probability considering all receivers in the group can be written as:  
\[\begin{array}{l}P_{Outage(2)}^{Group}=\frac{1}{N_{D}}\iint_{Group}P_{Outage (2)}(x_{D},y_{D})f_{D}(x_{D},y_{D})dx_{D}dy_{D}\\ =\frac{1}{N_{D}}\iint_{Group}(P_{SD}^{Outage}P_{RD}^{Outage}+P_{SD}^{Outage}P_ {SR}^{Outage}-P_{SD}^{Outage}P_{SR}^{Outage}P_{RD}^{Outage})f_{D}(x_{D},y_{D})dx _{D}dy_{D}\\ =\frac{1}{A_{Group}}\iint_{Group}(P_{SD}^{Outage}P_{RD}^{Outage}+P_{SD}^{Outage }P_{SR}^{Outage}-P_{SD}^{Outage}P_{SR}^{Outage}P_{RD}^{Outage})dx_{D}dy_{D}\end{array} \tag{31}\]  
assuming a uniform distribution of receivers.
## 1 Introduction
These studies are all related to the delay calculation results. In addition, some scholars have considered the delay calculation of intersections in the research of path planning using heuristic algorithms [14; 15; 16; 17]. The calculation error will affect the matching degree of the intersection signal timing plan and the traffic demand. When the coordinated control of the bus priority arterial road is carried out, the expected bus priority effect will not be achieved. Therefore, this paper takes arterial coordination control as the research object to realize the passive bus priority. Firstly, based on the existing research on vehicle delays of arterial coordination control, a multi-conversion standard delay calculation method is proposed for the mixed traffic flow of buses and cars of arterial coordination control. This method calculates the delay of cars and buses on each line separately. Then, arterial coordination control arithmetic cases are generated to calculate vehicle delays, and the accuracy of the method is verified by simulation software. Finally, on the basis of in-depth analysis of the impact of the stop time on the average speed of the bus, we comprehensively considered the bus stop and passenger capacity to establish an optimization model of bus priority arterial coordination control with per capita delay as the optimization objective and verify the validity and parameter sensitivity of the model through the calculation cases. The research in this paper can calculate the delay of two kinds of vehicles more accurately. On this basis, considering the bus stop and the passenger capacity of the line, from the point of view of people, this paper proposes an optimization method for the coordinated control of the bus priority trunk road, and, at the same time, it can give more priority to the bus line with more passenger capacity, which is more in line with the actual bus operation needs.
# An Adaptive Security Framework for Internet of Things Networks Leveraging SDN and Machine Learning
## 5 Enhanced SCAFFOLD Protocol
### Protocol Messages  
The Enhanced SCAFFOLD protocol consists of a sequence of structured messages exchanged between the IoT nodes to establish secure communication channels. The protocol messages encapsulate data, including public keys, nonces, session keys, and node identities. The messages are formally specified using a consistent notation that defines the content and encryption mechanism. For example, the preliminary key exchange involves the nodes sending their public keys encrypted with the recipient's public key. The channel request and grant messages allow the creation of session keys using encrypted nonces. Periodic reauthentication resets the session keys to provide freshness. This messaging notation unambiguously defines the protocol sequence, data transmitted, and encryption requirements. The formal definition facilitates implementation and interoperability. Rigorous security analysis can verify that the protocol messages are constructed properly and encrypted using the authorized keys. Any deviations represent a potential violation. The clearly defined protocol messaging syntax and semantics aid in translating the high-level protocol into a concrete implementation.  
* **Preliminary Key Exchange** ni \(\rightarrow\) nj: EN_nj(PKi, PKj); nj \(\rightarrow\) ni: EN_ni(PKj, PKi).
* **Channel Request** ni \(\rightarrow\) nj: EN_ij(IDi | Nni, Kij).
* **Channel Grant** nj \(\rightarrow\) ni: EN_ij(Nni, SKij).
* **Periodic Reauthentication** ni \(\rightarrow\) nj: ni, Nni; nj \(\rightarrow\) ni: EN_ij(SKij_new, SKij_old); where EN_k denotes encryption with key k.
# Research on Scene Classification Method of High-Resolution Remote Sensing Images Based on RFPNet
## 4 Results
### Experiment Settings  
This paper conducted experiments on the UC Merced dataset and the SIRI-WHU dataset, which are adopted for the following three reasons:  
(1) As benchmark datasets, these two datasets are widely used in the field of high-resolution remote sensing image classification, so as to facilitate the comparison with the methods proposed by other scholars.  
(2) These two datasets contain a relatively small number of images, so it can further prove the advantages of the dataset amplification method proposed in this paper.  
(3) The two datasets are acquired by different sensors and contain scene images of different regions. Therefore, experiments using these two different datasets can further prove the generalization ability of the proposed method.  
In the experiment, 80% of high-resolution remote sensing images were randomly selected as the training set, and the remaining 20% was used as the test set. The batch size which refers to the number of samples selected for a training iteration was set to 64. The regulation rate of \(L2\) regulation was set to 0.0001. The initial decay rate of the moving average model was set to 0.9999. The training iteration is set to 100,000. The experimental hardware platform was based on Intel E5-2680 V3 processor, TITAN V GPU, 64G memory. The software platform of the experiment was based on Ubantu16.04, and adopted CUDA 9.0, CUDNN 7, and TensorFlow1.12.0 environment.
# The Concept of Segmented Wind Turbine Blades: A Review
## 1 Introduction  
Over the past few decades, wind turbines have been developing rapidly. Most notably, the size of the rotor diameter and the corresponding power output have been increasing steadily to rotor diameters of up to 180 m, with rated powers as high as 9.5 MW [1; 2; 3]. This up-scaling trend is still ongoing, especially offshore, and is motivated by an expected reduced cost of energy (COE) for larger rotors as a result of increased economies of scale [4; 5; 6; 7]. However, this up-scaling leads to issues that can cause a steep increase in costs related to the production and handling of blades, to the extent that further up-scaling may no longer be beneficial. As a consequence, optimal rotor sizes exist for on- and off-shore turbines, which can increase as a result of technical improvements [2]. Furthermore, methods to reduce the loads on the rotor have proven successful for reducing the COE. The increase in size of the blades has led to interest in the concept of so-called \"segmented\" blades. Instead of the conventional single piece blades, these are manufactured as a number of segments, which can be transported individually and assembled at the site of the turbine. While the \"segmented\", \"split\" or \"modular\" blade concept is not new, it has recently gained increased interest. This paper intends to provide the reader with an overview of the concept. Design options include span-wise or chord-wise segmentation, the purpose and location of the division, as well as the use of a static joint or a variable mechanism. The available options are discussed along with their advantages and limitations. Furthermore, the feasibility of different methods is discussed.
# Forward-Looking Element Recognition Based on the LSTM-CRF Model with the Integrity Algorithm
## 2 Related Work  
The research objective of this article is similar to that of [PERSON] et al. [8] The goal is to obtain various elements of specified type at the sentence level, including entities, attributes, and attribute values. The existing element recognition methods can be largely divided into the following three categories.
# A Novel Evolutionary Algorithm for Designing Robust Analog Filters
## 2 Related Work
The active area of evolving robust systems is evolvable hardware [35; 36; 37]. Most of these approaches employ genetic algorithms or evolution strategies as the search procedures. Very recent work is the evolution of robust digital circuits [38; 39]. In this work, [PERSON], [PERSON], and their collaborators examine the feasibility of evolving robust digital circuits using a type of \"messy gate\". [PERSON] et al. [40] investigated how evolution may exploit non-perfectdigital gates to achieve fault tolerance, including tolerance to output noise and gate failure. However, the noise introduced to improve robustness is not applied to parametric values of the components, but to the analog outputs of the messy gates, and an evolution strategy (ES) is used as the open-ended topology search tool. This method is thus not as instructive as might be desired in exploring effects of alternative topologies. In addition to robust design and optimization in noisy environments, evolutionary algorithms have also been successfully applied to other optimization problems with uncertainty such as time-varying optimization problems. For a complete overview on the general topic of evolutionary optimization under uncertainty including the major techniques and issues, readers are referred to an excellent survey of [PERSON] and [PERSON] [28]. In this paper, we chose analog filter design as our test problems. The pioneering work of [PERSON] in automated analog circuit synthesis, including lowpass, highpass, and asymmetric bandpass filters, is described in [13; 41]. However, they did not specifically work on evolving robust circuits. [PERSON] and [PERSON] [15] proposed a linear representation approach to evolve analog circuits in which several lowpass filters were used as test problems. In another work, by [PERSON] and [PERSON] [42], a genetic algorithm with a linear direct encoding approach is used to evolve lowpass and bandpass analog filters. What is special in their work is that they constrained the component values to be chosen among the manufacturer's preferred component values. Finally, we have applied GPBG approach to a few benchmark and real-world problems including the lowpass filter design problem [43] and pump design problem [2]. Much work has been done in both evolutionary robust design and analog circuit synthesis. However, few studies specifically address how GP-based topologically open-ended synthesis may provide a new method of open-ended robust design. This may enable us to move robust design forward to the conceptual/functional design stage, i.e., at the very beginning, which will augment the current practice of designing for robustness in the parametric design stage.
# Experimental Evaluation of Graph Databases: JanusGraph, Nebula Graph, Neo4j, and TigerGraph
## 6 Conclusions and Future Work  
The main objective of this study was to examine and evaluate the latest versions of the most popular graph-type databases. We selected the JanusGraph, Nebula Graph, Neo4j, and TigerGraph engines as the top four databases in the DB-Engines 2022 Ranking. The LDBC SNB benchmark was used to evaluate the performance of the four databases. From the experiments, we conclude that Neo4j outperformed the other graph databases overall in terms of node load time and query execution time. It is a database that can handle a large volume of data and guarantees high scalability and low latency.  
Furthermore, it also offers better guarantees, is easy to use and compress, and has the Cypher query language, which is considered the most powerful and intuitive by users and programmers, although some prior knowledge is required. Neo4j also has a flexible data schema that allows users to easily access the data. Although TigerGraph has similar features to Neo4j, it is slower when dealing with larger amounts of data. JanusGraph and Nebula Graph are very different, as they are still under development. The applications of these databases are many and varied. We can use them for personal or experimental purposes. They are used in tax and financial fraud detection, in biochemistry and biology to identify drug components, and most importantly, in social networking.  
As to future work, we intend to evaluate other open-source graph databases using the LDBC SNB benchmark, compare them to other open-source graph databases and use higher scaling factors.  
Conceptualization, J.B. and F.S.; Methodology, J.M. and J.B.; Software, J.M.; Validation, J.M., F.S. and J.B.; Formal analysis, J.M., F.S. and J.B.; Investigation, J.M.; Resources, J.M.; Data curation, J.M.; Writing--original draft preparation, J.M.; Writing--review and editing, J.B. and F.S.; Supervision, J.B. and F.S.; Project administration, J.B. and F.S.; Funding acquisition, J.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data sharing is not applicable to this article.  
The authors declare no conflict of interest.
## 3 State-of-the-Art for WirelessHART Routing Schemes  
Since the present paper deals with wireless devices, the network topology is defined by the devices and their neighboring connections within radio range. However, due to fading and interference, many of these connections (links) are unusable. Hence, in the WSN context, routing consists of selecting, given a set of devices, the links to be used.  
Routing is very important for network lifetime and performance, since it defines the devices through which the data will pass, something that directly affects the delay, energy consumption and throughput, for example [50].  
In this section, we will present the most recent routing algorithms for WirelessHART and highlight their characteristics in order to aid the user's choice for a routing algorithm adequate for his/her needs.  
A previous evaluation of WirelessHART routing algorithms was made in [51] in which three algorithms were selected (the Han routing algorithm from [52], the Bellman-Ford [53] and ELHFR [54]) in order to test whether those algorithms were suitable for WirelessHART. From a graph-analysis, the author concluded that the Han algorithm and the Bellman-Ford algorithm were both adequate for WirelessHART networks and had similar performance on the tests (with a slight advantage for the Han algorithm). The ELHFR had worse performance than the others. For this reason, we will only consider the Han algorithm in the present paper.
# An Event-Driven Agent-Based Simulation Model for Industrial Processes
## 3 Agent-Based Model
### Agents Communication  
In order to deal with the event-driven system and the asynchronous messaging of the actor model, the communication mechanism adopted for the agents is based on the distributed event-based system, also called the Publish-Subscribe model [20]. This is an indirect communication method among distributed entities that communicate through a Dispatcher or Broker without any direct connection between the sender and the receiver. The Publish-Subscribe system supports cooperative work where a number of participants need to be informed on shared interest events. Moreover, it is used in several applications such as financial systems, Feed RSS, network monitoring, etc., [21; 22]. An example of a Publish-Subscribe communication scheme is reported in Figure 3.  
For instance, when a product X is ready to be loaded into the buffer of the machine Y (i.e., operation code 3), then Product Agent X, Machine Agent Y and Warehouse Agent Z are informed. In this specific case, the Machine Agent Y can check if is able to process the product X, Warehouse Agent Z can check where the product X is located and Product Agent X can control its own status. At the same time, other actions can be performed by the agents. Another example concerns the situation in which product X needs to know its own quality target parameters after being worked. The Product Agent X publishes a message requiring the necessary information so that the Customer Order Agent responsible for the product X is notified. The previous two examples are two situations depicting how the Publish-Subscribe mechanism is used to deal with both production operations events and message exchange among agents.  
Figure 2: Multi-Agent System main actors.
## 4 System Implementation
### Mess Core  
This is the heart of the system: a time-invariant, event-based sequence control of processes, based on the monitored statuses of resources (e.g., management of production processes). The MESS Core controls the synchronization with the CPSs and acknowledges the other system components about the occurring events. As illustrated in Figure 4, its major components can be listed as follows (from left to right):  
* _HTTP Service Brokering Manager_ for the front-end interface to the client components. It consists of an HTTP server to receive user requests, and an HTTP client to pass event messages to the connected clients;
* _OPC UA Server_ for external OPC UA compliant client interface. It embodies and leverages an extended version of the Milo open-source project for complete OPC UA stack management;
* _Process Manager_ is in charge for the execution of the process plans. It is one of the most crucial components of the MESS Core component. It manages the parallel execution of different process plans from different sources. It takes care of the proper locking and releasing mechanism of the resources. Nevertheless, it guarantees the precedence constraints between the operations (CPS functions) during the process plan execution, so that technologically irreplaceable operations cannot be interchanged. The sequence of CPS functions is to guarantee an acceptable and feasible production plan;
* _Event Message Handler_ implements and manages the event-based logic of the MESS Core. Clients can sign up in order to monitor various kinds of events, and the manager is in charge to notify them both on the status changes of the MESS core and the CPS components, as well as on changes in the physical variables' value. It can also dispatch messages from the CPS;
* _Database Manager_ is responsible for the persistent event logging. It records the events in the original form and manages the temporal labeling of the program, process, and operation life-cycles, as well;
* _MESS Manager_ controls the communication between the other program layers;  
Figure 4: Overall view of the MESS architecture.  
_CPS Mapper_ is finally entitled to provide a unified mechanism to synchronize and consistently manage the connection and the information exchanged between the MESS Manager and the CPS Twinning layer (and its various CPS components).  
The overall MESS also embodies a _Process Plan Editor_, which is part of the MESS Graphical User Interface (GUI). It provides a visual tool for the editing of process plan, to be executed by the MESS Process Manager later on. On the front-end side, the function inventory of every registered CPS is accessible and transparent with drag-and-drop feature to help users' interaction. Furthermore, the user interface provides a nearly real-time system overview during the process execution with the presentation of up-to-date production data. Beyond all of this, the admin users have a dedicated and secured interface for the configuration of the production and the MESS system.
## 2 System Model
### Relay Selection Methods
#### Conventional Opportunistic Relay Selection (C-ORS)  
As proposed in much of the literature such as [31; 34; 40; 49], the best relay is selected to maximize the end-to-end SNR of the data link:  
\[\text{R}_{c}\cdot\min\left(\gamma_{\text{SR}_{c}},\gamma_{\text{R}_{c}\text{D }}\right)=\max_{m=1,2,...,M}\left(\min\left(\gamma_{\text{SR}_{m}},\gamma_{ \text{R}_{m}\text{D}}\right)\right), \tag{28}\]  
where \(c\in\left\{1,2,...,M\right\}.\)  
Then, the e2e OP and e2e TP of the C-ORS protocol is computed as  
\[\begin{array}{ll}\text{OP}_{\text{C-ORS}}&=\Pr\left(\frac{(1-\alpha)\text{ T}}{2}\text{log}_{2}\left(1+\min\left(\Psi_{1c},\Psi_{2c}\right)\right)<C_{\text{th }}\right),\\ \text{TP}_{\text{C-ORS}}&=\left(1-\alpha\right)\text{TC}_{\text{th}}\left(1- \text{OP}_{\text{C-ORS}}\right).\end{array} \tag{29}\]  
It is worth noting that the implementation of C-ORS is simpler than that of B-ORS because it only requires perfect CSIs of the data links.