esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Hydraulic-Based Fuel Flow Control
### Metering-Based Flowrate Control
#### 3.1.2 Evolution of FMU  
In the past few decades, a large number of research studies on the performance improvement of FMU have been made. [PERSON] et al. [34] presented a simple FMU which only included a metering valve regulated by control unit. However, its control accuracy was low due to the lack of CPDCV. In order to solve this issue, [PERSON] et al. [35] investigated a novel electronic FMU with a diaphragm valve to maintain the pressure-difference constant. Moreover, in order to simplify structure and improve efficiency of FMU, [PERSON] et al. proposed a novel FMU with bypass valve [36]. Moreover, [PERSON] et al researched a gas turbine engine with a double control unit [37]. The novel FMU not only could transmit the fuel to combustor but also return the superfluous flow to tank. In 1999, [PERSON] [38] presented a new metering valve. The key merit was the simple structure and low cost. Today, with the development of aeroengine, the function of hydro mechanical fuel system has been gradually improved. Engine speed and engine inlet temperature have been introduced into the control system to complete the specific operation tasks. Several research studies on FCS concentrates on the structure simplification and performance improvement. [PERSON] et al. [5,6] proposed a new rotary FMU. The innovations were valve shape design and the direct drive actuation, but it was limited due to complex manufacture and high cost. [PERSON] [39,40] studied the complex fuel control system with hydro mechanical type and researched the mechanism of the model. In addition, due to the high control accuracy and wide range, the variable pressure-drop fuel metering device [41]  
Figure 4: Flowrate control system with DDV driven by VCA.  
Figure 3: Metering-based fuel flowrate control system: (1) Fuel pump, (2) safety valve, (3) pressure reducing valve, (4) servo valve, (5) metering valve, (6) directed constant pressure difference valve, (7) dynamic damping orifice, (8) main valve, (9) damping piston, (10) combustor.  
has been researched. Conversely, the drawbacks are the complex manufacturing, repairing, and maintenance costs.
# Influence of Directionality on the Seismic Response of Typical RC Buildings
## 4 Results and Discussion
### Interstory Drift
The results show the angles of incidence under which the maximum and minimum interstory drift occurred on both the \(x\) and \(y\) axes. Note that there is no common preferential angle of incidence for the four archetypes studied, although, for Archetypes 2, 3, and 4 on the \(x\) axis, maximum interstory drift values are maintained for an angle of incidence of 67\({}^{\circ}\)\(30^{\prime}\), while the minimum drift occurs for an angle of incidence of 157\({}^{\circ}\)\(30^{\prime}\), which means that there is a rotation of 90\({}^{\circ}\) between the angle of incidence of maximum and minimum interstory drift. The same happens for the results of Archetype 1, where interstory drift on the \(x\) axis reached maximum values for an angle of incidence of 292\({}^{\circ}\)\(30^{\prime}\) and minimum values for an angle of incidence of 202\({}^{\circ}\)\(30^{\prime}\). Regarding interstory drift on the \(y\) axis, it can be noted that the angle of incidence that produces the highest values is different for each of the four archetypes, while the minimum values of interstory drift occur for an angle of 135\({}^{\circ}\) in the case of Archetypes 1, 2, and 3, and Archetype 4 reaches minimum values under an angle of incidence of 157\({}^{\circ}\)\(30^{\prime}\). Meanwhile, Table 6 shows the interstory drift values calculated with the two components of the analysis using the SRSS method. The values show that there is no clear trend regarding the angles of incidence that produce the maximum and minimum interstory drift for the different archetypes considered.
## 2 Mission Concept
### Mission Concept of Operations
#### 2.3.2 Normal Mode  
In this state, the normal operation of the mission starts, and a beacon is transmitted periodically. While most of the subsystems will be powered ON continuously, certain will have conditions. During normal mode, different processes run simultaneously, this includes the beacon transmission, the ADCS, and the health check process. The latter is essential to keep monitoring the health of the CubeSat and acts accordingly in case of an abnormal telemetry reading. Negative feedback from the health check process can be due to low power, component's high/low temperature, failure of task execution within a time limit, high angular velocity, and any other sensor reading that can present abnormalities. This negative feedback will lead to switching Light-1 from normal mode to safe mode. The payload operation requires data from the Sun-horizon, in which the detectors must point toward the Earth's horizon to collect the specific data required. Hence, the ADCS subsystem will operate and will be responsible to always achieve this pointing requisite. When the CubeSat is within range of the ground station, the ADCS will change into target pointing toward the ground station as this has higher priority. Target pointing is required to achieve line-of-sight communication with the ground station to start downloading data using \(S-\)band. Thus, the \(S-\)band transceiver will be conditionally powered ON when payload data downlink is needed. Light-1 will receive commands and transmit housekeeping data during the ground station access time using a UHF transceiver.
## 3 Background and Preliminaries
### Wireless Sensor Networks Structure  
The structure of a WSN is based on the sensor node which is made up of four main components as illustrated in Figure 3. The main components are: sensing unit, data processing unit, power unit, and data transmission unit. The sensing unit is composed of two subunits which are the sensor that collects the data from the field and the analog to digital convertor (ADC) that transfers the analog signal produced by sensor to a digital form to be used by the processing unit. The processing unit uses a small storage media to store necessary data during processing task. The transceiver unit is used for the communication purposes between the node and other nodes or the base station. Three more additional units may be incorporated in the sensor structure depends on the application which are the energy generator that supplies the sensor with additional energy sources, the mobilizer which is sometimes used for supporting the mobility of sensor nodes, and the position finding system that determines the current position of mobile sensor nodes [1].  
Figure 3: The main sensor node structure [1].  
Figure 2: Some of WSN application areas. Adapted from [3].  
The structure of a WSN differs by its different layer design strategy from physical layer to network layer. For example, many strategies can be used for source encoding in the physical layer. The designer may choose between the pulse code modulation or delta modulation and the same goes for the channel coding and signal propagation. In the Medium Access Control (MAC) layer, many choices are available for the design of a suitable structure. In the MAC layer, the two main strategies are contention based medium access and contention free medium access. The IEEE 802.15.4 protocol is considered the standard specification for the physical and MAC layers for networks that have low data rates, low energy sources, and short range communications [34].  
On the network layer, routing protocols are classified according to network structure into flat-based, hierarchical-based, and location-based [35]. In a flat-based structure, all nodes are treated equally and given the same functionality. In a hierarchical structure, the nodes have different roles in the network according to their responsibilities. In this structure, a cluster head (CH) is given additional responsibilities and resources over other common nodes. In the location-based structure, the position of the nodes is used to determine the most suitable routing path to the target.
## 3 Performance Analysis
### Average Bit Error Rate  
In Section 2, we specifically introduced the system model. The BER performance is usually closely related to modulation method. There are many modulation methods that can be used in hybrid RF/FSO systems. To our knowledge, the common modulation method in deep space optical communications is PPM, and BPSK with good anti-interference performance is the classical modulation method in the near-Earth hybrid system.  
According to [33; 34; 36], the conditional BER of \(L\)-ary pulse position modulation (\(L\)-PPM) is:  
\[P_{LPPM}=\frac{1}{2}erfc\left(\frac{1}{2\sqrt{2}}\sqrt{\gamma\frac{L}{2}{\rm log }_{2}L}\right). \tag{18}\]  
Figure 4: Block diagram of the hybrid PPM–BPSK–SIM system; (**a**) transmitter, (**b**) receiver.  
By [11], the common unified expression of the conditional BER of binary modulation in Gaussian white noise is  
\[P_{e}(e|\gamma)=\frac{\Gamma(a,b\gamma)}{2\Gamma(b)}, \tag{19}\]  
where \(a\), \(b\) are modulation parameters, and when \(a=0.5\), \(b=1\), it is BPSK modulation.  
Base on \(L\)-PPM-BPSK-SIM schemes, We further assume an equivalent probable data transmission for both conditional and unconditional BER calculation over the channel model. So, the conditional BER with Gaussian white noise is [33]:  
\[P_{LPPM-BPSK-SIM}=Q\left(\frac{1}{4}\sqrt{\gamma L\text{log}_{2}L}\right), \tag{20}\]  
where \(Q(\cdot)\) is the Gaussian \(Q\) function.  
The unconditional BER of the system affected by the coronal turbulent media is defined as:  
\[P_{BER}=\int_{0}^{\infty}P_{e}(\gamma)f_{\gamma}(\gamma)d\gamma, \tag{21}\]  
where \(P_{e}(\gamma)\) is the conditional BER of modulation mode in Gaussian white noise, and \(f_{\gamma}(\gamma)\) is the PDF of the hybrid RF/FSO system, which can be obtained by differentiating (14):  
\[f_{\gamma}(\gamma)=\left(\frac{A_{2}}{\gamma_{1}}-\frac{B}{A_{2}}\right)\exp \left(-\frac{\gamma}{\gamma_{1}}\right)\times\left(G_{1,6}^{6,0}\bigg{[}B \gamma\bigg{|}\begin{array}{c}\frac{\xi^{2}}{2}+1\\ \frac{\xi^{2}}{2}\end{array}\right]+G_{2,7}^{6,1}\bigg{[}B\gamma\bigg{|} \begin{array}{c}o_{3}\\ o_{4}\end{array}\bigg{]}\right), \tag{22}\]  
where \(A_{2}=\frac{\xi^{2}2\tau^{2}+\phi}{8\pi\Gamma(\varphi)\Gamma(\varphi)},o_{2}= \frac{\xi^{2}}{2},\frac{\phi}{2},\frac{\phi+1}{2},\frac{\phi}{2},\frac{\phi+1} {2},0,o_{3}=-1,\frac{\xi^{2}-1}{2},o_{4}=\frac{\xi^{2}}{2}-1,\frac{\phi}{2}-1,\frac{\phi-1}{2},-1,0\).  
Substituting (20) and (22) into (21), the approximate closed-form expression of the unconditional BER of the hybrid RF/FSO system modulated by \(L\)-PPM-BPSK-SIM can be obtained:  
\[P_{BER}= -\frac{1}{2\sqrt{\pi}}\Gamma\left(\frac{1}{2}\right)+A_{2}\frac{ \sqrt{L\text{log}_{2}L}}{8\sqrt{2\pi}}\times\left(\frac{L\text{log}_{2}L}{32} -\frac{1}{\gamma_{1}}\right)^{-\frac{1}{2}}\!\!\!G_{2,6}^{6,1}\Bigg{[}\frac{B }{\frac{\gamma_{1}L\text{log}_{2}L}{32}-1}\bigg{|}\begin{array}{c}o_{5}\\ o_{2}\end{array}\Bigg{]}, \tag{23}\]  
where \(o_{5}=\frac{1}{2},\frac{\xi^{2}}{2}+1\).
# Benefit Analysis of Grid-Connected Floating Photovoltaic System on the Hydropower Reservoir
## 3 Economy-Financial Analysis
### National Economy Analysis of the Plant
#### 3.2.2 Impact on the Environment  
At present, the issue of environmental protection has become a global issue, attracting more and more attention from society; investment in the construction of PV plants and renewable energy sources is receiving more attention and becoming more popular.  
Solar power is considered a clean energy source that does not pollute the air when producing electricity. Thereby, solar power can significantly reduce the amount of \(\rm CO_{2}\) emitted into the environment by the power generation industry, contributing to a reduction in climate change and helping to protect the environment.
## 1 Introduction
### Cultural Heritage Annotation and Classification
#### 1.2.1 Feature Extraction Approaches  
In the past, visual recognition and image classification were performed using feature extraction approaches. These approaches are designed to examine the low-level features of an image by analyzing its pixel structure. The approaches used to extract these features include SURF, SIFT, HoG, GIST, Color Features, etc. Once extracted, the low-level features are used as inputs for supervised or unsupervised machine learning models such as SVM, KMeans, Random Forests, etc. The resulting models are then used to classify or cluster the images [16; 18; 19; 20]. These methods perform very well in some applications and scenarios, but their performance is limited in general classification tasks and big data environments where deep-learning-based approaches outperform feature extraction approaches [21].  
Using feature extraction techniques, many methods have been proposed to tackle the challenge of cultural heritage classification. The authors of [11] published a dataset collected from the Rijksmuseum of Amsterdam consisting of more than 100 thousand artifacts. They also provided baseline results of a four-task classification challenge using SIFT features and Fisher vector encoding. The authors of [10] combined HoG, SIFT, GIST and Color Features to classify painting genres, while the authors of [22] proposed a ranking-based method to classify paintings using Random Forests. The authors of [23] presented a histogram-based combination of local and global features for the classification of paintings. Several other researchers have attempted to combine feature extraction and feature learning. A comprehensive study investigating the correlations between feature maps for image style identification is presented in [24], and the authors of [15] combined GIST and CNN features for the classification of paintings. Additionally, in [25], the authors presented a hybrid method dedicated for the identification of copyrighted paintings in TV shows and movies. The technique uses deep learning to identify objects and a local feature detector to identify paintings. Furthermore, in [26], a study comparing CNNs to feature extraction methods clearly shows that CNNs have better performance than handcrafted features in the classification of cultural artwork. Nevertheless, feature extraction methods still perform well in many scenarios related to cultural heritage such as the identification of forged paintings [27].
## 3 The Proposed Semantic Internet of Things Middleware
### Ensuring Robust Security in the Middleware  
Healthcare data, by their highly personal and sensitive nature, necessitate top-tier security measures. The Semantic IoT Middleware (SIM), as shown in Figure 1, integrates a layered approach to safeguarding data integrity and privacy, especially when transiting through different components. Figure 4 further details the security capabilities of SIM, mainly the following:  
* Data Annotation: The process begins with the acquisition of raw health data and events from IoT devices. These raw data may include information such as the vital signs of a  
Figure 3: The AI Feedback and Analysis Module. This image shows all the different machine learning techniques this module uses.  
patient or specific medical events. Initially, interfacing with the Semantic Annotation Module occurs, where the data are labelled with relevant annotations, adding semantic context to raw readings. This step not only aids in data understandability but also prepares them for subsequent security measures.
* Encryption and Authentication: Once annotated, the health data enter the Data Security and Encryption Module. This pivotal component encompasses multiple operations to ensure data confidentiality and integrity:
* Encryption: Using the AES-256 encryption standard.
* Device Authentication: Through Public Key Infrastructure (PKI), the module authenticates the source IoT device. This ensures that the data origin is legitimate and trustworthy.
* Authorisation: Role-Based Access Control (RBAC) is employed to authorise the data transfer, ensuring that only entities with the requisite permissions can access the data.
* Digital Signing: The module employs the Elliptic Curve Digital Signature Algorithm (ECDSA) to digitally sign the data, ensuring the validity and integrity of the transmitted information.
* Blockchain Integration: Once the health data are secured, they are passed on to the Blockchain Component. This step amalgamates the robustness of blockchain technology with the previously applied security protocols, granting the data an added layer of protection against tampering or unauthorised access.
* Data Processing and Feedback: From the blockchain, the data progress to the Semantic Processing Component, where operations like query, analysis, and integration occur. The processed data are subsequently directed to the AI Feedback and Analysis Module. This AI-driven component harnesses the data to generate actionable insights or composed services, capitalising on rich, secured, and semantically rich information.  
Figure 4: SIM security module processes.
# TARS: A Novel Mechanism for Truly Autonomous Resource Selection in LTE-V2V Mode 4
## 4 System Model Assumptions and Proposed Mechanism
### Proposed Approach  
Enhancement in collision avoidance for the LTE D2D side link resource allocation provides significant benefits for LTE broadcasting. For the selection of RBs, our proposed approach utilizes the crypto-hash function. Through the use of the crypto-hash function, the aim was to achieve the following two tasks: (1) to provide randomness in the selection of an RB by exploiting the avalanche effect and (2) to predict the resource selection of the neighbors to a certain level through the use of the input to the crypto-hash.  
The avalanche effect is the property of algorithms for which, for a slight change in the input, there is a significant change in the output (almost half the bits flip) [34]. The proposed approach exploits this property in Message Digest 5 (MD5), which has a 128 bit hash value expressed as a 32-digit hexadecimal number. In addition, by handpicking the seed for the crypto-hash function, the RB selection of the neighbor vehicles can be predicted to a certain level.
# Multihop Latency Model for Industrial Wireless Sensor Networks Based on Interfering Nodes
## 1 Introduction and Related Work
Based on this analysis, they propose a mechanism called Bell-X, which adjusts this period dynamically, allowing the synchronization to be speeded up, not only during deployment but also to improve the connection of a new node during the steady state of the network. [PERSON] et al. [11] develop an analytical model to characterize the synchronization process in TSCH using shared slots and sporadic traffic. In order to characterize the behaviour in the different phases, an analytical model was proposed for the TSCH synchronization time and the formation of ascendent routes through RPL [12]. This model allowed the behaviour to be observed under different configurations of the parameters involved in the network formation process, providing a global view that allows the configuration of these protocols under different use cases to be optimized. The formation of downstream routes has other complications, since interfering nodes affect each hop in a localized manner. This paper proposes an analytical model to characterize the time it takes to perform the downstream routes formation in RPL, considering the number of interfering nodes in each hop. Using this model, it is possible to analyse the impact on the network formation time, due to different protocol parameters and physical configurations of the WSN, such as the number of interfering nodes that cause collisions to the RPL traffic. This multi hop message exchange covers the formation of downstream routes in RPL, and in combination with the published results of [12], including TSCH synchronization and upstream routes formation, the entire network formation process is temporally characterized. Thanks to these models, it is possible to predict the behaviour of a WSN based on TSCH and RPL under different configurations and deployments, in order to optimize the configuration of both protocols in industrial scenarios. The rest of the article is structured as follows. Section 2 introduces the complete network formation process, looking at both the TSCH synchronization phase and the creation of upstream and downstream RPL routes. Section 3 provides an analysis of the state of the art, addressing those articles that study the process of network formation based on TSCH and RPL. Section 4 proposes the analytical model for the upstream path formation time in RPL, while Section 5 describes the simulations performed to test the reliability of the model. Section 6 shows the results obtained in the different simulations. Finally, Section 6 presents the conclusions obtained after this analysis.
## 4 Open Issues
### Supporting Tools  
The fundamental role of WSAN is to connect the cyber space and the physical world. Cyberspaces are by nature discrete-time systems, whereas the physical world is composed of continuous-time systems. This hybrid feature of the integrated system challenges the development of simulation and design tools that can be used to evaluate the performance of QoS mechanisms for WSANs. An interesting question is whether or not it is technically feasible to develop such a tool based on a service-oriented architecture. If so, programming technologies for implementing various services need to be developed. In addition, benchmark testbeds and prototypes also deserve extensive research and development effort. Using these supporting tools, guidelines can be further developed that help implement new protocols, mechanisms, and algorithms for QoS management in practice.