esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work  
Energy efficiency in cellular networks and communication has been studied widely in the literature [2; 3; 4; 5; 7; 8]. Some research papers [2; 3; 4; 5] have proposed different algorithms to implement sleep mode in the LTE BSs. The authors in [5] proposed an approach to reduce energy consumption in mobile networks by introducing discontinuous transmission on the base station side. In some of the pioneering works, MDP has been used as an effective approach for sleep mode implementation [2; 7; 8; 9] for green communication as well as to solve some other optimization problems [10; 11]. The authors in [10] use a Markov decision process-based model to schedule consumers' behaviors in order to optimize the consumers' net benefits. They utilize a networked smart grid system, where future electricity generation is predicted with reasonable accuracy based on weather forecasts. In [11], the authors have proposed an algorithm to design efficient coding tools and optimize frame structure for transmission to facilitate view switching and contain error propagation in differentially coded video due to packet losses. In [2], the authors proposed an MDP-based optimal controller that associates to an activation/deactivation policy which maximizes a multiple objective function of the QoS and improve energy efficiency. Other papers such as [7; 8] consider a single user and use a Markov chain technique to evaluate the energy savings due to the sleep mode mechanism of a single user terminal. The authors in [8] take correlated packet arrivals into account to evaluate an MDP-based sleep mode mechanism. The authors in [9] consider a similar setting of one user and one station and show how to derive the optimal sleep policy numerically by formalizing the problem as a Semi-MDP. The authors in [3] proposed a novel scheme for the sleep scheduling based on decentralized partially observable MDP (Dec-POMDP). However almost all of the above mentioned papers have proposed sleep mode of the BS in low traffic condition and the BS will be in active mode for rest of the time. We refer to this model as '2-state model'. The authors of references [2; 3; 4; 5; 12] have shown that this 2-state power consumption model can reduce energy consumption of the whole network. However, the problem is that the BS takes significant time to wake up from sleep mode, which may cause call drop to new users. This wake up time can range from tens of seconds to couple of minutes for small cell and up to 10-15 min for macro cell [6]. This is clearly a constraint for an energy efficient system. The authors in reference [5] proposed the low power consumption mode, which consumes some small amount of power but wakes up within negligible time; their power consumption model is similar to what we propose as stand-by mode. In contrast to works done in the literature so far, we propose a MDP-based algorithm on the base stations so that they can intelligently switch among three different modes.
## 1 Introduction
Methods such as convolutional neural networks (CNNs) [27; 28; 29], combined convolutional neural networks (CNNs) and support vector machines (SVMs) [30], combined deep belief networks (DBNs) and restricted Boltzmann machines (RBMs) [31], long short-term memory (LSTM) neural networks [32; 33], artificial neural networks (ANNs) [34], neural network algorithms based on rough set and radial basis function (RBF) [35], and recurrent neural networks (RNNs) [36; 37] are used to predict disaster risk, identify affected areas, map landslide and flood vulnerabilities, and recognize post-disaster building damage. Meanwhile, most current research on disaster logistics employs exact or heuristic approaches in determining the location of emergency facilities and routes for sending aid. In this context, there is an unmet need to solve location facility problems for disaster logistics using a deep learning approach. Deep learning can provide innovation and excellence in handling LRP in a more efficient and accurate manner. The development and application of deep learning in the context of disaster logistics management therefore presents significant research opportunities. Therefore, this study aims to fill this research gap by developing a deep location routing problem (DLRP) model, which is dynamic and capable of recommending the opening of distribution centers (DC) based on demand point locations using the K-means method. It also utilizes deep learning to predict the locations of demand points at formed DCs in the context of disaster management, and the formation of disaster relief delivery routes using artificial neural networks (ANN). Additionally, a forbidden route model is established to select the existing routes and map them as either prohibited or permissible routes, resulting in a bi-objective mixed-integer nonlinear programming model with two objectives: minimizing cost and minimizing time. Thus, by incorporating a deep learning approach into solving location facility problems for disaster logistics, this research can significantly contribute to the field. The remainder of this paper is organized as follows. Section 2 begins with a description of the problem, model assumptions, the model objective function, formulating model constraints, and modeling by developing a location routing problem (LRP) model with a deep learning approach called the deep location routing problem (DLRP). This includesa dynamic emergency facility location model, prediction model, transportation route formation, and optimal route, which includes forbidden routes to generate an optimal route with the objective function of minimizing cost and time. Section 3 includes numerical studies to demonstrate the properties of the proposed model and algorithm. Finally, Section 4 concludes this paper.
# Service Life Prediction and Life Cycle Costs of Light Weight Partitions
###### Abstract  
This study investigates the life expectancy (LE) and life cycle costs (LCC) of three alternatives of interior partitions in residential units: gypsum board, autoclaved concrete block, and hollow concrete block partitions. The aim is to examine the sustainability and cost-effectiveness of these partitions in various service and occupancy conditions. Three different service conditions were analyzed: Standard (constructed without faults), Inherent Defect Conditions (with initial, non-progressing defects), and Failure Conditions (developing defects over time). To analyze the impact of occupancy conditions, six 'negative occupancy factors' were identified that accelerate partition deterioration, including non-ownership, poor maintenance, high residential density, the presence of young children, the presence of domestic animals, and the density of furniture. These factors define four occupancy condition categories: light, moderate, standard, and intensive. The research found that hollow concrete block partitions are the most durable, exceeding 100 years in light or moderate conditions. Gypsum board partitions, while cost-effective, have a lower life expectancy, needing replacement in 11-27 years in intensive conditions. Autoclaved concrete blocks offer moderate durability, with similar costs to hollow blocks in normal conditions. Overall, the study highlights the influence of service and occupancy on the lifespan of interior building components, and provides recommendations for partition type selection that are based on specific conditions. These recommendations are a pivotal outcome, highlighting the study's significant contribution to the understanding of the long-term performance and sustainability of building materials in residential construction.  
autoclaved aerated concrete; deterioration; durability; gypsum board; hollow concrete blocks; life cycle costs; lightweight partitions; service life prediction 2024  
13 January 2024
# Fuzzy Q-Learning Agent for Online Tuning of PID Controller for DC Motor Speed Control
## 5 Discussion  
This paper demonstrates a fuzzy Q-Learning agent for tuning online the gains of a PID controller, which is used to control the speed of a DC motor. The initial values of the controller's gains have been arisen from the Z-N method. The Q-Learning agent has as input variable, the error, and as output variables, the percentage changes of the gains. Fuzzy logic is combined with Q-Learning algorithm in order to deal with the continuous state-action space as the conventional Q-Learning algorithm can be applied to discrete state-action space.  
When a fuzzy Q-Learning agent is designed, three main points need to be taken into consideration. The first point lies on the selection of the state variables. A state should be descriptive enough in order to describe the conditions of the system, but without redundant information which will lead to a very large state space. The second point lies on the definition of the reward. The reward must  
Figure 8: Values of (**a**) kp, (**b**) ki and (**c**) kd through the simulation.  
Figure 7: \(ITAE\) of conventional PID control strategy (blue line) and fuzzy Q-Learning PID control strategy (red line).  
be accurate enough in order for the algorithm to converge quickly and accurately. Last but not least, the exploration/exploitation algorithm has to be chosen in dependence to the nature of the environment (stochastic, deterministic, single agent, multiagent, etc.) in order for the algorithm to converge quickly by exploring the possible paths.  
The fuzzy Q-Learning agent learns by interacting with the environment, and the total control strategy indicates a better performance than the conventional PID controller. The simulation results highlight this performance, as both the amplitude of the oscillations and the settling time are reduced. Additionally, the values of the IAE and the ITAE confirm the better performance.  
In future, it is our aim to add another state variable (change of error) in the fuzzy Q-Learning algorithm to enhance the performance of the agent. On the other hand, this addition will increase exponentially the state-action space and extensive exploration will be needed. Thus, it would be prudent to turn to multiagent system approaches in order to decompose the problem and reduce the state-action space.  
Investigation, P.K.; Methodology, P.K.; Software, P.K.; Supervision, A.I.D.; Validation, A.I.D.; Writing--original draft, P.K.; Writing--review & editing, A.I.D. This research received no external funding.  
The authors declare no conflict of interest.
## 2 Dynamic Modeling
### Analysis of the Interference Mathematical Model
#### 2.6.1 Sea Breeze Interference  
Sea breeze disturbances can be divided into average (steady) wind disturbances and pulsating wind disturbances. The equation of additional disturbance force, \(F_{wind}\), and torque, \(M_{wind}\), caused by average wind disturbance to the HAUV is as follows [40,43]:  
\[\left\{\begin{array}{l}F_{wind}=-C_{dw}|v_{rw}|v_{rw}\\ M_{wind}=r_{w}\times F_{wind}\end{array}\right. \tag{17}\]  
Assuming that the constant wind has a constant inertial reference speed,  
\(v_{w}\in R^{3}\), the relative speed of the HAUV moving in the wind is \(v_{rw}=v-v_{w}\); the geometric center of the HAUV body exposed to the air is the center of the wind pressure, and its position in the vehicle body is denoted by \(r_{w}\in R^{3}\); \(C_{dw}\) represents the wind damping coefficient.
## 3 Materials and Methods
### Convolutional Neural Network (CNN) Architectures
Inspired by the human brain, these computer programs exhibit high intelligence, akin to adept investigators capable of identifying complex patterns in photographs [43; 46]. These architectures are particularly suitable for detecting various features in crash diagrams, demonstrating a remarkable ability to differentiate between shapes, boundaries, and complex components within images [43]. Through an extensive analysis of crash diagrams involving pedestrians, CNNs can depict and classify pedestrian-related risk factors. In this investigation, CNNs serve as reliable collaborators in comprehending and identifying various pedestrian safety attributes by analyzing crash diagrams, showcasing the thoroughness of our approach. This study's selection of CNN architectures was based on their well-established performance in image classification and their ability to extract meaningful spatial features from pedestrian crash diagrams. AlexNet, VGG-19, and ResNet-50 were chosen due to their distinct advantages in feature extraction, computational efficiency, and deep learning capabilities. AlexNet was selected for its ability to efficiently process images with fewer parameters while maintaining competitive classification accuracy [9; 22; 45]. VGG-19, with its deeper architecture and smaller 3 \(\times\) 3 convolutional kernels, was included for its strong ability to capture fine details in complex images [22; 45]. ResNet-50 was incorporated due to its residual learning framework, which enables the training of deeper networks without vanishing gradient issues, making it highly effective for complex image classification tasks [22; 45]. These models have been widely applied in prior research, demonstrating their suitability for classification analysis and automated image processing [9; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37]. While newer models, such as Vision Transformers, offer potential advancements in classification performance, this study focuses on establishing a baseline for automated pedestrian crash diagram classification using well-documented CNN architectures. Three distinct CNN models were employed in this study: ResNet-50, AlexNet, and VGG-19. Each CNN model was trained to perform binary classification for multiple pedestrian crash attributes, including intersection type (three-leg or four-leg), roadway type (divided or undivided), crosswalk type (marked or unmarked), intersection angle (skewed or unskewed), the presence of a Michigan left turn (yes or no), and the presence of a nearby residential area (yes or no). These models, recognized for their proficiency in image classification, were chosen for their ability to identify critical pedestrian-related risk factors in crash diagrams. AlexNet and VGG-19 demonstrate high accuracy in extracting intricate details [9], while ResNet-50 excels in processing complex images due to its deep residual learning approach [22]. The integration of these architectures establishes a strong and reliable framework for accurately classifying various pedestrian crash attributes.
## 4 Data- and Layout-Aware Bloom Filter (DLBF)  
The two principal design considerations for using Bloom filters to support data integrity verification in object-based big data transfer systems are space efficiency and insertion order independence. However, as Bloom filters are probabilistic data structures, membership query result in false positives. This section presents a modified version of the Bloom filter, the DLBF [28], to handle false-positive errors more efficiently than a standard Bloom filter.  
As depicted in Figure 3, the DLBF filter employs the SHA-1 block hash engine to transform objects of varying sizes to a fixed size. The resulting deterministic block hash is used as input to the hash functions. Additionally, to prevent false positives in membership queries, the object layout (\(n\)-bits) is appended as additional information about the objects. Consequently, the overall size of the Bloom filter is increased to \((n+m)\) bits. Initially, all these \((n+m)\) bits are set to zero.  
* **Insert Operation:** Calculate the hash values \(h_{1}(s_{i})\),...., \(h_{k}(s_{i})\) for each object \(s_{i}\) and set the corresponding hashed bits, along with the object layout bit, in the Bloom filter \(B\), such that \(B[i]=B[h_{1}(s_{i})]=B[h_{2}(s_{i})]=\ldots=B[h_{k}(s_{i})]=1\).
* **Query Operation:** To test object membership, compute the hash values \(h_{1}(s_{i})\),...., \(h_{k}(s_{i})\) for each object \(s_{i}\) and check the corresponding hashed bits along with the object layout bit in the Bloom filter \(B\) are set to 1 or not. If all of \(B[i]=B[h_{1}(s_{i})]=B[h_{2}(s_{i})]=\ldots=B[h_{k}(s_{i})]=1\) are 1, then membership query is successful; otherwise, the element is not in the set.  
Figure 2(a) represents the Bloom filter state after inserting object\({}_{A}\), object\({}_{B}\), and object\({}_{C}\). Figure 2(b) illustrates the query operation of object\({}_{C}\), object\({}_{D}\), and object\({}_{E}\). The membership query of object\({}_{C}\) is successful as hashed bit positions \(B[20]\), \(B[24]\), and \(B[26]\) along with the layout bit \(B[3]\) is set to 1. Membership query of object\({}_{D}\) fails as one or more of the hashed positions \(B[11]\), \(B[14]\), and \(B[21]\) are not set to 1. However, the query for object\({}_{E}\) returns a false positive in the absence of layout bit, \(B[4]\), as all the hashed bit positions \(B[8]\), \(B[28]\), and \(B[32]\) are set to 1 by hash collisions. Therefore, by incorporating the object layout information, we can effectively mitigate the occurrence of false positives associated with the Bloom filter data structure.
# Design and Implementation of a Passive Optical Network for a Small Town +
## 3 Methodology
### Splitter Configuration and Optical Budget Considerations
The result is a reliable FTTH network design that meets the users' service requirements with sufficient power margin and throughput headroom for future growth.
# Shock Initiation of a Satellite Tank under Debris Hypervelocity Impact
## 4 Initiation of the Satellite Tank under Debris Impact  
It is of great significance for the risk assessment of satellite to determine whether the satellite tank explodes under the debris hypervelocity impact. As the hypervelocity impact tests are limited by the stability of launcher and the accuracy of measuring device. Numerical simulation provides a new method, which has the advantages of low cost, good repeatability and easy access to intermediate physical quantities. In this section, numerical simulation and the initiation criterion are adopted to study the shock initiation of the satellite tank under the hypervelocity impact.
# Automated Enemy Avoidance of Unmanned Aerial Vehicles Based on Reinforcement Learning
## 5 Simulation Environment
### System Construction  
The X-plane flight simulator is used in this paper to simulate the flying dynamics of both the decision making UAV and the enemy UAV, and each X-plane is controled by a Pixhawk (PX4) flight controller. The Pixhawk is a hardware which is also used in controlling the real UAVs. In the simulation system, there is a ground station which can broadcast the information of all the UAVs to every UAV. The PX4 can control the flying of the UAVs according to the desired attitude. The desired attitude is composed of the desired velocity, the desired heading angle, and the desired height. In each UAV, there is an on-board sensor system which is used to sense the position and the attitude information of the UAV. Besides, each UAV has an agent for the communication and the decision control. To be specific, the agent for the decision making UAV has three modules: communication module, decision making module, and translator module. The on-board sensor system sends the sensed information to the agent through the communication module, while the communication module also sends the received information to the ground station and receives the enemy UAV's information from the ground station. Based on all the information received by the communication module, the decision making module then decides which action the decision making UAV should take, while the translator module interprets the action into desired attitude and sends it to the PX4 for the UAV flying control. Since the enemy UAV in this paper is assumed to fly towards the goal point \(G_{e}\) directly all along the process, there is no decision making module in the enemy agent. Therefore, the enemy agent is composed of two modules: communication and control module. The communication module of the enemy agent has the same function as that of the agent for the decision making UAV. In the enemy agent, the control module sends the desired attitude to the PX4, where the desired velocity and the desired height are fixed at the initialization of the simulation, and the desired heading angle is calculated based on the relationship between the goal point and the position of the enemy UAV.  
The structure of the simulation system is illustrated by Figure 4.  
Figure 4: The structure of the simulation system.  
Since another purpose of this paper is to explore the collision avoidance solution for the fixed-wing UAV, the X-plane simulator is set to use the fixed-wing simulation model. The simulation environment is shown as in Figure 5.  
Beside receiving and sending the flying information of the UAVs, the ground control station also needs to send commands to all the UAVs. With these commands, the simulation process can be well controlled by the ground station.
# Hybrid Deep Learning Approach for Automatic DoS/DDoS Attacks Detection in Software-Defined Networks
## 3 The Proposed Method
### Dataset Types
#### 3.1.1 Real-World Datasets  
These datasets are collected from actual SDN networks and contain real traffic patterns and attack behaviors. An example of this dataset is the CIC-DDoS2019 dataset, which was collected from a large enterprise network and includes benign and DDoS attack traffic. This dataset has been widely used in research on DDoS attack detection and prevention in SDN networks (e.g., [25]).