esa-sceva/satcom-chunk-collection huggingface dataset:
# MAC Protocols for mmWave Communication: A Comparative Survey +
## 4 Centralized Protocols
### Brd-Mac  
Relaying is a great choice of approach for mmWave infrastructures experiencing blockage issues. In addition to the previously presented RD-MAC protocol, Ref. [44] presented a blockage robust and efficient directional MAC (BRD-MAC) protocol for 60 GHz based directional mmWave communication. Corresponding to the RD-MAC, the BRD-MAC also has one central node, and the remaining nodes are called common nodes. However, here the nodes are half-duplex, and they have directional antennas, which are electronically steerable. Similar to the RD-MAC, the time frames do not overlap with each other, and the central node keeps track of the clocks of all the nodes and maintains them to stay synchronized. Segmentation of an epoch into scheduling and transmission phases also exists in this protocol.  
* Advantages: The BRD-MAC is a versatile MAC protocol. This protocol considers relay selection, which is one of the most important future mmWave applications, making it more advantageous compared to other traditional protocols. It outperforms some other related MAC protocols such as FD-MAC and RD-MAC under different traffic patterns and blockage rates, demonstrating minimum transmission delay. It is a perfect protocol to tackle heavy load applications.
* Disadvantages: The probability of blockage increases as the number of hops increase. Therefore, the protocol is limited to operating under only two-hops. Such limitations have significant effect on the dynamic environments. As a result, the locations of the nodes and the propagation environment significantly impact the efficacy of the protocol.
# A Review of the Water Desalination Technologies
## 2 The State of the Art
### Forward Osmosis (FO)  
As introduced in the previous section, Forward Osmosis refers to the natural process via which solvent flows from a more diluted solution to a concentrated one if they are put in contact and separated by a semipermeable membrane.  
It is interesting to observe that two solutions with different solutes have the same osmotic pressure if they have the same equivalent concentration and temperature, as introduced by the van't Hoff equation (see the Reverse Osmosis paragraph).  
Therefore, it is possible to extract freshwater by using a solution of glucose more concentrated than the saline water. This approach is applied in the \"hydration bags\", which are an emergency kit equipped with a semipermeable membrane, which contain sugar inside. The bag is used to produce an ingestible draw solution in case of emergency, if a water source is available (rivers, seas, puddles, ponds), thereby avoiding contamination from pathogens or toxins [47].  
FO can also be used in a continuative process. In 2005, a research team from the Yale University proposed the utilization of ammonia carbon dioxide as thermolytic draw solute [48]. The extraction of freshwater from seawater produces the dilution of the ammonia carbon dioxide. These components can be easily recovered using a low temperature distillation. In this way, the main energy input is represented by thermal energy. Renewable energy sources could be exploited, such as solar and geothermal energy sources.  
However, this technical solution is not applicable for drinking purposes because of the presence of traces of ammonia in the freshwater [49]. In fact, according to the World Health Organization the maximal value of ammonia in freshwater for drinking purposes should be lower than 1.5 mg/L, but this condition cannot be achieved by this technology [48].  
Several researchers are investigating alternative draw solutions and better performing membranes, in order to use this technique in big desalination plants [28]. In order to minimize the energy expended to separate the draw solution, different techniques are under investigation, such as temperature or pH variation, the adoption of electro-magnetic field or light [50].  
A possible solution has been proposed by Trevi Systems. The layout of the plant is reported in Figure 21[51].  
In detail, after a preliminary saltwater filtration is introduced into the Forward Osmosis unit. As the solution on the other side of semipermeable membrane is more concentrated, freshwater is extracted from saline water, diluting the raw solution. Thanks to an external thermal supply, the diluted solution can be separated into two flows: the concentrated raw solution, which is sent to the FO unit, and the freshwater flow, which is further filtered before the storage [51]. This technology is in development phase.
# Multi-Media and Multi-Band Based Adaptation Layer Techniques for Underwater Sensor Networks
## 7 Proposed Scheme  
### Protocol Stack of Mult-Band and Mult-Medium Techniques  
Existing underwater communication consists of single medium and single band technology for transferring data through wireless communication. It is difficult to apply various types of applications underwater. Figure 3 shows a protocol stack of multi-band and multi-media underwater communication. The protocol stack consists of different layers, i.e., the physical, data-link, network, transport, and application layers. The adaptation layer is an extension of the data-link layer which contains multi-band and multi-media technology. The multi-band approach is used to split the bandwidth into different frequencies, while the multi-media approach is used to share the data through different channels such as acoustic, visual light, infrared, etc. Multi-band and multi-media approaches are used for the reliable transmission of data in underwater communication.
# Energy Efficiency Perspectives of PMR Networks +
## 3 Modelling Power Consumption in PMR Systems  
A TETRA base station is composed of several functional blocks that are depicted in the diagram of Figure 6. In particular, the main blocks are baseband unit (BB), radio frequency unit (RF) and power amplifier (PA), which together form the transmission chain. Moreover, the site control unit (SCU) is responsible for the management operations of the considered site. In order to maintain the opportune temperature for the whole site, a cooling system is considered. This system is responsible for cooling down the site environment coping with the temperature increase due to electronic operations, or, conversely, provides a heating function when the devices temperature gets below the operational target. Transformers and rectifiers are needed to adapt the network power in order to feed the base station devices, and their operation is considered in the main supply system.  
A TETRA base station power consumption model can be obtained by adapting the one proposed in [28]. The following equation is considered:  
\[P_{in}=N_{TRX}\frac{\frac{P_{out}}{\mu_{PA}(1-\sigma_{real})}+P_{RF}+P_{BB}+P_{ SCU}}{(1-\sigma_{DC})(1-\sigma_{MS})(1-\sigma_{cool})}, \tag{1}\]  
where \(N_{TRX}\) is the number of transceivers at the base station; \(P_{out}\) is the transmission power; \(P_{BB}\), \(P_{RF}\) and \(P_{SCU}\) are, respectively, the power spent for the base band operation, the RF stage and the site control unit. The feeder loss is modeled by the \(\sigma_{feed}\) parameter and \(\mu_{PA}\) is the power amplifier efficiency. Finally, \(\sigma_{DC}\), \(\sigma_{MS}\) and \(\sigma_{cool}\) model the DC loss, i.e., the loss in the transmission chain, the loss in the main supply system and the loss for cooling. The power consumption parameters are summarized in Table 2. Equation (1) shows that the power consumption in base stations can be divided into a static component and a dynamic component. In particular, the dynamic component is related to the output power: by varying \(P_{out}\), for instance when managing different amounts of traffic, and then the variable part of the total power consumption changes. On the other hand, the static component, represented by \(P_{BB}\), \(P_{RF}\) and \(P_{SCU}\), does not significantly change for different \(P_{out}\) values, or the variations are negligible. Therefore, the model can be simplified as follows:  
\[P_{in}=N_{TRX}(\Delta_{p}P_{out}+P_{0}), \tag{2}\]  
where the PA efficiency and losses effects are included in \(\Delta_{p}\) and \(P_{0}\) takes into account the static BB,  
Figure 6: TETRA base station model.  
RF and SCU power consumption. By considering the parameters in Table 2, in this paper, \(\Delta_{p}=7.5\) and \(P_{0}=280\) W have been assumed.
# An Energy-Efficient Approach to Enhance Virtual Sensors Provisioning in Sensor Clouds Environments
## 5 Performance Evaluation
### Comparing ACxSIMv2 and ACxSIMv1  
Regarding ACxSIMv1, we have used the results from [20]. Each scenario uses the same \(THRESHOLD\) parameter for all the dimensions, since, in ACxSIMv1, it is not possible to have different values. The simulations considered only the temperature and humidity from the Intel Lab dataset. Therefore, we have simulated ACxSIMv2 again using the same set of configurations. Table 4 summarizes the results from ACxSIMv1 and ACxSIMv2.  
From the results, we observe that ACxSIMv2 overcome ACxSIMv1 in all scenarios, which was expected since ACxSIMv2 enhanced the clustering and selection procedure. Regarding the energy consumption, as ACxSIMv2 chooses not only the physical sensor nodes but also the sensor devices it will use, the length the transmitted packets will be proportional the measurements taken, which explains why the overall energy consumption of the cloud is reduced.
# A Systematic Literature Review of Various Control Techniques for Active Seat Suspension Systems
## 8 Control Methods Used in the Active Seating Suspension System
### Preview Controller  
Preview controller is a type of controller that has been used to control the vehicle active suspension system [81; 82]. It was proposed by [PERSON] in 1968. It mainly depends on the road profile measurement of the wheels or vehicle suspension. There are two types of the preview control which are called the look ahead and the wheelbase. The look ahead preview control normally obtains the information from the vehicle ahead, and that information is fed to the front and rear wheels of the active suspension system. The wheelbase preview control collects the information from the front wheel dynamic characteristic variations and uses that information to control the rear wheel of the active suspension system [83].  
Preview information control was used in the active seat suspension where the actuator active force was calculated from the vehicle suspension dynamic information rather than the road disturbances [84]. The controller gain was optimized by genetic algorithm (GA). The proposed control method was validated using simulation and experimental tests where the quarter car suspension model was implemented with 1 DOF seat suspension model. The experimental test was performed under the bump and random excitation road profile with different vehicle speed. It is shown that a significant acceleration reduction of 10 dB is achieved across the frequency range 0-18 Hz.  
In addition, a preview information was obtained from the wheels, front left suspension and front axle, which was optimized by fuzzy logic controller to cope up with friction and actuator constraints [85]. State feedback of the seat suspension and feedforward of the vehicle suspension based on preview information were implemented in [86]. In general, the preview controller can guarantee a good performance in terms of vertical vibration attenuation. However, it is quite costly, as it consumes energy and takes longer time to preview the road as well as requires more sensors to predict the road condition [87].
## 2 System Framework  
### DQL Algorithm and DDQL Algorithm  
Double deep Q-learning (DDQL) is an improved version of deep Q-Learning (DQL) that overcomes the overestimation problem. In this paper, we aim to further enhance the stability and convergence speed of the DDQL algorithm. DDQL serves as the foundation for our work, and we introduce a refined version called R-DDQL. While DQL employs the same neural network for both action selection and action evaluation, DDQL separates the two processes, thus avoiding the issue of overestimation effectively. Specifically, DDQL decomposes the action selected from the action evaluation, which results in an improved accuracy of the action-value function. According to reference [17], the target Q values of DQL and DDQL are as follows:  
\[y_{t}^{DQL}=r_{t+1}+\gamma Q(s_{t+1},\underset{a^{\prime}}{\text{argmax}}Q(s_ {t+1},a^{\prime};\theta^{-});\theta^{-}) \tag{1}\]  
\[y_{t}^{DDQL}=r_{t+1}+\gamma Q(s_{t+1},\underset{a^{\prime}}{\text{argmax}}Q(s_ {t+1},a^{\prime};\theta);\theta^{-}) \tag{2}\]  
where \(y_{t}^{DQL}\) is the target Q-value of DQL, \(y_{t}^{DDQL}\) is the target Q-value of DDQL, \(\theta\) is the parameter of the online network, and \(\theta^{-}\) is the parameter of the target network. The two networks use the same structure. The target network is a lagging version of the online network.  
### DQL Algorithm and DDQL Algorithm  
In this section, we will introduce the method framework and the functionality of each component. The entire model is divided into three parts: 3D terrain processing, action selection, and state collection, as illustrated in Figure 1.  
The 3D terrain-processing module is responsible for converting the geographic information into image data that can be used by the model. The image data include the location of the agent, the location of the target, and the terrain information. The action-selection module employs heuristic knowledge or a neural network to select the next action. The retrospective mechanism analyzes the quality of the action chosen by the action-selection module and determines if there exists a better alternative. The state-collection module combines the next state obtained from the action-selection module with the state obtained from the retrospective mechanism to obtain a comprehensive state. These states will be utilized to train the neural network until a satisfactory strategy is obtained.  
Overall, the 3D terrain-processing module converts geographic information into a suitable format for the model to use, the action-selection module selects the next action using either heuristic knowledge or a neural network, and the retrospective mechanism  
Figure 1: Proposed system framework.  
analyzes the quality of the selected action to potentially identify a better alternative. The state-collection module combines the states from the previous two modules and is used to train the neural network to optimize the strategy.
# A Review of Smart Grid Anomaly Detection Approaches
## 5 Conclusions  
Cyber-attacks on the SGI will surely come from previous intrusions, so it is important to study how to detect a potential attack and the resulting anomalies proactively and intelligently from an intrusion.  
The models proposed in the different studies reviewed give great emphasis to the use of machine learning, regression, decision trees, deep learning, support vector machines, and neural networks. Other proposals are presented in novel ways, such as federated learning, hyperdimensional computing, and graph-based methods. An increasingly visible challenge for the new models is to be able to detect anomalies by optimizing computational resources in less time, with less data, in real time, and independently of the network model.  
In smart grids, experiments with synthetic data show that, compared to linear cases, nonlinear cases perform better in reducing the probability of false alarms related to anomalies.  
Usually, it is about discovering malicious activities or violations of security policies through intrusion detection systems. The reviewed works show that we are evolving from signature databases to AI techniques to achieve the ability to detect new attacks starting from a referential network model, considering activities that are outside the normal model as anomalies and discriminating false positives.  
There is a trend towards the formation of anomaly detection frameworks or hybrid solutions that consist of sets of various AI methods and tools combined to analyze the behavior of specific SG components.  
In future work, we will go deeper into each object of study to refine it and show the evolution and trend. A further review could improve the list of study objects currently identified. In addition, the digital twins applied to SG in anomaly detection will be explored.  
This research received no external funding.  
Not applicable.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflict of interest.
###### Abstract
A resource optimization method combining spectrum and speed is proposed in reference [18]. In order to meet the requirements of high average spectral efficiency and to maximize the minimum average energy, the authors of [19] worked on optimizing the transmitted power and trajectory of UAVs and the power splitting ratio of the ground terminals. Deep learning (DL) has been widely used in areas such as natural language processing and computer vision [20], as well as machine translation and automatic speech recognition [21]. The progress of reinforcement learning (RL) and deep convolutional neural networks (CNN) enabled machine learning (ML) to acquire unprecedented results in visual areas, for instance playing Atari games [22], image classification [23] and face recognition [24]. For the convolutional layer of one CNN, a single neuron is related to only a few adjacent neurons. One convolutional layer usually represents a few feature maps, and every feature map consists of certain rectangular neurons. The authors of [25] proved that some suitable learning mechanisms can help capture correlations and improve the performance of a CNN without additional oversight. The combination of deep learning and reinforcement learning is also known as deep reinforcement learning (DRL). Recently, DRL has also been put into use in the resource assignment problem of wireless networks. Compared to the traditional methods, DRL has its own superiority [26]. Dealing with large systems, a deep Q-network (DQN) is used to find a channel access strategy in the study [27]. A good strategy can be found directly from historical observations without knowing prior system information. Contrary to the above work, and to ensure the quality of communication, this manuscript considers the joint power and spectrum optimization of a UAV formation flight to maximize the transmission rate and increase the successful data transfer probability based on a DQN. The main contributions of this paper are as follows:Resource optimization based on the three-dimensional distribution of multiple UAVs, which is different from a random [28] or flat distribution [29] of UAVs, as used in the previous study. The multi-UAVs were located successively at edges or the eight vertices of a cube and kept a cube formation during their flight;
2. In order to achieve joint power and channel resource optimization for the UAV formation, a DQN was used. Moreover, a CNN was used after the state data were preprocessed in the training process;
3. Based on a DQN, the convolutional block attention module (CBAM) and value decomposition network (VDN) modules were introduced to further realize system performance improvement. The CBAM worked at the CNN layer to capture correlations of the feature map along both the channel and spatial aspects, and the VDN worked as the reward mechanism. The experimental results proved the effectiveness of the introduction of the CBAM and VDN.
## 3 Samples and Methods
### Contact Angle Experiment  
Static contact angles were measured using a KRUSS DSA30S drop shape analyzer (KRUSS, Hamburg, Germany) at room temperature and atmospheric pressure, with a droplet volume of about 2 \(\mu\)L. To ensure measurement precision, contact angle assessments were conducted at multiple sites on the sample surface, with 4 to 5 trials performed at each site.
## 2 Materials and Methods  
### Characterization of AuNPs  
X-ray diffraction (XRD) patterns of AuNPs were acquired at room temperature, utilizing an X-ray diffractometer (PANalyticalX'pert PRO MRD PW 3040, Enigma Business Park, UK) with CuKa (\(\lambda\) = 1.54050 A) to investigate their crystalline structure. The size of the sample was derived using transmission electron microscopy (TEM, Zeiss Libra 120, Oerzen, Germany) at 100 kV. The ImageJ software (1.52 v) was used to measure the particle size distribution of the sample from approximately 50 particles. The absorption properties of the AuNPs were determined using ultraviolet-visible (Uv-vis) spectrophotometry (UV-3600, Shimadzu, Santa Clara, US). The stability, polydispersity index (PDI) and hydrodynamic size of the AuNPs were measured using a dynamic light scattering (DLS) instrument (ZETASIZER Nanoseries Model ZEN 3600, Malvern Instruments, Enigma Business Park, UK).
# Progress in the Synthetic Holographic Stereogram Printing Technique
## 1 Introduction  
Holography is a three-dimensional (3D) display technology based on the interference and diffraction of light waves, and it was first proposed and realized by [PERSON] [1]. For almost two decades, computer technology and holography have been integrated more closely, and holographic printing techniques have developed rapidly. Compared with the conventional optical holography, the hologram of virtual scenes can be stored into the holographic recording medium with the help of holographic printing techniques. Holographic printing techniques have been widely used in various fields [2; 3; 4; 5; 6], such as military, architecture, commerce, automotive industry, and entertainment.  
According to the different sources of interference patterns and different approaches of recording, holographic printing techniques can be categorized into three types: synthetic holographic stereogram printing, holographic fringe printing, and wavefront printing. In synthetic holographic stereogram printing, the 3D scene is reconstructed by sequential perspective images that are captured by a tracking camera or modeled by a computer with rendering techniques. After being interfered with by the reference beam, the information of perspective images is stored in the holographic recording medium. During the reconstruction of holographic stereograms, stereoscopic vision occurs when different perspective images with parallax information is viewed, and the parallax is changing when eyes are moving [7; 8]. In holographic fringe printing, interference patterns are calculated by the computer, displayed by the spatial light modulator (SLM) and printed on the holographic recording medium directly [9; 10; 11; 12; 13; 14]. Thin transmission holograms, such as the Fresnel hologram, the rainbow hologram, the image hologram, and the holographic stereogram, can be achieved [15; 16; 17]. In wavefront printing,the holograms are calculated by a computer, but the algorithm is different from that of holographic fringe printing. Holographic fringe patterns are diffracted and propagated to the recording medium, interfered with by the reference beam, and reflection volume-type holograms can be achieved [18; 19; 20; 21; 22; 23; 24; 25; 26].  
Compared with holographic fringe printing and wavefront printing, since there is no complex diffraction calculation, synthetic holographic stereogram printing is the only holographic printing technique in application currently, and it is developing rapidly. In addition to the laboratory research, there are several companies worldwide engaging in synthetic holographic stereogram printing and providing related business services, such as Zebra Imaging Inc. in the United States as well as Geola Inc. and XYZ Imaging Inc. in Europe [3]. It is beneficial to readers to have a systematic review of the synthetic holographic stereogram printing technique. This paper introduces research on the synthetic holographic stereogram printing technique and other related topics.