esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Results
### Implementation Details  
We utilized three simultaneous multiscale capsule-like networks in the initial step by following the structure from Figure 1 (\(Conv\to BN\to LReLU\to Flatten\to Dense\)). For every iteration, one input batch is fed as an input to these shallow capsules. These small networks are trained with batch input, and we extract the feature representation occurring minimum loss from the convolved output of the same capsule. The selected feature representation is concatenated with input feature projection. The projection function \(S(x;\theta)\) in Equation (7) is implemented as the _maxpool2D \(\rightarrow\) conv2D with ReLU activation_ function. The capsule and PrimeNet's classifier model information is provided in the table below:  
Figure 3: PrimeNet training flow. The shallow _capsules_ network inside the selection block convolves at different scales, and the batch-wise loss is calculated as gradients are updated locally. The feature representation with minimum loss is forwarded as an input to the classifier, and classification is performed on locally learned feature representation.  
Here, the parameter column refers to the convolution scales and number of filters (e.g., \(1\times 1\) is the convolution scale, and \(\mathbf{32}\) is the number of filters). \(NA\) means that there are no trainable parameters involved. _GAP_ refers to the meaning Global Average Pooling in the Classifier configuration.  
We set the maximum iteration for 100 epochs in the training phase and use a mini-batch of size 200. For fine-tuning and evaluation, we use 50 iterations and a reduced batch size of 32. Moreover, we use the Adam optimizer with a default learning rate of 0.0002 with a beta value of 0.99. To recreate and compare with baseline ResNet50 and other SOTA models for computational results, we reshaped the MNIST input into three dimensions as per the experimental requirement. No data augmentation is used in PrimeNet experiments for any of the three datasets.
# Determination of Optimum Frequency for Electromagnetic-Assisted Nanofluid Core Flooding
###### Abstract  
The coupling of electromagnetic (EM) wave to nanofloading experiments, termed EM-assisted nanoflooding has gained enormous attention in recent years. This brings about the consideration of transmitter efficiency and the propagation pattern of the ensuing EM wave, both of which depend on reservoir electromagnetic properties. However, this has not been considered in previous works, despite its potential to improve the efficiency of the process through energy maximization. Hence, this study considers the effects of reservoir EM properties on the operation of a transmitter. The EM properties of saturated reservoir rock samples were measured. Afterward, a half-wave dipole antenna was modeled in a cylindrical reservoir based on the measured EM properties. The EM propagation pattern of the antenna was simulated in a frequency range of 100 MHz--2.0 GHz using the finite element method. The antenna was found to display dual resonance (at 800 MHz and 1.3 GHz) for sandstone saturated with oil, brine and nanofluid. Application of the EM wave at resonance frequency can help increase the efficiency of EM-assisted nanoflooding.  
Keywords: electromagnetic wave; transmitter; reservoir electromagnetic properties; resonant frequency; nanofluid +
Footnote †: journal: Applied
## 5 Symmetry Protected BSCs
### \(\pm m\) Degenerate BSCs with \(\beta=0\)  
The above described mechanism for partially symmetry protected BSCs with \(m=0\) can be exploited for even more complicated case \(m\
eq 0\). Their orbital angular momentum (OAM) \(m\
eq 0\) of these BSCs is the result of azimuthal rotation symmetry of the array and provide unique properties in the form of spinning or spiralling currents of the Poynting vector [58; 59]. Besides, the system has the time reversal symmetry which implies that these BSCs are degenerate over \(\pm m\). Let us start with the Type I BSC with \(m=1\) which has the odd \(E_{z}\) and the even \(H_{z}\) according to Tables 1 and 2. This BSC is symmetrically mismatched with the TM diffraction continuum \(m=1,n=0\) which is independent of \(z\). The coupling with the TE continuum can be cancelled by tuning the radius. The result of computation of this partially symmetry protected Type I BSC \((a_{2k},b_{2k+1})\) is the following  
\[m=1,\ \ k_{0}=2.847,\ \ R=0.3945,\ (a_{1}^{1},\ b_{1}^{1})=\left(\begin{array}[] {ccc}0&0.6662+0.4273i\\ -0.33+0.5145i&0\\ 0&-0.0048-0.0031i\\ 0&0\end{array}\right),\ l\geq 1 \tag{27}\]  
and shown in Figure 4a. The Type II BSC \((a_{2k+1},b_{2k})\) with \(m=2\) has even \(E_{z}\) and odd \(H_{z}\). It is symmetry protected against decay into the TE continuum with \(m=2,n=0\) and coupling with the TM continuum is cancelled by tuning the radius with the following result  
\[m=2,\ \ k_{0}=3.086,\ \ R=0.471,\ (a_{1}^{2},\ b_{1}^{2})=\left(\begin{array}[] {ccc}0&0.6545+0.2013i\\ -0.2142+0.6964i&0\\ 0&-0.0057-0.0018i\\ 0&0\\ 0&0\end{array}\right),l\geq 2. \tag{28}\]  
Figure 3: Pattern of the BSC (26) which is symmetrically protected in respect to the first TM radiation continuum and decoupled with the TE continuum by tuning of \(R=0.4805\).  
All components of electric and magnetic fields are nonzero and localized around the array as shown in Figure 4. We show the EM field around only one sphere because the pattern is periodically repeated along the \(z\)-axis. One can see that the value of OAM \(m\) reflects in the structure of force lines in the \(xy\)-plane while the number of the amplitudes \(a_{l}^{m}\) reflects in the structure of lines along the \(z\)-axis.
## 2 System Model
### Hydrogen Power Management  
During the photovoltaic and wind power generation phase, excess power is used to produce hydrogen through electrolysis. The produced hydrogen is stored in a hydrogen tank to be used later when there is no sunlight or less power production from the wind source and cannot meet any type of demand, either residential or industrial. The most common method of producing hydrogen is through electrolysis, which involves splitting water into hydrogen and oxygen using electricity. The hydrogen produced can then be stored in tanks or in underground salt caverns. The storage and transportation of hydrogen require careful consideration due to its highly flammable nature [39].  
The hydrogen-production rate should be optimized based on the current and future demand for electricity. The power output from the hydrogen fuel cell is optimized to meet the current load demand while not overloading the system. So, the hydrogen and oxygen parts are to be considered as lossless. The volume of hydrogen and oxygen can be measured by (8) and (9) [40].  
\[V_{H,n} =V_{H,n-1}+n_{H,n}t_{opt} \tag{8}\] \[V_{O,n} =V_{O,n-1}+n_{O,n}t_{opt} \tag{9}\]  
Volume of tank level disparity depends on the primary level, i.e., \(V_{H,n}\) and \(V_{O,n}\). Flow rate is described in \(n_{H,n}t_{opt}\) and \(n_{O,n}t_{opt}\), correspondingly. In this regard, flow rates can be considered in the optimization problem with first-order polynomial expressions. MATLAB polyfitZero function can give these expressions. The final flow rate values of hydrogen and oxygen can be found by (10) and (11):  
\[n_{H,n} =n_{H\,E,n}+n_{H\,F\,C,n} \tag{10}\] \[n_{O,n} =n_{O\,E,n}+n_{O\,F,n} \tag{11}\]  
where \(n_{H\,E,n}\) and \(n_{H\,F,n}\) are the hydrogen-based electrolyzer and fuel cell flow rates, respectively, and \(n_{O\,E,n}\) and \(n_{O\,F,n}\) are the electrolyzer and fuel cell flow rates of oxygen, respectively. Power and current of the electrolyzer and fuel cell are at the mercy of the polarization curve. The constraints of power are to be defined as in-between 20 to 80 percent of the determined power of fuel cell at any time. The stored hydrogen energy in the hydrogen tank (HT) must also satisfy specific constraints, and it can be measured by (12):  
\[VH_{2}^{min}\leq VH_{2}(\textbf{t})\leq VH_{2}^{max} \tag{12}\]  
Figure 5: Wind turbine characteristics [37; 38].  
where \(VH_{2}^{min}\) and \(VH_{2}^{max}\) are the minimum and maximum hydrogen energy stored in a tank. \(VH_{2}\)(t) is the total energy stored in a tank at time (t). Meanwhile, the electrolyzer power is well-thought-out in the ohmic zone of operation. Currents were found on the basis of pre-determined power and curve, respectively.
## 2 Continuous Model
### Aerodynamic Forces  
The aerodynamic part of the load is caused by the wind, which blows orthogonally to the plane of bridge, with a steady velocity \(U\). It triggers forces on the beam and on the cable, acting both in-plane and out-of-plane of the structure. Due to the higher out-of-plane stiffness of the beam and to the smaller dimensions of the cable (which, being of circular shape, is exclusively subjected to drag forces), only in-plane forces acting on the beam are taken into account.  
The quasi-static theory (see [29]) is used here to express such forces. The choice is corroborated by the discussion carried out in [28], where it is stated that: (a) the use of a nonlinear quasi-steady theory is justified in the range of low reduced frequencies, in which long-span bridges are expected to fall; and, (b) the previsions of the quasi-steady model are conservative, a few percent lower than those of a fully unsteady model. In particular, it is known that (see e.g., [43; 44]), the quasi-steady assumption is valid only if the main frequency of periodic components of fluid forces, associated with vortex-shedding, is well above the dominant structural frequency, i.e., \(n_{s}>>n_{0}\), where \(n_{0}\) is the natural frequency of the crosswind vibration mode of the structure and \(n_{s}=S_{t}U/b\), with \(S_{t}\) the Struhal number and \(b\) the deck width. The occurrence of the frequency condition will be verified in the numerical application. When the inequality is not satisfied, it is needed to resort to a non-steady theory. However, while this latter is well codified in the linear range, few studies concern nonlinear dynamics (e.g., [44; 45; 46]).  
In the framework of the quasi-steady theory, the forces depend on the structural velocity \(\dot{v}\) only (not on the frequency of the oscillations), as result of the aeroelastic interaction. By expressing them in series of powers of the \(\dfrac{\dot{v}}{U}\) ratio, by assuming the cross-section is symmetric with respect the horizontal axis, and truncating the series to the fifth order, they read:  
\[\begin{split} p_{b}^{a}&=-\dfrac{1}{2}\varrho_{a} DU^{2}\left[A_{1}\left(\dfrac{\dot{v}}{U}\right)+A_{3}\left(\dfrac{\dot{v}}{U} \right)^{3}+A_{5}\left(\dfrac{\dot{v}}{U}\right)^{5}\right]\\ &=-\left(b_{1}U\dot{v}+b_{3}\dfrac{1}{U}\dot{v}^{3}+b_{5}\dfrac{1 }{U^{3}}\dot{v}^{5}\right)\end{split} \tag{5}\]  
Here, \(\varrho_{a}\) is the air mass density, \(A_{i}\) are dimensionless aerodynamic coefficients (depending on the shape of the cylinder cross-section), \(D\) is a transverse characteristic length, and \(b_{i}:=\dfrac{1}{2}\varrho_{a}DA_{i}\) ( \(i=1,3,5\)) are dimensional coefficients, introduced to simplify the notation.
# On the Impact of Mobility on Battery-Less RF Energy Harvesting System Performance
## 2 Energy Trade-Offs for Mobile Systems
### Charging Problem  
As the amount of harvested energy depends on the environment, there is no guarantee that the capacitor voltage will be charged to \(V_{max}\) within one charging session. Due to mobility, a sensor node may leave the charging station before a \(V_{max}\) is reached, so any partial charge will gradually get dissipated before the node finds another energy source due to relatively the high self-discharge currents of capacitors, leading to energy wastage and inefficient operation. At \(V_{max}=1.25\) and \(V_{min}=1.02\), which is the standard operating range for PowerCast RF, the partial energy loss \(E_{loss}=CV_{residual}^{2}/2\) represents 67%-100% of energy harvested by the capacitor. The exact figure depends on the node mobility pattern with the worst case scenario in which all harvested energy is lost between the individual charging sessions. While the loss is also linearly proportional to the capacitor value with larger capacitors leading to more loss (2), the relationship is more complex, as the node may never boot when operating on a large capacitor.  
Figure 1: Block diagram of RF energy harvesting sensor node.  
Figure 2: The timing diagram of capacitor charging. The sensor activates when the capacitor voltage reaches \(V_{max}\) and keeps operating until the voltage drops to \(V_{min}\)[6].
## 2 Distributed Fair Rate Allocation Algorithm
### Problem Formulation under Fairness Considerations
Although there exist very efficient methods in the literature to solve generic convex problems, e.g., the so-called interior point methods [18] (Section 11.7), this approach is impractical in large-scale sensor networks as far as it implies an excess of signalling information. Therefore, there is a big interest in solving the problem as efficiently as possible, in a distributed manner. Efficient here means that: (i) the amount of signalling in the network and (ii) the computational load at the sensors must be kept small.
## 4 Results and Discussion  
### Convolutional Network Basic Model Comparison  
The process of training and testing the Handan City land use classification data set involved the utilization of multiple convolutional neural network models. Experimental and comparative analyses with some previous convolutional neural networks, as evidenced in Table 3, indicate that EfficientNetV2 has a clear advantage in terms of training speed and detection accuracy, with fewer parameters and a lighter model accuracy. The accuracy results of the testing sets show that the EfficientNetV2 model performs the best, with an accuracy rate of 80.97%. However, it is important to note that this accuracy rate did not reach a higher level due to several factors. One significant factor was the low resolution of the civil satellite remote sensing images used for classification. Additionally, confusion among the refined 14 categories of land use classification contributed to this limitation. In light of these challenges, it was found that adopting a transfer learning method holds promise for improving the accuracy rate.
## 1 Introduction
### Related Work  
Subsequent standardization of jitter by the IETF in RFC 5148 [9], its implementation in different routing protocols and research to evaluate and discuss the impact of these techniques in the performance of the protocols making use of them have been undertaken. [PERSON] _et al._[14] presented the relationship between the maximal jitter duration and the probability of successful transmission and provided a comparison between different strategies of implementing jitter mechanisms. This paperconcluded that implementing jitter at any layer above the IP (Internet Protocol) layer (e.g., at the transport or application layer) brings virtually no benefits. [PERSON] _et al._[15] introduced an analytical model for investigating the impact of the standardized jitter mechanism for link-state flooding (which includes jittering and packet piggybacking; see RFC 5148 [9] for details) on network-wide packet dissemination performance. Based on that model, [15] studied and quantified the additional delay incurred, the reduction in the number of transmissions and the effect of jitter in packet size. While this paper focuses on flooding in reactive routing, it borrows model assumptions and terminology from [15] in order to study analytically the impact of jittering. Similarly, [16,17] discuss and present preliminary studies of the issues related to the application of jitter in reactive routing protocols, which are extended and completed in this paper.  
This paper studies the use of jitter for improving the performance of reactive (on-demand) routing in wireless multi-hop sensor networks. Reactive routing protocols use flooding to disseminate route request messages in the network until they reach their destination, and the introduction of jitter techniques in route discovery is recommended to minimize systematic collisions. The \"delay inversion\" effect when using traditional uniform jitter [9] is identified and described, and an adaptive jitter mechanism is then proposed to reduce the route discovery overhead and improve path optimality. Both theoretical and experimental (simulation-based) analyses are performed and discussed; the corresponding results show that the proposed adaptive jittering substantially improves the flooding performance with little cost.
# Energy Saving in Wireless Sensor Networks via LEACH-Based, Energy-Efficient Routing Protocols
## 4 Threshold LEACH with Sleep-Awake Scheduling Protocol  
As aforementioned in Section 3, T-LEACH protocol achieves high energy savings by the usage of a specific energy threshold, which accordingly reduces the frequency of cluster head changes. For this reason, this protocol is considered to be one of the most cited descendants of LEACH. In this article, T-LEACH with sleep-awake scheduling (T-LEACH\({}_{\text{SAS}}\)) is introduced as a hierarchical protocol for energy-efficient routing in WSNs, which further enhances the performance of T-LEACH. In what follows in this section, the establishment of the theoretical background of T-LEACH\({}_{\text{SAS}}\) takes place, followed by an overview regarding first its operation and next its architecture.
## 4 Performance Evaluation
### Simulation Environment  
To evaluate the effectiveness of our proposed scheme, we conducted simulations using ns-3 [41], an open-source system-level network simulation tool capable of creating an environment for the easy transfer of states and actions between AI frameworks and the ns-3 simulation environment. To achieve this, we employ the ns3-ai [42] module, which enables seamless integration between ns-3 and open-source AI frameworks like TensorFlow and PyTorch by utilizing shared memory. The ns3-ai module consists of two components: the ns-3 interface, implemented in C++, and the AI interface developed in Python. These components work together to ensure the fast and efficient exchange of large data volumes from a C++ program to a Python program. By utilizing shared memory, the ns3-ai module facilitates communication between multiple processes. Unlike the ns3-gym framework [43], which relies on pipes or sockets, the use of shared memory allows for the creation of a highly efficient core module for data transfer.  
To implement our proposed scheme, we have designed a system architecture that combines the ns3-ai and the SDWSN architecture, as shown in Figure 2. The proposed architecture, depicted in Figure 3, consists of two main components: the ns-3 simulator and the AI framework. The ns-3 simulator serves as the data generator by providing environments to create simulation scenarios. It generates relevant information, which is then fed into the AI framework for training the model to make real-time decisions. The AI framework processes the data received from the ns-3 simulator and trains the model to make intelligent decisions. The shared memory pool facilitates the seamless data exchange between the ns-3 and the AI framework, allowing both sides to access and manipulate the data. Control signals, managed by four modules operating at the control layer, ensure smooth communication and coordination between the ns-3 and the AI framework. This integration enables efficient decision-making and the evaluation of our proposed routing scheme.
# Improved Dragonfly Optimization Algorithm for Detecting IoT Outlier Sensors
## 6 Conclusions  
The Internet of things is rarely discussed without a conversation about new ecosystem data and information. The intelligence and value of an Internet of things (IoT) system are using what may be learned from data, and sensors are the main sources of these data. The subject of collecting and sending information by objects specifically refers to sensors. Sensors can be used for sensing temperature, proximity, pressure, movement, position, humidity, light, air quality, or anything else. These sensors, coupled with Internet connectivity, enable them to automatically collect data from the environment, enabling stakeholders to make smarter decisions. Sensor networks are used in various fields, and there are primarily problems with sensor costs and battery replacements. Therefore, using the minimum number of sensors with full coverage and elimination is an incredibly important subject. In this paper, a new approach was attempted to detect outlier data and eliminate them in the Internet of things. Outlier sensors are one of the challenges of this type of network. In this paper, an improved version of the dragonfly optimization algorithm was used for optimal area coverage and energy consumption reduction. This paper used four parameters to evaluate its efficiency: the minimum quantity of nodes in the coverage area, the lifetime of the network, including the time interval from the start of the first node to the shutdown time of the first node, and the network power. The results of the suggested approach were compared with some published techniques. Simulations showed that the suggested technique achieved better results than the other investigated methods in terms of the provided performance parameters. The goal of future research is to construct IoT sensors that are appropriate for outlier detection modules (ODMs), analyze the sensors' produced spatial-temporal data in real time, and create a testable IoT prototype with an integrated ODM. The investigation of potential computational constraints for the ODM will also be a key component of the project. Additionally, the effectiveness of the IoT will be examined, as will its capacity to correctly identify any malfunctioning sensors as well as the type, source, and impact of errors on these sensors.  
Conceptualization, M.N.M. and S.K.; methodology, M.N.M.; software, H.T.R.; validation, M.N.M., S.K. and H.T.R. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.