esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 RPS Scheduling Policy
#### Computational Complexity of RPS  
The computational complexity of RPS is composed of three components:  
* Complexity of processing a new packet.
* Selection of a transmitted packet.
* Transmission complexity.  
Upon arrival, the RPS adds the packet to the queue according to the earliest deadline first order with a complexity of \(O(log|Q|)\) where \(|Q|\) is the size of the arrival queue; when the buffer size is limited, this is always less than \(log(buffersize)\). By our assumptions, the size of the arrival queue is bounded by:  
\[Q_{max}=\frac{\text{maximal deadline}\times\text{minimal packet length}}{\text{maximal bandwidth}\times\text{maximal number of beams}}. \tag{14}\]  
This complexity is similar to other sorted policies like EDF and priority greedy. In contrast, LIFO and FIFO have a \(O(1)\)complexity of packet insertion. However, even the logarithmic complexity is very moderate.  
The computational complexity of adding a new packet to a ZF beamformer (i.e., QR update) using the Gram-Schmidt process or Householder transformation is \(2N^{2}\)[47]. This computation is required regardless of the scheduling policy. After this step, we keep \(L\) and \(Q\) to avoid new computation. Selecting a new packet for transmission in RPS requires \(K-1\) partial QR updates per packet on average. Each partial QR update has a computational complexity of \(2N\), i.e., if the packet is not selected and a different packet is selected, the \(L\) and \(Q\) matrices are updated accordingly. When a packet ends, we perform a simple QR downdate by removing the relevant column and row, which has complexity \(2N\). Therefore, the total average complexity for processing a packet is bounded by:  
\[log(Q_{max})+2N^{2}+2N(K-1). \tag{15}\]  
Note that a packet transmission complexity for any scheduling policy with a sorted queue is \(log(Q_{max})\). Therefore, the updating of a ZF beamformer is \(2N^{2}+2N(\hat{K}-1)\) where \(\hat{K}\) is the average number of packets processed until an eligible packet is found. Therefore, the total average complexity of the beamformer processing per packet is bounded by:  
\[log(Q_{max})+2N^{2}+2N(\hat{K}-1). \tag{16}\]  
Optimal resource allocation even without beamforming is an NP hard problem [6] and [32]. This implies that our policy is likely to be sub-optimal. However, with a moderate polynomial complexity, we outperform all prior scheduling algorithms that are not designed jointly with the beamformer.
# Reflection Propagation Law of Electromagnetic Waves in U-Shaped Roadway
## 2 Mirror Image Method  
Due to the complex structure of mine roadways, sensors within these roadways are influenced by radiation sources located at various positions. This necessitates a more in-depth study of electromagnetic wave propagation in such environments, for which the mirror method is proposed as a suitable analytical tool. The mirror method is a raytracing technique based on the laws of light reflection and refraction, and the principles of geometric optics. When the positions of the transmitting and receiving sources are known, rays are used to represent electromagnetic waves in simulations. According to geometric optics, effective reflection paths can be identified by determining the mirror points. The reverse-tracing method is then employed, tracing the path from the field point to the source point, to exclude invalid rays. This method has proven to be highly accurate in analyzing the attenuation characteristics of electromagnetic waves in roadways [22] and is therefore adopted to analyze the reflection and propagation of electromagnetic waves in U-shaped roadways.  
In mine roadways, particularly U-shaped roadways, the presence of corners significantly alters the propagation of electromagnetic waves compared to traditional straight roadways. In straight roadways, reflection paths are relatively simple and regular. However, in U-shaped roadways, the corners cause multiple reflections, resulting in more complex reflection paths. After passing through two corners, electromagnetic waves undergo considerable attenuation and may even vanish entirely. Additionally, the presence of corners increases the number of reflection paths and the computational complexity. To address these challenges, this paper introduces a relay node. This node enhances the field strength of the electromagnetic waves and reduces the computational complexity associated with reflection lines. The relay node is positioned at the center of the roadway, maximizing its ability to cover electromagnetic waves on both sides and ensuring stable wave transmission at the node.  
The presence of corners in U-shaped roadways results in a smaller number of low-order reflection paths, while higher-order reflections contribute more significantly to the calculation of the reflected field strength. Considering both computational efficiency and accuracy, this paper sets the maximum number of reflections to 8.
## 4 Proposed Monitoring Approach
### Recognition of the Action  
In Section 2.2 we have presented a synthetic overview of existing methods for action recognition. In the following, we focus our attention on two of such methods for possible integration in our  
Figure 4: Schema of the proposed monitoring approach. Each processing step is implemented with algorithms based on Deep Learning. Subject localization is based on the Faster R-CNN network. For action recognition two networks are evaluated: 13D and DeepHar. We modified these networks to take advantage of a multi-branch approach where groups of actions and states are recognized separately.  
system for assisted living: I3D [33] and DeepHAR [42] (Deep Human Action Recognition). The I3D model has produced groundbreaking results in challenging domains such as the Kinetics datasets [25]. Given the large cardinality (up to 700 classes in the latest release), such a well-performing model is expected to successfully discriminate between subtle differences in very similar classes, a characteristic that we particularly cherish for the Daily-life action group. DeepHAR has been recently proposed as a model that infers the depicted action through an explicit representation of the subject inferred skeleton. We therefore expect this method to perform especially well for full-body actions such as those belonging to the Status and Alerting groups.  
Each action group presented in Section 3.1 has been designed to be independent from the others. In practice, this translates to the creation of three separate action recognition models, continuously processing a common stream of data when the system is running live. Although our proposed system does not have the constraint of real-time processing, a short-term response is indispensable, as the detection of potentially alerting situations must allow for timely intervention. To reduce the overall computational burden at inference time, we adopted the following approach during training:  
1. We pre-trained the action recognition model on a wide dataset, characterized by high cardinality both in terms of classes and examples. For DeepHAR we pre-trained on the NTU dataset [27] as suggested by the authors, while I3D was pre-trained on the Kinetics-400 dataset.
2. We fine-tuned three models (one per action group) on our aggregated dataset, freezing the gradient backpropagation in the first layers. Specifically, we blocked all pose estimation layers in DeepHAR, and all layers before and including the fourteenth layer of I3D, which is a \(2\times 2\times 2\) 3D max-pooling layer.
3. We combine the three models into a unique multi-objective neural network, which performs an initial common processing and then eventually branches out into three independent paths.  
This approach is inherited from the concept of transfer learning itself [58], where low-level features extracted in the first layers are hypothesized to represent pieces of information that can be exploitable throughout the entire domain.
## 5 Multiobjective Optimization Approach
### Improved NSGA-II
#### 5.2.2 Local Search using Achievement Scalar Function  
The Achievement Scalar Function (ASF) is employed as the evaluation function for local search. It is constructed by using a reference point \(\overline{z}\) and a weighting vector \(\delta\), which is scalarized to multiple targets. This approach guarantees that some targets which are not dominant in the maximum scalarized value are searched. Equation (43) is the formal representation of this function.  
\[\left\{\begin{array}{l}\min f(x)=\max\delta_{i}(f_{i}(x)-\overline{z}_{i})+ \xi\sum\limits_{i=1}^{m}\delta_{i}(f_{i}(x)-\overline{z}_{i}),x\in D\\ \delta_{i}=(\delta_{1},\delta_{2}\ldots,\delta_{k})\\ \delta_{k}=1/(z_{i}^{\max}-z_{i}^{\min})\\ \overline{z}_{i}=\min\limits_{e=1}^{N}\!f_{i}(x_{e})\end{array}\right. \tag{43}\]  
where \(f(x)\) is the objective function, \(D\) is the definition domain of the decision variables, \(\delta_{i}\) is the set of parameters used to adjust the weight of each objective function, \(\delta_{k}\) is the normalization factor to ensure that the weight of each objective function is within a certain range, \(z_{i}^{\max}\) and \(z_{i}^{\min}\) are the maximum and minimum values of each objective function, respectively, \(f_{i}(x)\) is the objective function, and the coefficients \(\xi\) are any extremely small positive numbers. The algorithm uses the Metropolis criterion to decide whether to accept a suboptimal solution from the parent generation as the initial value for the next generation of the population. This decision is based on an acceptance probability, which can be calculated by Equation (44).  
\[P=\left\{\begin{array}{l}1,F(X_{\text{new}})<F(X_{\text{old}})\\ \exp(-\frac{F(X_{\text{new}})-F(X_{\text{old}})}{BT_{c}},F(X_{\text{new}})\geq F (X_{\text{old}})\end{array}\right. \tag{44}\]  
where \(P\) is the acceptance probability, \(F(X_{\text{new}})\) is the child ASF value, \(F(X_{\text{old}})\) is the parent ASF value, \(B\) is the Boltzmann constant, and \(T_{c}\) is the current temperature.
## 4 Simulation Environment
### The Performance Metrics  
Various performance metrics were utilized in this study, such as Packet Delivery Ratio (PDR), average End-to-End Delay (E2E Delay), routing overhead, and average energy consumption [43, 44]. The performance metrics were used in the quantitative evaluation of the routing protocols in the network. This study adopted the following performance metric to evaluate the results for the proposed RALAR protocol:  
* Packet Delivery Ratio (PDR): This is the number of the packet received divided by the number of the packet sent;
* Average End-to-End Delay (E2E Delay): This is the difference of time between the packet arrival and packet send packet divided by the total number packet;
* Routing Overhead: The routing overhead metric refers to the total number of routing packets divided by the total number of delivered data packets [45];
* Average Energy Consumption: This is the total energy consumed by each node in the network divided by its initial energy [46].
# Assessing the Impact of an EV Battery Swapping Station on the Reliability of Distribution Systems
## 2 Overview of Battery Swapping Station  
With the vigorous development of EVs and the popularization of smart grids, EV swapping stations using V2G technology, as an important backup power source, have received widespread attention in improving the reliability of the distribution network [17]. The modeling of BSS is an important step in analyzing its impact on reliability. Before modeling the BSS, without loss of generality, we make the following assumptions:  
1. Assuming that all batteries are charged using slots, each battery corresponds to a slot. The battery needs to be continuously exchanged between the charging slot and the EV during the battery exchange process, while charging slots are fixed. Therefore, from the perspective of BSS, the number of charging slots is more meaningful. Therefore, a battery of a certain number refers to the battery in the slot of a certain number, not the battery itself.
2. The model in this paper takes one hour as the unit time interval. The charging and discharging power within one hour are the same, but the charging and discharging power in different hours are different. Since the time scale is one hour, and the battery replacement takes only a few minutes to complete, it is considered that the battery replacement does not require time.
3. We assume that the BSS has the ability to quickly replace batteries of EVs, and the traffic capacity of the road around the BSS is strong, which will not cause road congestion.
# Binary-Tree-Fed Mixnet: An Efficient Symmetric Encryption Solution
## 4 Security Analysis
### Properties  
Mixnets are usually compared based on a number of commonly known features [2]. The main one is anonymity, and both schemes (ESEBM and BTFM) provide it. However, ESEBM shows cases (2 and 3 in Table 1) where only one adversary can break privacy. In comparison, BTFM offers a higher minimum adversary threshold (\(\lfloor log_{2}(m+1)\rfloor\)). On the other hand, it is enough to have a single honest node in asymmetric mixnets to ensure privacy.  
When considering the other characteristics, there are some that no symmetric mixnet can meet. As far as we know, verifiability, robustness, and fault tolerance can only be accomplished by asymmetric encryption techniques. Nevertheless, scalability, throughput, and latency are vastly surpassed by symmetric mixnets, as indicated in experiments. In this area, BTFM shows better results than ESEBM, especially when processing huge amounts of messages, as described in the next section.
## 3 Strong Coupling Regime: Analytical Approximation
### Travelling Rabi-Waves
Using Equations (12) and (13), it may be transformed to  
\[\left|\Psi(t)\right\rangle=\frac{1}{\sqrt{N\Big{(}1+\Lambda^{2}\Big{)}}}{\sum _{p}}(A_{p}(t)\big{|}a_{p}\big{\rangle}+B_{p}(t)\big{|}b_{p}\big{\rangle})e^{- i\mu t} \tag{15}\]  
where \(\mu=(t_{a}+t_{b})\cos\varphi\), and  
\[A_{p}(t) =\Big{(}C_{1}e^{-i\frac{1}{2}\Omega t}+\Lambda C_{2}e^{i\frac{1}{2 }\Omega t}\Big{)}e^{-i\frac{1}{2}\omega t}e^{ip\varphi}, \tag{16}\] \[B_{p}(t) =\Big{(}C_{2}e^{i\frac{1}{2}\Omega t}-\Lambda C_{1}e^{-i\frac{1} {2}\Omega t}\Big{)}e^{i\frac{1}{2}\omega t}e^{ip\varphi} \tag{17}\]  
The constants \(C_{1,2}\) satisfy the normalization condition \(\left|C_{1}\right|^{2}+\left|C_{2}\right|^{2}=\Big{(}1+\Lambda^{2}\Big{)}^{-1}\), (guarantee the correct normalization for the wavefunction (15)). Let us assume the system initially prepared in the excited state (\(B_{p}(0)=0\)). In this case, we obtain from (16) and (17):  
\[A_{p}(t)=\frac{1}{1+\Lambda^{2}}\Big{(}e^{-i\frac{1}{2}\Omega t}+\Lambda^{2}e ^{i\frac{1}{2}\Omega t}\Big{)}e^{-i\frac{1}{2}\omega t}e^{ip\varphi}, \tag{18}\]  
\[B_{p}(t)=\frac{2i\Lambda}{1+\Lambda^{2}}\sin\frac{\Omega t}{2}e^{i\frac{1}{2} \omega t}e^{ip\varphi}, \tag{19}\]  
where the RO frequency is given by  
\[\upsilon_{1,2}(\varphi)=\left\{(t_{a}+t_{b})\cos\varphi\pm\sqrt{\left(t_{a}-t _{b}\right)^{2}\cos^{2}\varphi+\frac{\Omega_{R}^{2}}{4}}\right\} \tag{11}\]0  
It is important to note that it is corrected with respect to Rabi-frequency, due to the interatomic coupling via tunneling.
## 4 Functional Description of D-MSR Algorithms in Different Protocol Layers
### Selecting Advertisement Cell and Constructing Two-Hop Neighborhood Schedule-Matrix  
To let a new node choose the free advertisement cell in a distributed manner, the new device should listen to its neighbor's advertisement. The advertisement includes the advertisement cell numbers of a node and its neighbors. This effectively allows a receiving node to gather advertisement cell information about its two-hop neighborhood. The new device then chooses a free advertisement cell based on this information. A similar scheme is proposed to allocate the timeslot in a distributed manner in [22].  
In the _protocol_ interference model, the transmission on one edge (e.g., between node A and B) is interference-free and can only be activated in one timeslot-channel cell if there is no transmission on any edge that disturbs either A or B, as is shown in Figure 8(a). The conflicting edges (shown by black dashed lines) with edge (A, B) can be formulated, based on the [16] model, as follows:  
\[\begin{array}{l}\mathit{ConflictSet}\big{(}(A,B)\big{)}=\{(C,D)\in E\ \mid\ [\{C,D\}\cap\{A,B\}=\emptyset]\land[\{A,B\}\cap(R_{C}\cup R_{D})\
eq\ \emptyset]\}\end{array} \tag{1}\]  
\(\mathit{ConflictSet}\) denotes the set of conflicting edges with the edge (A, B). The set of all edges in the network is denoted by \(E\) while \(R_{C}\) denotes the set of nodes that are possible receivers of node \(C\). In addition to the \(\mathit{ConflictSet}\), other edges (shown by blue dashed lines) that are sharing a node with edge (A, B) cannot be scheduled in the same cell. That is because we assume that each node has a single radio transceiver and cannot simultaneously receive and transmit. In a realistic setting, the interference and transmission range of a node may not be equal. However, in D-MSR we assume, for simplicity, that the interference and transmission range of a node are equal. In case these ranges are not the same, considering an additional _virtual_ edge representing the interfering edges [16] can be a possible solution. The details of _virtual_ edge mechanism are discussed in Section 5.6.3.  
Each node maintains a schedule-matrix to keep track of the current cell usage in its two-hop neighborhood, as shown in Figure 8(b,c) for nodes A and B. The schedule-matrix is constructed based  
Figure 8: Selecting an interference-free cell on (**a**) edge (A, B) based on the constructed schedule-matrix of (**b**) node A and (**c**) node B.  
on the link table information that the node collects from its one hop neighbor's advertisements. In the received link table information from one hop neighbor, the links between the one-hop and two-hop neighbors are included. Any two nodes that wish to establish an interference-free link with each other can negotiate based on their schedule-matrix and find a common cell that is not used by any of their possible conflict edges in their own two-hop neighborhood.
## 4 Discussion  
### Effect of Operating Parameters on Newton Number  
Local power consumption increases with increased shaking frequency but decreases with increased filling volume. The power consumption increases with viscosity in the case of 4 L filling volume, but in case of 6 L to 10 L filling volume, power consumption decreases with viscosity. Table 3 shows an overview concerning the functional correlation between power consumption and shaking frequency that was assessed in shaking flasks and orbital shaken reactors in previous studies. All experimental results in this study are in very good agreement with results presented in preceding works.  
Figure 11: Effect of Reynolds number on mixing efficient number. (**a**) Water; (**b**) 5 vol% glycerin; (**c**) 10 vol% glycerin; (**d**) 20 vol% glycerin.  
These authors respectively found that the functional correlation between Newton number and Reynolds number as presented in Table 4. Equation (9) was found to be independent of liquid filling volume. [PERSON] et al. [16] introduced the \"in-phase\" operating conditions for 100 to 2000-mL sizes of the flask and \"out-of-phase\" operating conditions symbolized by a non-developed liquid flow and irreproducibly occurred for 100- to 5-L size flasks [52], the correlation of modified Newton number and Reynolds number is  
\[Ne^{\prime}=70\cdot\text{Re}^{-1}+25\cdot\text{Re}^{-0.6}+1.5\cdot\text{Re}^{- 0.2} \tag{24}\]  
Equation (24) was utilized to consider in the laminar, intermediate and turbulent flow regime. Re\({}^{-1}\), Re\({}^{-0.6}\) and Re\({}^{-0.2}\) terms in Equation (24) are related to the laminar, transitional and turbulent flow regimes, respectively.  
Using [PERSON]'s \(\pi\) theorem analysis, a new Reynolds number, the Froude number, and volume number are proposed for Newton number input in the cylinder orbitally shaken bioreactor. A new set of correlations have been proposed (Equation (15) to Equation (18)) for the Newton number in cylindrical orbitally shaken bioreactors. All experimental present Newton numbers are obtained versus the product (\(\text{Re}\times\text{Fr}\)).
# QFC: A Parallel Software Tool for Feature Construction, Based on Grammatical Evolution
## 2 Methods
### The Feature Construction Step  
This is the first phase of the proposed method and it has the following steps:  
1. **Initialization** step. 1. **Read** the train data. The train data contain \(M\) patterns as pairs \((x_{i},t_{i})\), \(i=1\ldots M\), where \(t_{i}\) is the actual output for pattern \(x_{i}\). 2. **Set**\(N_{G}\), the maximum number of generations. 3. **Set**\(N_{C}\), the number of chromosomes. 4. **Set**\(p_{S}\), the selection rate. 5. **Set**\(N_{f}\), the desired number of features. 6. **Set**\(p_{M}\), the mutation rate. 7. **Initialize** the chromosomes of the population. Every element of each chromosome is initialized randomly in the range [0, 255]. 8. **Set** iter = 1.
2. **Genetic step. 1. **For**\(i=1,\ldots,N_{g}\)**do** 1. **Create**, using the procedure of Section 2.2, a set of \(N_{f}\) for the corresponding chromosome \(g_{i}\). 2. **Transform** the original train data to the new train data using the previously created features. Denote the new train set as \(\big{(}x_{g,i},t_{j}\big{)}\), \(j=1,\ldots,M\). 3. **Apply** a learning model \(C\) (such as RBF) to the new data and **calculate** the fitness \(f_{i}\) as \[f_{i}=\sum_{j=1}^{M}\big{(}C\big{(}x_{g_{i},i}\big{)}-t_{j}\big{)}^{2}\] (1)
4. **Apply** the selection procedure. During selection, the chromosomes are classified according to their fitness. The best \((1-p_{s})\times N_{C}\) chromosomes are transferred without changes to the next generation of the population. The rest will be replaced by chromosomes that will be produced at the crossover. 5. **Apply** the crossover procedure. During this process, \(p_{s}\times N_{c}\) chromosomes will be created. Firstly, for every pair of produced offsprings, two distinct chromosomes (parents) are selected from the current population using tournament selection: First, a subset of \(K>1\) randomly selected chromosomes is created and the chromosome with the best fitness value is selected as parent. For every pair \((z,w)\) of parents, two new offsprings \(\tilde{z}\) and \(\tilde{w}\) are created through one point crossover as graphically shown in Figure 3. **Apply** the mutation procedure. For every element of each chromosome, select a random number \(r\in[0,1]\) and alter the corresponding chromosome if \(r\leq p_{m}\).
# Effects of Velocity Profiles on Measuring Accuracy of Transit-Time Ultrasonic Flowmeter
###### Abstract  
Ultrasonic wave carries the information for flowing velocity when it is propagating in flowing fluids. Flowrate can be obtained by measuring the propagation time of ultrasonic wave. The principle of transit-time ultrasonic flowmeters used today was based on that the velocity is uniform along the propagation path of the ultrasonic wave. However, it is well known that the velocity profiles in a pipe are not uniform both in laminar flow and turbulent flow. Emphasis on the effects of velocity profiles across the pipe on the propagation time of ultrasonic wave, theoretical flowrate correction factors considering the real velocity profile were proposed for laminar and turbulent flow to obtain higher accuracy. Experiment data of ultrasonic flowmeter and weighting method are compared to verify the proposed theoretical correction factors. The average relative error of proposed correction factor is determined to be 0.976% for laminar flow and 0.25% for turbulent flow.  
velocity profile; transit-time; ultrasonic flowmeter; accuracy; correction factor +
Footnote †: journal: Applied Physics Letters