esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Dnn  
DNN is a form of multi-layer perceptrons, which can be interpreted as universal function approximators [32]. The DNN consists of \(L\) layers and an input vector called the input layer, wherein \(L\) layers include \(L-1\) hidden layers, and the \(L^{th}\) layer is the output layer. We let \(N^{L}:R^{C_{i}}\to R^{C_{o}}\) denote a deep neural network of \(L\) layers, where \(C_{i}\) and \(C_{o}\) denote dimensions of the input vector and output vector, respectively, and \(N_{k}\) (\(1\leq k\leq L\)) denotes neurons in \(k^{th}\) layer (\(N_{L}=C_{o}\)). Let \(\mathbf{W}^{k}\in R^{N_{k}\times N_{k-1}}\) and \(\mathbf{b}^{k}\in R^{N_{k}}\) denote the weight matrix and bias vector of the \(k^{th}\) layer, respectively. The activation function in the \(k^{th}\) layer is denoted by \(\sigma^{k}(\cdot)\), regardless of its type. Commonly used activation functions are Sigmoid, ReLu, Tanh, etc. After the \(k^{th}\) hidden layer receives an input vector \(\mathbf{z}^{k-1}\) (\(\mathbf{z}^{0}=\mathbf{x}\)), it multiplies the vector with the weight matrix \(\mathbf{W}^{k}\), and then adds it to the \(\mathbf{b}^{k}\). If \(k\) is equal to \(L\), the result is output directly; otherwise, an activation function is applied to the result and then passed to the next layer. In summary, the feed-forward process of DNN is given in Equation (1). The first hidden layer's input vector is \(\mathbf{x}\) and output is \(\mathbf{z}^{1}\); the rest of the hidden layers will have an input \(\mathbf{z}^{k-1}\) (\(2\leq k\leq L-1\)) and output \(\mathbf{z}^{k}\). For the regression task, the last layer usually does not require an activation function, and the \(L^{th}\) layer takes in \(\mathbf{z}^{L-1}\), and outputs \(\mathbf{y}\), as follows.  
\[\begin{gathered}\mathbf{z}^{1}=\sigma^{1}(\mathbf{W}^{1}\mathbf{x }+\mathbf{b}^{1}),\\ \mathbf{z}^{2}=\sigma^{2}(\mathbf{W}^{2}\mathbf{z}^{1}+\mathbf{b }^{2}),\\ \vdots\\ \mathbf{z}^{L-1}=\sigma^{L-1}(\mathbf{W}^{L-1}\mathbf{z}^{L-2}+ \mathbf{b}^{L-1}),\\ \mathbf{y}=\mathbf{W}^{L}\mathbf{z}^{L-1}+\mathbf{b}^{L}.\end{gathered} \tag{1}\]  
Figure 1 shows the basic structure of DNN. In DNN, the hyperparameters are a set of parameters used to control the learning process, e.g., the number of hidden layers and the number of neurons in each layer.  
Figure 1: A basic schematic representation of DNN, which is composed of an input layer along with data \(\mathbf{x}=[x_{1},\dots,x_{N}]^{T}\in R^{N}\), an output layer \(\mathbf{y}=[y_{1},\dots,y_{M}]^{T}\in R^{M}\) and hidden layers.
## 2 Multilayered MM-Based Effective Medium Approach
### Effective Perpendicular Permittivity  
It is specifically known that the normal component of the electric displacement vector at a boundary should be continuous. The former is outlined by the following expression:  
\[D_{m}^{\perp}=D_{d}^{\perp}=D^{\perp} \tag{7}\]  
It is also known that a superposition of the electric field components generated by the sheets dramatically affects the total magnitude of the electric field. Consequently, it can be defined by:  
\[E^{\perp}=\rho E_{m}^{\perp}+(1-\rho)E_{d}^{\perp} \tag{8}\]  
where \(E_{m}^{\perp}\) is the perpendicular component of the electric field in the metallic area, \(E_{d}^{\perp}\) is the perpendicular component of the electric field in the dielectric area, and \(E^{\perp}\) is the total electric field in the MM. The boundary condition from Equation (7) and [PERSON]'s equation [20] can be used. Moreover, it is of particular importance to substitute them into Equation (8). If the common electric field components are cancelled out, and everything is solved for \(\varepsilon_{\perp}\), it is possible to derive the analytic formula for the perpendicular permittivity of the multilayered MM:  
\[\varepsilon_{\perp}=\frac{\varepsilon_{m}\varepsilon_{d}}{\rho\varepsilon_{d}+( 1-\rho)\varepsilon_{m}} \tag{9}\]
## 4 Simulation and Analysis  
Figure 17 depicts the power system considered in the simulation study: a 50 MW wind farm consisting of five 10 MW PMSG wind turbines controlled by an MMC system. The wind turbine generators (WTGs) are connected to each other by medium voltage collector power cables. Each collector power cable is represented by an equivalent \(\pi\) circuit model. The power outputs from the wind turbine generators are collected at a 33 kV main bus (B33) and then supplied to the main grid through a 33 kV/66 kV main transformer and double circuit 66 kV transmission line. The parameters of the PMSG and the MMC are presented in Tables 2 and 3, respectively.  
The power system model shown in Figure 17 has been analyzed with the detailed and the average models using PSCAD/EMTDC. The accuracy of the average model has been validated by comparing its responses under both steady-state and transient conditions with those obtained from the detailed model. The simulations have been performed on a personal computer (Intel (R) Core (TM) i7-9700 CPU @ 3.0 GHz Ram 64 GB).
## 3 Experiment and Result Analysis
### Data Collection and Processing Strategy
\begin{table}
\begin{tabular}{c c c} \hline \hline  & Traditional RTK & RZTD-constrained RTK \\ \cline{2-3} Observations (variance) & Uncombined DD pseudorange (\(e_{2}^{2}\) m) \\ \cline{2-3}  & Uncombined DD carrier phase (\(e_{2}^{2}\) m) \\ \hline Pseudo-observations (variance) & - & RZTD derived from GPT2 model (\(e_{Tent}^{2}\) m) \\ \hline Troposphere mapping function & GMF & GMF \\ \hline Initial troposphere RZTD (variance) & 0.0 m (\(0.15^{2}\) m\({}^{2}\)) & 0.0 m (\(0.15^{2}\) m\({}^{2}\)) \\ \hline Initial ambiguity (variance) & derived from pseudorange (\(30^{2}\) cycle\({}^{2}\)) \\ \hline Initial slant ionosphere delay (variance) & 0.0 m (\(0.05^{2}\) s \(BL^{2}/100\) m\({}^{2}\)) & 0.0 m (\(0.03^{2}\) s \(BL^{2}/100\) m\({}^{2}\)) \\ \hline Process noise of troposphere RZTD & \(10^{-4}\) m/\(\sqrt{s}\) & \(10^{-4}\) m/\(\sqrt{s}\) \\ \hline Estimator & Kalman filter & Kalman filter \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Processing strategies of traditional and relative zenith troposphere delay (RZTD)-constrained real-time kinematic (RTK) model. DD: double-differenced; GMF: global mapping function; GPT: global pressure and temperature; _BL:_ baseline length in kilometers. differ from second-best candidates [12], and the selected ambiguity subset will be fixed with the LAMBDA method again. To ensure the accuracy of the fixed solution, if they pass the FF-ratio test and the number of fixed NL ambiguities is not fewer than 6, these integer ambiguities will be applied as constraints to update coordinate parameters. The fixed rate of AR is defined as follows:  
\[P_{fixed}=\frac{N_{\text{fixed}}}{N_{total}}\times 100\%, \tag{7}\]  
where \(N_{\text{fixed}}\) refers to the epoch number of the fixed solution (fixed NL ambiguities are no fewer than six) and \(N_{total}\) represents the total number of epochs. Time to first fix (TTFF) is the time to initially fix ambiguity, which is another indicator to evaluate the performance of RTK positioning. To calculate the average TTFF, the ambiguities and atmospheric parameters were reset repeatedly for all baselines during the experiment:  
\[Average\;TTFF=\frac{\sum_{i=1}^{n}TTFF_{i}}{n}, \tag{8}\]  
where \(n\) is the number of sessions. In this study, the time span was set as 4 h for each session in order to acquire enough samples for calculating TTFF; data from 0:00:00-3:59:30, 0:30:00-4:29:30, 1:00:00-4:59:30,......, 20:00:00-23:59:30 were processed; and a total of 41 sessions were obtained for each baseline in one day--six of them independent sessions (0:00:00-3:59:30, 4:00:00-7:59:30,......, 20:00:00-23:59:30).
# Matching of Energy Provisions in Multihop Wireless Infra-Structures
## 5 Energy Differentiation Factor for Energy Provision in WMI Networks  
In Section 4, we showed that the optimal energy provision matching, Matching-EP, that achieves maximum network lifetime depends on the energy differentiation factors of the BSs in the network. The challenge of realizing Matching-EP in practice is that in general it is difficult to know precisely the traffic activities among nodes in a network. Hence it is difficult in practice to achieve optimal matching. However, Matching-EP can be used as a basic guiding principle for energy provision to improve energy efficiency and increase the lifetime of WMI networks. We present an analytical framework for various network models.  
Main notations in the energy consumption analysis are shown in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Description** \\ \hline \(d(i)\) & the topological distance from BS(\(i\)) to the gateway \\ \(N\) & the number of BSs in the WMI networks \\ \(E_{0}(i)\) & the initial energy of BS(\(i\)) \\ \(E_{0-all}\) & the total initial energy of all BSs for polarized traffic activities \\ \(E_{CR}(i)\) & the energy consumption rate at BS(\(i\)) \\ \(E_{c}(i)\) & the energy consumption at BS(\(i\)) \\ \(E(i)\) & the current remaining energy at BS(\(i\)) \\ \(E_{r}(i)\) & the remaining energy at BS(\(i\)) \\ \(zone(k)\) & the zone in which BSs have hop distance k to GW \\ \(Diff(i)\) & the energy differentiation factor at BS(\(i\)) \\ \(E_{Z}(h)\) & the energy consumption at zone(\(h\)) \\ \(Src(i)\) & the number of packets initiated at BS(\(i\)) in a WMI \\ \(TX(i)\) & the number of transmitted packets at BS(\(i\)) \\ \(RX(i)\) & the number of received packets at BS(\(i\)) \\ \(OH(i)\) & the number of overheard packets at BS(\(i\)) \\ \(E_{Others}\) & the energy consumption other than that of \(TX\), \(RX\), and \(OH\) \\ \(Oh(i)\) & \(E_{Others}\) at BS(\(i\)) \\ \(E_{Pt}\) & the average energy consumed for transmitting a packet \\ \(E_{Pr}\) & the average energy consumed for receiving a packet \\ \(N_{z}(k)\) & the total number of BSs in zone(\(k\)) \\ \(ZTX_{self}(k)\) & the number of packets initiated in zone(\(k\)) \\ \(ZTX(k)\) & the number of transmitted packets in zone(\(k\)) \\ \(ZRX(k)\) & the number of received packets at in zone(\(k\)) \\ \(ZOH(k)\) & the number of overheard packets in zone(\(k\)) \\ \(E_{Total-Z}(k)\) & the total energy consumption in zone(\(k\)) \\ \(E_{Total-B}(k)\) & the total energy consumption at a BS in zone(\(k\)) \\ \(Diff_{ZBS}(k)\) & the differentiation presenting factor of a BS in zone(\(k\)) \\ \(D\) & the density of BSs relative to hop-distance based area \\ \(H\) & the largest hop distance of BSs to GW \\ \(K_{RT}\) & the ratio of energy consumption for receiving a one-bit \\  & message to that of transmitting a one-bit message \\ \(K_{OH}\) & the ratio of \(OH\) energy consumption at a zone to the \(TX\) \\  & energy consumption of the upstream neighbour zone \\ \hline \hline \end{tabular}
\end{table}
Table 1: Definitions of notations.
###### Abstract
The stochasticity and time-varying nature of travel demand have an effect on the MFD shape (introduce scattering), through boundary conditions (rationing the traffic demand) well-defined MFDs are obtained [10]. An adaptative optimization scheme for perimeter control is proposed in [11], in this study a heterogeneous network was partitioned into four homogeneous regions in order to apply perimeter control to regulate the inflow and outflow between regions; the novelty is that the selected control points (where the flow is regulated) are in the boundaries between regions and the generated queues in this points are inside the area of analysis, therefore, these queues influence the traffic and affect the network performance (e.g., the total travel time and the space-mean speed). Similar is our study with the works that calculate the point at which if the accumulation of vehicles in the street network increases, the travel production decreases, since we compute the (upper) limit of the number of vehicles that can enter the network (during a time interval) for which the links' average speeds stay greater than a speed threshold. The data of various cities were analyzed in [12] to establish the critical point, i.e., the accumulation of vehicles from which the travel production of the network begins to drop. For signalized intersections in [13] was established a relationship between capacity (explained through followed-up headway, saturation flow rate, and proportion of time in which vehicles can leave the queue) and driving behavior parameters: driver response time during queue discharge, jam spacing, and queue discharge speed. The networks presenting a low flow than the predicted if traffic were evenly distributed are analyzed in [14], in addition, it was studied how the bifurcation's critical density changes (increases) with adaptive drivers (those choosing a route according to the current traffic). The relation between the quality of the Advanced Traveller Information System and the reserve capacity (i.e., the additional supported demand without exceeding the capacity of all links) of different networks is presented in [15], it was found that reserve capacity does not necessarily increases with traveler information level.
## 4 Cellular Component of the CRC microenvironment
### Vascular Cells  
The vascular cells include blood endothelial cells (BECs), lymphatic endothelial cells (LECs), and pericytes (characteristic only for blood vessels). These cells build transport pathways that maintain homeostasis in physiological processes by regulating vessel permeability and tension. However, their primary functions can be exploited by tumors for survival and invasion [214; 215; 216]. Tumor progression involves proliferation and the growth of the tumor mass. Substance exchange through diffusion only affects the external layers of the tumor mass, leading to strong hypoxia and acidosis in the growing pathological tissue. In such conditions, hypoxia-inducible factor (HIF) is activated, regulating the behavior of endothelial cells towards the formation of microvessels. Proangiogenic factors secreted by the malignancy-promoting phenotype of endothelial cells participate in this process, leading to vasculogenesis (formation of blood vessels from endothelial cell precursors) or angiogenesis (using existing blood vessels), resulting in tumor vascularization [1; 11; 217; 218; 219].  
In the tumor microenvironment, vascular cells are referred to as tumor-associated endothelial cells (TECs). They have different morphology and functions compared to normal vascular cells, resulting in the formation of mostly dysfunctional vessels. These vessels may be blind-ended, and their irregular arrangement and abnormal connections can slow down blood flow. This increases the malignancy of the tumor by maintaining compensatory mechanisms around the dysfunction of vessels, such as the production of proangiogenic factors and the activation of HIF, allowing energy acquisition in limited oxygen conditions [216; 219; 220]. On the other hand, partially dysfunctional vessels mediate substance exchange. Although this process does not guarantee full transport, it ensures the continued functioning and migration of cancer cells. Therefore, increased vessel density in the tumor niche correlates with a poor prognosis [216; 221]. The tumor vascularization process also depends on the location and type of tumor invasion, influencing the expression of such angiogenic factors as VEGF-A [222]. In patients with CRC, a high level of lymphangiogenic markers (VEGF-C and VEGFR-3) has been identified. In a mouse model, the VEGF-C/VEGF-3 axis initiated the formation of lymphatic vessels. Therefore, [PERSON] et al. identified these factors as molecular targets for preventing metastasis [223]. Moreover, factors released by endothelial cells can stimulate tumor growth and resistance to chemotherapy [224; 225].
## 1 Introduction
### Our Contributions  
In this paper, we focus on the method of enhancing the information necessary for each other between the SED and DOAE features, since SELD is a task predicting the temporal and spatial locations of a target event as two different aspects of one signal. Our model is based on the encoder-decoder structure. For the encoder, we adopt the embedding scheme of the EINV2 using soft parameter sharing, while the decoder part of the transformer [20] is modified to predict either sound events or source directions. Different from the conventional transformer that analyzes sequence data by feeding back the previous output as the current decoder input, embedded SED or DOAE features are directly used as the input to analyze the current sound in our decoder.  
In particular, we apply dual cross-modal attention (DCMA) in the decoder part, which is the first trial for multi-task learning including the SELD task as far as we know, although DCMA has been used in multi-modal tasks such as audio-visual speech recognition and audio-text emotion detection [21, 22]. Related information between features for SED and DOAE may be helpful for the SELD task, which needs to predict the class and direction of a specific sound event simultaneously. The related information can be enhanced by the DCMA using two different embedded features for the queries and keys/values in transformer decoder layers. Therefore, we expect our DCMA-based model structure to efficiently learn the association between the two embedded features.  
In addition, the decoder consists of one common block based on the DCMA despite the need for multiple predictions with the track-wise output format. Therefore, an efficient SELD model can be constructed even for the task with polyphonic sound events. We refer to our model as DCMA-SELD.  
Unlike speech, in which a feature extraction method suitable for recognition has been established, various feature extraction methods are evaluated for sound signal analysis, and a suitable combination for SELD is found among them. In addition, we try and evaluate data augmentation techniques of the mixup simulating polyphonic events and channel rotation for spatial augmentation to improve the generalization performance of our model.  
The core of the model architecture has been evaluated in DCASE2021 Task3 [4], and this paper further develops the model by the methods described above with a detailed description and demonstrates the appropriateness and effectiveness of the model through extensive experiments.  
The rest of the paper is organized as follows. In Section 2, we describe our approach including detailed explanations for DCMA-SELD, input features, and data augmentation techniques. Section 3 presents the experimental setup, results, and discussions. Finally, we conclude the paper with some remarks in Section 4.
## 2 Basic Theoretical Parameters of FE-SEA Method  
### Modal Density  
In the hybrid FE-SEA model, the three basic parameters required by the model are modal density, internal loss factor, and coupling loss factor between different subsystems. Within the research frequency range, the modal density is one of the important parameters of the subsystem, which represents the energy storage capacity of the subsystem.
# Effect of LNR-g-MMA on the Mechanical Properties and Lifetime Estimation of PLA/PP Blends
## 3 Results and Discussion
### Morphology  
The morphology of the tensile-fractured surfaces of the PLA, PP, and PLA/PP (70:30 wt%) blends with various contents of LNR\(-\)g\(-\)MMA was studied using SEM, as shown in Figure 3. In Figure 3a, PLA showed a brittle fracture, while PP showed a ductile fracture and whitening deformation in the micrograph spread over the entire surface (Figure 3b). It is generally known that the incorporation of PP into the PLA matrix results in immiscibility due to the different polarity of the two polymers, and it was found that phase separation occurred. PP dispersed in the form of small balls is distributed evenly in the PLA matrix, showing an incompatible blend morphology. The small holes can be seen as a result of the sample being pulled apart by the tensile force, which also caused PP particles to separate from the PLA matrix, as shown in Figure 3c. Therefore, adding LNR\(-\)g\(-\)MMA to the mixture enhanced the miscibility and reduced the interfacial tensile between PLA and PP. The PMMA component in the grafting shell had a good miscibility with the PLA phase [23], and the LNR component compatibilized PLA/PP system [2], which improved compatibility. The result in the droplet size of the PP particles of the PLA/PP blends in the presence of LNR\(-\)g\(-\)MMA was smaller than that of the PLA/PP blend without LNR\(-\)g\(-\)MMA. The droplet size of the dispersed PP particles decreased significantly when the LNR\(-\)g\(-\)MMA was increased, as shown in Figure 3d-f. However, the morphology of the PLA/PP blends with the LNR\(-\)g\(-\)MMA of 10 phr presented tiny PP particles and was almost homogenous, as shown in Figure 3f.
# GPRS Sensor Node Battery Life Span Prediction Based on Received Signal Quality: Experimental Study
## 3 Material and Methods
### Data Transmission Part  
This is the part that can even be called a sensor node. It is made with four subparts: microcontroller, GSM/GPRS Module [36], sensors, and the remote server; the circuit diagram is shown in Figure 9. During this work, we had an idea of sensing the temperature from five different locations and sending them to a remote database using the GPRS protocol. We were sending values every 20 s.
## 5 Reactive Protection Concept for Industrial Automation Systems
### Security Policies, Operational Policies and Reactive Actions in Real-Life Automation Systems  
The foundation for the presented concept for reactive protection are current architectural trends in IAS and the security and operational policies, as well as reactive actions suitable for these trends.  
Modern industrial networks are often divided into segments and segregated [20]. This improves their performance, facilitates their management and increases their security [20; 46]. The ISA/IEC 62443 series of Standards has included and further refined these concepts. It has also considered operational policies and the management of security policies on a zone-to-zone basis critical to maintain the security of IAS. This series of standards, as well as guidelines presented in [20], especially highlight the importance of security mechanisms located at the edge of each network zone.  
Considering the aforementioned architectural and security trends, a classification of security and operational policies, as well as possible reactive actions are presented in the following subsections. It is important to highlight that the policies and actions that may suit specific IAS should be analyzed on a case-to-case basis. However, in this section, they are presented in a more general way in order to outline the scope of the opportunities that the presented concept provides.  
First, security policies are derived from security requirements explicitly discussed in ISA/IEC 62443-3-3 (i.e., ISA/IEC 62443 Part 3-3 System security requirements and security levels). This standard part has been considered as it provides a set of well-defined security requirements that should be considered by system integrators in order to protect the correct operation of their systems [47]. Afterwards, operational policies are derived from guidelines [20] that present a better understanding of the behaviour of automation systems and the impact security may have on them. Following this, possible reactive actions are discussed. These reactive actions are derived from the security policies themselves, as well as suggestions provided by the aforementioned security standards and guidelines for IAS. Finally, a discussion regarding how security-, operational policies and reactive actions provide network segment reactive protection is provided.