esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Method
### Structured Prediction for Accurate Event Localization  
#### 3.4.1 RNN-Based Structured Prediction  
Segment-level prediction can be performed using DSNet as described above. However, segment-level predictions may not be robust since DSNet does not make good use of long-term contextual information. Better segment-level prediction results can be obtained by considering long-term contextual information and incorporating prior knowledge into our model. To consider long-term dependency between segment predictions, an RNN is applied at the top of DSNet. We refer to DSNet with RNN as DSNet-RNN. The structure of DSNet-RNN is almost the same with DSNet except that the dense layer marked with \(*\) in Figure 1 is replaced by a single layer RNN with bi-directional gated recurrent units (Bi-GRUs) [35]. However, in weakly supervised learning, there is a lack of accurate label information on each segment. The incorrect information on each segment may affect other segments through the RNN. To mitigate this problem, it is desirable to train our model by applying some prior knowledge that audio events are generally continuous. To utilize this prior knowledge, we define a prediction smoothness cost \(C_{ps}\) as  
\[C_{ps}=\sum_{i,j=1}^{N}\mu(y_{i},y_{j})s(i,j), \tag{7}\] \[\mu(y_{i},y_{j})=||y_{i}-y_{j}||,\] \[s(i,j)=exp(-\frac{||p_{i}-p_{j}||^{2}}{2\sigma_{ps}^{2}}),\]  
where \(y_{i}\) is the segment prediction of the \(i\)th segment and \(p_{i}\) denotes its normalized temporal position. The prediction smoothness cost \(C_{ps}\) encourages segment predictions to be continuous over time by penalizing nearby segments with different predictions. The cost function for training the DSNet-RNN is given by  
\[C=C_{cl}+\lambda C_{ps} \tag{8}\]  
where \(\lambda\) is a compromising parameter.
## 3 Spectrum Auction Structures
### Simultaneous Multiple-Round Auction  
First applied in 1994, the Simultaneous Multiple-Round Auction originated from the need for a structured format that would enable the FCC to maximize revenue from the allocation of spectrum band rights (according to [15]).  
The primary objective of the auction is to enable players to submit bids for both individual items and combinations of items. After each round, the winning bid is updated, and disclosed to the players, facilitating information sharing and strategic adjustments for all participants. New rounds continue until no additional bids are placed for any individual or combined bids.  
This structure aims to achieve market equilibrium (Walrasian tatonnement, a theoretical auction equilibrium achieved through price fluctuations, using trial and error, until supply and demand are satisfied; although auction prices cannot practically fluctuate downward, a sufficiently small increment would suffice to approach optimal prices), while the primary challenge is mitigating the negative impact of player exposure. This exposure, in turn, reduces bids due to the uncertainty of acquiring all desired items simultaneously.  
In the seminal article by [PERSON] [11], the author elaborates on the concepts of equilibrium, demonstrating both its existence and convergence in a scenario where licenses are mutually substitutable.  
However, spectrum licenses are often heterogeneous and frequently exhibit complementarities. Acquiring a 5G license may be more advantageous for a company that already has the rights to a 4G band. How do we address situations where the combined value of licenses surpasses the individual worth of each license? How can we safeguard the participant who values this combination? This challenge, known as exposure, is prevalent in all auction formats.
## 3 Results  
### Target Resolution  
Target size directly impacted the ability to resolve a target over the range of distances tested. In all cases, the MO\({}^{2}\)CA system was able to identify targets of each size (2, 5, and 10 mm), though with smaller targets having a shorter working distance range of detection. Specifically, the following working ranges of resolution were found: 2 mm = range of 1-6 ft, 5 mm = range of 1-13 ft, and 10 mm = range of 1-18 ft (Figure 7a; Supplementary Figure S1). In comparing functional distances between target sizes, a 5 \(\times\) 5 mm target afforded a 50% greater working distance of resolution versus the 2 mm target, with a maximum distance of identification and a resolution of 12 ft (Figure 7a,b). Further, the 10 \(\times\) 10 mm target demonstrated a 33% increase in operative distance over the 5 \(\times\) 5 mm target, with the maximum distance of identification of up to 18 ft (Figure 7b; Supplementary Figure S1). A single-factor ANOVA indicated significant differences between the three groups (F (2,6) = 1027, \(p\) < 0.001), with a linear trend between the size of the target and the distance of identification (r\({}^{2}\) = 0.99, \(p\) = 0.1). Post-hoc testing via the Bonferroni method indicated a significant difference in resolution between each group.
## 4 Results: An Experimental Analysis
### Performance of the ANN-Based Numerical Solution of (2)  
In this series of experiments, we investigate the model problem  
\[\begin{cases}\Delta u(x,y)=-16x\cos 4y\quad\text{in }\Omega\\ u(0,y)=y,\ u(\frac{\pi}{2},y)=y+\frac{\pi}{2}\cdot\cos 4y,\ u(x,0)=x\quad \text{on }\partial\Omega\\ u(x,1+\frac{\alpha\alpha y\,x}{4})=1+\frac{\alpha\alpha y\,x}{4}+x\cdot\cos 4 (1+\frac{\alpha\alpha y\,x}{4})\quad\text{on }\partial\Omega,\end{cases} \tag{14}\]  
where the computational domain is given by  
\[\Omega=\{(x,y):0<x<\frac{\pi}{2},\quad 0<y<1+\frac{\cos x}{4}\}.\]  
Note that (14) has the analytic solution \(u(x,y)=y+x\cdot\cos 4y\).  
The domain \(\Omega\) was discretized using a nonuniform grid. The motivation of this arises from the study of moving boundary problems for water waves [15], where the velocity potential exhibits more variation nearer to the free surface. In this way, for an equally accurate discretization, we need here more grid points compared to the bottom region. Also, the number of the grid points is frequently identical on each vertical segment. Using this convention, we can easily record the neighboring relations. A corresponding grid is shown in Figure 9.  
In concrete terms, we have 21 grid points along all vertical segments. Also, we have 21 segments in the discretization. Performing again a comparison between the ANN-based and conventional approximation of the Laplacian, the errors given by (12) and (13), we obtain 0.33 and 6.5, respectively. Here, we have a significant advance of our approach. Also, we have compared the final computational errors in the discrete \(L_{2}\)-norm to obtain  
\[\left(\sum_{\mathbf{x}_{j}}|\mathbf{x}_{j}-\mathbf{x}_{j+1}|\cdot|\mathbf{x}_ {j}-\mathbf{x}_{j+2}|\cdot|u(\mathbf{x}_{j})-(\mathbf{u}_{h,\text{NN}})_{j}|^{ 2}\right)^{\frac{1}{2}}\approx 0.018\]  
and  
\[\left(\sum_{\mathbf{x}_{j}}|\mathbf{x}_{j}-\mathbf{x}_{j+1}|\cdot|\mathbf{x}_ {j}-\mathbf{x}_{j+2}|\cdot|u(\mathbf{x}_{j})-(\mathbf{u}_{h,\text{FD}})_{j}|^{ 2}\right)^{\frac{1}{2}}\approx 0.065.\]  
Here, \(\mathbf{u}_{h,\text{FD}}\) denotes the numerical solution of (14) using the conventional finite difference method given with the (6) matrix based on the discretization in (13). Likewise, \(\mathbf{u}_{h,\text{NN}}\) denotes the solution of the equation \(D_{h,\text{NN}}\mathbf{u}_{h,\text{NN}}=\mathbf{f}_{h}\), where \(D_{h,\text{NN}}\) is the discretization matrix obtained by our ANN. The comparison of the errors above also shows the advance of our approach.  
Another way of obtaining an accurate discretization matrix is to perform the optimization procedure discussed in Section 2.3 pointwise in each \(\mathbf{x}_{0}\). This, however, significantly increases the computational time. Regarding this, a comparison is shown in Table 1. The proposed ANN-based algorithm becomes faster only for a large number of grid points. This is typically the case in real-life situations.
# Simulation Tool for Winter Navigation Decision Support in the Baltic Sea
## 2 Related Work
[PERSON] and [PERSON] (2020) presented a discrete-event simulation-based approach for assessing the operating performance of the Finnish-Swedish Winter Navigation System (FSWNS) under different operating scenarios. Different operating scenarios were specified in terms of ice conditions, volume of maritime traffic, number of icebreakers, and regulations, such as the Energy Efficiency Design Index (EEDI). Several performance indicators were considered, including transport capacity, number of instances of icebreaker assistance, and icebreaker waiting times. A validation against real-world data on maritime traffic in the Bothnian Bay indicated that the approach could capture the complex behavior of the FSWNS and roughly estimate its operating performance. Case studies were carried out in which the approach was applied to assess the impact of replacing various percentages of the present fleet of merchant ships entering the Bay of Bothnia by EEDI-compliant ships. As per [PERSON] and [PERSON] (2020), the approach is based on a number of generalized assumptions concerning convoy operations (a maximum of two ships are assisted by an icebreaker at a time), the time it takes for an icebreaker to reach a ship in need of assistance (probabilistically determined), criteria for when icebreaker assistance is to be provided (generalized criteria), and assumptions concerning the presence of brash ice channels (generalized assumptions), among others. Other limitations of the model include: (a) no ability to visualize the simulation results, (b) no optimizing capabilities, and (c) rigid airways. The above-mentioned studies have demonstrated that it is feasible to simulate the operations of the FSWNS, considering key factors, such as the prevailing ice conditions, the prevailing network of airways, the volume of ship traffic, the ice-going capabilities of individual merchant vessels, and the availability of icebreaking resources. This work aims to build on the previous work and take the concept further towards building a decision-support tool. In the field of winter navigation, most of the previous work with simulation models has involved numerical simulations that provided statistical estimates for speeds of vessels of different dimensions in varying ice conditions. The present authors' work models traffic flows, icebreaker-vessel interactions (including convoys and towing), along with visualization. Further, the framework allows for algorithms to be embedded in the model at multiple places to intelligently capture system decision making, such as in icebreaker choice making, where an icebreaker chooses which vessel to assist next.
# Multiple Object Tracking Using Re-Identification Model with Attention Module
## 1 Introduction  
Multiple object tracking (MOT) aims at estimating the trajectories of multiple objects and re-identifying the objects appearing in consecutive frames of videos and real-time cameras. It has been a longstanding objective in the field of computer vision including action recognition [1], sports video analysis [2], closed-circuit television analysis [3], and driving environment recognition [4].  
Most of the existing MOT methods [5; 6] are based on the two-stage detection-based tracking (DBT) model, which comprises a detection model and an association stage, as in Figure 1. The detection model estimates the bounding box position of the objects in an image, and then, the association stage extracts the features from each detected box to track the objects.  
Recent DBT systems have aimed at improving the performance of the detector or the association stage. For the detector, the rise of the convolutional neural network (CNN) [7] has greatly enhanced the accuracy and efficiency of object detection. Meanwhile, for the association stage, the simple online and real-time tracking (SORT) algorithm [8] has been proposed to handle the cross-regions by combining the Kalman filter with the detection system. DeepSORT [9] was found to show significantly improved performance when the SORT algorithm was combined with a re-ID network that considers appearance information. However, it still loses the ID when the appearances of the objects are similar.  
To address this issue, we introduced a triplet-based attention association network to fully leverage the visual information of the object. The network was trained using a triplet loss function to estimate whether a given sample is the same as a reference sample or not. To capture the important visual representation of the object, we employed convolutional attention modules in the association network. We further improved the association network by pretraining on the re-ID dataset, which included various perspectives of the object fromthe tracking dataset. The results, demonstrated on the KITTI tracking dataset, showed that the proposed method more effectively handles occlusive problems in multi-object tracking than previous studies. The contributions of this research can be summarized as follows:  
* We propose a triplet-based association network for multi-object tracking with a novel tracking batch sampler method that can discriminate objects from a similar viewpoint.
* We employed a convolutional attention mechanism in our association network to selectively focus on the features of the object, by assigning different weights to different regions.
* We demonstrate that the proposed method not only outperforms existing methods in terms of switching IDs, but also improves the detection and tracking performance.
To change antenna operation, switches like RF microelectromechanical systems (MEMS), varactor diodes, and PIN diodes, as well as the optically activated switches of fiber-optic cables, are being used [8; 9; 10]. Reconfigurable antennas with RF MEMS switches have a switching speed ranging from 1-200 usec. This is normally considered to be low for most applications [8]. An antenna combined with a varactor diode, the capacitance of which is controllable by a varying bias voltage, can have a wide tuning range. The design of the biasing network with varactor diodes has also been employed for the reconfigurable antenna design [9]. Hence, PIN diodes are preferred in reconfigurable antenna designs as they provide fast switching, and thus fast dynamic reconfiguration, with switching speeds ranging from 1-100 nsec [10]. In this paper, we present a comprehensive study of different types of reconfigurable antennas for mobile and wireless systems with simple or multiple reconfigurability functions. The paper is organized as follows: Section 2 discusses single reconfigurability in antennas. Different types of reconfigurable antennas with multiple reconfigurable features are studied in Section 3. Section 4 concludes the study.
## 3 Byzantine Fault Tolerance for Distributed-Ledger Systems
### Permissioned
#### 3.2.2 Classical Consensus  
The need for consensus has been studied extensively in the research area of distributed systems [3; 48]. As mentioned above, the reliability of services and coordination of multiple individual processes are major challenges for such systems. Thus, distributed systems employ different types of _failure models_ in an attempt to provide resilience against different types of failures: (i) _crash failures_, where nodes can fail at any time mostly by stop responding or disconnected; and (ii) _byzantine failures_, where nodes can act arbitrarily (i.e., maliciously) with an ultimate aim to interfere and eventually defeat the original purpose of the consensus protocol, e.g., by sending contradicting or even injecting malware messages, etc. [2; 6; 8]. In the context of DLS consensus, and especially in a public (or permissionless) environment--where participation to the network is dynamic and open to unknown participants--the Byzantine setting is of relevance [19]. Therefore, the consideration of classical consensus proposals, such as Paxos [16] or Raft [30], are not sufficient. DLS suggests the definition of improved versions of such protocols based on the classical consensus paradigm, suitable for dynamic and highly adversarial environments. BFT consensus protocols are algorithms that provide resilience to one or many byzantine failures while at the same time ensuring that the system is functioning as intended. The implementation of such algorithms originates from the paradigm of _state machine replication_[7], where consistency of data is ensured by consulting a set of replicas of databases [7; 15]. Often, the boundary condition that is satisfied for the operation of such algorithms in the presence of faulty processes \(f\) in a systems of \(N\) nodes is, \(f=\lfloor\frac{N-1}{3}\rfloor\) (the reader is referred to the work of [31] for a formal proof).
## Preliminaries
### Elliptic Curve Cryptography  
Let \(\mathbb{F}_{q}\) be a finite field with odd characteristic. An elliptic curve \(E\) over \(\mathbb{F}_{q}\) is defined by a short Weierstrass equation of the following form:  
\[E:y^{2}=x^{3}+ax+b,\]  
where \(a,b\in\mathbb{F}_{q}\) and \(4a^{3}+27b^{2}\
eq 0\).  
Because the most important operation in ECC is scalar multiplication \(kP\), it must be implemented efficiently. The basic method for computing \(kP\) is comprised of two elliptic curve operations: elliptic curve point addition (ECADD) and the ECDBL operations. Let \(P=(x_{1},y_{1})\) and \(Q=(x_{2},y_{2})\) be two points on an elliptic curve \(E\). The ECADD and ECDBL operations can be computed in affine coordinates as follows:  
\[x_{3}=\lambda^{2}-x_{1}-x_{2},\ \ \ y_{3}=\lambda(x_{1}-x_{3})-y_{1},\] \[\lambda=\frac{y_{2}-y_{1}}{x_{2}-x_{1}}\ \text{if}\ P\
eq Q,\ \ \text{and}\ \lambda=\frac{3x_{1}^{2}+a}{2y_{1}}\ \text{if}\ P=Q.\]  
The ECADD and ECDBL operations are composed of finite field arithmetic operations, such as field addition, subtraction, multiplication, squaring, and inversion. Therefore, to improve the performance of scalar multiplication, the internal algorithms such as field and curve arithmetic operations should be efficiently implemented.
# Long-Lifetime Event-Driven Wireless Monitoring System for Pole-Mounted Transformers
## 2 Conventional Monitoring Systems  
### Types of Conventional Monitoring Systems  
Figure 1(a) shows a simplified block diagram of a conventional wired monitoring system for large-scale power transformers. In the monitoring system of a power transformer, various physical quantities can be converted into electrical signals using sensors, such as PD, vibration, and ultrasound [8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24]. When an electrical signal is fed to a signal conditioner through a wire, the signal conditioner manipulates the input to prepare it for the next stage, a high-speed DAQ. The DAQ output is collected in a data center for monitoring and diagnosis. Even though this high-speed and real-time monitoring system is accurate and reliable, it cannot be applied to monitor pole-mounted transformers with limited accessibility or safety hazards, due to its high cost and large size. Before smart grid technologies were developed, noncontact-based remote sensors such as ultrasound and thermal imagers were widely used for monitoring pole-mounted transformers, as shown in Figure 1(b). High-frequency signals such as ultrasonic or acoustic emissions can be received by a remote sensor, followed by a filter as a signal conditioner, and then an audible frequency converter (AFC) that converts a high-frequency input signal to a lower audible frequency so that a specialist can analyze it by its sound. Even though the output signal needs to be analyzed by skilled specialists on-site, multiple remote locations can be monitored with only a single set of portable monitoring units [17]. However, there are several drawbacks to this remote monitoring system. As the number of pole-mounted transformers to be monitored increases, more specialists are needed for regular checkups. Furthermore, real-time monitoring for each pole-mounted transformer is not supported because of the lack of a network. Even though it is possible to realize real-time monitoring  
Figure 1: Various faults and their physical properties in a power transformer.  
by adding a network for remotely acquired data, the additional cost cancels out the benefits of remote monitoring.  
To accomplish real-time monitoring of pole-mounted transformers at a low cost, wireless monitoring has been developed as shown in Figure 2c [25]. Once a physical signal from a power transformer is detected by a sensor, a signal conditioner reduces noise and is followed by a DAQ. A wireless transmitter (TX) communicates the output of the DAQ to a receiver (RX). To realize small and low-cost solutions for real-time monitoring that enable long-term data transmission and analysis, battery-powered monitoring systems based on Internet-of-Things (IoT) devices can provide good solutions. However, continuous monitoring and data transmission using a wireless monitoring system consumes a large amount of energy, necessitating frequent replacement of batteries, resulting in higher O&M costs.
# Coverage, Rate, and Last Hop Selection in Multi-Hop Communications in Highway Scenarios
## 5 Conclusions  
Motivated by the need to reduce the expenditures on 6G V2X deployments, in this paper, we have formalized a model of relaying communication between vehicles and BSs accounting for sub-THz propagation effects and gradual penetration of the new technology components to vehicle manufacturing. To analyze the proposed system model, we have developed a system-level simulation tool capable of delivering critical quality of service parameters for network operator metrics of interest.  
The reported numerical results are based on IEEE 802.15.3d parameters and real measurements at 300 GHz. In this paper, we show that the bumper location is better in terms of bridge length, while the bumper and engine locations coincide in terms of the data rate. For reasonable road traffic conditions, the lower the traffic density, the higher the bridge availability, while a linear increase in the number of hops leads to a linear increase in the bridge availability. The multi-hop bridges even in relatively regular vehicular deployment are inherently opportunistic in terms of the data rate, i.e., forcing them to achieve a certain data rate leads to drastic performance degradation in terms of the bridge availability. Advanced last hop strategies are characterized by the different responses to the system parameters as compared to the \"first opportunity\" strategy. Specifically, there is an optimal value of vehicle density maximizing the data rate. Finally, LoS propagation is dominant for all the considered last hop selection strategies, contributing more than 90% of all the utilized paths.  
Conceptualization and methodology, Y.G. and K.S.; software, E.M. and V.B.; validation, V.B. and D.O.; formal analysis, D.O.; investigation, V.B., and D.O.; writing--original draft preparation, D.O.; writing--review and editing, K.S. and Y.G.; visualization, D.O. and E.M.; supervision, Y.G. and D.O.; funding acquisition, D.O. and K.S. All authors have read and agreed to the published version of the manuscript.  
Sections 2 and 3 were written by [PERSON] under the support of the Russian Science Foundation, project no. 21-79-10139. This paper has been supported by the RUDN University Strategic Academic Leadership Program (recipients [PERSON], Section 1, [PERSON], Section 4 and [PERSON], Section 5).  
Figure 11: Gains in the required BS density as compared to the baseline BS-only deployment.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.