esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
Machine learning-based algorithms have recently attracted attention in spectrum sensing for cognitive radio networks. The main advantage of these algorithms over conventional spectrum sensing algorithms is their heuristic nature and independence from needing prior information about the environment. An SVM-based algorithm is utilized in spectrum sensing with multi-class hypotheses. The transmission power of the SU node varies according to the received signal energy level and the stored energy in the battery. Altering the transmission power based on the probability of the PU's presence aids in reducing interference with the PU's activity; transmission with low power, if the stored energy in the battery is low, increases the number of transmission intervals or, alternatively, the throughput of the SU node. Input to the SVM is given in the form of four-dimensional vectors: three features extracted from the received signal concatenated with the percentage of residual energy. The SVM with a polynomial kernel function and the proposed features was able to achieve almost 75% accuracy, even in a low SNR range from \(-25\) dB to \(-15\) dB. This performance improved to 95% accuracy with an increase in the SNR up to \(-5\) dB. In addition, the simulation results show that throughput improved compared to conventional two-class hypothesis-based spectrum sensing.  
This work was supported by the National Research Foundation of South Korea by the MEST under Grant NRF 2015R1D1A1A0905707, NRF-2017R1D1A1B03029448, and 2018R1A2B6001714.  
All authors conceived and proposed the algorithm. [PERSON] and [PERSON] designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed experimental results; [PERSON] wrote the paper in the supervision of [PERSON] and [PERSON].  
The authors declare no conflict of interest.
## 5 Conclusions  
For the ESUs interfacing with boost converters, a fast balancing strategy based on CDRs was designed to eliminate the SoC differences during the discharge process. It is feasible for both parallel and series applications. Instead of energy conservation, accurate SoC estimation and equalization are achieved without the need for extra sensors. Moreover, the balancing duration was remarkably shortened by introducing current saturations into the CDRs. In consideration of the actual execution time, the least significant current, and high current deviation, the accelerating factors in the CDRs were separately chosen for two kinds of applications. Furthermore, the current limits were reasonably selected to reach an equilibrium point and avoid a step reference current. Moreover, the controllers of the dual-loop system were designed to account for enough stability margins. The experimental results obtained under different operating conditions have proven the effectiveness of the proposed strategy. However, this strategy is not feasible at present if the ESUs operate in the charging process or communication fails. Thus, determining how to enhance its reliability and usability will be our concern in the future.  
[PERSON] proposed the idea, conducted the experimental validation, and wrote the manuscript; [PERSON] edited the software procedure, improved the experimental scheme, and funded the research; [PERSON] provided the experimental equipment; [PERSON] coordinated the resources and reviewed the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grant number 52177211) and by the Heilongjiang Postdoctoral Research Starting Fund (grant number LBH-Q20020).  
**Institutional Review Board Statement:** Not applicable.  
Figure 22: Experimental curves of droop schemes in parallel applications: (**a**) voltage using the method in [2], (**b**) voltage using the method in [10], (**c**) currents using the method in [2], (**d**) currents using the method in [10], (**e**) SoC using the method in [2], and (**f**) SoC using the method in [10].
## 2 Materials and Methods
### QPI and Holographic Microscopy and Fluorescence Setting  
QPI was performed by using a Q-PHASE multimodal holographic microscope (Telight, Brno, CZ). The Q-PHASE is equipped with fluorescence module using a halogen lamp as a non-coherent source of blue light. In this work, the module was used as a source of blue light for treatment of observed cell lines. The 485 nm light waves are emitted by the fluorescence light source of the attached module. The Q-PHASE microscope optical setup can be seen in Figure 1 of the article [22]. Before the imaging experiment, cells were cultivated overnight in a concentration of 7000 cells/mL in flow chamber \(\upmu\)-Slide I Lauer Family (Ibidi, Martinsried, Germany). During the measurements, the chamber with cells was incubated in 37 \({}^{\circ}\)C humidified, 5% CO\({}_{2}\) atmosphere in H201-for Mad City Labs Z100/Z500 piezo Z-stages (Okolab, Ottaviano NA, Italy). Images and holograms were captured with lens Nikon Plan 10/0.3 and CCD camera (XIMEA MR4021 MC-VELETA, Munster, Germany) respectively. The fluorescence mode used was a plasma light source (Sutter Instrument Lambda XL Novato, CA, USA). Cells were irradiated with a 485 nm light with a 25 nm bandwidth. Light doses 0 mJ/cm\({}^{2}\), 24 mJ/cm\({}^{2}\) and 208 mJ/cm\({}^{2}\) were achieved by the combination of time exposition and light intensity shown in Figure 1.  
The images were acquired automatically from seven positions every 3 min for 24 h. Holographic images were collected by custom software and raw data were numerically reconstructed. The numerical reconstruction was performed by custom software where the established methods of the fast Fourier-transform [29] and phase unwrapping [30,31] are implemented. The output from the software is an unwrapped phase image. This image has high intrinsic contrast and can be processed by an available image processing software. The unwrapped phase image is integrated phase shift through the cell and it is proportional to integrated cell dry mass density [19; 32]. The values of unwrapped phase image \(\varphi\) (rad) are linearly transformed into cell dry mass density image \(m\) (pg/\(\mu\)m\({}^{2}\)) with the transformation  
\[m=\frac{\varphi\lambda}{2\pi\alpha} \tag{1}\]  
where \(\alpha\) (0.18 \(\mu\)m\({}^{3}\)/pg) is specific refraction increment and \(\lambda\) (0.65 \(\mu\)m in Q-PHASE) is a wavelength, where both values are assumed to be constant. This enables measurements of cell dry mass and analysis of cell mass distribution inside cells.
# Teletraffic Analysis of DoS and Malware Cyber Attacks on P2P Networks under Exponential Assumptions
## 2 Background  
Mathematical models developed to study the propagation of infectious diseases have been adapted to the case of worm propagation [8]. Worms are programs that spread themselves across a network by exploiting security or policy flaws in commonly used services [9]. In this regard, several studies of worm propagation have been conducted in which the main mathematical tool used is structural equation modeling (SEM), which is derived from epidemic models in which the host can only transfer from susceptible to infectious. The susceptible, infected, and recovered model improves on the SEM model by taking the removed state and the transition from infectious to removed into account [10]. The SIS model is another evolution of the SEM model; it assumes that infectious hosts can get back to a susceptible state with a certain probability [11]. Unlike these works, we assume that nodes are infected by the distribution of the chunks shared among peers, which is directly related to resource sharing and communication procedures. Moreover, we model a jamming attack based on the same dynamics of the P2P network. In the network security context, both deterministic and stochastic transmission models of worms, based on their respective equivalents in epidemiology, have been proposed. Deterministic propagation models of worms may be further classified into two categories: continuous-time and discrete-time. Since the propagation of worms is a discrete event process, discrete-time propagation models of worms are more accurate than their continuous-time counterparts in the deterministic regime. Some relevant works on deterministic propagation models of worms can be found in [8; 11; 12; 13]. All of the previous models consider a continuous-time propagation base except the last one, which considers discrete-time.  
Stochastic propagation models of active worms are based on the notion of stochastic processes. All of them are discrete-time in nature. Two prominent instances of stochastic propagation models of worms can be found in [12; 14; 15; 16].  
Attacks on P2P networks can be classified into two types, namely, general and specific attacks [17]. From the general network attacks perspective, this classification provides the most damaging attacks that threaten the network since they aim to disable the complete operation of the system. Malware, DoS, and DDoS (denial of service) fall under this category [18; 19; 20; 21; 22].  
From the specific attacks on P2P networks perspective, they can also be classified into network and application levels. At the network level, an adversary may try to break the routing system, block access to information by impeding the routing process, or obtain some particular identifiers. At the application level, an adversary can attempt to corrupt or delete data stored in the system. Some papers on network attack simulations can be found in [18; 23; 24; 25].
# RIS-Assisted High-Speed Communications with Time-Varying Distance-Dependent Rician Channels
## 2 System Model with Time-Varying Distance-Dependent Rician Channels
### Direct Link \(h_{d}(t)\) from the BS to the MR  
First, let us consider the LoS part of \(h_{d}(t)\). The channel gain of the direct link is given as [22]  
\[A_{0}(t)=\frac{\lambda}{4\pi d_{0}(t)}, \tag{1}\]  
where \(d_{0}(t)=\|D_{\text{MR}}(t)-D_{\text{BS}}\|\), and \(\lambda\) is the carrier wavelength. The delay of the direct link can be calculated as  
\[\tau_{0}(t)=\frac{d_{0}(t)}{c}, \tag{2}\]  
where \(c\) is the speed of light. Taking the derivative of \(\tau_{0}(t)\), we have the Doppler frequency shift for \(h_{d}(t)\) as [14]  
\[f_{0}(t)=-f_{c}\frac{d(\tau_{0}(t))}{dt}=-f_{c}\frac{v(x_{0}+v\cdot t-x_{\text {BS}})}{c\|D_{\text{MR}}(t)-D_{\text{BS}}\|}=-f_{c}\frac{v(x_{0}+v\cdot t-x_{ \text{BS}})}{cd_{0}(t)} \tag{3}\]  
where \(f_{c}\) is carrier frequency. Consider the transmitted passband signal for the MR during time slot \(t\in\{1,\ldots,T\}\) as  
\[s(t)=\sqrt{2}\mathfrak{R}\{x(t)\exp(j2\pi f_{c}t)\}, \tag{4}\]  
and \(x(t)=x_{r}(t)+yx_{i}(t)\), \(\mathbb{E}\{|s(t)|^{2}\}=1\). \(x_{r}(t)\) and \(x_{i}(t)\) are baseband signals bandlimited to \(B/2\), with \(B\) denoting bandwidth such that \(B\ll f_{c}\). Thus, the received signal in the MR is \(s(t-\tau_{0}(t))\). The phase of the LoS part of the \(h_{d}(t)\) can be calculated as  
\[h_{d}^{\text{LoS}}(t)=\exp\bigg{(}-j2\pi\big{(}f_{c}+f_{0}(t)\big{)}\tau_{0} \bigg{)}, \tag{5}\]  
where \(j\) denotes an imaginary unit.  
The NLoS part of \(h_{d}(t)\), which is denoted by \(h_{d}^{\text{NLoS}}(t)\), can be modeled as a random variable following a complex Gaussian distribution \(\mathcal{CN}(0,1)\) in a time slot \(t\). Let \(x_{d}^{\text{LoS}}\) and \(\kappa_{d}^{\text{NLoS}}\) be respectively the Rician factors for \(h_{d}^{\text{LoS}}(t)\) and \(h_{d}^{\text{NLoS}}(t)\). The direct channel \(h_{d}(t)\) can be obtained as [19]  
\[h_{d}(t)=A_{0}(t)\bigg{(}\sqrt{\frac{\kappa_{d}^{\text{LoS}}}{\kappa_{d}^{ \text{LoS}}+1}}h_{d}^{\text{LoS}}(t)+\sqrt{\frac{1}{\kappa_{d}^{\text{NLoS}}+ 1}}h_{d}^{\text{NLoS}}(t)\bigg{)}. \tag{6}\]  
**Remark 1**.: _It is important to point out that (6) is different from the direct link in the previous works [10; 11; 14; 15], which did not consider the Rician factors \(\kappa_{d}^{\text{LoS}}\) and \(\kappa_{d}^{\text{NLoS}}\), NLoS channel part \(h_{d}^{\text{NLoS}}(t)\), and DS term \(f_{0}(t)\)._
# Exploring Channel Properties to Improve Singing Voice Detection with Convolutional Neural Networks
## 2 Methods
### Channel Attention
#### 2.1.4 Channel Attention Based on a Squeeze-and-Excitation Module  
The squeeze-and-excitation (SE) module was initially introduced by [PERSON] et al. in the image classification field [27]. In addition to its use in studying the spatial structure of the CNN, the SE module strengthens its learning capabilities by making use of the dependencies among channels. This dependency is similar to our attention for the CNN; therefore, it was adopted to compute the channel attention or feature map importance. Our framework is shown in Figure 5.  
At the first step, we squeeze each channel, i.e., feature map, into a channel descriptor using a global mean pooling function:  
\[z=\text{mean}(F,\text{dim}=2) \tag{9}\]  
Figure 5: Channel attention based on a squeeze-and-excitation module.  
where \(\dim=2\) means operation on the channel dimension and each channel size is h \(\times\) w, where h is the height and w is the width. The vector \(z\in R^{c}\) denotes the compact information of the original feature maps.  
At the second step, a gating mechanism is carried out on the descriptor \(z\) aiming at limitation and generalization of the model, which comprises three units: a fully connected layer for reducing dimensionality, a ReLU, and a fully connected layer for increasing dimension. The sigmoid function is employed to extract the channel-wise weights, i.e., attention.  
\[\alpha=\sigma(\delta(z^{T}W^{z})W^{z}) \tag{10}\]  
where \(\sigma,\delta\) are the sigmoid function and the ReLU function, respectively, \(W^{z}\in R^{c\times r}\) and \(W^{a}\in R^{r\times c}\), and \(c\) is a multiple of \(r\).  
Finally, the original feature maps, \(F\), are rescaled by the attentions:  
\[F^{\prime}=\alpha F \tag{11}\]  
In this way, the feature importance is achieved by the channel attention based on a squeeze-and-excitation operation. The feature maps are adjusted in terms of importance before they enter into the next layer of the CNN.
# Throughput Enhancement in Downlink MU-MIMO Using Multiple Dimensions
## 4 Simulation Results
Figure 8a, b show the performance difference of the proposed scheme according to the number of users and antennas \((N_{t},N_{r},K)\). The cases of \((6,3,2)\) and \((6,2,3)\) have the higher throughput performance than \((4,2,2)\) case since \((6,3,2)\) and \((6,2,3)\) transmit more symbols by exploiting both space and power dimension. \((6,2,3)\) case has lower throughput performance in low SNR than \((6,3,2)\) case since \((6,3,2)\) case allocates more symbols to each user. On the other hand, in case of \((6,2,3)\), one more user can be serviced. In terms of BER, \((6,3,2)\) has better BER performance than \((6,2,3)\) since \((6,3,2)\) transmits more symbols to each user than \((6,2,3)\). \((4,2,2)\) has better BER performance than \((6,2,3)\) since more power is allocated to each symbol. However, \((6,2,3)\) has higher throughput than \((4,2,2)\) since \((6,2,3)\) transmits more symbols. Figure 7: Throughput and BER performance of conventional and proposed schemes: (**a**) Throughput performance with SIC error; (**b**) BER performance with SIC error; (**c**) Throughput performance without SIC error; (**d**) BER performance without SIC error. Figure 9a, b show the difference of the performance between the cases of linear MIMO detection and non-linear MIMO detection. The method with ML detection outperforms the method with ZF detection. The ML technique has better detection performance than the ZF technique before performing SIC on each antenna. Additionally, the ML detection technique mitigates the error propagation compared to the ZF detection scheme. As a result, the overall simulation results show better performance compared to conventional schemes by using two dimensions simultaneously. In the proposed scheme, by exploiting both power and space domains at the same time, the transmitted symbol is increased. The results show that the superiority of the proposed scheme, and the proposed scheme uses the dimensions appropriately. Figure 8: Throughput and BER performance of proposed scheme according to the number of users and antennas: (**a**) Throughput performance; (**b**) BER performance. Figure 9: Throughput and BER performance of ML detection and ZF detection: (**a**) Throughput performance; (**b**) BER performance.
## 3 Problem Formulation  
### Problem Description  
In this study, the collaborative helicopter rescue routing problem is formulated as an expansion of the VRPTW, incorporating limited survival time (referred to as the R-VRPTWLST). All rescue locations have time window constraints and material requirements, with each survivor having limited life strength. In R-VRPTWLST, two types of helicopters, i.e., transport and medical helicopters, are used to complete rescue tasks. Transport helicopters primarily deliver rescue materials to each rescue location. Medical helicopters are specifically designed to transport survivors and are critical in rescue operations. To make a great effort to rescue survivors, we have stipulated that when the transport helicopter's load is less than half of its maximum capacity, it can simultaneously perform medical helicopter rescue tasks.  
The R-VRPTWLST problem is based on the following assumptions:  
(1) All helicopters depart from and return to a single rescue center;  
(2) Each type of helicopter visits each rescue site only once;  
(3) The total demand for supplies of the rescue sites along the helicopter's route should not exceed the maximum carrying capacity of the helicopter;  
(4) The number of survivors to be rescued from the rescue sites along the helicopter's route should not exceed the helicopter's maximum carrying capacity;  
(5) Helicopters need to meet the time window requirements at each rescue site;  
(6) The return time of each helicopter to the rescue center should not exceed its maximum travel time;  
(7) The life strength of each survivor transported by a helicopter needs to exceed the corresponding threshold. Once a survivor is picked up by the medical helicopter, he receives initial treatment and his life strength remains stable so he can be safely transported to the rescue center.
# An Evaluation of Optimization Algorithms for the Optimal Selection of GNSS Satellite Subsets
## 5 Results and Discussion
### Multi-GNSS Optimal Satellites
Figure 15: (**Left**) The accuracy of the selection of the optimal GNSS satellite subset of ABC with respect to TM for the various cases of satellite constellations and parameter settings and (**right**) the required time of the TM and ABC algorithm to compute the selection of optimal GNSS satellite subset. Figure 16: A comparison of the performance of the ABC algorithm for the three sets of parameter settings with respect to TM, expressed as the difference between the CNR-WGDOP of the optimal satellite constellation of each ABC parameter setting and the corresponding CNR-WDGOP of TM. On the left axis, the CNR-WGDOP value of the optimal satellite constellation based on the TM is presented, and on the right axis is the difference between each of the ABC parameter settings and the TM. Figure 17: Sky plots of epochs 20 (**left**) and 88 (**right**), presenting the difference in the selection of optimal GNSS satellite subset between ABC setting 1 and the actual TM, by showing the common satellites (blue) and the differences between ABC’s (yellow) and TM’s (orange) satellite selection.
## 3 Post-Processing Methodologies
### Time-Domain Analysis
#### 3.1.2 Integrated-Bin Evaluation: Thermal Signal Area (TSA)  
Thermal signal area (TSA) integrates the thermal response \(T_{stand}(i,j,t)\) of each pixel \(p^{(i,j)}\) over a user-defined time range \([t_{1}\dots t_{2}]\)[24]:  
\[A^{(i,j)}=\sum_{t_{1}}^{t_{2}}T_{stand}(i,j,t) \tag{2}\]  
The integration range is a crucial parameter for the performance of the TSA. While a large integration time assured that deep defects could be detected, a short integration time reduces the effect of lateral heat diffusion and as such provides a better defect sizing. The optimal integration range corresponds to a time range around the moment of maximum thermal contrast [24] (see Figure 4a,b). This selection is complicated by the presence of defects at different depths (i.e., with different times of maximum thermal contrast) and the lack of prior information about any defect. The integration approach of TSA is graphically presented in Figure 4a,b, where the integration is performed over the time range \([t_{1}\dots t_{2}]\). To the best of the authors' knowledge, there were no studies on the defect depth inversion possibilities through TSA.
# Tunnel-Free Distributed Mobility Management (DMM) Support Protocol for Future Mobile Networks
## 3 Analytical Modeling and Performance Evaluation
### Modeling the Handover Latency
#### 3.2.3 Handover Latency of HMIPv6  
MIPv6 makes the MN exchange mobility messages with the HA/CN and configure a new CoA whenever the MN is on the move. In the case where the distance between the MN and HA/CN is large, it becomes very time consuming and also results in unnecessary signaling overhead. HMIPv6 [28] addresses this problem by introducing a MAP (mobility anchor point), a new functional entity for local mobility management. The handover latency for HMIPv6 can be given as:  
\[L_{HO}^{HMIPv6}=T_{L2}+T_{MD}+T_{DAD}+T_{MAP} \tag{4}\]  
where \(T_{L2}\) is the link layer handover latency, \(T_{MD}\) is the movement detection process delay, \(T_{DAD}\) is the duplicate address detection latency and \(T_{MAP}\) represents the time required to send the binding update message, receive the binding acknowledgement message and also receive the data packet sent from the MAP.
## 5 Securing ITS Applications: A Practical Case Study
### ETSI Security Layer: TS 103 097
#### 5.3.3 Security Profiles  
ETSI TS 103 097 [24] defines four main security profiles for CAMs, DENMs, generic messages and certificates. In this context, the standard recommends the usage of the ecdsa_nistp256_with_sha256 algorithm, _i.e._, elliptic curve digital signature algorithm [94] (ECDSA), for signing and verifying these messages and certificates, even though the standard is flexible enough to support other algorithms; however, no further details are given [24]. Each of the above security profiles defines the elements that should be part (or not) of a SecuredMessage, as well as their order and number of occurrences. One of the main difference between the CAM and DENM security profiles consists of the content of the signer_info field. For DENM messages, signer_info should always include one element of type certificate; whereas an element of type certificate_digest_with_sha256 should be included in the normal case for CAM messages. The element of type certificate is typically included in CAMs periodically (every 1 s) or in case a request_unrecognized_certificate is received from a nearby ITS station.  
The signature process works according to three main steps:  
1. A message digest is computed using a Secure Hash Algorithm (SHA) with a 256 bits block size (_i.e._, SHA-256) over the protocol_version, header_fields, payload_field, the length of the trailer_fields and the type of the signature trailer field;
2. The message digest is then signed using ecdsa_nistp256_with_sha256 and the private key of the ITS station certificate;
3. The signer information and the generated signature are stored inside the signer_info and signature structures, respectively.  
Once a signed message is received by an ITS station, the verification process undergoes three main steps:  
1. The content of the SecuredMessage is checked against the rules of the corresponding security profile;
2. The certificate is validated against timing, location and security considerations; in case the signer's certificate is unknown, a request_unrecognized_certificate is generated;
3. The signature of the message is checked using the same steps as in the signature process.