esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving Singing Voice Separation Using Curriculum Learning on Recurrent Neural Networks
## 3 Proposed Curriculum Learning Technique on RNN and U-Net
### Stacked RNN-based Separation Model  
Assume that \(\mathbf{x}_{t}\) is a STFT output obtained by a vector of dimension \(F\) at time \(t\). In order to add temporal variation to the input feature vector, the previous and next frames are concatenated as follows:  
\[\mathbf{z}_{t}=\left[\mathbf{x}_{t-1}\;\mathbf{x}_{t}\;\mathbf{x}_{t+1}\right]\;. \tag{5}\]  
The stacked RNN for the singing voice separation has three RNN layers followed by a fully connected (FC) layer as shown in Figure 3. The hidden state output at time \(t\) and level \(l\), denoted by \(\mathbf{h}_{t}^{(l)}\), is generated by passing the previous hidden state outputs, \(\mathbf{h}_{t}^{(l-1)}\), through the RNN unit at level \(l\), expressed as follows:  
\[\mathbf{h}_{t}^{(l)}=\sigma\left(\mathbf{W}_{hh}^{(l)}\mathbf{h}_{t-1}^{(l)}+ \mathbf{W}_{hz}^{(l)}\mathbf{h}_{t}^{(l-1)}+\mathbf{b}^{(l)}\right)\;, \tag{6}\]  
where \(\sigma\) is a sigmoid function, \(\mathbf{W}_{hh}^{(l)}\), \(\mathbf{W}_{hz}^{(l)}\), and \(\mathbf{b}^{(l)}\) are weight matrices and bias vector of the RNN at level \(l\). The input to the RNN at level \(1\) is the mixed sounds described in Equation (5), that is, \(\mathbf{h}_{t}^{(0)}=\mathbf{z}_{t}\). The outputs of the last RNN layer pass through an FC layer with rectified linear unit (ReLU) activation function. At time \(t\), the prediction of \(\mathbf{y}\) of source \(i\) is expressed as  
\[\hat{\mathbf{y}}_{i,t}=\max\left(\mathbf{W}_{i}^{(f)}\mathbf{h}_{t}^{(3)}+ \mathbf{b}_{i}^{(f)},0\right)\;,\;i=\left\{1,\,2\right\}, \tag{7}\]  
where \(\mathbf{W}_{i}^{(f)}\) and \(\mathbf{b}_{i}^{(f)}\) are the weight matrix and the bias vector of the fully connected layer. Figure 3 illustrates the data flow of the stacked RNN. To train the weights, the objective function in Equation (4) is minimized with appropriate optimization method. Learning the stacked RNN is briefly summarized in Algorithm 1. Detailed derivation of the gradients can be found in Reference [4].  
Finally, the time frequency mask is obtained by using the ratio of the outputs of the individual FC layers, \(\hat{\mathbf{y}}_{1,t}\) and \(\hat{\mathbf{y}}_{2,t}\). The magnitude spectrum vector of the separated sound is obtained by the element-wise multiplication of the time frequency mask and the spectrogram of the mixed input:  
\[\hat{\mathbf{y}}_{i,t}=\frac{|\hat{\mathbf{y}}_{i,t}|}{|\hat{\mathbf{y}}_{1,t}|+ |\hat{\mathbf{y}}_{2,t}|}\odot\mathbf{x}_{t}\,,\quad i=\{1,2\}\,. \tag{8}\]  
The original sound is reconstructed by inverse STFT on the magnitude \(\hat{\mathbf{y}}_{i,t}\) and the phase components of the mixed sound.
## 3 Results and Discussion
### Scenario 2
#### 3.2.2 Throughput Performance  
This part shows the throughput performance of both SUs, i.e., SCU and SEU, using scenario 2 for different modulation schemes. The results are given in Figures 18-21 and are based on the BER performance results in Figures 14-17, respectively.  
In Figure 18, the results are shown for conventional NOMA and WHT-NOMA with IRC using BPSK modulation. Like the results in scenario 1, it can be observed that using WHT-NOMA yields better throughput performance than conventional NOMA for both SCU and SEU. The receiver performance of WHT-NOMA is better for both ideal and nonideal SIC cases. For example, it can be observed that at 10 dB SNR, using WHT-NOMA and IRC results in an approximately 0.24 Mbps throughput increase for SEU compared to conventional NOMA with IRC. The results also show that using IRC with WHT-NOMA or conventional NOMA increases the throughput of both SCU and SEU in all cases, showing ICI suppression. The throughput performance because of IRC becomes even better at high SNR values, e.g., at 20 dB SNR, using IRC with conventional NOMA results in an approximately 0.05 Mbps throughput increase for SCU compared to the conventional NOMA without IRC. A similar trend is observed for all cases in Figure 19 where QPSK modulation is used considering a Hadamard matrix of order 4.  
Like the previous cases, the results in Figures 20 and 21 are produced for higher-order modulations, i.e., QAM and 8-QAM. These results also follow the same trend of throughput performance as followed by BPSK and QPSK in Figures 18 and 19, i.e., the throughput performance of both SUs, i.e., SCU and SEU, enhances with the use of WHT with NOMA compared to the conventional NOMA. Furthermore, the use of IRC suppresses ICI at the SCU and SEU receiver, yielding better throughput performance, especially at higher SNR values as visible from results in both figures.
## 7 Conclusions and Future Work  
Cabinet environmental monitoring represents a typical application for wireless sensor networks. We have proposed CENet for cabinet environmental monitoring. CENet uses two kinds of nodes: slave and master, to construct a hierarchical architecture. The slave nodes are the front sensing tier and the master nodes are the communication infrastructure which is responsible for forwarding data from slaves to BS. CENet implements a data-aided routing protocol which achieves 93% delivery ratio with fewer beacon transmissions compared to periodic beaconing. In addition, using our proposed scheduling, a slave node stays in sleep mode in most of the time and only wakes up for sensing data transmission, which helps prolong network lifetime.  
A CENet may consist of hundreds of nodes within high voltage cabinets in a substation. The communication interference can be serious. One way to increase data throughput and reduce data latency is using multiple BSs at different locations. Furthermore, we can take advantage of multi-channel sensor network to reduce interference. As future work, we intend to improve CENet to support multiple BSs and channel diversity.
# Evolution of V2X Communication and Integration of Blockchain for Security Enhancements
## 5 Issues and Challenges in 5G and Edge-Based V2X
### Traceability  
The data traceability is one aspect that can be considered for integrated 5G and edge computing. The vehicle mobility and driving path information could become vulnerable to identity leaks. The data tracking for vehicles in vehicular networks should be secure and only a legitimate entity should be able to trace the data, thus maintaining privacy of the users. The existing data traceability is not sufficiently strong and, thus, the shared data resources can be compromised by the malicious entity, or can be used unlawfully. The attackers may act as a legal entity and could eavesdrop and intercept the exchanged messages. They can trace the messages, and then modify, misrepresent, and monitor the traffic behavior [45].
## 3 Results and Discussion
### Effect of FMRF on Flame
The SO\({}_{2}\)-PLIF signal intensity along the radial direction at the downstream position of \(z=18\) mm (marked by the red dotted line in Figure 8b,d) for the cases with and without FMFRF is quantitatively compared in Figure 8e, highlighting the temperature rises in the shear layer on both sides corresponding to \(x\)--\(-\)5 and 3 mm (\(x=0\) mm is the burner center) under FMFRF. Local extinction events are rarely observed under FMFRF, and the  
Figure 8: A sequence of instantaneous CH\({}_{2}\)O-PLIF (**a**,**c**) and SO\({}_{2}\)-PLIF (**b**,**d**) images taken in flames at a mean fuel mass flow rate of 0.142 g/s and air velocity of 17.1 m/s at the unforced (**a**,**b**) and forced cases (**c**,**d**) with 80% FMFRF. The SO\({}_{2}\)-PLIF signal at \(z=18\) mm (red dotted line in (**b**,**d**)) is presented in (**e**) to highlight the temperature rise in the shear layer. Note that the CH\({}_{2}\)O- and SO\({}_{2}\)-PLIF were not simultaneous. CH\({}_{2}\)O- and SO\({}_{2}\)-PLIF distributions are continuous, which increases combustion efficiency with less fuel leakage, which explains the average temperature rise in the CRZ. Above all, the effects of FMFRF are classified into four aspects. First, the fuel jet penetrates further downstream, creating larger high-temperature zones. Second, the flame surface is stretched by the large vortex structure, increasing the flame surface area. Third, the fuel bulges are surrounded by preheated hot products, prompting the mass transfer of fuel and air and decreasing local extinction. Fourth, the temperature of the recirculating air rises, resulting in more intense chemical reactions. Thus, it can be realized that the FMFRT strongly affects flame stability.
## Appendix A Determination of the acid value (AV)  
for highly cross-linked porous polymers by the direct (standard) method may be subjected to a large error due to the relatively slow process of penetration of the titrant through the porous polymeric network. In order to ensure a sufficiently long time for diffusion and ion exchange, the determination of the acid number was performed using a back titration method as follows: the polymeric sample (100 mg) was weight into Erlemmeyer flask and 1 mL of methanol was added to improve the sample wettability, next 10 mL of KOH aqueous solution (0.081 M) was measured. Sample was mixed, corked and left at room temperature for about 60 min. Under these condition, only acidic species are able to react with diluted alkali solution. After this time, the excess of unreacted KOH was titrated with a standard HCl solution (0.05 M) at the presence of an indicator - methanol solution of methyl red and methylene blue. A blank test was carried out in the same way. The acid value was calculated according to an Equation (A1).  
\[AV=56.1\cdot C_{\text{HCl}}\cdot\frac{B-V}{w}\] (A1)  
where: AV-acid value (mg KOH/g), 56.1-molecular weight of KOH, \(C_{\text{HCl}}\)-molar concentration of titrant solution (mol/L), B-volume of titrant (mL) consumed for blank titration, V-volume of titrant (mL) consumed by the sample at the equivalent point, w-weight of the sample (g).  
To determine **iodine value (IV)** polymeric samples (200 mg) were suspended in 10 mL of chloroform and 15 mL of [PERSON] reagent (4.0 g bromine, 6.5 g iodine 500 mL of conc. acetic acid) was added. Next, samples were left in the dark place for 30 min. to allow the reaction between the iodine and the unsaturated bonds of the polymer. After this time 50 mL of distilled water and 15 mL of 10% aqueous KI solution was poured and the resulting mixture was titrated with standard Na\({}_{2}\)S\({}_{2}\)O\({}_{3}\) solution (0.179 M) until the contents of the flask turn orange. Then 1 ml of 1% starch solution as an indicator was added and the titration was continued up to the time of starch was completely discolored. The blank test was performed in the same way. To calculate IV following equation was used:  
\[IV=126.9\cdot C_{\text{Na}_{2}\text{S}_{2}\text{O}_{3}}\cdot\frac{1}{10}\cdot \frac{B-V}{w}\] (A2)  
where: IV-iodine value (mg I\({}_{2}\)/100 g), 126.9-molecular weight of I\({}_{2}\), \(C_{\text{Na}2\text{S}2\text{O}3}\)-molar concentration of titrant solution (mol/L), 1/10-is conversion factor (taking into account the conversion of milliliters per liter and the weight of the sample per 100 g) B-volume of titrant (mL) consumed for blank titration, V-volume of titrant (mL) consumed by the sample, w-weight of the sample (g).  
Figure 14: Spectra of the terpenes and DVB.
# Reliability and Maintenance Analysis of Unmanned Aerial Vehicles +
## 6 The UAV as a Complex Maintenance System
### Degradation Model for an UAV  
In this section, starting from the UAV degradation model, we will arrive at defining limits and probabilistic criteria to determine the optimal maintenance interval that does not exceed the corrective maintenance threshold: maintenance that yields effect when the gradual damage is intolerable by the agreed-upon operational standards.  
The random coefficient model is used to evaluate the level of degradation for the \(i\)th component for a time \(\hat{t}\)\(\in\)[0, \(\infty\)) in a cycle of single maintenance \(\Phi_{i}\) = {\(\{\phi_{i,1},\,\ldots,\,\phi_{i,Q}\}\), \(Q\in\mathbb{N}\), then a set of random parameters \(\Theta_{i}\) = {\(\{\theta_{i,1},\,\ldots,\,\theta_{i,V}\}\), \(V\in\mathbb{N}\) following a normal (Laplace-Gauss) distribution [62].  
The probability that the degradation at time \(T_{\chi}\) reaches the threshold \(\chi\) before time \(\hat{t}\) is:  
\[P_{r}\big{\{}T_{\chi}<\ \hat{t}\big{\}}=\ P_{r}\big{\{}X\big{(}\hat{t}\,;\, \Phi_{i}\,\Theta_{i}\ \big{)}>\chi\big{\}},\ \ \forall i\in I \tag{1}\]  
The calculation of this probability is necessary because, in the next discussion, we will introduce a certain degradation profile and calculate the probability that this has to overcome the various critical failure thresholds.  
We consider a complex system with the following degradation path (this type of degradation has been chosen because it is typical of this kind of complex systems):  
\[X_{i}\ (t,\ \Phi_{i},\Theta_{i})=\phi_{i,1}+\theta_{i,1}\cdot\hat{p}^{\hat{ \Phi}_{i,2}} \tag{2}\]  
where \(\Phi_{i}\) = {\(\{\phi_{i,1},\,\phi_{i,2}\}\) and \(\Theta_{i}\) = {\(\{\theta_{i,1}\}\) then:  
\[P_{r}\big{\{}T_{\chi}<\hat{t}\big{\}}=\ P_{r}\big{\{}\phi_{i,1}+\theta_{i,1} \cdot\hat{p}^{\hat{\Phi}_{i,2}}>\chi\big{\}} \tag{3}\]  
\[P_{r}\big{\{}T_{\chi}<\hat{t}\big{\}}=\ P_{r}\bigg{\{}\theta_{i,1}>\frac{\chi \ -\ \phi_{i,1}}{\hat{p}^{\hat{\[PERSON]}_{i,2}}}\bigg{\}} \tag{4}\]  
For a random variable, \(\theta_{i,1}\geq 0\), we evaluate the cumulative density function \(F_{\theta_{i,1}}\) :  
\[P_{r}\big{\{}T_{\chi}<\hat{t}\big{\}}=\ 1-\ F_{\theta_{i,1}}\bigg{(}\frac{\chi \ -\ \phi_{i,1}}{\hat{p}^{\hat{\[PERSON]}_{i,2}}}\bigg{)} \tag{5}\]  
Now we evaluate the probability in which, between the two time points (\(n\ -\ 1\))\(\tau\) and \(n\tau\), the control limit \(C_{i}\) is reached:  
\[P_{r}\big{\{}X_{i}((n-1)\tau;\ \Phi_{i},\Theta_{i})\big{\}}\leq C_{i}<X_{i}(n \tau;\Phi_{i},\Theta_{i}\ ),\ \ \forall n\in\mathbb{N} \tag{6}\]  
that is equal to:  
\[P_{r}\big{\{}(n-1)\tau\big{\}}\leq T_{C_{i}}<n\tau,\ \ \forall n\in\mathbb{N} \tag{7}\]  
The soft failure threshold \(H_{i}\) is reached before time point \(n\tau\) only if it has satisfied the following condition:  
\[P_{r}\big{\{}X_{i}(n\tau;\Phi_{i},\Theta_{i}\ )>H_{i}\big{\}}=P_{r}\big{\{}T_{H_{ i}}<n\tau\big{\}},\ \ i\in I \tag{8}\]  
Moreover, assuming the degradation path as monotonic (typical of this kind complex systems), we have: \(C_{i}\) < \(H_{i}\) with and \(T_{C_{i}}\leq\ T_{H}\).
## 2 SWS Design and Discussion  
### Design and Electromagnetic Parameters  
The schematic design of the proposed SV-SL SWS with natural dual beam tunnels is shown in Figure 1. This model is partitioned into the following three parts: the upper shell, the middle sheet, and the lower shell. The distance between the upper and lower shells is drawn out for easy observation. The center part is engraved with staggered vane patterns and fixed between the grooved upper and lower shells, while the gaps between the shell and centerpiece become nature beam tunnels. The dimensions of the structure are presented in Table 1.  
We simulated a single period of the proposed SWS with Floque periodic boundary conditions along the axial direction. The results are presented in Figure 2. To effectively avoid the occurrence of backward wave oscillations and band-edge oscillations, the electron injection voltage line should avoid intersecting the backward wave region (with phase shifts ranging from 0\({}^{\circ}\) to 180\({}^{\circ}\)) and move away from points with phase shifts of \(\pi\) and 2\(\pi\). Therefore, by increasing the length of the single period and decreasing the height of the electron injection tunnels, the 17.2 kV beam line and the fundamental mode of the SWS intersect in the forward wave region at 90 GHz, which means that signals near this frequency may be amplified.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Dimension (mm)** & **Parameters** & **Dimension (mm)** \\ \hline \(p\) & 1.2 & _ws_1 & 0.55 \\ _wa_ & 1.5 & _ws_2 & 0.1 \\ _ta_ & 0.375 & \(d\) & 0.05 \\ _ds_ & 0.65 & _ts_ & 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dimension parameters.  
Figure 1: Schematic of the single period SV-SL SWS. (**a**) Perspective view of the proposed SWS with cut shell and (**b**) cross-section view of staggered vane slot line.  
In addition, the variation in the normalized phase velocity and coupling impedance with frequency is shown in Figure 3. In the frequency range of 85-95 GHz, the dispersion characteristic curves are relatively flat, and the coupling impedance calculated for the fundamental forward harmonic mode of the SV-SL SWS is about 2.89-5.93 \(\Omega\) at 0.075 mm from the surface of the slot line.
## 2 Related Works
### A power-Efficient Routing Protocol for Underwater Wireless Sensor Networks  
[PERSON] et al. put forward a protocol conceived to route information in the least energetically costly possible way [28]. They suggest that a combination between acoustic communications and radio links can be offered in order to offer a holistic solution for networked communications in a deployment. The architecture that has been designed for the power efficient routing protocol consists of several elements: firstly, it makes use of a _forwarding node selector_, which receives as an input transmission distances, the angle between the nodes of the network (it has to be taken into account that this is a 3D network), and the remaining energy of the sensor itself. All these data are sent to a _forwarding tree mining mechanism_ that forwards the information as a packet that is transmitted to the sensor that was previously selected in the network. The other main purpose of this module is to prevent wasting power consumption due to fast spreading of packet forwarding procedures. Another development is the potential adoption of a fuzzy logic inference system, which would make use of fuzzing/defuzzing modules with an inference engine and a fuzzy rule base.  
This solution puts forward an optimized scheme in order to transmit information among several entities that are contained in the application domain. The main issue with this presented solution is that it cannot be used for transmissions at the data level, since the protocol has been conceived for routing and other network layer functionalities. As it happened before, the usability of this proposal in the application domain described in this manuscript is reduced, because it deals with problems that are alien to the data transmissions done in any higher layer, and does not solve problems that may be found at this level.
# The Experimental Characterization of Iron Ore Tailings from a Geotechnical Perspective
## 3 Results
### Physical and Identification Properties
#### 3.2.3 Plasticity  
The plasticity characteristics of the iron ore tailings tested, namely the plastic (w\({}_{\rm P}\)) and liquid (w\({}_{\rm L}\)) limits and the plasticity index (PI), were experimentally determined. The soil plastic limit was assessed through the soil hand rolling procedure defined by the ASTM standards [25]. The liquid limit was assessed through the cone penetrometer method (multipoint) defined by the BS standards, which is a more reproducible method that should not yield significant differences when compared to the alternative method employing the Casagrande apparatus [26].  
The results obtained (Table 1) show that the tailings tested are slightly plastic, with a plasticity index of just 5%. These results are consistent with the grain size distribution and composition of the tailings tested but also with the values observed in other iron tailings storage facilities. For example, the values obtained fit into the ranges for the plastic (0\(\sim\)19%) and liquid (0\(\sim\)28%) limits, as well as for the plasticity index (0\(\sim\)9%), measured in the Yuhezhai iron ore tailings [31].
## 2 Valve Structure and Principle  
The direct operating solenoid valve is composed of mechanical subsystem, electro-magnetic subsystem and hydraulic subsystem as shown in Figure 1. The electro-magnetic subsystem is composed of coil, armature and tube. The mechanical subsystem consists of a sleeve in which a spool and a spring are located inside. The position of the spool is mainly determined by an electromagnetic force, a spring force and a feedback force arising from the pressure difference of the orifice.  
The solenoid valve under study is a direct operating solenoid valve manufactured by Tosok (Kanagawa Prefecture, Japan). The valve is kept at its closed position by the return spring if there is no input current (Figure 3a). The control port is connected to the exhaust port.  
When the solenoid is energized, the magnetic force actuates the spool to move against the return spring. Then the supply port opens where the transmission fluid flows through to the control port. Meanwhile, the exhaust port is closed, and the fluid enters the control port to reach precise control pressure and finally flows to the clutch as shown in Figure 3b. Likewise, the fluid discharging from the clutch reenters the valve through the control port and flows to the tank as the electric current disappears and the spool returns to its initial position.
###### Abstract  
Due to VDES's higher data transmission speed and complex communication protocols, vulnerabilities within its data link infrastructure are more pronounced. To ensure the reliability of VDES data transmission, this manuscript proposes a credibility monitoring approach based on the combined detection method of radio interference detection and spoofing source identification and localization, focusing on key data link vulnerabilities outlined in the IALA G1181 VDES VDL Integrity Guide. Automated monitoring is achieved through VDES data link monitoring software (VDES(AIS 2.0)), which is based on a three-tier architecture and a Client/Server (C/S) model. The software validates monitoring techniques and software against various interference scenarios. Visualization of monitoring results, alarm notifications, and relevant data through the front-end interface enhances understanding of VDES data link credibility. This framework supports effective surveillance and detection of vulnerabilities, such as radio interference and spoofing sources.  
VDES; data link; credibility; monitoring approach; monitoring system Article
## 2 Routing Protocols Overview  
The performance and stability of network communication heavily rely on the choice and implementation of routing protocols. In business networks, two widely used interior gateway protocols are OSPF (Open Shortest Path First) and EIGRP (Enhanced Interior Gateway Routing Protocol). OSPF uses a link-state routing algorithm, which enables it to make efficient routing decisions based on a comprehensive understanding of the entire network topology. EIGRP, developed by Cisco, is designed to achieve fast convergence and low resource usage by incorporating both distance vector and link-state characteristics.  
For scenarios that involve internet access and the need to connect multiple autonomous systems, the exterior gateway protocol BGP (Border Gateway Protocol) plays a crucial role. BGP is essential for routing data between different networks, ensuring stable and reliable communication across diverse and geographically distributed systems.  
The study aims to assess the performance of EIGRP and OSPF, particularly in an IPv6 environment, focusing on their effectiveness in handling load sharing and link-failover scenarios. This evaluation could provide insights into how these protocols contribute to network resilience and traffic distribution, offering recommendations for enhancing failover capabilities and load-sharing techniques in IPv6-based systems.