esa-sceva/satcom-chunk-collection huggingface dataset:
# Brushless Field Excitation Scheme for Wound Field Synchronous Machines
## 1 Introduction
In [12], a brushless field excitation technique based on the installation of supplementary thyristors connected with the armature winding was proposed. The provided thyristor switches were operating at near zero-crossing of each phase current. This arrangement generated a zero-sequence third harmonic for the armature winding, which was then induced in the harmonic winding to achieve the brushless operation of WFSM. However, this technique requires a complicated drive system which increases the volume of the drive system. Furthermore, it requires several alterations to the structure of the machine. This paper suggests a new harmonic field excitation technique for the brushless operation of WFSMs which involves customary three-phase and single-phase inverters. The three-phase inverter is connected to the armature and injects three-phase currents to the armature winding. The single-phase inverter is connected to the armature star-connected common point. The inverters operate at different frequencies and supply input armature currents to generate a unique armature current shape. The composite output of the inverter's currents for the armature winding contains the fundamental as well as the third harmonic. The fundamental is utilized to develop the stator field, whereas the third harmonic component is employed for developing the pulsating MMF in the airgap. This MMF generates a harmonic current in the harmonic winding of the rotor that is later rectified using a full-bridge diode rectifier to provide DC current to the field winding of the rotor to develop the rotor field. The generated rotor field as it interacts with the stator field develops torque.
# Performance of the Transport Layer Security Handshake Over 6 TiSCH
## 7 Related Work  
The literature contains several works that study the performance of TLS and DTLS in the context of the IoT. They majorly focus the cryptographic overhead of the protocols and propose alternative schemes to offload the resource-demanding computations, in particular asymmetric cryptography [32; 33; 34; 35].  
The work presented by [PERSON] et al. [36] considers the performance of transport layer security in constrained environments. The authors focus only on the DTLS protocol, but they investigate its performance in combination with two duty-cycled MAC-layer protocols: X-MAC, a preamble sampling protocol [37] and beacon-enabled IEEE 802.15.4 [38]. The results on handshake duration in multihop, duty-cycled networks, obtained through the Contiki Cooja simulator [39], are similar to ours. Our work extends the previous by considering the TLS standard as well, using the constrained IPv6-enabled 6 TiSCH stack, implementing the experiments on state-of-the-art hardware, and presenting the side-by-side performance comparison with DTLS in different scenarios.  
RFC7925 [40] describes generic DTLS and TLS profiles for constrained IoT devices. It does not alter the protocols but recommends specific configuration options to make the protocols reasonably implementable on most devices. Some of the recommendations made are:  
* Set the initial timeout for the DTLS handshake to 9 s.
* Mandatory client support for the MFL extension.
* Session resumption (which requires less messages to be exchanged and only symmetric cryptography). In addition, when the server is constrained instead of the client, support for client-side handshake state storage [41].
* Use TLS-FALSESTART [42] to shave off one RTT and speed up the handshake duration.  
* Optionally, use of the (D)TLS heartbeat extension [43] to verify whether the peer is still alive and keep the connection up (preventing a new handshake).  
The first two recommendations are straightforward. Since the original default timeout value of 1s is too aggressive for most constrained networks, a more conservative choice is proposed. The RFC argues that 9s is big enough to absorb large latency variance due to slow computations or intrinsic network characteristics [40]. The mandatory client-side MFL support allows notifying the server of limited buffer space on the client. However, the server can ignore the extension. The remaining recommendations focus on preventing a new full handshake by either keeping the connection alive or using restricted versions of the handshake protocol. The TLS-FALSESTART and heartbeat extension necessitate additional code to the stack, while session resumption forces either the server or client to store state information.
## 2 Materials and Methods
### Sound Event Detection Algorithm Design
#### 2.6.1 Convolutional Neural Network (CNN) Architecture  
The CNN architecture designed for this study was designed to process and learn from 2D and 1D data.  
* Input layers--the CNN has two different inputs:
* A 2D input to the spectrograms passes through several 2D convolutional layers to extract spatial features.
* A 1D input for data such as duration is processed across densely connected layers.
* 2D convolutional layers: these layers extract features from spectrograms using 2D filters. After each convolutional layer, a ReLU activation function and a pooling layer are applied to reduce dimensionality.
* Densely connected layers: 1D data passes through these layers to learn non-linear relationships. Like convolutional layers, a ReLU activation function is applied after each dense layer.
* Fusion: after processing the 2D and 1D data separately, their features are fused and passed through several densely connected layers to combine the information and perform the final classification.
* Output layer: a dense layer with a softmax activation function to classify acoustic events into corresponding categories.  
This architecture was chosen based on the need to process and learn from data with mixed dimensions, taking advantage of the richness of both spatial and temporal information contained in spectrograms and other data.
## 5 Experimental Verification
### Experimental Verification with k > 1  
Figure 15 presents the experimental waveforms of the EIOS-TPS modulation strategy under a voltage conversion ratio of \(k\) = 1.5 (_V_\({}_{1}\) = 80 V, _V_\({}_{2}\) = 53.33 V). When the converter operates in mode F with an output power of 71 W, the transformer voltage and inductor current waveforms in Figure 15a closely match the theoretical analysis. The theoretical current stress is 4.8 A, while the measured value is 4.55 A, resulting in an error of less than 5.2%, thereby validating the accuracy of the theoretical model. Figure 15b illustrates the gate-source voltage \(v_{\text{gs}}\) and drain-source voltage \(v_{\text{ds}}\) of switches _S_\({}_{1}\), _S_\({}_{3}\), _S_\({}_{5}\), and _S_\({}_{7}\). The complementary switching pattern of each half-bridge exhibits symmetrical characteristics. The results confirm that the control system applies the gate signal \(v_{\text{gs}}\) only when the drain-source voltage \(v_{\text{ds}}\) naturally resonates to zero, ensuring ZVS for all switches in the DAB converter.  
Figure 16 shows the experimental waveforms of the EIOS-TPS modulation strategy under the same voltage conversion ratio \(k\) = 1.5. When the converter operates in mode D with an output power of 313 W, the transformer voltage and inductor current waveforms in Figure 16a exhibit excellent agreement with theoretical predictions. The theoretical current stress is 10.3 A, while the measured value is 9.3 A. As shown in Figure 16b, all switches achieve ZVS.  
Figure 17 shows the experimental current stress versus output power for SPS, CSO-DPS, CSO-TPS, and EIOS-TPS under a voltage conversion ratio of \(k\) = 1.5. At \(P\) = 71 W, the measured current stress for SPS, CSO-DPS, CSO-TPS, and EIOS-TPS is 7.4 A, 4.85 A, 4.65 A, and 4.55 A, respectively. The current stress of CSO-TPS and EIOS-TPS is nearly identical, while EIOS-TPS achieves a 38.5% reduction compared to SPS. At \(P\) = 313 W, the measured current stress for SPS, CSO-DPS, CSO-TPS, and EIOS-TPS is 11.5 A, 10.35 A, 9.2 A, and 9.3 A, respectively. EIOS-TPS exhibits comparable current stress to CSO-TPS, achieving  
Figure 15: Experimental waveforms of low power (71 W) by using EIOS-TPS when _V_\({}_{1}\) = 80 V and _V_\({}_{2}\) = 53.33 V. (**a**) Operation waveforms of the voltages and current. (**b**) Switching waveforms.  
a 12.5% reduction compared to CSO-TPS and a 20% reduction compared to SPS. These results demonstrate the advantage of EIOS-TPS in reducing current stress when \(k>1\).
# A Modified Compact Flexible Vivaldi Antenna Array Design for Microwave Breast Cancer Detection
## 2 Materials and Methods
### Comparison to Literature  
The proposed antenna was compared with various antennas reported in the literature. The results are summarized in Table 3. The proposed antenna has a smaller BW than most reported antennas. However, a BW greater than 1 GHz is considered sufficient for breast imaging. Moreover, the proposed antenna reduces the size of a conventional Vivaldi antenna by half while keeping a directional radiation pattern.
# Sunspot Detection Using YOLOv5 in Spectroheliograph H-Alpha Images
## 3 Related Work
### Summary  
As shown previously, space weather is very important for life on Earth; thus, the area of sunspot detection has been subject to heavy research. Spectroheliograph images have been available for almost a century, so they keep a long record of the history of the sun. Modern satellite images have higher quality, but they record only the last few decades. Hence, the following research questions remain open, and answers are sought in the present work: (i) How can deep learning methods be used to detect sunspots in spectroheliograph images from the OGAUC database? (ii) What is the best performance achievable in sunspot detection with one modern and fast CNN model, namely YOLOv5?
## 3 Simulation Results of the Elastoplastic SW-DVIB Method
### Analysis of Tunnel Deformation  
Empirical methods have been widely used by rock engineers to assess rock mass properties and design preliminary support systems, owing to their simplicity [24]. These methods often rely on systems for rock mass characterization, such as the Q-system [15], Rock Mass Index (RMi) [18], Rock Mass Rating (RMR) [20], and Geological Strength Index (GSI) [21], to evaluate rock mass characteristics. However, these classification systems are limited in their ability to analyze rock mass stability, understand fundamental mechanisms, and resolve practical failure modes. In such cases, numerical methods are essential for quantitative analysis. This section extends the elastoplastic SW-DVIB model to analyze tunnel wall deformation. The object of the analysis was a tunnel, as shown in Figure 19a. The domain of interest was 30 m \(\times\) 30 m. The dimensions of this tunnel are 5 m \(\times\) 6 m. The arch diameter is 3 m. The mesh generation strategy is illustrated in Figure 19b. In situ stress was applied to the outer boundary.  
Figure 19: Simulation object for the tunnel wall deformation analysis: (**a**) geometry of the interesting domain; (**b**) meshing scheme.  
Figure 18: Simulated failure mode vs experimental outcomes [49]: (**a**) \(\beta=15^{\circ}\), (**b**) \(\beta=45^{\circ}\), (**c**) \(\beta=60^{\circ}\).  
3.6.1. In Situ Stress and Its Effects on Tunnel Deformation  
To evaluate the influence of in situ stress on tunnel deformation, numerical simulations were conducted for four distinct cases, each characterized by varying in situ stress conditions. The input parameters are as follows: \(E=40.0\) GPa, \(\widetilde{\varepsilon}_{y}=0.15\times 10^{-3}\), \(\widetilde{\varepsilon}_{\rm b}=0.255\times 10^{-3}\), \(\kappa=0.6\), \(\rho=2600\) kg/m\({}^{3}\), and \(\
u=0.25\). The simulation incorporated in situ stress values obtained from direct field measurements [70]. Statistical analysis indicates that vertical in situ stress typically exceeds the horizontal in situ stress. Figure 20 illustrates the relationship between the in situ stress and depth.  
Figure 21 shows the simulated displacement contours of the surrounding rock under varying in situ stress conditions. The results indicate that as the in situ stress increases, the degree of deformation in the surrounding rock intensifies. When the vertical in situ stress exceeds the horizontal in situ stress, the primary areas of damage are concentrated at the top and bottom sections of the tunnel.  
Figure 20: Relationship between in situ stress and depth [70].  
The simulated results of the tunnel deformation in cases with different in situ stresses are illustrated in Figure 22. Tunnel deformation mainly manifests as distortion at the bottom of the tunnel wall. When the in situ stress was sufficiently high, upheaval occurred at the bottom of the tunnel (Figure 22c,d). This indicates that more attention should be paid to the bottom support of tunnels.
# Experimental Analysis of the Behavior of Mirror-like Objects in LiDAR-Based Robot Navigation
## 3 Problem Statement  
LiDARs can take two- or three-dimensional scans. A 2D LiDAR generally spins around an axis repeatedly, emitting a single beam and taking 360\({}^{\circ}\) angle scans of the surroundings in a single plane. On the other hand, a 3D LiDAR emits multiple beams while spinning around an axis, allowing it to take more details from the surrounding environment. Due to the nature of the data collected by 3D LiDAR, it is generally used in outdoor environments, while 2D LiDAR is more commonly used in indoor applications. 3D LiDAR is bulkier, more expensive, and more computationally expensive than 2D LiDAR.  
The LiDAR transmits a laser beam and can detect a barrier by using a sensor to catch the reflected beam (Figure 1). The distance between the detected obstacle and the receiver is calculated using time-of-flight (ToF). According to Formula (1), the distance can be calculated by measuring the amount of time that it takes for the emitted pulse to reach the object, reflecting some portion of the emitted ray and returning to the receiver lens of the LiDAR. It is assumed that the emitted and reflected rays travel at \(3\times 10^{8}\) m/s.  
\[\text{distance}=(\text{ToF}\times\text{speed of flight})/2 \tag{1}\]  
Surface properties affect the way that reflected light is scattered, which in turn impacts the way that LiDAR receives light information. There are four key ways that surface properties affect incident light: specular reflection, diffuse reflection, absorption, and transmission. Figure 2 shows the behavior of light according to the surface property.
###### Abstract  
In recent years, even if Fiber Reinforced Polymer (FRP) composites have been widely used for strengthening of civil buildings, a new generation of materials has been studied and proposed for historical masonry construction. These buildings, mainly made of stone work, are common in many areas of Europe and Asia and recent earthquakes has been the cause of many catastrophic failures. The brittleness of unreinforced historic masonry can be considerably reduced using new retrofitting lighter-weight materials such FRP, even if limitations were evidenced due to material and mechanical compatibility with poor substrates. Thus, fibrous reinforcements were used as long fibres incorporated into a cement or lime matrix, which better match with the properties of ancient masonry. The use of low strength fibers such as glass and basalt, respect to carbon, in presence of an alkaline matrix brought out durability issues, due to the chemical vulnerability of common glass and basalt fibres. The objective of this research is to explore the effects of selected aqueous environments and fatigue loading on the mechanical and physical properties of composite grids, made of E-CR (Electrical/Chemical Resistance) glass fibers and epoxy-vinylester resin, used as tensile reinforcement in new composite reinforced mortar systems. Glass-fiber-reinforced polymer (GFRP) coupons were subjected to tensile testing and a severe protocol of durability tests, including alkaline environment and fatigue tensile loads. Accelerated ageing tests were used to simulate long-term degradation in terms of chemical attack and consequent reduction of tensile strength. The ageing protocol consisted of immersion at 40 \({}^{\circ}\)C in alkaline bath made by deionized water and Ca(OH)\({}_{2}\), 0.16% in weight, solution for 30 days. GFRP specimens aged and unaged were also tested under tensile fatigue cycles up to 1,000,000 cycles and a nominal frequency of 7.5 Hz. After this severe conditioning the tests indicate a good tensile strength retention of the GFRP in absence of fatigue loads, while a significant loss in fatigue life was experienced when both alkaline exposure and fatigue loads were applied.  
**Keywords:** historic masonry; ageing; fatigue; composite materials; testing +
Footnote †: journal: Applied Sciences
## 5 Conclusions  
This paper implemented a new approach to develop a practical model of drilling torque in deep hole drilling. The new model consists of three components: the cutting, the chip-evacuation, and the stick-slip torques. Remarkable notes are concluded below.  
The cutting torque component can be modeled using several drilling torque models, as commonly found in engineering books. Experimental data can fit well with the available model with the regression correlation factor higher than 0.998. It was also revealed that ultrasonic assisted cutting can reduce the cutting torque by 27.3% in the dry drilling of aluminum alloys.  
The chip-evacuation torque can be modeled as an exponential function of the drilling depth with only one coefficient. The coefficient was carried out by experimental calibration with a correlation higher than 0.999. The only coefficient of the chip-evacuation torque in UADs smaller than that in CDs clearly reflects the improvement of ultrasonic assistance, making the chip evacuation in deep hole drilling easier than conventional drilling.  
The stick-slip was scrutinized as a separated component and was considered first in the deep drilling of metals. Further studies should investigate the component model and make it more applicable in practice.  
Using the model developed in this study, ones can predict the total torque required in deep hole drilling, either in conventional- or in ultrasonic assisted drilling processes. The model is applicable in other techniques, such as peck drilling, or expandable to other workpiece materials.  
All authors discussed and conceived the original idea. [PERSON], [PERSON], [PERSON] and [PERSON] designed and performed experiments. [PERSON], [PERSON] and [PERSON] analyzed and interpreted experimental data. [PERSON] wrote the manuscript with support from [PERSON], [PERSON], [PERSON] and [PERSON]. All authors provided critical feedback and helped shape the research, analysis, and manuscript. [PERSON] supervised this project.  
This research received no external funding.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Mean** & **StDev** & **SE Mean** & **95\% CI for \(\mu\)\_difference** & **\(t\)-Value** & **\(p\)-Value** \\ \hline
34.3 & 51.6 & 12.2 & (8.6, 59.9) & 2.82 & 0.012 \\ \hline \multicolumn{6}{c}{\(\mu\)\_difference: mean of (\(T_{3}\)CD–\(T_{3}\)UAD).} \\ \end{tabular}
\end{table}
Table 7: Estimation for Paired Difference.  
Figure 5: (**a**) A couple of conventional drilling (CD) and ultrasonic assisted drilling (UAD) stick-slip torques and (**b**) probability of the maximum values of stick-slip torques.
# A Novel Intermittent Jumping Coupled Map Lattice Based on Multiple Chaotic Maps
## 3 Analysis of Dynamic Behaviors
### Bifurcation Diagram Analysis  
The bifurcation diagram, which is a plot of the state variables versus the control parameter(s), is commonly used to analyze a dynamical system. Let all the systems, in this simulation study, work under the setting \(3.4\leq\mu<4\) and \(e=\{0.1,\ 0.5,\ 0.9\}\). In Figure 12, we depict the bifurcation diagrams, in which the state variables are harvested from the 1 st, 50 th, and 100 th lattices. For convenient comparison, the bifurcation diagrams belonging to different systems are coated with different colors.  
Figure 10: Resulting values of KSEB for the Lyapunov exponent analysis. (**a**) Result for the CML system. (**b**) Result for the NCML system. (**c**) Result for the DCML system. (**d**) Result for the NDCML system. (**e**) Result for the proposed IJCML system.  
Figure 11: Plots of the cumulative percentage functions.  
Figure 12: Cont.  
For the baseline systems, we can clearly observe that period-doubling bifurcations form a route to spatiotemporal chaos. For example, the NDCML system gets trapped in period-two oscillations when \(\mu=3.4\). Two bifurcation points at \(\mu=3.45\) double the periods of the orbits and give rise to pitchfork bifurcations. This process is repeated as \(\mu\) increases. Starting from \(\mu=3.57\), it is virtually impossible to observe the pitchfork bifurcations because the number of bifurcation points becomes large and the gaps between the bifurcation points are negligible. Unfortunately, the chaotic oscillations of the baseline systems may occasionally be interspersed with periodic windows in some cases. For example, when setting \(\mu\) to 3.63, we can observe the periodic windows in the NDCML system, where the oscillations suddenly degenerate into several periods. Comparing the baseline systems' bifurcation diagrams, we find that they roughly share the same phenomenon. That is, the period-doubling bifurcations trigger chaotic oscillations interspersed with periodic windows. By contract, the proposed IJCML system has a large number of  
Figure 12: Bifurcation diagrams. The setting of \(e\), from left to right, is 0.1, 0.5, and 0.9, respectively.  
bifurcation points with tiny gaps between them even when \(\mu=3.4\). This demonstrates that the IJCML system enters the chaotic regime early. Additionally, the IJCML system greatly reduces the periodic windows compared with the baseline ones. As shown in Figure 11(e), a higher coupling coefficient introduces more pseudo-random motions from the multiple chaotic maps, so as to further enhance the chaotic oscillations. Based on these comparisons, we claim that the IJCML system behaves better than the baseline ones in terms of the bifurcation diagram analysis.