esa-sceva/satcom-chunk-collection huggingface dataset:
# Double-Layer Compressive Sensing Based Efficient DOA Estimation in WSAN with Block Data Loss
## 2 Background
### DOA Estimation  
Without loss of generality, we consider a uniform linear array (ULA) consisting of \(H\) omnidirectional sensors, with an inter-element spacing of \(d\), \(d<\lambda_{\min}/2\), and \(\lambda_{\min}\) is the minimum wavelength of the impinging signals. Consider \(K\) far-field wideband signals \(s_{k}\left(t\right)\) incident on the ULA from direction \(\theta_{k}\), \(k=1,...,K\). The array output vector \(\mathbf{x}\left(t\right)\) is then given by:  
\[\mathbf{x}\left(t\right)=\sum_{k=1}^{K}\mathbf{a}\left(\theta_{k}\right)s_{k }\left(t\right)+\mathbf{n}\left(t\right)\text{, \ \ }t=1,...,T \tag{3}\]  
where \(\mathbf{a}\left(\theta_{k}\right)=\left[1,e^{-2\pi f\frac{d\sin\left(\theta_{k }\right)}{\varepsilon}},...,e^{-2\pi f\frac{\left(H-1\right)d\sin\left(\theta_ {k}\right)}{\varepsilon}}\right]^{T}\) is the \(H\times 1\) steering vector of the array corresponding to \(\theta_{k}\); \(\mathbf{A}=\left[\mathbf{a}\left(\theta_{1}\right),...,\mathbf{a}\left(\theta _{K}\right)\right]\) is the \(H\times K\) array manifold matrix; \(\mathbf{s}\left(t\right)=\left[s_{1}\left(t\right),...,s_{K}\left(t\right) \right]^{T}\) is the source signal vector. \(T\) is the number of samples; \(c\) is the wave speed. \(\mathbf{n}\left(t\right)\) is the noise vector, whose elements are assumed to be temporally and spatially white, and uncorrelated from the sources.  
Observing that signals impinging on the arrays are intrinsically sparse in the spatial domain, efficient strategies for DOA estimation based on sparse signal recovery have been proposed in [3; 21], where the sparsity constraints have been enforced through a \(l_{1}\)-norm minimization. The concept of group-sparsity has been exploited to cope with the problems of wideband DOA estimation [22; 23].
# Comparison of Heuristic Algorithms in Discrete Search and Surveillance Tasks Using Aerial Swarms
## 3 Proposed Search Algorithm, a Set of Behaviors: A1  
### Search Behavior  
This first behavior is the main one and leads the multicopter to search unexplored areas. It is based on the presence of virtual pheromones, whose dynamics and different classes are explained in the next subsections. The virtual pheromones are created on the search space and are absorbed by the robots as the area is observed. The robots are then attracted to unobserved zones, where the concentration of the pheromones is higher.
# Maintaining the Sense of Agency in Semi-Autonomous Robot Conferencing
## 5 Experiment
### Setups  
Figure 1 shows the experimental setup of the operator (i.e., subject) and the remote sides. On the operator side (a), our teleoperation interface, the mic capturing speech from the operator, and the speaker that reproduced the sound from the remote side, were positioned. The speaker was positioned behind the display.  
On the remote side (b), the robot and two chairs for the experimenters were placed at the apices of an equilateral triangle with a side length of 1.5 m. The speaker that reproduced the speech of the operator and the motion capture sensor that acquired the facial positions of the experimenters were positioned behind the robot. The mic that acquired the sound from the remote side was positioned behind the experimenters.  
As shown in the figure, the audio amplifiers and the Windows machines PC1 and PC2 connected the operator and remote sides. The eye, neck, and mouth motions of the robot were generated by pneumatic actuators. The control system of the robot adjusted the air pressure of the pneumatic actuators according to the motor command. The control system communicated with PC1 and PC2 by serial communication (RS23C). The microphones and speakers in the both sides were connected to the audio amplifiers (the speaker of the operator side and the mic of the remote side are omitted for simplicity). The mic of the operator side was also connected to the PC1 that generated the mouth motion (one degree-of-freedom) of the robot, based on the speech of the operator [32].  
The eye tracking sensor, display, and motion capture sensor were connected to the PC2 that generated the eye and neck movements of the robot. The figure ((b) remote side) also shows an example of robot movement when the operator's view changes from one experimenter to the other. The robot reproduced the movement of the vision line of the operator through neck movement (pitch, roll, and yaw) and eye movement (yaw). PC2 synthesized the green circle and the yarrow arrow, that is, the place where the operator is looking at, and the vision line of the robot, onto the live video, and it showed on the display.
## 1 Introduction  
### Motivation  
Great technological progress directly affects people's behavior. Humans have adapted themselves to the use of the Internet, computer systems, smartphones and sensors, among other devices to carry out different daily activities. In the same way, different types of applications have been developed to meet our needs, allowing problem solving in many areas of interest. One of the main areas that has benefited from technology is the agricultural sector. According to the Food and Agriculture Organization of the United Nations (FAO), the world should produce 70% more food in 2050 than what it produced in 2006 to feed the growing population around the world [1].  
The Agricultural sector is turning to the IoT for analysis, management and the search for greater production capacities as the demands for the products and its operation increase. The growth of communication technologies and sensors for agriculture, their easy usage and operation, as well as their low production cost, allow different parameters such as humidity, temperature and soil acidity to be measured in different places [2]. They can even be used in regions of difficult access, such as mountainous areas, slopes and deserts, and other possible places where agriculture can be developed.  
In this regard, the use of these technologies allows farmers to analyze the data collected by the sensors, predict future climatic conditions and, therefore, improve productivity, minimize expenses and preserve the resources used.
# Research on the UAV Sound Recognition Method Based on Frequency Band Feature Extraction
###### Abstract  
The unmanned aerial vehicle (UAV) industry is developing rapidly, and the application of UAVs is becoming increasingly widespread. Due to the lowering of the threshold for using UAVs, the random flight of UAVs poses safety hazards. In response to the safety risks associated with the unauthorized operation of UAVs, research on anti-UAV technology has become imperative. This study proposes an improved sound feature extraction method that utilizes the frequency distribution features of UAV sounds. By analyzing the spectrogram of UAV sounds, it was found that the classic Mel Frequency Cepstral Coefficients (MFCC) feature extraction method does not match the frequency bands of UAV sounds. Based on the MFCC feature extraction algorithm framework, an improved frequency band feature extraction method was proposed. This method replaces the Mel filter in the classic algorithm with a piecewise linear function with the frequency band weight as the slope, which can effectively suppress the influence of low- and high-frequency noise and fully focus on the different frequency band feature data of UAV sounds. In this study, the actual flight sounds of UAVs were collected, and the sound feature matrix of UAVs was extracted using the frequency band feature extraction method. The sound features were classified and recognized using a Convolutional Neural Network (CNN). The experimental results show that the frequency band feature extraction method has a better recognition effect compared to the classic MFCC feature extraction method.  
U
# An Accuracy-Efficiency-Power Consumption Hybrid Optimization Method for CNC Milling Process
## 3 Experiments  
### Experiment Design  
Basically, higher speed will produce higher power consumption, so an experiment with various speeds of spindle motor and X-, Y-, Z-axis servo motor were carried out to study the characteristic of the motor power consumption. The experiment was divided into two parts: without cutting load and with cutting load conditions. Assuming that the spindle motor speed sets 500 until 3000 RPM with the increasing step of 500, a power consumption measurement is performed for the spindle motor under these spindle speeds, to obtain the relationship between spindle speed and power consumption. Similarly, assuming that the servo feedrate sets 200, 500, 1000, 1500, 2000, 2500, 3000, 3500, 4000, 4500, 5000, 5500, 6000 mm/min, a power consumption measurement is performed for the servo motor under these servo feedrates, to obtain the relationship between servo feedrate and power consumption.  
For investigation of power consumption with cutting load, because of different cutting conditions, the cutting load is also different, hence, the motor will have different power consumptions. Therefore, different cutting parameters were taken to investigate the relationship between cutting loading and power consumption. The cutting conditions are shown in Table 1. The machining parameters are according to the tool material, hardness of workpiece material, milling speed, and material removal rate (MRR). By using the equation \(\rm V=\pi\ \times\ D\ \times\ N\ /1000\), we can obtain the required spindle speed, and using equation \(\rm F=f\ \times z\ \times\ N\), we can obtain the feedrate (mm/min), where \(\rm V\) is the milling speed (m/min); \(\rm D\) is the diameter of tool (mm); \(\rm N\) is the spindle speed (rpm); \(\rm F\) is the feedrate (mm/min); \(\rm f\) is the material removal rate (mm/t); \(\rm z\) is the number of teeth (t).  
\begin{table}
\begin{tabular}{l l} \hline
**Workpiece Material** & **Cast Iron (HB244)** \\ \hline Workpiece dimensions & \(550\times 100\times 100\) mm \\ \hline Cutting tool & Carbide end mills \\ \hline No. of teeth & 4 teeth \\ \hline Tool diameter & 25 mm \\ \hline \end{tabular}
\end{table}
Table 1: Cutting condition.  
Figure 3: Machining efficiency and power consumption hybrid optimization human machine interface (HMI).  
The machining parameters are shown in Table 2. Eight kinds of spindle speed, three kinds of feedrate, three kinds of feed per tool were taken, so a total of 24 combinations of parameters. Each parameter was tested five times.
### Proposed Approach  
To address the aforementioned challenges, we propose and implement a link-local retransmission design on a programmable data plane. Our proposed solution is based on a prototype programmable software switch, into which we have integrated this module. This module enhances link-layer reliability through retransmissions and extends the switch's bandwidth via link aggregation technology. In addition, it incorporates a cleverly designed feedback mechanism to optimize for out-of-order packets resulting from link aggregation. We employ a basic feedback mechanism that is distinct from existing link-layer retransmission methods for wired networks and introduce a new special protocol field. This field enhances the granularity of feedback provided to the sender, allowing for more precise control over retransmission decisions. The main contributions of this paper include the following:  
1. We develop a feedback message transmission strategy that dynamically adjusts timing based on the out-of-order packet conditions of the link. By including the mean length deviation, we select dupthresh to avoid triggering a certain portion of spurious fast retransmissions.
2. The implementation leverages the Data Plane Development Kit (DPDK) to achieve high-performance packet processing. The retransmission logic is integrated into the switch's architecture, enabling it to monitor packet delivery and initiate retransmissions as needed, thereby improving overall network performance and reducing packet loss.
# On the Competitiveness of Oblivious Routing:
## 5 On the Competitiveness of Oblivious Routing
### Statistical Competitiveness in Directed Graphs  
Ideally, we would like the PoC to be upper-bounded by some constant, say, \(\eta\leq\frac{1}{2}\) in all graphs. That would mean that there is more than a 50% chance that a traffic matrix routable by an optimal algorithm is also routable by the oblivious algorithm. If this is too illusory a desire, it would be sufficient if \(\eta\) approached 1 slowly. Unfortunately, it turns out that there is no general upper bound on \(\eta\) apart from the trivial \(\eta\leq 1\) and \(\eta\) can grow as \(1-O(\frac{1}{n})\) when \(\alpha\) is \(O(1)\) in certain directed graphs [42].  
Consider the directed graph of \(n=K+2\) nodes in Figure 1 for any \(K\geq 2\), let all link capacities be 1, and let the source-destination pairs be \((s_{k},K+2):k\in 1,\ldots,K\). The first source-destination pair has only a single path; thus, all its traffic is sent through this path. The rest of the users \(k\in\{2,\ldots,K\}\) have two paths.  
Due to the symmetry of the network, the oblivious routing function is determined by a single variable \(\beta\), the fraction of traffic sent at \(s_{k}\) to the path passing through node \(K+1\). The largest link loads are produced by the traffic matrices \([1,1,\ldots,1]^{\mathrm{T}}\) and \([0,0,\ldots,2,\ldots,0]^{\mathrm{T}}\). For the first one, the maximum load \(1+(K-1)\beta\) occurs on link \((K+1,K+2)\). The latter one causes \(2(1-\beta)\) maximum load on link \((s_{k},K+2)\). The load is minimal and equal to \(\alpha(K)\), when \(1+(K-1)\beta=2(1-\beta)\), resulting in \(\alpha(K)=\frac{2K}{K+1}<2\).  
Thus,  
\[\eta =\ 1-\frac{\mathrm{Vol}(R)}{\mathrm{Vol}(T)}\geq 1-\frac{ \mathrm{Vol}(R^{\prime})}{\mathrm{Vol}(T^{\prime})}\] \[=\ 1-\frac{\left(1+\frac{1}{K}\right)^{K-1}}{1+\frac{1}{2}(K-1)}= 1-\frac{\left(1+\frac{1}{K}\right)^{K}}{\left(1+\frac{1}{K}\right)\left(1+ \frac{1}{2}(K-1)\right)}\geq 1-\frac{2e}{\left(1+\frac{1}{K}\right)(K+1)}>1- \frac{6}{n}\]  
with \((1+\frac{1}{K})^{K}\xrightarrow[K\to\infty]{}e\) from below and with the substitution \(n=K+2\).
# An Active-Clamp Forward Inverter Featuring Soft Switching and Electrical Isolation
## 3 Operation Principles
### State 7 (\(t_{6}<t<t_{7}\), Figure 6g)  
At the time \(t=t_{6}\), the voltage \(v_{ds1}\) drops to \(V_{in}\), causing the primary voltage \(v_{p1}\) equal to zero again. The magnetizing inductor \(L_{m1}\) no longer participates in resonance, and its current remains constant. The voltage across the leakage inductor \(L_{r1}\) is \(V_{in}\), so its current \(i_{Lr1}\) rises linearly from a negative value and with the slope of \(V_{in}\)/\(L_{r1}\). In addition, the forward diode \(D_{1}\) is turned on again, so that the current \(i_{D1}\) increases, and the freewheel diode current \(i_{D2}\) decreases. The equivalent circuit is as shown in Figure 6g. The voltage \(v_{ds1}\) across the main switch and the leakage inductor current \(i_{Lr1}\) are expressed as:  
\[v_{ds1}(t)\ =\ V_{in}+i_{Lr1}(t_{6})Z_{2}\sin\ \omega_{2}(t-t_{6}), \tag{23}\]  
\[i_{Lr1}(t)\ =\ i_{Lr1}(t_{6})\cos\omega_{2}(t-t_{6}), \tag{24}\]  
where \(\omega_{2}\ =\ \frac{1}{\sqrt{L_{r1}}\zeta_{sd1}}\), and \(Z_{2}\ =\ \sqrt{\frac{L_{r1}}{\zeta_{sd1}}}\). Since the direction of the current \(i_{Lr1}\) cannot be changed instantaneously, the anti-parallel diode \(D_{m1}\) is forced to turn on, and the energy is recovered to the input. In order for the main switch \(S_{m1}\) to have the ZVS characteristic, Equation (23) should satisfy \(v_{ds1}(t)\leq 0\) before this state ends. The following conditions can be obtained:  
\[\left|i_{Lr1}(t_{6})Z_{2}\right|\geq V_{in}. \tag{25}\]  
When the voltage \(v_{ds1}\) continues dropping to zero, this state ends and the time it takes is:  
\[t_{7}-t_{6}\ =\ \frac{1}{\omega_{2}}\sin\ ^{-1}\left[-\frac{V_{in}}{i_{Lr1}(t_{6 })Z_{2}}\right]. \tag{26}\]
## 3 Development of the EMS and Obtained Results for a Specific Case Study
### Generalization and Adaptive Approach Applied to the FL EMS
#### 3.6.1 Generalization of the Optimized FL EMS  
In order to enrich price profiles considering the different periods of the year, one profile has been created for each season of the year, based on OMIE data [30]. For each season of 2022, the month with the closest price value to the average of the season has been selected. To choose a representative day, the same strategy has been performed. Thus, 14 January, 14 April, 10 July, and 10 October have been selected. The price profiles of these days can be seen in Figure 13.  
In addition to the grid price profiles, the different EV charging schedules that consider diverse bus timetables at the depot have been defined. Therefore, 20 different profiles have been created with an algorithm that charges buses randomly with varying demand power. In addition, the possible delay in the arrival of the buses at the depot has been considered, increasing the scheduled off-depot time with a certain probability. Moreover, the ten charging profiles obtained in the abovementioned RB EMS optimization process in Section 3.5.1 have been considered too, leading to 30 different charging schedules, one for each day of a month.  
A new EMS, EMS6, is developed in this context, optimizing the FL EMS with 90 chromosomes and 1000 iterations, and applying the same constraints explained in Section 2.8.1. Figure 14 shows the obtained optimal MF systems. It can be seen that the forms differ from what would be a homogeneous distribution of the MFs (Figure 11).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**EMS** & **BESS** & **Optimized** & **Operational Cost [pu]** \\ \hline EMS0, original charging strategy & No & No & 1 \\ EMS1, optimized charging profile & No & Yes & 0.9 \\ EMS2, RB (90\% _DoD_) & Yes & No & 0.87 \\ EMS2\_b, RB (60\% _DoD_) & Yes & No & 0.864 \\ EMS3, FL & Yes & No & 0.873 \\ EMS4, GA-RB & Yes & Yes & 0.857 \\ EMS5, GA-FL & Yes & Yes & 0.846 \\ \hline \hline \end{tabular}
\end{table}
Table 2: EMS comparison according to their overall operation cost.  
Figure 14: _Cont._  
Figure 13: Seasonally representative profiles of the electricity price.  
The obtained BESS output power behaviour for the month is shown in Figure 15, along with the electricity price profile. It can clearly be seen that the price changes have a direct effect on the behaviour of the BESS. Considering this influence, an adaptive FL EMS based on a one-day-ahead available forecasted price profile was designed, as explained in the next section.  
Figure 14: Optimized MF systems.
## 2 Non-Blind LP-DSS Watermarking Scheme  
### Concept  
The DSS method is implemented by spectrally spreading the security code \(m(n)\) with the PN signal \(c(n)\) and embedding it in the host signal \(x(n)\), as  
\[y(n) = x(n)+am(n)c(n), \tag{1}\]  
where \(a\) is the amplitude term to adjust the embedding strength and \(n\) is the samples. The PN signal \(c(n)\) has the properties \(E\big{\{}c(n)\big{\}}=0\) and \(E\big{\{}c^{2}(n)\big{\}}=1\). \(E\big{\{}\cdot\big{\}}\) is the expectation operator for \"-\".  
Linear predictive coding (LPC) is one of the most basic speech coding methods and is based on LP. It uses the LP coefficients and the LP residue \(r(n)\) obtained by LP analysis, as  
\[\hat{x}(n)=\sum_{i=1}^{P}\alpha_{i}x(n-i), \tag{2}\] \[r(n)=x(n)-\hat{x}(n), \tag{3}\]  
where \(\hat{x}(n)\) is the predicted speech, \(\alpha_{i}\) is the LP coefficients, and \(P\) is the LP order. The LP residue \(r(n)\) has the properties \(E\big{\{}r(n)\big{\}}=0\) and \(E\big{\{}r^{2}(n)\big{\}}=1\), which are the same as those of the PN signal used in the DSS method. On the basis of these properties, the DSS method can also be implemented using the LP residue instead of the PN signal. For this reason, we refer to the proposed method based on this concept as the LP-DSS scheme.
# Research on Application of Improved Quantum Optimization Algorithm in Path Planning
## 4 Analysis of Evacuation Path Planning Model Experiment
### Experimental Analysis of Single-Robot Path Planning
#### 4.3.3 Influence Analysis of BQGA and ACA, GA Stability Results  
With the expansion of the problem scale, the size of the problem scale has a strong influence on the algorithm's running time, and the overall trend of the running time of the algorithms increases from the analysis of the path instance. So the algorithms should be evaluated according to the size of the actual problem. Therefore, the results are further explored for each of the two algorithms in the case of increasing problem size, based on the fitting, using the control variables method. The number of location points and the running time are roughly linear in BQGA, while in ACA and GA, the number of location points and the running time tend to show a more quadratic correlation. When the number of location points is large, the running time of ACA and GA is obviously longer than that of BQGA. The relationship between the number of iterations and the degree of adaptation can be plotted according to the data obtained from the two algorithms, as shown in Figure 12.
## 4 Modelling Approach and Results
### Computational Simulation Approach
plastic damage model was deemed unnecessary, as the clamp frame deflections were not observed to deform plastically during the experiments, but it was anticipated that the oscillation of the clamp frames influenced the measured transient deformation of the V-structures. The contact between the clamp frames and the V-structure was modelled with a surface-to-surface penalty contact approach.
## 5 Analysis of Results  
### Comparative Analysis of Field Test Data  
Comparing the changes in surface settlement in Figure 6 (unreinforced section) and Figure 10 (reinforced section), it can be clearly found that the surface settlement in the reinforced section is controlled within 3 mm, which fully meets the specification requirements for the shield tunnel underneath the subway line; compared with the unreinforced section, the surface settlement in the reinforced section has been reduced by about 92%, which indicates that flower tube grouting reinforcing has a remarkable effect on the control of the surface settlement. The above analysis shows that flower tube grouting reinforcement can effectively reduce the surface settlement, significantly improve the stability of the soil body, and provide a reliable technical guarantee for the shield tunnel to penetrate the complex stratum.