esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Control Strategies  
### Local Discrete PI Controllers  
After performing mathematical modeling and obtaining the respective models for different operating points, local discrete PI controllers using the root locus technique were designed. This gain-tuning technique involves modifying the root locus by adding poles and zeros to the open-loop transfer function of the system. This allows for shifting the roots to the desired closed-loop poles in the frequency domain [12].  
A characteristic of this method is that it assumes the presence of a pair of dominant poles in the closed circuit based on the choice of a value for the overshoot and settling time (\(t_{s}\)). Therefore, the addition of zeros and poles does not significantly influence the characteristics of the response, and this technique then consists of modifying the location of the roots of the mathematical model using a compensator so that a pair of dominant closed-loop poles can be placed in the position desired [39].  
To tune and design the gains of a discrete PI controller using the root locus method, firstly, from the local models obtained for the motors' operating ranges, it was necessary to discretize the models for a sampling period \(T\), which was chosen to respect the processing time of the ESP32 microcontroller used to build the robots. For discretization, the ZOH (zero-order hold) method was used, which works as a D/A converter and can memorize the last sample of the signal until a new input is inserted into the system.  
To design the controller in the z domain, it is necessary to define an overshoot and a settling time. When starting the calculations, it is desired to determine the damping coefficient (\(\zeta\)), which can be obtained by  
\[\zeta=\sqrt{\frac{ln^{2}(overshoot)}{ln^{2}(overshoot)+\pi^{2}}}. \tag{8}\]  
Furthermore, to obtain the desired pair of poles (\(z_{d}\)) in the z plane, it is necessary to obtain the system's undamped natural oscillation frequency (\(w_{n}\)), the damped oscillation frequency (\(w_{d}\)), and the frequency of natural sampling (\(w_{s}\)), which are presented, respectively, by  
\[w_{n}=\frac{4}{t_{s}\cdot\zeta}\, \tag{9}\]  
\[w_{d}=w_{n}\cdot\sqrt{1-\zeta^{2}} \tag{10}\]  
and  
\[w_{s}=\frac{2\pi}{  
and  
\[\alpha+j\beta=\frac{z_{d}\cdot T}{z_{d}-1} \tag{16}\]  
with \(X\) and \(\alpha\) being the real part and \(Y\) and \(\beta\) being the imaginary part of Equations (15) and (16), respectively, we have that  
\[Kp=X-\frac{\alpha\cdot Y}{\beta} \tag{17}\]  
and  
\[Ki=\frac{Y}{\beta}. \tag{18}\]  
In this way, from the gains calculated via the discrete root locus method, it is possible to determine the transfer function of each local controller \(C(z)\), which is given by  
\[C(z)=\frac{U(z)}{E(z)}=\frac{(Kp+Ki\cdot T)z-Kp}{z-1}\, \tag{19}\]  
making it possible to determine the difference equation for each local controller, which is then implemented in the low-level code for effective speed control of the motors coupled to the right and left wheels of the robot [40].
# Energy Efficient Secure Communication Model against Cooperative Eavesdropper
## 2 System Model
### Iterative Block Decision Feedback Equalization Decoder  
All the communicating nodes use the SC-FDMA transmission technique and the decoders use IBDFE. It is assumed that \(R\geq T\) with perfect receiver synchronization for all antennas. The information symbol in the time and frequency domains are denoted as \(\{x_{n}^{(t)};n=0,1,...,N-1\}\) and \(\{X_{k}^{(t)};k=0,1,...,K-1\}\), respectively, where \(x\) and \(X\) are the information symbol in time and frequency domain, \(n\) and \(k\) are the index of the symbol in time and frequency domain, respectively. The received signal in the time and frequency domains are denoted as \(\{y_{n}^{(r)};n=0,1,...,N-1\}\) and \(Y_{k}^{(r)};k=0,1,...,K-1\), respectively. The received signal in matrix format is denoted as \(Y_{k}^{(r)}=\left[Y_{k}^{(1)},Y_{k}^{(2)},...,Y_{k}^{(R)}\right]^{T}\) and \(Y_{k}\) at the receiver is given as  
\[Y_{k}=H_{k}^{(r,t)}X_{k}+N_{k}, \tag{9}\]  
where \(H_{k}^{(r,t)}\) denotes the \(R\times T\) channel matrix with \(k^{\text{th}}\) frequency. \(N_{k}\) is AWGN with variance \(N_{0}/2=\sigma_{n}^{2}\). The linear minimum mean square error (LMMSE) decision of \(x_{n}^{(t)}\) from the IBDFE receiver is \(\tilde{X}_{k}=\left[\tilde{X}_{k}^{(1)},\tilde{X}_{k}^{(2)},...,\tilde{X}_{k} ^{(R)}\right]^{T}\), which is given as  
\[\tilde{X}_{k}=\frac{Y_{k}H_{k}^{H}}{H_{k}H_{k}^{H}+I_{N}(\Gamma_{X_{k}})^{-1}}, \tag{10}\]where \(\Gamma_{X_{k}}=\frac{|\mathbb{E}||[X_{j}^{(t)}|^{2}]}{|\mathbb{E}||[N_{k}]^{2}|}\), and \(\Gamma_{X_{k}}\) is assumed to be equal for all the values of \(t\) and \(r\). The LMMSE for the massive MIMO low complex receiver is given in [23],  
\[\tilde{X}_{k}=Y_{k}F_{k}-\tilde{X}_{k}B_{k}, \tag{11}\]  
where \(F_{k}\) and \(B_{k}\) are the feed forward and feedback of IBDFE receiver. \(B_{k}\) reduces the residual interface in each iteration. \(F_{k}\) and \(B_{k}\) are, respectively, given as  
\[F_{k}=\Psi\Lambda_{k}^{H}H_{k}^{H}, \tag{12}\] \[B_{k}=H_{k}F_{k}-I_{N},\]  
where \([\Lambda]_{(i,i^{\prime})}=exp(j\text{arg}[H]_{(i,i^{\prime})})\), and \((i,i^{{}^{\prime}})\) denotes the index of an element in the matrix. \(\Psi\) denotes a diagonal matrix and its \((t,t)^{\text{th}}\) element is given as \(\sum_{k=0}^{K-1}\sum_{\tau=1}^{R}|H_{k}^{(r,t)}|^{2}\).
# Dynamic Range Compression and the Semantic Descriptor Aggressive
## 4 Perceptual Listening Experiments
### Listening Experiment 2 Method
#### 4.2.3 Textural Analysis of Descriptors Used by the Participants  
Participants of the second listening experiment were encouraged to write descriptors to describe the sound of the vocal in the stimuli they had heard. WAET allows the test designer to include text boxes in the listening test's interface. Thus, participants recorded descriptors into these boxes during listening. A total of 88% of the participants noted descriptors and Figure 7 shows word frequency plots of the twenty most frequently used descriptors for each compressor.  
The word distorted is the most frequently used descriptor for the 1176 compressor. Moreover, descriptors which were shown to relate to distortion in Figure 1, namely gritty, crunchy and dirty also appear often for the 1176. Harsh is also a popular term for this compressor and may be related to distortion. However, one could argue it is a hedonic judgement of preference. Present and bright are two prevalent terms for the 1176, and this is commensurate with the long-term average spectrum (LTAS) plot shown in Figure 8. The LTAS measurements were plotted with a Matlab function [35] using 1/16 th octave smoothing. Only one of the songs used in the listening experiments is presented in Figure 8. However, all songs show a similar result, which is that the 1176 has more energy in the high end of the frequency spectrum compared with the uncompressed material and the clean software compressor output. In Figure 8, the increased energy occurs from 4 kHz onwards. Furthermore, the brightness, presence and harshness noted by the participants when listening to 1176 audio, may be related to the descriptor sibilance. Further work should investigate the association between these descriptors by conducting perceptual listening experiments in which the researcher controls these attributes.  
Figure 7b illustrates the descriptors used to elucidate the sound quality of the clean software compressor. The most common term is soft, and one can argue that this word is being used as an antonym for aggressive. Natural, smooth, compressed, round, weak and bright are also used by the participants to describe this compressor. Except for bright, they also appear to be terms used to describe the antithesis of an aggressive sound quality. A study carried out by [PERSON] and [PERSON] [36], which investigated descriptors used to describe piano timbre, found the terms soft, velvety, round and full-bodied were connected. It is worth noting that rounded and smooth are also connected terms in Figure 1. However, further work by the author aims to obtain a better understanding of the most popular descriptors shown in Figure 7 and how they relate to the timbre of DRC.  
Figure 7: (**a**) Descriptors used for the Universal Audio 1176 (1176) compressor; (**b**) descriptors used for the clean software compressor measured to have 0% THD. Descriptors for both ratios and all three songs have been combined for both compressors.
# Enhanced Scheduling of AI Applications in Multi-Tenant Cloud Using Genetic Optimizations
## 2 Problem Definition and the Basic Model
### Scheduling System Architecture  
As depicted in Figure 1, our proposed scheduling system architecture accommodates various types of ML jobs developed in frameworks like TensorFlow 2.1.0, PyTorch 1.11.0, and Graph-Learn 0.0.3. These jobs are parsed into tasks based on their roles, with each task comprising multiple task instances executed across several cluster machines. We monitor the CPU, memory, and GPU resource requirements from the user-submitted jobs and search for optimized pairs of machines and task instances that can satisfy these demands. Our scheduling method prioritizes pairs that significantly enhance GPU utilization within the cluster.
## 4 Experimental Results and Discussion
### Size Distribution of Heterogeneous Nanoparticles  
Figure 5 shows the particle size distributions of the various heterogeneous nanoparticles. Figure 5a,b show the size distributions of Ag and Au nanoparticles generated using the nanosecond pulsed laser, and Figure 5c,d the size distributions of nanoparticles generated using the femtosecond pulsed laser. Ag and Au nanoparticles without Pd ions created using the nanosecond and the femtosecond lasers had an average diameter of around 9.8 and 5.5 nm, respectively. The particle size deviations were relatively large, at 9.0 and 1.5 nm, respectively; thus, they were not uniform. In contrast, nanoparticles generated by femtosecond pulses had smaller diameters (3.6 and 3.7 nm for Ag and Au, respectively); they were also relatively uniform, with deviations of 0.7 and 0.8 nm, respectively. When Ag and Au nanoparticles formed in the presence of Pd ions, most were 3-4 nm in size irrespective of the laser pulse width. Thus, the average particle sizes of 5.5 and 9.8 nm in the absence of Pd decreased 1.9-2.5-fold in the presence of Pd, and particle uniformity was greatly enhanced, consistent with the simulation results. When small and large nanoparticles are randomly mixed, fragmentation seems to increase because of hot spots with electromagnetic field enhancement. When using the femtosecond laser process, this is difficult to discern because the original particles generated are both uniform and small. The nanosecond laser reveals the electric field concentration effect much more clearly; the addition of appropriate metal ions rendered small nanoparticles uniform in size.
## 3 Results and Discussion  
Effect of Laser Processing Parameters on Nanoparticle Count/Yield, Colloidal Density, Mean Diameter and Size Distribution  
The nanoparticle UV-Vis absorbance at 204 nm (this peak is observed for all 81 samples), mean diameter, and colloidal density (mg/mL) are shown in Figure 1, Figure 2, Figure 3, respectively, for each of the 27 sample groups. It was observed that all the inputs (ablation time, fluence and scan speed) have some influence on the outputs (nanoparticle size distribution, mean nanoparticle diameter, absorbance and colloidal density (mg/mL)). This section will examine the effect of each input on the outputs individually. The section will examine the trends in nanoparticle size distributions; particularly three types of distributions were observed: normal, positively skewed and bimodal distributions. Figure 1 shows a clear pattern in absorbance and sample number. This is attributed to ablation time; the absorbance increases with ablation time at any combination of processing parameters examined in this study as per the 3 \(\times\) 3 full factorial DOE (Table 1). A high absorbance is an indication of a high number of nanoparticles. The scan speed and the fluence do not affect the UV-Vis absorbance of the colloids as much as ablation time; however, they still have an effect as evident by comparing various samples synthesised at the same ablation time showing different absorbance values (e.g., samples 3 and 27 in Figure 1). The overall absorbance seems to increase from left to right signifying proportionality with laser fluence which increases after every nine samples (see Table 1 for DOE). A general increase in absorbance is observed from left to right in Figure 1 demonstrating a direct proportionality with scan speed which increases after every three samples (for every nine samples from left to right).  
Figure 1: Mean nanoparticle UV–Vis absorbance at -204 nm per sample group (1–27), giving an indication of nanoparticle count/yield per sample group (95% CI).  
It was discovered that the colloidal density tends to be affected by larger particles; this will be discussed in the sections that follow. Figure 2 demonstrates that similar colloidal densities can be achieved by multiple combinations of processing parameters, e.g., samples 10 and 11 had mean densities of 691 and 692 mg/mL, respectively, but different processing parameters were used. Incidentally, samples 10 and 11 have the same mean nanoparticle diameter of 101 nm (Figure 3). This is proof that the laser processing parameters have a combined effect on the nanoparticle size and yield.  
The nanoparticle mean diameter for each of the 81 samples ranged from 53 to 239 nm. The average of mean nanoparticle diameter per sample group (1-27) ranged from 77 to 171 nm. The mean nanoparticle diameter per sample group is shown in Figure 3. IPA is clear in colour but the colloid had a yellow colour after PLAL experiments due to the presence of magnesium nanoparticles (Mg NPs) due to their plasmonic effects.
## 3 Privacy Enhancement Proposals for 5G Core Networks
### Lawful Interception  
Lawful interception (LI) refers to the standardized approaches and technical mechanisms that allow law enforcement agencies to legally monitor a user's voice, data, and metadata on public 5G infrastructures specified in 3 GPP TS 33.106 [48]. These mechanisms are designed specifically and solely for these agencies to collect information on users for high-stakes scenarios: warrants, national security, public safety, etc. These standards, while useful for lawful surveillance, require strict policy enforcement to prevent misuse against innocent and law-abiding users. These policies could lead an individual to lose trust in 5G infrastructure as their implementations and usage are not always public knowledge. In a more technical sense, as these mechanisms are built into the system, they leave potential backdoor access for attackers who are looking to gather data on specific users. Having mechanisms in place to collect user data makes these attacks more realistic and attainable compared to in the scenarios where this type of technology access is not already implemented. Research carried out in [49] shows how 5G roaming security, built to prevent billing fraud, results in unprotected attack surfaces for state-sponsored attackers. The authors argue that there is no way for a user's smartphone to verify roaming decisions around the underlying trust assumptions in specific CNs. They tested their theories on an open-source 5G repository, Open5 GS [50], which could not prevent these attacks.
# Automatic Optimal Robotic Base Placement for Collaborative Industrial Robotic Car Painting
## 3 Pre-Processing: Point Cloud Rendering and Ellipsoidal Fitting
 \tag{8}\]  
where \(\mathbf{c}_{i}\) is the center of the ellipsoid \(\mathrm{MVEE}(\mathcal{P}_{i}(\mathbf{0}))\), and the matrix \(\mathbf{A}_{i}\) provides the shape of the ellipsoid \(\mathrm{MVEE}(\mathcal{P}_{i}(\mathbf{0}))\). In particular, by computing the singular value decomposition of \(\mathbf{A}_{i}\) as \(\mathbf{U}_{i}\Xi_{i}\mathbf{V}_{i}^{\top}\), the length of the ellipsoid semi-axes are given by \(r_{k,i}=1/\sqrt{\xi_{k,i}}\), \(k\in\{x,\,y,\,z\}\), where the \(\xi_{k,i}\)'s are the components of the diagonal of the matrix \(\Xi\). Furthermore, the orientation of the ellipsoid is given by the rotation matrix \(\mathbf{V}_{i}\). [PERSON]'s algorithm solves the Lagrangian dual problem of (8) using a conditional gradient ascent method [40] (see also [41]). In this paper, we utilized the implementation of [PERSON]'s algorithm provided in [44]. The point cloud rendering of the robotic forearm reachable spaces and [PERSON]'s algorithm result in \(M\) ellipsoids given by  
\[\mathrm{MVEE}(\mathcal{P}_{i}(\mathbf{0}))=\bigg{\{}(x,\,y,\,z)\left|\frac{( x-c_{x,i})^{2}}{r_{x,i}^{2}}+\frac{\big{(}y-c_{y,i}\big{)}^{2}}{r_{y,i}^{2}}+ \frac{(z-c_{z,i})^{2}}{r_{z,i}^{2}}=1\right\},\ 1\leq i\leq M. \tag{9}\]  
Algorithm 1 summarizes the computations needed for the pre-processing stage. As depicted by Figure 4, Algorithm 1 generates a 3D point cloud and an MVEE for each of the robotic arms. To prevent unnecessary computations, the if conditional in Algorithm 1 ensures that the point cloud rendering and ellipsoid fitting computations are performedonly for robotic arms having different kinematic parameters. For instance, if there are six ABB IRB 4600 robots, since their point clouds \(\mathcal{P}_{i}(\mathbf{0})\), \(1\leq i\leq 6\) and corresponding fitted ellipsoids \(\text{MVEE}(\mathcal{P}_{i}(\mathbf{0}))\), \(1\leq i\leq 6\) are the same, the rendering/fitting computations in the for loop are only run for the first robot. **Computational complexity for point cloud generation:** The forward kinematics for an RRR arm typically involves calculations using trigonometric operations (like sine and cosine) and matrix multiplications. These calculations are generally constant-time operations, i.e., \(O(1)\) for each configuration. Since we are computing forward kinematics for each configuration, the total computational complexity is the number of configurations multiplied by the complexity of forward kinematics (see, e.g., [45; 46; 47]). Thus, the total computational complexity for the point cloud generation is \(O(N_{i})\). **Computational complexity for ellipsoid fitting:** As demonstrated in [40], the original algorithm for computing MVEEs can be solved in \(O\left(N_{i}^{3.5}\ln\left(\frac{N_{i}}{\epsilon}\right)\right)\) arithmetic operations, achieving a relative accuracy of \(\epsilon\) in the volume.
## 3 Theory and Methods
### RNNs for Time Series  
Analysis: RNNs are a sort of neural network that can handle sequential data, making them ideal for time series analysis of road maintenance. RNNs use feedback connections to maintain information over time, allowing them to capture temporal dependencies in data. Standard RNNs, on the other hand, are susceptible to the vanishing gradient problem, which limits their ability to detect long-term dependencies in data.
# A Performance Analysis of VANETs Propagation Models and Routing Protocols
## 3 Proposed Work
### Routing Protocols
#### 3.2.3 Destination-Sequenced Distance Vector (DSDV)  
The protocol uses hop-by-hop routing and broadcasts updates periodically. It is based on Bellman-Ford routing mechanism; each node consists of a routing table listing the destination nodes, hops to reach each node, and sequence numbers of the information received. The mechanism avoids routing loops' formation as the node's location and its transmission information are broadcast to all nodes. When a node transmits, it updates its neighbours, allowing them to recognize that they are one hop from the source and update their distance vector information. In this manner, every node stores the information of the next routing hop in the routing table.
## 3 Application and Results  
To verify the effectiveness of the proposed method, the rolling bearing data disclosed by the Case Western Reserve University Bearing Data Center are used [34]. As shown in Figure 3, the test stand consists of a 2 hp motor (left), a torque transducer/encoder (center), a dynamometer (right), and control electronics. The motor speeds are from 1797 r/min to 1720 r/min. The test bearings support the motor shaft. Single point faults were introduced to the test bearings separately at the inner raceway, rolling element and outer raceway using electro-discharge machining with fault diameters of 7 mils, 14 mils, 21 mils, and 40 mils (1 mil = 0.001 inches). The test bearing is a deep groove ball bearing of SKF6205 and its related parameters are as follows: the inner diameter is 25 mm, the outer diameter is 52 mm, the number of balls is 9, the rolling body diameter is 7.94 mm, the pitch diameter is 39.04 mm, and the contact angle is \(0^{\circ}\). The vibration data were collected by accelerometers mounted on the housing with magnetic bases. Accelerometers were placed at the 12 o'clock position at both the drive end and fan end of the motor housing. The vibration signals were collected using a 16 channel DAT recorder, and were post processed in a MATLAB environment. The sampling frequency is 12 kHz. The fault data at the drive end with the smallest diameter (7 mils) at the inner raceway and the outer raceway are selected in this paper. The outer raceway faults were located at 3 o'clock. The rotation speed (RS), the rotation frequency (RF), the inner raceway fault frequency (IRF), and the outer raceway fault frequency (ORF) are shown in Table 1.