esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 _HoloSec_: Proposed Conceptual Holochain Security Framework for IoT Distributed Networks
### Data Integrity Verification  
Data integrity is ensured through a combination of cryptographic hashing and Chain of Custody (CoC) records. When an agent collects or transmits data, a cryptographic hash (e.g., SHA-256 or BLAKE2) of the data is generated, creating a unique fingerprint. A CoC record is then created, containing:  
* The data hash.
* Metadata (e.g., timestamp, origin ID).
* Signatures from a select group of trusted peer agents based on their TS.  
The CoC record is signed by the originating agent and a trust-based subset (i.e., at least two trusted peers). Having a subset of nodes validate transactions and co-sign records is the core idea of Holochain, which significantly reduces computational and energy costs [60]. The signed record is then stored on the global shared DHT, ensuring decentralization and immutability.  
To verify data integrity, the recipient agent:  
* Retrieves the CoC record from the DHT.
* Validates the signatures using the public keys of the originating and peer agents.
* Recomputes the hash of the received data and compares it with the hash in the CoC record.  
If discrepancies are detected (e.g., mismatched hashes), an alert is triggered via a gossip protocol, and predefined security protocols are activated, such as notifying stakeholders, isolating the compromised agent, and initiating data rollback or recovery.
# Interaural Level Difference Optimization of Binaural Ambisonic Rendering
## 2 Methods
### Ambisonic ILD Optimization
Each HRIR with AIO is then normalized as  
\[H^{AIO}\times\frac{\overline{RMS}(H)}{\overline{RMS}(H^{AIO})} \tag{13}\]  
The HRIRs with AIO are combined with the unprocessed HRIRs using the same linear-phase crossover network as used in the dual-band decode design in Section 2.1, such that the final pre-processed HRIRs are the same amount of samples and RMS amplitude as the unprocessed HRIRs, identical to the unprocessed HRIRs at low frequencies, but with AIO at frequencies above \(f_{alias}\). The complete pre-processed HRIRs are then switched into (6), and the process is repeated iteratively whereby the array of \(g^{\Delta}\) values is taken as the product of the \(g^{\Delta}\) values from each iteration \(i\):  
\[\mathbf{G^{\Delta}}=\prod_{i=1}^{l}\mathbf{G^{\Delta}}(i) \tag{14}\]  
where \(i=i+1\) for each iteration. The iteration runs until \(\overline{\prod\mathbf{G^{\Delta}}(i)}\approx\overline{\prod\mathbf{G^{\Delta }}(i-1)}\) is satisfied to an accuracy of 5 significant figures, where the overline denotes arithmetic mean. This method ensures that the final AIO pre-processed HRIR dataset will be subject to the crossover filter only once, regardless of the number of iterations. Implementing the AIO pre-processing as an iterative process also allows the consideration that changes in ILD to one loudspeaker may influence the resulting ILD of other loudspeakers in the configuration. Figure 3 presents the estimated ILD of binaural Ambsonic rendering and HRIRs on the horizontal plane, for \(\{M=1,M=2,...,M=5\}\), both without and with AIO. The \(M=36\) (without AIO) is included again for reference. The figure shows how horizontal ILD reproduction is greatly improved with the implementation of AIO, producing values of ILD closer to those of HRIRs for most angles around the horizontal plane. Though for the most part AIO produces an increase in reproduced ILD of binaural Ambsonic rendering, the example of \(M=5\) illustrates how AIO can also produce a reduction in ILD for some locations when necessary--see azimuth values between \(|75^{\circ}<\theta<105^{\circ}|\) in Figure 3e.
# Optimization of DLTS Hinges for the Assembly of the Solar Arrays of a Communication CubeSat
## 4 Results and Discussion
### Optimization Analysis--Taguchi Model
#### 4.2.3 Interaction Optimization Results for \(\sigma_{\rm v}\) and Steady-State Moment  
When designing the DLTS hinge components, a low \(\sigma_{\rm v}\) and high strength contribute to the overall performance. The research has shown that the process settings have an influence on the part specifications, and there are options to consider when selecting a particular criterion. In this section, contour plots are used to examine the relationship between the selected response variables of \(\sigma_{\rm v}\) and the steady-state moment and three predictor variables, curvature radius, subtended angle, and layer thickness.  
With a focus on optimizing \(\sigma_{\rm v}\) to be below 1700 N/mm\({}^{2}\), the following observations are found:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Descriptive Statistics**} & **Mean** & **SE Mean** & **StDev** \\ \cline{2-4}  & **209.4** & **19.7** & **59.1** \\ \hline Level & Curvature Radius [R] & Subtended Angle [\(\theta\)] & Layer Thickness [t] \\ \hline
1 & 206.9 [N/mm] & 140.3 [N/mm] & 209.4 [N/mm] \\ \hline
2 & 203.1 [N/mm] & 212.2 [N/mm] & 209.4 [N/mm] \\ \hline
3 & 218.3 [N/mm] & 275.7 [N/mm] & 209.4 [N/mm] \\ \hline Difference (\(\delta\)) & 15.2 [N/mm] & 135.4 [N/mm] & 0.0 [N/mm] \\ \hline Rank importance & 2 & 1 & 3 \\ \hline Prediction & \multicolumn{3}{c}{**133.9 N/mm (lowest steady-state moment)**} \\ \hline \hline \end{tabular}
\end{table}
Table 3: Response for mean steady-state moment.  
Figure 9: Main effects plot for steady-state moment.  
* It is possible to achieve a low \(\sigma_{\rm v}\) with a reduced subtended angle (<1.5\({}^{\circ}\)) and a curvature radius between 1.5 and 2.25 mm (Figure 10a).
* Similarly, a low \(\sigma_{\rm v}\) can be achieved with a subtended Angle (<1.5\({}^{\circ}\)) and a layer thickness between 1.75 and 2.75 mm (Figure 10b).
* A layer thickness between 1.75 and 2.5 mm and a curvature radius of 1.5-2.0 mm results in a low \(\sigma_{\rm v}\) (Figure 10c).  
When optimizing the steady-state moment below 150 N/mm\({}^{2}\), the following observations are found:  
Figure 10: Contour plots for \(\sigma_{\rm v}\) and steady-state moment.  
* It is possible to achieve a low moment with a reduced subtended angle (<1.2\({}^{\circ}\)) and a curvature radius between 1.25 and 2.0 mm (Figure 10d).
* A low \(\sigma_{\rm v}\) can also be achieved with an angle of 1.5\({}^{\circ}\) and a layer thickness between 1.75 and 2.2 mm (Figure 10e).
* A layer thickness between 1.75 and 2.25 mm and a curvature radius of 1.25-2.0 mm results in a low \(\sigma_{\rm v}\) (Figure 10f).  
A compromise for a low \(\sigma_{\rm v}\) and steady-state moment is possible. Optimization is achieved with a layer thickness of 1.75-2.25 mm, a curvature radius of 1.5-2.0 mm, and an angle of 1-1.2\({}^{\circ}\).
###### Abstract  
Spectral lines can be analysed to determine the physical properties of molecular clouds and the astrochemical processes in the formation area of massive stars. To improve the observation technology of radio astronomy, this paper proposes and compares two spectral analysis algorithms (improved weighted overlap-add (IWOLA) + FFT and IWOLA + weighted overlap-add (WOLA)). The proposed algorithms can obtain an ultra-high-frequency resolution for real-valued wideband signals, eliminate the signal overlapping interference between adjacent channels, substantially decrease the required hardware resources, especially multipliers, adders, and memory resources, and reduce the system design complexity. The IWOLA + FFT algorithm consists of an improved weighted overlap-add (IWOLA) filter bank, fast Fourier transform (FFT), a specific decimation for the output data from the IWOLA filter bank, and a selection for part of the output data from the FFT. The IWOLA + WOLA algorithm consists of the same modules as the IWOLA + FFT algorithm, with the second-stage FFT replaced by the modules of the weighted overlap-add (WOLA) filter bank and the accumulation for each sub-band. Based on an analysis of the underlying principles and characteristics of both algorithms, the IWOLA + FFT algorithm demonstrated a spectrum with a high frequency resolution and a comparable performance to an ultra-large-scale FFT, based on two smaller FFTs and a flexible architecture instead of a ultra-large-scale FFT. The IWOLA + WOLA algorithm contains the same function as the IWOLA + FFT algorithm and demonstrates a higher performance. The proposed algorithms eliminated the interference between the adjacent channels within the entire Nyquist frequency bandwidth. The simulation results verify the accuracy and spectral analysis performances of the proposed algorithms.  
channels; polyphase filter bank; WOLA filter bank; IWOLA filter bank
## 2 Experimental Setup
### Laser System  
The monolithic laser source in our setup is a fiber-coupled Fabry-Perot laser diode (FPL1009P, Thorlabs). It offers a small footprint, can be pumped electrically, is commercially available, and is inexpensive (\(<\)$1500) as compared to fiber lasers. Its emission wavelength is centered at 1550 nm, which makes the laser compatible with established telecom technology. The free spectral range (FSR) is 43.47 GHz, while the average output power exceeds 100 mW at an injection current of 500 mA. Although this laser is sold as a longitudinal multimode laser with no hint of mode locking capabilities, there was the conjecture from previous experiments that the broad emission spectrum with a full-width half-maximum exceeding 10 nm is a sign of four-wave mixing mode locking in this single section laser [25; 26]. In this case, the laser emits linearly chirped pulses, which can be compressed by the anomalous dispersion of a standard single mode fiber (SMF) [24].  
The rest of the laser system setup is as follows: To suppress unwanted feedback from the rest of the setup, a fiber optic isolator is employed. The inherent chirp of the laser can be compensated using a SMF with variable length. A fused fiber polarization splitter (PS) is used to ensure well-defined linear polarization at the output of the laser system. The polarization maintaining fiber (PMF) at the slow-axis output of the PS is connected to the PMF at the input of a fiber optic 50/50 beam splitter. The PMF at the fast-axis output of the PS is connected to a power meter. A polarization controller at the input of the section of SMF is used to minimize the power at the fast-axis output of the PS, thus maximizing the power at the slow-axis output. To check whether an ultrashort laser pulse exists, a second harmonic generation (SHG) autocorrelator (pulseCheck, APE Berlin) is used to determine the length of the pulse. The length of the SMF can then be varied to find optimal dispersion compensation and hence the shortest pulse. The autocorrelation function (ACF) is directly measured before the PCAs to determine the correct pulse duration. Further, an optical spectrum analyzer (MS9740A, Anritsu) is used to obtain the optical spectrum of the laser diode.
## 4 Quality Control
### Planimetric Analysis  
In this case, we are interested in obtaining planimetric precisions. We compared two images (see Figure 8) with the PNOA picture being used as a reference (right side in Figure 8), while the another one was obtained using a synthetic procedure (left side in Figure 8, using a blue color ramp) for a test zone (2 \(\times\) 2 km) in the city of Alicante (\(X_{min}=720,000\) m; \(X_{max}=722,000\) m; \(Y_{min}=4,246,000\) m; \(Y_{max}=4,248,000\) m, UTM 30N datum WGS84). Note that \(X_{min}\), \(X_{max}\), \(Y_{min}\) and \(Y_{max}\) refer to test area, so the pictures shown in Figure 8 cover more territory. A good way to compare these two images is to use a 2D transformation [17] by selecting at least three of the same points in bothimages to obtain six parameters for affine transformation (\(T_{x}\), \(T_{y}\), \(S_{x}\), \(S_{y}\), \(\alpha\) and \(\beta\)). In the example of Figure 8, the operator selected the same four points for both pictures and using the method of least squares [18], we obtained the following values for the six parameters (two translations, \(T_{x}\) and \(T_{y}\); two scale factors \(S_{x}\) and \(S_{y}\); one rotation \(\alpha\) and one perpendicularity \(\beta\)):  
\[\begin{array}{l}T_{x}=0.128\ \mathrm{m}\\ T_{y}=0.001\ \mathrm{m}\\ S_{x}=0.999\\ S_{y}=1.000\\ \alpha=-0.0185s\\ \beta=0.0248s\end{array}. \tag{36}\]  
There is a mean squared error of \(\pm 0.506\ \mathrm{m}\) for the adjustment. This estimated error is below the GSD used in generating the synthetic images (1 m).
## 3 Model Formulation
### State  
(PO)MDP states for the pipeline problem describe the degradation level due to pipeline corrosion. Degradation in the pipeline constitutes two processes: the formation of corrosion and the growth of the formed corrosion. These processes can be formulated either independently or in an aggregated form to determine the degradation state. In most cases, corrosion formation is considered a Poisson distribution process [16]. However, [PERSON] et al. [17] have shown that negative binomial distribution can also be used to model it. Deterministic and stochastic models have been developed to formulate the growth of corrosion [4]. Deterministic methods include single-value, linear, and nonlinear growth models, whereas stochastic methods include the Gamma process, Markov process (pure birth process), Monte-Carlo simulation, and time-dependent or independent generalized extreme value distribution. For the aggregated formation and growth model, a Markov process-based model has been frequently adopted in the literature [6; 18]. A Markov process is a continuous process where only the immediate predecessor state impacts the current state. Therefore, the degradation states extend over the life of the pipeline (from no corrosion to the maximum allowable corrosion defect level--failure). For a discrete state model formulation, the degradation level range was broken down into distinct states. After determining the number of states, dividing the total deterioration range by the number of states will give each state's deterioration range.
## 2 Kinematics of Wheeled Mobile Robots
### Friction Forces for Classic-Wheeled Mobile Robot with Differential Drive
#### 3.1.2 Rotational Movement  
If it is presumed that the differentially driven mobile robot rotates around a central point, and its wheels exhibit varying movements, one can consider the forces of longitudinal friction as well as the lateral friction forces that arise during the rotation process [8; 17].  
Lateral direction friction force \(\mathrm{F_{fl}}\) may arise from the speed difference between the left and right wheels during rotation. This lateral force may depend on the steering angle of the wheels and can be influenced by wheel slip. Because the lateral direction friction force is also similar to friction when sliding, one can assume the \(\mathrm{F_{fl}}\) is:  
\[\mathrm{F_{fl}}=-\mathrm{\mu_{s}}\cdot\mathrm{N}\cdot\mathrm{sgn}(\mathrm{v_{ Y}}), \tag{14}\]  
The total friction force \(\mathrm{F_{t}}\) during rotation can be the sum of the above forces:  
\[\mathrm{F_{ft}}=\mathrm{F_{fr}}+\mathrm{F_{fl}}. \tag{15}\]  
Figure 1: Friction forces acting on a differential drive mobile robot: (**a**) linear motion; (**b**) rotational movement.  
When a four-wheel drive robot rotates, both rolling friction and sliding (or lateral) friction come into play.  
Modified model with rolling and lateral friction generates the total friction torque for rotation:  
\[\text{T}\text{q}_{\text{f}}=-\text{F}_{\text{fr}}\cdot\text{b}\cdot\text{sgn}( \omega), \tag{16}\]  
\[\text{T}\text{q}_{\text{f}}=-(\text{F}_{\text{fr}}+\text{F}_{\text{fl}})\cdot \sqrt{\frac{\text{L}\text{x}^{2}}{2}+\frac{\text{Ly}^{2}}{2}}\text{sgn}( \omega). \tag{17}\]  
where b is the distance from the center of rotation to the wheel's contact point with the rolling surface. For the friction forces coefficients, the models use \(\mu_{\text{r}}=0.05\) and \(\mu_{\text{s}}=0.3\).  
These equations show that the lateral friction, which is dominant in this scenario, contributes significantly to the total frictional force and to the resulting torque required for rotation. The rolling friction, while present, has a smaller impact due to its typically lower friction coefficient. The above model provides a more comprehensive understanding of the friction forces during the rotation of a four-wheel drive mobile robot.
## 6 High Frequency Characterization and Modelling
### High Frequency Characterization of InP DHBTs
#### 6.1.2 Measurement Results and HiCuM Compact Model Validation Up to 330 GHz
Figure 14: Comparison between HiCuM extrapolation and measurements up to 330 GHz: (**a,b**) gain curves, (**c,d**) cut-off frequencies and (**e,f**) S-parameters at peak \(f_{T}\). Overall, this demonstrates again the accuracy of the HiCuM compact model to accurately reproduce the behavior of InP DHBT transistors in the sub-millimeter-wave frequency range, provided accurate and reliable measurements are performed with on-wafer TRL calibration.
## 4 Enhancement of Robot Position Control
### Enhancement Method  
To mitigate the applied force, we enhance the robot position control by considering network delay. The position vector of the robot is determined as follows (called _proposed method_\(1\) here):  
\[S_{t}=\frac{d_{t-1}^{(\mathbf{m}_{2})}\times\mathbf{M}_{t-1}^{(\mathbf{m}_{1})}+d _{t-1}^{(\mathbf{m}_{1})}\times\mathbf{M}_{t-1}^{(\mathbf{m}_{2})}}{d_{t-1}^{( \mathbf{m}_{1})}+d_{t-1}^{(\mathbf{m}_{2})}}+\mathbf{V}_{t-1} \tag{5}\]  
where \(d_{t}^{(\mathbf{m}_{1})}\) and \(d_{t}^{(\mathbf{m}_{2})}\) represent the network delay between the robot and haptic interface devices 1 and 2 at time \(t\), respectively. Note that we only consider cases where \(d_{t-1}^{(\mathbf{m}_{1})}+d_{t-1}^{(\mathbf{m}_{2})}>0\).  
As will be described later, we found that it is challenging to assert that proposed method 1 is effective. Therefore, we conduct a preliminary experiment to investigate the effect by using fixed ratios (1:1 for the conventional method, 2:1, 3:1, 4:1, and 5:1) of \(d_{t-1}^{(\mathbf{m}_{1})}\)  
Figure 8: Average of average force magnitude and average of maximum force magnitude when the sum of network delay 1 and network delay 2 is same.  
to \(d_{t-1}^{(\text{m}_{2})}\) in Equation (5), and obtain the optimal fixed ratio for each network delay. Through regression analysis, we derive the following equations (called _proposed method 2_ here):  
\[a=0.0057\times d_{t-1}^{(\text{m}_{2})}+0.5458 \tag{6}\]  
\[\text{\emph{S}}_{t}=\left\{\begin{matrix}\left(\text{\emph{M}}_{t-1}^{(\text{ m}_{1})}+\text{\emph{M}}_{t-1}^{(\text{m}_{2})}\right)/2+\text{\emph{V}}_{t-1} \qquad(if\ a\leq 1)\\ \left(a\times\text{\emph{M}}_{t-1}^{(\text{m}_{1})}+\text{\emph{M}}_{t-1}^{( \text{m}_{2})}\right)/(a+1)+\text{\emph{V}}_{t-1}\ \ \ (otherwise)\end{matrix}\right. \tag{7}\]  
where \(a\) is defined as the value when \(d_{t}^{(\text{m}_{1})}<d_{t}^{(\text{m}_{2})}\) and the ratio of \(d_{t}^{(\text{m}_{1})}\) and \(d_{t}^{(\text{m}_{2})}\) is \(a\):\(1\).
## 5 Analyses
### Performance Evaluation  
In this section, we will present the performance analysis of the proposed ECLAS scheme in terms of comparable feature with related research on the fields that gives merit to the proposed scheme. As such, performance comparison features are discussed in terms of computation cost analysis and communication cost analysis. We will assess the performance evaluation of the proposed work in terms of computation cost comparison against other related works by adopting the method presented in [17]. In [17] bilinear pairing on an 80 bits security parameter length is created as : \(G_{1}\times G_{2}\to G_{T}\). Here we consider \(G_{1}\) as an additive group of order \(q\) defined on a super-singular elliptic curve \(E:y^{2}=x^{3}+xmodp\) of embedding degree of 2. The recommended security parameter length for \(q\) and solinas prime number \(p\) are taken as 512 bits and 160 bits, respectively.  
For convenience, we will define the notations for execution time for different cryptographic computations in the schemes under discussion as portrayed in Table 3. We borrow the execution times directly from [17], which was evaluated using the MIRACL cryptographic library, to assess the efficiency of schemes. Operations which are very light like addition operation in \(Z_{q}^{*}\) and the multiplication operation in \(Z_{q}^{*}\) will not be considered.  
The notation for various computation operations are as follows.  
\(T_{bp}\): Denotes execution time for bilinear pairing operation defined as, \(e(P,Q)\), where \(P,Q\in G_{1}\)  
\(T_{bp,m}\): Denotes execution time for scalar multiplication operation \(x.P\), that is related to pairing operation defined as \(e(P,Q)\), where \(P,Q\in G_{1}\), and \(x\in Z_{q}^{*}\)  
\(T_{bp,sm}\): Denotes execution time for small scalar multiplication operation, \(v_{i}.P\), that is related to pairing operation \(e(P,Q)\), where \(P,Q\in G_{1}\) and \(v_{i}\in[1,2^{t}]\) is a small random integer, for a small predefined integer \(t\).  
\(T_{bp,a}\): Denotes execution time for point addition in bilinear pairing operation \(e(P,Q)\), such that \(R=P+Q\), where \(R,P,Q\in G_{1}\)  
\(T_{H}\): Denotes execution time for map-to-point hash function operation related to pairing operation \(e(P,Q)\), where \(P,Q\in G_{1}\).  
\(T_{c,m}\): Denotes execution time for scalar multiplication operation, \(x.P\), over ECC group, where \(P\in G\) and \(x\in Z_{q}^{*}\).  
\(T_{c,sm}\): Denotes execution time for small scalar multiplication operation, \(v_{i}.P\), for small exponent test, where \(P\in G\) and \(v_{i}\in[1,2^{t}]\) is a small random integer, for a small predefined integer \(t\).  
\(T_{c,a}\): Denotes execution time for point addition operation over an elliptic curve group, \(R=P+Q\), where \(R,P,Q\in G\).  
\(T_{h}\): Denotes execution time for one hash function operation.