esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Proposed PLL Architecture  
The classical PLL with the newly introduced circuit block to optimize the conventional architecture is shown in Figure 1. It consists of a reference frequency divider at the input, phase frequency detector (PFD), charge pump, passive loop filter, programmable VCO and automatic band selection circuit. A quartz crystal with a 25 MHz frequency is used as the reference frequency. It is further divided by a multi-modulus frequency divider, N = 5, to generate a 5 MHz reference frequency. The multi-modulus frequency divider is used at the feedback loop M = 66 to match the VCO central frequency with the input reference frequency. The PFD senses the phase difference, and, consequently, the charge pump generates an error current, which charges or discharges the loop filter. After filtering the high-frequency component, a DC control voltage Vctr is received at the input of the VCO. The VCO is replaced with a programmable VCO. The proposed VCO contains a voltage-to-current (V to I) converter with a programmable current digital to analog converter (iDAC) and a four-stage differential ring oscillator as the delay cell. The programmable iDAC is used to optimize the VCO delay cells. The programmable VCO is aided in its operation by the automatic band selection block. The inclusion of auto-ranging and a multiband selection circuit converts a classical PLL into a programmable PLL. It renders the programmable PLL capable of automatically adjusting the nominal center frequency of the VCO to an optimum required value suitable to operate over a wide frequency range. The proposed PLL architecture with a detailed automatic band selection circuit block is shown in Figure 2. On execution, it continuously monitors two important upper band voltages and lower band voltages with DC control voltage Vctr to remain in the desired frequency range. Its sub-block implementation and operation procedure are discussed in the following sections.  
Figure 1: Block diagram of the proposed frequency synthesizer.
## 7 ECC-based Key Agreement Protocol
### Evaluation of Key Agreement Protocol  
Our compiled code on the Tmote uses 30 KB of ROM and 4.4 KB of RAM, which leaves reasonable space for other applications. We note that most of this memory requirement comes from TinyECC, which includes numerous optimizations to speed up the ECC operations. As indicated by [PERSON] and [PERSON] [16], these optimizations can be selectively disabled to reduce the code size at the cost of increased computation time.  
We timed our protocol which ran on two motes and had a laptop PC (2.66 GHz Intel Pentium 4) as a base station. As illustrated in Figure 7, more \(A\) acts as a sender, while mote \(B\) serves as the interface between the sender and the PC by receiving \(A\)'s wireless packets and forwarding them over a serial connection (emulated via a physical USB connection). More \(B\) essentially serves as a bridge between the sending mote and the PC, as the PC lacks a native way of wirelessly receiving data from a mote. Mote \(A\) took 7075 ms to generate and send _KeyGenStart_, and 123 ms to check _KeyGenAck_ and send _KeyGenVerify_. The base station took 77 ms to check _KeyGenStart_ and send _KeyGenAck_, and 13.3 ms to check _KeyGenVerify_. Virtually all of the time required to generate and send _KeyGenStart_ is due to two ECC scalar point multiplications (computing the points \(rQ\) and \(rG\), as described previously).  
Although a CPU time investment of over 7 s may seem intensive, we note that the key agreement protocol only has to be run once to establish the initial symmetric keys, and once each time the keys expire. We explain in Section 8.5 that this periodic investment has minimal effect on the mote's battery life.
# Robo-HUD: Interaction Concept for Contactless Operation of Industrial Robotic Systems
###### Abstract  
Intuitive and safe interfaces for robots are challenging issues in robotics. Robo-HUD is a gadget-less interaction concept for contactless operation of industrial systems. We use virtual collision detection based on time-of-flight sensor data, combined with augmented reality and audio feedback, allowing the operators to navigate a virtual menu by \"hover and hold\" gestures. When incorporated with virtual safety barriers, the collision detection also functions as a safety feature, slowing or stopping the robot if a barrier is breached. Additionally, a user focus recognition module monitors the awareness, enabling the interaction only when intended. Early case studies show that these features present good use-cases for inspection tasks and operation in difficult environments, where contactless operation is needed.  
a +
Footnote †: 10: 10.  
if safety protocol is breached. [PERSON] et al. [7] have proposed a system that ensures the human safety in a robotic cell and enables gesture recognition for low-level robot control (e.g., start/stop).  
There have been many different implementations of contactless HRI [8; 9; 10; 11; 12; 13; 14; 15; 16]. [PERSON] et al. utilized a pointing gesture to point the robot to a certain location, where the direction of the pointing gesture intersects with the planar surfaces. The approach can be generalized by their proposed \"Laws of Linear HRI\" utilizing an intersection of a line, formed by any two joints of the detected human and a plane in the robots environment, creating a point of interest (POI) or a potential target for navigation. This pointing gesture method can also be used to distinguish between pre-programmed objects. The approach is specialized in finding POIs; however, it lacks user feedback, as it is unclear where the user was pointing until the robot executes the command [9].  
[PERSON] et al. [10] presented an augmented reality graphical user interface (AR-GUI) as the core element of their tour-guiding robot's interaction. A user sees himself together with augmentation on the screen of the laptop, placed on top of the mobile robot and can push virtual buttons and perform gestures and patterns after performing an initialization for hand detection. Many other approaches utilize head mounted displays [11; 12; 13; 14]; unfortunately these are not intuitive, require additional hardware for each user and are often difficult to integrate into the industrial environment. In addition to the time-of-flight sensors, there are approaches with capacitive [15], radar [17] or tomographic [16] sensors to detect gestures.
## 3 Results
### Stress-Strain Behaviour at Saturation  
Undrained triaxial shear strength tests conducted under saturation provided key insights into the long-term mechanical performance of soils stabilised with cement and guar gum, particularly following repeated wetting-drying (WD) cycles. All soil samples displayed strain-hardening behaviour under saturated conditions, except cement-treated samples subjected to one and five WD cycles (Figure 8). The undrained shear strength (\(S_{\text{u}}\)) of the untreated soil decreased from 28 kPa after 1 WD cycle to 17 kPa after 15 cycles, representing a 38% reduction in strength. Cement-stabilised samples exhibited a significantly higher initial \(S_{\text{u}}\) of 72 kPa after 1 WD cycle; however, this value declined to 27 kPa after 15 cycles (a 62% reduction), likely due to microcracking and calcium leaching. Despite this degradation, the cement-treated soil retained superior strength to the untreated control. Guar gum-stabilised soil demonstrated a \(S_{\text{u}}\) of 49 kPa at 1 WD cycle, decreasing moderately to 29 kPa after 15 cycles, amounting to a 40% reduction. It is worth noting that \(S_{\text{u}}\) for the guar gum-treated soil under 10 and 15 WD cycles is very similar to that of cement-treated soil (Figure 9a).  
Figure 9b presents the secant modulus of elasticity (\(E_{50}\)), determined under saturated conditions for untreated, cement-treated, and guar gum-treated soils across 1, 5, 10, and 15 WD cycles. Notably, the highest modulus of toughness (\(U_{\text{I}}\)) was recorded for the guar gum-treated samples, followed by cement-treated and untreated soils (Figure 9c),  
Figure 7: Variation in (**a**) undrained shear strength, (**b**) modulus of elasticity, and (**c**) modulus of toughness measured at \(-\)33 kPa matric suction for untreated soil and soil treated with cement and guar gum, subjected to 1, 5, 10, and 15 wetting–drying cycles.  
underscoring the energy absorption capacity and ductility enhancement afforded by the biopolymer treatment.
# Robot Localisation Using UHF-RFID Tags: A Kalman Smoother Approach +
## 4 Results
### Simulation Results
#### 4.1.4 Vehicle Dynamic  
In the last set of simulations, we investigate the impact of different linear and angular velocities on the localisation error using one single trajectory among the available set, which is represented in Figure 4 for the realistic warehouse scenario. Since different velocity profiles are used, ranging in the interval \((2.8,1.5)\) m/s for the linear velocity and in the interval \((|1.5|,|0.6|)\) rad/s for theangular velocity (we recall that the sign of the angular velocity is determined by the turn direction), different trajectories for the same sequence of via-points are generated (see Figure 4). For a more quantitative analysis, we consider sufficiently long trajectories, ranging from 70 to 164 s (the difference in the length are actually dictated by the different velocities on the same path). Results are reported in the RMSE plot of Figure 10, where we use the SEKF with the optimal window size of \(n=55\) samples and we denote with \(\bar{\omega}=|\omega|\), i.e., the magnitude of turning speed.  
Both the position and the orientation RMSEs are reported, which are computed on Monte Carlo trials of 100 executions. Due to the presence of a Kalman filter estimator, it is expected that the RMSE reaches a steady state value determined by the model and measurement covariances. This is certainly the case for linear systems, while for nonlinear systems (as the one at hand) it is expected a fluctuation of the RMSE, induced by the particular trajectory followed. Nevertheless, Figure 10 shows a clear divergence when the forward velocity \(v\) decreases. This apparently strange behaviour is a direct consequence of the longer trajectory followed when the forward velocity reduces, which implies a  
Figure 10: (**a**) Position and (**b**) orientation RMSE for different values of the angular and linear velocities.  
Figure 9: (**a**,**c**) Position and (**b**,**d**) orientation RMSE for different percentages of available observations for the EKF (**a**,**b**) and the SEKF (**c**,**d**).  
higher number of integration steps of the model noises with the same variance in the filter prediction step. This is usually compensated by the larger amount of measurements that can be collected along the trajectory. Unfortunately, this is not the case for RFID-tag measurements, where the difference of ranges are actually collected (see Section 3.2). Hence, smaller forward velocities \(v\) imply smaller displacements between two time steps, thus smaller differences among the range measurements. Therefore, the observations become comparable with the measurement uncertainties, thus reducing the amount of information carried by each observation. This fact is further proved by the substantial independence of the RMSE by the angular velocity \(\omega\), which does not affect the ranging measurements by design (see Figure 10).
# Vibration Compensation of the Frequency-Scanning-Interferometry-Based Absolute Ranging System
## 2 Theory Background
### Analysis of Vibration Effect  
In practical applications, the target cannot be guaranteed to remain absolutely stationary. According to Equation (3) even for a small change in \(\tau\), the phase modulation will be introduced into the beat signal because \(f_{0}\) is very large compared with the other terms. At the same time, as the target vibrates, the Doppler shift will also be introduced into the beat signal, resulting in a decrease in ranging accuracy. The specific derivation steps are as follows.  
Assuming the target vibration velocity is \(v\), the time delay corresponding to the distance to be measured can be calculated by:  
\[\tau(t)=\frac{2(R_{0}-vt)}{c}=\tau_{0}-\frac{2 vt}{c}, \tag{6}\]  
where \(R_{0}\) is the distance to be measured at the beginning of a single scanning period, and \(\tau_{0}\) is the corresponding time delay. In this case, the beat signal can be expressed by:  
\[I^{\prime}=\frac{1}{2}\eta{A_{L}}^{2}\cos[2\pi(\alpha_{0}\tau_{0}t-\frac{2v \alpha_{0}}{c}t^{2}-\frac{2v\beta_{0}}{c}t+f_{0}\tau_{0})],\tau\leq t\leq T_{m }/2. \tag{7}\]  
According to Equation (7), as the target vibrates, the beat signal is no longer a single-frequency signal, and the frequency of the beat signal can be expressed as:  
\[{f_{beat}}^{\prime}=\alpha_{0}\tau_{0}-\frac{2v\beta_{0}}{c}-\frac{4v\alpha_{ 0}}{c}t,\tau\leq t\leq T_{m}/2. \tag{8}\]  
Figure 1: Schematic diagram of the FSI-based absolute ranging system.  
In general, the third term in Equation (8) is negligible in the scanning period as its coefficient is very small and the scanning period is very short. Thus, the frequency of the beat signal can be simplified to:  
\[{f_{beat}}^{\prime}=\alpha_{0}\tau_{0}-\frac{2 vf_{0}}{c},\tau\leq t\leq T_{m}/2, \tag{9}\]  
where \(2 vf_{0}/c\) is the Doppler shift introduced by the target vibration. Substitution of Equation (9) into Equation (4) yields to:  
\[R^{\prime}=\frac{{f_{beat}}^{\prime}c}{2\alpha_{0}}=R_{0}\pm\frac{2 vf_{0}}{2 \alpha_{0}}=R_{0}\pm\frac{2f_{0}v(T_{m}/2)}{2\alpha_{0}(T_{m}/2)}=R_{0}\pm \frac{f_{0}}{B}\Delta R, \tag{10}\]  
where \(B\) is the laser scanning bandwidth, \(\Delta R\) is the small displacement of a target, and its sign is related to the sweeping direction of the frequency-swept signal. In Equation (10), it is noteworthy that the tiny displacement is magnified by a factor of \(f_{0}/B\), and this multiple can usually be up to several hundred or thousands resulting in a large measurement error. For instance, if the center frequency of the frequency-swept light is 1500 nm and the scanning bandwidth is 1 nm, the magnification of the tiny displacement will be equal to 1500.
## 1 Introduction  
In the future, tactical ad-hoc networks will require routing protocols to process large amounts of tactical sensor and mission-critical data securely and accurately, especially while constructing ad-hoc networks in harsh environments that have no infrastructure availability. Recently, IoT technology has been actively studied to apply to military needs such as base operations, situation awareness, healthcare, energy management, and boundary and harbor surveillance [1]. As the IoT devices increase, a networking management architecture is needed to handle the network traffic load efficiently [2]. Tactical ad-hoc networks are expected to generate large amounts of data from the various IoT sensors installed in unmanned robots, launchers, and munitions for fast and accurate command control, which emphasizes the importance of research and developments in reliable networking systems to deliver the data to the gateways. The gateway requires a technology to support essential services such as delay-sensitive applications and network management [3, 4].  
However, as the tactical ad-hoc networks become unmanned and the variety of devices increases, the associated cyber threats are likely to increase [5]. If a malicious node intercepts the information and consequently discards or disturbs it, mission-critical data may be lost, or the number of devices may increase, causing communication performance degradation in a resource-constrained network environment.  
To solve these problems, technologies have been studied to ensure trustworthiness and QoS, taking into account trust evaluation, expected transmission count (\(ETX\)) [6], and end-to-end delay [7, 8]. Moreover, bandwidth and queue management techniques have also been explored to ensure the reliability of data with high mission importance in terms of data priority.
## 2 Discussion
### Sonochemiluminescence  
We start with the discussion of a sonoluminescence regime that can be characterised as an intermediate type of bubble cavitation that bears signatures of both single and multi-bubble behaviour. In particular, this regime exploits the property of an oscillating bubble to \"dance\" [57], i.e., to undergo erratic spatial translation movement on a microscopic scale due to the Bjerknes force [73; 74] (the primary Bjerknes force is caused by an external acoustic pressure field, but the secondary Bjerknes force arises between several bubbles that oscillate in the same acoustic pressure field). While the optical frequency spectrum of the sonoluminescence signal produced by a dancing bubble was found to contain emission lines that are also present in a typical multibubble sonoluminescence spectrum, it was also shown that sonochemical reactions were possible when luminol--a chemical that fluoresces upon oxidation--was added to the liquid [75]. However, such reactions were not observed in the case of single-bubble sonoluminescence.  
The emission of light from luminol in a single-bubble experiment was also investigated in [60] at different peak amplitudes of the driving acoustic pressure waves. The results were compared with the data obtained using bubbles in degassed water (Figure 3). It was shown that while the behaviour of a single bubble in water followed a typically observed light emission pattern, where the onset of strong broadband sonoluminescence occurs at the peak acoustic pressure of about 1.05 bar and then the light intensity increases sharply, in a luminol solution, a typical emission band centred at the optical wavelength of 425 nm was observed at significantly lower acoustic pressures from 0.90 to 1.05 bar. It is noteworthy that this form of sonoluminescence is chemical in nature and, hence, generally speaking, distinct from conventional sonoluminescence since it occurs in the solution surrounding the collapsing bubble through an oxidative radical process [76]. However, this result is important in the context of the mainstream discussion in this article since it demonstrates that sonoluminescence-like processes can be amplified using luminescent chemicals and that bright light emission can be achieved using ultrasound waves of a lower amplitude compared with sonoluminescence in degassed water. This also means that sonoluminescence could be achieved under medically relevant ultrasound exposure conditions, where the acoustic pressure levels are lower that those used in studies of fundamental sonoluminescence properties.
###### Abstract  
Aim: In this study, we aimed to assess the correlation between diabetes mellitus (DM) and the retinal vein occlusion (RVO) based on skin autofluorescenceence (SAF) measurement, which reflects the accumulation of advanced glycation and products (AGE) in patients who have undergone an episode of central retinal vein occlusion (CRVO) or branch retinal vein occlusion (BRVO). Material and methods: In total, 23 patients (16 males, 7 females) with RVO were included in this study. Among these 23 participants, 12 (52%) had been diagnosed with CRVO and 11 (48%) with BRVO. The control group consisted of 14 healthy volunteers (11 females, 3 males). To calculate the risk of cardiovascular diseases (CVD) and DM, we conducted SAF examinations. We compared the SAF levels in three groups of patients: (1) with CRVO, (2) with BRVO, and (3) the control group. Basic demographic and clinical information and detailed history of the concurrent diagnoses of systemic diseases, such as systemic hypertension (HTN), DM, hyperlipidemia (HL), and heart diseases, were obtained. Results: In total, 10 (43.5%) patients were diagnosed with DM, 6 (55%) in the BRVO group and 4 (33%) in the CRVO group. The mean SAF value was significantly higher in the BRVO group than in the control group (2.64 a.u. and 2.35 a.u., respectively) (\(p=0.023\)). More patients with risk of DM were identified in the CRVO group than in the BRVO group (\(p=0.024\)). Conclusions: The advanced glycation end products (AGE) in the skin autofluorescenceence (SAF) is a viable method of evaluating the risk of DM in patients with RVO. We confirmed a correlation between RVO and DM, which was significantly pronounced in the CRVO form, although further carefully devised studies on the relationship between RVO and DM with a larger number of responders should be conducted in the future.  
retinal vein occlusion; diabetes mellitus; autofluorescenceence; advanced glycation end products (2021), 11, 7934. [[https://doi.org/10.3390/app11177934](https://doi.org/10.3390/app11177934)]([https://doi.org/10.3390/app11177934](https://doi.org/10.3390/app11177934))
# A Generic Block-Level Error Confinement Technique for Memory Based on Principal Component Analysis
## 4 Case Study
### Experimental Results
#### 4.2.1 Exploration of Modularization

Table 1: Results of dimension reduction and image reconstruction with proposed method for image (a) in Figure 7.  
Figure 9: Comparison of execution time and quality of the reconstructed _image (a)_ in Figure 7 under different cutting dimensions with 50% remaining principle components. Viewing across multiple images, it is discovered that different images show various PCCL under the same sub-block size. For instance, image (a) and (e) have the smallest and highest PCCL. By carefully viewing the images, it is shown that image (e) has significantly complex textures in majority of sub-blocks, other than the sub-blocks for sky, while image (a) has simpler textures in most sub-blocks, other than the hairs of Lena. Consequently, PCA on image (a) gives higher PSNR than image (e), which shows proves again how PCCL affects PSNR. However, despite the correlated trend, detailed observation implies that PCCL on a specified CR level does not result in a 1-to-1 correspondence with PSNR. For instance, image (b), which gives the lowest PSNR value, does not show the highest PCCL for all sub-block sizes. When the CR level of PCCL is increased to 99.9% as shown in Figure 10d, the PCCL of image (b) even improves to the third best. To investigate on such issue, we find out that PCCL of an image is the mean PCCL values of its sub-blocks, which does not reflect the differences of PC concentration for individual sub-blocks. Therefore, variance of PC to achieve 99% CR across individual sub-blocks is calculated for six images,  
Figure 10: Results of six images under four sub-block sizes on (**a**) PSNR with half retaining PC (**b**) execution time (**c**) PC concentration level (PCCL) for 99% CR (**d**) PCCL for 99.9% CR (**e**) variance of PC for 99% CR. which is plotted in Figure 10e. It is observed that image (b) has almost the largest variance, which leads to a disastrous deterioration to the PSNR, even though its PCCL is not the largest one. The key insights from the above analysis is that the features of images (PCCL, sub-block variance and even further features) have strong influence on the optimal strategy of PCA modularization to achieve a certain PSNR with minimal cost of execution time. Statistical methods such as machine learning can be used to form such strategy finding as a regression or classification problem, which will be explored in the future work.
# The CIPCA-BPNN Failure Prediction Method Based on Interval Data Compression and Dimension Reduction
## 4 Experiments Description
### Experiments  
#### 4.2.1 Data Compression  
Interval data can achieve the purpose of compressing data sample size. For flight data, we can split data from the time dimension. In the experiment, the original flight data of each sorting was set as a data interval every 0.1 s, and the maximum and minimum values were extracted from the data interval to form the interval data matrix according to the method in Section 2.1. The samples of each sort were in the same state, so the interval processing of flight data did not affect the label of the samples (normal state or fault state). The obtained interval data was used for the subsequent dimension reduction based on CIPCA and the establishment of a fault prediction model based on BPNN.