esa-sceva/satcom-chunk-collection huggingface dataset:
# 6G Goal-Oriented Communications: How to Coexist with Legacy Systems?
## 3 Coexistence of a Goal-Oriented and a Data-Oriented Communication System
### Edge Inference: Goal Value and Effectiveness
#### 3.2.1 Uplink Radio Performance of GO User  
Let us recall that the GO user offloads inference tasks to the MEH. From a wireless perspective, performance is affected by multiple factors: _(i)_ the uplink data rate; _(ii)_ the interference received from the DO user; _(iii)_ the target packet error rate. Let us denote by \(\mathbf{w}_{g}\in\mathbb{C}^{M_{g}\times 1}\) the combining vector at \(AP_{g}\), denoting by \(P_{g}\) the transmit power of \(UE_{g}\), and by \(P_{d}\) the transmit power of \(UE_{d}\). Assuming time as organized in slots \(t=1,2,3,\ldots\), the Signal-to-Interference-plus-Noise Ratio (SINR) at the GO receiver (\(AP_{g}\)), at time \(t\), reads as  
\[\text{SINR}_{g,t}=\frac{|\mathbf{w}_{g,t}^{H}\mathbf{h}_{g,t}|^{2}P_{g,t}}{N_{ 0}W+|\mathbf{w}_{g,t}^{H}\mathbf{h}_{g,t}|^{2}P_{d,t}}, \tag{7}\]  
where index \(t\) denotes the time dependence of the involved random and controlled variables; whereas \(N_{0}\) denotes the power spectral density, and \(W\) the uplink bandwidth, assumed to be fully shared between the two systems. Now, considering finite blocklength transmissions [51, 52, 53], and denoting by \(\gamma_{t}\) the target PER at time \(t\), we can write the achievable rate of \(UE_{g}\) (in bits/s) at time \(t\) as follows [51]:  
\[R_{g,t}=W\!\left[\log_{2}\!\left(1+\text{SINR}_{g,t}\right)-\frac{1}{\log(2)} \sqrt{\frac{V_{t}}{n_{g}}}Q^{-1}(\gamma_{t})\right]\!, \tag{8}\]  
where \(n_{g}\) is the blocklength, \(Q^{-1}(\cdot)\) is the inverse of the Gaussian Q function, while \(V_{t}\) is the channel dispersion, given by [52, 53]  
\[V_{t}=1-\frac{1}{\left(1+\text{SINR}_{g,t}\right)^{2}}, \tag{9}\]  
which is well approximated as \(V\approx 1\) with SINR above 5 dB [52]. Nevertheless, in this work, we keep it general, as our system may be required to work at low SINR regimes, provided that a target goal effectiveness is guaranteed. Finally, assuming (without loss of generality) that a batch of \(N_{t}\) new patterns is requested to be inferred at time slot \(t\), the uplink transmission delay for GO communication reads as follows:  
\[D_{\text{tx},t}=\frac{N_{t}n_{b}}{R_{g,t}}, \tag{10}\]  
where \(n_{b}\) denotes the number of bits encoding one pattern. In this work, we do not focus on the specific encoding scheme, which we assume to be fixed. Semantic and GO compression can also play a role in optimizing \(n_{b}\), which goes beyond the scope of this paper and is left for future investigations. Preliminary works on online compression level selection are available in [24, 25, 26, 27, 27, 30].
## 2 Materials and Methods
### Statistical Analyses  
Statistical analyses were conducted via SPSS (version 27; SPSS Inc., Chicago, IL, USA) and Microsoft Excel. The mean and SD of measured variables were recorded and subjected to statistical analysis. Normality of all collected variables was assessed with the Shapiro-Wilk statistic, and the homogeneity of variance was verified with the [PERSON]'s test (\(p>0.05\)). Within-session reliability of the metrics measured from the CMRJ and athletic performance tests was computed using a 2-way random effects model intraclass correlation coefficient (ICC) with absolute agreement and 95% confidence intervals (CIs) as well as the coefficient of variation (CV) with a 95% CI. The magnitude of the ICC was evaluated as an ICC value \(>0.90\) = excellent, 0.75-0.90 = good, 0.50-0.74 = moderate, and \(<\)0.50 = poor [28]. The CV was calculated as (CV% = SD/mean \(\times\) 100), with CV values considered good if \(<\)5%, moderate if between 5 and 10%, and poor if \(>\)10% [29]. The 95% CI for the CV was calculated as ((CV/\(\surd{(2n)}\)) \(\times\) 1.96), where \(n\) was the total numbers of subjects.  
[PERSON]'s \(r\) correlation (for parametric data) or [PERSON]'s \(\rho\) correlation (for non-parametric data) was used to investigate the relationships between the CMRJ metrics and independent measures of athletic performance, with statistical significance set at \(p<0.05\)[30]. Bonferroni corrections were applied to all correlations to account for multiple comparisons and the familywise type I error rate [30], resulting in statistical significance being set at \(p<0.006\). The strength of coefficient \(r\) was determined according to [PERSON] [31], as (\(r<0.1\): trivial; 0.1 to 0.3: small; 0.3 to 0.5: moderate; 0.5 to 0.7: large; 0.7 to 0.9: very large; \(>\)0.9: nearly perfect).  
A median split analysis was performed, creating higher and lower JH, RSImod, and RSI groups for each jump in the CMRJ test. This was completed to determine whether subjects with higher CMRJ metrics exhibited greater strength or faster performance during the linear sprint and 505 COD tests. The difference between the higher and lower groups was assessed with Mann-Whitney \(U\) tests, with statistical significance set at \(p<0.05\), and Hedges' \(g\) ES were used to determine the magnitude of differences between higher and lower jump performance groups. These ES were interpreted as \(g<0.35\) = trivial; 0.35-0.80 = small; 0.81-1.50 = moderate; and \(>\)1.5 = large [31].
# Neural Network Ensemble to Detect Dicentric Chromosomes in Metaphase Images
###### Abstract  
The Dicentric Chromosome Assay (DCA) is widely used in biological dosimetry, where the number of dicentric chromosomes induced by ionizing radiation (IR) exposure is quantified to estimate the absorbed radiation dose an individual has received. Dicentric chromosome scoring is a laborious and time-consuming process which is performed manually in most cytogenetic biodosimetry laboratories. Further, dicentric chromosome scoring constitutes a bottleneck when several hundreds of samples need to be analyzed for dose estimation in the aftermath of large-scale radiological/nuclear incident(s). Recently, much interest has focused on automating dicentric chromosome scoring using Artificial Intelligence (AI) tools to reduce analysis time and improve the accuracy of dicentric chromosome detection. Our study aims to detect dicentric chromosomes in metaphase plate images using an ensemble of artificial neural network detectors suitable for datasets that present a low number of samples (in this work, only 50 images). In our approach, the input image is first processed by several operators, each producing a transformed image. Then, each transformed image is transferred to a specific detector trained with a training set processed by the same operator that transformed the image. Following this, the detectors provide their predictions about the detected chromosomes. Finally, all predictions are combined using a consensus function. Regarding the operators used, images were binarized separately applying Otsu and Spline techniques, while morphological opening and closing filters with different sizes were used to eliminate noise, isolate specific components, and enhance the structures of interest (chromosomes) within the image. Consensus-based decisions are typically more precise than those made by individual networks, as the consensus method can rectify certain misclassifications, assuming that individual network results are correct. The results indicate that our methodology worked satisfactorily in detecting a majority of chromosomes, with remarkable classification performance even with the low number of training samples utilized. AI-based dicentric chromosome detection will be beneficial for a rapid triage by improving the detection of dicentric chromosomes and thereby the dose prediction accuracy.  
deep learning; neural network; object detection; ensemble; biological dosimetry; dicentric chromosome; radiation +
Footnote †: journal: _Sci_
# Review of Advancement in Variable Valve Actuation of Internal Combustion Engines
## 2 Variable Valve Timing (VVT) System  
The VVT technology was first applied to production engines by Alfa Romeo in 1980, and they are now widely used in most modern engines worldwide as shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classification** & **Company** & **System** & **Ind. Timing** & **Ind. Lift** & **Ind. Duration** & **Introd. Year,** **Comments \& Refs** \\ \hline DVVL & Audi & AVS & & 2-lobe, Both & 2006, [11] \\ \hline DVVL & Subaru & i-AVLS & & 2-lobe & 2007, [12] \\ \hline DVVL & Proton & CPS & & 2-lobe, Int & 2016, [13] \\ \hline DVVL & Yamaha & VVA & & 2-lobe & 2017, motor cycle \\ \hline DVVL + & Mitsubishi & MVEC & VVT, Both & 2-lobe, Int & 1992, [15] \\ \hline DVVL + & Nissan & VVL/VVL + & VVT & 2-lobe, Both & 1997, [16] \\ \hline DVVL + & Porsche & VarioCam Plus & VVT & 2-lobe, Int & 1999, [4, 17] \\ \hline DVVL + & Toyota & VVTL-i/VVT-IL & Cont & 2-lobe & 1999, [18] \\ \hline DVVL + & Honda & i-VTEC & Cont, Int & 2-lobe, Int & 2001, [19] \\ \hline DVVL + & Audi & AVS & Cont, Int & 2-lobe, Int & 2006, [11] \\ \hline CVVL & Hyundai & CVVL & & CVVL & 2012, [20] \\ \hline CVVL + & Great Wall & CVVL + VVT & Cont, Both & CVVL & 2018, [21] \\ \hline CVVL + & BMW & Valvertonic & Cont, Both & CVVL, Int & 2001, [22] \\ \hline CVVL + & BMW and & VFI & Cont, Ink & CVVL, Int & 2002, [23] \\ \hline CVVL + & Nissan & VVEL + CVTC & Cont & CVVL & 2007, [24, 25] \\ \hline CVVL + & Toyota & Valvermatic & Cont, Int & CVVL & 2014, [26] \\ \hline LMVVL & FCA & MultiAir & Cont & Cont & 2009, [27, 28] \\ \hline \end{tabular} Notes: Int = intake, Exh = exhaust, Both = both intake and exhaust, Cont = continuous.  
\end{table}
Table 1: Cont.  
VVT systems were initially stand-alone systems for timing control, and gradually they were integrated with variable lift mechanisms to become part of a cam-based VVA system, i.e., VVL + VVT. They are often applied to intake valves only. Their control evolved from two-stage, i.e., two discrete positions, in the early days to continuous control (\"Cont\" in Table 1) in more recent engines.
## 2 Operation Principle
### Data Processing  
Time-tagging of the 16 QAM signals is a crucial step to ensure an accurate reflection of the temporal order of each signal. Based on this time-tagged information, we then proceed with time-tagged QPSK partitioning.  
The principle of the time-tagged QPSK partitioning is as follows: using Euclidean distance, the Timing Marker Signal (TMS) is classified into Class I signals, denoted as QPSK-TMS, while Class II signals are to be excluded [25]. Figure 3a illustrates the constellation points for Class I (QPSK-TMS) and Class II signals. Class I and Class II signals are situated on concentric annular rings with different radii, which are approximated as circles with radii of R\(1=\sqrt{2}\), R\(2=\sqrt{10}\), and R\(3=\sqrt{18}\), respectively. Figure 3b shows that signals located on circles with radii R1 and R3 are classified as Class I signals, while signals on the circle with radius R2 are classified as Class II signals. To reduce the algorithm complexity, we use the square of the Euclidean distance to determine appropriate decision boundaries.  
Figure 2: Principle block diagram of the Time-Tagged QPSK partitioning (TTQP) FOE algorithm.  
Specifically, the squared values of the radii R1, R2, and R3 are represented by L1, L2, and L3, with values of 2, 10, and 18, respectively. Therefore, the classification criteria for Class I and Class II are as follows:  
\[\text{Class I}:L\leq\frac{\text{L1}+\text{L2}}{2}\text{ or }\frac{\text{L2}+\text{L3}}{2}\leq L\]  
\[\text{Class II}:\frac{\text{L1}+\text{L2}}{2}<L<\frac{\text{L2}+\text{L3}}{2} \tag{1}\]  
where \(L\) denotes the squared Euclidean distance of the signal point from the origin, and L1, L2, and L3 represent the squared values of R1, R2, and R3, respectively.  
As illustrated in Figure 4, the QPSK-TMS signal is obtained after processing the TMS signals through QPSK partitioning. Consequently, adjacent QPSK-TMS signals may be discontinuous, with time intervals present. Here, \(I\) denotes the time intervals between adjacent QPSK-TMS signals, while \(QPSK-TMS_{i}\) indicates the position of a signal within the QPSK-TMS sequence. In the subsequent frequency offset estimation phase, unlike traditional QPSK partitioning methods, the TTQP algorithm enhances estimation accuracy by mitigating the cumulative effects of these time intervals.
## 3 Results
### Disaster Management
#### 3.1.3 Infrastructure  
Roods and Transport  
Drones can assist in transport analysis in many ways. [PERSON] [40] compared the three types of autonomous vehicles for air and ground delivery, including drones, sidewalk autonomous delivery robots, and road autonomous delivery robots. The study investigated and analysed the number of deliveries, service time, area of service, and service distance, resulting that drones can act as an efficient last-mile delivery that reduce carbon emissions. Furthermore, the truck-drone delivery modes that combine one truck and multiple drones are proven to be effective to serve real-time requests [100].  
Drone technology has been used to observe transport methods. For example, [PERSON] [94] presented a method that can convert the footage collected by drones to analyse pedestrian and bicycle volume data. In the monitoring of pedestrian activities, drones provide an efficient way of counting and mapping pedestrians compared with on-the-ground observation tools [97]. To recognise vehicles accurately and effectively, [PERSON] et al. [98] proposed a method for locating and identifying vehicle models from videos captured by drones. Furthermore, [PERSON] et al. [99] presented a methodology integrating drones in real-time analysis of vehicle movement to prevent collision to ensure this is not mistaken for surveillance. The combination of drone images that captured at different times with deep learning models can identify the changes in road surface [95]. This helps local authorities to understand the road quality associated with ageing and deterioration of road surfaces.
# Improving GNSS Landslide Monitoring with the Use of Low-Cost MEMS Accelerometers
## 4 MEMS Sensor Features and Calibration Results
### Calibration Experiments and Results  
The six-faces test must be performed in strict compliance with the orthogonality conditions, which are not always guaranteed by the sensor casing. It is therefore necessary to verify, or mechanically realize, the supports to be used in the test with high precision mechanical machining. Figures 3 and 4 shows the images of some planarity and orthogonality measurements, to the hundredth of a mm, on a calibration metallic box. The references for flatness and orthogonality were calibrated stone plates--to assure the horizontality of the surface, simple precision-level vials were used. On this support, the six faces of eight TE4030 accelerometers were tested, with fifteen minutes of acceleration data acquisition for each face. The 12 estimated least-squares parameters (9) were written in the calibration matrix with the conventions reported in (5). An example is shown in Table 1, and in Table 2 we can see the corresponding standard deviation, which is at least one order of magnitude lower than the obtained values. The raw acceleration values were therefore corrected with the calibration values through the inversion of the formula (5). After the application of these parameters, it is possible to compute the tilts of the sensor (Table 3) through the formulas (4).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
1.024 & \(1.18\times 10^{-2}\) & \(-4.45\times 10^{-3}\) & \(5.12\times 10^{-2}\) \\ \(1.80\times 10^{-2}\) & \(1.025\) & \(1.32\times 10^{-2}\) & \(5.96\times 10^{-2}\) \\ \(5.62\times 10^{-3}\) & \(-2.25\times 10^{-2}\) & \(1.025\) & \(5.83\times 10^{-2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The calibration matrix: scale factor in diagonal, translational bias in the last column. See (5).  
Figure 3: Planarity and orthogonality test of the mechanical support for the six-faces test.
# Systematic Review of Mecanum and Omni Wheel Technologies for Motor Impairments
## 4 Discussion
Future work should focus on achieving cost-efficiency without compromising reliability or usability. A promising direction is transforming omnidirectional wheelchairs into multifunctional assistive platforms through technology integration. Researchers have explored adding robotic arms for task assistance and embedding IoT-based health monitoring for real-time vital tracking and emergency alerts [56]. These \"telehealth-enabled\" systems could enhance user independence and safety by merging mobility with healthcare support. However, challenges remain, including high power demands from continuous monitoringand concerns around data security and privacy [57]. Addressing these issues will be key as future designs increasingly adopt modular health and communication features, aligning with the broader shift toward IoT-driven assistive technologies.
# Lightweight Infrared and Visible Image Fusion Based on Nested Connections and Res2 Net
## 5 Experiments and Results Analysis
### Results Analysis and Comparison
#### 5.4.2 Objective Evaluation  
To further validate the effectiveness of our proposed algorithm, we selected eight objective evaluation metrics for comparative analysis, and the comparative results are shown in Table 5.  
Figure 10: Infrared and visible-light fusion results of each algorithm.  
From Table 5, it is evident that our proposed method achieves the optimal performance across all seven metrics except FMIw. Additionally, it demonstrates significant advantages in terms of model complexity and computational efficiency. This demonstrates that the algorithm can effectively preserve detailed information from visible-light images while highlighting significant features from infrared images. Moreover, it maintains a relatively small parameter count. The reduced number of parameters implies a lighter-weight model and lower training and deployment costs, which are particularly suitable for resource-constrained environments such as mobile devices and real-time systems. This highlights the high practical value of our method, not only theoretically and experimentally excellent but also highly applicable in real-world scenarios.
## 3 Structure Design and Optimization
### Design Optimization-Structural Loads  
All the analyses and verifications including model preparation were made using the software Within(Autodesk(r), San Francisco, CA, USA) FEMAP(Siemens PLM Software, Plano, TX, USA) and the solver Nastran(r) 2016 (Autodesk(r), San Francisco, CA, USA).  
In the design attempts many fails came from the balancing of the mass and the satisfaction of the dynamic requirements as will be clear in the following Section 3.6. Thus, a new optimization method was introduced in order to reduce the Von Mises stress to a minimum value by locally thickening those elements requiring more stiffness. For this purpose, the framework reported in Figure 12 was developed. Each module was designed in Autodesk(r) Within(r) by selecting the total thickness and adding all the necessary elements, namely the thermal plates, the upper, the lower and the wall junctions. For several lattice structures, the cell was defined by changing the dimensions and the wall skin thickness. Finally, the lattice geometry is generated only in selected regions.  
The geometry is loaded in Autodesk(r) Nastran 2016 Solver. For each structural load (ref. Table 4), the boundary conditions are given and a static analysis calculating Von Mises stress is provided. A thickening is applied to those regions exceeding the maximum value and a new geometry is generated. Based on the previous analysis this value was set at 130 MPa. The loop stops when all the regions satisfy the requirements. Figure 13a shows a local thickening applied to the zone between the upper junctions at the module corner. After generating the geometry, the module is subjected to a linear buckling analysis and a linear buckling safety factor is calculated. This verification allows establishing if the structure is subjected to buckling instability under the specified boundary conditions.  
If the factor is less than 1 the thickening value is increased, and a new thickening is applied to the previously modified regions. A new static analysis is provided, and the loop is repeated until a geometry satisfying the safety factor is created. As a result, the original regular lattice structure is transformed into an uneven pattern as shown in Figure 13b.  
Figure 11: AlSi10 Mg \(\sigma\)-\(\varepsilon\) curve for horizontal and vertical specimens, after Stress Relieving.
# Recent Advances in Bacterial Persistence Mechanisms
## 2 The Difference between Persistence and Resistance  
Resistance to antibiotics is generally inherited through genetic mutations in bacteria. Despite high antibiotic concentrations, these resistant cells are still capable of dividing. The mechanisms of resistance have been deeply studied by researchers worldwide and can be categorized into three common pathways [14]. Firstly, bacteria reduce the intracellular antibiotic concentration in the way of either activating efflux pumps or decreasing the permeability of their membranes. Secondly, they actively modify the intracellular targets of antibiotics, thereby avoiding antibiotic attacks. Furthermore, by generating specific enzymes, antibiotics are directly inactivated within bacterial cells. As opposed to resistance, persistence is a heterogeneous population behavior. The vast majority of the population presents susceptibility to the stress, which is not impacted by the non-growth persister component. This susceptible feature can remain stable over generations, suggesting that all bacteria within the population have the same genetic background as persisters. Therefore, antibiotic persistence is a phenotypic change that occurs in a small proportion of cells. Compared to the stable and heritable resistance of antibiotic resisters, the ability of persisters to survive lethal antibiotics is transient and reversible [15].  
Although the bacterial population containing persisters exhibited antimicrobial susceptibility, the killing curve of surviving persisters was independent of antibiotic concentration. However, the mortality rate of antibiotic-resistant bacteria was associated with antibiotic concentration when this was higher than the MIC [4]. Additionally, resistance is often specific to a certain class of drug, while persistence responds to a wide range of stimuli, including antibiotics. This reflects the differences in the underlying molecular mechanisms of the two species.  
Figure 1: Killing curves of different types of bacteria during antibiotic exposure. The resistant bacteria continue to grow. The susceptible bacteria are killed quickly. However, with the existence of persisters, the curve declines slowly.