esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Deep Dense Block-Based Model for Detecting Alzheimer's Disease
## 3 Materials and Methods
### Tensor-Based Morphometry  
The TBM is an image analysis modality that evaluates structural disparities in the brain via gradients of deformation fields that are employed to align images. To evaluate the three-dimensional configurations of structural brain changes over time, discrete Jacobian maps were generated for each individual. Equation (1) shows the computation of the Jacobian matrix [39].  
\[\mathbf{J}=\begin{bmatrix}\partial y_{1}/\partial x_{1}&\partial y_{1}/\partial x _{2}&\partial y_{1}/\partial x_{3}\\ \partial y_{2}/\partial x_{1}&\partial y_{2}/\partial x_{2}&\partial y_{2}/ \partial x_{3}\\ \partial y_{3}/\partial x_{1}&\partial y_{3}/\partial x_{2}&\partial y_{3}/ \partial x_{3}\end{bmatrix} \tag{1}\]  
Selecting 2D slices from 3D images is a critical choice for image-processing applications. Therefore, 3D volumetric data were sampled at 5-pixel intervals in a range of improvements covering the hippocampus and temporal lobe. This is because tissues affected by Alzheimer's are short sampled by manually selecting ranges to allow for the diversity of properties. This range of selection can be found with different selection techniques for different purposes [40].  
Figure 2 shows the MRI images of individuals with (a) AD, (b) MCI, and (c) CN, respectively. Figure 2d-f are TBM images corresponding to images (a), (b), and (c), respectively. MRI images in the ADNI_1 database and their corresponding processed TBM images were determined by comparing subject IDs. TBM images are obtained by normalizing the MRI images in Figure 2 to a reference space. Unlike easily interpretable MRI images, TBM images tend to be more complex and challenging to visually interpret. This is because the TBM images capture the local morphological variations of individual subjects in relation to the group average. Notably, these morphometric images were primarily intended for statistical analysis at the group level rather than visual inspection.  
Figure 2: MRI scans: (**a**) axial MRI image of an AD; (**b**) axial MRI image of an MCI; (**c**) axial MRI image of a CN; (**d**) TBM image of figure (**a**); (**e**) TBM image of figure (**b**); (**f**) TBM image of figure (**c**).  
In this study, TBM was used instead of raw or semi-processed MRI images. TBM has several of the following advantages over raw or semi-processed MRI images:  
* It can precisely and quantitatively measure the shape and size properties of brain structures with TBM.
* It can compress and reduce the data volume in brain images. This can help with faster data processing.
* It can be used to compare structural changes between different individuals, groups, or time points.
* TBM has the ability to make statistical analyses of brain structures.  
These advantages make tensor-based morphometry a powerful tool for deeper understanding of brain structures, diagnosis and follow-up of diseases, treatment planning, and neurological research [41].
## 3 Integration of 5G NPN Deployment Options and Edge Computing Scenarios
### Standardone NPN with Private, On-Premises Edge
#### 3.1.2 Federated Edge Data Centers  
Multiple data centers deployment in the factory can be handled and managed as a federation. This results in a high level of reliability (different application instances could be deployed on different data center infrastructure), as well as increased scalability, which can also be provided over the distributed edge cloud infrastructure, as shown in Figure 3.  
From the enterprise customer perspective, the multiple clusters are seen as a single distributed one, e.g., Kubernetes supports the federation of clusters and solves the orchestration of clusters in the same way that Kubernetes orchestrates containers, i.e., it leverages commonly used components in Kubernetes. Connecting multiple Kubernetes clusters only requires IP reachability between the gateway nodes of the clusters. An encrypted Virtual Private Network (VPN) tunnel is built over the IP transport network if the clusters are connected over a public network. In the case of two clusters, often, two independent VPN tunnels are set up for redundancy. Inside the factory premises, the VPN tunnels can be omitted, as the inter-cluster networking depends on the underlying network infrastructure of the edge domain.
# A 13.44-Bit Low-Power SAR ADC for Brain-Computer Interface Applications
## 2 Circuit Topology
### Dynamic Comparator Design  
The conventional StrongARM latch employs a single-stage topology, delivering rapid response times and zero static power consumption for inherently low-power operation [15]. However, the stacked configuration of the latch and input pair introduces severe kickback noise--the complementary output transitions directly couple to the input nodes through the C\({}_{\rm GS}\) of the input transistors, creating significant signal disturbance that degrades sampling accuracy in high-resolution ADCs [16].  
The double-tail architecture decouples the preamplifier stage from the latch, significantly reducing kickback noise through two-stage isolation--yet it incurs higher power  
Figure 11: Monte Carlo simulation results of unit capacitors.  
consumption as both differential load capacitors in the preamp stage discharge fully from VDD to GND during each comparison cycle.  
The adopted Elzakker architecture, as illustrated in Figure 12, improves upon the double-tail design by inserting cross-coupled PMOS pairs (M9, M10) in the latch stage while eliminating the tail current source, achieving direct power savings through two key mechanisms [17]:  
1. Static Current Elimination  
The proposed design eliminates the constant tail current dissipation characteristic of conventional double-tail latches, converting the structure into a fully dynamic implementation with zero static power consumption.
2. Controlled Regeneration  
PMOS pairs M9/M10 accelerate decision-making during metastability, reducing dead-zone time by 30% versus passive loads. This maintains kickback isolation (comparable to double-tail) via retained preamp decoupling.  
The operational sequence begins with the reset phase, where M9/M10 precharge internal nodes. In the evaluation phase, the input differential modulates the preamp outputs. Finally, during the regeneration phase, cross-coupled inverters resolve the digital level.  
The designed comparator circuit achieves a 0.12 us resolution time for discriminating 0.12 mV (%LSB@12-bit) signals, with Cadence Virtuoso transient noise simulations verifying an input-referred noise of 69.5 v RMS--meeting the critical requirements for a 12-bit SAR ADC operating at 320 kS/s. Key validation results include the following:  
1. Noise Compliance  
The measured 69.5 v noise, representing 0.58 \(\times\) LSB (where LSB = 120 uV at 1 V range), ensures quantization-limited performance, with its composition dominated by the input pair thermal noise (\(\sim\)60%) and latch metastability-induced uncertainty (\(\sim\)40%).  
Figure 12: Circuit of proposed Elzakker-type dynamic comparator.  
Monte Carlo Offset Analysis  
As illustrated in Figure 13, the simulated offset voltage exhibits 3\(\sigma\) < 0.5 LSB (120 \(\upmu\)V), achieved through large input devices for mismatch reduction and auto-zeroing during the reset phase.  
3 Speed-Noise-Power Tradeoff  
A delay of 0.12 \(\upmu\)s ensures a 16\(\times\) oversampling margin for 320 kS/s operation and consumes 8.982 \(\upmu\)[EMAIL_ADDRESS] V, optimized via dynamic bias control.
## 3 Functional Resonance Analysis Method (FRAM)
* Time (T): temporal requirements or constraints of the function, regarding both duration and time of execution. In the graphical language of FRAM, multiple hexagons can be displayed together, and the functions are interconnected by a set of lines (or arcs) through single or multiple vertices (or corners). In the FRAM vocabulary, lines or arcs are referred to as couplings [56],which are interpreted as paths for the spreading of variability throughout the system, as it can become the basis for functional resonance [57]. As noted in Figure 1, a function i could be linked with another function j through vertices \(v(I,\,O,\,P,\,R,\,C,\,T)\). Whilst the vertices \(v(I,\,P,\,R,\,C,\,T)\) represent input aspects that characterize the functioning of the function i, the vertex O feeds a function j or dozens of other system functions from the result produced by the function i. The FRAM Model Visualizer software [58] has been developed to help build FRAM networks as well as track functions and their aspects [59]. The second step is the characterization of the output variability of each function that constitutes the FRAM model. Variability analysis is provided to identify the points that most interfere in the work system performance. To classify the time-related variability, this study uses the following terms: (i) \"Too late\" when the output of a function does not occur within the exact time specified; (ii) \"On time\" when the output occurs within the exact time; and (iii) \"Too early\" when it happens earlier than expected. To classify the variability concerning the precision, this study uses the following terms: (i) \"Precise\", for outputs that meet the needs of a subsequent function; (ii) \"Acceptable\", for outputs that depend on some degree of regulation; and (iii) \"Imprecise\", for incomplete or ambiguous outputs that request additional interpretation or conferencing. The third step is to analyze how the potential variability of each function can become resonant in the entire system, leading to undesirable outcomes. [PERSON] [3] suggests that looking for functions with multiple couplings may be a first step in determining whether functional resonance can occur. If so, other actions must be taken to evaluate the likelihood and magnitude of variability and how it might affect other functions. The fourth and last step in the application of FRAM is the monitoring and managing of the performance variability. In this sense, [PERSON] [3] proposes that the most efficient strategy is to adopt actions to damping negative effects, eliminating those that can lead to undesirable outcomes and, conversely, propose actions to enhance positive effects, without losing control of the activities.
# A Novel Energy-Efficient Contention-Based MAC Protocol Used for OA-UWSN
## 2 Underwater Optical-Acoustic Hybrid Wireless Sensor Network Topology and Data Transmission Process  
As shown in Figure 1[15], the network topology of the OA-UWSN is composed of fixed nodes, mobile nodes, sink nodes, and control centers. The fixed nodes are deployed on the sea floor to collect monitoring data (such as temperature, salinity, and depth), images, or videos. The mobile nodes are suspended in the sea, completing the data collection and passing the data to the upper mobile nodes or the sink nodes and then to the control centers. Sink nodes and control centers are deployed in the sea and receive the collected information and send it to remote terminals.  
In the underwater optical-acoustic hybrid wireless sensor network, data communication is mainly divided into three phases. In the first phase, an acoustic handshake is performed to obtain the position information of a transceiver node to ensure that the channel is idle. Otherwise, a postponed access is performed to wait for the next time slot to compete again. In the second phase, according to the position information of the transmitting and receiving nodes obtained in the first stage, the node performs beam alignment (that is, optical handshake) to detect whether the signal-to-noise ratio or the bit error rate threshold required for optical communication is satisfied. In the third phase, if the optical communication requirement is met, data are transmitted between nodes through optical communication. If optical communication requirements are not met, data are transmitted between nodes through acoustic communication.
# Interpretability in Sentiment Analysis: A Self-Supervised Approach to Sentiment Cue Extraction
## 4 Methodology
### Generating Dataset for Sentiment Cue Extraction  
#### 4.2.1 Generation of Candidate Sequences  
As described earlier, for an input \(X\), it is necessary to generate several candidate sequences of pseudo labels, denoted by \(Y^{c}=\{y_{1},y_{2}^{c},\cdots,y_{n}^{c}\}\). Theoretically, for an input \(X\) of length \(n\), there are \(2^{n}\) possible configurations for \(Y^{c}\). This implies that for \(n=20\), \(Y^{c}\) could have over 1 million possible combinations--a daunting figure. This calculation pertains to just a single data instance, whereas training the SCE model requires thousands of such pseudo-labeled data instances. Enumerating all possible combinations is impractical, both in terms of time and computational resources. Therefore, we employ the Monte Carlo Sampling [40] method to randomly generate a specified number of candidate sequences, significantly reducing the time complexity associated with generating these candidate sequences. The Monte Carlo Sampling algorithm we use is outlined in Algorithm 1. This approach allows us to efficiently produce a manageable subset of potential label sequences for further analysis and selection, ensuring the feasibility of the SCE model training process.  
```
1:instance \(X\), sampling ratio \(p\)
2:\(Y^{c}\leftarrow\emptyset\)
3:for\(i\in[1,2,\ldots,n]\)do
4: generate \(g\) uniformly at random in the range [0, 1]
5:if\(g<p\)then
6:\(y_{i}^{c}\gets 1\)
7:else
8:\(y_{i}^{c}\gets 0\)
9:endif
10: Add \(y_{i}^{c}\) to \(Y^{c}\)
11:endfor
12:return\(Y^{c}\)
```  
**Algorithm 1** Monte Carlo Sampling for generating one candidate sequence  
In Algorithm 1, we commence by specifying a sampling ratio \(p\). For each element \(x_{i}\) in \(X\), we randomly generate a decimal number \(g\) uniformly within the range of 0 to 1. If \(g<p\), then \(y_{i}^{c}\) is set to 1; otherwise, it is set to 0. This mechanism ensures that each \(y_{i}^{c}\) has a probability \(p\) of being assigned the value 1. Consequently, it can be inferred that the proportion of elements labeled 1 in the generated sequence \(Y^{c}\) is expected, on average, to be \(p\).  
This method does more than simply allow for the manipulation of positive label density within candidate sequences; it also facilitates the emulation of varied labeling densities in scenarios devoid of pre-annotated data by modulating the \(p\)-value. Ideally, \(p\) should mirror the proportion of tokens in the text \(X\) that significantly influence the sentiment classification model's decision-making process, equivalent to the proportion of elements valued at 1 in \(Y\). However, this proportion is unknown. Therefore, to generate candidate sequences as comprehensively as possible, we employ multiple values for \(p\) during the sampling process, conducting sampling under these varied \(p\)-values.
# Sorting by Multi-Cut Rearrangements +
## 4 Conclusions  
We introduced Sorting by Multi-Cut Rearrangements, a generalization of usual genome rearrangement problems that, however, does not include reversals. We discussed its classical computational complexity (P vs. NP-hard) and its membership in FPT with respect to the parameters \(k\) and \(\ell\). For this, we distinguished the case where \(S\) and \(T\) are permutations from the case where \(S\) and \(T\) are strings.  
The obvious remaining open problem is the one indicated in Table 1, namely the FPT status of SMCR with respect to parameter \(\ell+k\) in strings (including cases where one of \(k\) or \(\ell\) is constant, with \(\ell>1\)). Another question is whether the approximation factor of 2 of Theorem 10 can be improved. We also recall that we conjecture SMCR to be NP-hard for any \(k\geq 5\) in binary strings. It would also be interesting to better understand the comparative roles of \(k\) and \(\ell\) in SMCR, for instance by studying the following question: assuming \(k\) is increased by some constant \(c\), what impact does this have on the optimal distance?  
Extensions or variants of SMCR could also be studied, notably the one allowing reversals (and thus applicable to signed strings/permutations), or the one where \(T\) is the lexicographically ordered string derived from \(S\), as studied, for instance, by [PERSON] et al. [13] in the case of reversals.  
**Author Contributions:** Conceptualization, L.B., G.F., G.J. and C.K.; methodology, L.B., G.F., G.J. and C.K.; validation, L.B., G.F., G.J. and C.K.; formal analysis, L.B., G.F., G.J. and C.K.; writing--original draft preparation, L.B., G.F., G.J. and C.K.; writing--review and editing, L.B., G.F., G.J. and C.K.; supervision, G.F. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was partially funded and supported by the PHC Procope grant number 17746 PC (G.F. and G.J.), and by the DAAD Procope grant number 57317050 (C.K.).  
**Data Availability Statement:** Not Applicable, the study does not report any data.
## 4 What Is NFV?
### Working Principle of NFV  
The NFV can be defined as a concept in the telecommunication field that aims to virtualize and abstract network functions, such as load balancing, firewalls, and routers, from dedicated hardware applications and run them as software on standardized hardware or in a virtual machine. By utilizing virtualization technology, network operators can deliver more cost-effective, flexible, and scalable network services. The working principle of NFV is based on techniques such as virtualization, abstraction, orchestration, and SDN integration.  
NFV uses virtualization technologies, such as hypervisors and containerization, to develop virtual instances of network functions. NFV also uses an approach defined as an abstraction to separate or decouple the network functions from the underlying dedicated  
Figure 9: 5G: network functions in dedicated and virtualized deployment, where (**a**) represents a 5G network deployed using traditional network functions and (**b**) shows a 5G network based on virtualized network functions.  
hardware. It enables network functions to be implemented as software applications and makes them more flexible and adaptable. To manage the different Virtual Network Functions (VNFs), NFV requires a Management and Orchestration (MANO) system to install, configure, and manage the VNFs. NFV mainly works integrated with SDN, where SDN provides the network control plane and allows the dynamic configuration and management of network resources, while NFV prioritizes the virtualization and deployment of network functions.  
In summary, the working principle of NFV involves the virtualization of network functions, abstracting them from dedicated hardware and orchestrating their deployment on standardized hardware platforms. Table 2 represents key differences between the NFV and SDN and Table 3 shows the differences between NFV and traditional devices.
## 3 Methods
### Features of EEG Signals
#### 3.1.3 Spectral Analysis and Autocorrelation  
Spectral analysis and autocorrelation can be used in the detection of epileptic seizures by analyzing electroencephalographic (EEG) signals and identifying characteristic features related to epileptic seizures [75; 76]. Techniques such as Fourier transform can be applied to extract spectral information from EEG signals (Figure 5). Spectral analysis enables the detection of patterns associated with seizures, such as increased power in low frequencies (e.g., slow waves) or abrupt frequency changes.  
We can express the formula for the Fourier transform as [77]:  
\[X[k]=\sum_{n=0}^{N-1}x[n]\cdot e^{-i2\pi\frac{kn}{N}} \tag{6}\]  
where \(X[k]\) represents the discrete Fourier transform of signal \(x\) for frequency component \(k\), \(x[n]\) is the value of signal \(x\) at time \(n\), \(N\) is the length of signal \(x\), and \(i\) is the imaginary unit.  
Autocorrelation allows for the analysis of similarity between shifted copies of an EEG signal (Figure 6). Regular patterns in EEG signals, such as periodic oscillations, can be associated with epileptic seizures. Autocorrelation analysis can aid in identifying these patterns and detecting seizures. Autocorrelation can be defined as [78]:  
\[R_{xx}[k]=\Sigma_{n=0}^{N-1}(x[n]\;*\;x[n-k]) \tag{7}\]  
where \(R_{xx}[k]\) represents the autocorrelation for shift \(k\), \(x[n]\) is the value of signal \(x\) at time \(n\), and \(k\) is the (temporal) shift between copies of signal \(x\). Autocorrelation measures the similarity between signal \(x\) and its delayed versions by a lag of \(k\). The sum of products of corresponding samples of the signal at time \(n\) and the shifted sample at time \(n-k\), for \(n\) ranging from 0 to \(N-1\), yields the autocorrelation result \(R_{xx}[k]\).  
Figure 5: Comparison of the averaged spectra for iEEG signals recorded during seizures (S) and during seizure-free period (F).  
Figure 6: Comparison of the averaged autocorrelations for iEEG signals recorded during seizures (S) and during seizure-free period (F).
## 2 Materials and Methods
### Experimental Set-Up and Testing Procedures
#### 2.6.2 Testing Procedure for the Receiver Block  
To validate these subsystems, tests were conducted by connecting the RIGOL DG5102 arbitrary function generator to the input of the signal-conditioning module and monitoring the 10 output bits of the ADC through the Integrated Logic Analyzer (ILA) programmed in the FPGA. Additionally, the comparator block was tested by activating it via the jumper on the board and observing the output bit through the ILA, as illustrated in Figure 30.  
For the tests associated with the conditioning stage and the ADC, signals of various frequencies were sent through the signal generator, and the ADC's output was monitored via the ILA programmed in the FPGA. Similarly, for the comparator block tests, signals of different frequencies were sent through the signal generator, and the comparator output was observed using both the ILA and the Tektronix 2012 oscilloscope.  
Figure 30: Implemented test to validate the receiver block.  
Figure 29: Tests carried out in the transmitter block utilizing a (**a**) visible LED and (**b**) IR LED.  
For instance, Figure 31 presents a capture from the ILA, showcasing a 10 MHz signal input delivered from the signal generator.
# Meta-Heuristics Optimization of Mirrors for Gravitational Wave Detectors: Cryogenic Case
## 4 The Optimization Methods
### Optimization Heuristic GDE3  
Generalized differential evolution (GDE3) is an algorithm that extends differential evolution in order to tackle multi-objective optimization problems [37] with an arbitrary number of objectives and constraints. The differential evolution [38] optimizes a problem by maintaining a population of candidate solutions and creating new candidate solutions by combining existing ones according to simple (linear) formulae. The candidates with the best fitness with respect to the optimization problem at hand are introduced into the population. In this way, the optimization problem is treated as a black box that merely provides a measure of the fitness of the candidates in the population (the gradient is therefore not needed).