esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Comparative Analysis
#### 4.3.4 Benefits of Cognitive and Neuroimaging Features and Their Relevance to DSM-5 Criteria  
Cognitive testing establishes a baseline for an individual's cognitive abilities in the absence of symptomatic indicators, serving as a foundation for future comparative evaluations if cognitive decline is suspected. Cognitive features are crucial for early detection and accurate staging of Alzheimer's Disease (AD). These features allow for the identification of nuanced cognitive changes before overt symptoms are present, establish benchmarks for monitoring deviations from healthy cognitive patterns, and provide objective measures conducive to accurate diagnoses.  
In relation to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) criteria, cognitive tests such as the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) offer valuable insights into several domains, including memory, executive function, and attention. These domains correspond with DSM-5 criteria for diagnosing neurocognitive disorders, including AD. Specifically, thedecline in one or more cognitive domains, evident through cognitive testing, is a key criterion for the diagnosis according to DSM-5.  
Neuroimaging, on the other hand, contributes critical baseline data about brain structure and activity. Subsequent scans can be compared to these baselines to identify structural and functional changes that might be indicative of AD. When cognitive and neuroimaging features are combined, they significantly improve the sensitivity and specificity of detecting AD in both its early and late stages, as demonstrated in Table 5.  
Our research adds a new dimension to the field by identifying a distinct set of cognitive and neuroimaging features critical for AD diagnosis. Unlike existing studies that have emphasized the importance of features such as CDRSB, AV45-PET, FDG-PET, and ADAS-Cog13 [31; 32], our model isolated 4 neuroimaging and 18 cognitive features as being more critical in the early and accurate detection of AD.
## Materials and Methods
### System Design  
The materials and methods for designing and constructing a SAR-OCT mouse retinal imager (Figure 1) are described.  
Similar to the [PERSON] design, the SAR-OCT design described here is a fiber-based system for ease of transport to the operating room. Because bulk optics systems are difficult and time-consuming to align single mode fiber optics were utilized when possible. SMF-28e fibers are used here which transmit the 1310 nm (\(\pm\)70 nm) broadband swept source (100 kHz repetition rate, 25 mW output, Axsun) in a single mode. The Axsun laser bandwidth is greater and the sweep repetition rate is faster than that of the [PERSON] design. FC-APC fiber connections are employed to minimize any reflected light from fiber-fiber interfaces and reduce ghost interference signals.  
Figure 1: Diagram of the SAR-OCT system for murine retinal imaging. The design includes a 1310 nm \(\pm\) 70 nm swept source laser and a fiber-based interferometer setup. (CP, fiber coupler; CR, fiber circulator). The sample arm includes a reflective collimator (RC), pathlength multiplexing element (PME), several gradient index lenses (L1–L5), a dual-axis MEMS mirror (MM) conjugate to the ocular pupil plane, and a fundus lens (FL) that interfaces with the mouse cornea. The reference arm includes three lenses for dispersion matching and a mirror pathlength-matched to the mouse’s retina. Permission was granted by the publisher to reprint this figure from [PERSON] et al. with minor modifications [19].  
A 90:10 fiber coupler directs 90% of the laser's output to the sample arm optics and 10% to the reference arm (Figure 1) to give 15 mW average power incident on the mouse cornea. Each arm of the interferometer has a fiber circulator with three arms. Light entering arm 1 exits arm 2, and light returning to arm 2 exits arm 3. After sample and reference optics, the light is interfered in a 50:50 fiber coupler, and each output is measured by a balanced detection scheme. Balanced detection eliminates the background signal to isolate the interference fringe.  
Concerning the laser source, a swept source OCT system typically has some advantages over spectral domain for SAR-OCT. The relevant optical design parameter for the pathlength multiplexing element (PME; described in Section 2.2.5) and SAR-OCT is the scan range. In swept source OCT, scan range is given by instantaneous coherence length of the swept laser source. In comparison, for spectral domain OCT, scan range is given by spectral resolving power of the readout spectrometer. In many OCT spectrometers, the scan range is on the order of 4-5 mm. Since the scan range of many swept laser sources is on the order of 10-100 mm, PME design and corresponding pathlength difference between distinct angular paths is greater than that in spectral domain OCT. Thus, most swept-source OCT systems provide greater PME design flexibility and separation of angular paths compared to spectral domain approaches.
## 2 Materials and Methods
### Data Collection  
We used the ERA5-land dataset from the European Centre for Medium-Range Weather Forecasts (ECMWF; [[https://cds.climate.copernicus.eu](https://cds.climate.copernicus.eu)]([https://cds.climate.copernicus.eu](https://cds.climate.copernicus.eu)) (accessed on 19 January 2024)), which offers a high spatial resolution of 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\). We downloaded hourly data for temperature, relative humidity, and potential evapotranspiration for the period from 2016 to 2022. Recognizing the coarse spatial resolution of the ERA5-land hourly data was inadequate for the precision required by our research, we employed a linear interpolation method to refine the data to a half-hourly resolution, thereby enhancing the accuracy of GPP estimation. Additionally, the vapor pressure deficit (VPD) was calculated using temperature and relative humidity data, and the aridity index was determined using the potential evapotranspiration (ET) data.  
Our study employed the photosynthetically active radiation (PAR) dataset provided by the Himawari 8 meteorological satellite, a geostationary satellite operated by the Japan Meteorological Agency (JMA). Himawari 8 offers a spatial resolution of 500 m in the visible band and 2 km in the infrared band. Covering the entire East Asia and Western Pacific region, including Japan, China, Korea, Southeast Asia, and Australia, the satellite provides comprehensive coverage for our region of interest. For the period from 2016 to 2022, we extracted solar shortwave radiation (SW) and PAR data at ten minutes intervals from the raster points. The SW data were utilized for radiation correction, while the PAR data served as input for the variables required in the GPP model.  
To validate the accuracy of daily GPP simulated by remote sensing data, we compared it with the measured flux data from the Huitong Chinese fir forest. Briefly, ecosystem-scale carbon-based gas fluxes were measured using the eddy covariance (EC) technique with the EC system installed in a relatively flat area. The system comprises an open-path methane infrared gas analyzer (LI-7700, LI-COR, Lincoln, Nebraska, USA), an open-path carbon dioxide and water vapor infrared gas analyzer (LI-7500A, LI-COR, Lincoln, Nebraska, USA), and a three-dimensional sonic anemometer. Positioned 26.7 m above the soil surface, these sensors recorded raw data at a frequency of 10 Hz, which were then stored by a data logger in half-hourly intervals.  
In brief, all data were obtained from (i) satellite datasets, (ii) model output based on satellite datasets, and (iii) field data from the flux tower. Additionally, we did not use field measurement from forest plots.
# Analysis of Power Allocation for NOMA-Based D2D Communications Using GADIA
## 6 Results and Discussion  
In this section, we demonstrate the performance of the NOMA scheme under consideration. First, we study the boundary rate comparison of OFMA and NOMA. Next, the performance of EE and SE is investigated. Finally, we consider a D2D system model for two distinct condition (i.e., rural and urban) and investigate the behaviour of GADIA algorithm. Figures 7-9 illustrate the main steps of our simulation process; i.e., random pairing algorithm, channel state sorting-pairing algorithm and orthogonal pairing algorithm. The simplest grouping method is the Random Pairing Algorithm (RPA) [54]. For grouping, the BS chooses users at random from the user pool. The algorithm has a Committed Information Rate when CIR0 is the rate threshold users establish based on their business needs. Users that meet the rate can be chosen at random by the BS. However, the system performance is poor because this method does not consider the user's channel state. The Channel State Sorting-Paring Algorithm (CSS-PA), on the other hand, considers that there are \(N\) users to be assigned. First, organize the candidate set's channel conditions in ascending or descending order. If the number of users is even, divide them in half and divide them into groups A and B. When the number of users is odd, the first \(\frac{N}{2}\) users are in group A, the \((\frac{N}{2}+1)\)th user is in group B, and the rest of the users are in group C. After the grouping, match pairwise in sequence A users and B users [55]. Moreover, the OPA algorithm allows users with the best channel orthogonality to pair [56].In turn, match each user in the candidate user set. Then, calculate the orthogonality factor for each match, and choose the user with the highest orthogonality factor. Therefore, delete the matched users from the group of candidate users.  
Figure 8: The flowchart of channel state sorting-pairing algorithm.  
Figure 7: The flowchart of random pairing algorithm.
# Service Interface Translation. An Interoperability Approach
## 3 Service Interface Translation
### Payload Translation Process  
The payload translation is one of the key aspects of the interface translation. Payloads are defined as the actual information or message exchanged via services. Even though tools and libraries to translate specific aspects of the payload can be found (eg. Jackson Project), the difficulty increases when different aspects are considered at the same time. In order to find a generic solution to the aforementioned problem, the translation uses as an input the service contract description metadata. The service contract can be described via interface description languages. It provides a common ground for a variety of heterogeneous technologies at the same time that describes all functional and non-functional aspects in detail [27].  
The current payload translation includes four parts: (1) the encoding translation, (2) mapping of semantic terms, (3) payload structure and mapping, and (4) casting of data types. The encoding conversion can be performed using libraries to address encoding-to-encoding translation. Nevertheless, the number of libraries to use grows faster than the number of encodings. To address this issue, the proposed translation utilizes Java objects as an intermediate format. The generator thus generates Java classes to map the different elements for both payloads. This approach also allows for capturing complex payload structures with nested objects.  
The mapping of the semantic terms and data types is performed at the same time. The generator uses the metadata defined in the interface description of both systems to match the key values of both payloads. Once the payload elements are matched, there is a conversion of the data type of each element according to the payload descriptions. To obtain an accurate translation, metadata about the payload elements are crucial. The definition of the metadata in the interface description document and the algorithm is currently in development. The initial implementation includes the use of the terms \"variation\" and \"unit\" for physical magnitudes. Each element in the payload description includes name, datatype, variation, and unit. The variation attribute is a generic term used to define the key value. An example is shown in Figure 1.
## 3 Results
### Platform Reliability  
To evaluate the mitigation efficiency, we measured the reliability of the system when injecting faults in the configuration memory at a period of 100 ms, namely one random fault injected every 100 ms. This period has been chosen because the mean time required by the scrubber to perform a complete verification of the FPGA used during these tests is about 50 ms. We allowed time for the scrubber to detect the fault and to perform all the steps for the correction, if possible, including the read of the essential bits information and the fault repair. A system failure occurs if the scrubber does not manage to repair the fault in time, i.e., before the faulty bit is used by the design and causes a computation error or a freeze of the system.  
The reliability is computed in four different configurations, or modes. The first mode is the basic mode (B_MODE) in which no mitigation mechanism is enabled. The second  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Module** & **FF** & **LUT** & **LUTRAM** & **BRAM** & **DSP** & **Power Consumption (mW)** \\ \hline Static Partition & 20568 & 17140 & 2382 & 7 & 1 & 2901 \\ \hline Grayscale Filter & 344 & 570 & 160 & 0 & 0 & 1 \\ \hline Sobel Filter & 807 & 1157 & 160 & 3 & 0 & 7 \\ \hline AES-256 & 1077 & 1452 & 0 & 0 & 0 & 12 \\ \hline SPHINCS\({}^{+}\) & 4405 & 4210 & 219 & 4 & 0 & 21 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Resources occupation and estimated power consumption.  
Figure 8: Estimated power consumption for the platform execution modes (mW).  
mode is the Safe mode (S_MODE) in which both the scrubber and the TMR are enabled. The third mode is the TMR mode (T_MODE) in which only the TMR is enabled. The last mode is the High-Performance mode (P_MODE) in which only the scrubber is enabled.  
For every mode, the injection period and the startup time are the same, and fault injection begins just after the application processor startup. Each mode is run until a system or computation failure occurs and is repeated 400 times. The mode, the period and the random seed are sent by the PC through the serial link at the beginning of the run. At the end, the monitoring processor gets access to the watchdog timer and transmits the elapsed time before failure and the number of injected faults to the PC. The results are reported in Figure 9.  
We can observe that, as expected, the different mechanisms improve the overall system reliability compared with the Basic mode (B_MODE). The best mode is the Safe mode (S_MODE) followed by the High-Performance mode (P_MODE). The TMR mode (T_MODE) does not provide any notable interest after the number of injected faults goes beyond a hundred, but coupled with the scrubbing, the performance reduction of the S_MODE is compensated by a significant reliability improvement.
## Theory
### The Extended Time-Domain Ptychography Algorithm  
In the following text, we summarize the relevant definitions of time-domain ptychography, introduce the extended ptychographic algorithm and explain the individual steps of the iterative reconstruction procedure. In time-domain ptychography, the X-ray pulse, \(E_{X}(t)\), and the gate, \(P(t)\) are sampled on an equidistant temporal grid with \(M\) samples equally spaced by \(\delta t\). Consequently, their Fourier transforms and the Fourier transform of their product exist on a frequency grid with \(M\) samples equally spaced by \(\delta\omega\), such that \(\delta\omega\delta t=2\pi/M\). In addition, time-domain ptychography uses a second grid which is defined by the time delays between the object and the gate. It consists of \(N\) samples equally spaced by the time delay increment, \(\delta\tau\). Without any loss in generality, we assume that both grids span the same time window which results in their frequency increments being identical, and \(\delta\omega\delta\tau=2\pi/N\) results. For each of the \(N\) time delays, a spectrum, \(I_{n}(\omega)\), is measured. The combination of all spectra resul in a spectrogram, \(S(\omega,\tau)\), sampled on an \(M\times N\) grid. The only constraint on the two integers is \(N\leq M\), but, in practice, \(N\) is orders of magnitudes smaller than \(M\). An important quantity in ptychography is the fundamental sampling ratio of ptychography, \(R\). It is defined approximately as the ratio of the full width at half maximum (FWHM) duration of the gate over the time delay increment, \(\delta\tau\). The fundamental sampling ratio has to be equal to or larger than one. For \(R>1\), the gate overlaps several times with parts of the X-ray pulse which increases the redundancy in the data recorded. It is well known that this redundancy can be used to reconstruct not only the X-ray pulse but also the gate [15; 16].  
As a starting point for the reconstruction algorithm, a Gauss function is assumed for the X-ray pulse, i.e., \(E_{j=1,n=1}(t)\), and a reasonable gate \(P_{j=1,n=1}(t)\) is used, as obtained, e.g., from the streak trace using the center of mass method [17]. In every iteration, \(j\), all measured spectra are processed. The algorithm first updates the current estimate of the X-ray pulse and thereafter  
and \(\beta_{P}>0\). As we have seen above, the gate is a pure phase gate, and thus, the additional constraint, \(P_{j,n+1}(t)=\exp\{\mathrm{i}\arg[P_{j,n+1}(t)]\}\), holds.  
In angular streaking, the time delay increment is determined by the streak laser frequency, \(\omega_{0}\), and the number of electron spectrometers, \(N\), i.e., \(\delta\tau=2\pi/(N\omega_{0})\). Moreover, \(M\) and \(\delta t\) are typically given by the resolution and total spectral range of the electron spectrometer.
# Dynamic Cooperative MAC Protocol for Navigation Carrier Ad Hoc Networks: A DiffServ-Based Approach
## 3 System Description and Related Models
### Propagation and Channel Models  
For simplicity, but without loss of generality, we ignore the unknown and heterogeneous factors of the signal propagation environment, and adopt a free space propagation model [27]:  
\[P_{R(s\to r)}=G\frac{P_{T}}{d^{\lambda}},2\leq\lambda\leq 4 \tag{1}\]  
where \(P_{R(s\to r)}\) denotes the received power of the packet from node \(s\) to \(r\), \(P_{T}\) denotes the transmission power, \(d\) denotes the distance between transmitter and receiver, \(\lambda\) is a path loss exponent related to the propagation environment. \(G=G_{T}G_{R}\), with \(G_{T}\) and \(G_{R}\) are antenna gains of transmitter and receiver. Thus, we have the absorption loss (in dB) \(PL_{dB}=92.44+10\lambda\log(f_{GHz})+10\lambda\log(d_{km})\), where \(PL_{dB}\) denotes the free space loss in dB, \(f_{GHz}\) is the electromagnetic frequency in \(GHz\), and \(d_{km}\) is the distance between transmitter and receiver in km [28]. Let \(f_{GHz}=1\) GHz and \(\lambda=2\), combine \(PL_{dB}\) with the practical test results of the TTNT data link for the data rate [8], and then the propagation parameters can be summarized as Table 1.  
For the sake of comparison with other protocols, let the propagation parameters (Table 1) combine with the channel assignment in IEEE 802.11b DCF [10]; the channel parameters can then be as shown in Table 2.
# Clustering-Based Data Dissemination Protocol Using the Path Similarity for Autonomous Vehicles
## 5 Conclusions  
As the demand for V2X communication increases and the number of IoT entities increases, the need for VANET capable of autonomous configuration between nodes is emerging. Among them, the clustering-based algorithm is widely adopted as a compromise between a fully connected mesh network and a central control network via a controller.  
The clustering algorithms can reduce the redundancy and control over the communication nodes without the central control by controlling the cluster head. However, it is fundamentally a method that is largely influenced by the survivability of the cluster. Therefore, there has been significant effort to overcome this problem, and the CPB protocol introduced in this paper is one such strategy.  
Despite much effort, however, all the research is based only on the information currently available. Therefore, its effectiveness is greatly reduced in future Internet environments and cannot exceed the limit of the clustering algorithm. Therefore, this paper assumes an autonomous mobile automobile age network and develops algorithms with more efficiency, high robustness, and less overhead.  
This paper presents the idea of clustering by using precise path information which has not been utilized or cannot be utilized. It is meaningful that the paper provided a clustering algorithm suitable for the autonomous vehicle era at present. In addition, this paper can be a stimulus to promote research in this field where such research is not yet active.  
However, since it is difficult to conduct a large-scale autonomous driving experiment, there are limitations in not verifying efficiency when various kinds of fading are applied on actual roads.  
Therefore, in the future, it is necessary to study the algorithm presented in this paper in actual road environment and overcome various physical limitations of vehicle nodes in V2X environment.  
All the authors contributed equally to this work. Conceptualization, Investigation, Writing--original draft, M.S.S.; Project administration, Supervision, Writing--review & editing, S.G.L. and S.L.  
This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (2014-3-00547, Development of Core Technology for Autonomous Network Control and Management).  
The authors declare no conflict of interest.  
Figure 21: Cumulative Data Dissemination Rate per Dissemination Trial.
## 6 Discussion
### Statistical Comparison of Regression Performance  
In previous sections, we visually compared the regression results on the degree of coordination for the two datasets. In this subsection, we present a statistical comparison of the two datasets and demonstrate that indeed our dataset \(O\) has statistical better regression performance \(R^{2}\). Due to the presence of negative \(R^{2}\) values in the testing results, we can only perform the statistical comparison using the training performance. We first check the \(R^{2}\) values for each dataset for normality. If the distributions of \(R^{2}\) for both datasets are both normal, then we can use the \(t\)-test. Otherwise, we compare using a form of non-parametric statistical method, i.e., confidence interval. The normality checks on the regression performance for both datasets for both LRPF and GBR fail. Hence, we have to resort to using confidence interval for statistical comparison.  
We also take this opportunity to compare with several other configurations: (1) using the seven-predictor set for each subsystem based on our dataset \(O\); (2) using the interpolated imputation method to fill the missing data; and (3) using CRITIC weighting method to calculate the weights. The comparison is made for both LRPF and GBR. As shown in Figure 42, all four different configurations using our dataset \(O\) are statically better than \(R\) because there is no overlap between the two confidence intervals. Three configurations (the original configuration with 12-predictor set for each subsystem, and alternative configurations 1 and 3) are statistically the same. However, the results are sensitive to the alternative interpolated imputation method, which lowers the performance when LRPF is used for regression. When GBR is used for regression, all four configurations are equivalent.  
Figure 41: LRPF training \(R^{2}\) and testing \(R^{2}\) for the coordination degrees using our dataset \(O\), where the weights are calculated using the CRITIC weighting method.