esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methodology  
L8 has several spectral bands whose wavelength overlaps with S2 spectral bands (see Table 2), and these bands exhibit a strong correlation across L8 and S2 [7]. One of our research aims is to understand the relationships between these common spectral bands across the two sensors. While the task of translating common bands--such as L8 green to S2 green or L8 NIR to S2 NIR--may appear straightforward, the complexity arises from the disparity in wavelength ranges covered by the spectral bands across L8 and S2. Indeed, translating S2 RE bands, which are absent in L8, introduces additional complexity. To address this challenge, [PERSON] et al. [25] employed hyperspectral data to simulate Landsat and Sentinel-2 bands; conversely, GAN techniques have been successfully applied in translating across more disparate domains like optical to SAR [45].  
We, therefore, opted to predict Sentinel-2-like RE bands by translating the L8 band exhibiting the highest correlation with each of the S2 RE bands. In ref. [31], the authors determined the correlation coefficients between the S2 bands across multiple scenes and discovered that the S2 green band had the highest correlation with S2 RE1; similarly, S2 NIR narrow exhibited the highest correlation with S2 RE2 and RE3. We developed two distinct strategies. The first involves directly predicting the S2-like RE bands from the L8 band that most closely correlates with them, as studied by [PERSON] and [PERSON] [7]. This is referred to as our direct approach.  
Our multistep approach introduces an intermediary translation phase: initially, we translate the L8 band to its corresponding S2 band--for instance, L8 green to S2-like green. Subsequently, we derive the S2-like RE band from the translated S2-like band, as evidenced in Figure 2. The multistep approach operates under two assumptions: first, deconstructing the complex translation task into simpler sub-tasks can enhance model performance; and second, the incorporation of additional reference imagery via an additional step could improve both model accuracy and robustness.
# Assessment for Alzheimer's Disease Advancement Using Classification Models with Rules
###### Abstract  
Pre-diagnosis of common dementia conditions such as Alzheimer's disease (AD) in the initial stages is crucial to help in early intervention, treatment plan design, disease management, and for providing quicker healthcare access. Current assessments are often stressful, invasive, and unavailable in most countries worldwide. In addition, many cognitive assessments are time-consuming and rarely cover all cognitive domains involved in dementia diagnosis. Therefore, the design and implementation of an intelligent method for dementia signs of progression from a few cognitive items in a manner that is accessible, easy, affordable, quick to perform, and does not require special and expensive resources is desirable. This paper investigates the issue of dementia progression by proposing a new classification algorithm called Alzheimer's Disease Class Rules (AD-CR). The AD-CR algorithm learns models from the distinctive feature subsets that contain rules with low overlapping among their cognitive items yet are easily interpreted by clinicians during clinical assessment. An empirical evaluation of the Disease Neuroimaging Initiative data repository (ADNI) datasets shows that the AD-CR algorithm offers good performance (accuracy, sensitivity, etc.) when compared with other machine learning algorithms. The AD-CR algorithm was superior in comparison to the other algorithms overall since it reached a performance above 92%, 92.38% accuracy, 91.30% sensitivity, and 93.50% specificity when processing data subsets with cognitive and demographic attributes.  
Alzheimer's disease (AD); classification; dementia; machine learning; neuropsychological assessments
## 1 Introduction
### Manual Inspection
[4] proposed an alternative, safer, cost and time-effective approach using unmanned aerial vehicles (UAVs) based on non-destructive inspection to provide a remote visual assessment. In summarising the drawbacks of manual visual inspection, the most common issues include the accessibility of the area being inspected, environmental conditions like weather and lighting, inadequate reporting, and human-related factors, all of which can lead to unreliable and inaccurate results. Further analysing the lighting conditions, the Illuminating Engineers Society (IES) [22] suggested direct, focused lighting for inspection checks inside a hangar. More specifically, for a space approximately 10 \(\times\) 6 square metres and 4 m high, 800-1100 lux per square metre is required. A high-reflectance wall and floor are essential for the correct and uniform light distribution. As commonly understood, these conditions are only sometimes the case, making the General Visual Inspection even more challenging. On the other hand, the human factor is also critical, with poor training, failure to follow the proper procedure, and time pressure playing significant roles [23; 24]. To incorporate quantitative insights into this literature review, [PERSON] et al. [25] reported that nearly 48% of maintenance aircraft inspection failures were attributed to skill-based errors. Contributing factors include insufficient training, a shortage of skilled workers, the absence of appropriate equipment, and unclear maintenance and operating procedures, all of which contribute to human error. In conclusion, manual visual inspection is questionable, and the introduction of automated visual inspection systems is vital to improving the performance of monitoring an aircraft's structure. In the same direction, [PERSON] et al. [26] explained the key benefits of an automated aircraft inspection system, highlighting the increase in flight safety, reduction in operating and maintenance costs, and improvement in the aircraft's reliability.
###### Abstract  
This paper explores the spatial coverage potential of a vision sensor to implement a dual service for a museum security system. Services include object monitoring and invisible visual Multiple-Input and Multiple-Output (MIMO) communication. For object monitoring, the Kalman filter algorithm is used. To implement a secret visual MIMO communication, an Adaptive Row-Column (ARC)-based LSB substitution technique is used along with the Integer Wavelet Transform method. These proposed services are implemented using existing electronic visual displays and surveillance cameras. Experiments are carried out using a sample object and four stego images. The performance of the object tracking system is measured in terms of accuracy by comparing the actual and estimated position of an object. Similarly, the performance of the covert visual MIMO system is measured at two different stages. On the transmitter side, differences between the cover and stego-images are used to measure imperceptibility. Likewise, at the receiver, differences between the original and the reconstructed data images are used to measure the accuracy. Results illustrate that the proposed system has improved imperceptibility, accuracy, and robustness, as compared to existing techniques.  
vision sensor; IWT; ARC; Hough line detection; Kalman filter; visual MIMO communication; object detection +
Footnote †: journal: Applied Soft. 2018, 8, 1918; doi:10.3390/app8101918
# Design of the CIEMAT Corrosion Loop for Liquid Metal Experiments
###### Abstract
The state of the art of liquid metal technologies and their future development in fusion research demands the construction of new infrastructure in Europe that could be devoted to the research on several topics, such as the training of scientists and engineers for the industrial development of liquid metal technology in the fusion field, together with the transfer of knowledge and technology among research centers and facilities, universities, and industry to establish competent human teams for the deployment of future fusion devices. For this reason, the prime objective of the CIEMAT Liquid Metal Laboratory (LML) is to achieve new experimental conditions able to cover the gap present in the literature and to study the main concerns related to PbLi technologies in future fusion reactors [25,26]. In this frame, a new facility, the CIEMAT Corrosion Loop, Internally Coated (CiCLo-C), has been designed with the aim of evaluating the corrosion behavior of EUROFER steel and other fusion relevant materials when they are exposed to the liquid metal alloy flow rate. According to the literature, a high mass loss has been observed in steel tested materials (-20-900 \(\upmu\)m/year) [27]. Thus, it is reasonable to think that this corrosion can also affect the steel pipeline of the system rather than only the specimens. In this scenario, the chemistry of the liquid metal can be severely modified, thus also affecting the corrosion process [28]. In order to prevent this situation, the pipes in CiCLo-C will be internally coated with tantalum. Tantalum has been demonstrated to have corrosion resistance [29]. The main purpose will be to ensure that observed corrosion products in the liquid metal are derived exclusively from the specimens, thus not modifying the experimental conditions (the chemistry of the liquid metal) and assuring the isolation of the corrosion effect on the specimens. In addition, this protection will extend the lifetime of the facility. An added value to the corrosion measurements is the inclusion of CIEMAT irradiation facilities [30; 31] since the effect of radiation on the corrosion of nuclear materials is still an open question. Thus, specimens can be irradiated or implanted (both with electrons and/or ions) before or after the corrosion experiments, with the samples then being damaged similar to that produced in the fusion reactor. Obviously, the mechanism of degradation, which is due to neutrons or lithium transmutation, is not the same, but it is still a powerful tool to understand the physics/chemistry behind the process [32]. The work provides an overview of the facility, focusing on the main components of the LiPb loop.
# A Service-Constrained Positioning Strategy for an Autonomous Fleet of Airborne Base Stations
## 3 Single Cell User Scheduling
### Equal Rate Scheduler  
In the equal rate scheduler, the instantaneous rate per user is also considered to assign the fraction of time per user in a way such that all the users obtain the same payload:  
\[t(x_{k},y_{k})\cdot c(x_{k},y_{k})=t(x_{j},y_{j})\cdot c(x_{j},y_{j})=K_{0}, \quad\forall k,j, \tag{19}\]where \(c(x_{k},y_{k})\) is the instantaneous rate for a user located at position \((x_{l},y_{l})\) in the region. Therefore, Equation (19) leads to the following value of \(T_{0}\):  
\[T_{0}=\int\int_{A_{i}}\frac{K_{0}}{c(x-x_{i},y-y_{i})}\cdot\lambda(x-x_{i},y-y_{ i})dxdy. \tag{20}\]  
Thus, using Equations (7) and (20), the equal average rate per user is detailed in Equation (21) assuming no fading losses. Regarding the proposed models in the system overview, the same changes as in the round robin scheduler can be applied: the transmit antenna gain is changed by Equation (3) and the fading produced by the presence of buildings is taken into account, which leads to the average rate per user detailed in Equation (22).  
\[\overline{R}\big{|}_{A_{i}}=\frac{\int\int_{A_{i}}K_{0}\cdot du}{N_{i}\cdot T_{ 0}}=\frac{1}{\int\int_{A_{i}}\frac{\lambda(x,y)}{BW_{A}\cdot\log_{2}\left(1+ \frac{k\cdot G_{T}}{BW_{A}\cdot((x-x_{i})^{2}+(y-y_{i})^{2}+h_{i}^{2})}\right)}dxdy} \tag{21}\]  
\[\overline{R}\big{|}_{A_{i}} = \tag{22}\] \[\frac{\int\int_{A_{i}}\frac{\lambda(x,y)}{BW_{A}}\left(\frac{P_{ LOS}(x-x_{i},y-y_{i},h_{i})}{\log_{2}\left(1+\frac{k\cdot h_{i}^{2}}{\xi_{ LOS}\cdot BW_{A}\cdot((x-x_{i})^{2}+(y-y_{i})^{2}+h_{i}^{2})^{2}}\right)}+\frac{P_{ NLOS}(x-x_{i},y-y_{i},h_{i})}{\log_{2}\left(1+\frac{k\cdot h_{i}^{2}}{\xi_{ NLOS}\cdot BW_{A}\cdot((x-x_{i})^{2}+(y-y_{i})^{2}+h_{i}^{2})^{2}}\right)}\right)dxdy}\]  
Thanks to this strategy, a fair distribution of the resources is fulfilled: the users closer to the ABBS and, therefore, with a higher instantaneous rate, will have a smaller fraction of time and vice versa.
## 2 Materials and Methods
### Datasets  
Sentinel-2 multispectral image (_MSI_)-level-1C product acquired on 4 December 2019, covering the study area has been chosen as test data of optical remote sensing. Figure 1b shows the false-color composite (_FCC_) image from Sentinel-2 (_L1C_) that encompasses the selected test site within the study area. The spatial resolutions vary between 10 to 60 m and spectral resolutions vary between 15 to 180 nm for different bands of the collected Sentinel-2 image (Table 1). Figure 1: (a) Geographic location, (b) field photographs, (c) false-color composite (_FCC_) image from Sentinel-2 of the study area. Sentinel-2 outperforms other available spaceborne datasets when using near-equivalent image bands when Sentinel-2 data are downsampled to 30 m pixel resolution (for example, Landsat 8). Additionally, Sentinel-2 includes high quality red edge band. Prediction of forest _AGB_ using Sentinel-2 confirmed better accuracy compared to Landsat 8 [45]. The field survey was carried out to collect ground forest information for remote sensing-based method development and result validation. In order to cover the complete forest variability of the study area, ground truth locations were chosen randomly based on accessibility in the forest and irrespective to tree height categories. Moreover, ground samples were collected from different locations in such a pattern to cover the entire forest diversity of the study area. The sample plots selected are a representation of the whole study area. The ground samples were collected from three different profiles separated by nearly 1 km. The distance between samples was also set at approximately 1 km. The ground samples were collected randomly from these profiles based on accessibility into the forest with independent directions. Field data collection was conducted using a global positioning system (_GPS_), altimeter, and ancillary field equipment. The ground samples, including tree height, tree _DBH_ and the number of trees per sample plot, tree stem density, and _FVC_ from 22 different locations with plot size approximately 30 m \(\times\) 30 m (approx. 0.09 hectare), were collected from the study site. The field samples from 22 locations comprised approximately 550 individual trees, and an average of 25 trees per plot were measured in different elevation strata. The average measured tree height for each field location was used as tree height of the location for further study. In the ground _FVC_ estimation at each sample location, initially, the total number of trees and the radius of each tree cover were calculated. The surface cover of individual trees was assumed circular. Therefore, surface area cover by individual trees was calculated using their covering radius. The total tree cover area at each sample location was estimated by summing individual tree cover area. Lastly, field _FVC_ was calculated by taking the ratio of area covered by trees to the total area of the sample
# A 0.3 V, Rail-to-Rail, Ultralow-Power, Non-Tailed, Body-Driven, Sub-Threshold Amplifier
###### Abstract  
A novel, inverter-based, fully differential, body-driven, rail-to-rail, input stage topology is proposed in this paper. The input stage exploits a replica bias control loop to set the common mode current and a common mode feed-forward strategy to set its output common mode voltage. This novel cell is used to build an ultralow voltage (ULV), ultralow-power (ULP), two-stage, unbuffered operational amplifier. A dual path compensation strategy is exploited to improve the frequency response of the circuit. The amplifier has been designed in a commercial 130 nm CMOS technology from STMicroelectronics and is able to operate with a nominal supply voltage of 0.3 V and a power consumption as low as 11.4 nW, while showing about 65 dB gain, a gain bandwidth product around 3.6 kHz with a 50 pF load capacitance and a common mode rejection ratio (CMRR) in excess of 60 dB. Transistor-level simulations show that the proposed circuit outperforms most of the state of the art amplifiers in terms of the main figures of merit. The results of extensive parametric and Monte Carlo simulations have demonstrated the robustness of the proposed circuit to PVT and mismatch variations.  
b +
Footnote †: journal: Applied Sciences  
1  
Dipartimento di Ingegneria dell'Informazione, Elettronica e Telecomunicazioni (DIET), Universita di Roma La Sapienza, 00184 Roma, Italy; [EMAIL_ADDRESS] (F.C.); [EMAIL_ADDRESS] (R.D.S.); [EMAIL_ADDRESS] (A.T.)  
Correspondence: [EMAIL_ADDRESS]  
Dipartimento di Ingegneria dell'Informazione, Elettronica e Telecomunicazioni (DIET), Universita di Roma La Sapienza, 00184 Roma, Italy; [EMAIL_ADDRESS] (F.C.); [EMAIL_ADDRESS] (R.D.S.); [EMAIL_ADDRESS] (A.T.)  
Correspondence: [EMAIL_ADDRESS]  
###### Abstract  
A novel, inverter-based, fully differential, body-driven, rail-to-rail, input stage topology is proposed in this paper. The input stage exploits a replica bias control loop to set the common mode current and a common mode feed-forward strategy to set its output common mode voltage. This novel cell is used to build an ultralow voltage (ULV), ultralow-power (ULP), two-stage, unbuffered operational amplifier. A dual path compensation strategy is exploited to improve the frequency response of the circuit. The amplifier has been designed in a commercial 130 nm CMOS technology from STMicroelectronics and is able to operate with a nominal supply voltage of 0.3 V and a power consumption as low as 11.4 nW, while showing about 65 dB gain, a gain bandwidth product around 3.6 kHz with a 50 pF load capacitance and a common mode rejection ratio (CMRR) in excess of 60 dB. Transistor-level simulations show that the proposed circuit outperforms most of the state of the art amplifiers in terms of the main figures of merit. The results of extensive parametric and Monte Carlo simulations have demonstrated the robustness of the proposed circuit to PVT and mismatch variations.  
body-driven; ultralow voltage; ultralow-power; operational transconductance amplifier; non-tailed differential pair; inverter-based
## 4 Discussion
Additionally, radiometric differences--due to factors like noise, sensor sensitivity, and atmospheric correction--can occur even for data captured on the same day [39]. The pansharpening method proposed in this study uses a coregistered image to simplify processing, principally because the images are taken simultaneously, thereby avoiding the need for overlapping acquisition dates and complicated preprocessing (geocoding and the spatial alignment of the two datasets). Thus, it is the more practical choice for biodiversity monitoring in urban contexts.
# Novel Dual Beam Cascaded Schemes for 346 GHz Harmonic-Enhanced TWTs
## 3 Cascaded Enhanced HHTWT
### The Simulation Results  
The output power of the CE-HHTWT is shown in Figure 14. The input signal is 200 mW at 86.5 GHz. Furthermore, a signal of 1100 mW is obtained at 346 GHz.  
Figure 15 is the Fourier transform of signal at end of the amplification section. The spectrum of the output signal is concentrated at the frequency of 346 GHz.  
Figure 14: The output signal of the CE-HHTWT at 346 GHz.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Beam voltage of beam 1 and 2 & 18.4 kV \\ Beam current of beam 1 and 2 & 10 mA \\ Beam radius & 0.04 mm \\ Length of modulation section & 38 mm \\ Length of drift tube & 12 mm \\ Length of radiation section & 34 mm \\ Length of amplification section & 34 mm \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of the CE-HHTWT.  
Figure 13: Power curve of the amplification section.  
Figure 16 shows the gain property and bandwidth property of CE-HHTWT. At 346 GHz, the output power is saturated when the input power is 200 mW. Figure 16b shows that more than 400 mW output power could be achieved from 340 to 348 GHz. The maximum power is 1100 mW at 346 GHz. The 3-dB bandwidth of CE-HHTWT is 5 GHz.
## 2 Materials and Methods
### Pattern Formation
Thus, the instability can only happen if \(b\big{(}k^{2}\big{)}\) becomes negative for some \(k\ \
eq\ 0\), i.e., the dispersion relation (10) has two roots with opposite signs. Carrying the function \(b\left(k^{2}\right)\), then the last condition requires that equation (11) be negative. \[\zeta_{s}d_{1}d_{2}k^{4}+\ (\psi_{s}g_{u}-d_{2}f_{u}-\ \zeta_{s}d_{1}g_{v})k^{2}+f_{u} \ g_{v}-f_{v}\ g_{u}<0, \tag{11}\]  
Since we required that the determinant \(|A|\ =f_{u}g_{v}-f_{v}g_{u}>0\) from the conditions (7), the only possibility for inequality (11) to hold, is that the sum of the roots is positive. That leads to Equation (12)  
\[\psi_{s}g_{u}-d_{2}f_{u}-\zeta_{s}d_{1}g_{v}<0, \tag{12}\]  
Referring to the condition (11), and since \(\zeta_{s}d_{1}d_{2}\) is positive, it is necessary that the discriminant of \(b\left(k^{2}\right)=0\) be positive. The discriminant of \(b\left(k^{2}\right)\) is represented by the following equation:  
\[\left(\psi_{s}g_{u}-d_{2}f_{u}-\zeta_{s}d_{1}g_{v}\right)^{2}-4\zeta_{s}d_{1} d_{2}(f_{u}\ g_{v}-f_{v}\ g_{u})<0, \tag{13}\]  
Applying inequality (12), we obtain from this inequality that  
\[\psi_{s}g_{u}-d_{2}f_{u}-\zeta_{s}d_{1}g_{v}<-2\sqrt{\zeta_{s}d_{1}d_{2}(f_{ u}\ g_{v}-f_{v}\ g_{u})}.\]  
In this paper we take specific functions in system (3), where \(f\) represents the cell density kinetics, the logistic growth, while \(g\) represents the chemo-attractant kinetics. A linear function depending on each of the variables \(u\) and \(v\) represents the chemoattractant growth with rate \(\
u\) and the decay with rate \(\delta\) due to dilution. They are given by the form  
\[f(u,v)\ =\mu u\left(1-\frac{u}{u_{c}}\right),\qquad g(u,v)\ =\
u u-\delta v \tag{14}\]  
Applying the same pattern formation analysis as before and for specific functions (14), we obtain the only necessary condition in Equation (15) for the generation of spatial patterns:  
\[\mu d_{2}+\delta D(u_{c})\ -\ \chi\varphi(u_{c})<-\ 2\sqrt{d_{2}\mu\delta D(u_{c})} \tag{15}\]  
where \(\varphi(u)=uq(u)\). In the sequel of this section, we investigate the range of unstable wave numbers for which, in addition to condition (15), we can expect pattern formation for system (3). To do this, we study the bifurcation with the chemo-sensitivity \(\chi\), the growth rate \(\
u\), and the decay rate \(\delta\) of the chemoattractant. For the bifurcation parameter \(\chi\), we compute the bifurcation value \(\chi_{c}\), and we obtain the following:  
\[\chi_{c}=\frac{2\sqrt{d_{2}\mu\delta D(u_{c})}+\mu d_{2}+\delta D(u_{c})}{ \upsilon\varphi(u_{c})} \tag{16}\]  
Furthermore, the range of unstable wave numbers \(k_{1}^{2}<k^{2}<k_{2}^{2}\) is obtained from the zeros of the equation \(b\left(k^{2}\right)\ =0\), and \(k_{1  
Performing the same analysis as for the bifurcation parameter \(\chi\), we obtain evidence that that the pattern formation can also be supported by increasing the value of \(v\) above the critical value \(v_{c}\) or by decreasing the value of \(\delta\) below the critical value \(\delta_{c}\).