esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
ML algorithms can reproduce with a reasonable level of accuracy the soil penetrometric profile from the drilling values of rigid inclusions. The results obtained represent an improvement compared to the traditional analytical formulas, which do not faithfully adapt to the blow values for the range and type of terrain analysed.  
Attention must be paid to possible errors introduced during the selection of the data set (especially important is the distance between the inclusions and their nearest penetrometer). In practice, a distance of 2 m presented good results, but this depends on the site, number of available penetrometers, etc. Practitioners must evaluate what suit is best for their current situation.  
Taking depth into account improves overall predictions, but engineers and researchers should remain alert to the possibility that abrupt changes in soil properties may be overlooked by algorithms relying heavily on depth as a predictor.  
Tree ensemble methods are confirmed as the algorithms to use in this kind of problem, especially the XGB regressor.  
The methodology to use DTW for drilling data proposed in this paper gives a useful metric to measure the similarity between obtained and expected results, going beyond traditional error measurements to compare overall profile shapes.  
In this way, ML algorithms applied to drilling data can become a useful tool to gain more information about the terrain and validate the design hypotheses in deep foundations and ground improvement. Nonetheless, the identified limitations and the sensitivity to dataset selection should guide practitioners in responsibly adopting these methods for real-world applications.  
Conceptualisation, E.M.G., A.A.A.A. and M.G.A.; methodology, E.M.G., A.A.A.A. and M.G.A.; software, E.M.G.; validation, A.A.A.A. and M.G.A.; investigation, E.M.G., A.A.A.A. and M.G.A.; data curation, E.M.G.; writing--original draft preparation, E.M.G.; writing--review and editing, A.A.A.A., E.M.G. and M.G.A.; visualisation, E.M.G., A.A.A. and M.G.A.; supervision, A.A.A.A. and M.G.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Restrictions apply to the availability of these data. Data were obtained from Menard Espana and are available from the authors with the permission of Menard Espana.  
Author [PERSON] was employed by the company Menard Espana. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 2 Background
### k-Nearest Neighbors  
Since [PERSON] and [PERSON] introduced the non-parametric method for pattern classification known as kNN, its strengths have become evident [55; 56]. kNN is valued for its simplicity, lack of a formal training phase, ability to handle a large number of classes, avoidance of parameter over-fitting, and reliance on distance measurement [55]. In kNN, the training process can be viewed as a single-shot memorization phase, contrasting sharply with the computationally intensive training required for neural networks.  
kNN assumes that all instances correspond to points in the n-dimensional space \(\mathbb{R}^{n}\). The nearest neighbors of an instance can be computed in by using any of the popular distance measurements, such as Euclidean distance. When\(f\) is the true concept function that gives the correct class value \(f\)(\(x\)) for each instance \(x\), and instance \(x\) can also be \(<a_{1}(x),a_{2}(x)\),..., \(a_{n}(x)>\) and \(a_{r}(x)\) denote the value of the rth attribute of instance \(x\). We can describe an arbitrary instance by vector \(<x_{f}\)(\(x\))\(>\). The Euclidean distance between two instances \(x_{i}\) and \(x_{j}\) is denoted as \(d(x_{i},x_{j})\) and can be calculated by Equation (2).  
\[d(x_{i},x_{j})=\sqrt{\sum_{r=1}^{n}(a_{r}(x_{i})-a_{r}(x_{j}))^{2}} \tag{2}\]  
The kNN algorithm is described by the pseudo-code in Algorithm 2. kNN is a supervised learning algorithm, where a training set \(D\) is employed to train the model to recognize patterns. Each instance in \(D\) comprises a pair of \((x_{i},y_{i})\) which are a pattern and its label. \(K\) represents the number of nearest neighbors to be considered when labeling the test patterns. The algorithm computes \(d(x,x_{i})\) for each training sample \((x_{i},y_{i})\), and the algorithm calculates the distance between the target sample \(x\) (the sample for which we want to determine the majority label) and the current training sample \(x_{i}\). The specific distance metric may use any of the distance measurement methods, such as Euclidean distance and Manhattan distance, amongst others. After computing the distance between the target sample \(x\) and each training sample \(x_{i}\), the algorithm selects the \(k\) samples from \(D\) that have the smallest distances. These \(k\) samples are from the set \(N\), which represents the \(k^{th}\) nearest neighbors of the target sample \(x\). Finally, the algorithm determines the majority label among the samples in \(N\). The majority label is the label that appears most frequently among the \(k^{th}\) nearest neighbors. This majority label is then returned as the output of the algorithm.  
In the next section, we propose encapsulating the aforementioned concepts in a tiny federated nearest neighbor approach to provide decentralized energy-efficient event recognition for wireless sensor networks.
## 6 Other Ongoing Preparatory Activities  
### End-to-End Scene Simulations  
The design of future Earth imaging systems, the optimization of fundamental instrument parameters and the development and evaluation of data pre-processing algorithms require an accurate end-to-end simulation of the entire image generation and processing chain. For this purpose, the EnMAP end-to-end simulation software EeteS has been developed [71; 72; 73]. The sequential processing chain starts with the EnMAP image simulator that consists of four independent parts, namely the atmospheric, spatial, spectral and radiometric modules. This forward simulator is coupled to a backward simulation branch consisting of calibration modules (non-linearity, dark current and absolute radiometric calibration) and a series of pre-processing modules (radiometric calibration, co-registration, atmospheric correction and orthorectification), forming the complete end-to-end simulation tool. The implemented modules allow flexible customization of a wide range of simulation input parameters. EeteS is capable of simulating EnMAP-like data products that also serve as a test bench for other pre-launch developments, such as new algorithms for the improved scientific-exploitation of future EnMAP data. Sample datasets generated with the EeteS are shown in Figure 9. The EeteS is offered on-demand to scientific users for the simulation of acquisitions over additional sites.
# Application of Thermal Image Data to Detect Rebar Corrosion in Concrete Structures
## 2 Research Methodology
### Theoretical Considerations
#### 2.1.2 Electrical Resistance of Rebar
The electrical resistances were measured three times, and the average value was calculated to obtain the values of electrical resistances shown in Table 1. These values were different from the values of electrical resistance calculated before the electrical resistance tests. However, the value of electrical resistance that was to be measured was very small, which was considered to be the result of an overall error, including the error between the nominal cross-sectional area and the actual cross-sectional area of the rebar that was used, the error in the length of the rebar, and the error that occurred in the measurement method. Despite this, the tests were able to confirm that the electrical resistance of the rebar increased with the increase in its corrosion ratio. \begin{table}
\begin{tabular}{c c c} \hline
**Corrosion (\%)** & **Expected Resistance (m\(\Omega\))** & **Measured Resistance (m\(\Omega\))** \\ \hline
**0** & 1.122 & 0.64 \\
**1** & 1.133 & 0.73 \\
**3** & 1.156 & 0.8 \\
**5** & 1.181 & 0.945 \\
**10** & 1.246 & 1.03 \\
**20** & 1.402 & 1.145 \\
**30** & 1.602 & 1.325 \\ \hline \end{tabular}
\end{table} 
Table 1: Electrical resistance of the rebar. Figure 2: Electrical resistance measurement circuit.
## 3 Method
### Water Vapor Retrieval
There are 38 AERONET sites in region 1 and 312 AERONET sites in region 2. In order to construct water vapor retrieval model for PSAC, we first matched PSAC L1 data containing TOA reflectance data, solar zenith angle data, and satellite zenith angle data with AERONET CWV data within region 1. In this paper, we use the temporal mean of AERONET PWV data within half an hour of observation time of PSAC to match the cloudless pixels where the AERONET site is located. Note that the TOA reflectance of band 2 and the TOA reflectance difference between adjacent pixels of bands 2 and 7 are used to judge whether a pixel is a cloud pixel. The specific judgment criteria are described in Section 3.2. There are a total of 839 valid matches. After that, we can calculate the natural logarithm of water vapor absorption transmittance of band 6 of PSAC (i.e., \(ln\big{(}T_{g}^{910\text{ nm}}\big{)}\)) and the slanted water vapor content in the path of the sun-ground-satellite (i.e., \(V_{C}\)/\(v*L\)) of each valid match based on the corresponding AERONET CWV data, solar zenith angle data, satellite zenith angle data, and TOA reflectance of bands 5 and 6. Finally, we can substitute the above matching results into Equation (3) to establish 839 equations containing only three unknowns (\(A\), \(B\), and \(C\)), and determine the values of \(A\), \(B\), and \(C\) by using the least squares method. The scatter plot of the slanted water vapor content in the path of the sun-ground-satellite and natural logarithm of water vapor absorption transmittance of band 6 of PSAC of the above matching results is shown in Figure 3.  
As shown in Figure 3, there is a significant correlation (R\({}^{2}\) = 0.98) between the slanted water vapor content in the path of the sun-ground-satellite (\(V_{CWW}*L\)) and natural logarithm of water vapor absorption transmittance of band 6 of PSAC (\(ln\left(T_{g}^{910\text{ nm}}\right)\)). This means that it is feasible to retrieve water vapor based on the data from bands 5 and 6 of PSAC. For band 6 of PSAC, \(A\), \(B\), and \(C\) in Equation (3) are equal to 13.944, \(-\)4.867, and \(-\)0.049, respectively. Therefore, the semi-empirical equation for retrieving water vapor content can be expressed as:  
\[V_{CWW}=\frac{13.944*\left(ln\left(T_{g}^{910\text{ nm}}\right)\right)^{2}-4.867* ln\left(T_{g}^{910\text{ nm}}\right)-0.049}{L}, \tag{5}\]
## 2 Removal of Optical Thick Clouds
### Interpolation Techniques  
The interpolation of pixels obscured by cloud cover can be regarded as an approximation of unknown values within an arrangement of known values [14]. By this definition it is obvious that missing values are reconstructed by analyzing a certain number of measurements to infer the value of a missing measurement. Interpolation can be accomplished in the temporal or in the spatial domain. The interpolation of time series to reconstruct cloudy pixels is mainly based on index composites like the normalized difference vegetation index (NDVI), gross primary production (GPP) or net primary production (NPP) [15]. The basic concept is the identification of outliers with respect to the time series and their replacement with estimated values of a certain model [5]. [PERSON] and [PERSON] [3] present an approach for the correction of NDVI time series with respect to this method. Based on the assumption that the NDVI of a cloud obscured pixel is lower than the NDVI of a cloud-free pixel--since atmospheric attenuation decreases the contrast between the VIS and the NIR parts of the EM-spectrum--and the assumption that a time series of NDVI composites will have a significant seasonal behavior, missing values can be reconstructed. The interpolation in the spatial domain is based on the general assumption that variability within an image is spatially dependent. In other words, neighboring pixels are more likely to be similar to each other than pixels in greater distances [16, 17, 18, 19]. Therefore the spatial structure of a cloudy image as well as the spatial cross correlation with a secondary cloud-free image can be employed to reconstruct cloud affected pixels [19]. The estimation of the DN values based on these assumptions can be carried out in several manners, for example with geostatistical interpolation methods like Cokriging (Ibid.). Other techniques are based on contextual approaches. For example, [PERSON] [20] employs a model for the estimation of a DN of a cloudy pixel, which is trained first with spatial and temporal information of adjacent regions in order to create context-based rules for the reconstruction of the cloudy pixel.
## 3 Received Power Model of a PV Array with Experimental Validation
### Experimental Validation of Received Power Model  
To validate a mathematical model of the received power on an LPBRX with a \(5\times 5\) VMJ PV cell array, we experimented with a 5S1010.4-A555555 from MHGP's standard VMJ PV array product and an MDL-H-808 from Opto Engine LLC. Experimental setup to validate the received power model is shown in Figure 3.  
The 5S1010.4-A55555 is suitable for HILPB applications requiring power up to 160 W at 975 nm with up to 36% power conversion efficiency. It consists of a \(5\times 5\) array of VMJ  
Figure 3: Experimental setup to validate the mathematical model.  
PV cells with an active cell area of dimensions 51 mm \(\times\) 52 mm. It is mounted to aluminum nitride (AlN) substrates that have a PPC functionality and converts laser beams to electric powers without further additions. We mounted the VMJ PV array onto a commercial off the shelf (COTS) thermoelectric cooler (TEC) with the liquid cooling plate to keep the array temperature around 25 \({}^{\circ}\)C. The MDL-H-808 is an infrared laser module at 808 nm with 6 W output power. Table 1 describes the four experimental setups when the laser module has located away from the VMJ PV cell array, and Table 2 shows the detailed technical specifications of the 5S1010.4-A555555 VMJ PV cell array and the MDL-H-808 laser module.  
Figure 4 illustrates the received power distribution of the four experimental setups. The sums of received power are 1.8298 W, 1.8370 W, 1.9745 W, and 2.0780 W that depend on the location of the highest irradiance cell, respectiveley. Figure 5 shows the normalized power distribution relative to the highest irradiance cell.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**5S1010.4-A555555**} \\ \hline
**Parameter** & **Value** \\ Technology & Photovoltaic receiver \\ Optimal wavelength & 975 nm \\ Input power & Up to 50 W cm\({}^{-1}\) \\ Output power & Up to 160 W \\ Power conversion efficiency (\(\tau_{PPC}\)) & Up to 36 \% \\ Dimension (A) & 51 mm \(\times\) 52 mm \\ Unit price & \$2000 \\ \hline \multicolumn{2}{c}{**MDL-H-808**} \\ \hline
**Parameter** & **Value** \\ Technology & DPSS laser \\ Wavelength & 808 nm \\ Operation mode & Continuous wave \\ Output power (\(P_{Tmax}\)) & 3 W to 6 W \\ Beam diameter (\(D_{L}\)) & 8 mm \\ Beam divergence (\(\alpha_{T}\)) & 3 mrad \\ Unit price & \$1200 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Technical specifications of the 5S1010.4-A555555 VMJ PV cell array and MDL-H-808 laser module.  
Figure 4: Received power when the laser module MDL-H-808 was located away from the 5S1010.4-A555555 VMJ PV cell array: suitable experimental setup in Table 1 is **(a)** setup #1, **(b)** setup #2, **(c)** setup #3, and **(d)** setup #4.
## 3 Shake-Table Tests of Model with Innovative Infill Connection (Model IW-SB)  
The shake-table testing of the 1:2.5 scale structural model (Model IW-SB) was conducted using a customized multi-phase testing protocol, developed to assess the seismic performance and safety of a reinforced concrete (RC) frame system with hollow-clay masonry infill walls under both linear and nonlinear dynamic excitation. The experimental program was divided into two principal phases:  
(a) Dynamic Characterization Tests:  
Figure 14: Elevation layout of the proposed connection for Model IW-SB.  
Figure 13: Stages of construction of the proposed connection for Model IW-SB.  
These included resonant frequency search tests performed prior to and following the seismic loading sequence. The objective was to identify the fundamental dynamic properties of the model--such as natural frequencies and modal stiffness--and to quantify stiffness degradation induced by cumulative damage (e.g., formation of micro- and macro-cracks) during testing.  
(b) Seismic Response Tests:  
This phase involved progressive shake-table tests using a selected earthquake ground motion record, applied in successive increments of input peak ground acceleration (PGA), ranging from 0.05 g to 1.6 g. The aim was to capture the transition from elastic behavior to inelastic response, including the onset of cracking, development of failure mechanisms, and potential collapse of the structural system under increasing seismic demand.  
The specimen was constructed at a 1:2.5 geometric scale, and standard similitude principles applicable to reduced-scale dynamic testing were observed. Specifically, the Cauchy-Froude similitude law was adopted to ensure dynamic consistency between the prototype and the scale model, satisfying the relation gm/EL2 in the prototype and the model, were equal (g = gravitational acceleration, m = mass, E = elastic modulus, and L = length). To satisfy the similitude requirements, two key adaptations were implemented:  
* Mass addition was employed to appropriately scale the inertial effects and simulate realistic axial stress states in the vertical load-bearing members.
* The ground motion time histories were compressed in accordance with the scaling law, ensuring that the dominant frequencies of the input motion matched the scaled dynamic characteristics of the model.  
These adaptations are well-established in the literature on earthquake simulation of RC structures, particularly since the 1970s [36; 37; 38; 39], and are routinely applied in experimental campaigns conducted at the IZIIS Dynamic Testing Laboratory [40].
# Ras Multimers on the Membrane: Many Ways for a Heart-to-Heart Conversation
## 1 Introduction
Many members in the Ras signaling network such as MEK [55; 56], extracellular signal-regulated kinase (ERK) [57; 58], NF1 (a Ras GAP) [59; 60], receptor tyrosine kinases [3], function as dimers. More broadly, dimerization and oligomerization have long been recognized as a general mechanism for regulating protein activities [61; 62]. Ras-GDP could also dimerize and cluster [63]. In addition to Ras homodimers, heterotypic dimers formed between different types of Ras molecules, for example between Ras-GTP and Ras-GDP, have been  
Figure 2: Ras dimers and nanoclusters in biological processes. Current evidence suggests that Ras exists as a mixture of monomers, dimers, and higher order multimers, where dimers and multimers could be homotypic, comprising either GTP- or GDP-bound Ras, or heterotypic, comprising both GTP- and GDP-bound Ras. At present, the physiological relevance of many of the Ras multimers remains unclear. proposed [64; 65; 33]. At present, the field is just starting to understand the makings and functions of the various Ras multimers (Figure 2). This review aims to give an updated survey of existing literature on Ras dimers as a complement to several recent reviews on this topic [66; 67; 32; 68; 69; 33]. We focus on Ras dimers since the same interactions are thought to mediate formation of Ras nanoclusters [70; 71; 72]. We begin with a summary of Ras dimer biology as it currently stands. Recent work has revealed some surprising features of Ras dimers, including the potential relevance of multiple dimer interfaces. We next discuss the various Ras dimer interfaces identified or proposed in recent works, followed by considerations on potential factors that regulate Ras dimer formation and function. Of these factors, we focus on anionic lipids, particularly phosphatidylinositol (4,5)-bisphosphate (PIP2) and phosphatidylserine (PS), which have been shown to interact with both the HVR and the G-domain of Ras on model membranes. In cells, the distributions of PIP2 and PS are heterogeneous, and both lipids interact with the cytoskeleton and participate in the dynamic remodeling of membrane topography. We conclude with a hypothetic model where versatile Ras signaling can be in part realized through Ras localizing to heterogeneous membrane nanostructures ('nanodomains'), each featuring a unique combination of lipids and proteins and promoting specific types of Ras dimers to carry out specific subsets of Ras functions.
# Dimensional Tolerances in Mechanical Assemblies: A Cost-Based Optimization Approach
## 2 Applicable Concepts and References Using Similar Proposal Approaches
### Tolerance Analysis  
The tolerance analysis process is used to accumulate the individual tolerances of components within an assembly to determine the overall tolerance range. This outcome must align with the specified design value. In the context of mechanical assemblies, this process takes into account the sequential order of component tolerances, referred to as chain dimensioning. The inclusion of plus and minus signs in the chain is dictated by the geometric positioning of the components within the assembly. In engineering design, the two most commonly utilized tolerance analysis models are the Worst-Case (WC) and Statistical Tolerance (ST) models. These models play a crucial role in assessing and managing tolerances within the design process.  
The Worst-Case (WC) method, commonly referred to as tolerance stack-up, involves the summation or subtraction of component tolerances at their maximum and minimum values in a given direction. This calculation yields the extreme limits of the assembly, representing the worst-case scenario [2; 3; 4]. The WC method is typically applied in situations where:  
1. information about the manufacturing processes of the components is unavailable;
2. the assembly consists of a limited number of components and is subjected to critical functional constraints.  
In the absence of process information, a 100% production inspection becomes necessary, resulting in the Worst-Case (WC) method being overly conservative. On the contrary, the statistical tolerance (ST) method is suitable for component populations. The ST method is derived from observation of measurements in well-controlled processes, where most component dimensions align closely with the center of their specified tolerance zone [5]. This allows the ST model to be applied to mass production scenarios using sampling inspection. However, it should be noted that the ST model allows a fraction of the process output to deviate from the specification, and this consideration must be taken into account during design development.  
The Statistical Tolerance (ST) model offers various methods for tolerance analysis, but two of the most commonly used approaches are the Root Sum Square (RSS) method and the Monte Carlo (MC) method. These methods are widely applied in the field of tolerance analysis.