esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Because the measurement setup was motivated by an aircraft-related structural element a typical quasi-isotropic material was used for both plates. Therefore, no significant direction dependency for the group velocity was measured. [PERSON] et al. [14] show that anisotropic effects are very pronounced for uni-directional and cross ply laminates. As in [15] it is shown by [PERSON] et al., that for quasi-isotropic layups and frequencies below the higher order GW modes, the group velocity is approximately direction-independent. In contrast to the previous datasets, an omega stringer was attached to both plates for representative aircraft components. The plates are again made of quasi-isotropic material, so that no anisotropic effects occur. Since measurements from the flat structure and the structure with the stringer are available, it is possible to analyse the scattering behaviour of guided waves at the stringer. For excitation a conventional narrowband toneburst, so as to reduce the impact of frequency dispersion similar to [16; 17], and a broadband chirp signal was used as presented in [18; 19]. Since GW-based SHM tests are limited by the high instrumentation effort and enormous costs, we have studied a reversible reference damage that could be attached to the plates surface [20; 21]. A representative damage is used, which is easy to model in numerical simulation and experiment. The representative damage is necessary to evaluate SHM systems in a standardized way. It should be able to represent the key features of the damage (size, interaction with the excited GW-modes and stiffness change). For this purpose, an elliptically shaped damage was chosen. Its size is motivated by the projected surface of real impacts. The reference damage has been placed at different locations on the structure in order to study spatially distributed probability of detection (POD) of GW-based SHM-systems [22]. The goal of the paper is to provide * measurements supporting current state-of-the-art GW-based SHM research for model-based POD [23; 24; 25; 26; 27],
* a dataset with multiple damage sizes for model validation,
* a finite shell element-based model as starting point for numerical model validation. The remainder of the paper is organized in the following way: Section 2 presents the CFRP (carbon fiber reinforced plastics) structures with stringers as well as the elliptical reference damage model. After that, Section 3 introduces the experimental setup for the _SHM plate_ and the _wave field plate_ with stringer element. Results derived from those measurements are presented in Section 4. Section 5 gives an overview of the modeling approach and some results for comparison. Finally, conclusions are drawn at the end.
## 5 Interference Analysis  
The signal received at the recipient V-N can be interrupted by the signals (or interference) of all the other nodes that are interacting via direct mode and shared mode transmission. The reception of a V-N's transmission is impacted solely by the transmission of V-Ns, which are broadcasting to neighboring V-Ns via direct mode transmission. For shared mode transmission, the transmission of V2M, V2L, or V2N links is going to be disturbed by transmissions from all other V2M, V2L, or V2N links.  
Interfering signals of MBSs, LOS-LAPs, NLOS-LAPs, and V-Ns are denoted as \(L_{I_{X}}(s)\), wherein \(X\) may correspond to MBSs, LOS-LAPs, NLOS-LAPs, or V-Ns (i.e., \(X\in\{M,L,N,V\}\)). The interference is provided as: \(L_{I_{X}}(s)=\mathbb{E}[e^{-sI_{X}}]\), \(I_{X}=\sum_{i\in X}P_{i}g_{i}\eta_{i}^{-1}g_{i}\mathcal{G}_{i,j}\mathcal{G}_{ i,j}\|z_{i,j}-z_{i,j}\|^{-\alpha_{i}}\), wherein \(P_{i}\) is the \(i\)-th node power. \(g_{i}\) is the small-scale fading gain for the \(i\)-th node, while \(\eta_{i}\) is the extra path loss for the \(i\)-th node. \(\mathcal{G}_{t,i}\) is the \(i\)-th sending node gain, \(\mathcal{G}_{r,i}\) is the \(i\)-th receiving node gain, \(z_{t,i}\) is the distance between the transmitting node and \(o\), \(z_{r,i}\) is the Euclidean distance between the receiving node and \(o\), and \(\alpha_{i}\) is the path loss of the \(i\)-th node. Thus, the Laplace expression can be expressed as \(L_{I_{X}}(s)=\mathbb{E}_{g,X}\Big{[}e^{-s\sum_{i\in X}P_{i}g_{i}\eta_{i}^{-1}g _{i}\mathcal{G}_{i,j}\mathcal{G}_{i,j}\|z_{i,j}-z_{i,j}\|^{-\alpha_{i}}}\Big{]}\), where the expectation is with respect to \(g\) and \(X\). By taking the expectation with respect to \(g\sim\exp(1)\), the expression can be written as  
\[L_{I_{X}}(s)=\mathbb{E}_{X}\Bigg{[}\prod_{i\in X}\Bigg{(}\frac{1}{1+sP_{i} \eta_{i}^{-1}\mathcal{G}_{t,i}\mathcal{G}_{r,i}\|z_{r,i}-z_{t,i}\|^{-\alpha_{ i}}}\Bigg{)}\Bigg{]}. \tag{22}\]
# Wind Lidar and Radiosonde Measurements of Low-Level Jets in Coastal Areas of the German Bight
## 5 Results and Discussion
### Weibull Distribution and Wind Roses for the Sites Heligoland and Norderney  
The Weibull wind speed distributions at Heligoland and Norderney (Figure 4), at a representative hub height (120 m), are similar: The scale factor, which is proportional to the most likely wind speed, is \(A=10.91\) m s\({}^{-1}\) for Heligoland and \(10.39\) m s\({}^{-1}\) for Norderney.  
Figure 3: Locations of the three WRF model domains with grid sizes of 15, 5, and 1.6 km, respectively [57].  
This is in agreement with typical long-term values in the North Sea obtained at FINO1 [33]. The shape factor is \(k=2.12\) for Heligoland and 2.33 for Norderney, which are typical values for the German Bight [33]. The wind roses are similar as well. However, the most frequent wind direction for Heligoland was SW, and for Norderney SSW. Note that the data were obtained during different years. Therefore, the differences may be attributed to generally different prevailing wind conditions in the different years, or to systematic differences due to different locations.
## 3 Methods
### Supervised Learning Based Methods
#### 3.1.3 Extreme Learning Machine (ELM)  
ELM is a type of feedforward neural network designed to simplify the learning process by utilizing a unique training approach. Unlike traditional neural networks, which use iterative optimization methods like backpropagation to adjust weights and biases, ELM initializes the input weights and biases randomly and determines the output weights analytically. This characteristic significantly reduces the training time and complexity, making ELM highly efficient and well-suited for large-scale data applications [21].  
The core idea of ELM is to approximate the desired target output using the activations of the hidden layer neurons. The process begins by establishing the hidden layer output matrix \(H\), which represents the responses of all hidden neurons to the input data. Each entry in this matrix is calculated based on the randomly initialized input weights and biases. Once the hidden layer activations are obtained, they remain fixed throughout the training process, as ELM does not employ backpropagation to fine-tune these parameters [22].  
Next, the relationship between the hidden layer output matrix \(H\) and the desired output, represented by the target matrix \(T\), is established. The target matrix \(T\) contains the expected outputs for each training sample and serves as the ground truth for the network to learn. To find the best mapping from the hidden layer activations to the target outputs, ELM uses the output weight matrix \(\beta\), which connects the hidden neurons to the outputlayer [23]. The weight matrix \(\beta\) is computed analytically by solving a linear system of equations, which can be represented as  
\[\beta= H^{\dagger}T \tag{5}\]  
In Equation (5), \(H^{\dagger}\) is the Moore-Penrose pseudo-inverse of the hidden layer output matrix \(H\). The pseudo-inverse is a generalized form of the matrix inverse that is used to obtain a least-squares solution for \(\beta\), even when \(H\) is not a square matrix or when it is singular (non-invertible). The use of the Moore-Penrose pseudo-inverse ensures that the calculated \(\beta\) minimizes the error between the predicted outputs of the network and the actual target values, making it an optimal solution in a least-squares sense.
## 2 System Model  
This section summarizes the key technological concepts that are needed to model the link level of the NB-IoT communication, as well as the different relaying architectures that could be used on-board the GEO satellite to interface the optical wireless signal (feeder link) into the radio wireless signal (access link) that is forwarded by the GEO satellite to the NB-IoT terminals.
## 2 System Model
### Problem Formulation
#### 2.3.2 Optimization Problem  
In this scenario, each battery-constrained node \(k\) seeks to minimize its transmission energy\[E_{k}=P_{k}\cdot t \tag{7}\]  
where \(P_{k}\) is the transmission power of node \(k\), and \(P_{k}=P_{k}\). In our work, network dynamics of remaining battery energy (\(E^{\tau}\)) and \(\gamma\) will be considered during the data transmission phase. The objective of each node is to minimize energy consumption while ensuring that \(\gamma\) remains above a set threshold \((\gamma_{\mathtt{th}})\). Thus, the optimization problem at each node is then formulated as  
\[\mathtt{Minimize} : E_{k}\] (8) subject to: \[\gamma_{k}\geq\gamma_{\mathtt{th}}\] \[E_{\mathtt{min}}\leq E_{k}\leq E_{\mathtt{max}}\]  
where \(E_{\mathtt{min}}\) and \(E_{\mathtt{max}}\) puts both lower and upper bounds on \(E_{k}\), \(E_{\mathtt{max}}\leq E^{\tau}\) and the value of \(\gamma_{\mathtt{th}}\) is constant for all the nodes.  
Since the nodes share the transmission medium, the transmission power and channel conditions of one node can affect \(\gamma\) and energy consumption of other nodes due to interference. This creates a conflict of interest, where the actions of one node can have negative externalities on others. Thus, optimizing each node independently, without accounting for interactions and dependencies among them, may not result in the most efficient utilization of energy from a global network perspective and could lead to sub-optimal overall performance.  
Game theory emerges as a viable optimization technique capable of considering node interactions [43; 44], offering a distributed solution to the optimization problem. In this context, node strategies are adjusted in response to evolving network conditions and the strategies of other nodes.
## 3 Results
### Laser Particle Size Analysis and Specific Surface  
A finer evaluation of proportion and median particle diameter (below 80 \(\upmu\)m) was carried out using the particle size analysis. Specific surface tests were used to estimate the clay content. The results presented in Table 5 show the median particle diameter (here, used on a volumetric basis, i.e., 50% by volume of the particles is smaller than this diameter, and 50% is larger) for particles below 80 \(\upmu\)m: for the BE42 and MTI soils its value is 16.7 \(\upmu\)m and 14.36 \(\upmu\)m, respectively, whereas for the Cabale and Morne Coco soils, it is 5.37 \(\upmu\)m and 6.14 \(\upmu\)m, respectively. It can be deduced that the fines present in the MTI, BE42, and Cabale soils are silty, whereas for the Morne Coco soil, the relatively small average size indicates that the fine fraction is sandy loam.  
The difference in the specific surface area between the BE42 and Cabale soils (14.2 m\({}^{2}\)/g and 36.2 m\({}^{2}\)/g) and the percentage of clay content (0.6% and 2.7%) indicate that the BE42 soil contains more of the silt fraction and therefore slightly fewer active particles. These results are consistent with the median particle size of less than 80 \(\upmu\)m.
## 2 Results and Discussion
### Method Development
As can be seen in Figure 2, the models are very similar and the factors that affect this ratio the most are x\({}_{3}\) and x\({}_{4}\), which should be set at the highest level in order to maximize the S/N ratio. The other factors have slight differences between the two models, but their contribution to the variation of the S/N ratio is very low. Figure 2: Plots of the coefficients computed for the S/N ratio models of cyclophosphamide (**a**) and iphosphamide (**b**). LEGEND: x\({}_{1}\), volume of methanol utilized to condition the \(\upmu\)SPE cartridge. x\({}_{2}\), volume of basified water utilized to equilibrate the \(\upmu\)SPE cartridge. x\({}_{3}\), volume of the washing solution. x\({}_{4}\), composition of the washing solution. x\({}_{5}\), sample loading speed. x\({}_{6}\), dispensing speed for  
the conditioning step. x7, dispensing speed for the equilibration step. x8, dispensing speed for the washing step. x9, dispensing speed for the elution step. x10 and x11 are fictitious (dummy) factors used to estimate the random error in the experiments. The grey area shows the magnitude of the random error estimated using the dummy factor x10. Factor x1, having a coefficient smaller than that of factor x10, is considered as not significant since it has an effect smaller than that produced by random error. In conclusion, the experimental design provides valid models to describe the sensitivity of the analytes, allowing the setting up of the sample extraction by performing a few experiments. The experimental conditions selected were: volume of methanol utilized to condition the uSPE cartridge (x1) = 200 uL; volume of basified water utilized to equilibrate the uSPE cartridge (x2) = 200 uL; volume of the washing solution (x3) = 500 uL; composition of the washing solution (x4) = 90:10 H2O:MeOH; sample loading speed (x5) = 25 uL/s; dispensing speed for the conditioning step (x6) = 55 uL/s; dispensing speed for the equilibration step (x7) = 55 uL/s; dispensing speed for the washing step (x8) = 25 uL/s; dispensing speed for the elution step (x9) = 10 uL/s.
## 3 Results and Discussion
### Behavior Study of the MSAMT
#### 3.3.4 Effect of the Decision Space Partition  
To evaluate the effectiveness of the decision space partition strategy, two variants, namely the proposed MSAMT and MSAMT-withoutDSP (only one RBF model is built to facilitate the global search conducted), were compared using the same four test functions as in Section 3.3.3. Figure 5 gives the convergence curves for the two variants. Figure 5 shows that the performance of the MSAMT was significantly better than MSAMT-withoutDSP without the decision space partition strategy. In particular, the MSAMT converged much faster than the MSAMT-withoutDSP in the early search stage.  
Figure 4: Performances of the MSAMT with different \(k_{max}\) values on 20D F2, F3, F5, and F9 (From (a) to (d) are the box plots of F1, F3, F5, and F9, respectively).
# Comparison of Methods to Identify and Monitor Mold Damages in Buildings
## 3 Results
### Dispersal of Fungi in the Apartments as Assessed by DNA Metabarcoding  
A low proportion of the OTUs (proxy of species), about 7%, were detected in both the mold and dust samples when calculated on an apartment-by-apartment basis (Figure 4a,b). This overlap increases up to \(11.4\%\) in an overall comparison of the mold and dust data in all the buildings combined (25 apartments; Figure 4c). In agreement with the species richness data (Figure 1c), the great majority of the OTUs (>96%) were found in the dust samples, while only 3.3% of the OTUs were on average uniquely detected in the mold samples. The overlaps between the mold and dust samples were slightly different (not significant; \(p\) = 0.14), depending on the room where the dust was collected, being to some extent higher in the damaged rooms (1.25%) compared to the central ones (0.57%) (Figure 4a).  
In addition, we assessed the spread of the molds by comparing the most abundant genera present in the mold samples, with their prevalence in the dust samples collected from the different rooms (Table 2 and Table S2). We here observed that (i) the majority of the genera identified in the mold samples were also detected in the dust from the same apartments, but sometimes with very low relative abundances, and (ii) there was no clear connection between their relative abundances in the dust and the distance to the mold damages (the potential sources of these species).
# Bio-Inspired Algorithms for Efficient Clustering and Routing in Flying Ad Hoc Networks
### Contribution of This Study  
This paper introduces a novel solution, a hybrid bio-inspired algorithm called HMAO, which is designed to address the persistent problems stated above, improve cluster stability, and enable predictive routing within dynamic FANETs.  
* In this study, an HMAO algorithm is proposed to optimize clustering and routing in FANETs by merging the best features of MGO for CH selection and AO for global path optimization.  
* Local density-aware clustering is proposed to avoid overloading UAVs in dense regions. The proposed method ensures that cluster sizes are balanced and prevents congestion in high-traffic areas.
* A predictive mobility-based routing mechanism is proposed to predict UAV movement, and this mechanism will improve data delivery performance and reduce the packet loss rate in a highly dynamic environment.
* Proposed HMAO dynamically balances the data load across UAVs by monitoring queue lengths and packet processing rates and directing traffic towards the least-congested nodes. This prevents overwhelming, reduces delays, and improves network efficiency, especially in dense scenarios.
* The fault-tolerant secondary path routing strategy is designed with the ability to dynamically activate secondary paths on congestion occurrences for the reliable transmission of data using global information.
## 4 Proposed System
### Performance Metrics  
#### 4.1.1 Late Elements Frequency  
Within the window operators, whenever a subtask receives any late elements, the operator will alert the master, which in this case is the Job Manager, of the arrival of such elements. The master will then compile the number of late elements of the system, including the recent arrivals, and use them, later on, to help determine the tradeoff mentioned at the beginning of the section between latency and throughput.  
Figure 9: Proposed system architecture: Event gathering systems collect the data, pipelining it to the data broker for validation, then passing it onto the workload analyzer, which finally passes the obtained results onto the Job Manager through workload prediction module (adapted from [12]).
## 3 Results
### How Well Can NCC and NEBF Distinguish between Correct and Wrong Correspondences?  
We assessed how well each of the measures (NCC and NEBF) can distinguish between the correct correspondence and wrong correspondences. We calculated the differences between the measure value at the correct correspondence (according to the ground truth) and at the most similar wrong correspondence. We call this difference NCC margin and NEBF margin, respectively. For example, for a given pixel of the left camera, the NEBF might be 62 at the correctly corresponding pixel of the right camera and the best NEBF value for any pixel which is more than two pixel away from the truly corresponding pixel might be 59; the NEBF margin is then \(62-59=3\). Large positive values mean that the true correspondence can be well distinguished from wrong correspondence candidates. Negative values mean that the wrong correspondence has a better measure value than the correct correspondence, which leads to a mismatch. It is also possible that several candidate correspondences have the same measure value (especially for the discrete NEBF). In that case, the margin is 0 and there are several equally good correspondence options. (The algorithm picks the first one. If this pick was wrong, it is likely to be removed in the reverse search. See Section 2.1.) We calculated the histograms of the margins for several sequence lengths (Figure 12).  
Figure 11: Relation between the two pixel similarity measures. 2D histogram of all pairs of normalized cross correlation (NCC) and Number of Equal Binary Features (NEBF) for all pixel comparisons of a measurement with a sequence length of 10 image pairs. For better visibility, the histogram is normed to the total number of pixels which fall in the same cross correlation bin.  
The NEBF shows more margin values close to zero than the NCC. This means that for some pixels, the NEBF performs worse at distinguishing between correct and wrong correspondences. It has more points with margins \(<0\). It thus produces more mismatches than the NCC. This explains the worse correspondence matching rate of BICOS(+) when omitting the median filter (Figures 13 and 14). However, this problem is reduced by the filter.
## 2 Instruments and Methods
### Spatially Resolved Acoustic Spectroscopy
To enable this specular reflection, the sample surface roughness should be less than the Rayleigh criterion [11]. \[\Delta h<\frac{\lambda_{d}}{8\cos\theta} \tag{2}\]  
where \(\Delta h\) is the standard deviation of the surface roughness, \(\lambda_{d}\) is the detection laser wavelength, and \(\theta\) is the angle of incidence. In practice, a signal is obtainable using this system if the sample has the surface roughness \(R_{d}<100\) nm. With rough surfaces such as those seen on as-deposited SLM samples, the return beam becomes a diffuse reflection--instead of a Gaussian beam, speckles are incident on the split photodiode of the KED, with no detectable signal measured when deflection occurs (the combination of multiple speckles moving across both photodiodes outputs an almost zero average signal). This concept is illustrated in Figure 2a,b. To measure an ultrasound signal on a rough surface, other detector schemes can be used. Adaptive interferometer techniques have been used to measured ultrasound on rough surfaces with some frequency restrictions [21; 22], with developed confocal Fabry-Perot interferometer systems potentially being able to make ultrasound measurements limited only by the electronic circuit response [23; 24]. A popular detection method of measuring high frequency ultrasound (up to 1 GHz) on rough surfaces consistently involves using two wave mixing (TWM) setup [25]. A photorefractive crystal is used to interfere the diffuse light return with a locally adapted reference beam, however, an amount adaption time is required before a measurement can be made, limiting scan speeds. Additionally, interferometric methods are often susceptible to microphonics and vibration. Other techniques that have been investigated include photodetector arrays with multiplexed laser interferometers [26] and quadrature demodulation for multi-speckle processing [27].
# Process-Oriented Stream Classification Pipeline: A Literature Review
## 6 Classifier Maintenance
### Evaluation
#### 6.4.2 Evaluation Metrics
[88] emphasizes that the duration of all preprocessing steps must also be considered. Based on our literature search, the methods presented above are conventional in the general stream classification setting. However, other metrics might be relevant for particular purposes or well-defined specific research areas. For evaluating multi-label classification problems, ref. [40] differentiate between example-based and label-based evaluation metrics. Example-based metrics assess the classifier's performance for all instances by combining and averaging the performance metric over the whole dataset, while in the label-based case, the performance for each label is assessed before it is averaged. Although advancements have been made toward stream classification-specific performance measures, authors still criticize the lack of a holistic performance metric. Current evaluation metrics for stream classification measure individual or local aspects along the stream but do not assess all problem dimensions for the complete stream [36; 86; 112]. Especially problem settings that complicate the standard settings for stream classification--such as the emergence of novel classes, multi-label classification, or highly imbalanced classes--lack appropriate measures [7; 86].