esa-sceva/satcom-chunk-collection huggingface dataset:
# MALDI-Based Mass Spectrometry in Clinical Testing: Focus on Bacterial Identification
## 4 Sample Preparation Strategies for MALDI-Based Bacterial Identification
### Ethanol and Formic Acid Extraction Method  
In some instance, the on-plate method still does not provide a sufficient MALDI mass spectrum, presumably as a result of low amounts of extracted protein. Under these circumstances, the ethanol-formic acid extraction method is recommended [52; 53]. This method is the basic approach for processing _Nocardi_, acid-fast bacteria, and fungi, which have cell wall structures that differ from general bacteria [54]. For example, beads may be used to physically break the cell wall [54].  
The MALDI Biotyper(tm) identifies bacteria using MALDI-TOF MS by pattern matching between mass spectra and bacteria registered in a database (Figure 2). A score of 2.0 or higher indicates high reliability at the species level, and a score of 1.7 to 2.0 indicates a match at the genus level. Since multiple strains of the same species are registered, accurate identification is possible even with pattern diversity among strains [30; 55; 56; 57].
## 2 System Model and Dynamic Optimization Problem
### Non-Outage Probability Calculated by Stochastic Geometry  
The non-outage probability is used to represent the quality of the channel [22; 23]; the non-outage event is defined as a successful packet transmission on the condition that the SIR is larger than the threshold \(\theta\):  
\[\mathbb{P}\left(SIR_{nk}^{V}\geq\theta\right) =\mathbb{P}\ \left(g_{nk}^{V}\geq\frac{I_{nk}^{V}\theta}{p^{V}\left(d^{V} \right)^{-\alpha}}\right)\] \[=\mathbb{E}\left(\exp\left(\frac{-I_{nk}^{V}\theta}{p^{V}\left(d^ {V}\right)^{-\alpha}}\right)\right). \tag{15}\]  
Let symbol \(t_{2}=\frac{\theta}{p^{V}\left(d^{V}\right)^{-\alpha}}\). Then, by substituting Equation (3) into Equation (15), we can rearrange Equation (15) and obtain:  
\[\mathbb{E}\left(\exp\left(\frac{-I_{nk}^{V}\theta}{p^{V}\left(d^ {V}\right)^{-\alpha}}\right)\right) =\mathbb{E}\left(\exp\left(-I_{nk}^{V}t_{2}\right)\right)\] \[=\mathbb{E}\left(\exp\left(-\left(I_{nk}^{MV}+I_{nk}^{VV}\right) t_{2}\right)\right)\] \[=\mathbb{E}\left(\exp\left(-I_{nk}^{MV}t_{2}\right)\right)\mathbb{ E}\left(\exp\left(-I_{nk}^{VV}t_{2}\right)\right). \tag{16}\]  
The last equality of Equation (16) is derived from the independence between the distribution of the platoons and the location of the MBS. As shown in Equation (12), the latter part of Equation (16) can be derived as follows [33]:  
\[\mathbb{E}\left(\exp\left(-I_{nk}^{VV}t_{2}\right)\right)=\exp\left(-\frac{2 \pi\lambda\left(t_{2}P^{V}\right)^{\frac{1}{\alpha}}}{\alpha\sin\left(\frac{ \pi}{\alpha}\right)}\right). \tag{17}\]  
By substituting Equation (1) into \(I_{nk}^{MV}\), because the geographical location and channel occupation of the MUs are broadcast to each platoon leader and \(g\sim\exp(1)\), we can derive the following equation:\[\mathbb{E}\left(\exp\left(-I_{nk}^{MV}t_{2}\right)\right) = \exp\left(\mathbb{E}\left(-t_{2}b_{k}P^{M}g_{k}^{MV}\left(d^{MV} \right)^{-\alpha}\right)\right) \tag{18}\] \[= \exp\left(-t_{2}b_{k}P^{M}\left(d^{MV}\right)^{-\alpha}\right).\]  
Let \(\xi_{nk}(t)\) denote the binary variable that indicates whether channel \(k\) is in a non-outage state to member \(n\) in the platoon in slot \(t\). We can replace \(t_{2}\) with \(\frac{\theta}{P^{V}\left(d^{V}\right)^{-\alpha}}\) in reverse in Equations (17) and (18), and substitute Equations (17) and (18) into Equation (16); then, the non-outage probability of a VU is derived as follows:  
\[\varphi_{nk} = \mathbb{P}\left(\xi_{nk}(t)=1\right) \tag{19}\] \[= \mathbb{P}\left(SIR_{nk}^{V}\geq\theta\right)\] \[= \exp\left(-\frac{2\pi\lambda d^{V}\theta^{\frac{1}{\alpha}}}{ \alpha\sin\left(\frac{\pi}{\alpha}\right)}\right)\exp\left(-b_{k}\frac{P^{M} \theta}{P^{V}}\left(\frac{d^{MV}}{d^{V}}\right)^{-\alpha}\right).\]
# CoMP-Aware BBU Placements for 5G Radio Access Networks over Optical Aggregation Networks
## 6 Conclusions  
In this paper, we proposed a BBU placement problem by taking into consideration the minimization of the number of inter-CoMP users, the number of BBUs, and the number of used wavelengths. First, we formulated an ILP model to place the BBUs and routed the connections, where the objective function is to minimize the number of BBUs and the number of used wavelengths. We took into consideration the link's capacity and trip delay constraints. Second, based on the output of the ILP model, we proposed a heuristic algorithm to relocate the RRHs with respect to the BBUs. The heuristic algorithm aims to reduce the number of inter-CoMP users. We used six different cases in the heuristic algorithm based on the minimum delay path, the minimum number of used fiber links, and the maximum multiplexing gain. The results showed that the maximum gain leads to the highest percentage of migrated users from inter-CoMP to intra-CoMP (97%). On the contrary, this resulted in an increased number of used wavelengths. The used wavelength increased by 94% when compared to the output ILP model. As a result, the overall X2 was minimized. Using Deep Reinforcement Learning (DRL) in the BBU placement problem while decreasing the number of inter-CoMP users can be considered as a future path in research.  
Conceptualization, M.S., S.M.G. and H.E.-B.; Investigation, A.M.A.; Methodology, A.M.A., M.S., S.M.G. and H.E.-B.; Software, A.M.A.; Supervision, M.S., S.M.G. and H.E.-B.; Writing--review & editing, A.M.A., M.S., S.M.G. and H.E.-B. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors declare no conflict of interest. The authors declare no conflict of interest.
## 2 Materials and Methods
### Seismic Data  
The Osservatorio Vesuviano (INGV-OV), branch of Istituto Nazionale di Geofisica e Vulcanologia, manages the permanent and the mobile seismic networks of Mt. Vesuvius (Figure 1). The permanent network is currently composed of 19 stations, both three-component digital broadband and analog short-period devices. The acquired data are telemetered in real time to the surveillance center in Naples. The mobile network consists of six temporary three-component digital broadband, standalone stations, which store the data locally. Details can be found in [25; 26].  
Since 1972, each VT earthquake detected by the seismic networks is noted down in a catalogue [27]. Each record of the catalogue reports origin time, epicenter coordinates, focal depth and MD.  
The seismological dataset used in this work consists of 24 VT earthquakes with MD \(\geq\) 2.0 that occurred at Mt. Vesuvius in the time span 2018-2020 (Table 1). Examples of seismograms are shown in Figure 2.  
Figure 2: **(a)** The 2018/04/26 18:04 VT earthquake. On the left, a beachball representing the focal mechanism estimated by FPFIT software, with the calculated parameters. On the right top, seismograms for the three components of BKSG station (black line). The selected S-wave packet is marked in red. On the right bottom, seismograms for the three components of BKWB station (black line). The selected S-wave packet is marked in red. **(b)** The same for the 2019/05/18 21:11 VT earthquake. ERZ and ERH values equal to 0.0 indicate location errors \(\leq\) 0.1 km.
## 5 Feature Fusion Based on Skewness Factor
### Pearson Correlation Coefficient between Features  
In order to research the change of the learning process of the degenerative model with an increase in the number of samples, five different numbers of training samples are used to train the degenerative model: 50, 75, 100, 125, and 150. In addition, the training samples need to be divided into training sets and verification sets. The former are used to train the degradation model and the latter are used to test the degradation model. This paper takes 80% of the training data as the training set and the remaining 20% as the verification set. The division of training set and verification set is shown in Table 5.  
The Pearson correlation coefficient is a linear correlation coefficient that reflects the linear correlation between two variables, the value of which is between \(-1\) and \(1\). The smaller the absolute value, the lower the correlation. Supposing two variables \(x\) and \(y\), each having n samples, correspond to \(n\) values, the formula for calculating the Pearson correlation coefficient \(r\) between \(x\) and \(y\) is shown in Equation (8):  
\[r=\frac{\sum_{i=1}^{n}{(x_{i}-\overline{x})(y_{i}-\overline{y})}}{\sqrt{\sum_{ i=1}^{n}{(x_{i}-\overline{x})}^{2}}\sqrt{\sum_{i=1}^{n}{(y_{i}-\overline{y})}^{2}}}. \tag{8}\]  
For all 150 training samples, the Pearson correlation coefficients between the two features are calculated. The calculation results are shown in Table 10.  
According to the results, it can be seen that the Pearson correlation coefficient between the skewness factor and other features is significantly lower than the Pearson correlation coefficient  
\begin{table}
\begin{tabular}{c r r r r r r r} \hline  & **Absolute** & **Shape** & **Crest** & **Impulse** & **Margin** & **Skewness** & **Kurtosis** \\  & **Mean** & **Factor** & **Factor** & **Factor** & **Factor** & **Factor** & **Factor** \\ \hline Absolute Mean & \(1\) & \(0.8192\) & \(0.8480\) & \(0.8633\) & \(0.8633\) & \(-0.3890\) & \(0.9206\) \\ Shape Factor & \(0.8192\) & \(1\) & \(0.9755\) & \(0.9817\) & \(0.9839\) & \(-0.0498\) & \(0.9317\) \\ Crest Factor & \(0.8480\) & \(0.9755\) & \(1\) & \(0.9967\) & \(0.9954\) & \(-0.0994\) & \(0.9440\) \\ Impulse Factor & \(0.8633\) & \(0.9817\) & \(0.9967\) & \(1\) & \(0.9998\) & \(-0.1470\) & \(0.9640\) \\ Margin Factor & \(0.8633\) & \(0.9839\) & \(0.9954\) & \(0.9998\) & \(1\) & \(-0.1462\) & \(0.9645\) \\ Skewness Factor & \(-0.3890\) & \(-0.0498\) & \(-0.0994\) & \(-0.1470\) & \(-0.1462\) & \(1\) & \(-0.3538\) \\ Kurtosis Factor & \(0.9206\) & \(0.9317\) & \(0.9440\) & \(0.9640\) & \(0.9645\) & \(-0.3538\) & \(1\) \\ \hline \end{tabular}
\end{table}
Table 10: Pearson correlation coefficients between features.  
between the other features. The skewness factor has a strong nonlinear relationship with other features. Therefore, we choose the skewness factor for the feature fusion.
###### Abstract  
In a packet switching network, the performance of packet loss concealment (PLC) is often affected by inaccurate estimation of phase spectrum of speech signal in the lost packet. In order to solve this problem, two kinds of PLC methods in the scene of continuous packet loss are proposed in this paper based on phase correction. One of them is based on waveform similarity overlap-add (WSOLA) and deep neural network (DNN), and the other one is based on the Griffin-Lim algorithm (GLA) and DNN. In the first method, considering the correlation of adjacent frames of speech signal, the periodicity of speech signal is well retained by the WSOLA method so that the phase spectrum of the lost signal is recovered. Combined with the prediction of amplitude spectrum of the lost signal by the DNN, the performance of the PLC in the case of continuous packet loss is effectively improved. In the second method, the phase spectrum of the lost signal is modified iteratively by the amplitude spectra estimated via the DNN based on the consistency of Fourier transform so that the phase spectra match the amplitude spectra. The PLC is greatly achieved as well. The experimental results show that the proposed PLC methods provide better speech quality than the reference methods.  
packet loss concealment (PLC); deep neural network (DNN); waveform similarity overlap-add (WSOLA); Griffin-Lim algorithm; phase correction 2022
# The Impact of Spatial Resolutions on Nature-Based Solution Suitability Mapping for Europe
## 4 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Metric** & **OSM** & **OSM (Sieved)** & **LUISA** & **LUISA (Sieved)** & **CLC** & **CLC (Sieved)** \\ \hline Land Cover (m\({}^{2}\)) & 36,825,700 & 14,381,000 & 54,319,900 & 24,027,300 & 57,086,100 & 25,345,300 \\ Number of Patches & 14,750 & 720 & 14,699 & 1196 & 15,464 & 1244 \\ Greatest Patch Area (m\({}^{2}\)) & 168,900 & 136,400 & 145,500 & 130,500 & 173,400 & 130,500 \\ Mean Patch Area (m\({}^{2}\)) & 2496.65 & 19,973.61 & 3695.48 & 20,089.72 & 3691.55 & 20,374.04 \\ Patch Density & 0.00040054 & 0.00005007 & 0.0002706 & 0.00004978 & 0.00027089 & 0.00004908 \\ Landscape Division Index & 0.99953 & 0.99792 & 0.99966 & 0.99879 & 0.99966 & 0.99884 \\ (dimensionless 0–1) & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Patch statistics of the suitability mapping output layers for each LULC input layer before and after removing isolated small areas (sieving). Figure 4: Coherence between suitability mapping output layers based on CLC, LUISA, and OSM. buffering the river. A general coherence between the output files was spotted in agricultural landscapes, which are, at the same time, more homogeneous areas than urban areas. Figure 5: Results from satellite imagery validation: (**a**) red marked areas presenting potential riparian forest buffers suggested by the OSM-based suitability map, but not by the other two outputs (coordinates: 4120537 3005590: 4124094 3007762); (**b**) red areas present non-appropriate suggested areas by the CLC-based suitability map, but not by the other two outputs (coordinates: 4146529 3027011: 4147856 3027821); (**c**) inaccuracy of input and output layers (coordinates: 4191466 2963021: 4194514 2964882); (**d**) the general agreement of all output layers is marked in green, while yellow presents the agreement of two layers, and red is only one layer (coordinates: 4162673 3014443; 4163541 3014973). Data storage size and processing timeThe data storage size of the three LULC layers varies greatly (ranging from 206.2 MB to 26 GB) for the entirety of Europe, depending on their resolution. However, the suitability output maps are of the same size. For the study area, no considerable differences in processing times were detected, perhaps because the area is rather small (compared to the European region). Nonetheless, the input data storage size is expected to have an impact on the computation time for the entirety of Europe.
# Rapid Detection of Key Phenotypic Parameters in Wheat Grains Using Linear Array Camera
## 4 Discussion
However, grain weight estimation may be influenced by factors such as variations in grain density under different growth environments, genetic differences between varieties, and internal structural characteristics, including the depth of the ventral groove. The determination coefficient and root mean square error for grain weight were 0.7635 and 3.8329, respectively, which are higher than those observed for grain length and grain width. Nonetheless, the method still captures grain weight characteristics to a meaningful extent, providing valuable phenotypic information despite inherent variability. Compared with the intuitive reflection of wheat phenotypic parameters, the determination of wheat grain bud points tends to be applied to grain sowing and seed vigor detection. For wheat seeds sown with oriented bud points, the root biomass is more developed, the root distribution is more uniform, the base of the stem is thick and strong, and the risk of lodging is reduced. The contact surface between the grain and the soil is also more stable, which can reduce the displacement caused by rainfall or irrigation and ensure population uniformity. The bud point determination method proposed in this study has an accuracy rate as high as 97.5%, the mean error of the distance between the positioning accuracy and actual germination point is within 0.10 mm, and the mean error of the angle is within \(\pm 3^{\circ}\). Accurately determining bud points is crucial for advancing wheat breeding and screening processes, as it provides key phenotypic reference data that support selection and genetic improvement. The methodology presented in this study for germ point identification is theoretically supported by previous studies conducted by [PERSON] et al. [43] and [PERSON] [31], who extensively investigated grain orientation and its impact on phenotype characterization. Their findings validate the reliability and precision of germ-point detection techniques, highlighting their significance for enhancing trait analysis in wheat breeding programs. If the identification of bud point information is applied to the seed suction actuator for precise orientation adjustment operations, aligning the bud points of multiple adsorbed grains in the same direction and directly placing them in the holes is conducive to improving the accuracy of the directional seeding device. The five wheat varieties provided by the institute represent a relatively limited sample and may not fully capture the complete range of characteristics present across all varieties. Furthermore, the image acquisition device utilized in this study is currently restricted to controlled laboratory settings, limiting its applicability in the field or under variable environmental conditions. When improving the current device to develop the next-generation device, it is necessary to consider the complex environments in actual application scenarios, such as illumination conditions, temperature, and humidity, which may impact the image quality and the detection results.
# Improving Landslide Recognition on UAV Data through Transfer Learning
## 4 Experimental Results and Discussion
### Recognition Result and Evaluation
#### 4.3.4 F1-Score  
The F1 score is the harmonic mean of recall and precision, which is a more comprehensive evaluation index. Values above 0.6 are considered satisfactory. The calculation formula is as follows:  
\[\text{F1}=2*\frac{precision*recall}{precision+recall} \tag{7}\]  
As shown in Figure 14, the F1 score of the transfer learning model is 0.84, which is higher than that of the SSD model.  
In terms of the comprehensive indicators, it is concluded that the transfer learning model performs better than the SSD model, with 0.95 precision, 0.9 recall, and 0.84 F1 measures. This shows that transfer learning is more effective for small sample detection. It not only improves the training efficiency, but also enhances the accuracy of the target detection. The experimental evaluation results are presented in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Index Model** & **SSD** & **Transfer Learning** \\ \hline Precision & 90.24\% & 95.1\% \\ Recall & 35.38\% & 90\% \\ P-R Curve & 0.64 & 0.88 \\ F1-Score & 51\% & 84\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental evaluation results.  
Figure 14: The F1 score of the SSD model (**left**) and the transfer learning model (**right**).  
Figure 13: The P-R Curve of the SSD model (**left**) and the transfer learning model (**right**).
# Optimization Techniques for Design Problems in Selected Areas in WSNs: A Tutorial
## 18 Dispatching of Mobile Sensor Nodes in a WSN to Sense a Region of Interest
### Solution Method: Distributed  
A distributed algorithm based on a greedy heuristic approach was proposed in [13] to obtain a suboptimal solution with lower communication and processing overhead. The algorithm requires periodically broadcasting a list of placement locations for the target area to the all sensor nodes in the network. The list identifies the locations that are occupied and those that are not. Initially all the locations are unoccupied and each node has a copy that may not be identical until convergence. Each node \(s_{i}\) selects the shortest target location from the list to move to. While moving to its target, the sensor node broadcasts the status of its list of target locations and the cost to move to the destination. All locations marked as occupied by \(s_{i}\) are also marked as occupied by the nodes receiving the list of \(s_{i}\). If \(s_{i}\) and \(s_{k}\) are moving towards the same location they compete their costs and the one with the lower cost wins and keeps moving towards the destination while the other searches for the other nearest target position. Each sensor keeps repeating the process, while sending and receiving location targets and costs until either it reaches a destination or looses to the other sensors and finds that all locations are marked as occupied.
## 2 Materials and Methods  
The migration and accumulation of anthropogenic radionuclides were studied in the western region of the Nenets Autonomous Okrug, Northwest Russia, in June 2022. A total of 46 peat samples were collected along a profile with a total length of about 20 km, perpendicular to the bed of the Nes River and extending from flat peat bogs in the southwest to a local watershed in the northeast. The selection of this area was based on the findings obtained earlier, which suggest that there is a probable zone of elevated levels of anthropogenic radionuclides in environmental objects [34; 35; 36; 37]. The activity and density of pollution with radionuclides Cs-137, Sr-90, Am-241, Pu-238, Pu-238+239, and the isotopic ratios of Sr-90/Cs-137, Pu-238/Pu-239+240, Pu-239+240/Cs-137, and Am-241/Pu-239+240 were determined in each peat sample, alongside the isotopic ratios of each radionuclide by layers 0-10 cm and 10-20 cm. The sampling profile scheme is illustrated in Figure 1. The angular coordinates of the site are as follows: N 66\({}^{\circ}\)30\({}^{\prime}\)55.9969\({}^{\prime\prime}\) E 44\({}^{\circ}\)28\({}^{\prime}\)13.8301\({}^{\prime\prime}\), N 66\({}^{\circ}\)40\({}^{\prime}\)45.7128\({}^{\prime\prime}\) E 44\({}^{\circ}\)28\({}^{\prime}\)53.3809\({}^{\prime\prime}\), N 66\({}^{\circ}\)40\({}^{\prime}\)45.7128\({}^{\prime\prime}\) E 44\({}^{\circ}\)54\({}^{\prime}\)08.6702\({}^{\prime\prime}\), N 66\({}^{\circ}\)31\({}^{\prime}\)08.8155\({}^{\prime\prime}\) E 44\({}^{\circ}\)54\({}^{\prime}\)13.6140\({}^{\prime\prime}\).  
To comprehend the patterns of radionuclide distribution in different types of elemental landscapes, the results of the pollution density calculations for each investigated radionuclide were plotted on the elevation diagram along the sampling route (Figure 2). The altitude diagram was developed as per the results of terrain digitization using the free and open-source cross-platform desktop Geographic Information System application QGIS.  
Field studies enabled the detailed characterization of the landscape of the Nes River basin. During the sampling route to the northeast of Nes (on the right bank of the river), shallow boggy peat bogs with a low moisture content were discovered (Figure 2). The cracking of the hillock surface was noted in the plakor zone and across most of the slope, an indicator of decreasing moisture content in these elementary landscapes. An elevation in wetness levels was observed as the river floodplain neared. A distinct contrast was noted on the southwestern side of Nes village, situated on the left bank of the Nes River. The terrain displayed shallow bogs with a significantly higher degree of wetness (Figure 2). High levels of peat wetness due to surface water were observed at all sampling sites. One of the reasons for these differences in the catchment area is the different types of landscape. This, in turn, affects the migration of elements, including radioactive elements. [38; 39; 40; 41; 42].  
Figure 1: Scheme of the study area in the Nes River basin (Kaninskaya tundra of the Nenets Autonomous Okrug).