esa-sceva/satcom-chunk-collection huggingface dataset:
# Find You: Multi-View-Based Location Inference for Twitter Users
## 4 Initialization
### Location Segmentation  
In reality, the majority of user location coordinates are concentrated in urban areas. This paper uses the \(k\)-d tree algorithm to discretize the user coordinates to prevent the model prediction results from being substantially biased towards large cities (such as New York City), wherein the hyperparameter bucket_size controls the maximum number of users for each location region. The \(k\)-d tree algorithm is then used to assign users in the training set with similar location distances to the same location region, and the central coordinate point of each region is set as the average longitude and latitude coordinates of all users within that region. For each user's coordinates in the validation and test sets, the centroid of each region is traversed and the _haversine_ formula is used to calculate the distance between the centroid's coordinates and the user's coordinates. Then, the users in the training and test sets are designated to the region in which the centroid is closest to them. The area number of the user's location is used as its label. Figure 3 depicts the result of using \(k\)-d tree to classify the user coordinates in the training set in GeoText. Each color block represents a location area.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Tweets** & **Mentions** & **Users** & **Train** & **Test** & **Dev** \\ \hline GeoText & 378k & 109K & 9475 & 5685 & 1895 & 1895 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specific information about the GeoText dataset used for the experiments.  
Figure 2: Heatmap of user location distribution in GeoText.  
* SGC4 Geo [36] is a simplified graph convolutional network that reduces the superfluous complexity of GCNs by removing non-linearities between GCN layers iteratively and collapsing the resulting function into a single linear transformation. It determines a user's residence based on his/her social posts and connections.
* M-GCN [14] employs graph convolutional networks to extract user text and link information from multiple perspectives to infer the location of the user.
* KB-emb [37] is a location inference technique that relies on entity linking and knowledge base embedding.
* MetaGeo [13] proposes a general framework for identifying user geolocation based on meta-learning to learn the prior distribution of geolocation tasks.
## 1 Introduction
Firstly, the ME effect must be sufficiently strong and easy to manipulate. Secondly, there is an issue of measurement methods used in investigations. The methods can be divided into qualitative (indirect, detection of the ME effect) and quantitative (direct, numerical representation of the coupling strength). The qualitative methods only confirm the existence of the coupling and do not provide any numerical information. On the other hand, most known direct methods require elaborate equipment and do not yield reproducible results. A review of existing measurement techniques can be found in [1]. In the present work, attention is focused on the selected composite material, i.e., multilayered PZT/Terfenol-D. This composite was chosen because PZT and Terfenol-D have among the strongest piezoelectric and magnetostrictive properties, respectively. Its simple synthesis, availability of materials, and reportedly large ME coupling coefficient are behind its unwaning popularity [9; 12; 13]. In this composite, the magnetic field generates strain via the direct effect of magnetostriction of Terfenol-D. Next, the strain is transferred to the PZT material and, due to the piezoelectric effect, an electric voltage appears at the edges of the sample. Since the composite components are mechanically coupled, the resonance state additionally occurs. As a result, the magnetoelectric coupling effect can be several orders of magnitude larger in comparison to that in single-phase materials or particulate composites. Most conventionally synthesized PZT-based composites exhibit single-frequency resonance characteristics, which makes the materials less useful as active components in magnetoelectric devices. In order to overcome this drawback, some authors have obtained the multiple resonance modes [14; 15] or band-like resonance characteristics [16] by creating complex mechanical structures. The goal of our studies was to synthesize and determine magnetoelectric properties of the multilayered PZT/Terfenol-D material. We aimed to achieve multi-frequency response by applying a piezoelectric patch instead of pure PZT. Our samples were investigated via the lock-in technique under different conditions (variable DC magnetic field intensity and AC magnetic field frequency). Particular emphasis was placed on examining properties in the resonance state.
## 6 Blockage-Free Scenario
### Blockage-Free Hop Distance  
We first derive the conditional \(m\)-th moments of the two routing schemes for \(\beta=0\). To derive \(\mathbb{E}\{d_{FN}|K\geq 1\}\) and \(\mathbb{E}\{d_{NN}|K\geq 1\}\) in this case, we first find the conditional PDFs for \(\beta\to 0\):  
\[\lim_{\beta\to 0}f_{d_{FN}|K\geq 1}(x) =\frac{\lambda\Phi xe^{\frac{1}{2}\lambda\Phi x^{2}}}{e^{\frac{1} {2}\lambda\Phi R^{2}}-1}, \tag{23}\] \[\lim_{\beta\to 0}f_{d_{NN}|K\geq 1}(x) =\frac{\lambda\Phi xe^{\frac{1}{2}\lambda\Phi(R^{2}-x^{2})}}{e^{ \frac{1}{2}\lambda\Phi R^{2}}-1}, \tag{24}\]  
where \(0\leq x\leq R\). Hence, the corresponding \(m\)-th moments are given by  
\[\lim_{\beta\to 0}\mathbb{E}\{d_{FN}^{m}|K\geq 1\}=\frac{2^{m/2}(- \lambda\Phi)^{-\frac{m}{2}}}{e^{\frac{\lambda\Phi R^{2}}{2}}-1}\] \[\qquad\cdot\left(\Gamma\left(\frac{m}{2}+1,-\frac{1}{2}\lambda \Phi R^{2}\right)-\Gamma\left(\frac{m}{2}+1,0\right)\right), \tag{25}\]  
\[\lim_{\beta\to 0}\mathbb{E}\{d_{NN}^{m}|K\geq 1\}=\frac{2^{m/2}( \lambda\Phi)^{-\frac{m}{2}}}{1-e^{-\frac{\lambda\Phi R^{2}}{2}}}\] \[\qquad\cdot\left(\Gamma\left(\frac{m}{2}+1,0\right)-\Gamma\left( \frac{m}{2}+1,\frac{1}{2}\lambda\Phi R^{2}\right)\right). \tag{26}\]  
With these moments, for a given beamwidth \(\Phi\), the baseline \(\mathbb{E}\{X|K\geq 1\}\) can be obtained from Equation (19). We note that if \(\lambda\to\infty\) on the top of \(\beta\to 0\), which corresponds to the high node density and blockage-free environments, \(\mathbb{E}\{d_{FN}^{m}|K\geq 1\}\to R^{m}\) and \(\mathbb{E}\{d_{NN}^{m}|K\geq 1\}\to 0\). These can be readily proved by taking the limit of Equations (25) and (26) as \(\lambda\to\infty\).
## 2 Alignment Calculation Model
### Experiment of Model Validation  
According to the standards for marine shafting alignment, the actual load of each bearing in the initial installation state was measured using the jack-up method and compared with the calculated values to validate the accuracy and credibility of the established numerical model. The specific experimental procedures of the jack-up method can be referenced in the relevant literature [8,21] and are not reiterated here. The theoretical slope was obtained by calculating the theoretical BLINs of each bearing, and the ascending and descending curves were plotted based on the measured results, as shown in Figure 3.  
Within the range of \(\Delta{}h\), curve segment OB represents the ascent phase from the start of jacking to the state that the tested bearing is totally unloaded, while AO represents the descent phase from the first contact between the journal and the tested bearing to the state that the jack is totally unloaded. The actual measured load \(L_{b}\) and the validity constraint of the tested bearing are:  
\[L_{b}=(\mathrm{A}+\mathrm{B})/2 \tag{1}\] \[s.t.(\mathrm{B}-\mathrm{A})<0.4L_{b}\]  
The trial vertical stiffness of the rear stern bearing and front stern bearing was set to 2.0 \(\times\) 10\({}^{5}\) N/mm and 1.7 \(\times\) 10\({}^{5}\) N/mm, respectively, and the lateral stiffness was set to 1.2 \(\times\) 10\({}^{5}\) N/mm and 1.1 \(\times\) 10\({}^{5}\) N/mm, respectively. For the thrust bearing, the trial vertical, lateral, and axial stiffness were set to 2.0 \(\times\) 10\({}^{5}\) N/mm, 1.7 \(\times\) 10\({}^{5}\) N/mm, and 1.2 \(\times\) 10\({}^{6}\) N/mm, respectively. The measured and calculated bearing load values are listed in Table 1.  
Table 1 shows that the measured results are close to the calculated results. Taking the measured results as a benchmark, the calculation errors of the three bearings are 3.34%, 2.57%, and 6.70%, respectively. The small initial clearance of the thrust bearing resulted in some errors in reading and recording during the jacking process of the journal. Experimental results demonstrate that the static analysis results derived from the numerical model are valid and reliable, and can be applied to the alignment characteristics analysis of the shafting.  
Figure 2: Numerical model of the shafting.  
Figure 3: Jacking curves. (**a**) Rear stern bearing; (**b**) front stern bearing; (**c**) thrust bearing.
## 4 Validation of Inelastic Seismic Analysis
### Analysis Modeling
#### 4.1.4 Seismic Input Motions  
Figure 15 presents the displacement time history input motions used for the inelastic seismic analyses, which are measured at the shaking table. The correlation coefficients are 0.015 between EW and NS, 0.0015 between NS and V and 0.0003 between V and EW, which are much less than the criteria value of 0.16 required for the seismic input motions [23]. Therefore, since the independence of the input motions is guaranteed, the inelastic seismic analyses can be performed by applying them simultaneously.  
The time interval (\(\Delta t\)) used in the inelastic seismic analysis is 3.91 ms; then, the cut-off frequency (\(1/2\Delta t\)), 128 Hz, can sufficiently cover the seismic cut-off frequency of 33 Hz in NPP seismic design. The total analysis duration is 32 s. Figure 16 presents the test response spectrum (TRS) calculated from the measured shaking table motions of Figure 15. Compared with the target required response spectrum in Figure 2, it is confirmed that the TRS envelops the target response spectrum.  
Figure 16: Test response spectrum of shaking table motions.  
Figure 15: Displacement input motions used for analyses for PGA = 0.3 g: (a) EW (X); (b) NS (Z); (c) V (Y).
# Three-Dimensional Printing and Digital Flow in Human Medicine: A Review and State-of-the-Art
## 5 Digital Flow in 3D Printing
### Three-Dimensional Modeling  
Three-dimensional modeling is a concept originating from computer graphics sciences. It refers to developing a \"mathematical representation\" of any surface of an object in three dimensions through specialized software [22; 24; 25]. The product is called a \"3D model\". Visually it is similar to creating a clay sculpture, only with digital tools developed by mathematical programming. One of the main advantages of digital 3D modeling is that it is easy to save versions and manipulate the geometries (mesh) completely. When we work on biomedical image manipulation, the 3D modeling stage is after image acquisition because we edit the geometry obtained from the previous step. If the case is to create a medical device, such as a spatula, or a retractor, we do not necessarily require prior image acquisition, and the process starts with 3D modeling. However, for example, if the objective is to create a fracture immobilization splint, the 3D modeling is from a previous surface image of the arm that requires immobilization, and the digital sculpting of the splint is completed using specialized software. Delving a little deeper into the principles, if the objective is to print, the nature of the design must obey not only the shape but also the manufacturing mechanism and final material. Due to physical, chemical, and biological properties, the overall safety assessment must be considered in terms of fidelity, reproduction, precision, compatibility, and others. Several specialized computer graphics programs exist, and their nature is in commercial licensing. They range from free and open source software (\"Free and Open Source Software--FOSS\", which allows modification of algorithms in the hands of programmers), free (\"Freeware\"), free with special paid functions (\"Freemium\"), and exclusive to paid commercial license (\"Commercial license\"). For the health area, there are examples such as Blender(r) (FOSS), Meshmixer(r) (Freeware), Zbrush(r), and Mimics(r) (Commercial license), among others. They can be exported in STL files and loaded to a 3D printer for the next step of additive manufacturing [22; 24; 26; 27].
# Comparison of Temperature Dependent Carrier Transport in FinFET and Gate-All-Around Nanowire FET
###### Abstract  
The temperature dependent carrier transport characteristics of n-type gate-all-around nanowire field effect transistors (GAA NW-FET) on bulk silicon are experimentally compared to bulk fin field effect transistors (FinFET) over a wide range of temperatures (25-125 \({}^{\circ}\)C). A similar temperature dependence of threshold voltage (\(V_{TH}\)) and subthreshold swing (\(SS\)) is observed for both devices. However, effective mobility (\(\mu_{\textit{eff}}\)) shows significant differences of temperature dependence between GAA NW-FET and FinFET at a high gate effective field. At weak \(N_{\textit{inv}}\) (= \(5\times 10^{12}\) cm\({}^{2}\)/V-s), both GAA NW-FET and FinFET are mainly limited by phonon scattering in \(\mu_{\textit{eff}}\). On the other hand, at strong \(N_{\textit{inv}}\) (= \(1.5\times 10^{13}\) cm\({}^{2}\)/V-s), GAA NW-FET shows 10 times higher \(d\mu_{\textit{eff}}dT\) and 1.6 times smaller mobility degradation coefficient (\(\alpha\)) than FinFET. GAA NW-FET is less limited by surface roughness scattering, but FinFET is relatively more limited by surface roughness scattering in carrier transport.  
GAA NW-FET; FinFET; temperature dependence; effective mobility; surface roughness scattering 2020
# Centralized MPPT Control Architecture for Photovoltaic Systems Using LoRa Technology
## 5 Experimental Results  
In order to validate the proposed LoRa-based, centralized MPPT controllers, some field experiments were conducted during the summer of 2023 with the equipment described in Section 3. The experiments were run repeatedly, alternating between the P&O and SMC algorithms, in 2 min periods for several days. In this way, the controllers were tested under different environmental conditions. As the two algorithms used were not run at the same time, it was not possible to compare the results under exactly the same environmental conditions. Therefore, we classified the experiments in the three scenarios that were representative of all the data collected: (1) constant irradiance, (2) smooth changes in irradiance, and (3) sudden changes in irradiance. For this manuscript, a couple of experiments with conditions as similar as possible were selected for each of the three scenarios.
## 2 Materials and Methods
### Comparisons and Statistics  
This section illustrates the working guidelines used for the result evaluation in the study. Result presentation of the classification performance was followed by a between-classifiers comparative analysis including significance of the highlighted trends about both the negative and positive class.  
Which classifier performs best for understanding the quality of the laser beam welds? It order to answer this question statistically, in the first instance, the performance of each classifier among its competitors trained on the same workflow is considered. This allowed to highlight best performances in terms of precision, recall, and f1 score on the same features; subsequently, for each metric distribution, all the classifiers' performances have been compared among all workflows in order to evaluate which classifier (trained on a particular workflow) stands out with respect to other implementations and how it ranks when compared with other workflows and classifiers combinations.  
In order to compare the classification scores, the performances have been computed on equal test sets obtained by using a five-fold cross validation with stratified splitting. The dependent variable is the overall score (for each metric on five-folds) and this is recorded in a separate variable for each method and for each workflow. This gives the possibility to derive an estimate of underlying performance distribution.  
The hypothesis of Gaussian distribution does not hold due to the limited number of tests, so nonparametric [PERSON] test was chosen.  
Nonparametric [PERSON] test and the [PERSON]'s pairwise post hoc tests with Bonferroni correction were carried out using medians to compare the scores for the different methods and workflows, to automatically check if pairwise comparisons were statistically significant once the main test was found globally consistent. In fact, [PERSON] first states the differences in terms of ranks between each pair of groups tested, and only if, once adjusted, \(p\)-values are less than statistical significance level, it allows for pairwise comparisons. The significance level was set to 0.05 and all the analyses were performed using the SPSS software (SPSS Inc., Chicago, IL, USA) Version 21.
## 3 Problem Formulation  
VM placement involves assigning VMs to PMs, either for initial placement or relocation after migration. When a user requests a VM, the scheduling algorithm looks for suitable hosts based on resource requirements like \(CPU\), \(RAM\), bandwidth, and storage. \(CPU\) utilization is the primary dynamic factor, while other resources are typically fixed. To initiate a VM, the virtual machine image is sent or mapped to a PM using a predefined template, and algorithms determine if the VM can be admitted and where it should be executed [28]. Table 1 summarizes the symbols and notations used throughout the optimization model. Let us assume there are \(N\)\(VMs\) and \(M\)\(PMs\). The set of VMs is denoted as \(VM=\{VM_{1},\)\(VM_{2},\)\(\ldots,\)\(VM_{N}\}\), and the set of \(PMs\) is denoted as \(PM=\{PM_{1},\)\(PM_{2},\)\(\ldots,\)\(PM_{M}\}\). Each VM, denoted as \(VM_{j}\in VM\), has \(CPU\), \(RAM\), and bandwidth (\(BW\)) requirements represented by \(VM_{j}^{CPU}\), \(VM_{j}^{RAM}\), and \(VM_{j}^{BW}\), respectively. Similarly, each server \(PM_{i}\)\(\in PM\) has \(CPU\), \(RAM\), and \(BW\)\(\\) capacities denoted as \(PM_{i}^{CPU}\), \(PM_{i}^{RAM}\), and \(PM_{i}^{BW}\), respectively. Figure 1 shows the overall system architecture. It illustrates the interaction between the cloud broker, datacenters, Cloud Information System (CIS), and renewable energy sources. The objective is to find an assignment solution, denoted as \(S_{a}\), with the minimum number of active servers, aiming to improve energy efficiency, reduce the network communication cost, and reduce the carbon cost. In the solution, each VM is placed on one and only one server.
# Detecting Multiple Mixed Bacteria Using Dual-Mode Hyperspectral Imaging and Deep Neural Networks
## 3 Results and Discussion
### Spectral Analysis of Multiple Mixed Bacteria  
Figure 4 displays the transmission hyperspectral image of the mixed sample (imaged at 40\(\times\) micro-objective) containing four types of bacteria: _S. aureus_, _E. coli_, _P. aeruginosa_, and _S. marcescens_. They can hardly be distinguished based solely on their morphological characteristics. The transmission hyperspectral images are used for sample observation and the calculation of the total concentration mixed bacteria using cell counting methods. Combined with the predicted proportion of the mixed bacteria from the MB-Net, the detailed concentration of each bacterium in the mixed sample can be calculated. Figure 4 shows the transmission spectra of several groups of mixed bacteria with varying proportions. The spectral characteristics varies with different mixed ratio. As for the fluorescence spectra, bacteria display similar fluorescent chromophores, but variations in the fluorescent chromophore content, bacterial cellular structure, and the extent of laser energy absorption by different bacteria give rise to distinct bacterial fluorescence spectra. Compared with other bacteria, _P. aeruginosa_ has a unique fluorescent peak located at 635 nm, which can be used as specific characteristic in the detection of mixed samples. Figure 4 shows the fluorescence spectra of several groups of mixed bacteria with varying _P. aeruginosa_ proportions. The _P. aeruginosa_ proportion of sample 01, sample 07, sample 28, and sample 77 are 0.1, 0.7, 0.1, and 0.3, respectively. The fluorescent intensity at 635 nm varies with different _P. aeruginosa_ concentration, which can be used to quantitatively measure the _P. aeruginosa_ concentration among pure or mixed bacterial samples. Sample 01 and sample 28 have the same _P. aeruginosa_ proportions, resulting in the similar fluorescent intensity at 635 nm. In our studies, the fluorescence spectra provided in the additional optical Information relate to bacteria composition and mixed ratio, improving the precision of the MB-Net in predicting mixed bacterial concentration.
## 5 Conclusions  
In this study, a new dual-electrode electrostatic resonant actuator (ERA) was proposed as a means to provide meaningful vibrotactile haptic feedback in large touchscreen display applications. A prototype dual-electrode ERA and custom controller were fabricated and compared to the single-electrode design to determine how the electrode configuration impacted the performance. The parameters of both actuators were experimentally measured, and the systems were determined to be comparable. The additional electrode added in the dual-electrode ERA led to a 73% increase in the maximum vibration intensity when compared to the single-electrode actuator. The top electrode in the dual-electrode actuator, when activated alone, produced a 35% lower vibration intensity compared to the bottom electrode, which can be attributed to the hole made for mounting the accelerometer. The actuators were also tested on a 100 g mock-up panel. The dual-electrode ERA produced a maximum intensity of nearly 65% greater than the single-electrode ERA. In summary, the dual-electrode ERA outperforms its single-electrode counterpart in all testing cases. These results show promising potential for the use of the dual-electrode electrostatic resonant actuator in generating vibrotactile feedback for large  
Figure 11: An illustration of dual-electrode ERA modules for a touch-sensitive display.  
Figure 10: Experimental acceleration response of the single-electrode ERA and the dual-electrode ERA on the 100 g mock-up panels.  
touch displays. In future extensions of this study, one or more dual-electrode actuators will be tested on actual touchscreen displays (>12 in) in multiple configurations to evaluate their vibrotactile feedback performance.  
Conceptualization, [PERSON]-H.Y., [PERSON]-[PERSON], and [PERSON]-[PERSON]; methodology, [PERSON]-H.Y. and [PERSON]-[PERSON]; validation, T.M., [PERSON]-[PERSON], [PERSON]-[PERSON] and [PERSON]-[PERSON]; formal analysis, [PERSON] and [PERSON]-[PERSON]; resources, [PERSON]-[PERSON] and [PERSON]-H.Y.; writing--original draft preparation, T.M.; writing--review and editing, [PERSON]-H.Y., [PERSON]-[PERSON] and [PERSON]-[PERSON]; visualization, [PERSON]-[PERSON] and [PERSON]-[PERSON]; supervision, [PERSON]-[PERSON]; project administration, [PERSON]-[PERSON]; funding acquisition, [PERSON]-H.Y. and [PERSON]-M.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by a research grant from the Korea Institute of Oriental Medicine, grant number KSN2012110.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.