esa-sceva/satcom-chunk-collection huggingface dataset:
# Studying Spatial Unevenness of Transport Demand in Cities Using Machine Learning Methods
## 2 Materials and Methods
### ANN Model Accuracy
The SOM is an unsupervised learning method and, unlike neural networks trained by backpropagation, does not require labeled data to produce results. This class of methods is used, as a rule, for data classification or clustering problems when it is necessary to find a pattern in previously unknown data. The method allows you to transform data from a multidimensional space into a two-dimensional space that is convenient for perception and interpretation, creating a map or mask of the input data that allows you to understand their structure. The neural network within the framework of this method is a rectangular two-dimensional structure (map), the neurons of which are connected only to the input neurons, containing data samples from their dataset. A neuron is an n-dimensional column vector containing weights. In the process of training on input data, the weights of neurons (maps) are modified, trying to become as close as possible to the input vector according to the criterion of minimizing the square of the distance between the weight of the neuron and the value of the input data vector. The map neuron with the best result, which is closest to the input vector based on the totality of weights, is called the Best Matching Unit (winning neuron) or BMU. Thus, the entire map is \"pulled\" to the data point. For data clustering tasks, a self-organizing map was used with empirically determined parameters of vertical and horizontal dimensions equal to three, as well as with a dimension (number of features) of the input space equal to six. To visualize the clustering of areas carried out using the method of self-organizing maps, the principal component method (PCA) was used, which makes it possible to visually represent multidimensional data in the space of two components that determine the dispersion of the input data, without reducing the quality of information. These components correlate with specific factors from the original dataset and can be conditionally interpreted as an integral indicator of the influence of these factors on the distribution of data by class. In the axes of the principal components, it is possible to see the most significant features of the source data, even despite the inevitable distortions.
# Energy Management Scheme for an EV Smart
## 3 Component Modeling and Control
### DG Model  
A mathematical DG model is devised to study the V2G and G2V scenarios. The DG is connected to the PCC bus, adjacent to the load and the EVs as shown in Figure 1. The three-phase sinusoidal currents \(i_{dg(abc_{c})}\) based on the output power \(P_{dg}\) with respect to time \(t\) are given by:  
\[\left(\begin{array}{c}i_{dg(a)}(t)\\ i_{dg(b)}(t)\\ i_{dg(c)}(t)\end{array}\right)=\left(\begin{array}{c}I_{dg}\cos(\omega_{g}t- \delta)\\ I_{dg}\cos(\omega_{g}t-\delta-\frac{2\pi}{3})\\ I_{dg}\cos(\omega_{g}t-\delta+\frac{2\pi}{3})\end{array}\right) \tag{3}\]  
\[I_{dg}=\frac{P_{dg}}{V_{pcc}\times\sqrt{\frac{2}{3}}} \tag{4}\]  
Figure 1: Schematic of renewables and EV integration with the grid. PCC, point of common coupling.  
where \(\omega_{g}\) is the grid angular frequency. The three-phase sinusoidal DG currents are in phase with the three-phase sinusoidal PCC bus voltage \(v_{pcc(abc)}\) to make it an active power source operating at the unity power factor. The DG is incorporated in the system as a current source.  
A positive and negative variation of 50% from the mean output power value is considered for the DG, which is based on the fluctuating characteristics of photovoltaics and wind turbines [3; 33; 34; 35]. Figure 2 shows the DG power output with deviations from the 1 pu mean value.
###### Abstract  
Electroporation (EP) allows for the transport of molecules into the cytoplasm with significant effectiveness by forming transient pores in the cell membrane using electric pulses. This can be used for cellular transport (RE--reversible electroporation) or ablation (IRE--irreversible electroporation). The first of described options fortifies medicine with novel possibilities: electrochemotherapy (ECT), which creates promising perspectives for cancer treatment, and gene electrotransfer (GET), a powerful method of DNA delivery as well as immunogen electrotransfer. The review constitutes a comprehensive explanation of the mechanism of EP in the case of GET, its present and prospective employment in medicine, including gene delivery, vaccinations, therapy, and transfection, are also presented.  
electroporation; gene delivery; electrovaccines; immunogen electrotransfer
## 3 Materials and Methods
### Datasets for Training and Testing
#### 3.1.2 Shenzhen CXR  
The Shenzhen dataset is collected as a collaboration between the Shenzhen People's Hospital and the Guangdong Medical College, Shenzhen, China. CXRs are collected from outpatient clinics within one-month period, mostly in September 2012, using a Philips DR Digital Diagnostic system. This dataset contains 662 frontal CXR, of which 326 are normal cases and 336 are cases with manifestations of tuberculosis (TB). All the images are provided in Portable Network Graphics (PNG) format. The size of the images in this collection varies, but it is approximately \(3000\times 3000\) pixels. Each image is provided with a clinical reading text file that contains the patient's age and gender as well as normal and abnormal labels. In addition, each image has a corresponding binary ground truth mask, generated by manually segmenting lung regions. Further details of the Shenzhen CXR dataset can be found in [32].
# Optically Transparent Honeycomb Mesh Antenna Integrated into OLED Light Source
## 2 Material Characteristic
### Invisibility to the Bare Eye  
In order to integrate the mesh antenna into the OLED, the parameters of the honeycomb structure were optimized so that the human eye cannot distinguish the structure. The performance of the human eye was taken into account in this optimization. Therefore, the angular discrimination of the eye is \(\theta_{min}=4.9\times 10^{-4}\) rad [38]. The distance between two distinct points can be expressed as follows:  
\[p_{min}=d\cdot\tan\theta_{min}\approx d\cdot\theta_{min} \tag{6}\]  
The distance \(d\) between the eye and the object depends on the intended application in which the mesh will be used (Figure 5). This distance is equal to the minimum value of the punctum proximum. Thus, a distance of 1 m was considered, which is quite sufficient for the antenna to be invisible in OLED light sources.  
The distance calculated using Equation (6) is approximately \(p_{min}=490\,\mathrm{\SIUnitSymbolMicro m}\). We can conclude that the mesh antenna using these parameters will be invisible to the bare eye at a minimum distance of 1 m.
# Joint Task Offloading and Power Allocation for Satellite Edge Computing Networks
## 5 Performance Analysis
### Delay Performance in General Conditions  
Indeed, the distances between the SSs and the MS are not necessarily similar and can vary significantly. To further validate the performance of the proposed DB-IPDD algorithm in a scenario closer to real-world conditions, we assume the distances are as follows: [5.1,6.8,60.1,2.1] \(\times\) 10\({}^{5}\) m, in which a significantly large value exists, which is shown in Figure 11. The convergence curve is shown in Figure 12, and we can see that the proposed algorithm is able to converge after 30 iterations in a relatively extreme scenario and the DB-IPDD method obtains a better performance than the IPDD method.  
Figure 11: The distances of each SS.  
Figure 10: Delay versus main frequency: simulation and ideal delay curves under different transmission powers.  
The delay performance of each SS and the MS is plotted in the bar chart in Figure 13. We can conclude that the proposed algorithm is able to find the optimal solution, even in a general relatively extreme scenario, because the approximate delay value among all the satellites meets the conclusion of Theorem 1.  
Based on our analysis, the procedure can be divided into two parts, i.e., transmission and computation. The comparison in each satellite is shown in Figure 14. Generally speaking, as the distance increases, the computation delay tends to decrease while the transmission delay increases, and the reverse holds when the distance decreases.  
Figure 12: The delay of different schemes with varying number of iterations in a general scenario.  
Figure 13: The delay in each SS.
###### Abstract  
Spatial population distribution data is the discretization of demographic data into spatial grids, which has vital reference significance for disaster emergency response, disaster assessment, emergency rescue resource allocation, and post-disaster reconstruction. The random forest (RF) model, as a prominent method for modeling the spatial distribution of population, has been studied by many scholars, both domestically and abroad. Specifically, research has focused on aspects such as multi-source data fusion, feature selection, and data accuracy evaluation within the modeling process. However, discussions about parameter optimization methods during the modeling process and the impact of different optimization methods on modeling accuracy are relatively limited. In light of the above circumstances, this paper employs the RF model to conduct research on population spatialization with multi-source spatial information data. The study primarily explores the differences in model parameter optimization achieved through random search algorithms, grid search algorithms, genetic algorithms, simulated annealing algorithms, Bayesian optimization based on Gaussian process algorithms, and Bayesian optimization based on gradient boosting regression tree algorithms. Additionally, the study investigates the influence of different optimization algorithms on the accuracy of population spatialization modeling. Subsequently, the model with the highest accuracy is selected as the prediction model for population spatialization. Based on this model, a spatial population distribution dataset of Sichuan Province at a 1 km resolution is generated. Finally, the population dataset created in this paper is compared and validated with open datasets such as GPW, LandScan, and WorldPop. Experimental results indicate that the spatial population distribution dataset produced by the Bayesian optimization-based random forest model proposed in this paper exhibits a higher fitting accuracy with real data. The Coefficient of Determination (R\({}^{2}\)) is 0.6628, the Mean Absolute Error (MAE) is 12,459, and the Root Mean Squared Error (RMSE) is 25,037. Compared to publicly available international datasets, the dataset generated in this paper more accurately represents the spatial distribution of the population.  
population spatialization; random forest; model parameter optimization
# An Application of an Unequal-Area Facilities Layout Problem with Fixed-Shape Facilities
## 4 Computational Results  
A set of test problems are used in this paper in order to test the performance of the proposed GA. More specifically, the first problem, P8, is an eight facility problem, which was generated randomly. The second and third problems (i.e., P20 and P50) are 20- and 50-facility problems available in the literature from the commercial software VIP-PLANOPT. Also, P20 is available in Imam and Mir [28]. For the last test problem, the floor space is unconstrained, and the Euclidean distance measure is used to determine the distances between pairs of facilities. However, the rectilinear distance measure is used on P8 and P20 test problems. Also, in all experiments, a PC (equipped with Microsoft Windows 10, an Intel Core processor with a CPU speed of 3.20 GHz and 16 GB of RAM) was used to solve the test problems, and the proposed GA was coded using the C++ programming language.  
Recall, the GA parameters are _Gen_Size_, _Max_Num_Cross_, _Num_Rand_Chrom_, and _Max_Duration_. The number of random solutions, _Num_Rand_Chrom_, generated at each generation was set to 0.1_Gen_Size_. The values used for parameters _Gen_Size_, _Max_Num_Cross_, _Max_Duration_ are shown in Table 3. Each test problem was solved five times, since the GA is stochastic, and the outcome can be different for different runs. Minimum TWDs, means, standard deviations, % Improvements, and run times are used for comparison purposes.
# Invariant Feature Encoding for Contact Handprints Using Delaunay Triangulated Graph
## 5 Results  
### Handprint Segments from Finger Region  
Experiments for fingerprints have been performed using partial-quality images which contain sufficient minutiae to form a graph in a print. The images have been divided into two different data sets with geometrical variations and without geometrical variations. From the images with geometrical variations, particularly with the scaled and rotated images, the following results are obtained as illustrated in Table 1. For the former experiment using 200 genuine and 8538 impostor matches, an equal error rate (EER) of 1.87% is obtained with a 97% genuine-accept rate (GAR) at a rate of 1% false-accept rate (FAR). For the later experiment using 962 genuine and 67,944 impostor matches, an EER of 1.97% was obtained with 94.25% GAR at a rate of 1% FAR.  
It is observed in the graph illustrated in Figure 8 that the algorithm performed well with the fingerprints that show geometrical variations. The algorithm proves its invariant nature for the images and opens a path to investigate other properties of the prints. It strongly gives a recommendation that if a hand segment is acquired from a finger region that can show sufficient minutiae, the algorithm would definitely support it. The next experiment has been planned to investigate the robustness with degradation present in the images.
# Development of a Robotic Airboat for Online Water Quality Monitoring in Lakes
## 8 Conclusions  
This work described the development, construction, and implementation of a robotic airboat to measure water quality in lakes. In general, it can be stated that the prototype obtained good results for an automatic field analysis, to provide relevant data on lagoon conditions. Along with the explicit benefits of data collection and analyses, other important contributions of this work are--this system helps to understand the biome and the behavior of aquatic animals in relation to water pollution, in a precise way; it automates the activity of the operator, since they sometimes do not have the resources, besides the lack of care in handling the collection containers; it provides security in the logistics; avoiding the reserve of laboratory services, which must be available on the day of collection, as well as the considerable increase in the number of evaluated points, thus, providing a better evaluation of the lagoon.  
In addition, the use of Wi-Fi, even with a limited range, did not cause a loss of performance to the vehicle. The boat operated entirely autonomously, through its embedded system, fulfilling the mission designed for it. The data collected was stored on board and transmitted to the station, when it was within the limit of the Wi-Fi transmission.  
For future work, more sensors should be added, such as a bathymeter for studying and mapping the depth of an aquifer; a 3D map can be generated for the silting studies, as well as the inclusion of 3G or satellite communication systems, whose data can be sent to the world wide web (Cloud) network, thus, providing a remote refresh rate for water analysis, anywhere in the world (IoT). A mechanized shuice system can be incorporated into the boat, to collect water samples in small reservoirs, for later and more detailed analysis, such as fecal coliforms, which demand a more time-consuming process for cultures in laboratory and whose result is only available after a few days.  
Conceptualization, methodology, validation M.M. and A.A.; project administration, supervision A.A.; writing--original draft preparation, formal analysis, M.M. and F.M.; writing--review and editing, M.M., F.M., V.A. and A.A.  
V.A. received support from the Brazilian National Council for Research and Development (CNPq) via Grant 304315/2017-6 and 430274/2018-1. A.A. received support from the Brazilian National Council for Research and Development via Grant 304790/2015-0. Authors thank [PERSON] for relevant contribution to this work.  
The authors declare that there is no conflict of interest.
## 2 Materials and Methods
### Measurement Protocol  
The kinematic (joint angles) and kinetic (joint torques and ground reaction forces) parameters of the perturbed and unperturbed gait were measured in the Interactive Gait Real-Time Analysis Laboratory (GRAIL, Motek Medical B.V., Amsterdam, The Netherlands). The GRAIL consisted of a 2200 mm-long and 2 \(\times\) 500 mm-wide dual-split-belt treadmill (1000 Hz), a motion capture system (Vicon Metrics Ltd., Oxford, UK) with ten Bonita cameras operating at 100 Hz, three video cameras, synchronised virtual reality environments (circular screen with 5 m diameter, 180\({}^{\circ}\), and 3 m height) with three projectors, and a safety harness suspended from the ceiling (Figure 1). The accompanying D-Flow software (Motek Medical B.V., Amsterdam, The Netherlands) was used for model adjustment, initiating perturbations, and data collection.  
When the participants arrived, anthropometric measurements were collected, and 25 reflective markers were placed on their bodies according to Human Body Model 2 (HBM2) guidelines (Figure 2). Throughout the study, participants wore their own comfortable sports shoes, which they typically use for walking on a treadmill or exercising at the gym.  
Subsequently, participants stepped on the treadmill and aligned in a T-pose to achieve an accurate body model fit in D-Flow 3.26 software. The test protocol included three trials. In each trial, the walking speed was constant at 1.2 m/s. Each trial lasted 80 s and contained five perturbations. The perturbations were introduced at the 30 th, 40 th, 50 th, 60 th, and 70 th seconds of each trial. The magnitude of the perturbations was five on a scale of 1 to 5. This setting resulted in a change in treadmill belt speed by about 0.5-0.6 m per second. Thus, during the perturbation, the velocity of the left lane of the treadmill was 1.7-1.8 m/s (Figure 3). This setup was in line with that proposed by [PERSON], et al. [33]. The duration of the perturbation was \(\sim\)0.82 s.  
Figure 1: A view of the Interactive Gait Real-Time Analysis Laboratory (GRAIL, Motek Medical B.V., Amsterdam, The Netherlands).  
the gait cycle was defined from the first contact of the left foot with the ground to its re-contact with the ground. Additionally, a visual verification was performed using motion data from OpenSim to ensure that the gait cycle was accurately identified, especially during perturbations. For each participant, the undisturbed gait cycle from the second trial, which occurred around the 20 th second of gait, was used for analysis, as participants were more accustomed to walking on a treadmill (Figure 5B). For perturbed gait, only the first perturbation gait cycle in each trial was considered, typically occurring around the 30 th second of gait (Figure 5C).  
Therefore, for analysis, there were 21 gait cycles without perturbations for the left lower limb, 21 gait cycles with perturbations in the IC phase, 21 gait cycles with perturbations in the MS phase, and 21 gait cycles with perturbations in the PS phase.
# Variational Mode Decomposition Based Time-Varying Force Identification of Stay Cables
## 4 On-Site Bridge Experiment
### Experimental Results and Analysis
#### 4.2.3 Recognition Results for Condition 3  
Condition 3 was in a state after the installation of the new support was completed, and it was the next morning, so it was basically stable. The acceleration data with a duration of approximately 160 s were selected for analysis. The acceleration signal diagram and the fundamental frequency are shown in Figure 17. According to the inspection report of past years, the completed cable force of A15 was 4300 kN, the cable force measured in December 2015 was 4480 kN, the cable force measured in April 2017 was 4450 kN, and the cable force measured in March 2018 was 4500 kN. As shown in Figure 17, though the proposed method has achieved good results in the lab experiments, it is found that there was a certain error in the on-site bridge measurement. The identified cable force value fluctuates around 4475.0 kN which was close to the values measured in previous years.  
It can be seen from the above results that the time-varying cable force identification method based on variational modal decomposition and HHT can basically identify the cable force change trend and can be used as an auxiliary monitoring method during the replacement of the support.
# Unmanned Autonomous Intelligent System in 6G Non-Terrestrial Network
## 3 UAV-Assisted Non-Terrestrial Networks
### Unmanned Aerial Vehicles Application in NTNs
UAV-assisted networks have facilitated a variety of applications, such as post-disaster area assessment, personnel search, and information collection [76; 77; 78]. With the emergence of new technologies in the domain of UAVs, UAV-assisted non-terrestrial networks demonstrate significant flexibility and mobility. However, there is still a need for further study on issues such as UAV-to-UAV or UAV-to-satellite collaboration communication, device-to-device communication, and UAV optical communication [79].
## Dynamic Attributes  
**[PERSON] *, [PERSON], [PERSON] and [PERSON]**  
Department of Computer Science, University of Victoria, 3800 Finnerty Road, Victoria, BC V8P 5C2, Canada; E-Mails: [EMAIL_ADDRESS] (J.-L.D.); [EMAIL_ADDRESS] (A.T.); [EMAIL_ADDRESS] (H.M.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +1-250-472-5719.  
Academic Editor: [PERSON]  
_Received: 29 January 2015 / Accepted: 29 April 2015 / Published: 12 May 2015_  
**Abstract:** Quality web service discovery requires narrowing the search space from an overwhelming set of services down to the most relevant ones, while matching the consumer's request. Today, the ranking of services only considers static attributes or snapshots of current attribute values, resulting in low-quality search results. To satisfy the user's need for timely, well-chosen web services, we ought to consider quality of service attributes. The problem is that dynamic attributes can be difficult to measure, frequently fluctuate, are context-sensitive and depend on environmental factors, such as network availability at query time. In this paper, we propose the Dynamis algorithm to address these challenges effectively. Dynamis is based on well-established database techniques, such as skyline and aggregation. We illustrate our approach using observatory telescope web services and experimentally evaluate it using stock market data. In our evaluation, we show significant improvement in service selection over existing techniques.  
**Keywords:** service discovery; service selection; quality of service (QoS); Dynamis; aggregation; skyline; histogram; top \(k\)