esa-sceva/satcom-chunk-collection huggingface dataset:
# Adversarial Validation in Image Classification Datasets by Means of Cumulative Spectral Gradient
## 3 Materials and Methods
### Corrupted Datasets  
MNIST-C is a dataset proposed by Google researchers from corruptions of MNIST images. It was proposed to facilitate the evaluation of OOD robustness in computer vision algorithms. According to its authors, the corruptions are realistic, diverse and are designed in such a way that they preserve the semantic content of the underlying image. Another important aspect is that a simple data augmentation does not trivially solve the challenge of this dataset. To the original MNIST dataset, 15 corruptions related to noise (shot noise, impulse noise), blurring (glass blur, motion blur), affine transformations (shear, translate, scale, rotate), digital processing (brightness, stripe, zigzag, dotted line, Canny edge detector) and weather (fog, spatter) have been applied [27]. Examples of the types of corruptions in MNIST are shown in Figure 2.  
As for the CIFAR datasets, there are modified versions for 19 corruptions. Each corruption has been applied with five levels of severity, where the lowest level was applied to the first ten thousand images, and the highest level to the last ten thousand. The corruptions can be grouped into four categories: noise (Gaussian, shot, impulse, speckle), blur (defocus, glass, motion, zoom, Gaussian), weather (snow, frost, fog, spatter) and digital (brightness, contrast, elastic transform, pixelate, jpeg compression, saturate). These corruptions took into account aspects such as semantic preservation of content or the use of real-world-like corruptions, and their severity was adjusted to identify a level that reduces model performance while retaining semantic integrity [28]. Figure 3 shows examples of corruption found in CIFAR-10-C and CIFAR-100-C datasets.  
Consequently, the idea in this study is to compare each of the classes of the MNIST, CIFAR-10, and CIFAR-100 training subsets with respect to the modified data in each of the MNIST-C, CIFAR-10-C, and CIFAR-100-C classes. This evaluation is performed under varying conditions, i.e., in each of the corruption types listed above, in order to evaluate the distribution shift using the method described below.  
Figure 3: Examples of the 19 corruptions included in the CIFAR-10-C and CIFAR-100-C datasets.  
Figure 2: Examples of the 15 corruptions included in the MNIST-C dataset.
# A Blockchain-Based Trust Model for Uploading Illegal Data Identification
## 5 Results and Discussion
### Trust Model Results and Analysis  
In order to test the blockchain-based trust model for uploading illegal data identification, we conducted simulation experiments for smart contracts on \"Rinkeby\". We set up several accounts on \"Rinkeby\" to simulate different roles, namely, IDBC, IBC, and INs. We use the interface by \"Ether\" for each account and pay different fees according to the incentive mechanism. For experimentation, we first deployed the most basic INPSC and registered several accounts in the identification node pool. Then, IDBC sends the hash value returned by IPFS to IBC to start to identify the data information. After that, we test all possible scenarios to test and validate all interfaces. The results show that the trust model in this paper basically meets the identification needs.  
The credibility of the trust model is proved by game theory and guaranteed by the reputation-based random selection algorithm and incentive mechanism. The credibility is supported by the technology of the blockchain itself. Therefore, we mainly analyze some performance information from experimental studies. Among them, performance refers to the complexity of each interface in the contract. It determines the fees that need to be paid by INs in IBC. Because INs need to execute certain programs defined in the method, the more complex the interface is, the higher the cost it needs to pay, that is, the higher the cost. This is measured by Ethereum's definition of \"Gas\". The identification fee is the product of \"Gas\" consumption and the price per unit. The cost is similar whether it is in the main network or in the simulation experiment. Therefore, we recorded all \"Gas\" consumption for each operation of the trust model.  
Figure 4 shows the simulation results of the trust model. It can be seen that compared with IDBC and IN, IBC needs more cost consumption in the whole identification cycle, and IDBC and IN use less interface consumption. This is in line with the original intention of the model design. In most cases, IBC is the maker of tokens and has more tokens, and IBCs main purpose is to identify the illegal data that IDBC is about to upload to the chain. INs consume less \"Gas\" but obtain more \"Gas\". IDBC pays less \"Gas\", which is very small compared to the identification service fee paid. These cost expenditures can persuade INs and IDSCs to participate in the trust model. In addition, \"Gas\" consumptions are implemented based on smart contracts. It is possible to further optimize the complexity to reduce gas consumption.
# European Geostationary Navigation Overlay Service Performance Prediction +
## 5 Machine Learning-Based Performance Prediction for EGNOS
### Algorithm Selection and Training  
Figure 2 shows the training process, for which we used over five years of data. The features (preprocessed historical GNSS and (space)weather data) were used in conjunction with the labels (historical UDREI and GIVEI broadcast values) to train different prediction algorithms, of which the best performing one was eventually selected for the product to be developed in the frame of this project.  
Machine learning algorithms are quickly evolving and there is an obvious 'best' algorithm for predicting the UDREI and GIVEI values based on our selected input features. Both UDREI and GIVEI values are categorical labels representing a given (discrete) class, the prediction of which can be based on regression, time-series, and classification algorithms. Because the 'unmonitored' UDREI and GIVEI values are assigned based on special rules, regression algorithms were deemed inappropriate. Similarly, the use of time-series was excluded due to the impact of sudden changes in geometry when satellites go in- and out of view of the RIMS stations. We therefore decided to focus only on multiclass classification algorithms. The multiclass classification problem type narrowed down the potential algorithms that are applied as part of best practices.  
There are two main approaches for classification: decision tree-based methods and (deep) neural network-based algorithms. We tested both types of algorithms, as well as mixed architectures running deep-learning models, and ended up trying the following  
Figure 2: The training process. GNSS and (space)weather data are gathered and pre-processed before being fed to the training module. The training produces both GIVEI and UDREI prediction models that will become part of the operational prediction system and related performance data.  
algorithms: decision tree, gradient boosted decision trees, XGBoost, LightGBM, CatBoost, CNN and CNN + MLP. For the deep-learning models, which were tried for GIVEI only, we have tested multiple values for number of layers, number of units per layer, pool types, padding, momentum, learning rates, optimizers, and architectures.  
For both GIVEI and UDREI, the decision tree algorithms were fast to train, but their accuracy figures are not impressive. LightGBM provided the best performance overall and an acceptable training time and therefore was the algorithm we ended up using for both UDREI and GIVEI prediction. Catboost, XGBoost, and deep-learning methods also provided acceptable performances and training times. The LightGBM models were eventually trained using over 5 years of training data, which were subsampled to provide 50,000 examples of each UDREI and GIVEI outcome.
## 1 Introduction
A PC slab not only supports in-plane guided modes that are confined by the slab completely (which cannot couple to external radiation), but also supports guided-mode resonances (referred to as quasi guided modes or leaky modes) which can couple to the external environment. Therefore, the maximum intensity of the electromagnetic field can be observed both in the high RI layer and in the evanescent part outside of the PC slab. When used in biosensing and bioimaging, the binding events of biomaterials interacting with the evanescent field atop of the PC slab and the associated RI changes can be obtained by detecting the peak wavelength shift (PWS) of guided-mode resonances in the reflection/transmission spectrum. Since the periodicity is in the plane, the lateral propagation of the modes is prohibited in the PC slab biosensor and, therefore, high spatial resolution can be realized in bioimaging. Figure 1: Photonic Crystal (PC) Surface Biosensor. (**A**) Schematic of the PC surface on a substrate with structure parameters: grating period (\(\Lambda\)), grating depth (d), refractive index (RI) of low-RI grating material (n\({}_{\text{low}}\)) and high-RI top layer (n\({}_{\text{high}}\)), thickness of high-RI layer (t); (**B**) Band structure of a photonic crystal biosensor calculated by FDTD simulation; (**C**) Normalized reflection spectrum from the PC surface with resonant peak wavelength value (PWV) of \(\lambda_{0}\); (**D**) Peak wavelength shift (PWS) of \(\Delta\lambda\) extracted from the normalized spectra with a background pixel (PWV of \(\lambda_{0}\)) and a pixel with surface-attached biomaterial (PWV of \(\lambda_{1}\)) on the PC biosensor.
# Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications
## 4 Smart Metering Applications--Smart Grid Projects
### Other Projects for Energy Management through ICT Tools
#### 4.4.3 EPIC-HUB  
The \"EPIC-HUB\" project, which involves participants from six countries, also deals with issues of improving the energy efficiency in neighborhoods through ICT tools. The port of Genoa, the airport of Belgrade and the exhibition center in Bilbao are the three main sites of the project. Monitoring of the energy produced and consumed and improving storage facilities as well as integrating RES are the main project objectives. The overall energy consumption monitoring is done through power meters located in the main substation through the GSM technology [94].
## 2 Experimental Setup
### Instrumental Techniques  
All zeta potential analyses were carried out using the Malvern Zetasizer Nano ZS. The scattered light was detected at a 135\({}^{\circ}\) angle with Non-Invasive Backscatter (NIBS) technology. The refractive index of the particle (1.59) polystyrene standard as well as the refractive index (1.33) and the viscosity (0.88) of ultrapure water were measured at 25 \({}^{\circ}\)C. All data analyses were performed in automatic mode. The measured size range was presented as the mean value of four runs. For zeta potential measurements, a universal dip cell with an applied voltage of less than 5 V was used and the Smoluchowski approximation was employed by the instrument's software to calculate the zeta potential values for aqueous solutions of Au NPs (F(Ka) value = 1.5).  
FTIR spectra were obtained using a PerkinElmer spectrum 100 Fourier Transform Infrared Spectrometer (PerkinElmer, Waltham, MA, USA). The KBr pellet technique is most commonly adopted for recording the spectra. However, in this work, the purified samples were lyophilized and the powdered solid were characterized directly in their free-standing state.
## 5 Discussion
### Speech Level Recognition and Users' Impressions  
The results of the previous section demonstrate that, although some users shifted their speech level according to the shift in the speech level of the dialog system, a certain number of users interacted without adjusting their speech to the level of the dialog system. Under the speech level shift condition, the speech level used by the dialog system changed depending on that of the user, and thus, differences in the user perception of the speech level of the dialog system may have caused variations in their impressions of the system. Based on the evaluation of Q6, namely \"I decided on my own wording while keeping the wording of the dialog system in mind,\" we clustered the experimental participants whose evaluation value was three or lower under the shifting speech level condition into the low speech level recognition group and the participants whose evaluation value was 5 or higher into the high speech level recognition group.  
We compared the evaluations of the humanness of the dialog system of the two groups (Figure 4). The results of the t-tests revealed a tendency (\(p<0.1,d~{}=~{}0.94\)) for the high speech level recognition group to rate the dialog system higher for Q3; that is, \"The utterances of the dialog system were attentive to its relationship with me\". Furthermore, the effect size was large. In this analysis, the \(p\)-values were corrected using [PERSON]'s method. No significant differences were observed in the other items. This result indicates that the users who determined their speech level by focusing on the shifting speech level condition tended to feel that the dialog system focused on its relationship with the user. For users in the high speech level recognition group, it is suggested that the dynamic speech level shift of the dialog system may facilitate the attribution of intentionality [55] and sociability [56] to the dialog system.
## 3 Results and Discussion
### GFET Flexibility and Reliability  
Mechanical flexibility of GFET is typically evaluated using custom-made manual bending test fixtures. DC probes were landed on source, drain and gate contacts. The bending of the device was increased and simultaneously DC measurements can be conducted for _in situ_ studies. This process can be repeated until device breakdown. Our contemporary graphene transistors with patterned dielectrics can sustain bending strain of around 8.6%. At this tensile strain, the channel-resistance and mobility remained within 80% of its initial value. Strains greater than 8.6% resulted in gate dielectric breakdown and increased gate leakage [8; 19]. To improve device reliability and robustness, GFETs were encapsulated with Si\({}_{3}\)N\({}_{4}\) and a fluropolymer (e.g., Cytop). The devices were exposed to DI-water and other wet environments to evaluate its liquid resilience. The device performance was within 80% of its initial properties even after two days of immersion in water [19].
# _Torusspora delbrueckii_ Strain Behaviour within Different
## 3 Results and Discussion
### Fermentation Monitoring  
The concentration of sugars in the apple juice depends on the fruit variety, harvest time, storage, and climatic condition. Several physicochemical parameters were measured for the juice obtained from the apple cultivar Topaz (Table S1). The juice was characterized by 12 \({}^{\circ}\)Brix, 59.33 g/L sugar (sum of fructose and glucose) and 2.34 g/L of sucrose, pH 3.44, and a total acidity of 6.5 g/L. The YAN content was given as the sum of the \(\alpha\)-amino nitrogen and ammoniacal nitrogen, both measured by enzymatic analysis. After the nutrient addition, a YAN concentration of 133 mg/L was estimated.  
After the yeast inoculum, the performances of the two _T. delbrueckii_ strains were assessed. The measurement of the sugar consumption showed that all fermentations had a regular kinetic, with the first fermentation being completed in 10 and 14 days by strain A and strain B, respectively (Figure S2). A similar fermentation trend was observed in apple juice fermented with _T. delbrueckii_ by [PERSON] et al. [28].  
Sparkling ciders were produced according to three different methods: Pet-Nat (PN), the traditional bottle-fermented method (TM), and the tank-fermented Charmat method (CM). Both strains tested in our study carried out fermentation to dryness with no difference between methods, showing the ability to be active also under different overpressure. The similar observation for two _T. delbrueckii_ strains utilized to produce sparkling wines have been described by [PERSON] et al. [29]. On the contrary, [PERSON] et al. [21] and [PERSON] et al. [30] observed a scarce tolerance to pressure higher than 350 kPa for other _T. delbrueckii_ strains, suggesting that, also for this yeast species, the tolerance to high pressure is a strain-dependent factor.  
For the PN cider, part of the fermentations products was bottled at a residual sugar content of 22.61 g/L, reached by strain A after 7 days, and 26.43 g/L, reached by strain B after 10 days. For each PNA and PNB bottle group, one manometer was mounted on one of the eight bottles to monitor the overpressure evolution during time. Both the PNA and PNB ciders showed a similar fermentation rate, reaching a constant pressure after 10 days. This was considered as the fermentation end point for all the bottles.  
To monitor the overpressure evolution, also for the TMA and TMB bottle group, one manometer was mounted on one bottle per type. In the TM ciders, according to the CO\({}_{2}\) development, the fermentation had a faster rate in the first 15-20 days, and a second slow phase, becoming constant within day 55 and day 56.  
The CM cider inoculated with strain A showed a pressure of 4.5 bar after 51 days, while for strain B, 3.5 bar were measured after 57 days. In CM, the pressure was measured at the time the pressurized tank was opened for isobaric filtration and bottling, and, therefore, the time which included fermentation and maturation, established according to the usual CM method applied in many local cider production facilities.
###### Abstract  
As the number of electric vehicles (EVs) significantly increases, the excessive charging demand of parked EVs in the charging station may incur an instability problem to the electricity network during peak hours. For the charging station to take a microgrid (MG) structure, an economical and energy-efficient power management scheme is required for the power provision of EVs while considering the local load demand of the MG. For these purposes, this study presents the power management scheme of interdependent MG and EV fleets aided by a novel EV charging/discharging scheduling algorithm. In this algorithm, the maximum amount of discharging power from parked EVs is determined based on the difference between local load demand and photovoltaic (PV) power production to alleviate imbalances occurred between them. For the power management of the MG with charging/discharging scheduling of parked EVs in the PV-based charging station, multi-objective optimization is performed to minimize the operating cost and grid dependency. In addition, the proposed scheme maximizes the utilization of EV charging/discharging while satisfying the charging requirements of parked EVs. Moreover, a more economical and energy-efficient PV-based charging station is established using the future trends of local load demand and PV power production predicted by a gated recurrent unit (GRU) network. With the proposed EV charging/discharging scheduling algorithm, the operating cost of PV-based charging station is decreased by 167.71% and 28.85% compared with the EV charging scheduling algorithm and the conventional EV charging/discharging scheduling algorithm, respectively. It is obvious that the economical and energy-efficient operation of PV-based charging station can be accomplished by applying the power management scheme with the proposed EV charging/discharging scheduling strategy.  
charging station; deep learning; electricity load; electric vehicle; microgrid; optimization; photovoltaic; power management; scheduling; vehicle-to-grid; vehicle-to-vehicle +
Footnote †: journal: Applied Sciences
## 4 Laboratory Experiment Verification of space state Transformation
### Theory and Design of the Indoor Model Experiment
The indoor experiment diagram of the spatial transformation problem is shown in Figure 8. In the indoor experiment, the similarity ratio was 1:300, and the internal size of the model was 1.0 m \(\times\) 1.0 m \(\times\) 1.0 m, which was used to simulate the surrounding rock with the actual size of 300 m \(\times\) 300 m \(\times\) 300 m. To simulate the spatial transformation relationship of VMTEM, high-resistance filler was used to simulate the actual air inside. The size of the high-resistance filler was 0.5 m \(\times\) 0.5 m \(\times\) 1.0 m, which was used to simulate the air with the actual size of 150 m \(\times\) 150 m \(\times\) 300 m. The model was filled with salt water, and the resistivity of salt water should be 0.01 \(\Omega\)-m, in theory. However, due to the saturation of salt water, it was difficult to achieve the theoretical resistivity of salt water in the laboratory experiments, so the resistivity of salt water in the laboratory experiments was approximately 5.0 \(\Omega\)-m (indoor temperature was 18 degrees Celsius). The critical distance was L = 1 m under the scale of 1:300, which was close to the size of the indoor model. Therefore, the transient electromagnetic response intensity of L = 150 m could be preliminarily determined, that is, the critical distance of the indoor experiment was L = 0.5 m (the model size met the size). Based on the above two similarity criteria, the variation law of the transient electromagnetic response curve in the prototype could be reflected by the transient electromagnetic response curve in the model, and only a certain coefficient ratio existed between the two conditions. In the laboratory experiment, the geometric similarity ratio was set to 1:300, so the scale of the physical model experiment could be presented to meet the background field requirements of the site electromagnetic environment. Figure 8: Schematic diagram of the vehicle-mounted transient electromagnetic spatial transformation problem.
# Effective Receiver Design for MIMO Visible Light Communication with Quadrichromatic LEDs
## 5 Conclusions  
This paper proposes a receiver design for the MIMO-VLC systems composed of QLEDs. The maximization problem of the minimum ED under constraints is obtained by using the normal vector of the receivers. Consequently, the Taylor expansion method is employed to transform the original optimization problem into a convex one that can be solved by an iterative algorithm. Moreover, to reduce the computational complexity, the proposed algorithm, which includes two stages, finds the optimal receiver orientation with different initial solutions. The simulation results show that the proposed receiver provides a better BER performance compared to the various conventional receivers, where the fixed orientation of PD is employed. Moreover, the receiver design algorithms with lower complexity and higher performance, or the consideration of turbulence induced noise [14] in specific scenarios such as underwater and open-path optical communications should be further investigated in the future research.  
Both authors discussed the contents of the manuscript and contributed to its presentation. [PERSON] designed and implemented the proposed scheme, analyzed the simulation results, and wrote the paper under the supervision of S.K.  
This research was funded by the Research Program through the National Research Foundation of Korea (NRF-2019R1A2C1005920).  
The authors declare no conflict of interest.  
Figure 4: BER performance of the proposed receiver in both locations with different crosstalk values when \(R=6\) bps.  
Figure 3: BER performance of the proposed receiver in Location 1 with different crosstalk values when \(R=4\) bps.