esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Deep-Learning-Based Channel Estimation Methods
### Overview of Neural Networks
#### 3.1.2 Cnn  
CNN is among the most representative model structures for deep learning, with the earliest proposition dating back to the publication of the seminal literature [48]. Figure 8 shows a basic CNN structure comprising an input layer, multiple convolutional and pooling layers, a fully connected layer, and an output layer. The input layer's primary function is to preprocess the data. After this, the convolutional layer extracts features from the processed data, and its output is then mapped nonlinearly via the activation function. Following this, the pooling layer is introduced to decrease computation and prevent overfitting. The convolutional and pooling layers are alternately stacked, and after a series of operations, the output is obtained through the fully connected and output layers. In the convolutional layer, the convolutional kernel is locally connected to its input feature map. For each position in the output feature map, the value is obtained by the weighted sum of the local inputs and connection weights, plus the bias. As this process is equivalent to the convolutional operation, the network is termed a convolutional neural network.
## 5 Discussion
### Relationship between Model Performance and Climate Classes  
Lastly, we investigated the effect of climate classes. To this aim, we used [59], which defines four classes in total: tropical (A), dry (B), temperate (C), and continental (D). Our selected stations are distributed as 23% in class B and 75% in class C. The remaining 2% belongs to classes A and D, with one station for each.  
In Figure 11, we present the model's prediction performance under different climate conditions as a boxplot. The stations in class B shows lower MAE values compared to those in class C (see Figure 10(a)). Considering the climate class properties, the rapid changes in the moisture affect the dielectric properties of the target [32,74]; at the same time, precipitation is a significant factor that negatively impacts the SM prediction due to the change in the interaction between SAR signals and land surface.  
We obtained better soil moisture predictions in arid climates (Bw) than those in semi-arid climates (Bs) regions due to less precipitation and more evapotranspiration. We also  
Figure 10: Time series of SM predictions during the testing period for stations 815, 1541, and 1569.  
observed a similar behavior between no-dry-season climate (Cf) and dry summer (Cs) temperate climate classes (see Figure 11b). The no-dry-season climate, as inferred by its name, has a high precipitation rate compared to a dry summer climate, which makes the stations located in this climate region challenging for SM prediction.
# GPR Data Interpretation Approaches in Archaeological Prospection
## 6 CNN Application to GPR Data
### Dataset Construction  
The training datasets were constructed from scratch using data collected from several archaeological sites, 50 of them being located in Greece, 1 in Cyprus, and 1 in Naxos,Sicily. Data collection was conducted under the framework of research projects of the Laboratory of Geophysical-Satellite Remote Sensing and Archaeo-environment (GeoSatRe-SeArch Lab), Institute for Mediterranean Studies - Foundation for Research and Technology Hellas (IMS-FORTH), Rethymno, Greece. All data were collected using the Noggin smart cart system, equipped with a 250 MHz antenna. The data were processed in MATLAB following standard methods and techniques, and C-scans were extracted (Figure 2). A preprocessing step was then followed by applying an overlapping sliding window to crop square subregions of selected C-scans. This step was mandatory in order to increase the number of images used for training. For the classification, three classes were defined based on dominant features observed in the data: unidentified geophysical anomalies, structures, and noise in stripe form. In total, 18,375 examples were selected manually, 6125 per class. One question raised was how to split the selected examples into a training and test set. For this reason, two datasets (A and B) were defined, each following a different splitting approach to examine which one leads to better generalization. For dataset A, the test set examples were derived exclusively from a single archaeological site, while for dataset B, the examples were split into training and test sets randomly.
# Stateflow-Based Energy Management Strategy for Hybrid Energy System to Mitigate Load Shedding
## 4 Execution of SF Chart and Operation Modes of HES  
Initially, the SF chart (referred to Figure 4) is inactive; it is triggered after getting input signals from Simulink. The state HES_operation is entered by executing its entry actions. It defaults into Grid_Connected_Mode. Due to the AND composition, its child-states PV_Mode and Grid_Mode are executed simultaneously. In the same way, child states of PV_Mode (PV_to_ESU_Mode, PV_to_Load_Mode) and Grid_Mode (Grid_to_ESU_Mode) are executed according to the set priority. After evaluation, the state data of the Grid_Connected_Mode are updated. Then, the CFT is checked; if P_Grid=0 is valid, the inter-state transition is activated; otherwise, intra-state transition is evaluated. The intra-state transition (if performed) activates the Grid_Connected_Mode again and repeats the state activity in the next time-step. Without loss of generality, we focus on the first case, the inter-state transition.  
Figure 8: (**a**) Hourly profile of power grid supply during summer (inclusive of load shedding durations). (**b**) The community load demand on an hourly basis.  
Figure 9: (**a**) Heat map view of annual solar irradiance, and (**b**) annual ambient temperature of Quetta.  
This transition results when the operation leaves Grid_Connected_Mode. Upon exiting, Grid_Connected_Mode (with all the child states) is marked inactive. During the inter-state transition, the event load_shedding is broadcasted, and therefore, the state Islanded_Mode becomes active. The execution in the Islanded_Mode is not independent. The solid borders in Figure 4 indicate the OR composition of the child states. Among the child states, either RES_Mode or Gen_Mode can be active at a time. Further, the states in the RES_Mode and Gen_Mode become active concurrently due to the AND formation. After executing all the child states, the Islanded_Mode is exited. The iteration continues for all the input data for each time-step. The chart transfers the output data to MATLAB and goes to the sleeping stage. The complete operation of HES controlled by EMS is shown in Figure 10.
# Recent Developments in Materials Science for the Conservation and Restoration of Historic Artifacts
## 2 Inorganic Nanomaterials in Cultural Heritage Conservation
### Challenges in the Application of Nanomaterials for Cultural Heritage Conservation  
While nanomaterials offer numerous advantages in conservation practices (including but not limited to enhanced mechanical properties, high surface area, and antimicrobial potential), several challenges must also be addressed and should be evaluated in future studies.  
The potential toxicity of certain nanomaterials poses risks not only to conservators but also to the artifacts themselves. As the treatment of cultural heritage objectives has the ultimate goal of their preservation for the general public (both present and future), the recipes used could have a significant impact on a societal level. Research is ongoing to assess the long-term effects of these materials on both human health and cultural heritage items [18; 34; 35], currently being one of the \"hot topics\" around the world. At the same time, the environmental implications of using nanomaterials must be carefully considered. Sustainable practices should be prioritized to minimize ecological harm while ensuring effective conservation outcomes [36; 37]. The lack of standardized regulations regarding the use of nanotechnology in conservation presents challenges for practitioners. Developing guidelines that ensure safety and efficacy is essential for the widespread adoption of recipes based on the engineered nanoparticles.  
Emerging from the overviewed aspects is that the future of nanomaterials in cultural heritage conservation is promising but requires continued research and innovation. Collaborations between scientists, conservators, and art historians will foster a holistic understanding of how nanomaterials can best be utilized while respecting the integrity of cultural artifacts.  
Also, the continuous improvement in characterization techniques (closely connected with the technological progress registered in the last decades) will enhance our understanding of how nanomaterials interact with different substrates, allowing for more tailored applications in conservation efforts. Finally, a pivotal role in the adoption of nanomaterial-based recipes in cultural heritage conservation is represented by the general acceptance of the nanomaterials. Raising awareness about the benefits and challenges associated with nanotechnology in cultural heritage conservation will encourage informed decision-making among stakeholders.
## 2 Results and Discussion
### Comparison with Previous Studies
prepared a fiber including a MOF layer for the extraction and detection of pesticides in fruits; the extraction time was 30 min, and the time taken for the desorption of analytes was 5 min. The method validation gave, for the limits of detection and quantification values, between 0.032 and 0.09 ng mL\({}^{-1}\) and 0.11-0.30 ng mL\({}^{-1}\), respectively. Additionally, the relative standard deviation (RSD%) was assessed as between 3.7% and 10.0% [55]. Comparing their optimized parameters with those obtained in the present study, our extraction time was 10 min lower. Additionally, the desorption time for the analytes was 5 min. Thus, the results showed a slight superiority of the homemade PDMS needle, with a less thick coating compared to their MOF fiber. The validated method proposed for the SPME microextraction of pesticides from water samples, followed by GC-MS, was compared to the results of previous studies. Table 4 shows a summary of this comparison based on the main validation parameters. It is clear that, for most investigated pesticides, the limit of detection obtained with the presently developed method is significantly higher than those reported in previous articles. However, this difference is particularly noticed when the detection was performed by tandem mass spectrometry, while the LOD results obtained using single quadrupole MS are close to those of the present study. A similar correlation is observed for the linear range, which shows a lower concentration range for the methods based on MS/MS detection.
# A Comprehensive Survey on Real-Time Image Super-Resolution for IoT and Delay-Sensitive Applications
## 2 Background
### Deep Learning-Based Super-Resolution Method
#### 2.3.1. Exampled-Based SR Method  
The example-based super-resolution (EBSR) method utilizes a database of HR sample images to increase LR inputs [PERSON] et al. (2016). This method follows a data-driven approach that utilizes the content information in the sample images to construct HR images. The EBSR contains six sub-approaches such as dataset collection, registration, patch extraction, patch similarity measure, patch fusion, and post-processing. In the dataset collection part, create a sample collection of HR images that resembles the images that are intended to enhance. In the registration part, the LR input image is aligned with the reference dataset's instances. This alignment is required to generate correspondence between the LR and HR images. In the patch extraction part, the LR input image and the HR reference image are separated into tiny overlapping images. Based on a similarity metric, such as pixel intensity or structural similarity, each LR patch is matched to similar HR images in the provided dataset. Apart from this image similarity measure working for each LR patch, a similarity metric is utilized to locate the most comparable HR patches from the reference dataset. This metric might be based on pixel intensity, gradient information, texture qualities, or other image properties. On the other hand, patch fusion works as a re-constructor in which the HR version of the image and HR patches from the reference dataset are fused together. Averaging, weighted mixing, and patch-based synthesis are some of the fusion techniques that may be applied. As a last part, post-processing works as the resultant HR digital frames to improve visual quality, eliminate artifacts, or polish features. Denoising, sharpening, and adaptive filtering are examples of such approaches.  
Apart from EBSR, sparse coding-based super-resolution (SCSR) is one of the well-known techniques used to enhance LR digital frames to HR digital frames. The main intuition behind the network is sparse coding, wherein natural digital frames can be sparsely displayed utilizing basis functions. SCSR methods target search sparse representation of LR digital frames in order to the dictionary, afterward utilizing this representation to construct HR digital frames. The above process involves producing solutions to optimization issues by finding the sparsest representation.  
Figure 2. Architecture of SRCNNs.
# Detecting Fake Reviews in Google Maps--A Case Study
## 4 Initial Experiments and Results
### Additional Features
#### 4.2.2 Geographic Dispersion  
We observed that owners of genuine accounts tended to review places located in clusters: first, close to the place they live (most probably) and next, in the places they visit when traveling (e.g., in tourist or business areas). On the other hand, places reviewed by fake accounts were geographically much more spread out; probably, the location of the places was not related to any itinerary but depended on the order for reviews placed.  
To measure the level of geographic dispersion, we decided to group the reviewed places into \(n\) clusters and measure distances \(d\) from the reviewed places to the centroids of the clusters they belong to. Clustering was performed using the \(K\)-means algorithm [33]. The number of centroids, \(n\), was set with Formula (1).  
\[n=\max\{number\_of\_reviews}/a+1,b\} \tag{1}\]  
The values of \(a\) and \(b\) were adjusted heuristically as \(a=7\) and \(b=20\). Figure 6(a),b show histograms of \(d\) values for fake and genuine reviews, respectively. As expected, the histogram for genuine reviews is heavily leaning to the left, which means that in many cases, the reviewed places were close to the centroids of their corresponding clusters. On the other hand, the centroids of the clusters with fake reviews were much further from reviewed places. This means that the geographical dispersion of places reviewed by a fake account was much higher.  
Figure 6: Histograms of geographic distribution of reviews. (**a** Fake reviews (zoomed); (**b**) genuine reviews.
## 6 Decision Engine for Multi-User Systems
### Multi-User Systems:  
This subsection simulates the method described in Section 6. As a reminder, a single transmitter is delivering content to multiple receivers using a broadcast link. Each receiver is only interested in a subset of the codewords, but can overhear the others. Our goal is to minimize the number of IR bits to be broadcast in order to guarantee a certain probability of success for all users who suffered failures in decoding their desired information.  
Figure 10 compares the average number of incremental redundancy bits resulting from Equation (36) with that required if we were to send extension bits for every codeword that failed decoding at its desired receiver. Each point in the plot is the result of 100 Monte Carlo simulations with rate \(R=0.5\) broadcast to eight users experiencing random SNR uniformly distributed between \(-2\) dB and \(-1\) dB. According to Equation (1), that yields a probability of decoding failure between 0.1 and 0.9 per codeword at each user.  
We used a logarithmic barrier method coupled with Newton descent to solve problem (36) and plotted the average \(\|\mathbf{\beta}\|_{1}\) (number of IR bits) for different values of \(\gamma\) (probability of error after receiving the IR). Then, we used Equation (1) to derive the number of extension bits that would be required to  
Figure 10: Average number of IR bits required to guarantee that the probability of decoding failure after IR is below \(\gamma\) for all users.  
Figure 9: Average throughput of different relay strategies in fading channel.  
guarantee the same probability of error for all users. As it can be seen, our proposed method requires significantly fewer bits regardless of \(\gamma\).  
In most practical instances, the solution to problem (36) is not unique. There is a whole subspace of optimal values for \(\beta\). In order to obtain a sparse solution, we introduced a small random perturbation in the objective, minimizing \(\sum_{k=0}^{\frac{p}{2}-1}(1+\epsilon_{k})\beta_{k}\) instead of \(\sum_{k=0}^{2^{p}-1}\beta_{k}\), where \(\epsilon_{k}\) are random noise variables distributed between 0 and \(10^{-2}\). The result was that, in most cases, the number of non-zero entries in \(\beta\) was lower than the number of failed codewords. This means that, on top of requiring fewer IR bits, our method is also able to group them into fewer types than a pure extension approach, reducing the amount of overhead.
# A Game Player Expertise Level Classification System Using Electroencephalography (EEG)
## 1 Introduction
In [21], an analysis of the experience of a video game player is presented based on recorded EEG signals and shows that wearable EEG devices can be used for game analytics and to differentiate various cognitive processes. In summary, there are various EEG-based methods presented in literature for game analytics and the evaluation of human response, as well as non-EEG-based methods for the classification of video game player expertise level. To the best of our knowledge, the same task has not been achieved using EEG and is presented in this study by using a method that classifies the expertise level of a game player into two classes, i.e., expert and novice. The aim is to explore whether, EEG can be used to tell how good a player is in a video game just by looking at the brain activity recorded during the game play and how certain brain areas play a more significant role than others. Game analytics and the human psychological response comprise a very interesting field of research as seen from the literature and the increasing use of mobile games in different fields of life. Video game play holds exciting promise as an activity that may provide generalized enhancement to a wide range of perceptual and cognitive abilities. Looking at these patterns, this study is conducted with a wider perspective of advancing research applications in psychology, BCI and the human cognition process. The study expands on the pilot study in [22] where initial data were recorded and the hypothesis of predicting the expertise of a game player was tested. In this study, data have been collected more rigorously to avoid artefactsand gender bias by adding more data to the experimental design. After noise reduction, features are extracted in the time domain from fourteen, as well as four selected EEG channels showing significant brain activity. Multiple classification algorithms are used to predict the expert-novice level of the player, and the results are evaluated using multiple performance metrics. The major contributions of this study are,  
1. EEG-based data are recorded from multiple participants during the play of a mobile game to automatically classify the player as expert or novice on the basis of brain activity. 2. Those significant brain areas are highlighted and selected as being affected during the game play after a careful statistical analysis. 3. Thirteen morphological features are extracted in the time domain for classification purposes. In Section 2, the proposed methodology and experimental setup to classify the players' expertise level are provided. Experimental results are presented and discussed in Section 3, followed by the conclusion and future work in Section 4.
# Wild _Saccharomyces_ Produced Differential Aromas of Fermented Sauvignon Blanc Must
###### Abstract  
Nine _Saccharomyces_ strains, previously isolated from vineyards in Southern Brazil, were used as starter cultures in fermentations of Sauvignon Blanc (SB) must at laboratory scale, to study inter-strain differences in aroma profiles. The molecular profiles differentiated the following isolates from the reference strain (SC2048), which is typically used in wine production: 06 CE, 11 CE, 33 CE, 01 PP, 12M, 13 PP, 26 PP, 28 AD, and 41 PP. Under the same conditions, each of these strains produced different concentrations and combinations of metabolites, which significantly influenced the aroma of the fermented SB must. Volatile compounds such as octanoic acid, diethyl succinate, and ethyl lactate were associated with the strains 26 PP, 41 PP, 01 PP, and 12M, while strains 33 CE, 28 AD, 13 PP, and 06 CE were associated with the production of ethyl acetate and 1-hexanol. Strain 06 CE produced 592.87 \(\pm\) 12.35 \(\mathrm{\SIUnitSymbolMicro g}\)/L 1-hexanol. In addition, the olfactory activity values (OAVs; we considered only values >1) allowed us to evaluate the participation of each compound in the aroma of the final fermented SB. In conclusion, the selected wild strains are promising candidates for improving the regional characteristics of wine.  
a 2022  
ensures control and wine quality inside a winery [9]. However, it reduces the possibilities to offer unique wines highlighting the added value of a regional characteristic [10]. In this sense, the particularities of wine currently depend only on the grape and geographical region, and not the aroma offered by autochthonous yeasts.  
\"Vinhos de Altitude\" is one of the vineyards grown in Santa Catarina, Brazil, which obtained geographical indication in 2021 [11]. The quality of wines produced in this region is recognized by the soil characteristics, altitude, grape varieties, and culture techniques. Sauvignon Blanc (SB) cultivars from this region are cultured between 947 and 1415 m of altitude, with soluble solids between 17.8\({}^{\circ}\) and 20\({}^{\circ}\) Brix, and a pH of 2.94-3.32. SB wines contain 12.2-12.4% \(v/v\) of ethanol and are described as wines with high aromatic complexity [10]. We hypothesized that the aroma profile of Sauvignon Blanc wines, produced in the traditional \"Vinhos de Altitude\" region, could be differentiated by the contribution of aromas provided by wild yeast originating from this region. Thus, we aimed to assess the flavor profiles of wild strains of _Saccharomyces cerevisiae_ in fermented Sauvignon Blanc musts at laboratory scale. We studied the chemical composition of the unfermented and fermented musts. These chemicals, consisting mainly of ethyl esters, higher alcohols, and fatty acids, are of great relevance in determining the quality of wine.
## 3 Observation of Scintillation in OEM7 Measurements
### Spatial decorrelation of phase under scintillation
Carrier phase DD measurements were used to monitor the phase variation during scintillation and to compare the frequency of its fluctuations to that of the amplitude scintillation, as illustrated in Figure 9. Through the use of two stations separated by 6.5 km, a reference satellite not experiencing scintillation at both locations was chosen (GPS PRN 5), and a satellite experiencing scintillation at both locations (GPS PRN 23) was selected for the carrier phase DD measurement. As shown in these plots, the intensity of the phase variation is much less than that of the amplitude variation. However, a slow varying phase delay across the baseline is observable in the carrier phase DD measurement plots.