esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
The difference between the valid SCP and that of the SZA for each date pair was plotted for each of the five subwatersheds to analyze the relationship between the variations in both SZA and SCP. The mechanism by which SCA overestimation occurred for large values of SZA was analyzed by exploring the different responses of surface reflectance at Bands 4 and 6 and the change in the NDSI value when the SZA increased. Surface reflectance data for Bands 4 and 6 were extracted from the MOD09 GA dataset to calculate the NDSI index. The minimum SZA value that introduces an overestimation in snow cover pixels (i.e., the sensitive SZA threshold) was determined by comparing these values to those of standard snow cover in the area when the SZA was at its nadir. To reduce cloud obstruction and to complement missing MOD10A1 data in areas with a large SZA, we performed a spatiotemporal refilling of the entire dataset. In particular, we assigned a land cover class to the most recent cloudless observation (within a window of a few days) to a MOD10A1 data cell that had cloud cover, and assumed that land cover changes would not have been very drastic within this intervening period. This processing technique was defined as a multiday refilling method. Finally, the modified dataset of snow cover was validated using the standard snow cover classification results from the HJ-1A/B satellite.
## 3 Results and Discussion  
### Antifungal Activity on Solid Medium, MIC, and MFC  
The results of the antifungal activity of nonvolatile or diffusible compounds obtained with the two _Trichoderma_ strains tested, _T. asperellum_ IMI 393899 and _T. atroviride_ TS, showed that the extracted antifungal compounds inhibited the growth of all pathogens in the agar diffusion assay, as shown in Table 1.  
The agar diffusion test of the extract of the 10 days culture filtrate at maximum concentrations incited a change in the colony morphology of the pathogen, but not a significant reduction in growth. For this reason, all subsequent inhibition tests were carried out using 30-day extracts. Both 30-day EtOAc extracts were effective on all tested pathogens, showing inhibition halos that varied with the concentration of the extract. In particular, the extract of _T. asperellum_ showed an inhibition halo at a concentration of 1 mg/mL on _P. expansum_, _P. camemberti_, _A. niger_, _A. steynii_, and _A. carbonarius_, with an average halo of 8 mm. The inhibition was visible on all pathogens at a concentration of 10 mg/mL with an average halo of 9.7 mm. At this concentration, the most susceptible was _A. steynii_ with an area of inhibition of 17 mm and the least susceptible was _C. gloesporioides_ with an area of inhibition of 5 mm. At the maximum concentration of 100 mg/mL extract, the average inhibition values were 24 mm and exceeded 30 mm for of _P. camemberti_, _A. niger_, _A. steynii_, _Neofusicoccum_ spp., _Ph. Parvispora_, and _Ph. nicotianae_. At this concentration, the lowest value was 18 mm of inhibition area for _P. verrucosom_ and _F. poae_. The extract of _T. atroviride_ was effective at the minimum concentration of 10 mg/mL on all pathogens with the exception of _A. parasiticus_, _F. verticillioides_, _F. langsethiae_, and _F. graminearum_; moreover, _A. parasiticus_ was not inhibited at the concentration of 25 mg/mL and the inhibition halo was observed at concentrations of 50 and 100 mg/mL with values of 7 and 11 mm, respectively. At a concentration of 10 mg/mL, the average inhibition area of all pathogens was 7 mm, the lowest value was 4 mm for _F. proliferation_ and the highest value was 13 mm for _Ph. nicotianae_. For the maximum tested concentration of 100 mg/mL, the mean inhibition area was 14 mm, species of _Neofusicoccum_ were the most susceptible to the extracts with an inhibition halo of 27 mm, while the minimum value of 10 mm was obtained on _P. brevicopactum_, _F. langsethiae_, and _F. poae_. Both extracts were shown to have inhibitory activity on all tested pathogens. In particular, the extract of _T. asperellum_ was more effective, as it was able to inhibit at the minimum concentration (10 mg/mL) of _A. parasiticus_, the major fungus responsible for aflatoxin formation in crop seeds [20]. Interestingly, a week after test initiation, the inhibition halo persisted (Figures 1 and 2), thus showing that these extracts have a great inhibition potential in vitro.
# DI-SLAM: A Real-Time Enhanced RGB-D SLAM for Dynamic Indoor Environments
## 2 Related Works
### Methods Based on Motion Segmentation  
Dynamic feature removal based on motion segmentation primarily includes three approaches: background-foreground initialization, optical flow methods, and geometric constraint methods. In terms of background-foreground initialization, ref. [16] establishes a rapid retrieval system by pre-storing SURF feature descriptors of dynamic objects [17], enabling efficient feature matching and motion region identification. Ref. [18] further improves dynamic feature filtering through a specialized template matching algorithm. For optical flow methods, ref. [19] presents an enhanced LK optical flow technique [20]that relaxes the grayscale consistency assumption, reducing gradient dependence while improving dynamic region detection accuracy. Ref. [21] implements a grid-based scene flow analysis combined with clustering algorithms to effectively separate dynamic and static features. Regarding geometric constraints, ref. [22] develops a point correlation segmentation method using motion consistency analysis, while ref. [23] constructs a geometric residual-based dynamic factor detection framework to enhance pose estimation. Ref. [24] optimizes computational efficiency through refined feature search ranges and outlier rejection strategies. Multi-view fusion approaches [25; 26; 27] demonstrate improved interference resistance, and ref. [28] introduces an innovative grid mapping technique employing probabilistic modeling for robust dynamic and static feature discrimination.
# A Deep Analysis of Brain Tumor Detection from MR Images Using Deep Learning Networks
## 3 Methodology  
### Proposed Architecture  
In our study, an input image with a size of 32 \(\times\) 32 pixels was sent to an initial convolutional layer with 16 filters, a 32 \(\times\) 32 \(\times\) 16 feature map, and a kernel size of 3 \(\times\) 3 in order to search for the most-generic features. The convolutional layer's output was then forwarded to a max-pooling layer feature map of 15 \(\times\) 15 \(\times\) 16 to decrease the size of the spatial data for the subsequent layer by half. The max-pooling procedure selected the greatest number of elements or pixels from the feature map area that the filter has covered. This result was then fed to a further convolutional layer with filter values of 32 and a 13 \(\times\) 13 \(\times\) 32 feature map with a 3 \(\times\) 3 kernel size. After that, the output was then forwarded to the max-pooling layer feature map of 6 \(\times\) 6 \(\times\) 32 to cut the amount of spatial data for the next layer in half. Another convolutional layer and another pooling layer came next. The feature map of 4 \(\times\) 4 \(\times\) 64 in size was made up of 64 filter values and a kernel size of 3 \(\times\) 3 in the final convolutional layer, while the final pooling layer had a feature map of 2 \(\times\) 2 \(\times\) 64. The newly created 4160-dimensional fully connected dense layer received the flattened final output of the previous convolutional layer. This output was sent to the final output layer, which included a softmax activation function. While the last layer employed a softmax activation with no dropout for the output, all the other layers utilized a dropout of 0.5 with a ReLU activation function. The above-proposed CNN architecture's configuration is depicted in Figure 2. The model was trained, validated, and tested using 80 epochs, a batch size of 18, and a learning rate of 0.01. Along with the Adam optimizer, a categorical cross-entropy-based loss function was calculated to find the loss value.  
The methodology is divided into a few important stages. First, we collected our data from an available online source (kaggle.com (accessed on 10 November 2022)), then we pre-processed our datasets. We used the holdout validation system in the validation stage. We applied various machine learning models to train our images. Our dataset was split into three groups: 80% for training, 10% for testing, and 10% for validation. We tried to validate four different types of brain images: glioma tumors, meningioma tumors, no tumor, and pituitary tumors. Then, in order to validate our findings, we considered several types of metrics including the accuracy, recall, AUC, and loss. Figure 3 shows the step-by-step breakdown of this research.
# Towards Explainable Artificial Intelligence for GNSS Multipath LSTM Training Models
## 5 Discussion
### Limitations and Future Work  
Understanding these practical implementation challenges and opportunities is crucial for transitioning this research from theoretical analysis to practical deployment. While computational requirements present significant challenges, continued advances in embedded computing capabilities and optimization techniques suggest that real-time LRP-based anomaly detection in GNSS systems is becoming increasingly feasible.  
These findings open new avenues for research in GNSS signal processing and machine learning interpretability. Future work could focus on developing real-time monitoring systems based on LRP score variations, data requirements and collection challenges, optimizing the anomaly detection thresholds, potential for comparative studies, and extending the approach to other types of GNSS errors and interference.
## 3 Dielectric-Based Passivation Layers  
The overview of the studies using a dielectric material as the rear surface passivation layer for CIGS solar cells to date is given in the Table 1. This table introduces the type of the material that is used for passivation and the deposition techniques used for these layers as well as the required contacting approaches and the thickness of the absorber layer. The light management techniques, anti-reflection coating (ARC) layer applications, additional sodium (Na) supply, and gallium (Ga) grading are also given in this table. If any of these four approaches are applied or not applied in a given study, it is indicated with a plus sign (+) or with a minus sign (-), respectively. Table 1 also gives the average JV results of the devices that have a passivation layer.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Dielectric and Its** & **Decposition** & **Coustering Approach** & **Light** & \multirow{2}{*}{**ARC**} & **Na Supply** & **Ga Crafting** & \multirow{2}{*}{**ICGS (sum)**} & \multicolumn{3}{c}{**Average**} \\
**Thickness** & **Technique** & & & & & & & & **V\({}_{\sigma}\) (mV)** & \(J_{\sigma}\) (mA/cm\  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Dielectric and Its** & **Deposition** & **Constructing Approach** & \multicolumn{3}{c}{**Light**} & \multicolumn{3}{c}{**Average**} \\ \cline{6-10}
**Thickness** & **Technique** & **Constructing Approach** & **Mangement** & **ARC** & **No Supply** & **Ga Grading** & **ICGS (sum)** & \multicolumn{3}{c}{**\(I_{\omega}\)(mV)**} & \(I_{\omega}\)**(ma/cm\({}^{2}\))** & \multicolumn{1}{c}{**\(I_{\omega}\)(ma/cm\({}^{2}\))**} & \multicolumn{1}{c}{**\(I_{\omega}\)(ma/cm\({}^{2}\))**} \\ \hline \hline \multirow{2}{*}{Al\({}_{\text{Q}}\)\({}_{\text{Q}}\)\({}_{\text  
After the creation of the passivation layer, the thickness of this layer was determined using high angle annular dark-field (HAADF) transmission electron microscopy (TEM) in [9], spectrally resolved ellipsometry in [12; 13; 16; 17; 24; 25], TEM in [14; 18], energy dispersive X-ray spectroscopy (EDX) in [15], scanning electron microscopy (SEM) in [11; 19; 20; 22], profilometry in [5], and atomic force microscopy (AFM) in [23]. The thickness of the passivation layer should be chosen to be sufficiently thin (\(\sim\)1-2 nm) to allow tunneling if there is no contact opening approach applied to this layer.  
In the following subsections, (3.1) approaches used to add the passivation layer, (3.2) contacting approaches, (3.3) light management techniques, (3.4) solar simulator results, and (3.5) other characterization techniques used for further analyses will be discussed.
# A Color Consistency Processing Method for HY-1C Images of Antarctica
## 2 Data Preprocessing and Analysis  
### Study Dataset  
In this study, 63 remote sensing images of Antarctica were collected, which were obtained by the HY-1C CZI instrument between December 2018 and March 2019. The projected coordinate system of the original images was World Geodetic System 1984. Because the images were located near the South Pole, all the remote sensing images used in this study were converted into the WGS 1984 Antarctic Polar Stereographic coordinate system for better visibility. The dataset was made up of 16 bit three channel multispectral images, which were converted to 8 bit images in the data preparation. The images were compressed according to the cumulative histogram and the pixel frequency. Some pixels with high values were compressed to improve the retention of the main order of the original images.  
The cumulative histogram of each image was calculated, and the minimum and the maximum truncation frequencies were set as \(\theta_{min}=0.005\) and \(\theta_{max}=(1-0.005)\). The truncation frequency was set to preserve as much order information as possible in the conversion process. For each image, the pixel values \(p_{min}\) and \(p_{max}\) corresponding to the truncation frequency on the cumulative histogram were recalculated as follows:  
\[p_{min}=Pix(\min(\theta))\quad(\theta>\theta_{min}), \tag{1}\]  
\[p_{max}=Pix(\max(\theta))\quad(\theta<\theta_{max}), \tag{2}\]  
where \(Pix(\cdot)\) is the pixel value in the cumulative histogram corresponding to the frequency and \(\theta\) is the cumulative histogram frequency. The original images were then translated to 8 bit images as follows:  
\[I(x,y)=\begin{cases}255&I_{16}(x,y)>p_{max}\\ 255\times\frac{I_{16}(x,y)-p_{min}}{p_{max}-p_{min}}&p_{min}\leq I_{16}(x,y) \leq p_{max}\\ 0&I_{16}(x,y)<p_{min}\end{cases}, \tag{3}\]  
where \(I_{16}(x,y)\) is the value of the pixel in the 16 bit image and \(I(x,y)\) is the converted 8 bit value. The images were stored in \(RGB\)color mode and compressed independently in each color channel.
## 3 System Model  
Figure 2 depicts the snapshot of the System model, where many devices are connected and participating in D2D communication. NTR is a non-transparent relay that provides extensive connectivity and coverage to nearby devices. MRBS is referred to as a master relay base station and is deployed by mobile network service providers; it is normally a high computational node. The certification authority (CA) has the responsibility of issuing the certificate to underlying nodes. Therefore, before any node provides communication, the facility must register with the CA that will publish the public key of all individuals in a whitelist maintained by the CA.  
Generation of public and private keys is achieved by the individual node to avoid the key-escrow problem. Thus, MRBS and NTR generate their public and private keys using Algorithm 1 with a huge key that can avoid DLP attacks.  
* _Certification Authority (CA)_: The Certification Authority (CA) is a globally recognized and trusted entity in which all nodes can trust. Its task is to verify the authenticity of nodes and publish the public key for each node on a secured publicly available Whitelist that can be accessed by each node ('node' indicates MRBS or NTR).
* _Master Relay Base Station (MRBS)_ is a node that is already registered with the CA and provides connectivity of all underlying nodes. Cellular companies deploy MRBS
* _Non-Transparent Relay (NTR)_ is a node that provides the facility of connection, communication, and coverage to underlying devices. These relays are decode and forward and thus have limited capabilities such as MRBS. NTR is very limited in power, storage and computation.  
There are already two keys generated by MRBS and NTR with CA using Public Key Infrastructure (PKI). The keys are as follows:  
\[\text{MRBS}=\{K_{\text{MRBS}},\ P_{\text{MRBS}}\}\] \[\text{NTR}=\{K_{\text{NTR}},\ P_{\text{NTR}}\}\] where \(K\) is the private key and \(P\) is the public key obtained through CA.  
Figure 2: Showing system model diagram for the proposed solution.
# Verifiable (2, \(n\)) Image Secret Sharing Scheme Using Sudoku Matrix
## 2 Sudoku Matrix  
This section introduces the concept of the Sudoku matrix \(M\)[39], which represents the core of our solution. The size of the Sudoku matrix \(M\) is 256 \(\times\) 256, as shown in Figure 1, calculated using Equation (1), where \(n=2\). As can be seen in the Figure 1, each pixel pair, represented by \(\alpha_{1}\) and \(\alpha_{2}\), can be mapped to a core digit. Based on the core number, there are eight different independent numbers surrounding the core number without any repeated numbers. For example, if \(\alpha_{1}=5\) and \(\alpha_{2}=2\), as shown in Figure 1, the core digit is 2, the surrounding eight neighboring digits are different from each other, and the range is between 0 and 8, excluding the number 2. By using the Sudoku matrix \(M\), a pixel pair can be moved to a new pixel pair, which is represented by \(\alpha_{1}^{\prime}\) and \(\alpha_{2}^{\prime}\) according to the hidden number \(d\). For example, if \(d=5\), \(\alpha_{1}^{\prime}\) and \(\alpha_{2}^{\prime}\), obtained through Equation (2), will be 5 and 3, respectively. The maximum distortion of a pixel is 1 in order to maintain good visual quality after secret embedding.  
\[\alpha=\sum_{i=1}^{n}3^{i-1}\ \alpha_{i}\bmod 3^{n} \tag{1}\]
# Marine Internet for Internetworking in Oceans: A Tutorial
## 6 Challenging Issues for Marine Internet
### Interconnecting Networks with Dynamic Borders  
An open network structure with hybrid communication interfaces is necessary to accommodate various types of networks and make them to cooperatively provide cost-effective service to marine Internet users. For interconnecting terrestrial networks or satellite networks, network borders are almost stationary or their traces are predictable. In this case, networking configuration and interfaces can be pre-studied and pre-setup for performance optimization as done in terrestrial networks. However, for marine Internet, many network nodes are vessels equipped with BSs for communication and move in oceans. Therefore, network borders are moving relative to coastline networks, vessels and other objects on the water surface, satellites as well as underwater networks. It is necessary to handle these issues for optimizing internetworking performance in real-time with heterogeneous air interfaces and network protocols in such large-scale networks. This issue becomes much more challenging when always-on connection between any nodes cannot be guaranteed in oceans.  
On the other hand, dynamic network borders also cause the conventional network addressing schemes based on structured network addresses unsuitable in marine Internet. A structured network address such as the IP address usually contains some information related to the geographic location of the network node. This information is important for internetworking, particularly for fast routing with a hierarchical network structure. As mentioned above, in marine Internet, many network nodes are vessels equipped with BSs, which often change their locations in large scale, e.g., from one continent to another. Therefore, it is necessary to study an addressing scheme able to adapt itself to constantly changing locations of network nodes. Furthermore, such moving BSs also cause problems to mobility management, as discussed in Section 6.4.
# Phytosterols in Seaweeds: An Overview on Biosynthesis to Biomedical Applications
## 2 Biosynthesis of Phytosterols
### Condensation of Isoprene into Triterpenoids and Their Exoxidation  
Isoprene, with a five-carbon atom, condenses together to generate monoterepenes (C10), sesquiterpenes (C15), dieterpenes (C20), and triterpenes (C30). Triterpenes serve as precursors for phytosterol synthesis. Isoprene from the MEP pathway catalyzes the synthesis of monoterepenes, dieterpenes, chlorophyll, carotenoids, and other phytohormones, such as gibberellin, strigolactone, and abscisic acid. It is indeed a case that the MVA pathway-derived isoprene is responsible for the synthesis of the phytosterol precursor, squalene (a triterpene), via geranyl pyrophosphate (GPP) and farnesyl pyrophosphate (FPP), which are the precursors of monoterepenes and triterpenes, respectively [22; 23]. FPP synthase (FPPS) and squalene synthase (SQS) catalyze these sequence reactions, which follow the epoxidation of squalene into 2, 3-oxidosqualene by squalene epoxidase (Figure 1 Bi).
# Prediction of Harvest Time of Apple Trees: An RNN-Based Approach
## 3 Materials and Methods  
### PredHarv Model Architecture  
Figure 2 presents the architecture of the PredHarv model and its main elements. Initially, the model must be trained before making the predictions, with data from the phenology and temperature historical series. The blue arrows in Figure 2 represent the training step flow. The model only needs to be trained once unless there is a need to incorporate new data into the training dataset. Briefly, the phenological data identify the beginning and the end of the periods, and the meteorological data are the daily values of the variables in this period. The data consistency is checked in the pre-processing module, ensuring that all data are in the proper format to be presented to the model. In this step, lost data imputation operations are also carried out, including data normalization, and transformation in input and output patterns when necessary. After training, it is assumed that the model is trained, minimizing the error. This step is represented in Figure 2 by the Trained model module. Based on what is learned from the training stage, inferences are made from the new inputs informed by the user. The red arrows in Figure 2 represent the prediction step flow after training. Scenarios A, B, and C in the output step of Figure 2 represent the output of the model, that is, the duration of the period. These perspectives are possible due to the flexibility and predictive capacity of the model based on input data and allow prospecting for future scenarios.  
The model inputs are time series of meteorological data, which are ordered in time, related, and contribute to the phenomenon's occurrence. We used six variables that serve as input for the model: daily minimum temperature (\(t_{min}\)), maximum daily temperature (\(t_{max}\)), daily average temperature (\(t_{med}\)), average daily minimum temperature (\(mt_{min}\)), the average temperature of the period (\(mt_{med}\)), and cumulative degree days (\(CDD\)). The input variables use the values of previous observations (\(t-n\),..., \(t-2\), \(t-1\)). These previous observations are called lags, which correspond to the value immediately preceding the series. This technique is called a sliding window [30]. We use seven lags for each input variable, that is, the values corresponding to seven lags immediately previous to the current time (t). We performed tests with window sizes of three, five, and seven lags to observe the variability of these models with windows of different sizes. The best results are obtained with seven lags. The output variable is a specific numerical value, which corresponds to the duration of the period in the prediction horizon one step ahead (\(t+1\)).  
Figure 2: PredHarv model architecture.