esa-sceva/satcom-chunk-collection huggingface dataset:
# A Blockchain Copyright Protection Model Based on Vector Map Unique Identification
## 4 Blockchain Copyright Protection Model Based on Vector Map Unique Identification
### Copyright Registration Process
#### 4.1.1 Watermark Embedding  
The basic steps for watermark embedding are as follows:  
Step 1: Generate a binary watermark sequence \(W\) from the copyright binary image, where \(W=\{w_{j},\,j=0,\,1,\,2,\,...,\,M-1\}\). \(M\) represents the length of the transformed watermark information.  
Step 2: Read the coordinate points \(V_{i}\) (\(i\) = 1, 2,..., \(n\)) in the vector map according to the order of the coordinate points in line and polygon features. Coordinate points of point features are read based on the order stored in the file. Calculate the corresponding distance ratio \(D_{i}\) according to Equation (11), as shown in Figure 5.  
\[D_{i}=\frac{\left|CD\right|}{\left|BC\right|}=\frac{\sqrt{\left(D_{x}-C_{x} \right)^{2}+\left(D_{y}-C_{y}\right)^{2}}}{\sqrt{\left(C_{x}-B_{x}\right)^{2} +\left(C_{y}-B_{y}\right)^{2}}} \tag{11}\]  
where \(B_{x}\), \(C_{x}\), and \(D_{x}\) represent the abscissa of the coordinate point \(V_{i}\), and \(B_{y}\), \(C_{y}\), and \(D_{y}\) denote the ordinate of the coordinate point \(V_{i}\).  
Step 3: Establish the mapping relationship between the watermark values and the watermark indices based on the mapping mechanism. Taking the distance ratio \(D_{i}\) as an example, stable numerical \(ID_{i}\) values are obtained by taking the first n digits of \(D_{i}\).  
\[ID_{i}=D_{i}\times 10^{n} \tag{12}\]  
The watermark synchronization mechanism is established as follows:  
\[Watemark_{index}=Hash(ID_{i})\%M_{N} \tag{13}\]  
where \(Watemark_{index}\) represents the watermark position to be embedded, \(M_{N}\) is the number of watermark bits, and \(Hash\) denotes the hash function, such as the logistic chaotic function.  
Step 4: Currently, prevalent methods for embedding and extracting watermarks in vector maps encompass classical techniques such as LSB (least significant bit), additive multiplicative methods, and spreadspectrum methods, among others. To meet the requirements for blind watermark extraction and robustness, a quantization mechanism is embraced for watermark embedding and extraction. Let the quantization step be denoted as \(l\), and the watermark information to be embedded denoted as \(W\) = 0 or 1. Consequently, the watermark embedding process based on the distance ratio \(D\) unfolds as follows:  
\[\begin{cases}D^{\prime}=D&if\ x\%2l<l\ and\ w=0\\ D^{\prime}=D-l&if\ x\%2l\ \geq\ l\ and\ w=0\\ D^{\prime}=D+l&if\ x\%2l<l\ and\ w=1\\ D^{\prime}=D&if\ x\%2l\ \geq\ l\ and\ w=1\\ \end{cases} \tag{14}\]  
where % denotes the modulo operation. The watermark embedding process is illustrated in Figure 6.  
The index of the watermark value to be embedded is derived from the mapping mechanism in Step 3. Based on the change in the distance ratio, it is necessary to calculate the coordinates of \(V_{i}\) after embedding the watermark.  
Figure 5: Calculation diagram of distance ratio.
## 5 Discussion
### Limitations and Future Research  
This research offers significant understanding, yet additional examinations must happen because of its identified restrictions. A research duration of 20 weeks proved insufficient for determining sustained AI feedback effects because longer-term studies would generate more complete data results. The research took place within a particulareducational environment, but future investigations should study its relevance to multiple educational environments and student demographics.  
Academic research must proceed to investigate AI's ethical consequences in education as well as risks associated with data protection and algorithmic discrimination together with technology dependence concerns. The insights students have about AI-based feedback systems will guide educational developers in creating both beneficial and ethically sustainable AI technological applications for educational settings.
# Optimized Implementation of Argon2 Utilizing the Graphics Processing Unit
## 2 Related Work
### Blake2b Hash Function  
Blake2b is a high-performance cryptographic hash function that efficiently generates fixed-size, short digests for data. It is based on the original BLAKE algorithm and is optimized for 64-bit platforms. The algorithm utilizes a block cipher-based round function to transform input data into a hash and incorporates bitwise operations and nonlinear transformations to modify the internal state. This process minimizes the risks of collision attacks and pre-image attacks. Blake2b can be easily applied to various use cases and security requirements by utilizing unique keys, salts, and personalization parameters.  
Figure 1: Algorithm of Argon2.  
Overall, Blake2b is recognized as a reliable cryptographic hash function that combines fast processing speed with high security.  
Additionally, Blake2b is an algorithm that allows flexible configuration of the output hash length. This means that the length of the generated hash can be adjusted according to specific needs. As a result, Blake2b can produce digests of various lengths, making it versatile for different purposes. This flexibility enables Blake2b to be widely used and facilitates the generation of optimized digests tailored to specific applications. Therefore, Blake2b is not dependent on a fixed output length and is evaluated as a cryptographic hash function that provides both flexibility and stability.  
Internally, the Argon2 algorithm utilizes the Blake2 hash function. Blake2 participated in the SHA-3 competition but ultimately was not victorious, losing to Keccak. Nevertheless, Blake2 remains fast and secure, supporting outputs of various lengths, making it valuable in many projects.  
In Argon2, the Blake2 hash function is employed in the following key parts:  
1. Initialization: Blake2b is used to generate the initial block.
2. Block Filling: Blake2b is used in the computation of each block, based on previous blocks.
3. Final Hash Generation: The final block is passed to the Blake2b hash function to generate the final password hash.
# Design, Challenges and Developments for 5G Massive MIMO Antenna Systems at Sub 6-GHz Band: A Review
## 5 Conclusions  
Massive MIMO is a crucial part of the 5G infrastructure. 5G massive MIMO uses a large number of antennas for both base stations and mobile devices. Increasing the number of antennas at the base station in relation to the number of users and smartphones enhances the performance of a massive MIMO system. Both the mutual coupling and spatial correlation between antennas play a role. For massive MIMO to work in either scenario, 5G antennas will need to have exceptional characteristics. As a result, many 5G antennas meet the criteria for the expansion characteristics of massive MIMO systems applicable to both methods. For sub-6 GHz frequencies, massive MIMO antenna systems in 5G mid-band base stations have been extended to support a maximum of 256 antenna elements, while for smartphones, the maximum is 20.  
Conceptualization, S.K.I. and S.S.A.-B.; Methodology, S.K.I. and S.S.A.-B.; Resources, M.J.S., S.S.A.-B., M.T.I., A.-A. and W.M.A.; Data curation S.K.I. and S.S.A.-B.; Writing--original draft preparation, S.K.I.; Writing--review and editing, M.J.S.; S.S.A.-B., H.H.I, M.T.I, W.M.A., A.A. and M.S.I.; Supervision, M.J.S., S.S.A.-B. and M.T.I.; Funding acquisition, M.T.I. and M.S.I. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Research University Grant, Universiti Kebangsaan Malaysia. Geran Universiti Penyelidikan (GUP), code: GUP-2022-075.  
Not applicable.  
The authors declare no conflict of interest.
## 2 Safety Analysis Framework for Complex Electromechanical Equipment System Based on STAMP  
STAMP treats safety as a control problem, and the focus of system safety is changed from preventing failures in implementing safety constraints. It still contains component failures, but it extends the concept of accidental causes to include component interactions. It contains three main concepts: safety constraints, hierarchical control structures, and process models. The most basic concept in STAMP is not an event but a constraint. The main causes of safety issues in the new theory are component failures, system external disturbances, interactions between components, and component behaviors that lead to dangerous system states.
# Fungi of the _Trichoderma_ Genus: Future Perspectives of Benefits in Sustainable Agriculture
## 5 Application of _Trichoderma_ Fungi in Promoting Plant Growth
### Colonization and Modification of the Root System of Plants
reesei_, and _T. virens_ produced indole-3-acetic acid in the range of 10.94-59.10 \(\upmu\)g/mL, making them potential candidates for biological plant growth promoters. [PERSON] et al. [137] observed that the increase in maize root length was correlated with the production of auxins by fungi of the genus _Trichoderma_. Among the analyzed isolates, _T. harzianum_ displayed the highest IAA production at 36.4 \(\upmu\)g/mL, which then accounted for the largest average enhancement in plant root length. Similar observations were made by [PERSON] et al. [138], who found that auxin production and root colonization were the mechanisms responsible for stimulating cucumber growth. The results of the studies showed that _T. harzianum_ T-E5, which produced auxins at a level of 14.2 mg/L, increased the dry weight and vigor of the roots by 0.10 g and 0.26 mg/g/h, respectively, compared to the control. In contrast, _T. harzianum_ strain SQR-T037, which produced indole-3-acetic acid at a level of 10.9 mg/L, enhanced the dry weight and vigor of the roots by 0.06 g and 0.16 mg/g/h, respectively, compared to the control. Therefore, IAA production was positively correlated with root growth parameters.
## 2 Materials and Methods
### Study Data
#### 2.2.3 GPP Products  
The GOSIF GPP [38] ([[https://globalecology.unh.edu/data/GOSIF-GPP.html](https://globalecology.unh.edu/data/GOSIF-GPP.html)]([https://globalecology.unh.edu/data/GOSIF-GPP.html](https://globalecology.unh.edu/data/GOSIF-GPP.html)) (accessed on 1 March 2025)) and MODIS GPP ([[https://modis.gsfc.nasa.gov/data/dataprod/mod17.php](https://modis.gsfc.nasa.gov/data/dataprod/mod17.php)]([https://modis.gsfc.nasa.gov/data/dataprod/mod17.php](https://modis.gsfc.nasa.gov/data/dataprod/mod17.php)) (accessed on 1 March 2025)) products were selected as control datasets for cross-validation. GOSIF-GPP is a global high-resolution GPP dataset derived from solar-induced chlorophyll fluorescence (SIF), which directly reflects photosynthetic activity and provides high sensitivity to GPP [38]. MODIS GPP is based on an LUE model driven by vegetation indices and meteorological data from the MODIS sensor. Although less physiologically direct, it offers a long-term, globally consistent GPP record and is widely used in carbon and climate studies [39].
## 2 Flexible Electronics Based on GnPs
### Environmentally-Friendly Graphene-Based Materials and Devices  
Another valuable and important parameter for the electronics of the future will be their sustainability (i.e., the biodegradability of the components and/or the green approaches employed to produce the materials) [68; 69]. Indeed, electronic goods production and waste management have become a major issue for environmental pollution [68; 69]. A novel method was proposed by our group [43] to fabricate isotropically electrically conductive biodegradable biocomposites based on cellulose and GnPs. It consisted of hot-press impregnation of porous cellulose networks after spray coating the flexible fibrous cellulose substrates with conductive GnPs-based inks. Since such ink was made employing a biodegradable thermoplastic polymer (Mater-Bi(r)), hot pressing at a temperature higher than the melting of the plastic led to the polymer-GnPs incorporation inside the fibrous network. The resultant green materials exhibited remarkable electrical conduction (\(\sigma\approx 10^{3}\) S/m) and a significant folding stability after severe weight-assisted 180\({}^{\circ}\) folding-unfolding cycles at 0 mm bending radius. Such conductive materials were used to fabricate simple circuitry [43], and as a top electrode for organic photovoltaics solar cells [62]. Another green approach developed by our group [63] to obtain reliable bio-based material for foldable electronics was to take advantage of the liquid absorbing properties of pure cellulose. A green conductive ink realized employing methanol and acetic anhydride as solvents, and cellulose acetate and GnPs as solid content, was spray coated onto pure cellulose. The ink thoroughly wet and impregnated the cellulose substrate after deposition, eliminating the need for hot-pressing. This cellulosic-GnPs bionanocomposite exhibited good folding stability and abrasion resistance. Proposed applications were sustainable THz electromagnetic shielding materials and electromyography signal detection (see Figure 7). The GnPs employed by our group for these studies were provided by Directa Plus (grade Ultra g+). For details on the lateral size and thickness of such nanoflakes, see this report [65].  
Figure 6: Image of the flexible GnPs-based antenna manufactured on transparent plastic substrate. Reprinted with permission from Materials Today 21, 223–230. Copyright 2018 Elsevier.  
Another green nanocomposite for flexible electronics was lately proposed by [PERSON] and collaborators [64]. They added GnPs and cellulose nanofibril into polylactic acid and conductive polypyrrole composite via a green, cost effective method. The addition of GnPs enhanced the electrical conductivity of the biocomposites from 12 to 106 S/m at 10 wt % nanoflakes concentrations. The nanocomposites exhibited also remarkable flexible stability, with only 7.5% deviation after 100 cycles. The GnPs and nanofibril addition also enhanced the mechanical properties of the biocomposite. The nanocomposite was employed as the electrode for flexible supercapacitors.
## 6 Discussion  
### System Performance  
Relating the accuracy of yield forecasts obtained by the proposed system to existing solutions is complex and difficult to do adequately. This is due to the facts that: (1) yield forecasting is carried out at different administrative levels, (2) the quality of reference data (in this case official statistics) can vary between countries, (3) the inter-annual crop yield variability varies between countries, related to the level of development of the agricultural practices and the use of state-of-the-art management methods to reduce the effects of unfavorable crop growth conditions, (4) models are trained and validated on data of different lengths, and finally (5) even if the compared systems operate in areas of a similar climate, other indicators may be a major crop yield-limiting factor. Therefore, embedding performance of the presented system in the European context cannot be interpreted as an exhaustive bench-marking of forecasting systems. Since very few studies have attempted to predict crop yields in Europe at a LAU level, the following discussion is focused on forecasting at a NUTS-2 level.  
Figure 17: An example of the graphical output generated by the crop yield forecasting system: crop yield forecasts for winter wheat (**upper row**), winter rapeseed (**middle row**), and maize (**bottom row**) for NUTS-2 (**left column**) and LAU (**right column**).  
The proposed system provides end-of-season winter wheat yield forecasts with a RRMSE of 8.15%. This outperforms the forecasting model based on the fraction of absorbed photosynthetically active radiation derived from SPOT VEGETATION, which provided winter wheat forecasts for Poland with a 10% RRMSE [17]. A common approach that employs MODIS NDVI seasonal peak was used to forecast the yield of winter wheat in Ukraine with a RRMSE of 15% [9] and 12% [42]. A very recent work of [PERSON] et al. (2022) [43] proposed a complex solution that uses deep learning methodology, and combines satellite-derived indicators with weather information and outputs of a crop growth model (World Food Studies Simulation Model, WOFOST). With this approach, RRMSE was 8.64% for winter wheat in Poland. It was also used for forecasting yields of maize for six European countries revealing an accuracy (RRMSE) from 12% (Spain) to as much as 29% (Romania), in comparison to the RRSME of 13% achieved here. Thus, it can be concluded that the operational system presented here provides crop yield forecasts at a NUTS-2 level with a similar or higher accuracy than similar approaches used in Europe.
# Control of an Omnidirectional UAV for Transportation and Manipulation Tasks
## 4 Motion Control  
The proposed motion control is a two-loop model-based scheme inherited from [35]; the outer loop performs the position controller, while the inner loop controls the platform orientation and the roll-pitch joint position. A sketch of the control block scheme is reported in Figure 2. More in detail, the position controller is in charge of computing the force,\(\mathbf{f}_{0}\), to be exerted to the vehicle's platform in order to achieve the desired vehicle's position trajectory and the reference angles, \(\alpha_{des}\) and \(\beta_{des}\), for the roll-pitch joints. The reference angles are fed to the inner loop, which computes, on the basis of the platform's desired orientation, both the torques \(\mathbf{\tau}_{c}\) for the roll-pitch joint actuators and the moment \(\mathbf{\mu}_{0}\).  
The control purpose is the tracking of an off-line planned desired trajectory for the platform (i.e., for the coordinate frame \(\mathbf{\Sigma}_{0}\)) defined by the vectors \(\mathbf{p}_{0,d}(t)\) and \(\mathbf{\phi}_{0,d}(t)\), their corresponding velocities, \(\dot{\mathbf{p}}_{0,d}(t)\), \(\dot{\mathbf{\phi}}_{0,d}(t)\), and accelerations, \(\ddot{\mathbf{p}}_{0,d}(t)\), \(\dot{\mathbf{\phi}}_{0,d}(t)\).  
**Remark 1**.: _It is worth noticing that the ODQuad, differently from usual underactuated quadrotors, allows us to control both position and orientation, provided that the assigned orientation, \(\mathbf{\phi}_{0,d}\), can be reached without violation of the limit of roll and pitch angles of the gimbal mechanism (i.e., \(\alpha,\beta\in[-25,\ +25]\) deg for the current design)._
## 4 Results and Discussion  
The proposed method was implemented using Matlab. For the ease of experiments' reproduction, the resources that participated in the process are presented onwards. Clock ticking sounds can be found online as free audio sound effects [41]. The effects of the sound propagation were investigated using acoustic impulse responses of two environments: a meeting room and a lecture room [42]. The reverberation time of the two acoustic environments used in the experiments were calculated using the Schroeder integration method. The found values are: RT\({}_{60\_\text{meeting}}\) = 0.244 s and RT\({}_{60\_\text{lecture}}\) = 0.861 s. The reverberation time (RT\({}_{60}\)) is the time it takes for the sound pressure level to decrease by 60 dB, after a sound source is abruptly switched off. The first 100 vocal signals available in the Carnegie Mellon University database [43] were used in the experiments. This section shows the results in two test scenarios:  
1. The watermark extraction performance depending on the power ratio (PR) between the voice signal and the sonic watermark, while keeping the power ratio between the watermark's components constant, and on the power ratio between the watermark components, while keeping the power ratio between the voice signal and the sonic watermark constant. This experiment was made in the two studied acoustic environments: a meeting room and a lecture room;
2. The performance of detecting forgeries done using audio cut operations.
## 4 Results  
### Exploratory Data Analysis  
The descriptive statistics of the standardized variables, which are shown in Table 2, are covered in this subsection. In order to facilitate comparability and interpretation within the random forest model, all variables were standardized to have a mean of zero and a standard deviation of one, as previously specified.  
While cloud adoption shows a moderate range of 4.11, the unemployment rate (6.14), which shows significant fluctuation among EU nations and years, has the widest range. Notably, the sampled countries' economic differences are reflected in the range of 5.46 for GDP per capita.  
Asymmetry and tail behavior are emphasized by skewness and kurtosis. The majority of the variables indicate moderate skewness, but the cloud adoption rate (0.84) and unemployment rate (1.64) show a noticeable right skew, suggesting a longer upper tail.  
On the other hand, GDP per capita (\(-0.30\)) and internet availability (\(-0.59\)) exhibit a slight left skew. In contrast to broadband access (0.00) and higher education (\(-1.03\)), which have relatively flat distributions when compared to a normal curve, the unemployment rate is notable for its high kurtosis (3.80), which indicates a distribution with heavy tails and possible outliers.  
These descriptive insights highlight the differences among EU member states, especially with regard to digital infrastructure and labor market conditions, which are anticipated to affect cloud adoption trends in the ensuing modeling.  
To illustrate the bivariate correlations between all variables, a correlation matrix (Appendix A.1) was created in addition to descriptive information. While the unemployment rate shows lower, negative associations, the figure shows large positive relationships between cloud adoption, broadband access, and ICT specialists.  
Furthermore, to ensure the reliability of predictor effects, we assessed potential multicollinearity among core variables using Variance Inflation Factors (VIFs). The results indicated low multicollinearity: VIF values were 2.89 for ICT specialists, 2.77 for broadband access, and 1.91 for higher education. All variables fell well below conventional thresholds (VIF \(<\) 5), suggesting that multicollinearity is not a concern in our setting. Moreover, as our primary models are tree-based ensemble methods (e.g., Random Forest and XGBoost), which are inherently robust to moderate collinearity, no additional regularization or dimensionality reduction was required. The stability of feature importance across different algorithms and cross-validation folds further supports this robustness.