esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Evaluation
### Results and Analysis
DEENT-Bert (83.10%) outperformed RF (77.68%), SVM (77.20%), XGBoost (80.44%), RNN (76.61%), CNN (77.53%), MentalBERT (82.02%), and DEENT-Generic (76.90%) regarding precision, highlighting the ability of DEENT-Bert to classify non-depressive tweets. DEENT-Bert (81.80%) accomplished better results regarding the f1-score than DEENT-Generic (78.47%), RF (76.74%), SVM (77.68%), XGBoost (75.88%), RNN (78.10%), CNN (77.38%), and MentalBERT (80.99%), highlighting DEENT-Bert as the most robust model solution to detect depressive and non-depressive tweets. Similarly to the accuracy and balanced accuracy results, the ones regarding precision, recall, and f1-score corroborated, firstly, that the attention mechanism used by DEENT allowed DEENT-Generic and DEENT-Bert to more successfully learn learn the complex words related to depressive tweets and non-depressive tweets than the non-transformers based models and secondly that, thanks to the fine-tuning, DEENT-Bert outperformed DEENT-Generic and MentalBERT regarding the f1-score since the former has already learned complex linguistic representations. Figure 7a,b show the accuracy and loss obtained by DEENT, MentalBERT, RNN, and CNN over four epochs when the early stop condition (there is no accuracy improvement) was triggered; note that RF, SVM, and XGBoost were omitted because they did not operate with epochs. DEENT-Bert (a: 84.54%, I: 0.3563) and DEENT-Generic (a: 81.23%, I: 0.4820) achieved their highest accuracy and the lowest loss in the second epoch. These accuracies were better than those of CNN (a: 80.72%, I: 0.5456) obtained in epoch three and  
Figure 5: DEENT vs. baseline models: accuracy and balanced accuracy. From the above results regarding accuracy, balanced accuracy, precision, recall, and f1-score, we concluded that DEENT-Generic and DEENT-Bert are promising solutions to identifying depressive and non-depressive tweets. In particular, DEENT-Generic more successfully learns to identify depressive tweets, while DEENT-Bert is more appropriate to classify non-depressive tweets. Furthermore, remarkably, they stabilize faster than the baseline models.
# Generic Methodology for Field Calibration of Nacelle-Based Wind Lidars
## 5 LOS Velocity Calibration and Uncertainties
### Measurement Uncertainties Assessment Procedure
#### 5.6.5 Expanded \(V_{los}\) Uncertainties  
Given a coverage factor \(k\), the expanded LOS velocity uncertainty is:  
\[U_{c,y}=k\cdot u_{c,y} \tag{21}\]  
Classically, the value of \(k=2\) is chosen in which case \(U_{c,y}\) corresponds to the half-width of a 95% CI for a normal probability distribution. If \(k=1\), it corresponds to a 68% CI; if \(k=3\), to a 99% CI.  
The combined and expanded uncertainty values depend on the encountered wind conditions (speed, direction, tilting, etc.). Thus, they are computed for each valid 10-min period. They are then averaged in each \(V_{LOS}\) bin and may be reported in the form of a table (see [4; 5]).  
The expanded \(V_{LOS}\) measurement uncertainties of 5B and ZDM averaged per bin are displayed in Figure 10. They vary linearly with the LOS velocity (\(R^{2}>0.99\)). High wind speed bins for the calibration of LOS0 of the 5B lidar do not feature enough data points and are invalid for \(V_{LOS}>13\) m\(\cdot\)s\({}^{-1}\). However, the linearity of the uncertainties suggests that extrapolation of the uncertainties to the invalid bins is reasonable.  
With a coverage factor \(k=2\), the expanded uncertainties vary from \(\approx\)3.2% at 3 m\(\cdot\)s\({}^{-1}\) to \(\approx\)1.9% at 16 m\(\cdot\)s\({}^{-1}\), as shown in Figure 10's right axis. The uncertainties results obtained show insignificant deviations between ZDM and all LOS of 5B.
# Exploring a Moderate Fallow Scale of Cultivated Land in China from the Perspective of Food Security
## 5 Discussion
### Practical Significance of Exploiting the Productive Potential of Cultivated Land  
With the rapid development of urbanization, a large amount of high-quality cultivated land has been occupied. To cope with the loss of cultivated land and guarantee grain outputs, the Chinese government has implemented land improvement projects. From 2001 to 2015, the cultivated land area increased due to nationwide land improvement projects, reaching \(413.5\times 10^{4}\) hm\({}^{2}\)[24], which was converted from barren grass and other land with poor fertility, such as land with soil erosion and fragmented land [10; 44]. It is noteworthy that the loss of cultivated land was mainly distributed in South China, with abundant water and heat resources, whereas the newly reclaimed cultivated land was mainly distributed in Northwest and Northeast China, with poor natural conditions and a mismatch of water and heat resources [35; 45]. The potential of cultivated land in the south has not yet been fully exploited, and the newly reclaimed cultivated land is less productive [46].  
Taking the change in MCI as an example, the reserve of GPP due to MCI reduction was \(4320.86\times 10^{4}\) t in the past 30 years. By referring to the land reclamation cost conversion method from [PERSON] et al. [24], this part of GPP was equivalent to a loss of \(702.24\times 10^{4}\) hm\({}^{2}\) of cultivated land according to the average grain yield of cultivated land in 1990. The average quality of cultivated land occupied by construction land in China was approximately 1.18 times that of newly reclaimed cultivated land through land development and consolidation projects [11]. Therefore, the grain yield reduction caused by the decline in MCI was equivalent to the grain production capacity of \(828.64\times 10^{4}\) hm\({}^{2}\) of newly reclaimed cultivated land. The minimum cost of land reclamation is 75,000 yuan per hm\({}^{2}\)[47]. If the GPP of this part of existing cultivated land is fully tapped, we will save 621.47 billion yuan in land reclamation costs.
# Modeling Trust in IoT Systems for Drinking-Water Management
## 3 Methods and Proposed Approach
### Data Collection
#### 3.1.1 Characteristics of the IoT System for Drinking-Water Treatment  
To understand the implications of trust in drinking-water management environments, let us examine the water life cycle in a drinking-water production facility using the example of the Sherbrooke Drinking-Water Treatment Plant. This plant has received accolades for its water quality, winning the first prize in Quebec in 2017 and the second prize in North America [27]. The process of producing drinking-water comprises four essential steps:  
1. Pumping Stage: The pumping station is located at the edges of Lake Memphremagog, extracting water 150 m offshore and 15 m deep. Water is pumped to Sherbrooke through a 90 cm diameter pipeline over a distance of 27 km. Upon arrival, the water passes through five pumping wells equipped with five raw water pumps.
2. Primary Micro-Screening Stage: Five small micro-screens eliminate particles of 300 microns (sand grains, small pieces of wood or plastic, etc.) to prevent them from reaching the subsequent treatment stages.
3. Membrane Filtration Stage: This crucial step in the treatment process uses nine membrane trains to achieve a processing capacity of at least 96,000 m\({}^{2}\)/day. Each membrane train contains thousands of tubes, each with 7000 small tubules with a porosity of 0.02 microns, allowing water passage while excluding bacteria and viruses.
4. Ozonation Stage: Ozone gas is produced from pure oxygen using ozonizers. This gas is injected into the filtered water basins to react with the water, producing oxygenated water. Ozonation improves water quality, destroys organic micro-pollutants, and neutralizes the odor, taste, and color of the water.
5. Chlorination Stage: Although the water may be entirely pottable at this stage, chlorine is added as a disinfect at a concentration of at least 0.3 PPM to prevent potential bacterial growth in the aqueduct network. This complex process is managed by PLCs and monitored by IoT sensors throughout the treatment cycle. These devices contribute to the collection of essential data to ensure the quality of drinking water and are key components of the IoT system deployed in this plant. Trust in these devices and the overall system is crucial to guarantee the supply of safe and high-quality drinking water to the population of Sherbrooke [28].
# Satellite Network Security Routing Technology Based on Deep Learning and Trust Management
## 3 Problem Modeling
### Intelligent Security Trust Model
In the fourth step, the loss function is set as follows:  
\[L(\theta,\varphi;x)=E_{q_{\varphi}(z|x)}[\log p_{\theta}(x|z,c^{*})]-D_{KL}(q_{ \varphi}(z,c^{*}|x)||p(z,c^{*})) \tag{13}\]  
The training of the VAE model can be carried out by a backpropagation algorithm, and the reconstruction probability is generally calculated by the Monte Carlo technique and reparameterization method. According to other studies, VAE can be calculated by the average reconstruction error of multiple sampling times based on prior probability, which can be expressed as follows:  
\[m=\frac{1}{L}\sum\limits_{i=1}^{L}p_{\theta}(x|\mu_{i}(x),\sigma_{i}(x)) \tag{14}\]  
where \(L\) represents the number of sampling times. A variable division self-encoder can obtain an average reconstruction error of \(m\). The \(m\) can be directly transmitted to the ant colony algorithm to guide the ants to find the way.
# Towards the Development of Agenda 2063 Geo-Portal to Support Sustainable Development in Africa
## 6 Discussion and Future Direction
From the functionality illustrated in this paper and feedback collected from the user seminars, it is evident that the Agenda 2063 Geoportal will play a significant role in providing geoinformation products and services necessary to support evidence-based integrated spatial planning. It is foreseen that with the demonstrated capability, the Agenda 2063 Geoportal has the potential to play an important role assisting the tracking of the sustainable development indicators over time, and will support data-driven decision making across sectors, within continental, regional, national, and sub-national spheres. The geoportal implemented in this project provides a credible mechanism to share credible, timely, and consistent data and information to support sustainable development enabling efficient monitoring and evaluation. Timely information enables timely decision-making. Future development options would look at the potential of a geographically distributed geoportal. While noting the data storage complexity associated distributed GIS databases over centralized databases, [PERSON] [37] highlights a number of distributed GIS databases are more efficient in terms speed, reliability, and storage efficiency when sharing large heterogeneous datasets. Distributed geoportals are particularly suitable when users require real-time responses of their queries and when the geoportal has interactive graphics. The deployment of the geoportal onto a cloud platform is another alternative that could be considered in order to achieve high operational efficiency in terms of data storage, upload, and analytical capabilities. Cloud computing is increasingly being used in the geospatial sciences to storage and analyse large datasets such as satellite imagery. Amazon web services (AWS) have already demonstrated the feasibility of hosting such as geoportal on a cloud platform through the hosting of the Africa Regional Data Cube and Open Data Cube Initiatives ([[https://aws.amazon.com/blogs/publicsector/tag/africa-regional-data-cube/](https://aws.amazon.com/blogs/publicsector/tag/africa-regional-data-cube/)]([https://aws.amazon.com/blogs/publicsector/tag/africa-regional-data-cube/](https://aws.amazon.com/blogs/publicsector/tag/africa-regional-data-cube/)), 2019). The integration of the geoportal with emerging digital platforms for accessing and analysing satellite imagery for assessing environmental conditions over the continent will enhance the capability of the geoportal. Interfacing the geoportal with the Digital Earth Africa data cube platform ([[http://www.ga.gov.au/digitalearthafrica](http://www.ga.gov.au/digitalearthafrica)]([http://www.ga.gov.au/digitalearthafrica](http://www.ga.gov.au/digitalearthafrica))), for instance, will open up opportunities for long-term time series analysis and change detection of urban areas, surface water resources, croplands, vegetation cover, land degradation, and other natural resources necessary to achieve Africa's development agenda.
## 3 Results
### Discussion of Results
Comparison of the results of modeling and selection of the effective operating mode of the hydrotreating unit based on the known and constructed models and real experimental data from the hydrotreating unit of the Atyrau refinery is shown in Table 3.  
The analysis of the data obtained and given in Table 3 allows us to conclude that the simulation results using the developed models, taking into account fuzzy information, are superior to the known deterministic approaches, since the simulation results coincide more accurately with the real (experimental-production) data. In addition, using the proposed approach, product quality indicators are determined, which are described indistinctly (\(\widetilde{y}_{2}\), \(\widetilde{y}_{3}\), \(\widetilde{y}_{4}\)), which cannot be determined by traditional modeling methods. The graphical results on the visualization of fuzzy inference and the \"inputs-output\" surface, built using the Fuzzy Logic Toolbox application, testify to the adequacy and effectiveness of the proposed method for solving the problem since the graphs obtained correspond to the results of the expert assessment. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Output and Input** & **Known Models [33]** & **Taking Into** & **Real,** \\
**Parameters** & **Known Models [33]** & **Account the Fuzzy Information of the Model** & **Experimental Data** \\ \hline hydrogneate output from & 76 & 77.1 & 77 \\ reactor R-1, m\({}^{3}\)/h & & & \\ unsaturated hydrocarbons in & - & \(\widetilde{\leq}0.97\) & \(\widetilde{\leq}(0.98)\) P \\ hydrogneate, \(\widetilde{y}_{2}\), \% & - & \(\widetilde{\leq}0.00005\) & \(\widetilde{\leq}(0.00005)\) P \\ sulfur in the hydrogenated product, \(\widetilde{y}_{3}\), \% & - & \(\widetilde{\leq}0.00005\) & \(\widetilde{\leq}(0.00005)\) P \\ water-soluble acids and alkalis in the hydrogenated product, \(\widetilde{y}_{4}\), \% & & \\ the volume of raw materials at & & & \\ the entrance R-1, \(x_{1}\), m\({}^{3}\)/h; & 83 & 80 & 80 \\ pressure in R-1, \(x_{2}\), kg/cm; & 30 & 30 & 30 \\ temperature in R-1, \(x_{3}\), \({}^{\circ}\)C & 345 & 340 & 340 \\ Volumetric velocity, \(x_{4}\), h\({}^{-1}\) & 3 & 3 & 3 \\ HCG circulation, \(x_{5}\), nm\({}^{3}\). & 420 & 400 & 400 \\ \hline \hline \end{tabular} Note: means that the corresponding parameters are not determined by this method, (.) P—means that these data are determined with the participation of people in the laboratory of the plant.  
\end{table} 
Table 3: Results of comparison of data obtained with known models, developed models and real, experimental data from the hydrotreating unit of the LG-35-11/300-95 unit of the Atyrau refinery.
## 2 Principle
### Hardware  
Figure 1 shows the hardware structure of the LED multispectral imaging system. It has a simple structure and mainly includes a LED ring for illumination and a RGB camera for capturing images. The diffuser is placed in front of the LEDs to produce a diffused uniform illumination. The controller has a USB connection for PCs. To avoid the effect of specular reflection, a polarizing coating can be applied on the diffuser and imaging lens.  
Figure 1: Hardware structure (1) diffuser, (2) LED ring, (3) RGB camera, (4) control module.  
The choice of LEDs is important for LED multispectral imaging systems. An optimal LED combination can reduce noise and improve the reconstruction quality [24]. Generally, five to six channels are enough for spectral reflectance [7; 17; 21]. In our system, the LED ring contains five types of LEDs, and every type has three LEDs distributed evenly in the ring. Considering the device cost and detection target, the spectral centers of the five LED types are 460, 530, 580, 630, and 650 nm, respectively. The LED spectral center covers more bands at the yellow and red range for the following tongue color detection. The RGB camera captures two frames in detection. One frame is the lighting of the 460, 530, and 630 nm LEDs. Three images are obtained from three r/g/b channels. The other frame is the lighting of the 580 and 650 nm LEDs, and two images are obtained from r/g channels. Therefore, five images are obtained from two frames. After detecting the images, an optimization algorithm is used to reconstruct multispectral images from the five gray images. The reconstruction process is shown in Figure 2: it is an inverse algorithm of Equation (1).  
\[I_{n}=\sum_{\lambda=1}^{L}S_{\lambda}C_{n\lambda}P_{\lambda}=\sum_{\lambda=1}^ {L}S_{\lambda}R_{n\lambda} \tag{1}\]  
where, \(I_{n}\) is the detected gray image, \(n=1\), \(2\),... \(5\), \(S_{\lambda}\) is the spectral reflectance of the object, \(\lambda=1\), \(2\),... \(L\) is the wavebands, \(C_{n\lambda}\) is the spectral response of the detector, and \(P_{n\lambda}\) is the emitted spectra of the LEDs. In our system, the spectral response of the LEDs and detector (\(R_{n\lambda}=C_{n\lambda}P_{\lambda}\)) is known information which is measured and calibrated using a spectrometer (AvaSpec-ULS2048 CL-EVO) and a white board. The objective of this system is to recover the spectrum \(S_{\lambda}\) from Equation (1).
## 4 Topological Arrangements of QS Genes in Bacterial Genomes  
Gene topology is a broad term that can include the arrangement of genes within chromosomes, with respect to the replication origin or other chromosomal elements. In this work we use the words \"topological arrangement\" or briefly \"topology\" to denote the arrangement within a close neighborhood of the QS regulatory genes, to denote whether the genes are convergent, divergent, synthase upstream, receptor upstream, or synthase and receptor separated by other genes, _etc_. The preliminary overview of the searches showed a variety of topological types. To illustrate these, we developed a concise notation based on a PROSITE-like syntax [43]. The _luxR_, _luxI_, _rsaL_ and _rsaM_ genes were abbreviated as \(R\), \(I\), \(L\) and \(M\), respectively, and X is used for all other genes. An arrow above each gene symbol then shows the direction of transcription. With this notation, for example, \(\vec{R}\vec{I}\) denotes adjacent _luxR_ and _luxI_ genes transcribed in the same direction. \(\vec{R}X(>5)\,\vec{I}\) denotes the same pattern with more than five genes between the _luxR_ and _luxI_, without specifying the direction of transcription of the X gene. A summary of the topological categories found in our survey is given in Table 1. Somewhat arbitrarily, we divided the patterns into two groups: simple topologies and complex topologies. Simple topologies consist of _luxI/luxR_ genes that are either vicinal or are separated by few genes. Such arrangements are characterized by typical patterns of transcriptional orientation that are conserved in many proteobacteria. This category makes up the majority of the observed cases in Table 1 and is the main subject of this review. On the other hand, there are complex topologies in which the _luxR/luxI_ genes are separated by a larger and more variable number of intervening genes. These topologies are characteristic of _Agrobacteriumand _Rhizobium_ species. As there are many excellent reviews on these species [44,45], we mention this class only for the sake of completeness. Within the class of simple topologies, the majority of the cases are made up of the \(\vec{R}\vec{I}\) and the \(\vec{R}\vec{I}\) topologies that [PERSON] termed type A and type B, respectively [40,41]. In Table 1 we see combinations that are outside these two known categories. For instance, all four arrangements that are possible for two vicinal genes appear. In a characteristic subgroup of the simple patterns there is
###### Abstract  
This study analyzed land use and land cover (LULC) changes to identify the levels of deforestation and forest degradation in two locations in the Amazon rainforest and their conservation units. Using Sentinel-2 satellite imagery and object-based image classification, yearly LULC maps were created from 2018 to 2023. Disturbances were then quantified by Primary Forest conversions. This study revealed a gain of around 22,362 ha in Secondary Forest areas in Manaus and 29,088 ha in Agriculture/Pastureland in Porto Velho within the study period. Differing yearly rates of deforestation and degradation were detected between the areas, with agriculture/pastureland expansion being observed as the primary driver of forest loss. State and federal units showed the largest conversion of primary to Secondary Forest, while state units experienced the most conversion to non-forest areas. Sustainable use units and buffer zones were particularly impacted by these disturbances. These findings suggest that factors beyond environmental policies contribute to these outcomes, highlighting the importance of understanding local contexts. Comparing areas with varying degradation levels provides insights into the effectiveness of restoration and conservation efforts.  
A +
Footnote †: journal: Applied sciences  
1
Footnote †: journal: Applied sciences  
1
Footnote 1: institutetext: Department of Global Agricultural Sciences, Graduate School of Agriculture and Life Sciences,  
The University of Tokyo, Tokyo 113-8654, Japan; [EMAIL_ADDRESS]  
2
Footnote 2: institutetext: Correspondence: [EMAIL_ADDRESS]