esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Patient Feedback
Technical Terms Explanation shows particularly strong performance, with over 50% of respondents being \"Extremely Satisfied.\" The chart also shows very low levels of dissatisfaction across all categories, with only a small percentage of \"Dissatisfied\" or \"Extremely \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Theme** & **Percentage** & **Representative Patient Comments** \\ \hline \multirow{4}{*}{Improved Understanding} & \multirow{4}{*}{92\%} & “Finally I understand what these numbers mean” \\ \cline{3-3}  & & “It’s like having a medical translator” \\ \cline{3-3}  & & “I particularly appreciated how the AI explained the relationship between different test results” \\ \hline \multirow{4}{*}{Reduced Anxiety} & \multirow{4}{*}{78\%} & “The explanations make complex terms accessible” \\ \cline{3-3}  & & “Knowing why a value is high helped reduce my worry” \\ \cline{3-3}  & & “The explanations helped me understand that not every ‘abnormal’ result is cause for panic.” \\ \hline \multirow{4}{*}{Better Physician Consultations} & \multirow{4}{*}{85\%} & “Clear explanations made abnormal results less scary” \\ \cline{3-3}  & & “I could ask more informed questions during my visit” \\ \cline{3-3}  & & “Helped me prepare better for my doctor’s appointment” \\ \cline{3-3}  & & “I felt more confident discussing my results with my doctor because I already had a basic understanding of what they meant.” \\ \cline{3-3}  & & “The explanations helped me formulate specific questions for my doctor instead of just general concerns.” \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Qualitative feedback analysis. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**Evaluation Criteria** & **Mean Score (1–5)** \\ \hline Overall Clarity of AI Interpretations & 4.3 \\ Technical Terms Explanation & 4.5 \\ Usefulness of Reference Range Context & 4.2 \\ Ease of Understanding Results & 4.4 \\ Helpfulness for Doctor Discussions & 4.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Likert scale responses (\(n=70\)). Dissatisfied\" responses. This visual representation reinforces the positive mean scores seen in Table 3 and provides additional insight into the distribution of user satisfaction levels. Together, these three data presentations demonstrate that the AI system successfully achieved its goals of improving patient understanding, reducing medical anxiety, and enhancing the quality of doctor-patient communications.
## 3 Wireless Communication Networks
### Energy Efficiency  
The efficient use of energy is a very important challenge in WCNs due to the limited source (battery). For this reason, energy considerations have been focused on maximizing the lifetime of the batteries of the WDs that form the WCN. According to [36], the energy efficiency protocols proposed can be classified into four strategies considering the battery outage, the time to unavailability of applications functionality and the time of the first network partitioning, This way, the strategies to energy efficiency are classified as:  
1. Energy efficiency routing that reduces energy consumption for each end-to-end transmission.
2. Scheduling the WDs status to save energy and ensure the network and application functionalities.
3. Topology control by turning node transmission power to find the optimum transmission power with the minimum energy consumption that ensures the connectivity.
4. Strategies that reduce the volume of information transferred.
# Groundwater Prediction Using Machine-Learning Tools
## 4 Groundwater Prediction Methodology
### Image Pre-Processing  
Predicting a full color image at the pixel level would be computationally expensive, since the full image consists of R,G,B values for \(360\times 180\times 3\) pixels. Hence, to reduce the computational cost, we transformed the images into grayscale, and cropped the images to focus on the southern area of the African continent with a size of \(47\times 51\). An image of the pre-processed data is shown in Figure 3 (left).  
The dataset provided from PO.DAAC had missing months. We imputed the data for the missing months by replicating the previous months' images. Out of the 174 frames, we deleted the first two frames because of a gap of about 100 days between the 2 nd and the 3 rd image in the data. This left 172 images, and after image imputation we ended up with a dataset of 190 images. Altogether 18 images were imputed, which amount to about 10% of the original 172 images. We then applied a sliding window to form 161 sequences of 12 consecutive images. The first 149 sequences were used for training, and the rest for testing. We did not use any of the imputed images as labels for the models to train on.
## 5 Discussion
### Graphical Comparisons of Predictive Maintenance Approaches  
To better illustrate the relative prevalence and performance trends of various predictive maintenance approaches, we present in Figure 8 a bar chart capturing their usage frequency and general effectiveness in industrial mining settings. The chart is derived from our bibliometric analysis of 166 articles, focusing on the proportion of studies employing each technique and the reported average accuracies (or F1-scores).  
In Figure 8, CNN-based solutions appear to have the highest adoption rate in recent works (32%), likely because of their strong ability to handle unstructured time-series sensor data [30]. Meanwhile, XGBoost and Hybrid Physics-ML methods exhibit somewhat lower adoption rates but consistently high average accuracies, reflecting their robust capabilities even under variable operational conditions [55,84]. Traditional ensemble methods (RF, SVM) remain competitive, especially in contexts where computing resources or data volumes are constrained [2,3].  
Insights:  
* CNNs and RNNs have gained significant momentum, particularly in high-dimensional sensor monitoring (e.g., acoustic and vibration data).  
Figure 8: Popularity (left axis, in %) and average predictive performance (right axis, in %) of major AI approaches for fault detection in mining, derived from 166 reviewed articles.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  
* Hybrid solutions show promise in bridging data-driven and physical modeling strategies, essential for large-scale mining operations with limited labeled data [4; 6].
## 4 Nazzfast Consensus Mechanism  
### Types of Validators Used by Nazfast Consensus  
There are four different types of validators used in the Nazfast consensus. Three types are based on the election results. For example, after a successful election process, we have the top 30 validators and an active validator pool to start the consensus mechanism, where an active validator is a validator that participated in the last two election rounds. These 30 validators are divided into three categories: proposer, V-validator, and corrector, according to their respective roles in the consensus. The fourth is the wildcard entry validator.  
1. Proposer validator  
Proposer validator nodes are the building block of this consensus mechanism. Proposer validators are the ones who propose a block in each block creation cycle of every round.  
2. V-Validator  
V-validator nodes are the nodes that verify the block produced by the block proposer during the block creation process. There are 15 V-validator nodes in the block creation process.  
3. Correctors  
Correctors are the validators who revalidate the block after V-Validator verification. There are five correctors to validate each block during the block creation cycle.  
4. Wildcard Entry Validator  
In each block creation cycle, we have a wildcard entry validator. It is an anonymous validator whose identity is unrevealed by all other selected validators. The validator is linked with any of the V-validators. The wildcard entry validator's responsibility is like a V-validator. There is a pool of active validators with hidden identities. The wildcard entry validator can be any validator among all other active validators from this pool. For round one's first block creation cycle, the election starter is selected randomly as a wildcard entry validator from the pool. If the election starter is inactive or not present in the system for any reason, then the network host will select the wildcard entry validator. After the first block creation cycle, every wildcard entry validator selects the next wildcard entry validator for the next block creation cycle in the new height phase. For the genesis block  
Figure 3: Block structure to save the election result in the main chain.  
creation process, the host will select the wildcard entry validator. After the wildcard entry validator selection, the host will release the actual identity of the wildcard validator to the consensus group and its connections just before the start of the block creation process.
### Consensus Theory  
The problem of consensus achievement, an active research field with a long history in computer science, has been attracting great attention from both academia and industry over recent years [20]. In general, the term \"consensus\" means achieving an agreement upon a certain value determined by the inner states of all the agents in MAS [20; 21]. In MASs, the term \"consensus algorithm\" (sometimes referred to as a consensus protocol) is understood as a set of rules specifying how the agents in MAS interact with their neighbors [20]. The goal of distributed consensus algorithms is to ensure agreement among a set of independent autonomous entities interconnected by potentially faulty networked systems [21]. Over the past decades, the consensus algorithms have found a wide range of applications in various fields such as wireless sensor networks, robotic systems, unmanned air vehicles, clustered satellites, etc. [19; 21]. In this paper, we focus our attention on the distributed consensus algorithms, which are iterative schemes for determining/estimating various aggregate functions (e.g., the arithmetic mean, the sum, the graph order, the extreme, etc.). The authors of [22] defined two categories of distributed consensus algorithms:  
* _Deterministic_ algorithms include the Metropolis-Hastings algorithm, the Max-Degree weights algorithm, the Best Constant weights algorithm, the Convex Optimized weights algorithm, etc.
* _Gossip_ algorithms include the Push-Sum protocol, the Push-Full protocol, the Randomized gossip algorithm, the Broadcast gossip algorithm, etc.
## 2 Results and Discussion
### Odor-activity Values (OAVs)  
Though dozens of volatiles were detected in each terrain wine, but not all of the components have the same impact on the overall aroma character of this wine. Of all the compounds detected, only those displaying OAVs greater than \(1\) were deemed to contribute to wine aroma [5]. By the OAVs we can estimate the contribution of specific compound to the overall wine aroma. Table 3 shows total 13 OAVs for compounds that exceeded their thresholds in the three terrain Cabernet Franc wines, and thereby they all possibly contributed to the wine aroma. Three of these were the most powerful compounds in three terrain wines: ethyl hexanoate, ethyl octanoate and isoamyl acetate, especially ethyl hexanoate, although aroma synergy and suppression exist, all of them are byproducts of yeast metabolism, they were responsible for the fruity, floral and anise sensory properties of young wine. Aside from isoamyl acetate, the OAVs of both ethyl hexanoate and ethyl octanoate from the slope lands were the higher than that in the flat wine; they could exert a strong influence on wine aroma. Such differences might be attributed in part to the specific \"terrain\" factor. The vineyards are located in slope lands (LS-Land and HS-Land) with average altitude of 1352 m above sea level. It provides with a lower temperature, a wide swing in diurnal temperature differences distinguished by lower night-time temperature, high UV radiation and light intensity. These specific characteristics might stimulate the ethyl octanoate metabolism. The results in our study partially agreed with previous report [13] indicating that these were also the upper three most powerful odorants according to the OAVs of aroma compounds in the Cabernet Franc wines from Huailai County of China, but ethyl octanoate was the first predominant odorant among of them, which accounted for 71.8% of the global aroma of Cabernet Franc wine rather than was ethyl hexanoate as present study. Five components had OAVs higher than or very close to unity in three terrain wines: ethyl acetate, octanoic acid, ethyl decanoate, 3-(methylthio)-1-propanol and isoamyl alcohol. Among them, 3-(methylthio)-1-propanol and octanoic acid had some bad effect on the overall wine aroma, this is because both compounds share boiled potato, rubber and rancid, harsh sensory properties; but the other three give a pleasant character which are described as having fruity and cheese odor. Finally, the other compounds quantified in Table 3 can be considered as occasional odorant, they can reach OAVs higher than their corresponding odor threshold in some wine samples, but lower in other wines. Although some aroma compounds could be present at sub-threshold concentrations (i.e., OAVs \(<\) 1), their potential contribution to wine aroma should not be excluded, because they can enhance some existing notes by synergy with other compounds [44].
## 2 Position Analysis of the Planar Four-Bar Linkage  
Figure 1 shows the vector loop diagram of the planar path generating four-bar linkage [49], where \(r_{1}\) is the length of fixed link, \(r_{2}\) is the length of input link, \(r_{3}\) is the length of the coupler link, \(r_{4}\) is the length of output link, and \(r_{cx}\) and \(r_{cy}\) are the projection and normal distance of the coupler point \(C\) to the line connecting joint \(A\) and joint \(B\), respectively. Angle \(\theta_{0}\) is the inclined angle of the fixed link on the horizontal axis, and \((x_{0},y_{0})\) is the coordinate of the fixed pivot \(O\) of the mechanism on the \(X\)-\(Y\) coordinate system.  
From the vector loop defined on the planar four-bar linkage, we obtain:  
\[\vec{r_{3}}=\vec{r_{1}}+\vec{r_{4}}-\vec{r_{2}} \tag{1}\]  
By taking the inner product of its own vector:  
\[\vec{r_{3}}\cdot\vec{r_{3}}=(\vec{r_{1}}+\vec{r_{4}}-\vec{r_{2}})\cdot(\vec{r _{1}}+\vec{r_{4}}-\vec{r_{2}}) \tag{2}\]  
The above equation can be expanded to:  
\[{r_{3}}^{2}={r_{1}}^{2}+{r_{4}}^{2}+{r_{2}}^{2}+2{r_{1}}{r_{4}}\cos{\theta_{4} }-2{r_{1}}{r_{2}}\cos{\theta_{2}}-2{r_{2}}{r_{4}}\cos({\theta_{2}}-{\theta_{4 }}) \tag{3}\]  
After expanding the trigonometric function, we obtain:  
\[A\cos{\theta_{4}}+B\sin{\theta_{4}}=C \tag{4}\]  
where  
\[\begin{array}{l}A=2{r_{4}}({r_{1}}-{r_{2}}\cos{\theta_{2}})\\ B=-2{r_{2}}{r_{4}}\sin{\theta_{2}}\\ C={r_{3}}^{2}-{r_{1}}^{2}-{r_{2}}^{2}-{r_{4}}^{2}+2{r_{1}}{r_{2}}\cos{\theta_{ 2}}\end{array} \tag{5}\]  
Figure 1: Vector loop diagram of a planar path-generating four-bar linkage.  
Using the half-angle formula of tan\(\theta\) we derive:  
\[(C+A)\tan^{2}(\frac{\theta_{4}}{2})-2B\tan(\frac{\theta_{4}}{2})+(C-A)=0 \tag{6}\]  
By solving for \(\theta_{4}\)  
\[\theta_{4}=2\tan^{-1}\left[\frac{B\pm\sqrt{A^{2}+B^{2}-C^{2}}}{A+C}\right] \tag{7}\]  
By decomposing Equation (1) into the x-y components, \(\theta_{3}\) can be solved and expressed as:  
\[\theta_{3}=\tan^{-1}\left(\frac{r_{4}\sin\theta_{4}-r_{2}\sin\theta_{2}}{r_{1} +r_{4}\cos\theta_{4}-r_{2}\cos\theta_{2}}\right) \tag{8}\]  
The coordinate components of the coupler point C on the Xr-Yr coordinate system, \((C_{xr},C_{yr})\), can be expressed as  
\[\left\{\begin{array}{l}C_{xr}=r_{2}\cos\theta_{2}+r_{cx}\cos\theta_{3}-r_{ cy}\sin\theta_{3}\\ C_{yr}=r_{2}\sin\theta_{2}+r_{cx}\sin\theta_{3}+r_{cy}\cos\theta_{3}\end{array}\right. \tag{9}\]  
The trajectory of the coupler point C on the X-Y coordinate system,\((C_{x},C_{y})\), can be expressed as  
\[\left[\begin{array}{c}C_{x}\\ C_{y}\end{array}\right]=\left[\begin{array}{cc}\cos\theta_{0}&-\sin\theta_{0} \\ \sin\theta_{0}&\cos\theta_{0}\end{array}\right]\left[\begin{array}{c}C_{xr}\\ C_{yr}\end{array}\right]+\left[\begin{array}{c}x_{0}\\ y_{0}\end{array}\right] \tag{10}\]
## 3 Results and Discussion
### Wear Resistance  
The wear loss and wear resistance of the coatings with different Mo contents are shown in Figure 8. The wear loss of the coatings decreases gradually with the increase of Mo content. The wear resistance of the 6.0 wt. % Mo coating is the best, which is about 3.7 times of that of Mo-free coating. Obviously, the addition of Mo in the stainless steel powder can greatly improve the wear resistance of the coatings.  
To reveal the wear mechanism of the coatings with different Mo contents, the worn morphologies of the coatings are observed by SEM, as shown in Figure 9. The worn surface of the Mo-free coating (Figure 9a) has a large number of deep furrows, which are typical features caused by abrasive wear. When the contents of Mo are 2 wt. % and 4 wt. % (Figure 9b,c), the depths of the furrows are reduced and the amount of debris is gradually increased. Both abrasive wear and adhesive wear exist for these two cases. When the Mo content is increased to 6 wt. %, there is no furrow on the worn surface, but there is a large amount of debris. The main wear mechanism is adhesive wear. From the above analysis we can see that when the Mo content in the stainless steel coating increases, the wear mechanism of the coating changes from abrasive wear to adhesive wear.  
Figure 8: Effect of Mo content on the wear loss and wear resistance of the coatings.  
Figure 7: Effect of Mo content on the microhardness of the coating.  
In summary, the wear resistance of the stainless steel coating can be improved by adding the Mo element. The main reasons for the increase of wear resistance of the coatings are as follows. First, from the microstructure analysis it can be seen that the addition of Mo will refine the microstructure of the coatings, which can improve the wear resistance of the coating [44]. Second, the addition of Mo can change the phase types of the stainless steel coatings from the XRD results. Mo\({}_{2}\)C hard phase is formed when the Mo content is greater than 4 wt. %. Therefore, the wear resistance of the coatings can be effectively improved with the increase of Mo [45]. Third, a higher hardness generally represents a higher brittleness and lower ductility, and the lower hardness shown in Figure 6 therefore represents a ductility of the coating when Mo is added [46]. With the increase of Mo content, the hardness of the coating will decreases, and the ductility will increase. The increase of ductility will increase the wear resistance of the coatings [47]. Fourth, it is found from the worn morphologies that the depths of the furrows on the coating surfaces gradually decrease while the amounts of debris increase with the increase of Mo content. Such changes indicate that the wear mechanism is changed from abrasive wear to adhesive wear with the increase of Mo content. Therefore, the wear resistance of the coating is improved with the increase of Mo content.
## 3 Results and Discussions
### Characterization of Fine Lateritic Materials
#### 3.1.4 Compaction Characteristics  
The maximum dry density (MDD) and OMC values determined from the Proctor curves are presented in Table 2. The maximum dry density values exhibit a very slight variation between 1.6 and 1.8 t/m\({}^{3}\), with both lowest and highest values reported in the BG1 soil profile (Table 2). Meanwhile, the OMC values vary between 14.8 and 27.5%. These minimum and maximum values are also reported in the BG2 soil profile (Table 2). However, Refs. [72,80] recommended the following variation of MDD and OMC for subgrade layers of roadways: 1.9 t/m\({}^{3}\) < MDD < 2.1 t/m\({}^{3}\) and 7 < OMC < 13%. Additionally, 1.90 t/m\({}^{3}\) is the minimum value recommended for the use of materials as a sub-base layer [81]. Therefore, the studied clay-rich laterites, with their MDD lower than 1.90 t/m\({}^{3}\) and their OMC greater than those mentioned above, also require a treatment or a correction before use, both as subgrade and sub-base layer materials.  
Figure 7: Particle size distribution of fine lateritic materials in Bangangüe.  
The CBR indices at 95% of MDD after 4 days of immersion of the studied clay-rich laterites are summarized in Table 2. As indicated by [82; 83], CBR appears to be one of the most important criteria for the selection of natural materials in road construction. Therefore, [72] prescribed some uses of soil materials in road pavements based on their specific values of the CBR. Consequently, since all fine-grained laterites have a CBR > 10, they can be conveniently utilized for subgrades (Table 2). However, some materials from BG1, namely BL13 and BL27, had CBR values greater than 30%, as shown in (Table 2), and were classified as the S5 bearing class and suitable as a sub-base for high traffic (T2/T3) loadings or as a base course or wearing course for pavements with a low volume of traffic [72]. The other samples from BG1 had a CBR lower than 30% (Table 2). Consequently, they will have a low to tolerable application as a sub-base for pavements unless they are treated. Of all the samples tested, BL31 and BL32 had the lowest CBR values (Table 2) and were, therefore, chosen for soil stabilization with PKS powder.