esa-sceva/satcom-chunk-collection huggingface dataset:
# A Solution for Predicting the Timespan Needed for Grinding Roller Bearing Rings
## 4 Results and Discussion
### Causal model tree
#### 4.2.2 The Application of the Algorithm for Comparative Assessment  
The comparative assessment algorithm was applied to a set of three variables selected from the sets generated by applying the causal identification algorithm (as shown above). Thus, the set of cause variables [_l, v_, _t_] is considered as having a good modeling capacity (see Figure 5). The effect variable is the timespan, \(TS\).  
The database has four columns (the first three for the _l, v_r, t_ cause variables and the last for \(TS\), the effect variable) and \(n=77\) lines.  
We suppose that the current case (_l_ = 0.9, _v_r = 0.5, \(t\) = 0.35) needs to be ranked relative to the instances database from above. At first, the pivot (_lv_ = 0.842008037, _v_r = 0.466505582, _t_v = 0.310301508, _TSv_ = 0.292046553) was chosen from instances database and the algorithm for the case ranking assignment was iteratively run, with the results being presented in Table 7. The modeling by nonlinear multiple regression was performed in MATLAB (_Optimization tools_ package).  
The coordinate differences \(\Delta x=x-xv\), \(\Delta y=y-yv\), \(\Delta z=z-zv\) for each \(n_{i}\) cases (_x, y, z, T_) in \(N_{i}\) are calculated.  
The form adopted for the nearness function is  
\[\Delta TS=b_{4}\cdot\mathrm{sgn}\Delta x\cdot|\Delta x|^{b_{1}}+b_{5}\cdot \mathrm{sgn}\Delta y\cdot|\Delta y|^{b_{2}}+b_{6}\cdot\mathrm{sgn}\Delta z \cdot|\Delta z|^{b_{3}} \tag{1}\]  
The values for the \(\epsilon\) parameter have been selected at each iteration; as in the current case, the neighborhood includes the same number of cases (here, 24). The algorithm stabilizes rapidly, after only one iteration--the two iterations give the same results as the previous one. Here, the vector \(P_{i}\) introduced in Section 3.2, was re-denoted by \(B\) (\(b_{1}\), \(b_{2}\), \(\ldots\), \(b_{6}\)).  
We applied the algorithm, and found the values for the parameters of vector \(B\) (\(b_{1}\) = 0.31692, \(b_{2}\) = 0.12741, \(b_{3}\) = 1.3259, \(b_{4}\) = 60.453, \(b_{5}\) = 1.7365, \(b_{6}\) = 3.4573) under the conditions that \(\Delta x\) = 0.057991963, \(\Delta y\) = 0.033494418, \(\Delta z\) = 0.039698492. Consequently, relationship (1) can be used (in the form resulting after the last modeling by multiple nonlinear regression) for calculating \(\Delta TS=TS-TS_{v}\). The obtained value is \(\Delta TS=0.2205\), and then the value of the effect variable is \(TS=0.51254655\).
## 6 Results and Discussion
### Rating Prediction Results  
In this subsection, we evaluate the performance of our multi-tasking learning model in rating prediction. We compare CLtrans with several mainstream baseline models, including PETER, NRT, and EMER. These models, similar to ours, are frequently used in comparative experiments. LightGCN and SGL, which are based on graph comparisons, serve as commonly used baseline models in the field of recommender systems. The experimental results are presented in Tables 1-3, respectively.  
As shown in Table 1, GCLTE significantly outperforms other models on the 3-core dataset (REASONER) in terms of RMSE and MAE metrics. This improvement is attributed to the addition of random noise in the graph convolution process during the rating prediction task, which enables a broader range of embedding representations and yields better rating prediction results, even with relatively sparse interaction data.  
For the 5-core datasets (Amazon_Music and Amazon_Instant_Video), the experimental results, presented in Tables 2 and 3, show that GCLTE continues to outperform most other models in terms of RMSE and MAE metrics. Notably, the performance of SGL is comparable to ours. This similarity is attributed to the increased interaction information for each user-item in these datasets, where the dropout-based graph enhancement operation does minimal damage to the graph's original structure. The slight gap between SGL and GCLTE in RMSE and MAE metrics, which are significantly lower than those of other models including LightGCN, demonstrates the significant impact of the contrastive loss shared by SGL and GCLTE during the training process.
# CFD Simulations of Flows in a Wind Farm in Complex Terrain and Comparisons to Measurements
## 2 Methodology  
The RANS and LES simulations are performed using the in-house developed flow solver EllipSys3D [11,12] with the AD and AL methodologies as described in [13,14], respectively, in neutral atmospheric conditions. EllipSys3D solves the Navier-Stokes equations in general curvilinear coordinates using a block structured finite-volume approach [10]. Both the AD and AL include controllers for the rotational speed and pitch. Section 2.1 contains information about the wind turbine model used in the AD and AL techniques. Sections 2.2 and 2.3 describe the AD-RANS and AL-LES implementations in more detail, respectively. Section 2.4 summarizes the flow cases used in the present study.
# Monitoring Land Cover Change on a Rapidly Urbanizing Island Using Google Earth Engine
## 4 Discussion
### Advantages of Using GEE and Data Limitations  
GEE provides an unprecedented opportunity to handle time-series images efficiently. Previous research reported combining NDVI, NDBI, and other indexes to improve classification accuracy [19,45]. For instance, [PERSON] et al. (2020) [14] proposed to add the annual minimum built-up composites, texture features' usable image, and other indices to bands for image classification. [PERSON] et al. (2019) [46] used NDVI, EVI, GRVI, MSAVI, and NDWI for classification in Greenville, South Carolina. In this study, NDVI, NDBI, MNDWI, and tasseled cap (brightness, greenness, wetness) were added to Landsat images and achieved satisfactory classification. For better accuracy of classification, it has not only high-resolution images (e.g., Sentinel datasets) but also supports HSV-based Pan-Sharpening algorithm for Landsat in GEE, which is helpful for classification. However, it is limited to processing post-classification [14] and lacks an interactive interface to modify the classification.  
In addition, combining images from different time periods can improve the accuracy of classification. For instance, Figure 8 shows the Landsat image of Haitan Island in April 2020 and June 2020, respectively, where the increased pixel values from cultivated land are evident in the June image. Cultivated land can be identified accurately by evaluating both growing season and fallow season images. It is, however, challenging to acquire both the growing season and fallow season images in each of the study years due because there is often cloud coverage in the study area.  
For long-time temporal observations, Landsat datasets are the only option for tracking the continuous land cover change from the 1990s. In this study, the maximum NDVI composition was selected from all the available images in a given year to evaluate the influence of LULC changes. However, although GEE provides algorithms to combine multiple images for re-assembling a cloud-free image, Landsat series datasets and other optical satellite data always contain more cloud cover in the coastal area. Some NDVI values might not represent the maximum NDVI values due to the limited images in a given year because of the influence caused by haze and clouds in the image [30]. Therefore, it is more challenging to achieve long-term observation and evaluation in coastal areas [24].
## 6 Summary and Conclusions  
In this paper, the problem of calculating the elastic settlement of footings relying on the stress-strain method has been revisited, offering a great number of strain influence factor charts covering the most common cases met in civil engineering practice. The calculation of settlement based on strain influence factors has the advantage of considering elastic moduli values rapidly varying with depth and also the convenient calculation of the correction factor for future water table rise into the influence depth of footing. As known, when the water table rises into the influence zone of footing, it reduces the soil stiffness, and thus additional settlement is induced. The proposed factors refer to flexible circular footings (at distances 0, \(R\)/3, \(2R\)/3 and \(R\) from the center; \(R\) is the radius of footing), rigid circular footings, flexible rectangular footings (at the center and at the corner), triangular embankment loading of width \(B\) and length \(L\) (\(L\)/\(B\) = 1, 2, 3, 4, 5 and 10) and trapezoidal embankment loading of infinite length and various widths. The strain influence factor values are given for a Poisson's ratio value ranging from 0 to 0.5 with 0.1 interval.  
The compatibility of the so-called \"characteristic point\" of flexible footings with the stress-strain method has also been investigated. The study of settlement values from 210 different cases of rigid footings on homogenous medium (values derived from 3D finite element analysis) showed that the characteristic point concept is highly reliable but only for an analysis under drained conditions. Despite of the effectiveness of the \"characteristic point\" concept in homogenous soils, the method in question is not suitable for non-homogenous soils, as it largely overestimates settlement near the footing (for \(z\)/\(B\) < 0.35) and underestimates it at greater depths (for \(z\)/\(B\) > 0.35; \(z\) is the depth and \(B\) is the footing width).  
The proposed charts can also be used for replacing the original non-homogeneous medium with a homogenous one having equivalent elastic constant values. The equivalent homogenous medium gives the same settlement profile as the original one. This is an intermediate step for calculating the settlement of rigid rectangular footings on sands or clays based on the settlement of the respective circular footing. The reduction of the problem of a \(B\times L\) footing to the problem of the respective circular one is possible due to the effect of the shape of footing on the elastic modulus of soil. Finally, the equivalent elastic modulus corresponding to a Poisson's ratio value equal to zero can also be obtained. This value is of particular importance, as this is the proper value for use in a Winkler spring type of analysis as a Poisson's ratio value equal to zero better represents the deformation pattern of springs.  
This research received no external funding.  
The author would like to cordially thank [PERSON] for his help in running the finite element models.  
The author declares no conflicts of interest.
## 3 Results and Discussion
### Thermal Cracking Property (Low Temperature)
This indicates that CRM has the ability to alleviate the heightened rigidity generated by TLA, hence improving the performance of the mixture at low temperatures. Nevertheless, despite having a 20% TLA and 5% CRM, the m-value remains below the minimum threshold, suggesting that the 5% CRM is not enough to completely offset the rigidity caused by the increased TLA content. An absence of TLA in a 10% CRM blend results in a notable rise in the m-value, indicating that a greater proportion of CRM significantly enhances the low-temperature characteristics. When the 10% TLA and 10% CRM are combined, the m-value closely resembles that of the virgin binder, suggesting that the detrimental impact of TLA is successfully counteracted by the inclusion of CRM. Ultimately, the binder having 20% TLA and 10% CRM exhibits the highest m-value compared to all other blends containing TLA. This indicates that the addition of 10% CRM is highly effective in enhancing the performance of binders with a high TLA content at low temperatures and the statistical analysis for the stiffness and m-values mentioned above was also conducted and is presented in Tables 9 and 10 below. Figure 7: m-value at \(-12\)\({}^{\circ}\)C of the modified asphalt binders after RTFO + PAV aged condition. To summarize, TLA has a detrimental impact on the m-value, which indicates a decrease in low-temperature performance. However, the inclusion of CRM can effectively mitigate this impact. Blends containing 10% CRM exhibit significant enhancements, rendering them appropriate for cold climates where the issue of thermal cracking is a problem. This discovery is essential for the development and choice of asphalt binders in areas with cold climates, guaranteeing resilient and enduring pavement functionality. By precisely adjusting the ratios of TLA and CRM, it is feasible to attain the ideal performance attributes, guaranteeing both stiffness and adaptability as needed for particular uses.
## 4 Partial-Exploit-Sensitive POC Generation
### Partial Exploit Sensitive Fuzzing  
Once some partial exploit carrying out an address leakage-event is generated by the back end analysis-time exploit generation engine, as depicted in Section 5, the above hybrid fuzzer has to generate test cases that not only exhibit the same address leakage effect exposed by the partial exploit, but also trigger a new vulnerability afterwards. To implement this kind of partial-exploit sensitive fuzzing, we make the following adaptions to both the feedback-directed fuzzing engine and the generation-based symbolic execution engine.  
On the feedback-directed fuzzing engine side, we make address leakage an additional feedback source. More specifically, we collect the address space layout information during the execution monitoring phase, along with implementingdynamic hooks on API functions including write, recv, etc, that would send data to the output channel. An example hook on a POSIX write function in Linux 1386 platform is demonstrated in Figure 5. After checking the output file descriptor pointing to our desired channel (line 7-9), this hook extracts the buf parameter and nbytes parameter denoting the memory region to write (line 11-13), and checks if this region contains some address pointing within the memory region of the main executable or the libc.so module, which is the most common runtime library in the GNU Linux platform (line 15-17). If some address information is located in the output buf, we set the corresponding leakage context (line 18-24) for this test case, marking it as definitely promising for further generating a test case upon it.  
On the generation based symbolic execution side, given a test case _tcase_ generated by feedback-directed fuzzing based on a partial exploit input \(exp_{partial}\), asumeneing that _tcase_ would lead the single-path symbolic execution to obtain a path constraint \(c_{0}\) AND \(...\) AND \(c_{i}\) AND \(c_{i+1}\) AND \(...\) AND \(c_{nt}\), where conditions ranging from \(c_{0}\) to \(c_{i}\) are derived by the prefixing \(exp_{partial}\) part and conditions ranging from \(c_{i+1}\) to \(c_{n}\) are derived by the remaining part in _tcase_, the generation-based symbolic execution engine would only flip the latter conditions, while keep the former unchanged in test-case generation.
## 2 Materials and Methods
### FE Modeling Approach
#### 2.4.2 Ring Design and Boundary Conditions  
In evaluating ring design modifications, the analysis considered configurations featuring a 160 mm diameter ring and three K-wires (1.8 mm in diameter), each subjected to 500 N and 1000 N of pre-tension. Four distinct ring designs were analyzed to assess their structural performance and adaptability under varying pre-tension loads, as presented in Figure 8. The configurations are inspired from designs currently available on market: full rings and rings with connecting flanges [44].  
Figure 8: Ring configurations analyzed for design improvement: (**a**) baseline ring; (**b**) new 5/8 ring; (**c**) full ring; and (**d**) new full ring.  
Figure 7: Reaction area at bone-to-pin interface.  
The baseline configuration remained consistent with the initial stress assessment and early healing stage analysis. In the new 5/8 ring design, the net section was increased to enhance transverse stiffness. Additionally, extra holes were incorporated to allow for potential connections with additional rings, facilitating future EF designs that involve stacked ring configurations.  
In the full ring configuration, an additional segment (visible in yellow color in Figure 8c,d) was incorporated to fully enclose the ring, creating a complete structure. The same modification was applied to the new 5/8 ring design to assess the impact of increased transverse net section on structural performance. These configurations were analyzed comparatively, considering both metallic rings and 3D-printed PLA-CF alternatives.  
As shown in Figure 9, boundary conditions were consistent with the initial assessment. For the metallic ring (Figure 9a), three pre-tension forces of 500 N simulated wire loading, and one node was fixed to ensure isostatic constraint. For the PLA-CF configuration (Figure 9b), \(Z\)-axis directional deformation is presented for two geometries under 100 N pre-tension. In both cases, the fixed node was placed at the cylindrical coordinate origin (\(\rho=0\), \(\varphi=0\)), corresponding to a zone of minimal displacement. This setup ensures consistency across simulations and enables direct comparison of deformation responses.
# Predictive Maintenance on the Machining Process and Machine Tool
## 1 Introduction  
The Oxford Dictionary (2015) defines maintenance as: \"the process of preserving a condition or situation or the state of being preserved\". Maintenance is applied mostly to everything, not only in manufacturing processes [1], but also to railways, bikes, cars, computers, etc. Its importance has increased recently while enterprises have noticed the value and cost reduction it implies. In the industrial sector, maintenance has the aim of maintaining a process's functions over time [2].  
At the beginning of the industrial revolution, workers were responsible for equipment maintenance. As the complexity of the machines grew and the maintenance operations increased, enterprises started to include maintenance departments in their business plans. The concept of reliability appeared at the beginning of the 20 th Century when maintenance began to be concerned not only with solving failures, but also preventing them. With the arrival of computer science, maintenance strategies had another opportunity to develop more complex models.  
Since those beginnings, computational and data analysis technologies have evolved. Not all maintenance methods work for all the processes and assets, but the ones that have been useful have been adapted for new purposes. Currently, the specific needs of each company determine the most appropriate way of performing maintenance to optimize its resources.  
Maintenance can be divided into three main types: corrective, preventive, and predictive maintenance. The next paragraphs will describe each of them.
## 3 Results and Discussion
### Operations on Other \"Solar Days\" (With and without Fully Charged Batteries)
This is due to the fact that the directly coupled PV-membrane system exhibited a higher efficiency, as discussed above, thus producing a  
Figure 4: Cumulative performance of the PV-membrane system with/without (grey curves) fully charged battery storage (100% initial SOC, black curves) on the “very cloudy day” (left graph) and “sunny day” (right graph) in terms of (**A**,**F**) the motor power and SOC, (**B**,**G**) TMP\({}_{\text{RO}}\), (**C**,**H**) production, (**D**,**I**) permeated EC and (**E**,**J**) SEC. much higher permeate for most of the day (from 9:30 to 15:15) at SI > 800 W/m\({}^{2}\). Amongst these three \"solar days\", the SEC with batteries on the \"sunny day\" exhibited the highest value. This occurred as the high-average SI was capable of providing a higher current to the pump from the PV and a small current from the batteries, hence slowing down the discharging rate of the batteries (a fraction of \(\sim\)20% of the current supplement to the pump when the SI reached the maximum value during the middle of the day). Thus, the batteries played a minor role in producing sufficient permeate throughout the day, but losses were still encountered, resulting in an increase of the SEC. It indicated the likelihood of system redundancy by involving an additional device. The performance on the \"sunny day\" indicated that the water production increased from 770 to 892 L/d, the permeate EC improved from 353 to 290 uS/cm and the average SEC increased from 4.0 to 4.3 kWh/m\({}^{3}\). This represented an improvement in the water production by 15.8% and average permeated EC by 17.8%, respectively (see Table 1 in Section 3.3). From the operation of the system with/without batteries over the three different days, it can be concluded that batteries play a significant role in smoothing fluctuations and intermittency, reducing shutdown events of the pump and improving the water quantity and quality. These results are not surprising, given the fact that Li-ion batteries exhibit a high efficiency and energy intensity. Nevertheless, the main drawback was the expense of increasing the cost of the system due to its special packaging and internal overcharge protection circuits, which ultimately affected the cost of the water (discussed in Section 3.4). This is anticipated to reduce over time as the technology matures.
# Physicochemical Characterization and Antioxidant Activity of Jara Honey Produced in Western Georgia
## 3 Results and Discussion
### pH  
The pH is one of the most important characteristics of honey [59], as it may influence honey texture, stability, and shelf life [33]. In particular, it prevents the development of microbiological processes. [56].  
The pH values between 3.4 and 6.1 indicate the freshness of the honey samples [60]. All of the investigated Jara honey samples were acidic and were within the limit (pH 4.23 to 5.63) (Table 2) and within the standard limit (pH 3.40-6.10) [61], ensuring the freshness of the honey samples. In particular, the average value of the pH in the honey ridge samples is 5.214, in the Shuakhevi samples--4.96, and in Khulo samples--5.02.