esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The TASIPPS Conceptual Framework
### The TASIPPS Framework Components
#### 2.1.2 The Semantic Interoperability Module
This is achieved using the same OpenAI text-embedding-3-small model that was used to vectorize the data in the database. This conversion ensures that both the query and the stored medical data are represented in the same vector space, making meaningful comparisons possible. With the vector representation of the search query in hand, the system performs a cosine similarity search to determine the relevance of the vectorized data to the search query. Cosine similarity measures the cosine of the angle between two vectors. This metric reflects how similar the vectors are in terms of their direction in the vector space, focusing on the orientation rather than the magnitude, which highlights the semantic closeness between the query and the stored data. The vector representation of the search query is compared with the vector representations of all data entries in the vector database. The system calculates the cosine similarity score for each comparison, indicating the degree of similarity between the query and each data entry. The results are then ranked based on their similarity scores, with higher scores indicating greater relevance to the query. The system retrieves the top three most relevant results based on their similarity scores. These results contain information from across all connected hospitals, including Hospital B. The system then filters these results to ensure that the information returned is relevant to Hospital A, focusing on the specific needs of the querying system. As part of the filtering process, the system translates the patient's diagnosis and other relevant medical information from Hospital B's terminology into a format that Hospital A's system can understand. This step ensures that the medical information is not only relevant but is also comprehensible to the healthcare professionals at the requesting hospital. The most relevant results from the semantic search are then returned to the requesting system at Hospital A. This process ensures that healthcare professionals have access to pertinent patient medical history in familiar terminology, enabling them to make accurate medical diagnoses, formulate timely treatment plans, and ultimately improve patient outcomes.
## 5 Common Systems
### Data Acquisition
#### 5.3.4 DataBuffer/ImageBuffer  
All beam synchronous data is buffered and stored for a configurable amount of time. By doing so users are able to retrieve historical data if needed. The systems taking care of the buffering are DataBuffer and ImageBuffer. Both systems take care of and are optimized for different classes of data. DataBuffer takes care of small data, i.e., of all scalar and one dimensional arrays (waveform) data. The ImageBuffer system takes care of all beam synchronous images from cameras and detectors running at the facility except the JUNGFRAU detector data.  
The DataBuffer system is currently a cluster of 12 machines and uses Cassandra [204] for persistent data. The ImageBuffer system is based on Spectrum Scale [205] and an in-house data format. Data from both systems is accessible to users via the DataAPI interface described in Section 5.3.5.
# Prediction of Unconfined Compressive Strength in Cement-Treated Soils: A Machine Learning Approach
## 3 Results and Discussion
### Uncertainty Quantification System Performance
#### 3.3.5 Contextualization with Previous Research  
The comparison of our findings with previous studies reveals both similarities and differences. The strong influence of cement content on UCS development aligns with established literature [96; 98], but the minimal impact of compaction rate observed in our study must be interpreted within the specific context of our experimental methodology. All specimens were compacted to the same target density regardless of the compaction velocity used. Therefore, the total compaction energy was effectively adjusted to achieve the same final density. This finding does not contradict studies such as [PERSON] et al. [105], who reported substantial effects of compaction energy on strength development in cement-stabilized soils. In their study, different levels of compaction energy produced variable densities, which then directly influenced the strength of the material.  
Our results instead suggest that when the final density is the same, the rate at which compaction is applied has limited effect on the mechanical properties of cement-treated soil. This observation is significant for practical applications in geotechnical engineering, as it indicates that as long as the target density is achieved, the precise rate of compaction application may be a less critical parameter than previously assumed. This aligns with the fundamental understanding that density, rather than the specific process through which this density is obtained, is the primary determining factor for strength development in cement-treated soils.
# A Multiple Criteria Decision Analysis Framework for Dispersed Group Decision-Making Contexts
## 4 Scenarios of Application  
In this section, we walk through some of the framework's functionalities. As mentioned before, GDM processes with dispersed elements are extremely complex. The limitations associated with the communication is one of the main reasons. In order to benefit from the advantages associated with GDM processes in this kind of context (to exchange ideas, to understand the reasons behind other decision-makers' preferences, to create new intelligence, among others [39]), specific conditions need to be created. First, the support systems should have the capability of understanding that the best decisions generally take time, i.e., the period of time that comprehends the decision process should be respected. Second, regardless of how important it is to propose solutions, the main focus should be to support the process, i.e., how much a decision-maker can benefit from interacting with the system? That means, besides the basic information that can be deduced from the decision-makers' configurations and through a superficial analysis of their preferences (over alternatives and criteria), the system should include methods to see beyond that data. This framework arises from the need to overcome those limitations and aims to be an important tool for researchers and developers that intend to include mechanisms capable of supporting groups in attaining higher quality decisions in their systems or projects. So, we will show how the framework can be used to get important information for the decision processes, by starting with the simpler tasks and then advancing to the more complex ones.
# Harnessing Biotechnology for the Remediation of Organic Pollutants in Coastal Marine Ecosystems
## 4 Biotechnological Methods for Remediation  
Remediation refers to the set of procedures that remove, reduce or neutralize contaminants in the environment, bringing the affected ecosystems closer to their natural or near-natural state. In the case of coastal marine ecosystems, remediation has become important because it addresses the impacts that organic pollutants have on biodiversity by degrading ecological balance and utilizing ecosystem services. Traditional remediation techniques, including chemical and physical, can often be expensive and potentially contribute to secondary pollution; they are also poorly suited to their application in sensitive marine environments [52].  
Biotechnology gives sustainable and eco-friendly alternatives in the remediation of organic pollutants. Biotechnological remediation takes advantage of biological systems such as microbes, plants, and enzymes to utilize organisms through natural processes of breaking down, transforming, or removing pollutants from the environment. This approach minimizes its impact on the environment and contributes towards making the entire remediation process much more effective. It is based on the use of biological processes to ameliorate environmental contamination, offering sustainable and eco-friendly alternatives to other remedial strategies. These methods comprise degradation, transformation, and removal of organic pollutants with efficiency, cost-effectiveness, and the least possible ecological disturbance. The major types of biotechnological remediation, such as bioremediation and phytoremediation, utilize the natural abilities of organisms to regenerate environments polluted in coastal marine ecosystems.
# Efficient Argan Tree Deforestation Detection Using Sentinel-2 Time Series and Machine Learning
## 2 Materials and Methods
### Change Detection Method  
To create a deforestation map of organ forests in the study area between 2017/2018 and 2022/2023, four scenarios were considered:  
1. Detected in both 2017 and 2022. 2. Not detected in either year. 3. Detected in 2017 but not in 2022 (deforestation). 4. Detected in 2022 but not in 2017 (regeneration or new plantations).  
The deforestation map was generated by comparing each pixel in the 2017/2018 map to its corresponding location in the 2022/2023 map using a patch-based approach. Due to the 10-m resolution of Sentinel-2, individual organ trees could not be identified, necessitating the use of spatial kernels to improve classification accuracy. Five kernel sizes were evaluated to balance detail preservation and noise reduction. Smaller kernels provide finer details but are prone to increased classification errors. Larger kernels smooth classification results, reducing noise but potentially overlooking small-scale deforestation events [124]. For sparse and heterogeneous forests like organ, medium to large kernels are generally preferred to enhance stability while retaining relevant patterns [125]. The tested kernel sizes were:* Patch 1 \(\times\) 1 (Pixel-based): Direct comparison of individual pixels between the two maps.
* Patch 3 \(\times\) 3: Each pixel in one map was compared to all pixels within a 3 \(\times\) 3 kernel centered on the corresponding pixel in the other map.
* Patch 5 \(\times\) 5, 7 \(\times\) 7, 9 \(\times\) 9: Similar to the 3 \(\times\) 3 kernel but using larger sizes.  
After generating the deforestation maps, the deforestation rate of organ forests was calculated for each kernel size (Equation (1)).  
\[rate\ =\ \frac{Argan\_2017}{Argan\_17\_22\ +\ Argan\_2017\ +\ Argan\_2022}, \tag{1}\]  
where _Argan_17_22_ is the number of pixels classified as organ trees in both 2017 and 2022, _Argan_2017_ is the number of pixels classified as organ trees in 2017 but not in 2022 (deforestation) and _Argan_2022_ is the number of pixels classified as organ trees in 2022 but not in 2017 (regeneration).  
The optimal patch size was determined based on:  
* Visual assessment of classification accuracy and deforestation pattern preservation.
* Comparison with deforestation rates from previous studies and official reports.  
These methodological choices were crucial in ensuring a reliable estimation of deforestation, despite the limitations of Sentinel-2 imagery and the absence of ground truth data for 2017/2018.
## 4 Discussion
For individual stimuli, from a, b, and c in Figure 7, all three types of stimuli (W, V, and P) showed ERD and ERS in the \(\beta\) band. For warmth (W), the ERD did not appear in the \(\alpha\) band, but for vibration (V) and pressure (P), it appeared after 0.5 s. Also, for both stimuli (V and P), the ERD lasted longer than for the temperature stimulus. This is likely due to the small muscle movements. In the peak-to-peak analysis of ERD and ERS, warmth was the largest and pressure was the smallest. This is because thermoreceptors, which detect warmth, are more sensitive than mechanoreceptors [11,21,25]. Vibration and pressure sensations differed from thermal sensations in that the frequencies activated in the \(\beta\) band of the ERD were similar in duration and time. In sensory receptors that detect sensations, vibration is predominantly responded to by Pacinian corpuscles, while pressure is predominantly responded to by Merkel discs [8]. In physiology, these are classified as mechanoreceptors because they respond together depending on the intensity and duration of the sensation. Thermal sensation is primarily mediated by free nerve ending receptors classified as thermoreceptors that respond without physical deformation, leading to a different trend compared to vibration and pressure sensations. The peak of the vibration sensation was larger than the pressure sensation, which is likely due to the fact that the intensity of the pressure presented in this study was relatively not high and also, we used a frequency of 200 Hz, which is the most sensitive in vibration. But this may change depending on the tactile stimulation parameters (frequency, intensity, and time) of other tactile studies [17,19,26]. For the combined stimuli, from d, e, and f in Figure 7, in all cases, the ERD was larger in all \(\alpha\) bands of the individual stimulus and commonly appeared after 0.5 s. This is likely due to the inclusion of vibration stimuli. Although the same stimuli were presented simultaneously to individual tactile receptors, the superimposed results of the responses to each individual stimulus did not appear. This is thought to be because information processing in the brain is performed integrally rather than individually. Further detailed research on the functional and effective connectivity of the brain is needed. In the case of two simultaneous stimuli, the duration of the \(\beta\) band ERS was longer than that of a single stimulus. This means that more complex cognitive processes are required than for a single stimulus. For the W + V + P simultaneous stimuli, the ERD peaked at \(-\)3.63 dB in the \(\beta\)-band and had the maintain largest duration. This suggests that neuronal activity is more focused on attention-related processing. Also, the interaction between the types of stimuli would have resulted in different results than in the case of a single stimulus. This suggests that the response of sensory receptors in the skin may be different depending on the type and combination of stimuli, and that there may be differences in information processing in the brain.
## 5 Direct Simulations
Figure 16: The influence of the phase-velocity mismatch, \(\chi=1\), on the evolution of the MI in the bar (**a**) and cross (**b**) channels in the anomalous-GVD dispersion regime. Other system parameters are \(A_{1}=A_{2}=\omega_{0}=\alpha=\Gamma=\kappa=1,\rho=0\) and \(a_{0}=0.0001\). Figure 15: The influence of the group-velocity mismatch, \(\rho=1\), on the evolutions of the MI in the bar (**a**) and cross (**b**) channels in the anomalous-GVD regime. Other system parameters are, \(A_{1}=A_{2}=\omega_{0}=\alpha=\Gamma=\kappa=1,\chi=0\) and \(a_{0}=0.0001\). Figure 19 reveals the impact of the ratio of nonlinearity coefficients between the two cores. In this case, the MI generates a chain of very narrow solitons with a higher amplitude, whose peak powers are growing in the cross channel and growing peaks on an oscillating background with a relatively low amplitude in the bar channel. Next, we plug in all of the parameters, to identify their combined effect on the MI evolution in the anomalous-GVD regime, in Figures 20 and 21. In the former case, it is observed that the MI gives rise to a single soliton in the bar channel, whereas the field in the cross channel decays into radiation. In the latter case, a single soliton is generated too (which is natural for the case of the anomalous GVD), but with components in both cores. Focusing our attention on the asymmetric coupler in the normal-GVD regime, in Figure 22, we address the case when the amplitudes of the two CW components are equal. In this case as well, a periodic array of peaks with growing amplitudes is generated in both the bar and cross channels. However, its shape is essentially different from the soliton chains displayed above in the anomalous-GVD regime, as in the present case, the array is built of alternating peaks and wells. Lastly, if the amplitudes of the two CW states are widely different, such as in the case of a large  
Figure 19: The influence of the ratio of the nonlinearity coefficients in the two cores, \(\Gamma=2\), on the MI evolution in the bar (**a**) and cross (**b**) channels in the anomalous-GVD regime. Other parameters are \(A_{1}=A_{2}=0.75,\omega_{0}=2,\alpha=\kappa=1,\rho=\chi=0\) and \(a_{0}=0.0001\). Figure 20: The MI-driven evolution in the bar (**a**) and cross (**b**) channels in the anomalous-GVD regime (\(\sigma=1\)) for initial CW amplitudes \(A_{1}=0.75,A_{2}=0.5\) and perturbation parameters \(a_{0}=0.0009,\omega_{0}=1\). Other parameters are \(\alpha=2,\Gamma=1,\rho=0.01,\chi=0.001\) and \(\kappa=1\).
## 3 Results and Discussions
### Low Frequency Mini-Brillouin-Zone Mode
#### 3.2.2 MBZ-Center Mode Excitation
The combined frequency at around 63.1 GHz is thus enhanced greatly compared to the pure second order MBZ-center mode in the DBR, which could partwise explain why the second order MBZ-center mode dominates rather than the first order MBZ-center mode (we cannot filter out the pure contribution of MBZ center modes from the spectrum). Secondly, the amplitudes of the spectral components of the signal depend not only on the generation but also on the detection processes. While all the above considered three frequencies could potentially be detected both in the MQWs spatial region and in the DBR spatial region, the lowest of these frequencies could be additionally efficiently generated and detected in the parts of the samples which are not spatially structured. In other words, the generation and detection of the phonons around 32-33 GHz frequency could take place in spatially-homogeneous materials constituting our samples, for example in spacers between the MQWs. The counter-propagating light fields of pump laser in the MQW-SESAM structure could efficiently excite phonons at these frequencies by stimulated Brillouin scattering (via the electrostriction mechanism due to intensity modulation in their interference pattern, Figure 4c). Probe light could be efficiently scattered by the propagating acoustic waves at these frequencies in our sample via the acousto-optic effect because the probe light and acoustic wave vectors are matched via momentum conservation law in the backward Brillouin scattering process. We found that in all materials of our sample, the so-called Brillouin frequencies of phonons, satisfying the momentum conservation law which is essentially the same for the efficient generation and the efficient detection, are very close to the MBZ center mode 32 GHz at \(k\cong 0\) (31.4 GHz in GaAs, 30.8 GHz in In\({}_{0.27}\)Ga\({}_{0.73}\)As and 31.8 GHz in Al\({}_{0.95}\)Ga\({}_{0.05}\)As). The listed Brillouin frequencies here and in Table 4 have been calculated using \(f_{i}\)= \(2n_{i}v_{i}/\lambda_{probe}\)[44], where \(n_{i}\) denotes the refractive index, \(v_{i}\) denotes the longitudinal sound velocity in sample constituent materials and \(\lambda_{probe}\) denotes the probe light wavelength in vacuum. Thus, the Brillouin frequencies could probably suppress the MBZ center mode at 32 GHz due to destructive interference between them when the phase difference is close to \(\pi\), which could potentially explain why the first-order mode has a weaker amplitude than the second order one.
## 3 Results  
The deflection curves of the stalk with the ear, leaves, and extra stalk removed, which was separately loaded with 0.03, 0.04, 0.05, 0.06, and 0.07 N, are shown in the Figure 4. It was clear that higher the load, the larger the deflection. A one-way analysis of variance (ANOVA) test was conducted, the result indicating that load was significantly related to deflection (\(p<0.05\)).  
Figure 3: Testing rig: (**1**) adjustable table, (**2**) hand-operated support, (**3**) digital force meter, (**4**) rope, (**5**) stalk, (**6**) coordinate board, (**7**) clamp.  
The deflection values of the stalk, corresponding to the deflection of point A, were then used to calculate \(EI\) by substituting them into Equations (2), (14), and (15). The results were shown in Figure 5. It was obvious that the \(EI\) values were basically consistent by using Equations (14) and (15). A one-way ANOVA test was also conducted, the result indicating that differing deflection had no significant influence on flexural rigidity (\(p<0.05\)), while the values of Equation (2) differed widely when the deflection increased. The experiment validated the appropriateness of [PERSON]'s formula for the calculation the flexural rigidity. On the other hand, the maximum deflection value measured in this paper exceeded the common deflection range of 100-200 mm, which means this method could meet the requirement of flexural rigidity calculation in rice harvesting.  
By substituting the \(EI\) values into Equations (3) and (5), the deflection curves of stalk AO could be fitted, and the results are shown in the Figure 6. The deflection curves of 0.06 N were fitted as an example and compared to the measured curve. The results showed that the curve fitted by the large deflection formula in combination with [PERSON]'s formula was close to the actual one, with the free end error and the maximum error being only 1.83% and 8.95%, respectively, while the curve fitted by the conventional equation greatly deviated from the actual curve, with the free end error and the maximum error both being 37.15%. The experiment validated the availability of the large deflection  
Figure 4: Rice stalk (measured values).  
Figure 5: Deflection vs. flexural rigidity.  
calculation formula in combination with [PERSON]'s formula to compute the deformation curve of rice stalks. Using the accurate calculated curve, a proper cutting height of rice stalk could be determined to optimize power configuration of harvesters and satisfy the requirement of special crops.
## 3 Materials and Methods  
The work methodology in this study included two main phases: the fieldwork phase and the office work phase. The fieldwork included selecting the study area, defining and establishing ground control points and checkpoints, measuring their coordinates with a global positioning system (GPS), and conducting aerial photography with a UAV. The office work included the preparation of the flight plan, data processing, extraction of map products (dense point cloud, DEMs, and orthophotos), and evaluation of positional accuracies.  
In this study, the effects of flight direction and camera orientation on UAV photogrammetry geospatial products were studied and analyzed. For this purpose, photogrammetric flight missions were conducted using low-cost UAVs equipped with consumer-level image sensors and low-quality navigation and attitude sensors. Different combinations of image sets were used to evaluate the effects of some parameters such as the use of GCPs, the image acquisition angle, and the flight direction on the positioning accuracy of the UAV products.  
Figure 1 shows the methodology proposed in this study, which included the main phases mentioned above and the steps and tasks performed for each phase. The UAV flights in this study were performed using a Mavic 2 manufactured by the Chinese company DJI-Innovation Technology Co. LTD (Figure 2a). This multi-rotation rotary drone has a diameter of only 30 cm and weighs only 2 kg. The Mavic 2 can fly autonomously and is controlled by route-planning software that also takes off and returns automatically. The autonomous flight time of the drone is about 25 min per battery group. The Mavic 2 is equipped with a Hasselblad L1D-20c camera from the manufacturer. This camera provides an image of 12.825 \(\times\) 8.550 mm, a focal length of 10 mm, and an image size of 5472 \(\times\) 3648 pixels.