esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling a Milling Dynamometer as a 3 DOF Dynamic System by Stiffness Identification
## 3 Static Stiffness Determination
### Analytic Stiffness Calculation  
The values for stiffness for octagonal rings can be calculated according to different relations presented in technical papers. In [16; 17; 18; 19], for longitudinal and transverse stiffness calculations, the following relations are presented:  
* For the longitudinal direction: \[\text{k}_{\text{long}}\ =\ \frac{\text{Ebt}}{40\text{r}}\] (1)  
Figure 4: Ring stiffness determination set-up: (**a**) longitudinal direction of the ring; (**b**) transverse direction of the ring.  
Figure 5: Ring stiffness experimental curves: (**a**) stiffness curve in longitudinal direction of the ring; (**b**) stiffness curve in transverse direction of the ring. Blue—load; Red—unload; Yellow—Average.  
* For the transverse direction: \[\mathrm{k_{tr}}\ =\ \frac{\mathrm{Ebt^{3}}}{3.6\mathrm{r^{3}}}\] (2) where \(E\) is [PERSON]'s modulus, in [N/mm\({}^{2}\)], \(b\) represents the width of the ring measured in [mm], \(r\) is the radius expressed in [mm], and \(t\) represents the thickness of the ring measured in [mm] (Figure 6).  
Considering the geometrical data, where \(a\) = 50 mm, \(b\) = 18 mm, \(t\) = 4.6 mm, \(r\) = 15.4 mm, and \(E\) = 2.1 \(\times\) 10\({}^{5}\) N/mm\({}^{2}\), the following values for stiffness were obtained:  
* For the longitudinal direction (1): \[\mathrm{k_{long}}\ =\ 28.227\ \times\ 10^{3}\mathrm{[N/mm]}=22.227\ \times\ 10^{6}\ \mathrm{[N/m]};\] (3)
* For the transverse direction (2): \[k_{tr}\ =\ 27.983\ \times\ 10^{3}\mathrm{[N/mm]}\ =\ 27.983\ \times\ 10^{6}\ \mathrm{[N/m]}\] (4)
# Leveraging Large Language Models for Sensor Data Retrieval
## 3 Architecture of the System
### Data Retrieval
#### 3.2.3 Search and Retrieval  
The search and retrieval phase aims to identify sensor data that can be related with the user's input dataset. Specifically, this process entails seeking out other tables with highly relevant content that can be combined with the initial table to augment its content. The bottom part of Figure 3 shows the search and retrieval process.  
In the same way as in the previous step for indexed tables, the input table is split into columns, and word embeddings are generated for each column. Following this, the system uses each column's embedding to query the index, retrieving the top-k similar columns that have been indexed. The similarity between columns is computed using cosine similarity, ranging between \(-1\) and \(1\), where a value closer to \(1\) indicates greater similarity. The k-value, representing the total number of documents retrieved by a query, was set to 1000 in the experiments carried out in Section 5.  
Next, to calculate the overall similarity for a table, the retrieved columns that belong to the same table are grouped, and their similarity scores are aggregated. The resulting sum is divided by the total number of columns in the input table, yielding a similarity score for each resultant table. Finally, the tables are ranked based on this score, providing an ordered list according to their relevance.
## 2 Materials and Methods
### Network Latency  
The propagation delay of a link (\(t_{pr}\)), frame/packet transmission/reception delay of a link (\(t_{tr}\)), average waiting time in queue per link for one way (\(t_{qu}\)), fusion time (\(t_{fu}\)), Maximum Link length out of links connected to neighbor nodes (\(l_{i}\)), data rate (R), frame/packet size (P), number of Hops (N) of the longest path of the network and Average number of retransmission attempts (K), and career propagation speed (c) are the main determinants of Network Latency of WSNs. Using these parameters, the network latency (\(TL\)) for a hierarchical network employing proposed method and AVG can be given as shown in Equation (20).  
\[TL= ((2*(N-1)(K+1))*(t_{pr}+t_{tr}+t_{qu}))+((N-1)*(t_{fu})) \tag{20}\] \[= (N-1)*(((2(K+1))*(t_{pr}+t_{tr}+t_{qu}))+(t_{fu}))\] \[= (N-1)*(((2(K+1))*(\frac{l_{i}}{c}+\frac{P}{R}+t_{qu}))+(t_{fu}))\]  
Network latency is calculated for the longest path in the WSN for AVG and proposed methods. Equation (20) considers both data and acknowledgment frame/packet transmissions. The frame/packet has to travel from hop to hop for networks employing proposed method and AVG. However, for networks employing EBDKF, the frames/packets are only exchanged among neighbors such that the corresponding latency Equation is as shown in Equation (21).  
\[TL= ((6*2*(K+1))*(t_{pr}+t_{tr}+t_{qu}))+(t_{fu})\] \[= ((6*2*(K+1))*(\frac{l_{i}}{c}+\frac{P}{R}+t_{qu}))+(t_{fu}) \tag{21}\]  
The network latency is independent of N for diffusion techniques as proved by Equation (21). For bidirectional Ad-Hoc networks the proposed method and average methods will also have to fuse only information from neighbors as described in Section 2.10. Therefore, the network latency of such networks employing AVG method or proposed method can be given as in Equation (22).  
\[TL_{chain}= ((1*2*(K+1))*(t_{pr}+t_{tr}+t_{qu}))+(t_{fu})\] \[= ((1*2*(K+1))*(\frac{l_{i}}{c}+\frac{P}{R}+t_{qu}))+(t_{fu}) \tag{22}\]
## 4 Results and Discussion
Figure 6: Layout of the OPC showing locations and assigned departments. From literature, the Ant algorithm in [19] provided the single best optimal solution for the layout with the objective of minimizing the walking distances. The proposed model in [19] provided one best solution. The optimized layout obtained from that algorithm is suitable for new OPC buildings as these buildings can adapt easily, whereas for existing OPCs there are multiple constraints. Having only one optimal layout and changing the existing layout to optimize workflow is challenging and difficult. In such situations, OPCs need sub-optimal solutions/layout redesigns. The changes suggested as per sub-optimal solutions can be adapted in the existing layout and also optimize the workflow. There are various solution layouts which reduce the walking distances compared to the existing layout and require few changes/modifications in the layout. The layout redesign that has a greater number of walking steps/distance requires less alteration to the layout, and with a smaller number of steps, requires more alteration to the existing layout. This is because the farther locations are reallocated to the same departments as before and this does not require any alteration. Only if the locations are allotted with different departments does OPC require a greater number of alterations. With this size of layout problem, the execution time for genetic algorithm is 34 s. With the increase in number of locations, the search space becomes wider and the execution time will increase. The complexity depends on mutations, crossovers, population size and number of generations for GA. Unlike other facility layouts, OPCs/hospitals have a lot of variabilities and uncertainties in services offered. The OPC may add or stop providing certain services. The long-term requirements are considered for 1-5 years. But the short-term requirements are considered for 3-6 months. The OPC building lasts for a long time, so their reuse is necessary. For this, the OPCs should be flexible to restructure or design its layout based on operations management. With time, the dynamics in OPCs keep changing. At times, implementing the optimal solution will not be possible because of cost and other constraints. This study helps in hospital layout planning and aids in decision-making. The decisions about layouts need to be made based on system-status/real-time need of the OPC. The cost associated with making the changes in the existing layout by moving equipment is not incorporated in this model. Also, the change is services offered and change in pathway is not implemented. Waiting hall capacity also needs to be incorporated with increase in patient demand. With increase in demand, the number of departments will also increase. The OPC can have demand based resource allocation that minimizes patient waiting time [1]. To optimize in this way, OPCs should be flexible to adapt accordingly to the changes.
## 1 Introduction
For example, [23] showed that mixed Saharan dust events can be especially powerful during the period of stratification. A positive correlation between dust deposition and chlorophyll-A concentration has been found in large parts of the Mediterranean Sea with a clear gradient of decreasing correlation coefficient from south to north [24]. The chlorophyll concentration increased drastically after significant dust deposition events with concentration peaks between days 1 and 6 after the event; the chlorophyll growth ranged from 13% to 345% in different cases. The study [22] considered weekly data (2000-2007) on chlorophyll concentration obtained from the satellite observations by SeaWiFS, and dust deposition data obtained using the BSC-DREAM8b model. The data were examined for the entire Mediterranean basin to describe the geographic distribution and dynamics of both variables and to find the potential relationships between them. The results indicated that the central Mediterranean had the highest chlorophyll-A sensitivity to mineral dust deposition, with an annual contribution of seasonal variability, and stimulation from major events. These results highlight the importance of African and Middle Eastern dust deposition in the potential stimulation of phytoplankton production in the nutrient-depleted surface layers of the Mediterranean. In recent years, the Black Sea has shown a tendency to an increase in the role of coccolithophorids in the formation of the biomass of communities [25]. Cocolithophorids produce half of CaCO3 in the ocean [26], so additional input of phosphates with dust aerosol can largely affect the biological characteristics of the surface layer of the Black Sea. Thus, most of the previous studies of the Saharan dust impact on phytoplankton have been limited to a few specific areas of the Mediterranean. In recent years, a number of papers about registration of the dust aerosols in the Arctic region have also been published [27; 28; 29]. However, such aspect for the Black Sea has been insufficiently explored, the first estimates are provided in [30]. The purpose of this research is to identify patterns and features of the Saharan and Syrian dust deposition and determination of the dynamics and distribution of surface chlorophyll in the Black Sea.
## 3 Results and Discussion
### Future Field Studies
In this study, we chose to monitor pre- and post-treatment samples in a controlled laboratory environment rather than in the treated areas in Lake Cochituate due to challenges in controlling environmental conditions in open water, such as surface and water column mixing between the treatment zones. However, it may be beneficial to test the CyanSTUN(tm) in controlled waterbodies such as experimental ponds to observe changes in the complete water body post-treatment. Future field studies with the CyanoSTUN(tm) should also consider alternative motor configurations and improved flow metering capabilities, as it was challenging for the field researchers to maintain the speed of the vessel at the low velocities required in this study using the current electric motor configuration. Additionally, the CyanoSTUN(tm) had a fixed reactor depth, which was challenging to establish at 12.7 cm (5 in.) and keep constant throughout the field studies as the field researchers moved about the vessel, causing the boat platform and affixed reactor to tip side-to-side and front-to-back. An alternative engineering design should be considered to enable variable depth control of the reactor and potentially independent leveling relative to the boat platform, especially since chlorophyll \(a\) and phycocyanin concentrations showed a slight positive correlation to depth in this study, with the lowest readings occurring near the surface and the highest readings occurring at 91.44 cm (36 in. ; farthest from the UV reactor). Improved depth control would also be advantageous when environmental conditions are such that the target cyanobacterial biomass is more distributed in the water column rather than concentrated at the surface.
# Lower-Body Positive Pressure Treadmill Training for Pediatric Gait Disorders: A Scoping Review
## 4 Discussion
### Effect of Training on Body Functions  
After anti-gravity treadmill training, a few studies reported an increase in lower-extremity strength (as measured in isometric or isokinetic mode) [18; 27; 28], a decrease in spasticity [26] and improvement in gross motor function [20]. These improvements are relevant and need to be confirmed by further studies with larger sample sizes. Spasticity and a lack of muscle strength play a key role in gait impairment, given the strong relationship between these parameters and walking ability in children with cerebral palsy [42; 43; 44; 45]. Indeed, lower-limb muscle strength explains approximately 21-47.8% of ambulatory capacity in these children [44; 45]. Strength and spasticity might be considered to determine whether a child with spastic cerebral palsy may benefit from an intervention to improve walking capacity.
# Experimental and CFD Simulation Studies on Bell-Type Air Nozzles of CFB Boilers
## 2 Experimental Research
### Structure of Air Nozzles  
The pressure drop characteristics of traditional bell-type air nozzles (nozzle A) and the newly-designed bell-type air nozzle (nozzle B) at different air flows were tested by the test rig. The shape, structure, and size of the two air nozzles are shown in Figure 2 and Table 1.  
Figure 1: Schematic diagram of cold-state experiment rig. 1—fan; 2—air flow regulating device; 3—air flow speed measuring device; 4—air duct; 5—temperature measuring device; 6—air pressure measuring device; 7—wind box; 8—distributor; 9—air nozzle.  
Figure 2: Schematic diagram of bell-type air nozzle: (**a**) traditional design (nozzle A); (**b**) new design (nozzle B).  
Nozzle A comprised two parts: a cover and an inner tube (as shown in Figure 2a). Its design concept was to adopt the orifice's structure for the upper part of the inner tube, and outlets on the lower part of the outside cover. Thus, the pressure drop of the air nozzle was mainly formed at the orifices of the inner tube, so that the air nozzle will possess reasonable pressure drop characteristics. Since the outlets on the cover were relatively large, and the air velocity here was relatively slow, it facilitated a reduction in the abrasion of the air nozzle.  
The structure of nozzle B is shown in Figure 2b, which replaced the screw threaded connection of nozzle A by top welding and the fixing method. Nozzle B could select a lower outlet velocity in cover than nozzle A, so as to reduce the abrasion of the air nozzle. At the same time, the outlets of the cover were oval structured and inclined downward, which can not only reduce the mutual influence between adjacent air nozzles, but also facilitate the coarse particle flow, such as stones and coal, to avoid blocking.  
Since nozzle A is an air nozzle structure that is actually used in engineering, three representative nozzles (A1-A3) were obtained from the site of the cold-state experiment rig: nozzle A1 with large abrasion of outlet holes on the outer cover, nozzle A2 with blocked holes in the outer cover, and nozzle A3 with a gap between the top and outer cover in the deformed inner tube. The three kinds of nozzles are shown in Figure 3.
## 4 Results
### Frequency-Domain Analysis  
From the power spectral density subfigure of reference noise (Figure 12a), frequency bands of 6-7 Hz and 12-13 Hz should be considered the noise-dominant frequency bands of PPS tactile sensors. Figure 12b shows that the traditional wavelet de-noising method could not filter noise in these two frequency bands. The VMD method correctly found the distribution of the noise (Figure 12c), but the noise in particular frequency bands was only partly filtered out. However, all RPCA-based methods (Figure 11d-f) could successfully filter out the main frequency bands of the noise. In addition, it is worth noting that our method could eliminate the noise better in a lower-frequency band (0-2 Hz) compared with other RPCA-based methods. Other noise frequency bands were strongly mixed with pulse signals, which are considerably hard to eliminate.
## 2 Materials and Methods
### Coastal Flooding Analysis  
To support the results obtained through the vulnerability index, a coastal flood analysis of the study area was conducted using the Flood Hazard Index (FHI). Despite the last tsunami in Galicia being associated with the Lisbon earthquake of 1755, and the fact that the earthquake was more perceptible than the subsequent tsunami, Galicia is considered an exposed area to this natural hazard due to several proximal and distant focal mechanisms in the North Atlantic that could trigger a tsunami. These mechanisms include volcanic activity in the Azores and Madeira islands, seismic activity in the Mid-Atlantic Ridge, the Caribbean Plate, and the Azores-Gibraltar oceanic convergence zone, as well as large sediment masses mobilized along the North American coast. In the case of the Lisbon tsunami, maximum elevations of up to 3.3 m were recorded [38]. However, for this study involving extreme values, the maximum elevation reached by this tsunami on the coast was set to 8 m [39].  
The coastal flood analysis is based on the parameters of significant wave height (Fw), sea-level change (Fsl), and extreme tidal range (Ftr), which are used to estimate flood hazard in different scenarios. The values for significant wave height and extreme tidal range are considered constant, while the sea-level change parameter values vary, capturing both minimum and maximum rates of rise (mm/year) from the interpolation conducted using Ports of State data.  
As a result, the scenarios used for flood risk analysis include present conditions, defined as a 25-year interval (Xa); 100 years (Xb); 500 years (Xc); 1000 years (Xd); and extreme storm (Xe) and tsunami (Xf) scenarios. These data and values are presented in Table 1, where summing the obtained values provides the maximum and minimum absolute extent of sea-level rise for each scenario.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Scenario** & \multicolumn{2}{c}{**Fw**} & \multicolumn{2}{c}{**Fsl**} & \multicolumn{2}{c}{**Ftr**} & \multicolumn{2}{c}{**Total**} \\ \hline  & **Min.** & **Max.** & **Min.** & **Max.** & **Min.** & **Max.** & **Min.** & **Max.** \\ \hline Xa & 0.11 & 4 & 0.027 & 0.4 & 4.38 & 4.47 & 4.52 & 8.51 \\ Xb & 0.11 & 4 & 0.11 & 0.16 & 4.38 & 4.47 & 4.60 & 8.63 \\ Xc & 0.11 & 4 & 0.55 & 0.8 & 4.38 & 4.47 & 5.04 & 9.27 \\ Xd & 0.11 & 4 & 1.1 & 1.6 & 4.38 & 4.47 & 5.59 & 10.07 \\ Xe & 0.11 & 4 & 2 & 2 & 4.38 & 4.47 & 6.49 & 10.47 \\ Xf & 0.11 & 4 & 8 & 8 & 4.38 & 4.47 & 12.49 & 16.47 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Analyzed scenarios and their corresponding parameter values (in meters).
## 2 Materials and Methods
### Performance Measurement of Machine Learning Algorithms (MSE, RMSE, MAE, and R\({}^{2}\))  
To evaluate the performance of machine learning models, four standard error and success metrics are used in this study: Mean Squared Error (MSE), Root Mean Squared Error(RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R2). These metrics are used to evaluate the differences between the predicted values (\(y_{i}^{pred}\)) and the actual values (\(y_{i}^{true}\)) from different perspectives. MSE is a fundamental measure for evaluating the model and calculates the mean squared prediction error [42]. It is expressed mathematically as follows:  
\[MSE=\frac{1}{n}\sum_{i=1}^{n}\left(y_{i}^{pred}-y_{i}^{true}\right)^{2} \tag{3}\]  
where \(n\) is the total number of data points. MSE measures the overall error rate of the model by penalizing large deviations of errors more. A low MSE indicates that the model performs accurately [42]. RMSE is obtained by taking the square root of the MSE measure and bringing the error units to the same level as the predicted variable [43]. This makes the error magnitude more intuitive:  
\[RMSE=\sqrt{MSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(y_{i}^{pred}-y_{i}^{true} \right)^{2}} \tag{4}\]  
RMSE is widely used to understand how close the model is to the actual values and is especially important in applications that require high error sensitivity [44]. MAE calculates the average of the absolute differences between the predicted and actual values [45]. This is a more balanced measure for evaluating the model because it considers the magnitude of the errors equally:  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}\left|y_{i}^{pred}-y_{i}^{true}\right| \tag{5}\]  
MAE is useful for understanding the average magnitude of the errors, primarily as a measure that does not penalize significant errors and reflects how sensitive the model is to overall error [46]. \(R^{2}\) is a measure of success used to assess the model's explanatory power [47]. It shows how much of the variance of the actual values is explained by the following model:  
\[R^{2}=1-\frac{\sum_{i=1}^{n}\left(y_{i}^{true}-y_{i}^{pred}\right)^{2}}{\sum_ {i=1}^{n}\left(y_{i}^{true}-\overline{y}^{true}\right)^{2}} \tag{6}\]  
where \(y_{i}^{true}\) is the mean of the true values. As the value of \(R^{2}\) approaches 1, the predictive power of the model increases. A negative R2 indicates that the model performs poorly and is less accurate than random predictions [42]. The evaluation of these four criteria allows the models to be analyzed from different aspects. While MSE and RMSE emphasize significant errors more, MAE treats error sizes equally. \(R^{2}\) summarizes the overall explanatory power and predictive ability of the model. These criteria play a critical role in evaluating and comparing the effectiveness of the algorithms in this study.
###### Abstract  
Electromagnetic tomography technology (EMT) is widely used in underground energy exploration. Limited by objective conditions, the collected projection data of electromagnetic waves are sparse and incomplete. Therefore, a study of the tomographic inversion algorithm of EMT based on incomplete projection data has an important guiding significance for the exploitation of underground energy. As a global optimization probability search algorithm, the simple genetic algorithm (SGA) has been widely used in the process of tomographic inversion. However, SGA evolves through a single population, and the values of crossover and mutation probability are always fixed, so there are risks of premature convergence and poor local search ability. To improve the performance of the SGA, a new approach of adaptive multi-population parallel genetic algorithm (AMPGA) with constraints is proposed in this paper. First, the AMPGA makes full use of multi-group adaptive co-evolution to improve the local and global search ability of SGA and restrain the risk of premature convergence. Then, the introduction of prior information as a constraint makes the results clearer and more accurate. The proposed algorithm has been verified in a numerical experiment and field tests, and the results show that the proposed algorithm can well balance global and local search capabilities, which offers a more realistic and stable tomographic result.  
E 2019 9 2611; doi:10.3390/app9132611