esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Flexometric Method to Evaluate Range of Movement: A Validity and Reliability Study
## 3 Results  
The means, standard deviations, PCC, and ICC from the SLR and HA tests are presented in Table 1. The data show very strong PCC values ranging from 0.95 to 0.97 and very high reliability (ICC = 0.99).  
The mean angular measurement values for the intra- and inter-rater reliability analyses with SD, SEM, MCD90, and ICC (95% CI) are presented in Tables 2 and 3. The data show excellent inter- and intra-rater reliability and for right and left SLR and HIP-ABD. Overall, the intra- and inter-rater reliability ICC ranged from 0.97 to 0.99. The MCD90 for the inter- and intra-rater reliability indicates that a change of equal to or greater than 5.7\({}^{\circ}\) (SLR-R), 5.6\({}^{\circ}\) (SLR-L), 5.1\({}^{\circ}\) (HA), 6.3\({}^{\circ}\) (SLR-R), 4.9\({}^{\circ}\) (SLR-L), and 5.4\({}^{\circ}\) (HA) indicates certainty that the change is not due to inertial variability or measurement error.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**ROM Mean Angle \({}^{\circ}\) (SD)**} & \multicolumn{3}{c}{**Correlations**} \\ \cline{3-7}
**Modality** & **Movement** & **FM** & **WI** & **PCC** & **ICC** & **95\% CI** \\ \hline FM/WI & SLR-R & 70.9 (10.8) & 69.2 (10.5) & 0.97 ** & 0.99 & 0.98–0.99 \\  & SLR-L & 71.2 (10.3) & 70.2 (9.9) & 0.96 ** & 0.99 & 0.98–0.99 \\  & HA & 79.1 (13.3) & 79.1 (17.0) & 0.95 ** & 0.99 & 0.98–0.99 \\ \hline \hline \end{tabular}  
* \(p<0.001\).  
\end{table}
Table 1: Correlations between the FM and WI from the two raters. Values are mean (SD). FM: flexometric method; WI: WIMU system; ROM: range of movement; PCC: Pearson’s correlation coefficient; ICC: interclass correlation coefficient; CI: confidence interval; SLR: straight leg raise test; SLR-R: right; SLR-L: left; HA: hip abduction.  
The Bland and Altman plots with accompanying 95% limits of agreement illustrate the wide range of measurement scores. For validity plots, comparisons between the FM and WI methods over the HA, SLR-L, and SLR-R tests include RA, RB, M1, and M2 scores. For the inter-reliability plots, comparisons between both raters RA and RB over the HA, SLR-L, and SLR-R tests include FM, WI, M1, and M2 scores. For the intra-reliability plots, comparisons between both measures M1 and M2 over the HA, SLR-L, and SLR-R tests include FM, WI, RA, and RB scores. The results (Figures 3 and 4) indicate that both methods, the FM and WI, were unbiased in scoring SLR-R and SLR-L, and also the FM in the HA test. In contrast, a small bias existed whereby the  
Figure 4: _Cont_.  
Figure 3: Bland and Altman plots for validity visual illustration. HA: hip abduction test. SLR-R: straight leg raise test for right leg. SLR-L: straight leg raise test for left leg, illustrated with FM: flexometric method and WI: WIMU system comparisons, where M1: Measure 1, M2: Measure 2, RA: Rater A, and RB: Rater B scores were included for each plot.
## 3 Results and Discussion
### Major and Trace Element Assessment in Crops
#### 3.5.2 Other Trace Elements in Plants  
The PIGE method has proven to be a highly effective tool for identifying various elements that play an essential role in plant growth and development. This method has enabled the identification of a range of essential or toxic elements, including Mg, F, Na, Al, Fe, Si, P, and Cl. The chemical composition of plants provides vital insights into their growth and development, revealing a close association with the soil on which they are cultivated. Table 13 presents the elemental concentration (dry matter) in plant sections.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Plant**} & \multirow{2}{*}{**Plant**} & \multicolumn{6}{c}{**Element Concentration (mg kg\({}^{-1}\)\(\pm\)\(\sigma\))**} \\ \cline{3-8}  & & F & **Na** & **Mg** & **Al** & **Si** & **P** & **Cl** & **Fe** \\ \hline T-1-1 & \multirow{2}{*}{wheat} & 17 \(\pm\) 6 & 830 \(\pm\) 20 & 9870 \(\pm\) 867 & 748 \(\pm\) 48 & 61300 \(\pm\) 5500 & 3870 \(\pm\) 220 & n.d. & 3940 \(\pm\) 1110 \\ T-1 & & \multirow{2}{*}{
\begin{tabular}{} \end{tabular} } & \(<\)18 & 120 \(\pm\) 20 & 4441 \(\pm\) 1314 & \(<\)140 & \(<\)16,900 & 4750 \(\pm\) 490 & n.d. & \(<\)4420 \\ \hline \hline \end{tabular}
\end{table}
Table 13: The content of macro- and micro-elements (in mg kg\({}^{-1}\)\(\pm\) standard deviation (\(\sigma\))) in wheat, corn, and sunflower plants collected from the Lower Danube Basin, determined by PIGE.
# A Data-Driven Approach for Generating Synthetic Load Profiles with GANs
## 4 Discussion
### Limitations  
Despite its strong performance, the proposed approach has several limitations. First, it assumes a fixed 24 h profile length, limiting its applicability to datasets with variable time horizons or irregular sampling. Second, the model currently treats each profile independently and does not explicitly learn longer-term dependencies across days or weeks (e.g., seasonal patterns and weekly routines). Furthermore, although convolutional layers effectively capture short-term structures, their receptive field is limited compared to transformer-based or attention-enhanced models. Lastly, the training process remains sensitive to hyperparameters such as batch size, critic iterations, and gradient penalty, which require empirical tuning to achieve optimal results.
## 3 Method and Experiment
### Experimental Test and Calculation Method
#### 3.2.2 Calculation Method  
There are two main methods to calculate the fracture pressure [23; 24; 25; 26; 27; 28; 29]: direct and indirect. The direct method is mainly obtained through the above experiments. The data obtained by this method are accurate, but the method can only measure the pressure of a single depth, and cannot obtain the fracture pressure of the continuous caprock. The cost of the experiment is high, and the cost performance is low when applied to large thickness rock formations. The indirect method is based on empirical formula or mathematical modeling. This method can obtain the continuous fracture pressure curve, and the error is small compared to the experimental data, so it is widely used. At present, the main formulas used are the Eaton method, the Willis method, the Holbrook method, and the Huang Rongzun method [30]. In practical application, the Willis method is less effective for different regions. The Holbrook method is mainly aimed at sandstone strata, which are different from the lithology of the target layer. The Huang Rongzun method needs to consider the horizontal principal stress, the parameter requirements are too high, and the cost is too high. The Eaton method is suitable for continuous sedimentary basins with relatively new strata and less tectonic movement, which is in line with the actual situation of more stratigraphic and lithologic traps in Junggar. Therefore, this study selected the Eaton formula to calculate the fracture pressure [31], as shown in Equation (7):  
\[\left\{\begin{array}{l}P_{f}=\frac{\mu}{1-\mu}(P_{o}-P_{p})+P_{p}\\ P_{p}=P_{o}-(P_{o}-P_{pn})(\frac{\Delta t_{h}}{\Delta t})^{x}\\ P_{o}=H\rho_{r}g\\ \mu=\frac{V_{p}^{2}-2V_{r}^{2}}{2(V_{p}^{2}-V_{r}^{2})}\end{array}\right. \tag{7}\]  
Figure 3: Fracturing dynamic and static evaluation process.  
In the formula, \(P_{f}\) is fracture pressure, MPa; \(\mu\) is [PERSON]'s ratio of strata, dimensionless; \(P_{o}\) is the overburden pressure, MPa; \(P_{p}\) is the formation pore pressure, g/cm\({}^{3}\); \(P_{pn}\) is the normal pore pressure, g/cm\({}^{3}\); \(\Delta t_{n}\) is the acoustic time difference under normal compaction; \(x\) is the Eaton index, using a value of 2.8 [32]; \(H\) is the depth of the overlying strata, m; \(\rho_{r}\) is the average density of the overlying sediment, kg/m\({}^{3}\); \(g\) is the acceleration of gravity, 9.8 m/s\({}^{2}\); \(V_{p}\) refers to p-wave velocities; \(V_{s}\) refers to s-wave velocities, m/us.
## 6 Results and Analysis
### Parameters Setup  
In RL algorithms, determining the appropriate values for the learning rate (\(\alpha\)) and exploration rate (\(\epsilon\)) is a crucial issue. To address this, we use the number of hops in the path between the source and destination addresses as a criterion. Initially, we run the RSCR routing algorithm, continuously adjusting \(\alpha\) and \(\epsilon\) to explore potential paths between Switch 2 and Switch 22. When link costs are equal, the shortest path found by [PERSON]'s algorithm is three hops. For each \(\epsilon(0.8,0.6)\), we test four \(\alpha\) values \((0.9,0.7,0.5,0.3)\) for 200 iterations each time. Figure 4 illustrates the number of hops between Switch 2 and Switch 22 under different parameters. As observed in the figure, reducing \(\epsilon\) leads the RL agent to explore random actions in the action space, increasing the number of hops. Conversely, the agent tends to leverage previous experiences when \(\epsilon\) is high. For \(\alpha=0.9\) and \(\alpha=0.7\), it is evident that the convergence speed of \(\alpha=0.9\) is superior to that of \(\alpha=0.7\). Based on these results, the most suitable values for the parameters of the RL agent in RSCR are \(\alpha=0.9\) and \(\epsilon=0.8\).  
Figure 3: GEANT topology.
## 1 Introduction
used a laser scanner and a drone to generate 3D point clouds, after which the damage assessment of the building was performed by analyzing the three-dimensional structure of the building [12]. The scheme is expensive and is not suitable for large-scale 3D reconstruction. SMART SKY EYE used commercial software for 3D reconstruction and found defects in building structures using 3D models to complete the damage assessment of buildings [5]. However, the commercial software was developed from conventional methods [13; 14; 15], such as pix4d [16], smart 3d [17], and PhotoScan [18]. They improve the quality of 3D model, but the efficiency cannot meet the urgent needs of post-earthquake assessment. In recent years, some multi-view stereo (MVS) networks based on deep learning have been widely used in 3D-reconstruction-related research [19]. The basic principle of MVS based on deep learning is to calculate the depth map of all images to complete the 3D reconstruction of the whole scene [20]. Based on the _DTU_ dataset [21], MVSNet [20] completed the 3D reconstruction end-to-end for the first time. RMVSNet [22] used the GRU structure [23] to improve its regularization method, making large-scale 3D reconstruction possible. Subsequent improvements proposed by \(D^{2}\)HC-MVSNet [24], AA-RMVSNet [25], and CasMVSNet [26] have further improved network performance. Among them, CasMVSNet uses a multi-layer cascading method to compute the coarse-to-fine depth information [27; 28], with higher computational efficiency and reconstruction quality. However, all of the models mentioned above were tested in public datasets, which are mainly used to validate and evaluate different improved MVS networks. In addition, to the knowledge of the authors, there is little research that has used deep-learning-based MVS models to complete the damage assessment of buildings. The generalization ability of the model is the most important ability to move towards engineering applications, so it is necessary to use real post-earthquake image data analysis to evaluate the performance of the method. At the same time, these data also fill the gaps in the application of this method to the damage assessment of buildings after earthquakes [29; 30]. Therefore, the objective of the study is to propose a deep-learning-based MVS method that is suitable for assessing building damage after an earthquake. Most importantly, the applicability of different 3D reconstruction models for post-earthquake building damage assessment is explored from the point view of time efficiency and the construction performance. The remainder of this paper is organized as follows. Section 2 introduces the dataset used in this study. The methodology is presented in Section 3. In Section 4, the experimental details and results are shown. In Section 5, the performance of different methods is discussed. Finally, the conclusion is shown in Section 6.
## Materials and Methods
### Spectral Feature Selection Algorithms
#### 2.3.2 Successive Projections Algorithm  
The successive projection algorithm (SPA) is a typical spectral feature extraction method [31]. SPA is a forward variable selection algorithm that minimizes collinearity in linear space. Assuming the number of samples in the dataset is n and the number of spectral wavelengths is m, the spectral matrix X is n \(\times\) m. SPA starts by selecting any wavelength as the starting point and iteratively searches for the parts of the projection that have not been incorporated into the wavelength combination. Each cycle continuously introduces the maximum projection direction until W times (w \(<\) n\(-\)1), thus forming a wavelength subset that minimizes the linear relationship between any adjacent wavelengths in the subset. In this study, the probability of wavelength selection was calculated and the first few wavelengths with higher probability were selected as the final candidate variables.
# Memory Model for Morphological Semantics of Visual Stimuli Using Sparse Distributed Representation
## 4 Experimental Results  
The experiments are conducted in a desktop environment equipped with a GPU with TensorFlow 2.5 version installed in the python 3.7 execution environment.  
To verify the memory model proposed in this paper, we have conducted two ways of experiments shown in Figure 14. The first experiment is conducted without prior memory as shown in Figure 14a. Whenever a new image is released, it is structured to generate and store new memories if the similarity level between memorized patterns is low, or to update the stored existing memories if the similarity level is high. In the experiment of (a), since the memory of morphological semantics matching the input image is not predetermined, many images tend to be created as new memories.  
Figure 14: Experiment overview: (**a**) Procedures for generating progressive memories, (**b**) The process of creating a pre-memory.  
Figure 13: An example of long-term memory obtained from experiment and represented by superposition SDR; The location \((x,y)\) axis of activated bits and its intensity \(z\) axis are embedding information about each memory pattern.  
Contrary to expectations, it is necessary to supplement the problem that morphologically similar images are formed by multiple memories. In the second experiment, we have conducted additional pre-memory process as shown in (b) by simulating that memory is effectively formed through the process of showing multiple picture cards in object recognition education for infants. In the pre-memory process, 100 images with high morphological similarity are continuously exposed to create an average pre-memory of morphological similarity in advance. After generating the prior memory in this way, the experiment in (a) is conducted to analyze the results.
# On the Association between Dental Implants, Osteoporosis and Bone Modulating Therapy
###### Abstract  
Background: Osteoporosis affects bone metabolism and may result in fragility fractures. Medications include bone modulating therapy (BMT), which come with the risk of osteeonecrosis of the jaws (ONJ). The literature is contradictory about the impact of osteoporosis and/or BMT on dental implant success and the incidence of ONJ. Purpose: The aim of the present study was to assess the effect of osteoporosis and BMT on early implant failure (EIF) and ONJ incidence (primary outcome parameters) following dental implant placement. Materials and Methods: Retrospective, cohort study based on dental records. Implant-supported prostheses (ISP) were delivered by experienced oral and maxillofacial surgeons and prosthodntists. Inclusion criteria: diagnosis of osteoporosis, delivery of ISP, consecutive individuals, and available data. Confounding variables included--age, gender, smoking status, BMT, bone grafting, and implant length/diameter. Results: There were a total of 72 osteoporosis patients, 46 using and 26 not using BMT. There were a total of 279 inserted implants, 154 in those using and 124 in those not using BMT. Univariate analysis of factors that may affect EIF showed no statistically significant differences between non-osteoporotic, osteoporotic using BMT, and osteoporoptic patients not using BMT regarding EIF and ONJ incidence following dental implant delivery. Multivariate model using logistic regression demonstrated one factor associated with increased risk for EIF--bone augmentation. No cases of ONJ were reported up to one year post ISP delivery in any group. Conclusions: Within the limitations of the present study, it can be concluded that installing dental implants in osteoporotic patients, treated or not with BMT, is a safe procedure with EIF comparable to non-osteoporotic patients. The short-term risk for ONJ following dental implant insertion in osteoporotic (even when using BMT) patients is negligible.  
osteoporosis; bone modulating therapy (BMT); early implant failure (EIF)
## 6 Results and Discussion
### Effect of Downsampling  
During the experiments, we observed that the training time of the proposed SampleCNN is about four times longer than the frame-level mel-spectrogram model because the proposed model has more network parameters with deeper layers. In order to reduce the training time, we downsampled the audio with a set of lower sampling rates including 2000, 4000, 8000, 12,000, 16,000, 20,000 Hz. This can be regarded as a time-domain counterpart of in linear-to-mel mapping in that both reduce the dimensionality of input and preserve low-frequency content. The results in Table 5 show that the performance is maintained down to 8000 Hz but it starts to be degraded from 4000 Hz. This may indicate that the relevant information to the task is concentrated below 4000 Hz (the Nyquist frequency of 8000 Hz). Also, we report the training time ratio of the models taking re-sampled audio to the model using 22,050 Hz signal as input. At the expence of the accuracy, the training time can be reduced to about half.
# Sigmoidal Mathematical Models in the Planning and Control of Rigid Pavement Works
## 3 Materials and Methods
### Evaluation of the Models  
Statistical methods were used to select the optimal sigmoidal model, using 20% of the data for validation, using different performance indicators: Correlation coefficient (R) (see Equation (24)), which measures the linear relationship between the real data and the estimated data [7]. The coefficient of determination (R\({}^{2}\)) (see Equation (25)) indicates the proportion of model variability; Mean Square Error (MSE) (see Equation (26)) and Root Mean Square Error (RMSE) (see Equation (27)) allow quantifying the magnitude of the prediction errors [30]; Mean Absolute Error (MAE) (see Equation (28)), reflects the average deviation of the predictions for the actual values [19], and Akaike Information Criterion (AIC) (see Equation (29)) compares the quality of the different sigmoidal models according to their complexity and explanatory power [31].  
\[R\ =\frac{\sum{(X_{i}-\overline{X}_{i})(Y_{i}-\overline{Y}_{i})}}{\sqrt{\sum{(X _{i}-\overline{X}_{i})^{2}\sum{(Y_{i}-\overline{Y}_{i})^{2}}}}} \tag{24}\]  
Figure 7: Gompertz model.  
\[R^{2}=1-\frac{\sum\limits_{i=1}^{n}\left(Y_{i}-\hat{Y_{i}}\right)^{2}}{\sum \limits_{i=1}^{n}\left(Y_{i}-\overline{Y}_{i}\right)^{2}} \tag{25}\]  
\[MSE=\frac{1}{n}\sum\limits_{i=1}^{n}\left(Y_{i}-\hat{Y_{i}}\right)^{2} \tag{26}\]  
\[MAE=\frac{1}{n}\sum\limits_{i=1}^{n}\left|Y_{i}-\hat{Y_{i}}\right| \tag{27}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum\limits_{i=1}^{n}\left(Y_{i}-\hat{Y_{i}}\right)^{2}} \tag{28}\]  
\[\text{AIC}=2k-2\ln(L) \tag{29}\]  
where \(X_{i}\) is the time variable, \(Y_{i}\) is the cost variable, \(\hat{Y_{i}}\) is the predicted cost, and \(n\) is the sample size; \(\overline{X}_{i}\) and \(\overline{Y}_{i}\) are the means of the variables, \(k\) is the number of parameters in the model, and \(L\) is the likelihood function.
## 3 GPR Image Characteristics Analysis
### Characteristics of Void Defect  
In the instantaneous amplitude image (see Figure 12a), when the travel time of the electromagnetic wave was 6 ns, an anomalously strong reflection wave appeared between the 70 th and 130 th traces. At this time, the normalized amplitude of a single-trace wave for the loose defect was approximately 0.41. The middle part of the upper boundary of the cavity defect presented an almost straight-line reflection wave, accompanied by diffraction phenomena at both ends. The reflection wave below it deformed into a \"single-hump shape\". The single-trace waves at approximately 2 ns, 6 ns, and 8 ns exhibited peak instantaneous amplitudes, indicating significant electrical differences between the structural layers within the subgrade, resulting in abnormal reflection waves. The peak at 2 ns corresponded to the air-surface interface of the subgrade structural layer, with a normalized amplitude of 1. The peak at 6 ns corresponded to the interface between the surface and base layers, with a normalized amplitude of 0.23. The abrupt change in the reflection wave at 8 ns was attributed to the propagation of the electromagnetic wave to the upper boundary of the void defect, where the instantaneous amplitude reached its maximum at the peak, with a normalized amplitude of 0.145. The amplitude gradually decreased as it extended to both ends. From the fluctuations in the instantaneous amplitude signal of the void defect and the response characteristics of the instantaneous amplitude image, information about the subgrade structural layers and the boundaries of the target anomaly could be deduced.  
In the instantaneous phase image (see Figure 13a), at the travel time of 6 ns, discontinuities in the in-phase axis occurred between the 60 th and 140 th traces. The instantaneous phase image characteristic of the cavity defect showed discontinuities and intersections of the in-phase axes. At an 8 ns travel time, the single-trace instantaneous phase exhibited an anomalous reversal, corresponding to the upper boundary of the void defect. At 12 ns, a second anomalous reversal in the single-trace instantaneous phase was observed; based on the instantaneous amplitude characteristics, this was likely caused by the electrical differences at the lower boundary of the void defect.  
In the instantaneous frequency image (see Figure 14a), between the travel times of 6 and 30 ns, the high-frequency reflection waves of the cavity defect were mainly concentrated between the 70 th and 130 th traces. The high-frequency distribution of the cavity defect was relatively chaotic and irregular. In the single-trace instantaneous frequency variation image, abrupt high-frequency reflection waves were generated when the electromagnetic waves encountered the interface with differing physical properties. The physical changes in the subgrade structural layers could be inferred based on the variations in the instantaneous frequencies of the electromagnetic waves.  
Figure 12: Void defect instantaneous amplitude features.
# Towards the Unmanned Aerial Vehicles (UAVs): A Comprehensive Review
## 6 UAV Applications Areas
### Construction and Infrastructure Inspection  
As-built mapping, construction monitoring and site inspection have become efficient, easy and fast through UAVs. Monitoring of construction projects from start to finish ensures quality progress of site work. It can provide reports containing imagery, video and 3D mapping to potential stakeholders. This technology can significantly support infrastructure and construction inspection applications. There is rapidly expanding interest in UAVs used for GSM towers inspection, gas pipeline monitoring, power lines inspection and construction projects monitoring [102].