esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Performance Evaluation of SM-OGFC
#### 2.4.3 Water Stability Test  
Immersion Marshall test was carried out, to evaluate the water stability of SM-OGFC according to Chinese standards GB/T0709-2011 in this study. Due to the interconnected pores and large porosity of OGFC, most of the water can freely discharge in the interconnected pores. However, there is always a part of the moisture remaining in the mixture, due to the surface tension and capillary action of the water, and the presence of the semi-intersecting void semi-connected pore. The volume of free water has expanded after freezing, which generates the internal temperature stress in the OGFC, and this effect can enlarge the micropore and the original crack in the OGFC. The ice crystalloid is transformed into free water, which caused moisture damage with the increase of temperature.  
In order to better evaluate the water stability of OGFC in spring-thawing season, this paper use the spring-thawing stability index to evaluate the water stability of OGFC in spring-thawing season. The specimens were treated by vacuum saturation in 97.3 kPa for 15 min and submerged in a container containing water, then the container with specimens were placed in the precision temp-enclosure at \(-15\)\({}^{\circ}\)C and frozen 12 h. Then, the specimens were soaked in water at \(15\)\({}^{\circ}\)C for 12 h through controlling the precision temp-enclosure. As described above, a complete freeze-thaw cycle is completed. After 5 freeze-thaw cycles, damaged specimens were collected for Marshall test according to Chinese standards GB/T0709-2011.
## 7 Limitations and Improvement Opportunities  
We identify two significant limitations of the research described above. It must first be noted that, while the methodology and dashboard that we developed are generic, they target global time series models constrained to machine learning algorithms whose input features are intelligible to the human. Furthermore, many of the explanations assume the features convey a particular meaning to the user--which is, in our view, a criterion that handcrafted features can only meet. Due to this limitation, we consider this approach is not suitable for deep learning models. In addition, given how the features are framed for such machine learning models, insights regarding the time dimension can be conveyed through the features and their metadata. Second, the current approach to selecting train data instances similar to the data instances creating the anomalous forecast requires some tuning to find an appropriate instances' similarity threshold and set proper constraints towards the maximum number of instances to consider for each case. While this could be replaced by an automatic procedure searching for the best similarity measure, threshold, and instances' limit, such a procedure would come at a high cost, since it would require computing the similarities across all instances for each run. Further research is required to understand better alternative procedures to identify similar train data instances that drive the forecast towards an anomalous value.  
From experience that we obtained through the experiments and results described above, we identified improvement opportunities. Following research done by [122], finding the most influential instances in the train set for an anomalous forecast can be done using influence functions. Other possible enhancements would be to use a more stable model to estimate feature relevance for each prediction. In particular, we could replace LIME for DLIME, since LIME is not deterministic, and changes in feature ranking computations can  
Figure 5: Dashboard screenshot. We highlight different areas devoted to explaining a given forecast, providing a context within the time series, indicating the most relevant features to the forecast, train instances that most likely influenced the forecast, counterfactual examples, and alternative values expected to make a good forecast.  
affect the instance selection based on the similarity to the detected anomalous forecasts. Another improvement can be made regarding the anomaly detection module. Reducing the number of false positives correlates to the number of instances removed from the train set. Furthermore, removing only instances similar to the ones where the global model and fallback provide anomalous forecasts has been shown to improve the performance of the model trained without such instances. Following this intuition, we can use our current anomaly detector for unsupervised anomalies labeling. By labeling instances as anomalous only when the current anomaly detector predicts the actual value is not anomalous when the forecast was, we can later train more precise supervised machine learning models to detect outliers.
## 2 Numerical Modelling  
The numerical model is constructed by means of 3 DEC, which is a three-dimensional numerical program employing the distinct element method for discontinuum modelling. In the following analysis, rock specimen models with different joint conditions (single, multiple parallel, and intersecting joints)are built, and the effects of joints on the tensile properties are investigated by applying a uniaxial tension load. The specimen model is a cylinder with 50 mm in diameter and 100 mm in height, as suggested by the International Society of Rock Mechanics (ISRM) [33]. All joints are considered to cut through the specimen. A constant rate of 0.005 mm/step is applied to the upper and lower boundary to simulate the uniaxial tension load. After the specimens fail in tension, 1000 extra steps are calculated to capture the after-peak behavior. With the obtained stress-strain curves, \(\sigma_{t}\) and \(E_{t}\) under different joint conditions are calculated, and the effects of joint condition on the tensile strength (\(\sigma_{t}\)) and \(E_{t}\) are hereby investigated.  
In 3 DEC, joints or fractures are considered as elements with mechanical properties. The rock and joint properties [34] (listed in Tables 1 and 2) are applied according to a study on tunnel stability analysis of an underground mine using 3 DEC.  
To study the effects of joints on the tensile properties of rocks, the rock specimens with various joint conditions were described and modelled with four geometry parameters of joints: \(\alpha\) (the angle between the joint and the horizontal direction), \(d\) (the perpendicular distance between the two joints), \(\beta\) (the angle at which the two joints intersect), and \(n\) (the joint density).  
In cases of rock specimens with two joints, the effects of the perpendicular distance between the two joints (\(d\)), the angle between the joint and the horizontal direction (\(\alpha\)) of parallel joints, and the joint density (\(n\)) on the complete stress-strain response were studied by numerical simulation. When \(d\) was kept constant at 10, 20, 30, and 40 mm, four different homogeneity indexes (\(\alpha\)) were considered, which were 20, 30, 40 and 50\({}^{\circ}\), respectively. Similarly, when \(\alpha\) were kept constant, \(d\) was varied as 10, 20, 30, and 40 mm. Moreover, when \(d\) and \(\alpha\) were kept constant, \(n\) was changed from 2 to 6.  
In cases of rock specimens with multiple joints, the effects of the perpendicular distance between the two cracks (\(d\)), the angle at which the two joints intersected (\(\beta\)), and the joint density (\(n\)) of intersecting joints on the complete stress-strain response were studied by numerical simulation. When \(d\) and \(n\) were kept constant as 0 mm and 2, there were six different homogeneity indexes (\(\beta\)) of 20, 40, 60, 80, and 100\({}^{\circ}\). When \(\beta\) and \(n\) were kept constant, \(d\) was varied as 10, 20, 30, and 40 mm. As a comparison, when \(d\) and \(n\) were kept constant, \(\beta\) was separately varied as 40, 60, 80, and 100\({}^{\circ}\). When \(d\) and \(\beta\) were kept constant, \(n\) was changed from 4 to 12.
# Measurement of Head-Related Transfer Functions: A Review
## 7 Discussion  
### Measurement Uncertainty  
Many factors can lead to HRTF measurement errors, such as nonlinear distortions of the electro-acoustic systems, environmental noises, reflections from measurement environments, characteristics of sound sources, and temperature changes [35]. These issues can generally be addressed with suitable excitation signals, playback levels, repeated measurements, and some post-processing methods as described in Sections 2 and 6. In addition, an alignment between the subject/dummy head and the loudspeaker setup should be done to avoid an offset of the measured HRTF dataset. The \"blocked ear technique\" has been widely applied for the HRTF measurement of human subjects.  
However, a minor deviation in the microphone position at the blocked ear causes a noticeable change in the HRTF spectra at high frequencies because of the short sound wavelength. [PERSON] et al. [35] suggested to place binaural microphones slightly inside the entrance of the ear canal to alleviate this issue.  
In most HRTF measurement systems, subjects should be kept still during the measurement, and slight inevitable head or body movements may degrade measurement accuracy [102; 191; 192; 193]. Hence, the alignment of the head position only at the beginning of the measurement is not sufficient. Headrests or mechanic supports are often used to physically fix the head position and limit the degree of head movement. Alternatively, some authors used tracking systems based on inertial sensors or cameras to monitor listeners' head positions during the HRTF measurement [128; 148; 161; 193; 118]. For instance, [PERSON] et al. [193] placed a head tracker device (inertial sensor) on the subject's head to monitor the head movement. The misalignment of the head position was visualized to the subject in real-time, so that the subject could control her/his head position during the measurement. The measurement results showed a substantial improvement of the stabilization of head positions compared to that by using a headrest.  
For typical continuous HRTF measurement systems, subjects or the loudspeaker systems rotate continuously during the measurement. As described in Section 4.2, the rotational speed must be carefully chosen as a compromise between the measurement time and accuracy. In addition, a low-noise turntable/motor for rotating the subject or the system should be selected to ensure that the measurement results have sufficient SNR [140]. Some novel HMD-based HRTF measurement methods require subjects to actively rotate their heads. In these cases, there is no need to consider the measurement errors caused by small head movements described above. However, HMDs can alter the original HRTF spectra and a suitable compensation filter may be considered to compensate this influence. Additionally, the orientation data and microphone signals must be highly synchronized to avoid offsets in the measured HRTF datasets.
## 3 Method
### Conventional DBSCAN  
DBSCAN is a widely used density-based clustering algorithm that considers connectivity between samples based on their local density to determine the final clustering results. This involves extending the clusters by adding new connectable samples continuously. The algorithm employs two parameters: Eps and MinPts, which describe the sample distribution's proximity in terms of neighborhood. Specifically, Eps is defined as the radius of the adjacent area surrounding a point, while MinPts stands for the minimum number of points required to form a cluster. The DBSCAN algorithm initially identifies a set of points satisfying at least MinPts within the Eps neighborhood as the starting cluster. Then, it gradually expands cluster by consistently including points that also satisfy the Eps and MinPts criteria until no more points qualify. A schematic depiction of the DBSCAN clustering principle is shown in Figure 4. The blue point represents that the pixels are clustered according to the clustering definition, while the red point does not conform to the clustering definition and is defined as the noise class.  
Figure 3: Hollow sliding window.  
Figure 2: Probability distribution of target and background.  
However, the conventional DBSCAN algorithm has some drawbacks in processing 3D SAR data with uneven point cluster density and strong sidelobes. Firstly, selecting appropriate values for Eps and MinPts can be challenging since density changes in the cluster affect these parameters' sensitivity. Because the template is spherical shape, if the Eps value is too large, it is hard to distinguish strong sidelobe point clusters diffused within the 3D point cloud, while a too-small Eps value might miss some free target point clusters. Secondly, even after performing the two-step CFAR algorithm to extract the initial 3D target point cloud, residual, randomly-distributed strong sidelobe points may still be present. Therefore, we propose a modified DBSCAN clustering algorithm that changes the clustering template with cylinder shape to suit the data's own characteristics, so that the false alarm can be reduced. After clustering, a discrimination step is conducted to filter out strong sidelobe classes automatically based on target angle, shape, size, and position information present in the clustering results, leading to more accurate 3D point cloud extraction.
# Energy Harvested and Cooperative Enabled Efficient Routing Protocol (EHCRP) for IoT-WBAN
## 4 System Model
### Energy Consumption Model  
The consumption of energy by the SNs during the transmission and reception of data packets are estimated via a first order ratio model [28]. Equation (5), states the usage of energy during transmission of \(L\) bits to the destination receiver SN at distance \(D\):  
\[E.T\left(L,Di,j\right)\ =\ L\times E_{Tmn}+L\ \times E_{amplifier}+\ D_{i,j}^{2} \tag{5}\]  
Figure 3: Node deployment in EHCRP.  
where \(E.T\) represents energy consumption during data transmission, \(L\) represents the bits/packet and \(D\) represents the distance from sending sensor to the receiver sensor, \(E_{amplifier}\) represents the energy spent by the electric circuit. The energy consumption to receive \(L\) bits of data is expressed in Equation (6):  
\[E.R\left(L,D_{i,j}\right)\ =\ L\ \times\ E_{rcp} \tag{6}\]  
where \(E.R\) represents energy consumption during data reception, \(L\) represents the length of bits/packet received by a sensor and \(E_{rcp}\) is the energy consumed during reception by the receiver circuitry, \(Di\,j\) is the distance between sensing SN and receiving SN.  
Furthermore, Equation (7) represents the consumption of energy during aggregation of data:  
\[E.A=L\ \times V_{supply}I_{0}V_{supp}/K_{p}V_{t}M_{cycle} \tag{7}\]  
where \(E.A\) represent the energy aggregation, \(I_{0}\) denotes the current loss, \(K_{P}\) represents constant and its value depends on processor type, \(V_{t}\) represents the transmission voltage, \(M_{cycle}\) denotes the cycle of the machine or CPU spent during cycle.  
The SNs deployed/attached on human body senses for physiological data of human body. Energy spent during this process is referred as energy sensing (\(E.S\)), which is represented in Equation (8):  
\[E.S=L\ \times\ (V_{sen}+\ I_{sen}+\ T_{sen}) \tag{8}\]  
where \(L\) is the packets of bits and \(V\) is the voltage supplied during sensing, \(I\) represent the actual present consumed and \(T\) represent the time lapsed during sensing.  
The energy consumed during a cycle span of bit transmission could be calculated and is represented in Equation (9):  
\[E_{consumed}\ =\ E.S+\ E.A+\ E.T+\ E.R \tag{9}\]  
The remaining energy after the completion of all types of processing is considered as residual energy which is represented in Equation (10):  
\[E_{residual}=E_{total}-\ E_{consumed} \tag{10}\]
## 5 Discussion of Experimental Findings  
This work contributes to a better knowledge of how a CNN algorithm may contribute to accurately categorizing lung nodules from CT images. The following conclusions were drawn from the experiments carried out as part of this research.  
* The classification accuracy is affected by various algorithm factors, notably patch size, hidden layer nodes, and architectural decisions regarding the CNN.
* Optimizing the CNN architecture to obtain the highest possible accuracy in classification is vital.
* It is possible to reduce false positives by iteratively training the CNN algorithm. In addition, using a training set comprising both unseen true and false positives is of great assistance in reducing the number of false positives to an acceptable level.
* Optimization of the CNN relies on parameters such as the dropout value, the activation function, the number of convolutional layers, and the number of max pooling layers.  
Figure 10: Results for the proposed method.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method Name and Reference** & **Accuracy Score** \\ \hline CNN (Luna 16 Challenge) & 82.700\% \\ [31] & 82.000\% \\ [30] & 79.000\% \\ [32] & 78.400\% \\ CADIMI (2017) & 63.700\% \\ CNN (This study) & 84.800\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of results.  
Observation of the distribution of the dataset is necessary for both the statistical analysis and the machine learning-based categorization of lung nodules. Adjusting the voxel size is one of the steps that should be taken to optimize the learning advantage while simultaneously lowering the MSE (the patch size option should be utilized). Significant nodules may be missed if a smaller patch size is used instead of a larger one. If, on the other hand, a considerable number were selected for the patch size, the tiny nodule threshold would not be taken into consideration.
* During the process of detecting lung nodules, it is essential to give careful consideration to both the patch size and the threshold value to achieve the highest achievable level of classification precision.
## 4 Results and Analysis
### Validation of Heat Fluxes Calculated Using MODIS LSTs
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Date**} & \multicolumn{3}{c}{**MODIS-LST-H (W\(\cdot\)m\({}^{-2}\))**} & \multicolumn{3}{c}{**MODIS-LST-LE (W\(\cdot\)m\({}^{-2}\))**} \\ \cline{2-7}  & \(R^{2}\) & **MBE** & **RMSE** & \(R^{2}\) & **MBE** & **RMSE** \\ \hline
19 June & 0.29 & 57.71 & 59.86 & 0.67 & \(-\)57.65 & 55.40 \\
30 June & 0.60 & 31.49 & 55.14 & 0.79 & \(-\)52.16 & 84.54 \\
8 July & 0.59 & 51.23 & 84.16 & 0.81 & \(-\)55.83 & 92.02 \\
27 July & 0.65 & 53.20 & 78.70 & 0.85 & \(-\)70.41 & 95.93 \\
3 August & 0.44 & 62.78 & 80.48 & 0.70 & \(-\)83.98 & 61.39 \\
15 August & 0.36 & 32.10 & 75.84 & 0.76 & \(-\)54.16 & 95.90 \\
22 August & 0.36 & 57.55 & 84.52 & 0.71 & \(-\)68.77 & 55.43 \\
29 August & 0.58 & 54.11 & 74.01 & 0.79 & \(-\)87.93 & 60.29 \\
2 September & 0.36 & \(-\)35.55 & 75.72 & 0.57 & 14.75 & 71.48 \\
13 September & 0.35 & \(-\)41.93 & 92.41 & 0.37 & \(-\)21.24 & 55.42 \\
14 September & 0.37 & 28.81 & 79.29 & 0.36 & \(-\)46.34 & 61.40 \\ overall & 0.63 & 38.90 & 77.26 & 0.75 & \(-\)59.78 & 73.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Validation results of MODIS-LST heat fluxes. Figure 7: Maps of the four energy components, (**a**) \(R_{ni}\); (**b**) G; (**c**) H; and (**d**) LE, calculated using MODIS LST data corresponding to 19 June 2012.  
land. The points in the scatterplot with large H values represent desert areas [24], where the H values reach approximately 300 W\(\cdot\)m\({}^{-2}\). We also note that some points in the H scatterplot are less than zero, possibly due to the oasis effect or irrigation.
## 3 Results
### Retriaval Performance from Nadir Reflectance  
The Scenario 0 allows comparing a traditional approach using only the red and NIR spectral bands at 275 m spatial resolution at nadir (e.g., MODIS) with the Scenario 1, which includes two additional spectral bands (blue and green) at nadir. The results showed that for both H\({}_{\text{mean}}\) and CC, the relative error value improved significantly when the number of bands was increased, with a decrease of 12.7% and 12.6% in rRMSE for H\({}_{\text{mean}}\) and CC, respectively (Table 2). The performance improvement of Scenario 1 model was found to be statistically significant (\(F_{2,\,7340}=19.49,\,p<0.05\)), for both parameters.
## 5 Results and Analysis
### Statistical Validation of Samples  
To validate our proposed solution, we first examined the behavior of the extracted data and identified the relationships between variables. We performed 450 measurements in total across three different SOIC packages and three different dielectric fluids. For each possible combination, we completed 50 measurements and reported the sample deviations for each set in X and Y coordinates. To identify the direction of measurements, we assigned a negative sign to deviations to the left on the X-axis and the front on the Y-axis. We assigned implicit positive values to deviations to the right on the X-axis and backward on the Y-axis. The model did not permit deviations in the Z direction, so we did not consider them in the analysis.  
Figure 7 shows boxplots of the X and Y axes, respectively, illustrating the median, the highest significance percentiles, and the minimum and maximum values of the deviation of the SOIC packages in Cartesian X and Y coordinates for each type of dielectric fluid.  
In each boxplot, the ends of the vertical lines indicate the minimum and maximum data values. The Points outside the extremes indicated by the symbol (*), present outliers up to a maximum of 1.5 times the interquartile range. We observed a conservative quartile concentration trend around the maximum alignment point in the box plots, with minimal variations in the upper and lower quartiles not exceeding 0.1 mm. The SOP16 packages showed minor deviations from other SOIC packages but did not maintain symmetric behavior across different dielectric fluids. Conversely, SOP24 packages had a symmetrical deviation ratio in various dielectric fluids and a higher concentration of deviations than SOP16 and SOP20 packages. Significantly, we recorded the lowest concentration of deviations with the dielectric fluid FR3. The higher friction coefficient and greater contact area of SOP24 with the dielectric fluid likely caused a thinner cohesion layer and higher surface tension at the edges.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Dielectric** & **SOIC** & **Splash** & **Splash** \\
**Medium** & **Package** & **Percentage (\%)** & **Threshold** \\ \hline \multirow{3}{*}{FR3} & SOP-16 & 0.169 & 0.063 \\  & SOP-20 & 0.168 & 0.110 \\  & SOP-24 & 0.210 & 0.167 \\ \hline \multirow{3}{*}{DTE-150} & SOP-16 & 0.184 & 0.064 \\  & SOP-20 & 0.184 & 0.112 \\  & SOP-24 & 0.230 & 0.170 \\ \hline \multirow{3}{*}{DPMS} & SOP-16 & 0.200 & 0.065 \\  & SOP-20 & 0.202 & 0.114 \\ \cline{1-1}  & SOP-24 & 0.252 & 0.174 \\ \hline \end{tabular}
\end{table}
Table 12: Splash threshold and sloshing percentage for selected dielectric fluid interfaces and SOIC packages.