esa-sceva/satcom-chunk-collection huggingface dataset:
# A Linkage Representation of the Human Hand Skeletal System Using CT Hand Scan Images
## 3 Results  
Joint centers of all the participants' right hands were obtained. Table 1 shows the locations of the estimated joint centers for a participant in the defined global coordinate system. Figure 5 shows the established linkage representation of the participant's hand by connecting the estimated joint centers. Intra- and interobserver variation of the estimated joint centers was evaluated for a participant. For intraobserver variation, the mean difference \(\pm\) standard deviation between two measures was 0.4 \(\pm\) 0.7 mm (ranging from -1.3 to  
Figure 4: Linkage system of the hand. For the thumb, Segment 1 is the carpal, Segment 2 the metacarpal, Segment 3 the proximal phalangeal, and Segment 4 the distal phalangeal. For the four fingers, Segment 1 is the carpometacarpal, Segment 2 the proximal phalangeal, Segment 3 the middle phalangeal, and Segment 4 the distal phalangeal. 1.8). For interobserver variation, the mean difference \(\pm\) standard deviation between two measures was 0.7 \(\pm\) 2.0 mm (ranging from \(-\)1.9 to 4.1). \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Finger**} & \multicolumn{3}{c}{**Joint 1**} & \multicolumn{3}{c}{**Joint 2**} & \multicolumn{3}{c}{**Joint 3**} & \multicolumn{3}{c}{**Bone Tip**} \\ \cline{2-13}  & \(x\) & \(y\) & \(z\) & \(x\) & \(y\) & \(z\) & \(x\) & \(y\) & \(z\) & \(x\) & \(y\) & \(z\) \\ \hline Thumb & \(-\)21.28 & 20.81 & \(-\)22.29 & \(-\)56.92 & 46.32 & \(-\)14.02 & \(-\)71.19 & 74.12 & \(-\)9.77 & \(-\)85.52 & 93.31 & \(-\)4.01 \\ Index & \(-\)22.85 & 85.41 & 0.00 & \(-\)29.84 & 125.91 & 0.28 & \(-\)33.45 & 148.23 & \(-\)3.80 & \(-\)34.53 & 167.84 & \(-\)3.30 \\ Middle & 0.00 & 83.51 & 0.00 & \(-\)3.06 & 131.01 & \(-\)4.84 & \(-\)4.38 & 158.07 & \(-\)10.28 & \(-\)4.26 & 178.90 & \(-\)8.80 \\ Ring & 15.85 & 76.70 & -6.95 & 20.05 & 120.37 & \(-\)13.07 & 22.94 & 145.64 & \(-\)20.02 & 23.66 & 165.85 & \(-\)18.04 \\ Little & 34.44 & 69.58 & -9.72 & 42.07 & 104.60 & \(-\)13.93 & 44.92 & 120.93 & \(-\)18.17 & 45.94 & 140.19 & \(-\)19.38 \\ \hline \hline \end{tabular} Note: For the thumb, Joint 1 is carpometacarpal (CMC), Joint 2 metacarpophalangeal (MCP), and Joint 3 interphalangeal (IP). For the four fingers, Joint 1 is MCP, Joint 2 proximal interphalangeal (PIP), and Joint 3 distal interphalangeal (DIP).  
\end{table} 
Table 1: Locations of hand joint centers and bone tips for a participant.
## 3 Results  
The number, mean, and standard deviation (SD) of the bubbles found in the different third of the canals are listed in Table 1.  
Five bubbles were detected in the coronal third of the samples with a mean of 0.625 and a SD of 0.74. In the middle thirds, four bubbles were detected with a mean of 0.5 and SD of 0.76. Seventeen bubbles were detected in the apical third of the samples with a mean of 2.125 and the SD of 0.99.  
The average percentage of the volume occupied by bubbles in the coronal, middle, and apical thirds is shown in Table 2.  
The percentage value of the coronal third is 7.773% with SD equal to 14.54. In the middle third, the percentage is 3.768% with SD 6.09, while in the apical third, the value rises  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Sample**} & **Bubbles in the** & **Bubbles in the** & **Bubbles in the** \\  & **Coronal Third (n\({}^{\circ}\))** & **Middle Third (n\({}^{\circ}\))** & **Apical Third (n\({}^{\circ}\))** \\ \hline
2 & 2 & 1 & 2 \\ \hline
3 & 0 & 0 & 3 \\ \hline
4 & 0 & 0 & 2 \\ \hline
5 & 1 & 0 & 2 \\ \hline
6 & 1 & 0 & 3 \\ \hline
7 & 0 & 1 & 0 \\ \hline
8 & 0 & 0 & 2 \\ \hline Tot & 5 & 4 & 17 \\ \hline Mean & 0.625 & 0.5 & 2.125 \\ \hline SD & 0.74 & 0.76 & 0.99 \\ \hline \end{tabular}
\end{table}
Table 1: Bubbles number in each sample.  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Sample**} & **Volume Occupied in** & **Volume Occupied in** & **Volume Occupied in** \\  & **the Coronal Third** & **the Middle Third** & **the Apical Third** \\  & **Expressed in** & **Expressed in** & **Expressed in** \\  & **Percentage** & **Percentage** & **Percentage** \\ \hline
1 & 1.308 & 15.764 & 98.881 \\ \hline
2 & 42.151 & 10.482 & 98.202 \\ \hline
3 & 0 & 0 & 99.010 \\ \hline
4 & 0 & 0 & 99.943 \\ \hline
5 & 11.611 & 0 & 99.294 \\ \hline
6 & 7.11 & 0 & 97.212 \\ \hline
7 & 0 & 3.897 & 0 \\ \hline
8 & 0 & 0 & 79.22 \\ \hline Tot & 7.773 & 3.768 & 672.671 \\ \hline Mean & 7.773 & 3.768 & 84.084 \\ \hline SD & 14.54 & 6.09 & 34.68 \\ \hline \end{tabular}
\end{table}
Table 2: Air percentage in the canal space.  
to 84.084% with SD 34.68. Table 3 shows the statistical analysis of the cross comparison between the coronal, middle, and apical third in relation to the number of bubbles found.  
There were no statistically significant differences between the coronal third and the middle third (\(p\) = 0.743). There was a significant statistical difference between the coronal and apical third (\(p\) < 0.05) and between the middle and apical third (\(p\) < 0.05). The cross-comparison relating to the volume occupied by the vapor lock in the different areas of the canal showed a non-significant difference between the coronal and middle third (\(p\)-value 0.484) (Table 4).  
There is a statistically significant difference between the coronal and apical third (\(p\) < 0.01) and between the middle and apical third (\(p\) < 0.01).
## 3 Methodology
### Forecast Accuracy Metrics  
The accuracy of a forecast can be assessed in several different ways. In this study, three standard metrics, MAD, MSE, and MAPE, are used to compare the performance of different forecasting methods. The MAD and MSE are scale-dependent metrics, and the magnitude of the error varies with the magnitude of the underlying series. On the other hand, the  
Figure 2: Proposed neural network structure (\(N^{(p-q-1)}\)) [6].  
MAPE is a relative-error metric ranging between 0 and 1. The scale-independent nature of MAPE makes it a convenient assessment tool in a one-to-one comparison of time series with varying magnitudes. The formulation for each error metric is given in Equations (2)-(4).  
* Mean Absolute Deviation (MAD) \[MAD=\frac{1}{n}{\sum\limits_{t=1}^{n}}{\left|Y_{t}-F_{t}\right|}\] (2)
* Mean Squared Error (MSE) \[MSE=\frac{1}{n}{\sum\limits_{t=1}^{n}}{\left(Y_{t}-F_{t}\right)^{2}}\] (3)
* Mean Absolute Percent Error (MAPE): \[MAPE=\frac{1}{n}{\sum\limits_{t=1}^{n}}{\left|\frac{Y_{t}-F_{t}}{Y_{t}}\right| }*100\] (4) where \(Y_{t}\) and \(F_{t}\) represent the actual and the forecast. As we propose a new forecasting approach in this study, our primary focus is to compare the performance of the proposed method with respect to previous work. For a one-to-one comparison of different models, each model has to be tested on the same data set. In test runs, we used MAD and MSE to evaluate forecast accuracy. These two metrics are easy to compute and understand [23; 24].
## 2 Results and Discussion
### Water Holding Capacity (WHC)  
As shown in Figure 1a, the WHC of all turbid samples showed a declining trend during storage. This could be attributed to the advancement of protein denaturation that reflects a decrease in the water-protein interactions with increasing storage time [14; 15]. The initial WHC value of turbid samples was 89.20%, and AP, VP, MAP1, MAP2, MAP3, and MAP4 treated samples reached significantly (\(p<0.05\)) to 81.91%, 82.57%, 84.06%, 83.90%, 82.41%, and 81.4%, at day 18, respectively. From day 0 to 15, VP treated samples had higher WHC values than other turbid samples, which indicated that turbid samples under the VP treatments combined with superchilling have an advantage in maintaining water content from the beginning to middle stages of storage. Decreased MHC in VP treated samples may be related to the myosin denaturation during storage. However, MAP1 and MAP2 treated samples had significantly higher (\(p<0.05\)) WHC values than others after day 18 as the higher CO\({}_{2}\) MAP treatments were more effective in preventing the protein degradation during superchilling storage.
# Weighted Cluster-Range Loss and Criticality-Enhancement Loss for Speaker Recognition
## 5 Conclusions  
In this paper, a light-weight dual-path attention module and two novel loss functions are proposed for text-independent speaker recognition. By combining the CBAM and SA, the model achieves better performance than either self-attention or CBAM only in most experiments, whilst bringing quite small extra burden on the training time. To enhance the performance of CRL, two improvement schemes are respectively presented. First, a comprehensive version of CRL is proposed for the speaker identification task, i.e., the weighted cluster-range loss. By slightly increasing the weight of \(s_{a,n}\) and \(h^{n}\) in the loss function, the proposed weighted cluster-range loss could speed up pushing those indecisive samples towards the correct region of classification and serves to converge in a more efficient way. No complex operation, but only a simple hyperparameter is added, and we achieve significant improvement compared with the baseline. In addition, the proposed criticality-enhancement loss centers on the most easily optimized samples. By combining CRL and CEL, both the hardest samples and the most easily, but necessarily optimized samples are considered concurrently. Both theoretical and experimental results prove the superiority of this loss function. Our methods could be generalized to other similar tasks using CNNs and triplet loss. Future work will focus on how to combine the proposed two loss functions and further optimize the model structure.  
Conceptualization, investigation, and writing, original draft preparation and editing, J.M.; writing, review and editing, L.X. All authors read and agreed to the published version of the manuscript. This research was funded by the Robotics Institute of Zhejiang University under Grant No. K11801.  
**Acknowledgments:** The authors would like to thank [PERSON] for his contribution to the basic work of the project and the support from Robotics Institute of Zhejiang University.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 10 Experimental Analysis
### Evaluation of Accuracy of Localization
^{T}\)m.  
The functional changes of RMSE curves with the change of the number of transmitters employed in the localization for the considered algorithms are displayed in Figure 8, along with the appropriate CRLB, for the variance of measurement noise \(\sigma^{2}=1\) m\({}^{2}\). Figure 8 shows that as the number of transmitters grows from 4 to 10, the RMSE performance of all algorithms under consideration improves noticeably. We observe that the proposed method is able to reach CRLB accuracy in each of the cases. Furthermore, we observe that CWLS algorithm deviates from the CRLB accuracy. If we consider the cases of lowest number of employed transmitters (4) and the case of highest number of transmitters (18) taken for simulation, we observe the improvement in accuracy of location estimation of 52.4% in the proposed method. Based on simulation results, we came to the conclusion that the proposed ICDEBOA algorithm's performance was less affected by large-scale changes in measurement noise and network topologies than other algorithms we looked at. Figure 7: The obtained CDFs of \(LE\) for considered passive TDOA-based localization problem.
# Deep Learning- and IoT-Based Framework for Rock-Fall Early Warning
## 4 Methodology
### Rock-Fall Detection Model
#### 4.2.2 Rock-Fall Detection-Based Micro-Seismic Wave  
The seismic wave is generated in two cases: at the moment rocks crack or when rocks fall. Thus, the seismic wave sensor can be used to detect rock-fall events. This study characterized the micro-seismic wave by its frequency component, classified into three frequency domains. A significant frequency spectrum band between 100 and 1000 Hz  
Figure 2: Rock-fall detection model.  
was present in the first domain. These signals are generated several hours prior to the rock's fall. The second domain is in the higher frequency band, between 500 and 1000 Hz.  
The third domain is in the lower frequency spectrum, 100 to 500 Hz. These signals precede the rock-fall event by a few moments. The relationship between rock-fall incidents and seismic wave frequency domains was quantified using the spectral amplitude ratio (R). The spectral amplitude ratio (R) is calculated according to Equation (2) [28].  
\[R=\frac{A_{MAX}(100\text{ Hz--500 Hz})}{A_{MAX}(500\text{ Hz--1000 Hz})} \tag{2}\]  
where \(A_{MAX}\) (100 Hz-500 Hz) is the maximum amplitude of the frequency spectrum 100 Hz to 500 Hz, and \(A_{MAX}\) (500 Hz-1000 Hz) is the maximum amplitude of the frequency spectrum 500 Hz to 1000 Hz. The average amplitude ratio R for all frequency domains is shown in Table 2.
## 2 Models and Algorithms
### Osculating Elements of Aperiodic Motion on Quasi-Conic Section  
As in the case of Model 1, the perturbing force \(\ddot{F}\
eq 0\) defined by expression (3) affects the motion of the body \(P_{1}\), and its orbital elements must vary with the time. To obtain the differential equations determining the behavior of the orbital elements in time, we need to represent formally the corresponding solution (24) in the form (13), where the orbital elements \(a(t),e(t),i(t),\Omega(t),\omega(t)\), and \(\Theta(t)\) are unknown functions of time, and the functions \(x,y,z\) are given by (24). The time derivatives of the functions (24) are also represented in the form (15), where the functions \(\dot{x},\dot{y},\dot{z}\) are defined by expressions (26) and  
\[F_{x}=-\frac{\bar{\gamma}(t)}{\gamma(t)}x,\ F_{y}=-\frac{\bar{\gamma}(t)}{ \gamma(t)}y,\ F_{z}=-\frac{\bar{\gamma}(t)}{\gamma(t)}z, \tag{28}\]  
are the corresponding components of the force (3).  
Using expressions (24), (26), and (28), and substituting them into Equations (14) and (16), we obtain a system of six differential equations determining the time derivatives of the orbital elements. By performing all the symbolic calculations described for Model 1, we finally obtain the following differential equations for the orbital elements:  
\[\frac{da}{dt}=-\frac{2a^{5/2}e\sin E}{\sqrt{\kappa}}\gamma(t)\bar{\gamma}(t), \tag{29}\]  
\[\frac{de}{dt}=-\frac{a^{3/2}(1-e^{2})\sin E}{\sqrt{\kappa}}\gamma(t)\bar{ \gamma}(t), \tag{30}\]  
\[\frac{di}{dt}=0,\ \ \frac{d\Omega}{dt}=0, \tag{31}\]  
\[\frac{d\omega}{dt}=\frac{a^{3/2}(\cos E-e)\sqrt{1-e^{2}}}{e\sqrt{\kappa}} \gamma(t)\bar{\gamma}(t), \tag{32}\]  
\[\frac{dE}{dt}=\frac{\sqrt{\kappa}}{a^{3/2}(1-e\cos E)\gamma^{2}(t)}+\frac{a^ {3/2}}{e\sqrt{\kappa}}\Big{(}2e-(1+e^{2})\cos E\Big{)}\gamma(t)\bar{\gamma}(t). \tag{33}\]\[\frac{dM}{dt}=\frac{\sqrt{\kappa}}{a^{3/2}\gamma^{2}(t)}+\frac{a^{3/2}}{e\sqrt{ \kappa}}\Big{(}-(1+3e^{2})\cos E+e(3+e^{2}\cos(2E))\Big{)}\gamma(t)\dot{\gamma}( t). \tag{34}\]  
Again, the inclination \(i\) of the orbital plane and the longitude of the ascending node \(\Omega\) remain constant (see (31)) because the force (3) acts in the orbital plane. As the masses of celestial bodies vary quite slowly, the second derivative \(\dot{\gamma}(t)\) in the right-hand sides of Equations (29)-(34) is small. Therefore, the perturbing force (3) is also small and results in slow variation of the orbital parameters. Only the eccentric and mean anomalies are increasing functions of time undergoing small oscillations (see (33) and (34)). Note that in the case of constant masses (\(\gamma(t)=1\)), the right-hand sides of Equations (29)-(32) become zero while Equations (33) and (34) for the eccentric anomaly and the mean anomaly, respectively, reduce to the well-known equations for the two-body problem with constant mass.
## 4 Signal Processing Algorithms
### Fixed Pattern Noise Removal  
Fixed pattern noise refers to items in the captured signal that are separate to the interference modulation pattern which contains the depth information as a response to OPD between the reference reflected signal and the signal reflected from the measured sample [46]. For example, variations in response of pixels in the CCD camera or spurious etalons in the interferometer due to back-reflectance within the interferometer or stray rays and the DC signal term. There have been various software methods to remove these unwanted signal additions suggested in literature with the most common techniques being high-pass filtering, subtracting the measured reference signal [69] and subtracting the mid-point between the upper and lower signal envelopes [12].
## 3 Results and Discussion
### Comparison between Input-Coupled HMT and Input-Coupled HMT with Lockup Clutch--UNI EN 15429-2  
In order to highlight the advantage of using a lockup clutch in the input-coupled HMT, the global efficiency and fuel consumption were analyzed for both architectures. At first, the comparison was made through a regulated cycle of UNI EN 15429-2. In this  
Figure 16: HMT global efficiency. Highlighted is the FMP at which the transmission reaches the best efficiency.  
Figure 15: Hydraulic transmission global efficiency.  
case, the control strategy allows the mechanical branch to be engaged during the working mode. The working speed settled at 5 km/h, according to Figure 13. Figure 17a,b show the comparison between the global efficiency and fuel consumption of the input-coupled HMT and the input-coupled HMT with lockup clutch, respectively.  
The comparison of the global efficiencies shows that the use of a mechanical branch during the working mode leads to a consistent improvement. In fact, as shown in Figure 17a, during the working mode, the global efficiency for the HMT with lockup clutch reaches the value of 0.9, while this value is 0.6 for the power split. The efficiency improvement is also reflected in the fuel consumption, as shown in Figure 17b. The results are summarized in Table 5.  
Figure 17: Comparison between input-coupled HMT and input-coupled HMT with lockup clutch through UNI-EN 15429-2 cycle. (**a**) Global efficiency. (**b**) Fuel consumption.
## 3 Methods
### NPP Estimation Model  
Based on the modified CASA model from [31], the parameters required for the models were processed accordingly to finally estimate the vegetation NPP with a temporal resolution of one month and a spatial resolution of 1 km. The model estimation equation is  
\[\mathrm{NPP}(x,t)=APAR(x,t)\times\varepsilon(x,t) \tag{3}\]  
where NPP(\(x\),\(t\)) is the NPP of pixel \(x\) in month \(t\) (g C/m\({}^{2}\cdot\)month\({}^{1}\)), \(APAR(x\),\(t)\) is the photo-synthetically active radiation absorbed by pixel \(x\) in month \(t\) (g C/m\({}^{2}\cdot\)month\({}^{1}\)), and \(\varepsilon(x\),\(t)\) is the actual light energy utilization of pixel \(x\) in month \(t\) (g C/M).
## 3 Results and Discussion
\[h=a-b\cdot c^{-P} \tag{24}\]  
where \(a\),  
Again, for the tests, carried out with a power of 200 W, the measurement uncertainty increased slightly, which is characterized by larger ranges of confidence and prediction bands. The reason for this phenomenon is the intensive mixing in the vicinity of the sample. The results presented in Table 2 show that the heat transfer enhancement achieved strongly depends on the sample size. For the smallest sample, a small cylinder, the gain was more than nine times; for medium-size samples, the cylinder and cube, the gain was over five times; and for the largest sample, the cuboid, the gain was 4.5 times. These results can be compared with the results shown in other articles. For example, in the work [36,38], heat transfer was improved by 10-75%. In the work [40], the heat transfer was increased to a level of 50-250%. The greatest enhancement of convective heat exchange is reported in [39]. The authors achieved a maximum amplification of 25 times. It follows that the gain obtained in our research should be classified as high. Unfortunately, the lack of literature reports on the intensification of convective heat exchange in the air using ultrasound does not allow for a wider comparison of the results obtained. In Figure 5, the heat transfer enhancement coefficient \(n\) calculated in accordance with Equation (22) is presented for particular shapes and processes. The values of the heat transfer coefficients were taken from Table 2, \(h_{0}\) for processes without ultrasound and \(h_{\pi}\) for processes with ultrasounds. It is clearly seen that the application of ultrasonics accelerated the cooling and increased the heat transfer coefficient for the small cylinder. For the cylinder and the cube, the intensification was lower but at a level similar to each other. The smallest increase was observed for the cuboid. Taking into account the influence of the ultrasound power, it may be stated that increasing the ultrasonic power results in the growth of the heat transfer coefficient. However, the highest gain was observed for 50 W and 100 W, which is particularly visible for a small cylinder. For 150 and 200 W, the heat transfer coefficient still rises, but the change in reference to the previous ultrasonic power is smaller. Figure 4: Approximation of the heat transfer coefficient: (**a**) cube; (**b**) cuboid; (**c**) cylinder; (**d**) small cylinder. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & \(a\) & \(b\) & \(c\) \\ \hline
**cube** & 219.9 \(\pm\) 8.1 & 177.4 \(\pm\) 9.3 & 9.85 \(\times\) 10\({}^{-1}\)\(\pm\) 2.10 \(\times\) 10\({}^{-3}\) \\
**cuboid** & 219.8 \(\pm\) 14.6 & 184.4 \(\pm\) 13.8 & 9.94 \(\times\) 10\({}^{-1}\)\(\pm\) 8.84 \(\times\) 10\({}^{-4}\) \\
**cylinder** & 256.5 \(\pm\) 14.0 & 210.0 \(\pm\) 14.6 & 9.87 \(\times\) 10\({}^{-1}\)\(\pm\) 2.25 \(\times\) 10\({}^{-3}\) \\
**small cylinder** & 519.3 \(\pm\) 38.6 & 466.3 \(\pm\) 39.1 & 9.88 \(\times\) 10\({}^{-1}\)\(\pm\) 2.50 \(\times\) 10\({}^{-3}\) \\ \hline \end{tabular}
\end{table} 
Table 3: Values of the \(a\), \(b\), and \(c\) constants.
## 3 Results and Discussion
### Comparison of Base Case of NMC of STY/DVB (Case 4) against Reference Situations (Cases 1-3)  
A comparison of a representative formulation of NMC of STY/DVB in scCO\({}_{2}\) (Case 4) against the reference situations of FRP of STY (Case 1), NMP of STY (Case 2), and FRC of STY/DVB (Case 3) is shown in Figure 2. As expected, the slowest case is the NMP of STY (Case 2), where the controller slows down the polymerization rate and there is no crosslinker which accelerates it, and the fastest case corresponds to the conventional FRC of STY/DVB, where opposite conditions prevail. Intermediate between these two are the NMC of STY/DVB (Case 4), which is the second slowest system, and the FRP of STY (Case 1). In this situation, the reduced polymerization rate caused by the controller dominates over the action of the crosslinker.