esa-sceva/satcom-chunk-collection huggingface dataset:
# Metrics for Evaluating Synthetic Time-Series Data of Battery
## 4 Results and Analysis
### Application of the Proposed Evaluation Method
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Data**} & \multirow{2}{*}{**Evaluation Method**} & \multicolumn{3}{c}{**Iteration**} \\ \cline{3-5}  & & **100** & **1000** & **10,000** \\ \hline \multirow{3}{*}{RW9} & Existing & Training loss & 0.3900 & 0.1800 & **0.1200** \\ \cline{2-5}  & Proposed & Rate of change in correlation coefficient & Not used & 0.0813 & **0.0027** \\  & Proposed & Dunn index & 0.3127 & 0.3030 & **0.2540** \\  & Silhouette coefficient & 0.1053 & 0.0308 & **0.0073** \\ \hline \multirow{3}{*}{RW 10} & Existing & Training loss & 0.2850 & 0.2550 & **0.1450** \\  & Proposed & Rate of change in correlation coefficient & Not used & 0.0325 & **0.0291** \\  & Proposed & Dunn index & **0.2718** & 0.3533 & 0.2719 \\  & Silhouette coefficient & 0.0381 & 0.0889 & **0.0089** \\ \hline \multirow{3}{*}{RW 11} & Existing & Training loss & 0.4700 & 0.2200 & **0.1050** \\ \cline{2-5}  & Proposed & Rate of change in correlation coefficient & Not used & 0.0382 & **0.0320** \\  & Proposed & Dunn index & 0.3223 & 0.2722 & **0.2639** \\  & Silhouette coefficient & 0.2172 & 0.0199 & **0.0084** \\ \hline \multirow{3}{*}{RW 12} & Existing & Training loss & 0.2850 & 0.3200 & **0.1540** \\ \cline{2-5}  & Proposed & Rate of change in correlation coefficient & Not used & 0.1444 & **0.0414** \\ \cline{1-1}  & Proposed & Dunn index & 0.6826 & **0.2900** & 0.2938 \\ \cline{1-1}  & Silhouette coefficient & 0.0267 & 0.0295 & **0.0156** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Result of the proposed evaluation method (bold indicates best performance). of iterations increased, and the reliability of the evaluation method was improved compared to that of the existing evaluation method based on learning loss. Contrary to the learning loss method, the proposed evaluation method can quantitatively evaluate data similarities by directly comparing the datasets. The silhouette coefficient demonstrated the best accuracy for similarity based on the number of iterations. This is because this coefficient does not depend on data characteristics (such as linearity) and uses the average distance of all data rather than the distance between specific data.
## 3 Results
### SIT Opportunity Windows--Timing and Patterns of Female Medfly Emergence in Successive Hosts  
During summer and autumn, successive batches of progeny of the founding cohort of medfly females emerged on successive seasonal fruits. The periods of medfly emergence on the successive fruits follow fruit phenology with a degree of temporal overlap (Figure 3). In the three hypothetical sites, on very early apricot, the first adult female emerged on days 161-164 (mid-June), and the emergence continued until days 188-193 (mid-July). In mid-season peach, adult emergence lasted from days 181-184 (early July) to 262-266 (late September). In late apple, adult emergence lasted from day 241-244 (late August) to 313 (mid-November).  
The time when new females emerge determines the temporal window of opportunity for SIT intervention. Newly emerged virgin females will reach their maturity within 5-15 days (depending on temperature) and become ready-to-mate and thus vulnerable to SIT. This means that in our hypothetical locations, the release of sterile males should begin around day 150 (beginning of June) and continue unabated until day 325 (late-November) or slightly longer depending on weather. Such timing was adopted in the simulation of all scenarios of SIT intervention, as discussed below.  
Figure 2: The influence of site structure and role of seasonal fruits on annual medfly population profile in the absence of any control measures (daily numbers of females present on different fruits). (VE, M and L denote very early, mid-season and late fruit, respectively).
###### Abstract  
There is a growing interest in topic modeling to decipher the valuable information embedded in natural texts. However, there are no studies training an unsupervised model to automatically categorize the social networks (SN) messages according to personality traits. Most of the existing literature relied on the Big 5 framework and psychological reports to recognize the personality of users. Furthermore, collecting datasets for other personality themes is an inherent problem that requires unprecedented time and human efforts, and it is bounded with privacy constraints. Alternatively, this study hypothesized that a small set of seed words is enough to decipher the psycholinguistics states encoded in texts, and the auxiliary knowledge could synergize the unsupervised model to categorize the messages according to human traits. Therefore, this study devised a dataless model called Seed-guided Latent Dirichlet Allocation (SLDA) to categorize the SN messages according to the PEN model that comprised Psychoticism, Extraversion, and Neuroticism traits. The intrinsic evaluations were conducted to determine the performance and disclose the nature of texts generated by SLDA, especially in the context of Psychoticism. The extrinsic evaluations were conducted using several machine learning classifiers to posit how well the topic model has identified latent semantic structure that persists over time in the training documents. The findings have shown that SLDA outperformed other models by attaining a coherence score up to 0.78, whereas the machine learning classifiers can achieve precision up to 0.993. We also will be shared the corpus generated by SLDA for further empirical studies.  
machine learning; personality detection; PEN model; topic modeling +
Footnote †: journal: _Algorithms_  
16 December 2021  
Accepted: 3 March 2022  
Published: 8 March 2022
## 3 Results
### Experimental Results  
Figure 10 presents the experimental results of the 3D reconstructed images using the GT and the proposed imaging sequence (full-aperture transmission, Nr = 5 and Ns = 3). The AW is applied on the GT to have a fair comparison. The red, yellow, green, and blue arrows show the foam, the two needles, the grating lobes, and the clutter in the azimuth direction, respectively. The smaller size of foam in Figure 10b,e is due to a smaller effective aperture size in the elevation direction. A better visualization on the needles is achieved in Figure 10c compared to Figure 10d due to the AW technique.  
Figure 8: The reconstructed images using (**a**) the column-by-column (CBC) imaging sequence (ground truth—GT) and (**b**) the proposed imaging sequence (full-aperture transmission, Nr = 5 and Ns = 3), along with (**c**) their corresponding lateral variations. The AW is applied on the GT to have a fair comparison. (**d**) The clutter level using the green rectangular region in (c).  
Figure 9: The reconstructed images using (**a**) the proposed imaging sequence (full-aperture transmission, Nr = 5 and Ns = 3) and (**b**) the CBC imaging sequence (GT). (**c**) Statistical analysis of the image intensities indicated by two regions of interest (ROI1 and ROI2) which are the red and yellow circles, respectively.  
The maximum intensity projection (MIP) over a depth of 1-23 mm of the 3D experimental images obtained with the two-needle phantom is shown in Figure 11a-c. The elevation FWHM obtained with the proposed imaging sequence at the depth of about 10 mm is doubled, in accordance with the results provided in Figure 8a,b. This is the reason that the targets look stretched in the elevation direction, as seen in Figures 11a and 10b. The azimuth FWHM for different receive parameters stays the same (about 620 um) as the proposed imaging sequence does not significantly affect the performance of the 2D transducer in the azimuth direction. The same clutter patterns for different Ns, but the same Nr, also prove this matter (see Figure 11d). The clutter level in azimuth direction is higher than elevation direction due to the larger pitch (300 um) and lower number of elements (48).  
Figure 10: The 3D images obtained with (**a**,**c**) the CBC imaging sequence (GT) and (**b**,**d**,**e**) the proposed imaging sequence (full-aperture transmission, Nr = 5 and Ns = 3). The red, yellow, green, and blue arrows show the foam, the two needles, the grating lobes, and the clutter in the azimuth direction, respectively. The dynamic range was 40 dB. The numbers in (**c**) are related to those shown in Figure 3e.
## 4 Discussion
Comparing basal assessments (T0) with one-month follow-up (T1) for each limb (paretic and non-paretic), using the L-STIFF tool, the measured values of the stiffness of the paretic legs at T1 were lower than those at T0 but this decrease was not statistically significant. Using L-FORCE and L-ROM tools, the joint torque and the ROM for the paretic legs at T1 was greater than that at T0 but the differences were not statistically significant except for hip flexion torque (\(p<0.05\)) and hip ROM (\(p<0.05\)). This means that paretic legs improved their stiffness, joint torque and ROM after one month of intensive rehabilitation treatment including Lokomat, as expected, and the L-STIFF, L-FORCE and L-ROM tools were able to measure the small changes but the low number of patients influenced the statistical significance. However, it is important to mention that a poor statistical significance does not mean that the results do not have practical importance. As well as several studies on care and rehabilitation focused on patients with strokes, the aim of the treatments is early intervention and intensive training [31] since studies of animal models demonstrated a time-limited period of heightened plasticity after focal brain injuries [32; 33]. Regarding the non-paretic legs, one month later the stiffness and the joint torque fluctuated without a consistent trend and without statistical significance. On the other hand, the ROM was improved without statistical significance. These results were expected since the training of the non-paretic legs is limited by the performance of the paretic legs. Therefore, the non-paretic legs do not train to the best of their ability and thus they do not present significant changes. The main limitation of this study was the low number of patients. Accordingly, the absence of statistical significance in the improvement of joint torque, increase in joint excursion, and reduction of stiffness was probably influenced by this. Unfortunately, during the early stages of the disease, the evaluation of joint torque and joint excursion cannot include patients who were hemiplegic, or who were not able to understand indications. Indeed, to evaluate joint torque and joint excursion, the active and voluntary participation of the patient is required, as well as the skills to adequately understand the task. Another limitation of this study is the high cost of Lokomat that may reduce its widespread use and, therefore, the utilization of the data it can provide. An additional limitation is that no measurements were carried out with another instrument to compare the Lokomat results.
###### Abstract  
In the construction industry, the lack of supply and demand for high-quality natural aggregates is a problem. In the case of South Korea, according to data from the Ministry of Environment, it is predicted that the depletion of aggregate resources will occur in 20 years, considering the amount of aggregate used in construction every year and the amount of natural aggregate. Therefore, it is necessary to develop recycled aggregates that can replace natural aggregates for construction. The purpose of this study is to evaluate the applicability of recyclable air-cooled slag (ACS) aggregates as a substitute material for natural aggregates applied to rural-road pavement concrete. That is, the applicability of rural-road pavement concrete is evaluated by evaluating the strength and durability of rural-road pavement concrete to which an ACS aggregate is applied. Durability was assessed in terms of the chloride ion diffusion, repeated wetting-drying, abrasion resistance, impact resistance, and repeated freezing-thawing tests. The test result showed that the diffusion coefficient of the mixture to which the ACS aggregate was applied was slightly larger. In addition, the diffusion coefficient was slightly larger in the case of applying the air-cooled slag coarse aggregate (GG) than in the case of applying the air-cooled slag fine aggregate (GS). The results of abrasion and impact resistance tests of ACS-aggregate-incorporated rural-road concrete indicated that abrasion and impact resistance decreased as the aggregate content increased. The ACS retained some of the properties of the blast furnace slag. Thus, in repetitive wetting-drying tests, which can cause changes in chemical properties, the ACS aggregate increased the concrete's long-term residual strength. In addition, the results showed that the relative dynamic elastic modulus targeting repeated freezing-thawing resistance satisfied the 80% target. The freeze-thaw resistance improved as the ACS aggregate content increased. In conclusion, the results of this study showed that the durability of rural-road pavement concrete can be improved experimentally by applying both GG and GS at the same time. Therefore, it is shown that ACS aggregates can be applied to rural-road pavement concrete as a substitute for natural aggregates.  
air-cooled slag (ACS) aggregate; concrete aggregates; durability; rural-road concrete pavement; steel manufacturing by-product +
Footnote †: journal: Applied Sciences
# Compact 50 W All-Solid-State Picosecond Laser System at 1 kHz
## 2 Experiment and Results
### Regenerative Amplifier  
The seed pulses were input to a Nd:YAG regenerative amplifier. The crystal in the cavity was a 0.3 at % Nd-doped YAG crystal with a size of \(3\times 3\times 40\) mm\({}^{3}\), which was wrapped with indium foil and mounted tightly on a thermoelectric cooler (TEC) copper heat sink. The crystal surface was coated with 1064 nm and 808 nm antireflection films. The M7 to M12 were high reflectors. M7 curved mirrors had 780 mm radius of curvature, while M8 had \(-\)1400 mm radius of curvature, and the other mirrors were plane mirrors. Due to thermal effects, the crystal acted as a thermal lens in the cavity. It was found that as the focal length of the crystal thermal lens becomes shorter, the beam waist radius at the crystal also becomes larger in this cavity. Therefore, under different pump powers, the cooling power of the TEC for the crystal should also change accordingly. The pump laser was a 300 W fiber-coupled quasi-CW (QCW) laser diode with a 600 \(\upmu\)m core diameter centered at 808 nm (DILAS, Mainz, Germany), which was controlled by a trigger signal with gate width of 180 \(\upmu\)s at 1 kHz. By optimizing the focal length of the thermal lens crystal, the regenerative cavity can achieve maximum output power of 6.5 W at pump power of 41.5 W. Figure 3 shows the beam quality (M\({}^{2}\) factor) of the output pulses measured by a commercial M\({}^{2}\) factor instrument (M2-200S-FW, Ophir-Spiricon Inc., North Logan, UT, USA). The M\({}^{2}\) value in x and y direction were 1.327 and 1.445 respectively with the output power of 6.5 W.
# Design and Implementation of Inverse Kinematics and Motion Monitoring System for 6 DoF Platform
## 3 PID Controller  
In the synchronous d-q frame, the voltage equations can be expressed as  
\[v_{d}=r_{s}i_{d}+L_{d}\frac{d}{dt}i_{d}-\omega_{re}L_{q}i_{q} \tag{20}\]  
and  
\[v_{q}=r_{s}i_{q}+L_{q}\frac{d}{dt}i_{q}+\omega_{re}L_{d}i_{d}+\omega_{re} \lambda_{m} \tag{21}\]  
where \(v_{d}\) is the d axis voltage, \(v_{q}\) is the q axis voltage, \(r_{s}\) is the stator resistance, \(i_{d}\) is the d axis current, \(i_{q}\) is the q axis current, \(L_{d}\) is the d axis inductance, \(L_{q}\) is the q axis inductance, \(d/dt\) is the differential operator, \(\omega_{re}\) is the electrical motor speed, and \(\lambda_{m}\) is the flux linkage of the permanent magnet mounted on the shaft rotor. Through realizing \(i_{d}=0\) by applying field-oriented control, Equations (20) and (21) can be rewritten as  
\[v_{d}=-\omega_{re}L_{q}i_{q} \tag{22}\]  
and  
\[\begin{array}{c}v_{q}=r_{s}i_{q}+L_{q}\frac{d}{dt}i_{q}+\omega_{re}\lambda_ {m}\\ =u_{q}+\omega_{re}\lambda_{m}\end{array} \tag{23}\]  
The electro-magnetic torque is  
\[T_{e}=\frac{3}{2}\frac{P}{2}\lambda_{m}i_{q}=K_{T}i_{q} \tag{24}\]  
where \(T_{e}\) is the electro-magnetic torque of the motor, \(P\) is the number of poles of the motor, and \(K_{T}\) is the torque constant. The mechanical speed and position of the motor are expressed as  
\[\frac{d}{dt}\omega_{rm}=\frac{1}{J_{t}}(T_{e}-T_{L}-B_{t}\omega_{rm}) \tag{25}\]  
and  
\[\frac{d}{dt}\theta_{rm}=\omega_{rm} \tag{26}\]  
where \(\omega_{rm}\) is the mechanical speed, \(\theta_{rm}\) is the mechanical position, \(J_{t}\) is the total inertia of the motor and gearbox, \(T_{L}\) is the load torque, and \(B_{t}\) is the total viscous coefficient of the motor and gearbox. The electrical position and speed, which can be expressed as \(\theta_{re}\) and \(\omega_{re}\), are shown as follows:  
\[\theta_{re}=\frac{P}{2}\theta_{rm} \tag{27}\]  
and  
\[\omega_{re}=\frac{P}{2}\omega_{rm} \tag{28}\]  
The PID control law is  
\[u=Kpe(t)+K_{I}\int e(t)dt+K_{D}\frac{d}{dt}e(t) \tag{29}\]  
where \(e(t)\), \(K_{P}\), \(K_{I}\), and \(K_{D}\) are the control error, proportional, and integral coefficients, respectively. In Figure 4, \(G_{\theta}\), \(G_{\omega}\), and \(G_{C}\) of the servo driver are the PID controllers, namely, the position loop, speed loop, and current loop controllers, respectively. The control system was designed and applied to each of the six actuators independently.
## 4 Experimental Results
### Performance Comparison  
On the MICCAI 2018 test set, comparison is made between the method and other prevalent segmentation methods such as FCN, Conv-Deconv [39], U-Net, Indices-JSQ [40], and SegNet to evaluate its effectiveness. As shown in Table 1, the DM and JC of the proposed method reach 0.908 and 0.834, respectively, with a certain extent of improvement compared to that of other segmentation methods. These results suggest that the model is capable of more accurately determining the class of each pixel point and then achieving higher segmentation performance.  
Figure 4: Segmentation results for four subjects. The arrows point to places where the proposed method can be seen to almost overlap with the GT manually delineated by the experts, indicating better segmentation. (**a**) Cardiac MR image. (**b**) Results of segmentation using the suggested method. (**c**) GT. (**d**) The segmentation contours obtained by the proposed method are marked by red curves, and the corresponding GT manually delineated by experts is marked by green curves.
# A Review of Power System Fault Diagnosis with Spiking Neural P Systems
## 2 Power System Fault Diagnosis with Spiking Neural P Systems
### Fault Location of Distribution Network
The firing rules in the proposed model in [67] are also different and along with traditional firing and forgetting rule, there exist backward firing rule and backward forgetting rule which are associated with backward reasoning process. Moreover, the neurons are divided into two types, i.e., input/output and output/input neurons. Next we explain the reasoning process for the proposed fault location identification method in distribution network with distributed generations. Since the electrical synaptic transmission-based spiking neural P system is a bidirectional system and it performs bidirectional reasoning. So the reasoning process in [67] is divided into forward reasoning process and the backward verification process. Moreover, note that results contained in the neuron are different during the forward reasoning process and the backward verification process. Unlike in [82,84,88,89,91], the neurons are divided into input/output (IN/OUT) and output/input (OUT/IN) neurons where instead of truth values, the neurons contain discrete values, i.e., the values associated with the neurons are from \(\{-1,0,1\}\). Furthermore, during the backward reasoning process, the synaptic connection between the IN/OUT and OUT/IN neurons and the connection between the section neurons and OUT/IN neurons are represented by a direction matrix. Next, the information is used to verify whether fault current information is wrong. The proposed method in [67] is efficient and it has the following advantages: (1) it can efficiently express the relationship between the various parts of the distribution network and the logical relationship of the graphics and also can represent the fault location process visually;  
(2) strong information processing capability because of parallel-distributed architecture;  
(3) bidirectional characteristics of electrical synaptic transmission which is combined with electrical quantity (direction of current) for fault location of distribution network with distributed generations (DGs);  
(4) high accuracy, less computation time, the fault diagnosis model and reasoning process is simple. Another important feature of the reasoning algorithm is that it is applied according to the bidirectional power flow characteristics of a distribution network with DGs;  
(5) single faults, multiple faults, and misinformation faults in the distribution network with DGs can be located accurately and quickly by the proposed method. (6) this method is applicable to a variety of fault conditions for distribution network with DGs. Moreover, in fault location, it can verify the fault current information, improves the reliability of the fault current information and the accuracy of the fault location result.
# Bearing Capacities and Failure Behaviors of F-Type Socket Joint in Rectangular Pipe Jacking Tunnel
## 1 Introduction
[25] used ABAQUS to study the joint shear test of rectangular top pipe joints by numerical simulation and investigated the force characteristics and sealing properties of the waterproof rubber ring at the joints. Rectangular pipe joint construction usually employs F-type socket joints, which can meet the requirements of tunnel waterproofing and fine deformation. Nevertheless, the joint stiffness is much smaller than the stiffness of the tube body and is a weak part. Thus, the bending and deformation of rectangular pipe tunnel joints occur easily due to a series of factors such as disturbances during construction, changes in groundwater, and drastic variations in the load above the tunnel. Ultimately, the joint waterproofing failed, even causing damage and endangering the safety of the tunnel operation. Based on the above analysis, the bending performance, deformation characteristics and damage mechanism of rectangular pipe jacking joints with F-type socket joints are still unclear and need to be further studied. To this end, indoor bending tests of F-type socket joints of rectangular pipe jacking were carried out and a series of numerical simulations were established. The results show that under the same loading displacement, the greater the foundation stiffness, the greater the bending moment borne by the joint, and the higher the joint bending capacity. The bending moment of the joint is mainly borne by the steel ring, and eventually the steel ring will be damaged by bending. The larger the foundation stiffness and the harder the strata, the smaller the joint bending deformation; the larger the section size of the pipe joint, the smaller the joint bending deformation; the larger the size of the steel collar, the smaller the joint bending deformation.