esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods  
### Subjects and Measurements  
We obtained forearm images of 23 subjects using peripheral CBCT (PHION, Nano Focus Ray, Jeonju, Korea). The scanning parameters included 130 kvp, 8 mA, scanning times of 7.5 s, and image matrixes of 1024 \(\times\) 1024 pixels with slice thickness of 0.2162 mm. The scanning regions were obtained roughly from the carpal bone to the elbow, which included the forearm midshaft. The total slice image number was 797. The study subjects consisted of 3 men and 20 women with a mean age of 63.3 years; among them, 14 and 9 were patients with osteoporosis patients and non-osteoporosis subjects, respectively. Their detailed demographic data are summarized in Table 1.  
We used the forearm images to estimate femoral neck BMD, which were compared with reference femoral neck BMD values obtained by DXA (Discovery-W scanner, Hologic Inc., Bedford, MA, USA). The entire measurement procedure was performed by three radiologists in Wonkwang University Hospital (WKUH), who are inter-operators. The WKUH Institutional Review Board (IRB) approved the measurement and analysis protocol.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Subjects**} & **Non-Osteoporosis** & **Osteoporosis** & **All Subjects** \\  & **(_n = 9_)** & **(_n = 14_)** & **(_n = 23_)** \\ \hline \multirow{2}{*}{Sex (n)} & Male 2 & Male 1 & Male 3 \\  & Female 7 & Female 13 & Female 20 \\ Age (years) & 63.3 \(\pm\) 9.57 & 69.7 \(\pm\) 6.62 & 67.2 \(\pm\) 8.32 \\
50–59 (n) & 3 & 1 & 4 \\
60–69 (n) & 5 & 5 & 10 \\
70–79 (n) & 1 & 8 & 9 \\ Height (cm) & 159.7 \(\pm\) 8.81 & 151.9 \(\pm\) 8.34 & 155 \(\pm\) 9.20 \\ Weight (kg) & 62.2 \(\pm\) 7.68 & 51.2 \(\pm\) 8.80 & 55.5 \(\pm\) 9.85 \\ BMI (kg/m\({}^{2}\)) & 24.4 \(\pm\) 3.14 & 22.1 \(\pm\) 2.19 & 23.0 \(\pm\) 2.81 \\ BMD\({}^{\text{f}}\) (g/cm\({}^{2}\)) & 0.698 \(\pm\) 0.09 & 0.5 \(\pm\) 0.07 & 0.6 \(\pm\) 0.11 \\ \hline \hline \end{tabular} BMD\({}^{\text{f}}\): femoral neck bone material density (BMD) obtained from X-ray absorptiometry (DXA). Values are means \(\pm\) standard deviation.  
\end{table}
Table 1: Demographic data of the subjects.
## 5 Experimental Results
### Threats to Validity  
In this section, we identify potential threats to the validity of our study.  
* Internal validity: The performance difference between our proposed method and BugSum is not huge; however, the experimental result for RQ2 shows that the difference comes from the combination of three sentence significance factors. In addition, the experimental result for RQ4 shows a significant difference between performance of our proposed method and that of BugSum, based on the bug reports without evaluated sentences.
* External validity: In our evaluation, we only used two datasets, SDS and ADS. By using the datasets previously used in other summary methods, we could objectively evaluate the proposed method; however, we need to evaluate our proposed method with larger datasets. It is challenging to create large datasets, however, because humans have to create the golden summaries.
* Construct validity: We regarded the summary quality to be represented by the value of F1-score. To compensate for this threat, we used a different metric (e.g., ROUGE-1 score) to evaluate summary quality for RQ1.
* Conclusion validity: Although the performance difference between our proposed method and BugSum in the result of RQ1 is not huge, we showed that the difference is not a coincidence by showing the result of combining several sentence significance factors in RQ2. We also analyzed the influence of each step in RQ2.
# Modified Uncertainty Error Aware Estimation Model for Tracking the Path of Unmanned Aerial Vehicles
## 3 Simulation Analysis and Result  
Here, an experiment is conducted to evaluate the performance of UAV tracking with the proposed KFUEA compared to various existing methods such as particle filter (PF) [40] and diffusion maps Kalman (DMK) [36] in a nonlinear environment with measurement functions and system dynamics. The experiment shows that KFUEA provides better state estimation compared to existing PF and DMK-based UAV tracking approaches.
## 2 Numerical Models and Schemes
### Turbulence Model and Boundary Conditions  
The change in flow structures in the pump-turbine during runaway process is very complicated, and strong shear flows will occur at the blade-surface boundary layer by the large attack angle. The SAS-SST (Scale Adaptive Simulation-Shear Stress Transport) model was adopted to resolve various vortices on a different scale. It is a hybrid turbulence model, and it introduces the von Karman length-scale in the transport equation of the turbulence eddy frequency. The information provided by the von Karman length-scale allows the SAS-SST model to dynamically adjust to the resolved structures in the unsteady Reynolds Averaged Navier-Stokes (URANS) simulation. Therefore, the SAS-SST turbulence model could provide better solutions for the off-design condition due to involving various scale vortices [28; 29].  
The boundary conditions were defined as follows: constant total pressure was defined at the spiral-case inlet; constant static pressure 0 Pa was applied at the outlet of draft-tube extension part; no-slip wall condition was used for all the walls. The total pressure at the spiral-case inlet is different for the four operating conditions. The smaller the value of the GVOs, the larger the total pressure at the spiral-case inlet. The pressure setting in the CFD simulation is only to obtain a better initial flow fields at each GVO.
## 2 Study Area and Datasets  
The study area is Yuyao City in Zhejiang Province in eastern China (Figure 1), which is located at the south shore of Hangzhou Bay whose coordinates are 29\({}^{\circ}\)-30\({}^{\circ}\)N, 120\({}^{\circ}\)-121\({}^{\circ}\)E. Yuyao City is located in a relatively open and flat plain while the Yuyao River flows through the middle of the whole city from west to east. The study region has a total area of 521.2 km\({}^{2}\) with an elevation range of 1 to 331 m and a slope range of 0\({}^{\circ}\) to 39.4\({}^{\circ}\). The main land cover types include woodland (e.g., broad-leaved forest and coniferous forest), cropland (e.g., paddy rice, oilseed rape, leaf mustard), built up area (e.g., urban and suburban regions), bare soil (e.g., non-vegetated bare ground) and water (e.g., rivers, lakes, and reservoirs). Besides, the study area has an annual temperature of 16.2 \({}^{\circ}\)C and an average annual precipitation of 1547 mm. There is a typhoon season between May and October with an average precipitation of 5.5 mm/day in the last 60 years. Influenced by Typhoon Fitow, Yuyao experienced extreme precipitation on 7 October 2013, which led to the most serious floods in the last 60 years [30]. Typhoon Fitow brought about an accumulated rainfall of 496.4 mm in three days and the mean precipitation is about 165.5 mm/day. Most downtown areas were inundated for more than 5 days while about 833 thousand people were impacted by the disaster and the direct economic losses were more than 69.61 billion RMB (about 11.33 billion USD).  
Remote sensing data used in this study were acquired by HJ-1B satellite of China on 11 October 2013. HJ-1B belongs to a small satellite constellation (HJ-1A/1B), was launched in September 2008 and is targeted for rapid mapping and monitoring of natural hazards and disasters [39]. Sensors on board  
Figure 1: Study area. Red-Green-Blue composition: near-infrared, red and green bands of the multispectral charge coupled device camera (HJ-CCD) after the flood.  
HJ-1B consist of a multispectral charge coupled device camera (HJ-CCD) and an infrared camera (HJ-IRS) which have similar wavelengths to that of Landsat TM. The revisit time is four days which can meet the requirements of dynamically monitoring the flood events. The basic parameters of HJ-1B data can be viewed in Table 1.  
Since Band-4 and Band-5 have similar wavelengths, we only utilized Band-4 in the analysis due to its higher spatial resolution. Besides, the data quality of Band-7 is quite low due to severe stripe noises and Band-8 belongs to a thermal infrared band; they were discarded in the following research. Thus, we only focused on the visible, near-infrared and short wave infrared wavelengths and Band-1-4 and Band-6 were selected for further analysis.
## 3 Our Proposed TCDE Scheme
### Time Complexity  
The computational complexity of the TCDE algorithm is mainly represented by three core stages: (1) DE terrain exploration, (2) topographical contour construction, and (3) niche search.  
First, DE terrain exploration is a standard differential evolution (DE) algorithm with computational complexity of \(O(N*(D+T_{f}))\). Here, D represents the number of data dimensions, and \(T_{f}\) denotes the time cost for a single fitness function evaluation. Second, the computational complexity of constructing a topographical profile is demonstrated via Kriging interpolation, which is \(O(H^{3}+M_{grid}*H)\). Here, \(H\) is the total number of historical individuals used to build the contour, and \(M_{grid}\) is the number of grid points for interpolation. Finally, the niche search stage uses DBSCAN to cluster potential peak points for niche construction. The standard time complexity of DBSCAN is \(O({N_{c}}^{2})\), where \(N_{c}\) is the number of potential peak points used for clustering. After superimposing the local search, the complexity of this stage becomes \(O({N_{c}}^{2}+k_{niche}*T_{de\_local})\), where \(k_{niche}\) is the number of niches generated after DBSCAN clustering, and \(T_{de\_local}\) is the cost of performing a local DE search in a single niche.  
Therefore, the time complexity of TCDE is \(O(N*(D+T_{f}))+O(H^{3}+M_{grid}*H)+O({N_{c}}^{2}+k_{niche}*T_{de\_local})\). Since H > N, the time complexity of TCDE is demonstrated by \(O(H^{3})\).
# Occluded Pedestrian Detection Techniques by Deformable Attention-Guided Network (DAGN)
## 2 Related Works
### Attention- and Deformable-Convolution-Related Methods  
Attention and deformable convolution have been proposed to enhance the representation capability of the network for object-detection and crowd-understanding fields. In [29], the deformable convolution is used in the context-feature-embedding module during the forward pass to obtain unevenly distributed context information, whereas the attention filter is applied during the backward pass. ADCrowdNet [30] proposes the attention map generator (AMG) to get the attention map, then uses the density map estimator (DME) with deformable convolution to generate the density map. In these methods, the attention and deformable convolution are developed in separate parts. On the contrary, the attention module is integrated into the DCAM in our approach to pay attention to the deformed appearance of occluded pedestrians.  
In this work, a new deformable attention-guided pedestrian detector is proposed to achieve improved detection performance in occluded instances. Deformable convolution brings adaptive receptive field learning for pedestrians under different poses and occlusions. The non-local (NL) attention block is integrated to capture global context information. For classification and regression, an IoU-based loss function is used to optimize the model. Furthermore, instead of greedy NMS, we apply distance-IoU NMS (DIoU-NMS) [13], which generates the dynamic threshold with the IoU factor. This is to effectively suppress redundant boxes, which is useful in handling the detection of occluded instances. Additionally, adaptive local tone mapping [15] is implemented to further improve the detection performance by enhancing the visibility of objects with poor exposure.
# Computational Complexity of Modified Blowfish Cryptographic Algorithm on Video Data
## 2 Materials and Methods
### Blowfish Encryption  
With the Blowfish encryption algorithm, a 16-round Feistel arrangement is used to encrypt information. A key-subordinate replacement and an information-subordinate replacement occur in each cycle. All of the tasks are 32-bit XORs and augmentations. Four filed exhibit information queries every cycle are the other major activities. Blowfish employs a wide range of techniques. Before any encryption techniques or decrypting, these keys must be pre-registered. The key clusters, often known as the P-exhibit, are made up of 18 32-bit sub-keys: P1, P2...P18.  
There are four 32-bit S-boxes with 256 entries each: S1, 0, S1, 1...S1, 255; S2, 0, S2, 1... S2, 255; S3, 0, S3, 1...S3, 255; S4, 0, S4, 1... S4, 255.  
The encryption requires a function that iterates the network 16 times (see Algorithm 1). Each round includes a key and data-dependent permutation as well as a key and data-dependent substitution. For 32-bit words, all operations are XORs and additions. For each cycle, four indexing array data retrieval banks are the only additional procedures. The x is  
Figure 1: Methodology workflow diagram of the proposed modified algorithm.  
a 64-bit communication-instrumental variable's data. Gap x is split up into two 32-bit parts: xL and xR. The steps involved in the encryption process are as follows:  
```
Divide x into two 32-bit halves: xL, xR For i = 1 to 16: xL = XL XOR Pi xR = F(XL) XOR xR Swap XL and xR Swap XL and xR (Undo the last swap.) xR = xR XOR P17 xL = xL XOR P18 Recombine xL and xR
```  
**Algorithm 1** Blowfish F function.  
For Function F: partition xL into four eight-piece quarters: a, b, c, and dF(xL) = ((SI, a + S2, b mod 232) XOR S3, c) + S4, d mod 232.  
Decryption is identical to encryption, with the exception that PI, P2, and P 18 are used as part of the switch configuration. Blowfish executions that require the fastest speeds should unroll the circle and ensure that all subkeys are stored in the cache-store.
# Particle-Velocity-Based Mixed-Source Sound Field Translation for Binaural Reproduction
## 6 Experimental Verification
### Discussion  
Based on the above experimental results, we give the following comments:  
* Statistically, the least squares solution of the particle-velocity-based expansion is equivalent to the closed-form solution of the sound-pressure-based expansion, whereas the sparse solution of the the particle-velocity-based expansion provides better perceptual performance than the sparse solution of the sound-pressure-based expansion at a random translated position. The experimental results are consistent with the discussions in Section 5.3.
* Having particle velocity as the design criterion contributes to more precise reconstruction of the interaural phase difference than sound pressure, which is of utmost important for direction perception and therefore, contributes to improved localization of reconstructed sound when there are only a limited number of sources (the sparse solution). In addition to direction perception, audio quality can also be improved by  
Figure 12: Results of the listening experiment scores for the source localization test and the basic audio quality test. Each box bounds the 25 th and 75 th percentile of the scores with the central red line indicating the median value, and the whiskers are the outward extension of the 25 th and 75 th percentile by 1.5 times of the interquartile range. The v-shaped notches represent the 95% confidence intervals. The symbols + denote the outliers and the symbols \(*\) denote the mean values. (**a**) Source localization test; (**b**) basic audio quality test.  
controlling particle velocity for the sparse solution, which may be due to the more accurate reproduction of the sound field.
* There are some limitations for the experimental verification. We only evaluate the single source scenario in the perceptual experiment, where the sound field exhibits sparsity. The perceptual performance may degrade for the multiple source scenario or the highly reverberant sound fields. In addition, the source signal is a single type of music, and the performance of the proposed method in terms of the type of audio remains unexplored. We should note that the equipment used (e.g., headphone and sound card) and the listening environment may differ for different subjects due to the remote experiment.
## 3 Results and Discussion
We attribute this to the damage of the graphene and the unwanted heating of the SA by the residual pump, since the graphene was not attached to a cooler. The improvement of the pulse train and the pulse energy and reducing the pulse duration of Q-switched laser should be achievable by optimizing layer number of graphene and transmission coefficient of the output coupler. Figure 4: Repetition frequency, as a function of absorbed pump power. Figure 3: Pulse width, as a function of absorbed pump power. Figure 6 plots a typical pulse envelope, having full width at half maximum (FWHM) 265.2 ns with \(T=30\%\). Figure 7 shows the pulse train for a typical laser output at the maximum output power 2.2 W, which was achieved at the absorbed pump power of 9.3 W, the corresponding pulse width was 265.2 ns and the PRF was 131.6 kHz. With the increase of the pump power, damage was shown on the surface of graphene and we observed an instable pulse train on the oscilloscope. The stability of the pulse train is not good, which can be attributed to the absorbed heat on the graphene, as mentioned before. With the stable operation of the Q-switched laser even at the large loss brought by the unwanted heating, we believe that if we further wrap the graphene with a cooler, shorter pulses and higher single pulse energy should be possible. To determine the beam-propagation factor (\(M^{2}\)), we routed the Ho laser's radiation through a 150-mm-focal-length lens and measured the beam radius by using a knife straight edge, which is shown in Figure 8. A typical two-dimensional beam profile is shown in Figure 8 as well. The data were fitted by least-squares analysis to standard mixed-mode Gaussian beam propagation equations. Derived from standard beam-propagation expressions, the fit yields \(M^{2}=1.7\). We attribute it to the mode of the pump laser. By using a spectrum analyzer (Bristol Instrument 721A), the output wavelength of Ho:YVO\({}_{4}\) laser in both CW and PQS modes were measured with absorbed pump power of 2.2 W and \(T=30\%\), which are shown in Figures 9 and 10. The shift of the central wavelength resulted from the varied resonator loss. The inversion population density was very high when the  
Figure 5: Pulse energy, as a function of absorbed pump power. Figure 6: Temporal profile of single-pulse. Figure 7: Typical pulse train for 2.2 W output power. laser pulse began to build as a result of the intra-cavity losses increased greatly when inserting the graphene SA into the cavity, which resulted in the output central wavelength under the PQS operation blue-shifting relative to the CW operation. While changing the transmission of the output coupler, the wavelength did not change.
## 5 Literature Review Summary
### Robot-Aided Rehabilitation of Upper Limb Motor Function
#### 5.2.1 Data Analysis Capability  
One of the most significant advantages of using robot-based devices is the data acquisition capability, which is objective, reliable, and automatically stored. In agreement with that, the reviewed systems offered the possibility of analyzing the user's performance based on the metrics gathered by the robotic devices themselves. This data analysis can be performed during the therapy session or when it is finished (post analysis). In other words, it can be in an online or offline mode, respectively. In this way, we identify the data analysis capability of the robotic systems themselves rather than the post analysis using stored data. In this regard, three levels of data analysis capability of robotic systems have been defined: low, moderate, and high. Systems with a low level of analysis capability are those that only stored the patient scores and sensors measurements. In this category, we have included end-point systems such as ACT-3D, MIME, and BI-MANU-TRACK and exoskeleton systems such as L-EXOS, MYOPRO, WREX, Mentor ProTM, HEXOR, SUPINATOR-EXTENDER, WOTAS, and RUPERT. A moderate level of analysis capability is denoted for those systems that provided the therapist with a rapid report about the user's performance. Thus, it we obtain an overview of the patient's evolution. This report is generated in online mode, and it is based on the outcome comparison between the current therapy session and the previous one. End-point systems such as MEMOS, NEREBOT, REHAROB, or HWARD and exoskeleton systems such as RUTGERS MASTER or GENTLE/G are included in this category. Finally, a high level of analysis capability is defined for those systems that can provide extended metrics. Besides, they can provide additional information by online analysis of the raw data of the sensors. A total of ten systems (eight end-point: ARM-GUIDE, BRACCIO DI FERRO, GENTLE/A, InMotion ARM(tm), AMADEO(r), ReGo(tm), DIEGO(r), and ADLER; and two exoskeletons: ARMEO(r)SPRING and ARMEO(r)POWER) were classified as high level.  
The storage and sharing of big data from patients, and in an efficient way, comprise two of the most important issues for the ongoing digital healthcare era. In this way, the properties of robotic rehabilitation systems could contribute to enhancing the management of a large amount of information. This fact could make the integration of robotic technology into a digital healthcare framework easier.
# Analytical Model for Angular Distortion in Multilayer Welding under Constraints
## 4 Conclusions  
Most of the structures for assembly are welded under unknown constraints. In this study, the angular distortion mechanism of welding joints was analyzed, and an analytical formula for its prediction with regards to the degree of constraint was developed. Experiments under varying heat input, thickness, and groove shape demonstrated suitable prediction accuracy. The main conclusions of this study can be summarized, as follows:  
1. The bead area was introduced instead of the heat input, and its relationship with angular distortion was determined and evaluated. The angular distortion is linearly proportional to the ratio of the bead area to the square of thickness, and it decreases after a threshold.
2. An analytical formula for efficiently and accurately predicting angular distortion during multilayer butt welding was derived based on the above linearity and geometrical principle of the welding joint.
3. For the prediction curve, the initial thickness and corresponding initial angular distortion should be obtained by the first measurement, and the degree of constraint by the second measurement.
4. According to the prediction formula, the angular distortion decreases as the degree of constraint and root face increases and as the root gap and groove angle decrease.
5. The degree of constraint significantly increased with strongback when compared to the free (unconstrained) condition. The difference in degree of constraint due to that in thickness of strongback was relatively small.
6. Although no fixture is installed, the degree of constraint increases for small bead size. Fixtures can be used to increase the degree of constraint, increase the heat input up to at least two layers at welding onset, and subsequently keep a low heat input to minimize angular distortion in multilayer welding.  
Conceptualization, W.-J.S.; methodology, W.-J.S., S.-C.P. and H.-K.L.; investigation, W.-J.S., S.-C.P. and H.-K.L.; Validation, W.-J.S. and S.-C.P.; Formal analysis, W.-J.S.; software, W.-J.S.; data curation, W.-J.S.; writing--original draft preparation, W.-J.S.; writing--review and editing, W.-J.S.; visualization, W.-J.S.; Project administration, H.-K.L.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors acknowledge [PERSON]. [PERSON] in DSME Welding R&D Engineering Department for active support of the research.  
The authors declare no conflict of interest.