esa-sceva/satcom-chunk-collection huggingface dataset:
The Relationship between Skinfold and Ultrasound Measures of Subcutaneous Fat in Untrained Healthy Males  
[PERSON]  
1 Faculty of Health and Wellbeing, University of Winchester, Winchester SO22 4 NR, UK; [EMAIL_ADDRESS] (A.O.); [PERSON]SS] (E.P.); [PERSON]] (J.F.); [PERSON]SS] (S.J.)  
1 Faculty of Health and Wellbeing, University of Winchester, Winchester SO22 4 NR, UK; [EMAIL_ADDRESS] (A.O.); [PERSON]SS] (E.P.); [PERSON]] (J.F.); [PERSON]SS] (E.P.)  
1 Faculty of Health and Wellbeing, University of Winchester, Winchester SO22 4 NR, UK; [EMAIL_ADDRESS] (A.O.); [PERSON]SS] (E.P.); [PERSON]] (J.F.); [PERSON]SS] (S.J.)  
1 Faculty of Health and Wellbeing, University of Winchester, Winchester SO22 4 NR, UK; [EMAIL_ADDRESS] (A.O.); [PERSON]SS] (E.P.); [PERSON]] (J.F.); [PERSON]SS] (E.P.)  
1 Faculty of Health and Wellbeing, University of Winchester, Winchester SO22 4 NR, UK; [EMAIL_ADDRESS] (A.O.  
transported [14]. However, the upper limit of skinfold callipers (\(\sim\)9 cm) and the increasing error rate associated with larger skinfolds [19; 20; 21] results in a restriction of their application in particularly overweight populations.  
Research comparing ultrasound imaging and skinfold site measurement to assess body composition is limited. Previous research has demonstrated a strong relationship between ultrasound imaging and skinfold measurements as methods of measuring body composition [7; 21; 22]. However, the studies involved either solely female participant groups or a mixed sex cohort. Male and female adipose tissue appears to possess structural differences [23], and so should be assessed for measurement comparison separately. [PERSON] et al. [22] also indicated that there was greater agreement between measurement techniques at specific skinfold sites, with images in locations such as the subscapular and trunk being difficult to interpret. More research is required that compares the two techniques for various populations at selected sites, so the use of ultrasound as an alternative measure of adipose tissue can be ascertained.  
This study will determine the relationship between skinfold measures of subcutaneous adipose tissue and ultrasound imaging techniques at three skinfold sites (biceps, triceps and front thigh). It is posited that the findings of this study will assist in obtaining accurate and accessible measures of body composition in a clinical, health or elite athlete setting.
## 4 Dynamic Model Updating Examples
### Proof Examples: Dynamic Model Updating of a Thin Plate
#### 4.1.1 Updating Based on the Simulated \"Experimental\" Data
The MUUM and CMPES method were employed to update the initial FE model, and the same optimized parameters are listed in Table 4.  
The updated natural frequencies and MAC values are shown in Table 5. Compared with Table 2, the updated MAC values were closer to 1. The FRE values and \(RMSE_{FRE}\) of the updated models were within the range of \(\pm\)1%. Therefore, both the updating methods worked well. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter Name** & **Value** & **Parameter Name** & **Value** \\ \hline Number of & 50 & Migration direction & forward \\ individuals & 5 & Migration Interval & 15 \\ Number of elite & 0.75 & Migration probability & 0.1 \\ Crossover probability & 0.75 & Migration Tolerance & 10-6 \\ Maximum number of & 200 & Function Tolerance & 10-6 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Optimize parameters of evolutionary algorithm. Figure 4: Uncorrelated mode pairs for the MUUM method based on the simulated “experimental” data. (**a**) Swapping between the third and fourth modes. (**b**) Swapping between the sixth and seventh modes. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Mode** & \multicolumn{4}{c}{**MUUM**} & \multicolumn{4}{c}{**CMPES**} \\ \cline{2-7}
**No.** & **Frequency (Hz)** & **FRE (\%)** & **MAC** & **1-MAC** & **Frequency (Hz)** & **FRE (\%)** & **MAC** & **1-MAC** \\ \hline
1 & 162.45 & 0.82 & 1.00 & 0.00 & 161.13 & 0.01 & 1.00 & 0.00 \\
2 & 211.12 & 0.75 & 1.00 & 0.00 & 209.55 & \(-\)0.01 & 1.00 & 0.00 \\
3 & 449.25 & 0.79 & 1.00 & 0.00 & 445.72 & 0.00 & 1.00 & 0.00 \\
4 & 458.70 & 0.77 & 1.00 & 0.00 & 455.08 & \(-\)0.02 & 1.00 & 0.00 \\
5 & 738.63 & 0.80 & 1.00 & 0.00 & 732.60 & \(-\)0.02 & 1.00 & 0.00 \\
6 & 787.00 & 0.79 & 1.00 & 0.00 & 780.86 & 0.00 & 1.00 & 0.00 \\
7 & 861.69 & 0.72 & 1.00 & 0.00 & 855.35 & \(-\)0.02 & 1.00 & 0.00 \\
8 & 899.26 & 0.74 & 1.00 & 0.00 & 892.64 & 0.00 & 1.00 & 0.00 \\
9 & 1163.85 & 0.79 & 1.00 & 0.00 & 1154.77 & 0.01 & 1.00 & 0.00 \\
10 & 1229.49 & 0.80 & 1.00 & 0.00 & 1219.92 & 0.01 & 1.00 & 0.00 \\ _RMSE_ & & 0.78 & & & 0.01 & 0.00 & \\ _CV_ & & & & & & 0.00 & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the updated results by the MUUM and CMPES methods based on the simulated “experimental” data.
# Analysis and Design of Protection Device for Anchor Cable Pull-Out in High-Stress Roadways
## 6 Discussion  
The working conditions of anchor cables in coal mine roadways present a challenge in observing the ejection speed of a breaking anchor cable wire in the field. This difficulty is also mirrored in the laboratory due to the unique hinged structure of the anchor wire. To overcome this, Section 2 cleverly utilizes the strain energy density of the anchor cable wire during the unloading stage to accurately determine the maximum strain energy and the initial speed at which maximum breakage ejection occurs. This research forms the basis for the development of an anchor cable protective device. In Section 3, a tensile test of the protective device is conducted to ascertain its energy absorption limit under static load conditions, relative to the breaking ejection speed of the anchor cable. This test serves as a mechanical performance parameter for the protective device and sets the stage for a numerical simulation study under dynamic load conditions, such as when an anchor cable breaks and ejects. Numerical simulation, with its advantages of strong intuitiveness, high repeatability, and low cost, is employed in Section 4; the stress and deformation data for the protective device under varying speeds of the anchor wire can be obtained intuitively  
Figure 24: Engineering application of spring protection devices.  
by using ANSYS Workbench simulation. These results provide pertinent parameters for experimental design under laboratory conditions, thereby accelerating research progress. Section 5 introduces the application of the protective device in coal mine roadway anchor cable support and verifies its effectiveness. During field application, potential defects in the protective device can be identified for further optimization and improvement. The findings from this research offer valuable insights into the ejection and protection of anchor wires.
## 3 Fabrication, Measurements and Discussions
### Measurement and Results
#### 3.2.3 Actuation Voltage  
The actuating voltage of the can be calculated by Formula (15).  
The calculated value of effective elastic coefficient \(k\) is 15 N/m, and the actuating voltage \(V_{p}\) is approximately 13 V. However, the measurement of actuating voltage is 16 V, which is different from the calculation value using Formula (15). The inhomogeneity of thickness and the inadequate release of polyimide are the main causes. The actuating voltage will decrease to the evaluating value when the manufacturing process has a good release and flatness.
# Record Linkage of Chinese Patent Inventors and Authors of Scientific Articles
## 3 Experiments and Discussion  
### Evaluation of ASJC Code Prediction  
Evaluation of multi-label models for ASJC code prediction was performed on article abstracts. The dataset consisted of 788,335 instances, i.e., vectors representing contents of articles, which was randomly split into training and testing subsets in a proportion of 80-20. All instances have from ground-truth labels 1 to 8 (ASJC codes).  
We evaluate three multi-label models, described in Section 2.1, i.e., binary relevance with a decision tree (BR-DT), label powerset with Gaussian naive Bayes (LP-GNB) and a multilayer perceptron with adjusted back-propagation (BP-MLL). For DT a maximum tree depth of 3 was chosen, for MLP three layers, with 64, 32 and 26 neurons were used, ReLU (rectified linear unit) was used as activation in hidden layers and sigmoid in the output layer. MLP was trained for 50 epochs using an Adagrad optimizer with a learning rate set to 0.001. Outputs from the last layer were transformed into binary label indicator vectors using a constant threshold with value 0.8.  
The results of multi-label classification were evaluated using a Hamming loss (HL) and example-based version of precision (\(P_{e}\)), recall (\(R_{e}\)) and \(F_{\beta}\), depicted in Equations (7)-(10), where \(n\) is the number of instances, \(L\) is the set of all labels, \(\Delta\) is the symmetric difference operator, \(Y_{i}\) is the set of ground-truth labels for instance \(i\), \(Z_{i}\) is the set of predicted labels for instance \(i\) and \(\beta\) is a positive scale parameter.  
\[HL =1/n\sum_{i=1}^{n}|Y_{i}\Delta Z_{i}|/|L| \tag{7}\] \[P_{e} =1/n\sum_{i=1}^{n}|Y_{i}\cap Z_{i}|/|Z_{i}|\] (8) \[R_{e} =1/n\sum_{i=1}^{n}|Y_{i}\cap Z_{i}|/|Y_{i}|\] (9) \[F_{\beta} =(1+\beta^{2})\cdot(P\cdot R)/((\beta^{2}\cdot P)+R) \tag{10}\]  
Evaluation scores are presented in Table 2. In a record linking problem high recall is preferred over high precision, because, during the final record pair classification, false positives are preferred over false negatives, therefore we report \(F_{2}\) (\(\beta=2\) in Equation (10)). This way \(R_{e}\) is considered twice as important as \(P_{e}\).  
The best performance was achieved by the BP-MLL model, while the worst was by the LP-GNB. Both the BR-DT and BP-MLL favored the prediction of multiple labels per instance, which is noticeable in high values of \(R_{e}\). \(P_{e}\) of BP-MLL is superior to the other models; hence, labels predicted by this model are more accurate. The final model was created by refitting TF-IDE, tSVD and BP-MLL on whole training data.  
Finally, the classifier was used for prediction of ASJC codes using patent abstracts, previously transformed using the fitted TF-IDF and tSVD models. ASJC codes decoded from the predicted binary vectors were used directly in the comparison step of the record linkage pipeline.
## 2 UAV Communication
### Characterizing the UAV Network
#### 2.1.2 Infrastructure-Based or Adhoc UAV Networks  
UAV networks are usually referred to as ad hoc networks in the existing literature. Most of the discussion in available research relates VANETs to UAV networks; moreover, MANETs are also related to UAV networks. However, the stated studies do not address the  
Figure 3: Multi UAV system and varying links’ quality demands.  
explicit properties of UAV networks entirely. Depending upon the utilization and nature of the mission, the UAV network could be attributed to slow moving, feature-like hovering, high mobility missions and slow mobility profiles. One of the pertinent applications of UAV nodes is to function as a sky-based communication-based station, ensuring provision of reliable coverage over a good span of area [44]. UAV networks could perform functions such as infrastructure-based systems for applications dissimilar to VANET and MANET networks. In such scenarios, UAVs would exchange data with one another in parallel with designated control centers [45]. Such network would be identical to a wireless network that is fixed with UAVs acting as aerial base stations. The infrastructure-based UAV network clearly diversified the authentication requirements. Moreover, there is a group of roles in which nodes have high mobility and collaborate, construct and communicate with the network in an ad hoc manner [45]. The aerial network of UAVs demonstrating adhoc base station systems is illustrated in Figure 4. In this scenario, the architecture and nodes involved in data forwarding can be ascertained on a dynamic basis. A considerable number of challenges are faced by both UAV adhoc and UAV infrastructure-based networks [46]. Such as replacing power exhausted nodes or failing nodes by new nodes.  
Any wireless mobile network is vulnerable to link disruptions. The networks of unmanned aerial vehicles (UAVs) are no exception. In situations where UAVs provide communication coverage across a large region, the UAVs hover, thus the likelihood of disruptions is low. On the other hand, disruptions are more likely in applications that require rapid UAV mobility [47]. The amount of disruption is determined by the mobility of the UAVs, the amount of power transmitted, inter-UAV distances and external noise. Delays in data transmission could be caused by low network quality or by one or more UAV nodes holding the data due to a lack of an end-to-end path [48].
## 3 Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Group (n)**} & \multicolumn{2}{c}{**Mean of Marginal Gap Change (SD)**} \\ \cline{2-4}  & **Post-Thermocycling** & **Post-Cyclic Loading** & **Post-Five-Year** \\  & **Post-Thermochemical Aging** & **Post-Cyclic Loading** & **Thermomechanical Aging** \\ \hline Titanium (12) & 0.04 (2.07) & 0.93 (2.71) & 0.71 (1.23) \\ PEKK (12) & 3.24 (2.01) & 0.32 (1.07) & 3.56 (2.02) \\ \(p\)-value & 0.003 a & 0.45 & 0.002 a \\ \hline \hline \end{tabular}  
* indicates statistically significant difference (\(p\)-value \(<\) 0.05).  
\end{table} 
Table 2: Mean (\(\mu\)m) values of marginal gap change after different aging conditions. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Group**} & \multirow{2}{*}{**Position**} & \multicolumn{2}{c}{**Mean Marginal Gap Change (A) According to Position (\(\mu\)m)**} \\ \cline{3-5}  & & **APost Thermocycling** & **APost Cyclic loading** & **APost Five-Year Aging** \\ \hline \multirow{5}{*}{Titanium} & 1 & \(-\)0.45 & 1.90 & 1.45 \\  & 2 & 0.61 & 1.38 & 1.99 \\  & 3 & 1.49 & \(-\)0.84 & 0.65 \\  & 4 & 0.85 & \(-\)0.03 & 0.33 \\  & 5 & \(-\)0.63 & 1.53 & 2.75 \\  & 6 & \(-\)0.53 & 1.34 & 0.81 \\  & 7 & 0.50 & \(-\)0.80 & \(-\)0.70 \\  & \(p\)-value & 0.679 & 0.829 & 0.631 \\ \hline \multirow{5}{*}{PEKK} & 1 & 3.17 & 0.25 & 3.42 \\  & 2 & 2.18 & 0.97 & 3.15 \\  & 3 & 2.04 & 0.19 & 2.22 \\ \cline{1-1}  & 4 & 3.77 & 0.65 & 4.41 \\ \cline{1-1}  & 5 & 2.36 & 0.84 & 3.20 \\ \cline{1-1}  & 6 & 4.46 & \(-\)0.80 & 3.66 \\ \cline{1-1}  & 7 & 5.11 & 0.12 & 5.23 \\ \cline{1-1}  & \(p\)-value & 0.514 & 0.532 & 0.532 \\ \hline \hline \end{tabular}  
* indicates statistically significant. (\(p\)-value \(<\) 0.05).  
\end{table} 
Table 3: Mean (\(\mu\)m) values of marginal gap change after different aging conditions according to positions.
## 2 Research Methods and Data Sources
### Research Methods
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Hard**} & \multirow{2}{*}{**Land Use Type**} & **Cultivated** & \multirow{2}{*}{**Woodland**} & \multirow{2}{*}{**Grassland**} & **Water** & \multirow{2}{*}{**Wetland**} & **Construction** & **Unused** \\  & & & **Land** & & & **Body** & & **Land** & **Land** \\ \hline \multirow{4}{*}{\begin{tabular}{c} Provisioning \\ services \\ \end{tabular} } & Food production & 1.105 & 0.474 & 0.3 & 0.8 & 0.51 & 0 & 0.005 \\  & Raw material production & 0.245 & 0.482 & 0.445 & 0.23 & 0.5 & 0 & 0.015 \\  & Water resources supply & \(-\)1.305 & \(-\)0.286 & 0.245 & 8.29 & 2.99 & 0 & 0.01 \\ \hline \multirow{4}{*}{\begin{tabular}{c} Regulating \\ services \\ \end{tabular} } & Gas regulation & 0.89 & 1.748 & 1.555 & 0.77 & 1.9 & 0 & 0.065 \\  & Climate regulation & 0.465 & 4.68 & 4.115 & 2.29 & 3.6 & 0 & 0.05 \\  & Environmental purification & 0.135 & 1.372 & 1.36 & 5.55 & 3.6 & 0 & 0.205 \\  & Hydrological regulation & 1.495 & 3.532 & 3.015 & 102.24 & 24.23 & 0 & 0.12 \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Supporting \\ services \\ \end{tabular} } & Soil conservation & 0.52 & 1.86 & 1.895 & 0.93 & 2.32 & 0 & 0.075 \\  & Nutrient cycling maintenance & 0.155 & 0.18 & 0.145 & 0.07 & 0.18 & 0 & 0.005 \\  & Biodiversity maintenance & 0.17 & 1.734 & 1.725 & 2.55 & 7.87 & 0 & 0.07 \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Cultural \\ services \\ \end{tabular} } & \multirow{2}{*}{Aesthetic landscape provision} & \multirow{2}{*}{0.075} & \multirow{2}{*}{0.76} & \multirow{2}{*}{0.76} & \multirow{2}{*}{1.89} & \multirow{2}{*}{4.73} & \multirow{2}{*}{0} & \multirow{2}{*}{0.03} \\  & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Equivalent factors for ESVs per unit area.
# Blind Detection of Broadband Signal Based on Weighted Bi-Directional Feature Pyramid Network
## 2 Methods  
In this paper, blind detection of broadband communication signals is formulated as a deep learning-based target detection task, aiming to obtain the moment of occurrence, modulation type, and other relevant parameters of each constituent signal in the composite signal. The workflow is shown in Figure 1. Next, in this paper, our signal model and algorithm flow are presented in parts according to the indications of the dashed boxes in the flow block diagram.  
The signal detection framework is shown in Figure 2, where a single receiver receives communication signals from multiple non-cooperative transmitters. Next, the received signal sequences are integrated into a matrix with a time-domain superposition between individual signal sequences. Then, STFT is applied to the integrated signal matrix to extract its time-frequency features, and a series of images that can be judged by vision is obtained through normalization. After that, the series of images are made into data sets and input into the target detection algorithm (DL method) proposed in this paper for training. Finally, a model that can detect relevant parameters can be obtained.  
Figure 1: Deep learning method workflow block diagram.  
Figure 2: The paper deep learning signal detection framework.
## 3 Design of the Management System for an Influencer Marketing Campaign
### The Functions of the ADVO System
#### 3.2.1 Data Crawling  
There are three separate databases used to organize the collected data: the raw crawled database, command and mapping database, and processed database. The structures of the crawling data system are shown in Figure 3.  
Figure 2: The architecture of the ADVO system.  
The crawling system consists of four main components:  
* _Command receiver_: the system's input is implemented as a set of external system APIs to request the crawling and when to start and end the crawling commands. A \"command and mapping\" database is used to save the commands for creating messages that will be sent to the message queue to process.
* _Message queue_: it receives and sends requests to different parts of the system. It ensures stability by using the style of asynchronous communication. This approach warrants the steadiness and performance of the system. Some popular message brokers are used, such as RabbitMQ [29], ZeroMQ [30], or streaming platforms--Kafka [31].
* _Crawling worker_: the core of the system, responsible for collecting the requested data. While the data collecting is sent to crawling workers by the message queue, the workers know how to obtain the needed data and which information would be saved to the \"raw\" database. Due to the wide variety of data sources, the system has to deal with, there are two sets of crawling workers: API requesting and user emulating. Besides, simple matching steps are used to restrict the amount of collected data. Workers will notify the \"data processor\" through the message queue if the desired data is saved.
* _Data processor_: this component pre-processes to normalize the crawled data. Those are preparation steps to make the raw data ready for external systems. This processor will remove some redundant components in raw data, such as HTML tags, and add more missing information by gathering the information from other databases. From that, the crawled data will be structured and stored in a database entirely. The results of the data processor will be saved to the processed database. At this stage, the data is considered clean and ready to be processed by external systems. External systems can consume the processed data through APIs provided by a sub-component of the crawling system or read the database directly.
## 7 Summary and Conclusions  
The blast performance of a plate-reinforced moment connection with a large square CFT column was investigated numerically using LS-DYNA. In previous research, there has been no precedent of applying plate reinforcement and a large, square CFT for beam-column connections. Blast loadings were determined using five IEDs. This blast analysis aimed to provide insight into the structural performance of moment-resisting connections using a large CFT column and reinforcing plate for blast loading. The key findings and recommendations are as follows:  
1. The numerical model of the proposed plate-reinforced large CFT connection in LS-DYNA was validated using past experimental data and verified in comparison to finite element analysis using  
Figure 16: Comparison of end rotation between two different connections. (**a**) Beam; (**b**) column.  
Figure 17: Deflection ratio of through-diaphragm to plate-reinforced connection.  
ABAQUS. Furthermore, the blast loadings generated in LS-DYNA were verified using the design charts of UFC 3-340-02.  
2. For cargo van (\(W\) = 1814 kg) and delivery truck (\(W\) = 4536 kg) explosions, the plate-reinforced connection resulted in better blast resistance than the through-diaphragm connection. Both connections for compact sedan (\(W\) = 227 kg) and sedan (\(W\) = 454 kg) explosions led to similar deflections, as did for the largest explosion (\(W\) = 11,000 kg).  
3. For a typical moment-resisting frame with an 8 m beam and 8 m column, the beam can fail for the cargo van (\(W\) = 1814 kg) and greater explosions and the column can collapse for the delivery truck or greater explosion (\(W\) = 4536 kg) for both the through-diaphragm and plate-reinforced connections.  
4. Shear failure modes for large blast loads were observed at the beam on the column face. The plate reinforcement was more efficient because there was no shear reinforcement in the beam of the through-diaphragm connection.  
5. The amounts of explosive were considered sufficient for evaluation of practical design of structures, but computational fluid dynamics analysis would be required if very near-field detonations were to be considered because of the limitation of LBE for very small-scaled distances. In addition, further study needs to be performed for various blast events, namely, varying charge weights, standoff distance, member sizes, story heights, beam lengths, and connection details.  
The authors contribute as follows: Conceptualization, J.S. and J.K.; Data Curation, J.K.; Formal Analysis, J.S. and S.-H.H.; Funding Acquisition, J.S.; Investigation, J.S. and J.K.; Methodology, J.K.; Project administration, J.K.; Resources, S.-H.H.; Software, J.S.; Validation, J.S. and J.K.; Visualization, J.S. and S.-H.H.; Writing--Original Draft, J.S.; Writing--Review & Editing. J.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean Government (MSIT) (No. NRF-2018R1C1B6006275).  
The authors declare that they have no conflict of interest.