esa-sceva/satcom-chunk-collection huggingface dataset:
# Reliability Analysis of CFRP-Packaged FBG Sensors Using FMEA and FTA Techniques
## 3 Reliability Analysis of CFRP-Packaged FBG Sensors Using the FMEA Technique
### Results and Discussion  
Under the coupling influence of harsh working conditions and long-periodic alternating load, the CFRP-packaged FBG sensors showed a variety of failure modes. By evaluating the severity (S), occurrence (O), and detection (D), the RPNs of the 31 failure modes can be obtained (Table 3). In order to provide more information for operation and maintenance of the CFRP-packaged FBG sensors, the relative importance of the three indices is compared by computing their proportionalities in the RPN of each failure mode (Figure 8). From Figure 8, we can infer that most failure modes of the CFRP-packaged FBG sensor happen slightly or occasionally. Once the CFRP-packaged FBG sensor is in fault, the measurement accuracy cannot be ensured. Fortunately, most failure modes are easily detected; thus, periodical inspection and maintenance are essential.  
Furthermore, critical failure modes of each subsystem are analyzed by computing the RPN share (Figure 9). For FBG sensors, most failure modes are related to specific installation operations, such as failure cause #1-#7; thus, a standardized operation process is necessary. Significantly, the failure mode FBG-FM7 has the highest RPN share (10.20%), for it is directly related to measurement accuracy and is comprehensively affected by the packaging layer, bonding layer, and others; thus, periodical calibration is essential. It is noteworthy that the unusable FBG sensors (caused by #1, #4, #8, #9, #28, and #29) do not have a higher RPN share, and although unusable FBG sensors will cause a short data loss, it is easy for the failed FBG sensors to be immediately detected and replaced. The most concerning problem in the CFRP-packaged FBG sensors is the falling measurement accuracy caused by system performance degradation, especially the degradation of PL and BL. For PL, most failure modes happen in the later stage of WTB fatigue testing, and they are mainly caused by complex force conditions (see failure cause #11, #12, #14, #15, and #17-#19). The failure mode PL-FM8 (with a total of 7.65% RPN shares) should be taken note of, for it is difficult to observe without the help of instruments. For BL, most failure modes are related to the bonding process (see failure cause #21-#24), which are manually operated. The adhesive types, the size of the bonding layer, the stress conditions during the fatigue test, etc., will affect the bonding performance. Though the influence of the bonding process on the measurement accuracy of FBG sensors has been studied previously [8,10], a unified pasting process has not been formed at present. For CC, CC-FM17 (with a total of 9.56% RPN shares) is the main failure mode. It reduces the transmission distance and immediately affects the allowable number of FBG sensors on each channel. For FBG-D, its reliability is relatively high compared with other subsystems, as the FBG demodulator is a fairly mature product and it is placed indoors.  
Figure 8: The RPN of failure modes of CFRP-packaged FBG sensors.
# Low-Cost Rapid Fabrication of Conformal Liquid-Metal Patterns
## 3 Results  
### Characteristics of Sprayed Galinstan Films  
#### 3.1.1 Thickness of LM Films  
The thickness of the sprayed LM can be controlled by varying the spraying time and the spraying pressure. The spraying time was varied from 1 s to 5 s, and the spraying pressure was varied from 100 kPa to 500 kPa. The nozzle of the air gun was 5 cm or 10 cm from the target substrate (Figure 2).  
As expected, the thickness of the LM film increases as the spraying time increases. The thickness of the LM film decreases as the distance between the nozzle and the substrate increases. The droplets of the sprayed LM disperse as they exit the nozzle of the sprayer, so the sprayed LM covers a larger area when it is sprayed from a greater distance to the substrate. The volume of the sprayed LM was kept constant, so it is expected that the thickness of LM film decreases as the distance increases.  
Interestingly, the thickness of the LM films have irregular results when the distance between the nozzle and the substrate is only 3 cm (Figure 3). It is hypothesized that this is due to the effects of the air flow on the LM. During LM deposition, LM droplets and high-pressure air strike the substrate at the same time. Unlike sprayed conductive inks, LM does not contain solvents. Although the LM is covered with a very thin oxide layer, the oxide is fragile enough to break when it is struck by other LM droplets or high-pressure air flow. If the nozzle is close to the substrate, the air can cause lateral flow of LM away from the nozzle, decreasing the LM film thickness.  
Figure 3: Characterization of sprayed LM line thickness when sprayed onto a substrate 3 cm, 5 cm, and 10 cm away from the nozzle for 1 s, 3 s, and 5 s. The spraying pressure is varied from (**a**) 100 kPa, (**b**) 150 kPa, and (**c**) 200 kPa.  
Figure 2: Characterization of sprayed LM thickness under various spraying pressures: LM thickness when it is sprayed from a nozzle (**a**) 5 cm and (**b**) 10 cm away from the substrate.
# A Method of Accuracy Increment Using Segmented Regression
## 4 Step-by-Step Procedure for Accuracy Increment during Segmented Regression Usage
\begin{array}{l}\frac{\partial}{\partial a_{0}}\sum \limits_{i_{1}=1}^{w}\ldots\sum\limits_{i_{q}=1}^{w}\Bigg{(}\sigma\big{(}x_{ \text{br 1 \ }i_{1}},\ x_{\text{br 2 \ }i_{2}},\ \ldots,\ x_{\text{br \ \ }q}\big{)}-\Bigg{(}\alpha_{0}+\sum_{j=1}^{q}\alpha_{j}x_{\text{br \ \ }j_{i}}^{2}+\sum_{j=1}^{q}\beta_{j}x_{\text{br \ \ }j_{i}}\Bigg{)}\Bigg{)}^{2}=0,\\ \frac{\partial}{\partial a_{1}}\sum\limits_{i_{1}=1}^{w}\ldots\sum\limits_{i_{ q}=1}^{w}\Bigg{(}\sigma\Big{(}x_{\text{br 1 \ }i_{1}},\ x_{\text{br 2 \ }i_{2}},\ \ldots,\ x_{\text{br \ \ }q}\big{)}-\Bigg{(}\alpha_{0}+\sum_{j=1}^{q}\alpha_{j}x_{\text{br \ \ }j_{i}}^{2}+\sum_{j=1}^{q}\beta_{j}x_{\text{br \ }j_{i}}\Bigg{)}\Bigg{)}^{2}=0,\\ \frac{\partial}{\partial\beta_{1}}\sum\limits_{i_{1}=1}^{w}\ldots\sum\limits_{i _{q}=1}^{w}\Bigg{(}\sigma\Big{(}x_{\text{br 1 \ }i_{1}},\ x_{\text{br 2 \ }i_{2}},\ \ldots,\ x_{\text{br \ \ }q}\big{)}-\Bigg{(}\alpha_{0}+\sum_{j=1}^{q}\alpha_{j}x_{\text{br \ \ }j_{i}}^{2}+\sum_{j=1}^{q}\beta_{j}x_{\text{br \ \ }j_{i}}\Bigg{)}\Bigg{)}^{2}=0,\\ \frac{\partial}{\partial a_{q}}\sum\limits_{i_{1}=1}^{w}\ldots\sum\limits_{i _{q}=1}^{w}\Bigg{(}\sigma\Big{(}x_{\text{br 1 \ }i_{1}},\ x_{\text{br 2 \ }i_{2}},\ \ldots,\ x_{\text{br \ \ }q}\big{)}-\Bigg{(}\alpha_{0}+\sum_{j=1}^{q}\alpha_{j}x_{\text{br \ \ }j_{i}},\ \mu_{j}^{2}+\sum_{j=1}^{q}\beta_{j}x_{\text{br \ \ }j_{i}}\Bigg{)}\Bigg{)}^{2}=0,\\ \frac{\partial}{\partial\beta_{q}}\sum\limits_{i_{1}=1}^{w}\ldots\sum\limits_{i _{q}=1}^{w}\Bigg{(}\sigma\Big{(}x_{\text{br 1 \ }i_{1}},\ x_{\text{br 2 \ \ }i_{2}},\ \ldots,\ x_{\text{br \ \ }q}\big{)}-\Bigg{(}\alpha_{0}+\sum_{j=1}^{q}\alpha_{j}x_{\text{br \ \ }j_{i}},\mu_{j}^{2}+\sum_{j=1}^{q}\beta_{j}x_{\text{br \ \ }j_{i}}\Bigg{)}\Bigg{)}^{2}=0.\end{array} \right.\]  
Let us simplify the first equation in the system. After derivative calculation, it can be presented as follows:  
\[-2\sum\limits_{i_{1}=1}^{w}\ldots\sum\limits_{i_{q}=1}^{w}\Bigg{(}\sigma\Big{(}x _{\text{br 1 \ }i_{1}},\ x_{\text{br 2 \ }i_{2}},\ \ldots,\ x_{\text{br \ \ }q}\big{)}-\Bigg{(}\alpha_{0}+\sum_{j=1}^{q}\alpha_{j}x_{\text{br \ \ }j_{i}}^{2}+\sum_{j=1}^{q}\beta_{j}x_{\text{br \ \ }j_{i}}\Bigg{)}\Bigg{)}=0\]  
or  
\[\sum\limits_{i_{1}=1}^{w}\ldots\sum\limits_{i_{q}=1}^{w}\Bigg{(}\alpha_{0}+\sum_{j =1}^{q}\alpha_{j}x_{\text{br \ }j_{i}},\mu_{j}^{2}+\sum_{j=1}^{q}\beta_{j}x_{\text{br \ }j_{i}}\Bigg{)}=\sum\limits_{i_{1}=1}^{w}\ldots\sum\limits_{i_{q}=1}^{w}\sigma \Big{(}x_{\text{br 1 \ }i_{1}},\ x_{\text{br 2 \ }i_{2}},\ \ldots,\ x_{\text{br \ \ }q}\big{)}\]  
Making simplifications in the left side of equation, we can get  
7. The optimal SLR model is calculated for the obtained breakpoint abscissa. The final equation is \[\phi_{1\ \ \mathrm{opt}}(X)=15.256-0.0513X+0.03945(X-197.031)h(X-197.031)\]  
The standard deviation for the optimal SLR model is 0.313. The result of the model building using the SLR model is shown in Figure 1.
# Soft X-ray Microscopy Techniques for Medical and Biological Imaging at TwinMic--[PERSON]
## 2 Materials and Methods
### TwinMic Imaging Modes
#### 2.1.2 Full-Field X-ray Microscopy (TXM)  
TwinMic was originally designed to be able to operate both in STXM and TXM mode with an easy switch between them [14]. Finally, the STXM mode resulted in the most requested one, because of the increasing interest of the scientific community to the newly developed LEXRF. Indeed, scanning instruments are well suited to combine imaging and spectroscopy measurements for a given fixed X-ray energy. On the other hand, the TXM mode offers a faster acquisition time, of the order of seconds, and a higher spatial resolution. For this reason, it is usually well suited for monitoring dynamic changes in the sample or for the tomography experiment, where multiple images are taken sequentially.  
The TwinMic TXM mode is summarised in Figure 3. A condenser ZP (CPZ) optics illuminates an area of the sample, which is magnified through a high-resolution ZP on an imaging detector. The CZP used at TwinMic is 1 mm in diameter and consists of 256 grating elements in 5 rings. The grating constant is 218 nm for the innermost ring and 126 nm for the outermost ring. Each grating covers an area of 50 \(\times\) 50 \(\upmu\)m\({}^{2}\). CZPs were fabricated by Zoneplates.com. The transmitted X-rays are collected by a back-illuminated Princeton CCD camera with 1340 \(\times\) 1300 pixels, a pixel size of 20 \(\times\) 20 \(\upmu\)m\({}^{2}\), and a 2 s readout time when a full frame is acquired [7; 17].
## 2 Materials and Methods
### Quantification of the Secreted Proteins  
Secreted tartrate-resistant acid phosphatase 5 b (TRAP5b), bone morphogenetic protein 2 (BMP2), cystatin C, interferon-\(\beta\) (IFN-\(\beta\)), interleukin 10 (IL-10), interleukin-17A (IL-17A), interleukin-18 (IL-18), interleukin-4 (IL-4), interleukin-6 (IL-6), interleukin-23 (IL-23), osteopontin (OPN), free active transforming growth factor-\(\beta\) 1 (TGF-\(\beta\)1), and tumor necrosis factor-\(\alpha\) (TNF-\(\alpha\)) were analyzed out of the supernatant of each sample with a customized human BioLegend's LEGENDplex(tm) multiplex assay (Biolegend, San Diego, CA, USA). Analysis was performed following the manufacturer's instructions, and internal standards served to determine the concentration of each protein. The volume of used samples, standards, and kit reagents was adjusted to 15 \(\mu\)L. The samples and standards were incubated with the beads overnight. The multiplex assay was measured with a BD FACSverse(tm) (Becton, Dickinson and Company, Franklin Lakes, NJ, USA).  
To identify the initial concentration of the cytokines secreted by electrically stimulated osteoblasts, the supernatant used for stimulation was diluted 1:1 with RPMI medium, as explained in 3.3. The multiplex assay was measured directly with a BD FACSverse(tm). Further, the diluted supernatant was incubated under standard cell culture conditions (5% CO\({}_{2}\) and 37 \({}^{\circ}\)C) for 48 h and was then handled as the PBMC and pre-osteoclast samples. This measurement served as an internal control to validate the number of proteins that would be degraded during the 48 h incubation. The internal control was named \"control incubated without cells\" (control\({}_{\text{invc}}\)).  
The total protein content of each supernatant was determined to normalize the protein concentration measured with the LEGENDplex(tm) multiplex assay. Therefore, the supernatant was analyzed with the Invitrogen Qubit Protein Assay Kit and the Qubit fluorometer Q3285  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Gene** & **Abbreviation** & **Sequence** \\ \hline Caspase 8 & CASP8 & For: TGTTTCACAGGTTCCTCCTT \\  & & Rev: GAGAATATAATCCATCCATCC \\ Receptor Activator of NF\(\kappa\)B & RANK & For: AGAAAACCACCCAAAATGAAATCC \\  & & Rev: GCCAGAGCCTCATTGATTC \\ Tartrate Resistant Acid phosphatase 5 & ACP5 & For: GGGGAGATGGTGAGCCAGTG \\  & & Rev: GTCCACATGTCCTCACAGG \\ Carbonic anhydrase 2 & CA2 & For: TGGGGTCACTTGATGACACACAGCTGGA \\ Cathepsin K & CTSK & Rev: GGGGCCACTCTCCTCCTCATC \\ Matrix Metallopeptidase 9 & MMP9 & For: ACCAGCAACGTCTCAC \\ Hypoxanthine-Guanine & & Rev: AACCAGTTGAGACCACGAC \\ Phosphoribosyl & HPRT & For: CCCTGGCGTGTGTATTAGTG \\ Transferase & & Rev: TCGAAGACAGACGTTCAGTC \\ \hline \hline \end{tabular}
\end{table}
Table 1: Primer sequences for the genes of interest.  
(both: Thermo Fisher Scientific, Waltham, MA, USA) following manufacturer instructions. The quantification of the total protein content was ensured through included standards.
# Speech Intelligibility Analysis and Approximation to Room Parameters through the Internet of Things
## 4 Conclusions  
In this work has been designed, prototyped and deployed a IoT system for SII measurement, from the ANSI standard for acoustic assessment of rooms. At present, and due to the necessary social distance that has been implemented in schools, it is of particular interest to have a system such as the one designed to assess speech intelligibility in different spaces. It is true that there are systems on the market that offer similar analyses, at a very high price compared to the presented system, and with a technological complication also excessive for a first and quick analysis.  
The complexity of the system has been reduced to a minimum so that the user does not have to worry about the technological part and only focuses on the positions he wants to analyze in each room. Likewise, to facilitate this task the nodes of our system are totally autonomous in terms of energy and communicate wirelessly, so it facilitates its deployment and location in any area. The designed system implements a data visualization in a clear, clean and fast way, since in a matter of seconds it allows graphical depiction of the measured results of SII allowing their evaluation by the user.  
In addition, the system can store the desired data in a cloud server and be able to retrieve and display it at any time. Thanks to this, different speech intelligibility improvement techniques can be tested, such as those based on loudspeakers hardware systems and those based on signal processing software, and the results obtained can be measured and compared easily and quickly. All this allows analysis and improvement of speech intelligibility not only in teaching or lecture rooms, but also in any type of enclosure where speech transmission is critical, such as operating heaters, airport control rooms or hospital waiting rooms.  
Conceptualization, J.L.-B. and J.S.-G.; methodology, M.C.; software, J.L.-B.; validation, J.M.A.C.; formal analysis, M.C.; resources, S.F.-C. and M.G.-P.; writing--original draft preparation, J.L.-B. and S.F.-C.; writing--review and editing, J.M.A.C. and M.G.-P.; funding acquisition, J.S.-G. All authors have read and agreed to the published version of the manuscript.  
This research has been partially funded by the Ministry of Innovation and Economy within the project with reference BIA2016-76957-C3-1-R (co-financed with FEDER funds) and the grant with reference BES-2017-082340, and by the Generalitat Valenciana, with grants BEST/2020/117 and AEST/2020/048.  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Intelligent Handover Decision-Making for Vehicle-to-Everything (V2X) 5G Networks
## 4 Performance Evaluation
### Simulation Settings  
The effectiveness of the mechanism is measured by considering different network performance metrics. In addition to handover Key Performance Indicators (KPIs), measurements of packet loss, delay, and energy consumption are considered to better capture all facets of network performance and provide a comprehensive picture of how the network behaves in various scenarios. The simulation was coded in Python version 3.10.13, leveraging libraries such as Numpy, Matplotlib, OpenPyXL, and SQlite3. The experiment was conducted within a network environment with dimensions of \(100\times 100\) km\({}^{2}\). The network grid is divided into 25 network sectors. The simulation was tested using an Intel (R) Xeon (R) with 3.10 GHz, 4 Cores, 8 Logical Processors, and 64 GB of memory. The simulations were run for 1200 iterations. The simulation parameters are listed in Table 4.
## 2 Materials and Methods
### Methods  
This study utilizes the Google Earth Engine (GEE) platform to obtain Sentinel-2 imagery and digital elevation model (DEM) data, thereby generating a dataset of samples and labels through the intersection of processed product data. Initially, features were extracted from the Sentinel-2 images and DEM data, including spectral bands, vegetation index, and terrain factors. Subsequently, random forest was applied to evaluate feature importance for reducing data dimensionality and selecting high-quality features for classification. Following this, multi-level spatial features and deep semantic information were extracted to improve classification performance, incorporating texture and morphological features. During this process, a pre-constructed dataset of samples and labels was utilized to extract and integrate deep semantic features using a 3D convolutional neural network (3 DCNN), resulting in high-quality features with strong discriminative power for subsequent land  
Figure 2: Sample distribution. (**a**) Sample distribution in the East experimental area; (**b**) sample distribution in the South experimental area.  
cover classification. Finally, a classification model was designed to accomplish the ultimate land cover classification, utilizing RF as the base classifier and employing the AdaBoost strategy in ensemble learning. The technical workflow of this study is depicted in Figure 3.
# Spatio-Temporal Behavior Detection in Field Manual Labor Based on Improved SlowFast Architecture
## 4 Experiments and Results
### Scenario Test  
We conducted tests on realistic scenes of agricultural labor using the trained CA-ACTION-SlowFast model. Figures 16-20 illustrate selected video frames after behavior detection with CA-ACTION-SlowFast in different scenes. In Figure 16, rice seedling transplantation in a rice field is depicted, while Figure 17 captures watering on land reclaimed for crop cultivation in a farm yard. Figure 18 showcases hoeing in a weedy field, Figure 19 portrays weed removal from the edge of the field, and Figure 20 demonstrates pesticide spraying in a vegetable field. The model consistently maintains high recognition accuracy across all scenarios, achieving remarkable performance, particularly in actions such as rice planting and hoeing where the target is not occluded. Even in backgrounds with an abundance of green plants, the model exhibits high recognition accuracy for weeding behavior, distinguishing it from pesticide spraying actions. This suggests that the model emphasizes human behavioral characteristics during feature extraction. The enhanced CA-ACTION-SlowFast model, augmented with the CA and ACTION modules, demonstrates robust adaptability across diverse scenarios. These improvements are anticipated to yield superior results in various practical applications.
# Optimizing Support Locations in the Roof-Column Structural System
## 6 Conclusions  
This study presented a new optimization method to design column locations under a flat roof. The new method was developed on the basis of the optimality-criterion (OC) algorithm, which allowed for all columns' relative density to be simultaneously updated throughout optimization. The optimality condition was set on the basis of the columns' axial stress, as columns were hinged and only loaded in the axial direction. Three sub-strategies were developed to improve optimizer performance. First, adjusting locations of candidate columns gives different loading areas, where columns with small and large loading areas tend to be removed and preserved, respectively. Second, enforcing constant relative stiffness between roof and columns ensures that the algorithm solves the equivalent structural system during optimization. Third, employing a filter can result in a grid-independent solution without losing solution accuracy. The key finding of this study is that optimizing column locations significantly reduces structure compliance.  
The new method was validated using a variety of roof-column structural models ranging from simple to complex roof shapes: square, eye-shaped, and bean-shaped roofs. The optimization algorithm was highly capable of finding efficient column layout distributions from a large number of potential combinations. Manual column location analysis was carried out to confirm that the optimizer had successfully obtained optimal designs. Lastly,  
Figure 9: Design of pavilion with bean-shaped roof. (**a**) Roof–column structural system. (**b**) Installation of curved panels. (**c**) Intermediate optimization results.  
this paper demonstrated the practical application of the proposed method in a pavilion design.  
Conceptualization, Y.M.X.; methodology, Y.M.X., X.H., and X.M.; software, X.M. and Y.X.; validation, Y.M.X. and X.H.; formal analysis, T.-U.L.; investigation, X.M.; data curation, X.M. and X.H.; writing--original-draft preparation, X.M. and T.-U.L.; writing--review and editing, T.-U.L. and Y.M.X.; visualization, X.M.; supervision, Y.M.X.; project administration, Y.M.X.; funding acquisition, X.M. and Y.M.X. All authors have read and agreed to the published version of the manuscript.  
This project was funded by the Australian Research Council (FL190100014), the China Scholarship Council (201906195003), and the National Natural Science Foundation of China (51778283).  
Not applicable.  
The results of the optimized designs and the basic code of this work are available from the corresponding author on reasonable request.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.