esa-sceva/satcom-chunk-collection huggingface dataset:
# Typhoon Loss Assessment in Rural Housing in Ningbo Based on Township-Level Resolution
## 5 Conclusions  
This paper proposed a new approach to achieve quantitative and accurate typhoon loss assessment of disaster-bearing bodies at township-level high-resolution. The rural housings in Ningbo area were taken as the target disaster-bearing bodies and a typhoon loss assessment model in rural housing based on township-level resolution was established by utilizing the RBF neural network with the help of public catastrophe insurance data. The specific findings are as follows:  
1. The RBF neural network could effectively establish a typhoon loss assessment model from the causal factors to the losses of the disaster-bearing bodies, and the RBF neural network converged faster and had a smaller overall prediction error compared to the commonly used BP neural network. 2. Overall, the insured loss rate of rural housing due to typhoons showed a positive correlation with the typhoon wind speed affecting Ningbo area. Under the impact of typhoon disaster, the insured loss rate of rural housing was higher in the townships of southern Ningbo than in the townships of northern Ningbo. The townships with larger insured loss rates were concentrated in mountainous and coastal areas that are prone to secondary disasters under the attack of the typhoon's peripheral spiral wind and rain belt. It should be noted that although the typhoon loss assessment model for rural houses proposed in this paper was well applied in most townships in Ningbo, there were still a few townships where the predicted value of insured loss rate decreased with the increase in design wind speed, and even the situation of 0 insured loss rate exists. In order to further improve the model prediction accuracy, subsequent improvements can be made in terms of increasing the model input layer variables and improving the completeness of insurance claim records. For example, the model can be improved by adding typhoon rainfall, typhoon central pressure, distance from typhoon landfall to target site, and other disaster-causing factors as model inputs. In addition, the insurance loss estimator should clearly distinguish typhoon disaster-causing factors and secondary disasters such as strong wind, heavy rain, landslide, flood, mudslide, etc. when making notes on the cause of the subject matter. Since the RBF neural network-based typhoon loss assessment model for disaster-bearing bodies proposed in this paper was purely data-driven, future research work will build on this research to analyze the structural vulnerability and damage mechanisms of disaster-bearing bodies under extreme typhoon loads by establishing physical finite element models for disaster-bearing bodies, and then establish a link with economic losses to finally realize a physically-driven quantitative typhoon loss assessment model for disaster-bearing bodies. Conceptualization, Q.L. ; Writing--original draft preparation, H.J. ; Writing--review and editing, Q.L. ; Supervision, J.Z., J.M., W.F., M.H., and B.Z. All authors have read and agreed to the published version of the manuscript.
## 5 Conclusions  
This study presents the design and validation of a lightweight periodic scheduler for microcontroller-based wearable sensor devices, aimed at enabling reliable, low-latency communication for real-time biomechanical feedback (RTBF) applications. The proposed system supports sensor sampling frequencies up to 650 Hz when transmitting one sample per packet and up to 1750 Hz when transmitting 26 samples per packet. These two values represent the extremes of system performance, while the scheduler is capable of accommodating a wide range of real-world application needs by balancing sampling frequency and data packet size.  
The main contribution of this work is the demonstration that a simplified, cooperative scheduling approach can achieve real-time performance on resource-constrained devices, offering a practical and low-cost solution for RTBF systems in sports and physical rehabilitation. By minimizing computational overhead and memory usage, our scheduler allows the use of energy-efficient, low-cost hardware--making the design suitable for both prototyping and potential industrial-scale implementation.  
We acknowledge that the limited sample size--five measurements on four hand-assembled systems--reduces the statistical power of our results. For instance, a 100% success rate in 20 trials corresponds to a Clopper-Pearson 95% confidence interval lower bound of approximately 0.83, which indicates encouraging, though not statistically conclusive, reliability. Nevertheless, the results offer valuable performance benchmarks and conservative estimates that practitioners can use when developing or evaluating similar systems.  
Future work will focus on extending the analysis to include receiver-side performance, such as delay and jitter under varying environmental conditions, including Wi-Fi quality and frequency spectrum occupancy. We also plan to explore adaptive scheduling strategiesand further optimize packet transmission protocols, particularly for more demanding multi-sensor or multi-user scenarios.  
All authors contributed in conceptualization, methodology, investigation, and reviewing. Writing, A.K. and I.F.; software, A.B., M.H. and J.P.; Editing, A.U. and S.T.; visualization, A.K., M.H. and I.F.; funding acquisition, S.T. All authors have read and agreed to the published version of the manuscript.  
This work is sponsored in part by the Slovenian Research and Innovation Agency within the research program ICT4 QoL-Information and Communications Technologies for Quality of Life (research core funding no. P2-0246).  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
This article is a revised and expanded version of a conference paper [21].  
The authors declare no conflicts of interest.
# A Structure for Accurately Determining the Mass and Center of Gravity of Rigid Bodies
## 2 Background and Related Work  
### Method for Obtaining the Mass and CG of a Body  
As shown in Figure 1, the multi-point weighing method is a static method, which includes three load cells and a support platform. These three load cells are placed on the same horizontal flat and angularly spaced by 120\({}^{\circ}\). This proposed method depends only on gravity force acting through the CG [1,4].  
We record the readings of the three load cells, and the sum of the three load cell readings is the mass of the whole system. Similarly, we also record the mass of the system without the rigid body, and the mass of the rigid body is obtained from the difference of the readings shown in Equation (1).  
\[M=\Delta m_{1}+\Delta m_{2}+\Delta m_{3} \tag{1}\]  
where: \(M\) is the mass of the rigid body, and \(\Delta m_{1}\), \(\Delta m_{2}\), \(\Delta m_{3}\) are the difference readings of the three load cells.  
Based on the static equilibrium (first law), the CG location can be obtained by Equation (2).  
\[\left\{\begin{array}{l}x=\frac{x_{1}\cdot\Delta m_{1}+x_{2}\cdot\Delta m_{2} +x_{3}\cdot\Delta m_{3}}{x}\\ y=\frac{y_{1}\cdot\Delta m_{1}+y_{2}\cdot\Delta m_{2}+y_{3}\cdot\Delta m_{3}}{M} \end{array}\right. \tag{2}\]  
According to Equation (1) and Equation (2), there are two factors affecting the accuracy of the results.  
(a) Accuracy of the load cell.  
The load cell is sensitive to side forces, bending and torsional moments, which should be avoided in the measurement systems [11; 12; 13; 14; 15; 16]. Figure 2 shows some examples of correct loading as well as incorrect loading methods on a load cell [13; 14; 15].  
Figure 1: Diagram of the multi-point weighing method.  
(b) Location of the reaction forces (\(F_{1},F_{2}\) and \(F_{3}\)) of the load cells.  
The load cell always has a designed axis of loading. If the location of the reaction force is out of this designed axis, it will cause errors of the load cells' readings, and errors of the CG results can be calculated according to Equation (2).  
To avoid these issues, some efforts are done by designing the structure act on the load cell properly.
## 2 Materials and Methods
### Quantitative Evaluation of the Acquired Image
#### 2.4.2 Similarity Evaluation  
The root mean square error (RMSE) and peak signal to noise ratio (PSNR) were measured to quantitatively evaluate the similarity between an image segmented from a standard image (referred to as the original image) and a filtered segmented image after adding noise (referred to as the comparison image). RMSEs measure the difference between the original and comparison image, and a smaller value indicates greater similarity between the two. RMSEs quantify the average difference between the pixel values of two images. PSNRs quantify how closely the signal of the comparison image approximates the signal distortion. A high PSNR indicates that the comparison image is closer to the original image in terms of signal fidelity. The formula for calculating RMSE and PSNR is as follows [22]:  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{N}{(f_{i}-g_{i})^{2}}}{N}} \tag{6}\]  
\[PSNR=10\text{log}_{10}\Bigg{(}\frac{S_{Peak}^{2}}{RMSE^{2}}\Bigg{)} \tag{7}\]
# A New Indicator to Assess Public Perception of Air Pollution Based on Complaint Data
## 3 Results  
### Descriptive Statistical Analysis  
This paper's application examples consist of 11,699 air pollution public complaints data in Shandong Province from 2012 to 2017. As of December 2017, this comprises 17 cities in Shandong Province, including Jinan, Qingdao, Zibo, Zaozhuang, Dongying, Yantai, Weifang, Jining, Taian, Weihai, Rizhao, Binzhou, Dezhou, Liaocheng, Linyi, Heze, and Laiwu.  
Table 4 lists statistical results to understand the data characteristics of public complaints about air pollution in Shandong Province from 2012 to 2017. In Table 4, the column \"Counts\" represents the total number of complaints, columns \"Mean\" and \"SD\" represent the average and standard deviation of the number of complaints or the Emotional intensity of complaints, respectively. The column \"Annual changes\" of the number of complaints indicates the total number of complaints changes each year. The column \"Annual changes\" of the emotional intensity of complaints shows the average complaint intensity changes every year.  
Table 4 shows that the air pollution complaints were identified with a mean of 1949.83 (SD = 877.74) per year in Shandong Province. From 2012 to 2017, both the number of air pollution complaint data and the public's complaint emotional intensity in Shandong Province showed a trend of first increasing and then decreasing. However, the annual changes in the number of complaints and the intensity of air pollution complaints were notcompletely consistent. For example, when the number of complaints is small, the intensity of complaint emotion is not necessarily weakened. When the number of complaints is large, the intensity of complaint emotion is not necessarily strong.  
Therefore, from the perspective of statistical analysis, it is not enough to consider the number of public complaints about air pollution or the intensity of complaint emotion alone.
## 3 Materials and Methods
### Assay of ANAC074 Binding to the Two Novel Motifs and the MybSt1 Motif  
The two novel motifs were designed as NRS1 and NRS2 (NRS: NAC Recognized Sequence). Three tandem copies of the NRS1 sequence \"TAGTT\", NRS2 \"GAATC\" and the MybSt1 motif sequence \"GGATA\" together with their respective mutants \"GCGTT\", \"TAGGG\", \"GGGGG\" (mutants of NRS1, named NRS1-M1, NRS1-M2 and NRS1-M3, respectively); \"TCATC\", \"GACGG\", \"TCCGA\" (mutants of NRS2, named NRS2-M1, NRS2-M2 and NRS2-M3, respectively); \"TIATA\", \"GTCGA\" and \"TTCGC\" (mutants of MybSt1, named MYB-M1, MYB-M2 and MYB-M3, respectively) were cloned into pHIS2 vector (see Supplemental Table S1 for primers used), respectively. Y1H screening analysis was performed to study their interactions with ANAC074.  
To further verify the Y1H result, the DNA-protein interactions were further performed in tobacco leaves. For reporter plasmids construction, three tandem copies of the core sequence of the NRS1 \"TAGTT\" and its complete mutant \"GGGGG\", NRS2 \"GAATC\" and its complete mutant \"TCCGA\", the MybSt1 motif \"GGATA\" and its complete mutant \"TTCGC\" were respectively fused to the minimal 35S promoter (\(-46\) to \(+1\)) to drive the _GUS_ reporter gene (designed as pCAM-NRS1, pCAM-NRS1-M3, pCAM-NRS2, pCAM-NRS2-M3, pCAM-MYB, pCAM-MYB-M3, respectively) (see Supplemental Table S1 for primers used). The effector was constructed by cloning the ORF of ANAC074 into pROKII under 35 SCAMV promoter (named as pROKII-ANAC074).  
Each of these reporters was co-transformed with effector into tobacco leaves using a particle bombardment method (Bio-Rad, Hercules, CA, USA). Firstly, detached leaves from tobacco (_Nicotiana benthamiana_) were tiled on Murashige & Skoog (MS, PhytoTechnology, Lenexa, KS, USA) agar medium. The procedure of transformation followed the manual for the Biolistic PDS-1000/He Particle Delivery System (Bio-Rad). For coating particles with DNA, 50 uL gold powder suspensionwith a diameter of 1 \(\upmu\)m (60 mg/mL), 5 \(\upmu\)L plasmid DNA (1 ug/uL), 50 \(\upmu\)L CaCl\({}_{2}\) (2.5 M), and 20 \(\upmu\)L spermidine (0.1 M) was added and then proceeded as follows. Vortex for 10 min, then let sit for 1 min. Spin down briefly (\(\sim\)10 s) to settle loose gold and abandon supernatant. Wash once with 140 \(\upmu\)L 70% ethyl alcohol, then spin down briefly. Wash once with 140 \(\upmu\)L of 100% ethyl alcohol, then resuspend gold pellet in 48 \(\upmu\)L 100% ethyl alcohol. Vortex well before spreading the coated gold out on each flyer. The transformation conditions were 9 cm target distance, 1100 psi helium pressure, and 2 times for the number of bombardments. Transfer to a MS agar medium after bombardment, incubate at 24 \({}^{\circ}\)C in the dark for 2 days before examining GUS activity. GUS activity levels were determined according to [PERSON] [46]. There were three independent experiments and leaves were taken from 25-30 separate plant individuals per experiment.
# Landslide-Tunnel Interactions and Control Countermeasures under an Orthogonal System
## 3 Numerical Simulation of Tunnels and Landslides
### Displacement Distribution of Surrounding Rock  
The displacement distribution of the surrounding rock after tunnel excavation is shown in Figure 9. After tunnel excavation, the stress is released throughout the range of the landslide, which changes its original three-dimensional stress equilibrium state and further aggravates it. From the perspective of the overall deformation law, the displacement deformation of the tunnel's crown is greater than that of the arch bottom, and the maximum displacements are 32.5 mm and 12.3 mm, respectively. Owing to the influence of tunnel excavation, the landslide mass slides downward along the landslide surface. The disturbance stress from the landslide near the mountainside causes considerable damage to the tunnel, which leads to the large area settlement of the crown on the right side. Figure 9b shows that the maximum horizontal displacement of the surrounding rock is 22.4 mm; the subsidence phenomenon appears at the top of the tunnel near the mountainside, and the displacement deformation is obvious. There is a large displacement above the landslide and the tunnel crown, indicating that tunnel excavation exacerbates the sliding displacement changes in the landslide mass.  
The displacement of the tunnel support structure is primarily characterized by the settlement of the tunnel crown on the near-mountainside. Therefore, the crown, spandrel, and arch bottom are monitored. The monitoring depth is 20 m, and the displacement change is shown in Figure 10. Owing to the influence of the landslide zone above, the crown and the right arch shoulder show large displacement changes, and the change displacement pattern manifests as follows: tunnel crown > tunnel right shoulder > tunnel left shoulder > tunnel arch bottom.  
Figure 10: Displacement changes in the surrounding rock.  
Figure 9: Displacement distribution of surrounding rock. (**a**) Vertical displacement. (**b**) Horizontal displacement (unit: MPa).
# Consumer Perceptions and Attitudes Towards Ultra-Processed Foods
## 5 Data Analysis
### Clustering  
To identify groups of consumers with similar characteristics and determine the variables influencing their attitudes, an unsupervised machine learning approach--cluster analysis--was performed using the k-means algorithm (Figure 2 and Table 3). The optimal number of clusters was determined using the Elbow and Silhouette methods, with both indicating that two clusters provided the best solution. These clusters consisted of 134 and 156 respondents, respectively. As shown in Figure 2, the clusters are well defined when k = 2, demonstrating that the k-means algorithm effectively differentiates consumer groups with comparable attitudes towards UPFs.  
The first cluster (Cluster 1) comprised 134 customers, demonstrating more awareness of the consumption of UPFs. They exhibited higher ratings for their health concerns (Question 9, HC), better knowledge (Question 10, UPF), less social pressure (Question 11, SN), environmental concerns (Question 12, EC), attitudes towards UPFs (Question 13, ATT), buying intention (Question 14, WP), and actual buying behaviour (Question 15, ABB) (Table 3). Among the indicators, knowledge (Question 10, UPF), attitudes (Question 13, ATT), and health consciousness (Question 9, HC) exerted the strongest influence on overall UPF perception. Meanwhile, the second cluster (unconcerned consumers) reflected fewer concerns about social norms (Question 11, SN) and less knowledge about UPFs (Question 10, UPF), with a minimal intention to buy (Question 14, WP) and consume these harmful foods (Question 15, ABB). Table 3 illustrates the average values of the indicators for both clusters, along with the differences between these estimates.  
Figure 2: Consumer clusters created with \(k\)-means (\(k\) = 2) using 19 construct indicators.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **HC1** & **HC2** & **HC3** & **UPFK1** & **UPFK2** & **SN1** & **SN2** \\ \hline Cluster 1 & 4.119 & 4.552 & 3.858 & 4.045 & 4.455 & 2.828 & 3.858 \\ Cluster 2 & 2.628 & 3.660 & 2.840 & 3.256 & 3.654 & 2.160 & 2.449 \\ Difference & 1.491 & 0.892 & 1.018 & 0.788 & 0.801 & 0.668 & 1.409 \\ \hline  & **EC1** & **EC2** & **EC3** & **ATT1** & **ATT2** & **ATT3** & **WP1** \\ \hline Cluster 1 & 4.194 & 3.672 & 3.985 & 4.172 & 4.463 & 4.097 & 3.090 \\ Cluster 2 & 3.385 & 2.577 & 3.173 & 3.128 & 3.571 & 3.128 & 2.128 \\ Difference & 0.809 & 1.095 & 0.812 & 1.043 & 0.892 & 0.969 & 0.961 \\ \hline  & **WP2** & **WP3** & **ABB1** & **ABB2** & **ABB3** & & \\ \hline Cluster 1 & 4.187 & 3.694 & 3.097 & 3.381 & 3.970 & & \\ Cluster 2 & 2.994 & 2.596 & 2.212 & 3.237 & 2.667 & & \\ Difference & 1.193 & 1.098 & 0.885 & 0.143 & 1.303 & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average values by cluster and the absolute differences between clusters shown by construct indicator.
## 3 Petri Net Representation
### Assumptions and Rules  
In this section, we make the following assumptions such that the obtained executable model is easily simulated and dynamic behavior analysis can be done.  
1. In the urban traffic network, when one vehicle is approaching to an intersection, usually, the driver prefers to decelerate the vehicle and ensures that the vehicle safely passes through the intersection or waits in a queue. Hence, we set the nominal travel speed at \(V_{\text{tr}}\) km/h, and the RSU sensor for transit detection is deployed at a distance of \(D_{RSU}\) from the stop bar;
2. In each phase, the traffic lights turn on in the following order: green lights for \(T_{G}\) seconds, yellow lights for \(T_{Y}\) seconds, and red lights for \(T_{R}\) seconds. In order to ensure safety, when a cycle is completed, all-in-red lights turn on for \(T_{AR}\) seconds. Therefore, a complete signal cycle is \(T_{G}+T_{Y}+T_{R}+T_{AR}\) seconds;
3. The event that the RSU sensor detects an approaching transit vehicle is stochastic and uncertain, but the traffic signal controller's response to the event is deterministic. Therefore, to cope with the complexity of the TSP control model, it is crucial to reduce some redundant states while a full state space is constructed and analyzed.  
Different RSU sensors may detect transit vehicles and send conflicting requests to the traffic signal controller simultaneously. In order to handle this dilemma, we have the following rules:  
1. If the traffic signal controller receives more than two green extension requests in each phase, these requests are handled in accordance to the principle that only the first one is served and the others are neglected;
2. The same principle can be used to the two or more red truncation requests;
3. If the controller receives two different requests at the same time, the response to the green extension request is superior to the response to the red truncation request.  
For example, in Figure 1a, four approaching transit vehicles are detected in four different road sections, such as Lanes 1, 3, 5, and 7, and they send priority requests in sequence. Obviously, transit vehicles on Lanes 1 and 3 request green extension, while transit vehicles on Lanes 5 and 7 request red truncation. With the help of above rules, the requests made by transit vehicles on Lanes 5 and 7 are ignored first, and then the request made by transit vehicle on Lane 3 is ignored too, only the request issued on Lane 1 can get a response.
## 1 Introduction
To improve the performance of multiple user equipments (UEs), the BS utilizes the UAV-mounted IRS to flexibly serve ground users [18]. The direct non-line-of-sight links between the BS and UEs are required to power the IRS relaying links. The main performance metric is presented, i.e., the outage probability. The model of IRS-NOMA in [19] allows this cell-edge user to be paired with a cell-center user to form the NOMA scheme. In this way, the coverage can be improved at the cell-edge user. The phase shift plays an important role in the design of an IRS-aided NOMA system, i.e., the impacts of random phase shifting and coherent phase shifting are further investigated [20]. In [21], IRS-assisted NOMA benefits from enabling BS's beamforming vectors. To minimize transmission power, the beamforming vectors and the IRS phase shift matrix could be optimized. One can place the IRS at preferred locations, and line-of-sight (LoS) can be adopted for the links between the transmitters and the IRS before reflecting to receivers [23; 24; 25]. The links can be enhanced if the system enables LoS and optimized passive beamforming vectors to implement a NOMA-assisted IRS. In [23], by considering ideal and non-ideal IRSs, the system performance was evaluated for the link from source to destination via IRS. In [24], a tremendous performance improvement could be confirmed in the approach of an IRS-assisted NOMA system. This means that IRS-assisted NOMA exhibits sufficient benefits to be incorporated into the existing IoT systems.