esa-sceva/satcom-chunk-collection huggingface dataset:
# Development and Application of Digital Twin-BIM Technology for Bridge Management
## 4 Building Information Management
### Digital Twin of Comoro Bridge for Structural Performance Evaluation
#### 4.4.2 Structural Performance Evaluation Based on FE Analysis
In the double-cell box girder, the middle web was removed and added to the left and right webs of the single cell-box girder, as illustrated in Figure 20:2 Consequently, the evaluation of shear-lag effects in both the double-cell and single-cell box girders was as follows:  
* The shear-lag effect was observable at the top and bottom flanges in the box-girder section located at the top of the piers, as depicted in Figure 30a. * The results indicated that the shear-lag effect was more prominent in the single-cell box compared to the double-cell box. This disparity was attributable to the geometrical differences, with the double-cell box featuring an intermediate web, while the single-cell box lacked one (refer to Figure 30b,c). Consequently, the shear-lag effect was significantly more pronounced in the single-cell box girder. However, as the location transitions towards the midspan, the shear-lag effect diminished linearly and became aligned with the assumptions of the Euler-Bernoulli theory (refer to Figure 31). Figure 20:3 Normal stress distribution on the girder section at midspan. (a) Contour map of the normal stress distribution; (b) at the top flange; (c) at the bottom flange. The evaluation results of the shear-lag effect for both the double-cell box and single-cell box were as follows:  
1. Both the double-cell and single-cell boxes exhibited a shear-lag effect, especially at the top of the piers. Figure 20:4 Bridge digital twin for structure maintenance. Figure 20:5 Double-Cell Box and Single-Cell Box Girder. (**a**) Double-Cell Box Girder; (**b**) Single-Cell Box Girder. Figure 20:6 Normal stress distribution on the girder section at pier 3 (single-cell box-girder case). (**a**) Contour map of the normal stress distribution; (**b**) at the top flange; (**c**) at the bottom flange. As the location progressed towards the midspan, the shear-lag effect diminished linearly and became aligned with the assumptions of the Euler-Bernoulli beam theory. 3. At the midspan in the case of the double-cell box, the shear-lag effect was minimal and closely resembled the behavior predicted by beam theory. However, in the single-cell box, where no middle web was installed, the shear-lag effect slightly increased compared to that of the double-cell box. A comparative study can be performed using a full-scale bridge model, which is easily developed from the BIM technology.
# Active Contours in the Complex Domain for Salient Object Detection
## 5 Conclusions and Future Research  
In this paper, a new complex domain model is proposed, while using a low-pass filter as a penalty term and a complex force function to distinguish the object from the background for the active contour. It is well known that there is no perfect method that can succeed for all types of images. The detection results depend on many factors, such as the texture, intensity distribution, image content, and problem domain. Therefore, it is impossible to consider a single method for all types of images. We focus on the initialization and reinitialization problem and propose a novel complex-domain-based transformation for joint salience detection and object boundary detection. First, we transform the input image to the complex domain. The complex transformation provides salience cues. We use a low-pass filter for distinguishing the salient region from the background regions. Finally, the complex-force function is used to fine-tune the detected boundary. We compare the proposed model with state-of-the-art techniques and find that the proposed technique outperforms other methods and produces better results at low computational cost.  
In the future, the following advantages are furnished by the ACM in the complex domain according to the analysis that is offered in the existing study. Here, we would like to provide some recommendations for researchers. First, the complex domain can be used in other areas; for example, the complex domain can help to efficiently extract more local information in medical images. Second, initialization and parameter settings play a very important role in the final results. The optimization algorithm for setting parameters should be studied in the future, and more suitable initialization methods for active contour models in the complex domain should be proposed in order to reduce the computational cost in the future. Finally, combining complex domain with other theories is a feasible and efficient way to address the deficiencies of ACMs; for example, the complex domain could be combined with a neural network to obtain a high level of accuracy.  
The authors' contributions are as follows: U.S.K. conceived the algorithm, designed and implemented the experiments and wrote the manuscript; resources were provided by X.Z.; Y.S. reviewed and edited. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Key Research and Development Program of China under Grant 2016 YFB0200902.  
The authors declare no conflicts of interest.
# Modal Identification of Train Passenger Seats Based on Dynamic Tests and Output-Only Techniques
## 3 Case Study: Alfa Pendular  
The Portuguese CPA 4000 series Pendolino, traditionally named as the Alfa Pendular (AP) train, started its operation in 1999. Figure 3 shows the CPA 4000 series on a regular passenger service. The train is a conventional electric train with an active tilting system and a total length of 158.9 m. It is operated as a single unit and comprises six vehicles, four motor and two hauled vehicles [51; 52].  
Cars 1 and 2 are classified as comfort classes, while the others are standard class. Despite being visually similar, the seats of the two classes are different in terms of their dimensions. The comfort seat is larger than the standard one. Therefore, comfort class seats present a higher seating area, thicker seat surface and seatback foams, larger seat frame, and higher distance between seats.  
In 2017, the AP train was renovated, leading to a significant change in seat design while retaining the same seat structural frame. This renovation introduced several changes to the seat cover and foam design. While the pre-renovation seats presented a free foam covered by fabric, the renovated ones exhibited a restricting leather cover with large hems and new foams [53; 54]. Figure 4 illustrates the renovated comfort and standard class seats and their respective location. Seats instrumented on this work were positioned in the same location inside vehicles 1 and 6, respectively, for comfort and standard classes, more specifically near the rear bogies.  
The comfort class seats have a seat surface foam thickness of 190 mm, while for the standard class seats, the same thickness is equal to 130 mm. Regarding the seatback, the foam thickness is 127 mm and 110 mm, respectively, for comfort and standard seats. Moreover, both seats and foams have a 2 mm thick leather cover. The aluminium structural frame underneath that supports the seats is presented in Figure 5. The frame comprises two vertical columns and a transversal girder, and provides the seat's connection to the carbody platform. These elements compose the structural frame of both seat classes. As the seats differ in dimensions, the elements constituting the comfort seat frame have higher dimensions than those of the standard seat [54].  
Figure 3: CPA 4000 series train in operation.
## 3 Results  
### Phytochemical Analysis  
HS-SPME and GC-MS techniques were used to evaluate flower volatiles of \(H\). _agayanensis_, \(H\). _lacunosa_, and \(H\). _coriacea_. A total of 59 metabolite compounds with varying percentages were found. Nine chemical classes were found consisting of terpenoids, alcohols, alkenes, aldehydes, benzenoids, ester, ketone, acid, and sulfur groups. Twenty-three metabolites were identified in _H. _cagayanensis_, 14 in \(H\). _lacunosa_, and 36 in \(H\). _coriacea_. Many profiled metabolites were found to be unique to a particular species only, with 15 unique metabolites in \(H\). _cagayanensis_, 6 unique metabolites in the \(H\). _lacunose_ and only 28 unique metabolites in the \(H\). _coriacea_ (Table 2). Only six metabolites (\(\beta\)-ocimene, (Z)-3-hexen-1-ol, 1-octen-3-ol, butanoic acid (Z)-2-methyl-3-hexenyl ester, (Z)-butyric acid-3-hexenyl ester, and methyl salicate) from terpenoid, alcohol, ester, and the benzenoid group were found to have a relative amount more than 10% in all species studied (Table 2). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \begin{tabular}{c} **Formula and** \\ **Chemical Group** \\ \end{tabular} & **CAS. No** & **Compound** & \multicolumn{2}{c}{**Plant Species/ Relative Percentage (\%)**} \\ \cline{3-5}  & & & \
\begin{tabular}{c} _H. cagayanensis_ \\ \end{tabular} & **H. _lacunosa_** & **H. _coriacea_** \\ \hline
\begin{tabular}{c} Terpenoids \\ Monoterpene \\ \end{tabular} & & & & \\ \(\text{C}_{10}\text{H}_{6}\) & 013877-91-3 & \(\beta\)-Ocimene & 25.78 & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 2: List of secondary metabolites compounds found in flowers of \(H\). _cagayanensis_, \(H\). _lacunosa_, and \(H\). _coriacea_ species. The relative amount (in %) of scent compounds are listed according to compound class.
## 3 Results
### Acidity of CSO  
The predicted acid value of CSO in the three center-point STDs was 0.92 mg KOH/g of oil. The observed acid values of CSO center-point STDs were 1.18, 0.93, and 0.95 mg KOH/g of oil (Table 2). The smallest acid values of CSO were predicted (0.50 mg KOH/g of oil) and observed (0.85 mg KOH/g of oil) in STD 5 (size of the restriction die: 1 cm; temperature: 40 \({}^{\circ}\)C; and duration of dry heat exposure at 100 \({}^{\circ}\)C: 30 min), STD 8 (size of the restriction die: 1.4 cm; temperature: 60 \({}^{\circ}\)C; and duration of dry heat exposure at 100 \({}^{\circ}\)C: 30 min), and STD 9 (size of the restriction die: 1 cm; temperature: 50 \({}^{\circ}\)C; and duration of dry heat exposure at 100 \({}^{\circ}\)C: 15 min) (Table 2). The 2 FI model was employed to evaluate variances in the acid value of CSO (Table 4).  
The 2 FI model for the acid value of CSO was significant (\(p\) = 0.0056) with adjusted R\({}^{2}\) and predicted R\({}^{2}\) values of 0.8279 and 0.6874, respectively, and a nonsignificant lack of fit (\(p\) = 0.2351). The results showed that the 2 FI model equation was suitable for predicting the acid value of CSO.  
The CCD-generated 2 FI model equation for the acid value of CSO (mg KOH/g of oil) was as follows (Equation (6)):  
\[\text{Acid value = 2.9194 -1.5386A - 0.0377B - 0.0984C + 0.0286 AB + 0.0467 AC+ 0.0009 BC} \tag{6}\]  
where A is the size of the restriction die, B is the pressing temperature, and C is the duration of dry heat exposure.  
The size of the restriction die (\(p\) = 0.0007), temperature (\(p\) = 0.0032), size of the restriction die and temperature (\(p\) = 0.0058), size of the restriction die and dry heat exposure time (\(p\) = 0.0008), and temperature and dry heat exposure time (\(p\) = 0.0012) significantly affected the acid value of CSO (Table 5). RSM prediction indicated that a lower temperature and reduced size of the restriction die could produce CSO with a lower acid value. All factors greatly influenced the acid value, while dry heat exposure time was not an influencing factor for the acid value (Figure 4).  
Figure 3: Response surface plot illustrating the influence of restriction die size, temperature, and duration of dry heat time on the total phenolic content of CSO. (**a**) Influence of restriction die and temperature (**b**) restriction die and dry heat time, and (**c**) temperature and dry heat time on the total phenolic content of the CSO.  
Figure 4: Response surface plot illustrating the influence of restriction die size, temperature, and duration of dry heat time on the acid value of CSO. (**a**) Influence of restriction die and temperature (**b**) restriction die and dry heat time, and (**c**) temperature and dry heat time on the acid value of the CSO.
## 3 System Model and Problem Statement  
Given a set of \(N\) sensor nodes and a sink node, the network is structured as a tree-topology rooted at the sink. At every time interval \(\Delta t\), each node \(i\) sends a data packet along the tree towards the sink node, and monitors a set of nodes \(W_{i}\subseteq L_{i}\), such that \(L_{i}\) denotes the set of \(i\)'s neighbors. We consider two forms of the selective routing attack:  
* An attacker \(i\) drops some/all data packets coming from all its upstream neighbors.
* An attacker stops generating its own data packets.  
At every time interval \(\Delta_{j}=\Delta\), called the monitoring period, each node \(i\), which monitors node \(k\)'s behavior, constructs a vector (or profile) \(a_{j}^{ik}\in\mathbb{N}^{d}\) that is composed of \(d\) attributes \(\{a_{jl}^{ik}:l=1\ldots d\}\). The attributes can denote: the number of dropped packets, the number of received packets, the number of generated packets, etc. To be able to perform monitoring, \(\Delta_{j}\) must be larger than \(\Delta t\). We remove the superscripts \(i\) and \(k\) to ease the notation burden.  
After a time period \(T\), each sensor node has collected a set of \(n\) d-attribute profile vectors \(a_{j}=\{a_{jl}:l=1\ldots d\}\), called the training data set. The latter can also be written as \(d\times n\) matrix \([a_{jl}]\), where \(a_{jl}\) is the number of occurrence of feature \(l\) in profile \(a_{j}\) and \(n\) is the number of profiles. A profile \(a_{j}\) can be a one-attribute vector that records, for example, the number of packets sent/forwarded by the monitored node during the monitoring period \(\Delta_{j}\).  
During the testing phase, the intrusion detection problem is to check at each \(\Delta_{j}\) (such that \(j>n\)) if the observed profile \(a_{j}\) is normal or not.
# Performance of Differential-Phase OFDM Systems over Selective Multipath Fading Channels
## 3 Problem Statement  
### The Transmitted OFDM Signal  
The baseband transmitted OFDM signal can be expressed as  
\[s(t)=\frac{1}{\sqrt{N}}\sum_{i=1}^{\infty}\sum_{n=0}^{N-1}u_{n,i}f(t-iT_{f})e^ {i2\pi f_{n}(t-iT_{f})} \tag{10}\]  
where \(f(t)\) is the rectangular frame pulse waveform defined by  
\[f(t)=\left\{\begin{array}{ll}1&-T_{g}\leq t\leq T_{u}\\ 0&\text{elsewhere}\end{array}\right. \tag{11}\]  
Other types of frame pulse waveforms could be used, such as the Nyquist shape window and the raised cosine window [17; 18].  
In the differential-phase OFDM scheme similar to the one shown in Figure 1, the modulated symbol \(u_{n,i}\) is an \(M\)-ary DPSK modulated signal carried by the \(n\)-th subcarrier and transmitted over the \(i\)-th OFDM signaling interval. The modulated symbol \(u_{n,i}\) can be expressed as [16]  
\[u_{n,i}=\sqrt{\frac{2E_{s}}{T_{s}}}\cdot e^{i\theta^{(n,i)}} \tag{12}\]  
where \(E_{s}\) is the energy per symbol, and \(\theta^{(n,i)}\) is the transmitted phase in the interval \(nT_{s}<t<(n+1)T_{s}\) from the \(i\)-th OFDM frame. \(\theta^{(n,i)}\) is given by \(\theta^{(n,i)}=\Delta\phi_{n,i}+\theta^{(n,(i-1))}\) modulo \(2\pi\) where \(\Delta\phi_{n,i}\) is one of the \(M\) possible differential phases chosen from the set \(\{2\pi m/M,m=0,1,2,...,M-1\}\) and \(\theta^{(n,(i-1))}\) is the transmitted phase in the interval \(nT_{s}<t<(n+1)T_{s}\) from the \((i-1)\)-th OFDM frame
# An Interactive Real-Time Locating System Based on Bluetooth Low-Energy Beacon Network +
## 5 Conclusions  
Since the invention of iBeacon and given the extreme popularity of smartphones, the BLE-based beacon technology has been deployed extensively as a basis to provide various locating services for smartphone users. In this research, our main contribution is that we combined the Bluetooth broadcast and mesh topologies to extend the applicability of beacon solutions. Specifically, apart from broadcasting beacon messages, our beacon devices also serve as beacon readers that can discover the presence of specific users, and then forward the presence state hop-by-hop to the backend server over the Bluetooth beacon network. With the knowledge of a specific customer's presence, the backend server can respond to the customer with a personalized message, again via the relay of the Bluetooth beacon network. In some use cases, such as welcoming a VIP customer at the airport, our interactive locating system can give the customer a much improved user experience, since all the communications rely on a single network technology--BLE. Neither costly mobile Internet connections nor the troublesome Wi-Fi connections are needed for the customers.  
Figure 12: Deployment with multiple subareas to reduce the hop count to the Mesh Router.  
Our current implementation of messaging over the Bluetooth beacon network is based on rebroadcasting in the advertising channels. It is easy to implement, and it precludes the need to pair Bluetooth devices before sending messages to each other. Furthermore, without having to run a routing protocol and maintain a routing table, the complexity and memory consumption of the Bluetooth devices can be minimized. Although sending messages by broadcast is less efficient, the overhead can be well-controlled if we design the rebroadcast mechanism carefully as well as dividing a large network into smaller subareas properly. In the future, we plan to replace the Bluetooth interfaces with version 5.0 and incorporate the official Bluetooth mesh protocol into our system, to achieve better locating performance and the interoperability between BLE-based mesh networks.  
Both authors conceived and designed the experiments; [PERSON] performed the experiments; both authors analyzed the data; [PERSON] wrote the paper.  
This research was funded by The Ministry of Science and Technology, Taiwan, grant number 105-2221-E-032-032-MY3.  
The authors declare no conflict of interest.
# Linear Algorithms for Radioelectric Spectrum Forecast
###### Abstract  
This paper presents the development and evaluation of two linear algorithms for forecasting reception power for different channels at an assigned spectrum band of global systems for mobile communications (GSM), in order to analyze the spatial opportunity for reuse of frequencies by secondary users (SUs) in a cognitive radio (CR) network. The algorithms employed correspond to seasonal autoregressive integrated moving average (SARIMA) and generalized autoregressive conditional heteroskedasticity (GARCH), which allow for a forecast of channel occupancy status. Results are evaluated using the following criteria: availability and occupancy time for channels, different types of mean absolute error, and observation time. The contributions of this work include a more integral forecast as the algorithm not only forecasts reception power but also the occupancy and availability time of a channel to determine its precision percentage during the use by primary users (PUs) and SUs within a CR system. Algorithm analyses demonstrate a better performance for SARIMA over GARCH algorithm in most of the evaluated variables.  
c pacs: 12.38.-b, 12.38.-b, 12.  
primary user (PU) occupancy models can be used as empty channel indicators replacing the spectrum sensing procedures [2; 8]. A key issue for spectrum sensing operations is the evaluation of the so-called hidden node margin (HNM). The HNM has been evaluated in sharing scenarios between white space devices and digital terrestrial television systems [9].  
Different initiatives for radioelectric spectrum channel modeling, mainly using deterministic and stochastic models, have been proposed [3; 10; 11; 12; 13; 14; 15; 16]. Differences among those proposals and the present work lie in that in the first initiatives, the duty cycle (the average percentage of the channel utilization) time series is modeled for different types of channels, whereas the proposal presented here models the time series for received power in three GSM channels with different occupancy levels. For this purpose, firstly we used seasonal autoregressive integrated moving average (SARIMA) algorithm, which is adequate for analyzing time series with seasonality, in accordance with different studies [13; 14; 15]. Secondly, the generalized autoregressive conditional heteroskedasticity (GARCH) algorithm, which had been applied in traffic modeling and forecast for different communications networks, was also used [17; 18; 19].  
The evaluation of the results obtained in algorithm forecasts is based on the following variables: channel availability time (the time interval where the channel is not used by the PUs), channel occupancy time (time interval where the channel is used by the PUs), observation time and error criteria analysis (symmetrical mean absolute percentage error, SMAPE, mean absolute percentage error, MAPE, and mean absolute error, MAE) [20; 21; 22].
# Nutritional Recommendations for Breath-Hold Divers
## 2 Mediterranean and Alkaline Diet
### Different Nutrition Concepts Concerning the Specifics of Diving Disciplines  
Freediving encompasses a wide range of disciplines that differ significantly in performance, as well as in functional-metabolic energy demands. Static apnea (maximum breath-holding on the surface at rest) is based exclusively on aerobic capacity, while dynamic disciplines such as long-distance and deep diving include pronounced anaerobic requirements [22]. On the other hand, spearfishing is characterized by the alternation of frequent shallow dives with short surface rest intervals, breath-holding at rest on the bottom (dive hold), and deep dives with longer recoveries, which requires a flexible and durable energy system with aerobic and anaerobic components [23]. Nutritional approaches to practicing these different diving disciplines must be adapted to the specifics of training and competition. While apnea training in freediving usually lasts up to an hour, spearfishing can last more than six hours, which creates different nutritional needs. Optimal nutrition for divers is based on a balanced and planned intake of calories and macro- and micronutrients, as well as adaptation of nutrition to the training and competition demands.  
The three main nutrition concepts include preparation for statics, for dynamic and deep-sea disciplines, and for the specifics of spearfishing, with a common emphasis on healthy eating habits and avoiding undesirable foods before diving.