esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Characterization of the _On/Off_ Periods
This outcome is particularly pronounced when \(\lambda_{2}\) is significantly larger than \(\lambda_{1}\). For the case of the Erlang distribution corresponds to the sum of \(k\) exponential distributions. Then the pdf is obtained as the \((k-1)\)th convolution of exponential distributions with parameter \(\lambda\). Thus, the Erlang distribution is obtained as Equation (20). Figure 5:3  
The mean of an Erlang random variable is calculated as k times the mean of the exponential distribution, \(1/\lambda\), presented in Equation (21)  
Figure 5:4  
and \(E[x^{2}]\) is \(k\) times \(E[x^{2}]\) of the exponential distribution, then we get Equation (22):  
Figure 5:5  
From this, we can obtain the CoV for the Erlang distribution as presented in Equation (23):  
Figure 5:6  
Note that we already have the values of the mean, variance and CoV of the empirical data, directly obtained form the recordings of the bird's sounds. Then, we can obtain the parameters of these distributions by solving for \(\lambda\) when the \(CoV=1\), for \(\lambda_{1}\), \(\lambda_{2}\), and \(p\) when the \(CoV\geq 1\) and for \(k\) and \(\lambda\) when the \(CoV\leq 1\). Once we have found the parameters of the proposed distributions to approximate the _On/Off_ distributions, we calculate the accuracy of the approximation using the Chi-square method as shown in Equation (24):  
Figure 5:7  
The _Chi-squared_ analysis, is evaluated by a \(p_{value}\), which is dependent of the \(\chi^{2}\) and the freedom degrees, which are the number of independent variables minus one (\(IV-1\)). Then, the accuracy of the fit with the proposed phase-type distributions is obtained as follows: if the \(p_{value}\) is less than the critical value, the hypothesis should be rejected. In our project, we selected a \(p_{value}=0.05\). We analyzed the audio recordings by categorizing them into statistical subgroups, which we refer to as Stratum. The 'General Stratum' encompasses all analyzed audios. The 'Pine-Oak Forest Stratum' includes recordings of the bird species _Pheucticus melanocophilus_, _Myadestes occidentalis_, and _Turdus migratorius_. The 'Meztitlan Stratum' comprises audios featuring _Toxostoma curriostr_, _Campylorhynchus brunneicapillus_, _Campylorhynchus brunneicapillus_, and the _Cardinalis cardinalis_. The parameters obtained for different birds and the distributions used to approximate to both the _On_ and _Off_ periods were calculated and for all the birds considered, both active and inactive times have a \(CoV>1\). Hence, we propose the Hyper-Exponential distribution to characterize both _On_ and _Off_times. Where the minimum value for the CoV reported was for the _Pheucticus melanocophilus_ in the Silence times with \(CoV=1.065180\); and the major CoV reported was for the General stratum in singing times with \(CoV=2.264716\). Additional data have been included in Appendix A.
# SCMC: An Efficient Scheme for Minimizing Energy in WSNs Using a Set Cover Approach
## 3 Approach
### Energy Optimization  
Once the clusters are created, our objective is to minimize the energy consumption when routing data to the base station. First, we minimize energy consumption within each cluster, and then we minimize the routing energy consumption.
## 3 UWOC System Model
### UWOC Propagation Channel  
In UWOC systems, water is the communication channel via which the optical signal \(s(t)\) propagates. One of the challenges of the UWOC is that there is no definite mathematical expression for the impulse response function (\(h_{c}(t)\)). Hence, \(h_{c}(t)\) must be reliably modelled to assess the scope of impacts on the propagated \(s(t)\) due to water channel impairments like absorption, single/multiple scattering, scintillation, and turbulence. These degradations degrade \(s(t)\) temporal and spatial quality, reducing the received OSNR [71] at the surface of the photodetector. There are many studies (e.g., [70; 72; 71]) that focus on solving the radiative transfer equation (RTE) analytically and numerically, which account for different sets of inherent optical properties (IOPs) that mainly include absorption and scattering. The analytical solutions of the RTE are based on a wide range of assumptions or rather simplifications. These solutions are considered benchmark limits for the numerical ones. The simplest and most well-known benchmark is the Beer-Lambert law (BLL) [71; 72; 73]. The main aim of numerical solutions is to conclude an extrapolated mathematical close form using a double gamma curve-fitting to conclude a temporal profile for \(h_{c}(t)\), which accounts for impairments' impact limits for different water types and given link configurations. Once the \(h_{c}(t)\) format is defined, we will be able to conclude the convolution \(s(t)\) with \(h_{c}(t)\), the product of which is the optical received signal (\(r_{opt}(t)\)).  
In this study, we utilised the following \(h_{c}(t)\) format versions: (1) double gamma functions (DGFs), (2) weighted double gamma functions (WDGFs), (3) a combination of exponential and arbitrary power functions (CEAPFs), and (4) Beta Prime (BP). The impairment scope of each \(h_{c}(t)\) model is shown in Table 1. The CEAPF and BP formats might look different from the foundation DGF but can be reduced back to the DGF.
# Smart Mobility and Aspects of Vehicle-to-Infrastructure: A Data Viewpoint
## 3 V2I Communication
### Communication Technologies
#### 3.2.2 Cellular Vehicle-to-Everything  
Cellular vehicle-to-everything (C-V2X) is one of the researched communication technologies for communication within the intelligent transport system (ITS). It also covers communication between vehicles and infrastructure devices (V2I). This technology is managed by the 3 rd Generation Partnership Project (3 GPP). Data transmission is performed via a wireless mobile data network 4G LTE (Long-term evolution) or 5G NR (New radio). The services of mobile operators are used for this communication, which causes several disadvantages compared to DSRC technologies, for example, communication is usually charged, takes place in the licensed band and the device must be registered with the operator [13].  
When using a 5G network, data latency and error rate should be lower, and data throughput and network reliability higher. The problem is in areas where there is not enough coverage of the territory by 5G networks. In this case, the C-V2X communication will be performed via a 4G LTE network, which is not built on as many devices as can occur, for example, in traffic jams in larger cities. In this case, it is questionable what will be the latency and throughput of this communication. [13]  
The use of 5G networks will facilitate the transition from single-vehicle intelligence to connected intelligence, where the vehicle will be controlled not only by data obtained from its sensors but also by data from other vehicles and other ITS elements. As a result, the driver will have a greater overview of the traffic situation in the area in which he/she is located and not only about its immediate surroundings. At the same time, connected intelligence is a necessary step for the complete autonomy of vehicles. In addition to the above reasons, the construction of 5G networks is also essential for the use of HD maps in road transport [25].
## 6 Discussion  
This study adopted and validated a deep learning model with a simple structure, as we observed that employing modern models with a large number of parameters could easily lead to overfitting. Consequently, it was determined that a shallow network with fewer parameters would be most effective for the given datasets. In the future, as the size of the data increases, it may be feasible to use larger models. In such cases, the proposed loss function could be employed to enhance performance.  
The proposed system is based on NIRs. This approach has the advantage of collecting data closely related to food composition. However, it faces challenges, such as the difficulty of developing mobile-sized hardware and the limitation of scanning only localized areas of the sample. To make this system practically useful, a more integrated approach is required. Such a system would leverage NIRs in combination with camera-based visual recognition and user input, among other user-friendly features.  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline  & **KNN** & **LR** & **DT** & **RF** & **XGB [(29)]** & **CNN [(28)]** & **MM [(31)]** & **MM* [(31)]** & **Proposed** & **Proposed* \\ \hline Mean & 0.34 & 0.57 & 0.46 & 0.41 & 0.35 & 0.28 & 0.26 & 0.24 & 0.26 & 0.24 \\ \hline Z-score & 0.91 & 1.24 & 0.97 & 0.92 & 0.87 & 0.83 & 0.85 & 0.80 & 0.82 & 0.64 \\ \hline Z-score (worst 30%) & 1.42 & 2.06 & 1.52 & 1.44 & 1.30 & 1.23 & 1.25 & 1.20 & 1.21 & 0.86 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the baselines and the proposed model in terms of mean and Z-score. The proposed model achieves the lowest mean error (0.24) and Z-score (0.64), indicating superior performance.  
As highlighted in Section 2.2, it is observed that uncertainty does not always align with the magnitude of error. Consequently, the model might reject a result that is relatively accurate due to a high perceived uncertainty. The analysis of Figure 4 indicates that such instances often arise when data samples fall outside the customary distribution range, potentially linked to data sparsity issues. Our future research efforts will focus on addressing concerns related to data sparsity.
## 4 IoT-Enabled Traffic-Aware Telemedicine Architecture (ITTA)
### Wearable-Device-based Monitoring Algorithm for Edge Device (WDMA-Edge)  
The IM\({}^{2}\)E node consists of a telemedicine ambulance integrated with healthcare equipment and wearable devices for continuous monitoring of the patients. Such an integrated approach in edge nodes is required for handling COVID-19 pandemic situations [36]. Secondary transfers are intra- or inter-hospital transfers required for the survival of almost all critical cases in the emergency department (ED) and can sometimes lead to adverse risk levels of patients [14; 37]. All these clearly indicate risk scoring, real-time monitoring, and risk assessment of emergency patients. Vital parameters such as pulse rate (PR), blood pressure (BP), blood oxygen saturation level (SPO\({}_{2}\)), breathing rate, and body temperature are some of the most important vital parameters that can reflect patients' health conditions and risk levels. Many IoT-based systems for health monitoring are proposed in the literature [38]. AI-enabled autonomous devices employ a route source recommendation protocol [39], while blockchain-based solutions integrate lightweight advanced identity management systems [40]. Additionally, traffic congestion is addressed through rule-based management systems for the Internet of Vehicles in smart cities [41]. Therefore, considering the importance of monitoring patients during transit, it has been realized that the health risk level of a patient needs to be quantified on the fly, in order to make better decisions. This has led to the development of various risk-scoring mechanisms over time. A few of the relevant ones are Rapid Emergency Medicine Score (REMS), Revised Trauma Score (RTS), Trauma Score (TS), Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS), and Rapid  
Figure 2: IoT-based Telemedicine Services Framework (ITSF). Acute Physiology Score (RAPS) [42]. However, these risk-scoring mechanisms consider parameters which require a lab environment, which is challenging in an ambulance. After analyzing the existing scoring mechanisms and integrating the suggestions provided by doctors practicing emergency medicine, a modified scoring mechanism, On Fly Emergency Health Score (OFEHS), has been proposed. The focus of this scoring mechanism is on the analysis of patients during transit from the patient location to the hospital location, keeping in mind the requirement for faster first-level decision making. The scoring chart for the same is provided in Table 1. To monitor the patient's vital signs and to determine the first-level risk analysis using OFEHS, a wearable-device-based monitoring algorithm for edge device (WDMA-Edge) is proposed, as shown in Algorithm 1. The details of hardware components and devices required for the same, as well as their functioning, have been discussed in [43; 44; 45].
# Application of Virtual Reality for Learning the Material Properties of Shape Memory Alloys
## 2 SMA Properties and Applications
In addition, its heat conduction and shape memory properties can be used to drive a heat engine to save electric power. For medical applications, the transition temperature can be set to the body temperature if the SMA is used for the production of medical devices [32]. For example, it can be used to design fixation devices in orthopedic surgery or dental braces in orthodontics treatment to exert constant forces on teeth [33]. There are some other real-life applications of the SMA, which include:  
* Vascular filter  
When implanted into the vein vascular, it will gradually return to the net shape to prevent the sludged blood from flowing to the heart and the lung. It is effective for capturing and breaking apart particles in the blood. * Vascular stent  
The vascular stent is stretched to become a thin piece for inserting into the blood vessel. After the implant surgery, the blood will flow through the vascular stent and increase the temperature to restore the original shape. It is used for treating the vascular sclerosis and angiembraxis. Figure 4: Different SMA shape memory effects. * Bone lamella  
The bone lamella is used in osteosynthesis to fix two pieces of broken bones, where the pressure generated by the shape memory effect can force the broken bones to heal faster. * Fire alarm  
At room temperature, the SMA connector is bent and has no contact with the power. When a fire occurs and the temperature rises, the SMA connector will stretch automatically and touch the power to send out the alarm signal. * SMA-spring motor  
The length of SMA spring can change with the repeated heating and cooling process caused by the electric power to drive the motor and generate kinetic energy.
# An Overview of Cooperative Driving in the European Union: Policies and Practices
## 2 The European Framework
Yet, with the advent of LTE (long term evolution) and fifth generation cellular communication 5G, cutting edge technologies, defined as C-V2X (cellular-V2X), have been developed. Such innovative technologies are known as LTE-V2X (or car-LTE) [30] and 5G NR-V2X (new radio-V2X) [31]. According to their advocates, they can operate equivalently to DSRC, in ITS bands and independent of cellular networks, thus ensuring a direct channel between the interlocutors involved with a very low latency [32; 33]. An extensive description of LTE-V2X communication and its evolution towards fifth generation technologies can be found in [34; 35; 36]. Finally, [37; 38; 39] provide a comparative discussion about features and applications of the above communication systems together with the identification of the related optimal application ranges of distance and safety requirements. The European Commission set in 2019 the start time of deployment of mature C-ITS services and identifies, in the C-ITS Platform Phase II Report [40], a list of services to be considered as overriding, given their high safety potential. On the basis of the priority required for their deployment, services are classified as Day-1 and Day-1.5; however, the report specifies that the above list should not be seen as the 'official C-ITS service list' but only as a first benchmark for making the various deployment activities interoperable across the EU. Table 1 represents an excerpt from Annex 1 to the C-ITS Platform Report [40] and shows a cooperative services classification with reference to:  
* interlocutors involved (i.e., V2V and V2I)
* kind of standardised messages implemented, (i.e., CAM, DENM, SPaT, MAP, and IVI)
* communication technology adopted (i.e., ETSI G5 and traditional cellular networks)
* application field (i.e., urban environment and motorway)
* safety-related features (i.e., safety-critical (SC) and nonsafety-critical (nSC))
* priority in deployment (i.e., Day-1 and Day-1.5)
###### Abstract  
The accelerated life testing (ASLT) method was used to evaluate the effect of increasing the storage temperature from 10 to 40 \({}^{\circ}\)C on the aerobic plate count (APC), the pH, and the colorimetric parameters (L*, a*, b*) of pasteurized orange juice during 40 days of storage. For APC growth, a polynomial model was found to fit better, and at the lower temperatures of 10 and 15 \({}^{\circ}\)C, the shelf life was longer, as expected. More specifically, 15 and 10 days were needed, respectively, until the rise in the APC population to 1000 cfu/mL. However, for the temperature range of 30-40 \({}^{\circ}\)C, only approximately 3 days were needed to reach 1000 cfu/mL APC. Regarding pH, according to an exponential 3P model, a stable trend was apparent at all temperatures until 30 days of storage, followed by a more abrupt decreasing trend at 25 \({}^{\circ}\)C. The lightness (L*), redness (a*), and yellowish (b*) of the juice showed a decreasing trend with the temperature increase, and this trend was more profound at higher temperature levels. The multiple regression analysis between the predictors L*, a*, b*, pH, storage temperature, and the APC response showed an increase in APC growth when the colorimetric parameters decreased and the temperature increased.  
accelerated life testing (ASLT); orange juice; shelf life; storage temperature; microbiological evaluation; colorimetric parameters +
Footnote †: journal: Applied Sciences
## 5 Conclusions  
In this paper, the seismic ultimate capacity of beams reinforced with SFCBs is studied by testing and numerical simulation. First, the method and main results of the experiment are briefly introduced, and based on the OpenSees software, a simplified constitutive model of composite bar material was applied to simulate the seismic behaviors of the concrete beams reinforced with SFCBs by fiber element modeling. The validity of the model was verified with the experimental results for the concrete beams reinforced with composite bars under monotonic loading and a low reversed cyclic load. Based on the numerical simulation method, a parametric study was then conducted to illustrate the effects of the FRP types in composite bars (i.e., basalt, carbon FRP and E-glass FRP), the concrete strength, FRP content of the SFCBs and shear span ratio. The results showed that (1) the fiber type of the composite bar has a great impact on the mechanical properties of the frame beam, with beam reinforced with BFRP composite bar having higher seismic ultimate capacity and better ductility; With the increase of the fiber bundle in the composite bar, the post-yield stiffness and ultimate capacity of the component increases and the ductility strengthens; (2) at pre-yield stage, concrete strength has little influence on seismic performance of concrete beam while after yielding, the seismic ultimate capacity and post-yielding stiffness of specimens are increased slowly with the increase in concrete strength, however, the ductility is reduced accordingly; (3) as the shear span ratio of beams increases from 3.5 to 5.5, the seismic ultimate capacity decreases gradually while the ultimate drift ratio increases by more than 50%. Through sensible setting of the fiber content and shear span ratio of the composite bar reinforced concrete beam, concrete beams reinforced with composite bars can have good ductility while maintaining high seismic ultimate capacity. Due to the limited number of the tested beams, more tests and analyses are needed to derive sound conclusions concerning the use of SFCB in concrete structural members in seismic prone regions.  
All authors have made a substantial contribution to this study. [PERSON] designed the experiment and wrote the paper. [PERSON] provided the concept and design of the study. [PERSON] performed the FEM simulation and analyzed the data.  
This research was funded by the National Natural Science Foundation of China [No. 51708107] and the Natural Science Foundation of Colleges and Universities in Jiangsu Province, China [No. 17 KJB560005].  
The authors would like to acknowledge financial support from the National Natural Science Foundation of China (No. 51708107) and the Natural Science Foundation of Colleges and Universities in Jiangsu Province, China (No. 17 KJB560005).  
The authors declare no conflicts of interest.
## 4 Results
### Interannual ET Simulations Using the SEBS  
Interannual ET dynamics (unit: mm-year\({}^{-1}\)) estimated by the SEBS model over the QM forest from 2000 to 2012 are provided in Figure 5. Due to diverse environmental conditions (various meteorological and forest stand conditions) during the simulation period, the figure shows the regional distribution of interannual forest ET over a wide range. In all of the maps, due to the cold and arid conditions of the QM forest, the estimated forest ET is generally lower than 400 mm-year\({}^{-1}\). In general, forest ETs located in the northern portion are lower than those in the southern portion.  
Figure 5: _Cont_  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Year** & **ET\({}_{\textbf{Mean}}\)** & **ET\({}_{\textbf{Min}}\)** & **ET\({}_{\textbf{Max}}\)** & **ET\({}_{\textbf{sd}}\)** & **ET\({}_{\textbf{Sum}}\)** & **P** & **ET\({}_{\textbf{Mean}}\)/P(\%)** \\ \hline
2000 & 244 & 136 & 1016 & 80 & 190,426 & 454 & 53.74 \\
2001 & 281 & 165 & 1008 & 76 & 219,356 & 344 & 81.77 \\
2002 & 299 & 166 & 1025 & 79 & 233,380 & 453 & 66.02 \\
2003 & 317 & 194 & 985 & 77 & 247,049 & 279 & 113.59 \\
2004 & 271 & 158 & 1075 & 82 & 211,567 & 386 & 70.21 \\
2005 & 290 & 165 & 949 & 62 & 225,890 & 454 & 63.77 \\
2006 & 248 & 143 & 1242 & 97 & 193,126 & 509 & 48.65 \\
2007 & 277 & 165 & 1150 & 80 & 216,346 & 671 & 41.34 \\
2008 & 255 & 136 & 1051 & 82 & 198,537 & 481 & 52.97 \\
2009 & 201 & 124 & 882 & 69 & 156,904 & 512 & 39.33 \\
2010 & 222 & 131 & 969 & 75 & 173,053 & 482 & 46.05 \\
2011 & 236 & 137 & 1024 & 79 & 184,386 & 550 & 42.99 \\
2012 & 290 & 175 & 1015 & 75 & 225,943 & 499 & 58.11 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The statistics for interannual simulated forest ETs and precipitation (unit: mm\(\cdot\)year\({}^{-1}\)) downscaled from WRF estimates over the QMs from 2000 to 2012.  
Figure 5: The interannual dynamics of forest ET simulated by the SEBS over the QMs from 2000 to 2012.  
Correspondingly, the interannual statistics of ET (including average, minimum, maximum, stand deviation, and total amount), precipitation (downscaled from the WRF result), and ET/precipitation in the forested areas are provided in Table 3. In total, the statistics calculated from the 780 forest pixels (1 km resolution) also represented the diversity of the estimates. The table indicates that the highest annual average of forest ET (317 mm\(\cdot\) year\({}^{-1}\)) occurred during 2003 and the lowest (201 mm\(\cdot\) year\({}^{-1}\)) occurred during 2009. Interestingly, the highest ratio of annual ET/precipitation was also determined during 2003 while the lowest was found for 2009. The ratio in 2003 was higher than one, implying that forest growth during 2003 was largely supported by soil moisture.