esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analytical Results
### Bulk-Rock Major and Trace Elements  
The major and trace element compositions of the studied Baiyinhushuuo granodiorite are shown in Table 2. This study analyzed two samples and collected data from four samples. The SiO\({}_{2}\) content of the granodiorite ranges between 66.93% and 69.40%, with an average of 67.75%. The Na\({}_{2}\)O content is 4.35-5.45%, with an average of 4.63%. The K\({}_{2}\)O content is 2.48-2.95%, with an average of 2.60%, and the Na\({}_{2}\)O/K\({}_{2}\)O ranges from 1.56 to 2.05. The Al\({}_{2}\)O\({}_{3}\) content was 15.13-15.53%, with an average of 15.39%. In the TAS diagram, the samples fall in the granodiorite fields, subalkaline magmatic series (Figure 5a,d). The total alkali (Na\({}_{2}\)O + K\({}_{2}\)O) amounts are 6.82-8.12% and show a calc-alkaline to high-K calc-alkaline series (Figure 5b). The Al\({}_{2}\)O\({}_{3}\) content (11.45-14.11%) is moderate to low, reflecting A/CNK values of about 0.94-1.01 and A/NK values of 1.30-1.54 (Figure 5c), and is known for the metaluminous granitoids (Figure 5c).  
The granodiorite samples showed low rare earth element (REE) abundances, with \(\Sigma\)REE ranging from 54.39 to 76.84 ppm; they showed a right-leaning chondrite distribution curve with a strong differentiation of light REE (LREE) and heavy REE (HREE) (Figure 6a). The LREE/HIREE value was 7.38-10.68, with an average of 8.53; the (La/Yb)\({}_{\rm N}\) value was 8.14-13.93, with an average of 10.10; the \(\delta\)Eu value was 0.79-1.02, with an overall weak negative Eu anomaly. The trace element primitive mantle spider diagram is enriched in large ionic lithophilic elements (LILEs), such as Rb, Ba, Th, and U, and depleted in high-field-strength elements (HFSEs), such as Nb, P, and Ti. (Figure 6b) The trace elements have high Sr (424.00-570.14 ppm) and high Sr/Y (42.79-62.91) (Table 2).  
The geochemical composition of the granodiorite sampled in this study is consistent with the findings of [PERSON] et al. [15]. The SiO\({}_{2}\) varies from 66.93% to 69.40%; the MgO ranges from 1.35% to 1.55%; and the Al\({}_{2}\)O\({}_{3}\) is between 15.37% and 15.43%; the rock is characterized by high Sr, low Y, and high Sr/Y, which is consistent with the geochemical characteristics of typical adakites.
# A Survey of Advanced Border Gateway Protocol Attack Detection Techniques
## 6 Attacks that Require Advanced Detection
### MED Modifications and RFD/MRAI Timer Exploitation  
MED modification and RFD/MRAI Timer attacks manipulate specific BGP attributes or timers to influence route selection subtly. Both attacks exploit the complex decision-making process of the BGP. Understanding the subtle manipulations of MED or the exploitation of RFD/MRAI timers requires a nuanced understanding of how ASs interact and make routing decisions based on these attributes. These attacks are designed to be stealthy, altering route selection and stability without overt disruptions. Detecting such subtle manipulations benefits from techniques that can aggregate and analyze data from multiple sources, identifying inconsistencies or anomalies in routing behavior that might indicate an attack.
## 6 Discussion
Consequently, reciprocal forces are avoided, and unwanted movements in the premolar and incisor regions are minimized, which not only shortens the treatment duration but also provides the clinician with a more predictable treatment outcome [55; 80]. With the increasing use of miniscrew-supported molar distalization systems, these screws have started to be used in modified forms alongside traditional distalization appliances [81]. However, these appliances still require laboratory procedures, occupy space in the oral cavity that may cause patient discomfort, and impose additional financial costs for clinicians. For these reasons, distalization methods that utilize screws independently of appliances--such as those placed in the buccal, infrazygomatic crest, and maxillary tuberosity regions--are gaining prominence. Among these, the use of extra-alveolar screws, which has gained popularity in recent years, offers clinicians a new perspective. Extra-alveolar screws are less limited by anatomical constraints, resulting in a lower risk of root damage and a safer working environment. Compared with intra-alveolar screws, extra-alveolar screws can be used with larger diameters and lengths. Additionally, since they do not require surgical procedures, they can be placed by orthodontists in the clinic with local anesthesia. Using an infrazygomatic crest screw, a variety of malocclusions can be corrected in the sagittal, vertical, and horizontal planes. If distalization with an infrazygomatic screw proves as effective as anticipated, it could replace hybrid distalization devices that require extensive laboratory procedures. In this narrative review, the effectiveness of appliances used for maxillary molar distalization over the past two decades has been examined, with an emphasis on identifying the most effective methods for current clinical practice. The primary limitation of this study is the lack of comprehensive research in the existing literature directly comparing all appliances, which restricts the objective evaluation of their relative effectiveness. Nevertheless, through an extensive literature review, the clinical advantages and limitations of tooth tissue-supported and skeletal anchorage distalization methods have been systematically analyzed. This review aims to serve as a valuable resource for clinicians in selecting the most appropriate distalization method on a patient-specific basis and to lay the groundwork for future comparative studies.
## 3 Challenges of LC-RIS and Future Outlook
Efficient power management strategies [32] must be developed to minimize energy consumption while maintaining the desired performance (for which low-maintenance, self-learning calibration algorithms are technically demanding). Arguably, real-time digital twins and efficient algorithms governing the aforementioned tasks are highly sought after for LC-RIS's self-learning control and optimization. Figure 3: Challenges of LC-RIS. The manufacturing of RIS using LC technology can still be challenging in today's miniaturized high-throughput workflows, especially when it comes to achieving the necessary precision and uniformity across the entire surface. This can affect the overall performance and reliability of RIS, presenting the same challenge faced by LCOS [28]. Furthermore, an LC-RIS must be able to integrate with existing wireless communication systems seamlessly. This requires additional development of low-loss (radiation-free) interfaces [33] and protocols that can facilitate communication between LC-based RIS and other devices, e.g., antennas and transceivers. It remains to be seen how the total cost of LC-RIS can compete with other technology-based RIS or not. Notably, physics-aware machine learning [34] and artificial intelligence (AI) technologies, which can operate both at the link- and system-levels, are envisaged to have a significant impact on the design of LC-RIS communication systems across all layers of the communication architecture. The trends of cognition and self-organization, smart spectrum access, operation of the physical and medium access layers up to resource allocation and network organization are all envisaged to be further accelerated by these technologies. Although AI does not produce new physics, leveraging it can greatly accelerate the rollout of LC-RIS in the post-5G roadmap, unlocking new frontiers of knowledge and innovation. For example, new applications that combine sensing, positioning, imaging, and mobility with communication capabilities will be made available with ease. Last but not least, the long-term reliability of RIS based on LC technology is still not fully understood, and more research studies are needed to identify potential failure mechanisms (as researchers have identified for LCOS [35]) and develop strategies to mitigate them. This is particularly important for mission-critical applications, e.g., in aerospace and defense. For a network targeted with embedded trust in such a situation, security should be in place at all levels, including the physical layer [36].
# Calibrating Microscopic Traffic Simulation Model Using Connected Vehicle Data and Genetic Algorithm
## 2 Literature Review
[17] aimed to calibrate the Simulation of Urban Mobility (SUMO) to create a testing environment for automated driving systems. They captured high-resolution trajectory data using devices installed in the vehicles driving through the network. A GA was used to find the best solution and minimize the error between simulated and observed data. [PERSON] et al. [18] integrated drone capabilities to collect high-resolution data in a roundabout. In a similar study, [PERSON] et al. [19] used drones and helicopters to capture vehicle trajectories and used them to calibrate the traffic models. Both studies were able to calibrate the traffic model using collected data continuously, yet the duration of data collection in the suggested approaches was limited. Practical implementation of the suggested methods demonstrates their performance in improving the traffic model calibration. In a study conducted by [PERSON] et al. [6] in India, data collected by cameras during a short period in a segment of roadway without any intersection or interruption with pedestrians were used to calibrate the Wiedemann 99 car-following model, one of the most used models in traffic engineering. The study showed significant improvement in the model, although there is a need to test the same approach in a more complex network. Additionally, the Wiedemann 99 car-following model was calibrated for bicycle traffic by [PERSON] et al. [20]. This study compared data captured from a bicycle simulator to the results obtained from the VISSIM traffic simulation. The calibrated parameters performed better than the default ones, although capturing the wide range of behaviors was unsuccessful. Some studies tried to calibrate the simulation model in specific situations such as work zones [21; 22], roads with limited sight distance [23], and exact locations with a particular traffic behavior [24]. Table 1 compares some of the recent efforts to calibrate microsimulation models and provides details about them. Some challenges of calibrating microsimulation models still need to be solved despite significant progress. Issues such as improving the accuracy of the models, generalized solutions, and fast and reliable methods need to be considered. In this study, the high-resolution trajectory of vehicles inside the network was used to process the calibration. This approach helps to obtain the best possible accurate results compared to past studies. Because of its shown effectiveness in resolving complicated optimization problems and its capacity to efficiently explore vast solution spaces, the GA was selected. Its advantage over conventional techniques in calibrating traffic models was shown in earlier studies [8; 17; 25], which makes it a reliable tool for parameter adjustment in a variety of circumstances. Moreover, the nature of the proposed platform makes it suitable for all types of networks with every type of traffic and environment.
## 3 Results and Discussion
### Identification of Key Volatile and Non-Volatile Compounds in Raw Meat during Refrigeration  
#### 3.2.1 Key Volatile and Non-Volatile Compounds  
Volatile compounds with odor-activity values (OAVs) > 1 made a dominant contribution to raw meat, while compounds with OAVs < 1 contributed less [16]. The concentration of a volatile compound was divided by the published odor threshold in water to determine its OAV. As shown in Figure 2a, 16 volatile compounds with OAVs > 1 were set out below: nonanal, octanal, decanal, (E)-4-decanal, (E)-2-nonanal, (E)-2-octanal, (E, E)-2,4-decadienal, (E, E)-2,4-nonadienal, heptanol, heptanal, 1-octen-3-ol, (E)-2-octen-1-ol, hexanoic acid, 2-pentylfuran, 2,3-octanedione, and methyl hexanoate. To intuitively explain the contribution of volatile compounds to raw meat, the contribution rate was used to elaborate. As shown in Figure 3b, the higher contribution rates of volatile compounds in raw meat were 1-octen-3-ol (19.31-56.21%), (E, E)-2,4-decadienal (10.7-25.34%), and nonanal (9.50-20.34%), and they accounted for almost 80% of the contribution rates. 1-octen-3-ol, (E, E)-2,4-decadienal, and nonanal could be recognized as key volatile compounds in raw meat during refrigeration. Consistently, several studies demonstrated that nonanal, (E, E)-2,4-decadienal, and 1-octen-3-ol had higher OAVs in raw and cooked meat [15; 34; 35]. Alcohols were typical volatile markers of raw and unprocessed meat. They were mainly oxidative decomposition products of fat, especially 1-octen-3-ol, which had a mushroom odor and was reported to be generated by \(\beta\)-oxidation [36; 37; 38]. The aldehydes with the largest contribution rate of volatile compounds were (E, E)-2, 4-decadienal, and nonanal, which had the odor of fat [17; 39].  
Taste activity values (TAVs) identified the main non-volatile compounds. As shown in Figure 2b, aspartic acid (Asp), Glu, 5\({}^{\prime}\)-GMP, 5\({}^{\prime}\)-IMP, and 5\({}^{\prime}\)-AMP had a TAV greater than 1. Therefore, they were key non-volatile compounds in raw meat. Glu, 5\({}^{\prime}\)-GMP and 5\({}^{\prime}\)-IMP were widely regarded as monosodium glutamate (MSG)-like components that gave a pleasant broth or a rich meaty odor [39; 40]. Previous studies showed that nucleotides, such as 5\({}^{\prime}\)-GMP, 5\({}^{\prime}\)-IMP, and 5\({}^{\prime}\)-AMP, usually be added to raw meat in order to improve the sweetness and umami taste, while some free amino acids such as Asp and Glu were also umami-taste-related free amino acids [41; 42; 43]. In addition, nucleotides, and umami amino acids (UAA) created synergies to enhance the taste of meat. In agreement with previous studies [44], MSG-like components were the key non-volatile components in raw meat.
## 5 Conclusions  
Based on our analysis of the effects of wheel polygons on the dynamic behavior of high-speed railway tracks using a 3D FEM of the wheel-track coupled system, we conclude that the OOR amplitude and harmonic order of a wheel polygon have the greatest impact on the track dynamics. The following specific conclusions can be drawn:  
1. Both the vibration displacement and the von Mises equivalent stress in structural components of the track increase with an increase in the OOR amplitude of a polygonized wheel. For a given OOR amplitude, the maximum vibration displacement and von Mises equivalent stress due to wheel polygons occur in the rail head.
2. The vibration displacement in the rail first increases and then decreases as the harmonic order increases from 3 to 21, with the displacement reaching a maximum for a ninth-order polygonized wheel. This is because of the excitation of the resonances of the rail and the slab at \(\sim\)259.6 Hz. Special attention should be paid to such wheel polygons.
3. There are differences in the dynamic responses of the track caused by wheel polygons for train speeds of 200 to 350 km/h, which implies that a change in train speed influences the displacement, acceleration, and stress in the track structure.
4. For Cases 1, 2, and 3 (defined in Section 3), the dynamic responses of the concrete slab layer, CA layer, and support layer decrease from top to bottom.Thus, significant vibration attenuation reduces the dynamic responses of the support layer and CA layer.  
Further work is needed to develop a more complex model using ANSYSLS-DYNA, which will enable the analysis of wheel-rail rolling contact at high speeds. A new line of research must establish a non-track monitoring system to detect wheel defects on the basis of the dynamic response of the track caused by wheel polygons.  
Conceptualization, Y.S.; Formal analysis, X.Z.; Writing--review & editing, Y.D. and B.S.  
This research was funded by National Natural Science Foundation of China grant number11372199, China Postdoctoral Science Foundation Funded Project grant number 2018M643521, and Natural Science Foundation of Hebei Province grant number E2019210152.  
We would like to thank Editage (www.topeditsci.com) for English language editing.  
The authors declare no conflict of interest.  
The numerical simulation data used to support the findings of this study are available from the corresponding author upon request.
## 3 System Model of HAP Drones and LEO Satellite Collaborative Networks
### Computing Model  
Each user's task can be divided into four parts: one part can be processed locally, while the remaining parts can be processed on the HAP drone's edge server or LEO satellite's edge server. Below, we introduce each part in detail.  
* Local processing: Terrestrial users can put part of the task on the local CPU for processing. We denote \(x_{j}(x_{j}\in[0,1],j\in\mathcal{J})\) as the proportion of task processed locally. Therefore, the data size of the task processed locally is \(x_{j}D_{j}\), and the required CPU numbers can be expressed as \(x_{j}D_{j}c_{j}\).
* Processing on directly connected HAP drones: In addition to the amount of data processed locally, the user will transmit other parts to the HAP drone directly connected to it. The amount of data in this part is \((1-x_{j})\). After the HAP drone receives this part of the data, it will directly put part of it on its own server for processing. We express the proportion of this part as \(y_{j}(y_{j}\in[0,1],j\in\mathcal{J})\). Therefore, the data size and required CPU numbers can be denoted as \(y_{j}D_{j}\) and \(y_{j}D_{j}c_{j}\).
* Processing on forwarded HAP drones: The HAP drone can also offload part of the task to the HAP drone connected to it and process it on its server. The proportion of this part of the task is \(z_{j}(z_{j}\in[0,1],j\in\mathcal{J})\), and the amount of data and the required CPU numbers are \(z_{j}D_{j}\) and \(z_{j}D_{j}c_{j}\).
* Processing on LEO satellites: The HAP drone is connected to the LEO satellite, so the remaining task can be forwarded to the LEO drone and processed on the LEO's server. The proportion of this part of the task is \(w_{j}(w_{j}\in[0,1],j\in\mathcal{J})\).
# The Evaluation of Industry 5.0 Concepts: Social Network Analysis Approach
In order to give insights on the state of Industry 5.0 in developing countries, as well as improvement suggestions, the authors propose the following research questions:  
RQ1: _Which are the key indicators for measuring the level of implementation of human-centricity in the manufacturing sector in developing countries?_  
RQ2: _Which are the key indicators for measuring the level of implementation of sustainability in the manufacturing sector in developing countries?_  
RQ3: _Which are the key indicators for measuring the level of implementation of resilience in the manufacturing sector in developing countries?_  
According to the research questions, this paper contributes by acknowledging the Industry 5.0 pillars individually and providing information on the indicators of each pillar, as well as data on the current state of the pillars in the Republic of Serbia, one of the developing countries. Research has shown that human-centricity is achieved by investing in production employees' training and competence development with a task-specific focus, as well as by establishing bonus systems for outstanding performances in production and/or innovation. When talking about sustainability, developing countries are achieving this by implementing certain measures for improving efficiency in material consumption and energy use; possessing a certified environmental management system is the third most important factor in achieving sustainability. The use of standardized and detailed work instructions has the biggest contribution to becoming and staying resilient, followed by the implementation of activities that raise employees' awareness of data security and the use of software specifically as two important factors for resilience. To successfully answer the research questions and to give insights on the manufacturing sector of the Republic of Serbia, the structure of this paper includes an introduction in Section 1, a literature review covering Digital Product-Service Systems and three Industry 5.0 approaches in Section 2, methodology with information on data collection and data analysis in Section 3, results and discussion in Section 4, and a conclusion in Section 5.
# Development and Field Validation of Wireless Sensors for Railway Bridge Modal Identification
## 2 Description of the Wireless Monitoring System
### Wireless Sensor Nodes
#### 2.2.1 Architecture of the Sensor Node  
The main components can be divided into three main areas, depending on the task for which they were designed, which are power, logic, and communication. The power supply management is very important to guarantee a long autonomy, and it represents one of the most innovative features of the developed sensor.  
The power supply of the sensor node is provided by a 3.7 V lithium polymer (Li-Po) battery and a photovoltaic (PV) panel, which allows recharging of the battery when a sufficient solar radiation is available. The component which manages these two power sources is the ADP5091, an ultralow power energy harvester power management unit (PMU). The maximum power point tracking (MPPT) control keeps the input voltage ripple in a fixed range, to obtain a DC-DC boost conversion as stable as possible. The dynamic sensing mode and no sensing mode allow the extraction of the highest possible energy from the power source. Such a component also provides the stabilized 3.3 V power supply for all components on the PCB. Moreover, a standard micro-USB port is available, which is used to fast-charge the battery and to get information about the node, like the configuration parameters stored in the flash memory.  
The microcontroller is the main element of any digital device, since it is the programmable component that manages the entities of the whole system. An ultralow power Arm Cortex M4 STM32 device is used. The Cortex-M4 core features a floating-point unit (FPU) single precision, which supports all arm single-precision data-processing instructions and data types. It also implements a full set of DSP instructions and a memory protection unit (MPU). The sensing part of the board presents four MEMS accelerometers with \(\pm 8\) g or \(\pm 16\) g measurement range, which can communicate with the microcontroller through both the SPI and the I2C protocols. The sensor used in this work is provided by TDK InvenSense (IIM-42351), a three-axis accelerometer with low noise and low power features, a \(\pm 16\) g measurement range, and a sampling frequency up to 8 kHz. The main features of the adopted MEMS accelerometer are reported in Table 2. Connected to the microcontroller, there is also a flash memory able to store custom configuration setups, lookup tables, and manage a circular buffer with the processed data. The presence of the microcontroller allows performing on-board processing on the acquired data such as the fast Fourier transform (FFT) or RMS.  
The communication between the sensor node and the gateway takes place through a wireless communication channel, which is based on the Fanstel Bluetooth Low Energy 5.0 (Fanstel Corp., Scottsdale, AZ, USA) (BLE) stack. Such a protocol ensures a good communication range (up to 200 m in line of sight) with a low power consumption (10-500 mW).