esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Descriptive Characteristics  
Table 1 provides an overview of the maternal and perinatal characteristics. We observed a higher prevalence of nulliparity in the acidotic group (\(p<0.001\)). Fetal growth restriction was more frequently observed in the acidotic group (15.47%) compared to the non-acidotic group (10.79%), with statistically significant differences (\(p=0.011\)). Additionally, the incidence of cesarean section was twice as high in the acidotic cases (23.66% vs. 8.99%). The median values of arterial and venous pH were 7.06 and 7.15, respectively, in the acidotic and non-acidotic groups, showing a significant difference (\(p<0.001\)).  
In Table 2, we provide a comparative analysis of the deceleration area and reperfusion time measurements during the last 30 min of fetal ECG among the acidotic groups. We also present the parameters measured during the last deceleration of the fetal ECG and their difference from the first deceleration. Additionally, we include the odds ratio and the AUC of univariate logistic regression models for all predictor variables to predict acidosis.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Total Sample n = 502** & **Non-Acidotic n = 278** & **Acidotic n = 224** & \(p\)**-Value** \\ \hline Maternal age & 34 (30–37) & 34 (30–36) & 34 (30–37) & 0.440 \\ Nulliparity & 302 (60.16\%) & 148 (53.24\%) & 149 (66.52\%) & \textless{}0.001 \\ Age gestational & 280.5 (274–286) & 280 (273.75–286) & 281 (274.25–286) & 0.205 \\ Sex & & & & \\ Male & 262 (52.19\%) & 146 (52.51) & 116 (51.78) & 0.87 \\ Female & 240 (47.81\%) & 132 (47.48) & 108 (48.21) & \\ Newborn percentile weight & 3251.74 (471.07) & 3281.09 (454.45) & 3215.31 (489.34) & 0.123 \\ \(p<10\) & 73 (14.54) & 30 (10.79\%) & 43 (15.47\%) & 0.011 \\ \(>\)90 & 60 (11.95) & 33 (11.87\%) & 27 (12.05\%) & 0.999 \\ Birth eutocic & 303 (60.36\%) & 193 (69.42\%) & 110 (49.11\%) & \textless{}0.001 \\ Instrumental delivery & 121 (24.1\%) & 60 (21.58\%) & 61 (27.23\%) & 0.141 \\ Cesarean section & 78 (15.54\%) & 25 (8.99\%) & 53 (23.66\%) & \textless{}0.001 \\ Apgar 5 m \textless{}7 & 36 (7.17\%) & 4 (1.44\%) & 32 (14.29) & \textless{}0.001 \\ Umbilical arterial pH & 7.12 (7.07–7.20) & 7.18 (7.14–7.27) & 7.06 (7.01–7.09) & \textless{}0.001 \\ Umbilical venous pH & 7.18 (7.13–7.22) & 7.23 (7.18–7.26) & 7.15 (7.09–7.19) & \textless{}0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Maternal characteristics and perinatal results.  
The variables measured during the 30 min fetal monitoring window demonstrate good discriminatory ability. The deceleration area achieves an AUC value of 0.807, while the reperfusion time exhibits a slightly lower value of 0.750. When considering the parameters of the last deceleration, the slope of the deceleration demonstrates the highest AUC value of 0.853, surpassing those measured over the entire 30 min period.
# Noise Reduction Based on a CEEMD-WPT Crack Acoustic Emission Dataset
## 2 Basic Theory  
### CEEMD Noise Reduction  
In order to solve the problem of mode aliasing in EMD, scholars proposed EEMD, which first added multiple groups of white noise to the original signal, then repeated EMD, and finally averaged the overall IMF component. However, it did not completely solve the problem of mode aliasing, but led to increased computation and noise residue. Different from the EEMD method, CEEMD adopts the method of adding white noise in pairs to solve the problem of noise residue, and its process is as follows:  
(1) First, white noise is added in pairs to the original signal.  
\[x_{i}(t)=\begin{cases}x(t)+\varepsilon^{i}(t)\\ x(t)-\varepsilon^{i}(t)\end{cases},i=1,2,\ \ldots,n \tag{1}\]  
In the above formula, \(x(t)\) is the original signal; \(\varepsilon^{i}(t)\) is white noise; and \(x_{i}(t)\) is the signal pair after adding noise.  
(2) The \(x_{i}(t)\) signal pair is decomposed using \(EMD\).  
\[IMFs=EMD(x_{i}(t)) \tag{2}\]  
(3) The average value of the 2n group \(IMFs\) component is calculated, which is the result of CEEMD.  
\[\overline{IMF}=\frac{1}{2n}\sum_{i=1}^{2n}IMF_{ij}(t) \tag{3}\]  
In the above formula, \(IMF_{ij}(t)\) is the JTH component of the \(i\)th signal decomposition.
# Maximizing UAV Coverage in Maritime Wireless Networks: A Multiagent Reinforcement Learning Approach
## 3 System Model and Problem Statement
### Problem Formulation  
The objective of the optimization problem is to maximize this coverage rate. The coverage path-planning problem consists of one objective function and ten constraint conditions, which represent different aspects of task performance. We formalize the optimization problem as follows:  
\[\max_{x_{1},x_{2},\ldots,x_{n}}\frac{1}{N}\sum_{i=1}^{N}\mathbb{I}((x_{i},y_{i}, z_{i})\in\mathbf{A}) \tag{24}\]  
where \((x_{i},y_{i},z_{i})\) represents the path of the \(i\)-th UAV, \(\mathbf{A}\) denotes the coverable area, and \(\mathbb{I}(x_{i}\in\mathbf{A})\) indicates whether \(x_{i}\) falls within \(\mathbf{A}\).  
**s. t.**  
\[\begin{array}{ll}(1)&d_{mm^{\prime}}\geq R_{\mathrm{cov}}\quad\forall m,m^{ \prime}\in\mathcal{M},m\
eq m^{\prime}\\ (2)&0\leq d_{m,t}\leq d^{\max},\quad\forall m\in\mathcal{M},t\in\mathcal{T}\\ (3)&H_{\min}\leq H\leq\frac{l_{x}}{2\pi\tan\left(\frac{lOA_{x}}{2}\right)}\\ (4)&\mathcal{D}_{m,m^{\prime},t}\geq\mathcal{D}_{\min},\quad\forall m,m^{ \prime}\in\mathcal{M},m\
eq m^{\prime}\\ (5)&\frac{V_{m,t}-V_{m,t-1}}{\Delta t}<max(\mathcal{A}_{m}),\quad\forall m\in \mathcal{M}\\ (6)&\int_{0}^{t}\mathbf{r}\Big{\|}\frac{dx}{dt}\Big{\|}dt\leq C-E_{0}\\ (7)&E_{m,max}^{turn}+E_{mm^{\prime},max}^{comm}+E_{m,max}\leq\int_{0}^{T} \mathbf{r}\Big{\|}\frac{dx}{dt}\Big{\|}dt\\ (8)&|\theta_{m}(t)-\theta_{m}(t-1)|\leq\Delta\theta,\forall m\in\mathcal{M},t \in\mathcal{T}\end{array}\]
## 3 Background
### Kernel-Based Virtual Machine with Intel Virtualization Technology  
The kernel-based virtual machine (KVM) is a virtualization platform that utilizes a Linux kernel as a hypervisor (e.g., Xen in other virtualization platforms). KVM runs as a loadable kernel module (i.e., kvm.ko) in the Linux kernel (i.e., host OS) and constructs virtualized execution environments (i.e., VMs) by exploiting the hardware-assisted virtualization techniques, such as Intel virtualization technology (VT) [33]. Intel VT offers two different execution modes, the root mode and non-root mode: the host OS runs in the root mode and VMs run in the non-root mode [34]. The mode transition that switches the execution mode of the CPU from the root mode to the non-root mode is called VMentry, which occurs when the instructions of VMs are executed. On the other hand, when the applications in VMs execute privileged instructions, the execution mode is switched from the non-root mode to the root mode, which is called VMexit. This is because the privileged instructions cannot be handled in the VM. For example, when a hardware interrupt occurs, it triggers VMExt to execute the corresponding interrupt handler in the host OS [35]. Upon every mode transition, the state information (e.g., the values of the registers and memory address) of the current execution mode is stored in the specialized data structure, the virtual machine control structure (VMCS), so that the states are restored later.  
In addition, Intel VT provides an extended page table (EPT) [36] that enables the translation from the virtual address of a VM into the physical address of the host server. MMU with EPT offers two page tables: the guest page table for the VM and the host page table for the host OS. For each virtual memory accessed by the VM, MMU retrieves the physical address of the host server from the virtual address of the VM using the guest and host page tables. The EPT translation requires two page table walks so that inherently it is slower than native (without KVM) address translation. Moreover, when the memory access made by a VM is not permitted, it causes an EPT violation that is a page fault, which needs extra processing to update the page tables [37]. KVM typically utilizes QEMU to support virtualization together. QEMU is a fast machine emulator using a dynamic translator. QEMU supports full system emulation, where a complete and unmodified kernel runs. Also, QEMU provides the CPU and device emulation, memory management, and I/O for VMs.
## 5 Case Study: Cryptographic Hardware Coprocessor for the Secure Subsystem of Rhea GPP
### Interface Security Policy  
According to the security functionalities included in the Crypto-Tile and described in Section 5.1, it is possible to individuate the main security-critical assets that require additional protection mechanisms when they are installed, used, and erased. We targeted the security-critical assets at three different levels:  
* Global Crypto-Tile level: this level concerns the global configuration of the Crypto-Tile;
* Cryptographic Operation level: this level concerns data that are processed during the execution of the cryptographic operations;
* Key material level: this level concerns the keys, their installation in dedicated registers inside the Crypto-Tile, and their storage, access, and utilization statistics.  
At the Global Crypto-Tile level, we integrated seal/unseal mechanisms that rely on a unique identifier that is configured during the secure boot process and the support of two authorization levels for access to the Crypto-Tile: a privileged level and an unprivileged level. For the cryptographic operation level, we integrated dedicated resources that strictly regulate the execution of each cryptographic operation and the access to the related data. For instance, Figure 8 shows the state diagram of the FSM that handles the cryptographic operations for the AES cryptoprocessor and manages access to the processed data. The auxiliary data such as initialization vectors can be provided in input only in the CONFIGURED state, while the input data to be encrypted can be written only in the RUNNING state, only once and only after the encryption of the previous data block is completed. In addition, the corresponding output data can be read only once and only in the RUNNING or ENDED state depending on the executed cryptographic operation. Similarly, the commands that can be provided to the AES cryptoprocessor strictly rely on the current state of the FSM.  
Figure 8: FSM regulating the data access during the execution of a cryptographic operation.  
Concerning the key material level, the Crypto-Tile embeds dedicated seal/unseal mechanisms similar to the ones used for the Global Crypto-Tile level. A dedicated FSM for each key regulates its usage similarly to how it is illustrated for the cryptographic operations in Figure 8. Counters and configuration registers limit the key utilization according to the target operation. For instance, the usage of cryptographic keys can be configured for specific AES operations such as the GCM one or only for encryption processes. In such a case, the usage of that key in other AES operations different from the GCM one or in decryption processes is not permitted.  
In addition, each level presents logging and alerting resources to check unauthorized access and interrupt all the ongoing operations in case of misusage, as well as triggers for two distinct levels of panic mode. According to the severity of the panic mode, the key material can be preserved to allow for retrieving it after the termination of the panic mode, or it can be flushed to prevent an attacker from taking it over.
## 2 Literature Review
[PERSON]'s 'adaptive-awareness' dual-factor model developed in Ghana demonstrated that culturally adapted smart training can increase the acceptance of safety behaviors by 2.3 times [14]. These findings guide us: the advancement of intelligent coal mining must be accompanied by 'technology-culture' adaptation mechanisms, such as developing intelligent terminals with ethnic language interfaces, which can reduce operational error rates by 35%. The association between technological innovation and psychological costs is reinforced in [PERSON]'s and [PERSON]'s research. [PERSON]'s BP-ARIMA model (MAE = 1.02%) reduced miners' water hazard anxiety indices by 62% through precise predictions, demonstrating that technological reliability directly impacts psychological costs [15]. [PERSON]'s digital twin system showed that visualizing the carbon footprint across the entire lifecycle increased management's willingness to invest in intelligent upgrades by 28 percentage points, with this 'visible benefit' significantly alleviating transition anxiety [16]. The above literature establishes that intelligent coal mine transformation operates through two interdependent mechanisms: (1) techno-psychological feedback, where safety climate enhancements via intelligent systems (e.g., digital twin visualization [6; 7]) reduce equipment resistance by 4.2-29.2%; (2) cultural-leadership mediation, where empowering leadership [8] and localized interfaces [13; 14], respectively, improve compliance by 19.7-23% and reduce errors by 35%, albeit with 18% cost premiums. Crucially, these studies collectively identify--but do not quantify--the nonlinear value perception gap between enterprises (ROI-focused) and governments (safety-priority), a theoretical void our prospect-theoretic game model (Section 3) specifically addresses by integrating [PERSON]'s anxiety reduction coefficients [15] and [PERSON]'s visualization elasticity [16] into bilateral utility functions. Specifically, when the safety-effectiveness perception of intelligent systems among enterprise miners increases by one unit, their technical acceptance increases by less than one unit, while the government's investment willingness in intelligent construction is significantly positively correlated with accident cost avoidance expectations. This imbalance in bilateral value perceptions is the core of the current game-theoretic dilemma, where the government prioritizes immediate safety gains while enterprises focus on the long-term return on investment. Therefore, focusing on the critical point calculation of intelligent adoption, that is, finding the Pareto optimal solution between psychological acceptance costs and corporate investment pressures, is the current research priority. On this basis, this paper addresses the issue of intelligent construction in coal mines under China's current reform policies from the perspectives of traditional game theory, prospect theory, and mental accounting theory.
# Bitumen and Bitumen Modification: A Review on Latest Advances
## 1 Introduction
### Bitumen Chemistry
#### 1.2.1. Asphaltenes  
Asphaltenes, whose percentage in bitumen varies from 5 to 25% are amorphous brown/black solids at room temperature with particle dimensions between 5-30 \(\upmu\)m, insoluble in n-heptane, but soluble in toluene [17; 18]. Asphaltene contains oxygen, nitrogen, sulfur, and heavy metals (V, Ni, etc.) in the form of complexes such as metallo-porphyrins with long aliphatic chains (up to 30 carbon atoms), and pyrrolic and pyridinic rings. As demonstrated by UV-fluorescence spectroscopy [19; 20], Fourier transform infrared spectroscopy (FTIR) [20; 21], X-ray raman spectroscopy [22], and NMR spectroscopy [20; 23; 24], asphaltene molecules consists of fused aromatic rings, most probably between 4 to 10 units, together with some aliphatic chains as ring substituents, as shown in Figure 1.  
Owing to the presence of many condensed aromatic rings, asphaltenes form an almost planar structure that can associate by \(\uppi\)-\(\uppi\) bonding interaction to form graphite like structures (see Figure 2) [21; 27].  
Figure 1: Asphaltene hypothetical structure: (**a**) Archipelago structure, and (**b**) continent structure (reprinted from [25; 26] with the permission of the American Chemical Society and OnePetro).  
X-ray diffraction spectra of solid asphaltenes [29; 30; 31] have shown two main peaks: one amorphous at a \(2\theta=19^{\circ}\), mainly due to the aliphatic structures of the molecules, and a very wide diffraction peak at \(2\theta=26^{\circ}\), corresponding to the Bragg angle scattering of the (0,0,2) planes of graphite, meaning that the graphite-like crystals of asphaltene aggregates are very small [4]. As reported by [PERSON] et al. [30] based on the half-width of the diffraction peak, they estimate the graphite-like structure size in the order of 2-5 nm with an average of five-unit sheets (see Figure 3).  
Asphaltene aggregation is still maintained in solvent, where they associate to form the so-called \"micelles\" [14; 32; 33; 34; 35] whose size is principally determined by the solvent nature, asphaltene content, and temperature [28; 29; 35]. Asphaltene graphite-like stacking of the molecules has as consequences a  
Figure 3: [PERSON] model of solid asphaltenes (reprinted from [28], with permission of the American Chemical Society).  
Figure 2: Monomeric, dimeric, trimeric, and tetrameric structure of a Venezuelian asphaltene molecule (reprinted from [28] with permission of the American Chemical Society).  
high degree of electron delocalization, giving an overall negative charge [27; 36] a permanent electrical charge [37], and a dielectric constant between 5 and 7 [38; 39; 40]. Finally, asphaltene association also has particular effects on the rheological behavior of bitumen [41; 42; 43]. A more detailed study on the asphaltene nature can be found in [44].
# Application of Natural Antioxidants from Fruits Waste for Improving Egg Quality Characteristics
###### Abstract  
Fruit waste poses a significant global challenge, with the Food and Agriculture Organization (FAO) reporting that about 1.3 billion tons of food is wasted annually, contributing to environmental and economic challenges. The major fruit-producing countries (United States, China, India, and Brazil) are responsible for a large portion of this waste. Fruit waste contains different bioactive compounds like polyphenols, flavonoids, vitamins, and carotenoids, which are, in large part, underutilized regardless of their antioxidant properties. These compounds can play an important function in poultry nutrition, offering natural alternatives to synthetic antioxidants. Reintegrating fruit waste into poultry diets might have potent benefits on the nutritional quality and shelf life of eggs. Some studies reported that bioactive compounds from fruit waste improve the oxidative stability of eggs and the nutritional content, potentially extending shelf life during storage. Additionally, antioxidant compounds from fruit waste have been shown to improve yolk coloration and some egg quality parameters while reducing reactive oxygen species, which cause lipid oxidation. Their inclusion in poultry diets can lower feed costs, promote animal welfare, and reduce the environmental impact of fruit waste. This review highlights some key fruit wastes, such as citrus, apple, grape, rosehip, sea buckthorn, and banana, among others, which are natural reservoirs of bioactive compounds beneficial for poultry nutrition. This strategy aligns with FAO sustainability goals, addressing both food waste and animal nutrition challenges. Future studies should focus on optimizing the inclusion rates of these natural antioxidants in poultry diets to maximize their benefits.  
food quality; preservation; antioxidants; fruit waste; bioactive compounds; poultry; egg quality; oxidation +
Footnote †: journal: applied sciences
## 4 Result and Discussion
All these results evidently support that the given HEN cleaning schedule optimization problem is very complicated even with the time-horizon of 18 months and there could be several local minimums very close to the global one. In Figure 4, it can be also noted that both methods give the same result that the heat exchanger unit 9 and 10 should be cleaned more often than the others, indicating they are important in the network. This is because they have a faster fouling rate constant than the others (see Table 1). Figure 5 shows the CIT (the crude inlet temperature to furnace) profiles obtained by applying the solutions of two compared methods for the extreme case (\(t_{F}=60\) months). From this figure, it can be seen that well-optimized cleanings could effectively restore the performance of the HEN system. Without cleaning, the CIT can be decreased from 401 \({}^{\circ}\)F down to 304 \({}^{\circ}\)F due to the fouling, which requires a tremendous extra fuel cost of 4574 k\(\varepsilon\). With optimized cleanings, the minimized \(Obj_{opt}\) values were 2271 k\(\varepsilon\) (moving window) and 2393 k\(\varepsilon\) (fixed time horizon), respectively (see Table 2), which implies that 122 k\(\varepsilon\) could be further saved if using our moving window algorithm. All these results clearly support that our moving window decision-making algorithm is not only fast but also robust especially when dealing with an extremely complex HEN cleaning optimization problem with a long-time horizon. Figure 4: Detailed cleaning schedules for the case of \(t_{F}=18\) months.
## 6 Discussion
### Safety
According to the simulation results in this study, due to the randomness of departure sequences and differences in drivers' desired risks, even with a yellow duration of 5 s, it is rare for two or more vehicles to run a yellow light within the same signal cycle. As a result, the situation described in the study of [PERSON] et al. [81] did not occur in this study. If we consider the total number of yellow- and red-running vehicles under the same departure rate, speed limit, and yellow light duration as a measure of unsafe driving behavior, the analysis suggests that when traffic flow evolves from sparse to dense conditions, increasing the yellow light duration within the range of 3-5 s can help suppress the occurrence of unsafe driving behaviors. Therefore, traffic managers may consider appropriately extending yellow phase duration within this range under high traffic volumes at intersections, which is consistent with the conclusions in Section 6.1 and offers a pragmatic solution for traffic managers seeking to enhance safety at intersections. However, traffic volume and speed limits should be jointly considered when optimizing yellow phase duration. While longer yellow light durations may improve safety at lower speeds, they must be carefully calibrated under higher speed limits to prevent unintended risk-taking behaviors. Combining these insights with advanced traffic signal control technologies and driver assistance systems can further enhance safety outcomes.
# An Integrated DQN and RF Packet Routing Framework for the V2X Network
## 4 System Simulations and Performance Evaluations
### Simulation Environment
In this study, the average overhead ratio is the sum of the total number of packets that need to be transmitted through each relay node \(j\) in the forwarding path of packet \(I\) from the source vehicle to the destination vehicle, including the data packets sent by the source vehicle and the control packets sent by various routing protocols, denoted as \(np_{j}^{i}\). The overhead for the forwarding path of packet \(i\) is defined as \(\sum_{j=1}^{h}np_{j}^{i}\), where \(h\) is the total number of relay nodes, i.e., hop counts, between the source and the destination vehicles. Next, the overheads of all packets transmitted by the source vehicle are summed and divided by the total number of transmitted packets by the source vehicle (Equation (12)). \[\textit{Average Overhead ratio}=\frac{\sum_{i=1}^{Number of transmitted packets by source}\sum_{j=1}^{h}np_{j}^{i}}{Number of transmitted packets by source} \tag{12}\]