esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Simulation Results
### Experimental Results
#### 6.6.2 Security Performance  
Since all the damage that comes with vampire attacks is reflected in increased energy consumption, \"security performance\" is then naturally converted to part of energy efficiency performance under a specific malicious ratio in overall performance evaluation. Thus, the overall cost is a comprehensive \"energy overhead.\" Both estimated energy costs after successfully delivering a data packet and the extra energy consumption caused by vampire attacks in this transmission along the route discovered by a specific routing protocol are inclusive. That is to say that higher energy consumption is linked to longer average route length in data transmission. The overall cost, in other words, the so-called comprehensive \"energy overhead\", as aforementioned, can be regarded as an indicator of security level as well. That is to say that higher energy overhead is linked with less security level, and lower energy overhead means higher security level. Consequently, earlier in Section 6.6.1, it has already presented an overall performance evaluation that involves both energy efficiency and safety performance.
## 2 Deterministic Model  
### Boundary Conditions  
For the SPT molten salt receiver tube, its length and inner/outer radius are 3 m and 10.5/12.5 mm, respectively. The heat transfer fluid is molten nitrate salt (60% NaNO\({}_{3}\) + 40%KNO\({}_{3}\)), and the tube material is 316H stainless steel.  
Figure 1 depicts the physical model of the receiver tube. The inlet temperature and inlet velocity of the heat transfer fluid are, respectively, 673 K and 3 m/s. The incident solar flux onto the receiver tube is generally nonuniform and the expression is as follows:  
\[q=\begin{cases}M\text{-}e^{-1.33z^{2}}&\cos\theta\geq 0\\ 0&\cos\theta<0\end{cases} \tag{1}\]  
where the peak incident solar flux \(M=900\) kW/m\({}^{2}\) is imposed at the tube crown, and \(\theta\) and \(z\) are described in Figure 1. Only half of the receiver tube is subjected to the high solar flux, which is assumed to have a normal distribution along the axial direction [2; 28].  
The heat losses \(q_{\text{loss}}\) of the receiver tube caused by reflection, convection, and radiation can be calculated as follows [29]:  
\[q_{\text{loss}}=q(1-\alpha)+h(T_{\text{wall}}-T_{\text{amb}})+\varepsilon \sigma(T_{\text{wall}}^{4}-T_{\text{amb}}^{4}) \tag{2}\]  
where \(\alpha\) and \(\varepsilon\) are, respectively, the absortivity and emissivity of the tube coating material and both taken as 0.93. The convective heat transfer coefficient outside tube wall \(h\) is assumed as 10 W/m\(\cdot\)K, and the ambient temperature \(T_{\text{amb}}\) is 25 \({}^{\circ}\)C. \(T_{\text{wall}}\) is the outer wall temperature of the receiver tube, and \(\sigma\) is the Boltzmann constant.  
In addition, Figure 1 shows that there is no constraint at both ends of the receiver tube. Consequently, the thermal stress is caused by the temperature gradient and geometry.
## 4 Discussion
### Effects of Intercropping on System Productivity and Silage Quality
For the nutritive values of the mixed silage, the proportion of alfalfa, which has a high CP content but low WSC content, was one of the key factors affecting the CP and WSC of the mixed silage [12; 13]. Besides, consistent with previous studies, both the CP and WSC content of the corn was improved by the increasing N fertilizer and decreased by the increasing corn plant density [42; 43]. Thus, we found that the treatments of low corn densities (D30, D45) combined with high N rates (N120, N180) had a relatively higher CP and WSC content, while they were relatively low under treatments of a high corn density (D60) (Figure 3). The DM content of the mixed silage was affected by the N rate \(\times\) corn density interaction in both years (Table 3), but there was no linear correlation relationship with the alfalfa proportion [34]. Previous studies also have shown that neither corn plant density nor N fertilizer affected corn NDF [44; 45], which agree with our results that only minor differences of NDF, ADF as well as RFV were found among different N rates or corn plant densities in both raw materials or the mixed silage (Table 3 and Table S1). For the fermentation characteristics, consistent with other studies [40; 43], the NH\({}_{3}\)-N content was significantly increased by N fertilizer. Given that pH values and LA contents were affected by the N rate and corn plant density, the differences of fermentation characteristics among treatment combinations of the mixed silage were relatively small (Table 4). Proper field management is important for a new cropping system to achieve high productivity and forage quality. To evaluate the better N rate and corn plant density of the alfalfa-silage corn intercropping system we proposed, the multicriteria assessment on all agronomic, nutritive and fermentation characteristics was used, and we found that the treatment combinations under a high N rate and medium corn density (N120-D45 and N180-D45) could achieve high scores on both productivity and quality characteristics during the summer season (Figure 3). We have also proven that although the alfalfa stand density was decreased, the alfalfa productivity during spring was not affected, except for the treatment combination N180-D60 compared with the monocultured alfalfa, and the treatment combination of N120-D45 which achieved a higher annual system DM yield, annual system protein yield and land equivalent ratio with relatively low environmental costs [14; 15]. Conclusively, we suggested the N120-D45 as the most advantageous alfalfa-silage corn intercropping combination for this system. However, further studies should be conducted in a variety selection, water management, etc., to improve the productivity and flexibility of the intercropping system.
# Determination of Geometrical REVs Based on Volumetric Fracture Intensity and Statistical Tests
## 5 Discussion
### Geometrical REV Calculation  
In this work, based on the field survey at the Maji dam site, we established a relatively reasonable process for calculating geometrical REVs. Based on the current investigation of geometrical REVs, the calculation procedure is described as follows:  
1. Generate a reliable 3D fracture network model based on the joint data obtained from the field survey.
2. Select several random regions in the generated 3D fracture network model.
3. Calculate the geometrical parameters of fractured rock masses by varying cube sizes and locations. Generally, the fracture intensity index is widely applied.
4. Generate \(n\) samples with the same cube size, which vary from 1 to \(n\) m.
5. Analyze the REV size by using several different statistical test methods.  
In this paper, the proposed method comprehensively considers the anisotropy of rock masses, simplicity of calculation and differences between different methods. Specifically, the volumetric fracture intensity P\({}_{32}\) and two statistical test methods (i.e., the likelihood ratio test and Wald-Wolfowitz runs test) were adopted to estimate the REV sizes at the dam site of Maji hydropower station. Because different parameters and methods have different considerations and impact factors, the REVs obtained from these parameters and methods may be varied. To ensure a result that both the likelihood ratio test and Wald-Wolfowitz runs test accept, we select the larger critical value as the REV of the studied rock mass.
# Machine Learning in Medical Triage: A Predictive Model for Emergency Department Disposition
## 1 Introduction
### Related Work
We leverage a comprehensive Open Source AutoML Python Module, H2O.ai, to develop a predictive model that not only forecasts hospital admissions but also integrates context-aware preprocessing methodologies specifically tailored to address the unique and often irregular data patterns found in emergency medical settings. Key contributions of this research include:  
1. Advanced Data Handling and Machine Learning Techniques: Extending beyond traditional applications of AutoML in healthcare, our study employs advanced data preprocessing techniques to manage the irregular, incomplete, and anomalous data patterns typical in emergency department settings. This allows for more accurate and reliable predictions, improving upon the granularity of data handling noted in foundational studies by [PERSON] et al. [9] and [PERSON] et al. [10]. 2. Application of Context-Aware AutoML: Unlike previous studies that have applied AutoML generically across healthcare data, our approach is meticulously designed for the idiosyncrasies of ED data, enhancing both the model's accuracy and its practical usability for end-users in clinical environments. 3. Integration of Explainable AI (XAI) Techniques: In response to the critical need for transparency and accountability in AI applications within healthcare, our model incorporates XAI principles. This ensures that the decision-making processes of our model are transparent, fostering trust among medical practitioners and supporting ethical AI usage in clinical settings. 4. Focus on Real-World Clinical Settings: The predictive model is intended as a decision-support tool to aid medical staff under high-pressure conditions, particularly in overcrowded and understaffed environments, where rapid and accurate decision-making is crucial. 5. Empirical Evaluation and Ethical Consideration: We conduct a thorough empirical evaluation of the model's performance and discuss its ethical implications, ensuring that our contributions are not only technically sound but also ethically responsible. By addressing these aspects, our study not only contributes to the academic field of medical informatics and emergency care but also sets a new benchmark for the integration of AutoML in enhancing emergency department operations. This research underscores the potential of machine learning to transform emergency medical services by providing timely and accurate decision support, thereby potentially improving patient outcomes and operational efficiency in EDs.
# A Review on Internet of Things for Defense and Public Safety
## 3 Target Scenarios for Mission-Critical IoT
### Logistics
#### 3.3.2 Individual Supplies  
The deployment of RFID tags, sensors and standardized barcodes allow for tracking individual supplies. IoT provides real-time supply chain visibility (whether it is being shipped, transferred, deployed, consumed... ), and allows the military to order supplies on demand and simplify logistics management for operational units. This smarter procurement of goods avoids delays caused by out-of-stock parts or inventory-carrying costs. Likewise, it can increase accountability, enhance mission reliability, reduce losses and theft of military equipment, and help with the time criticality on the military maintenance.  
At the soldier level, tracking is useful in order to follow a proactive approach to logistics or to meet operational requirements. Soldier material (e.g., water, food, batteries or bullets) can be monitored with alerts issued for a necessary resupply. Aggregate data (e.g., groups of soldiers, companies, battalions... ) might also be studied for further enhancements of supply for tactical and emergency units. The analytics might be focused on considering environment, body type, consumption... among other variables.
## 5 Conclusions  
In summary, we characterized, for the first time, 46 volatile components in \(\text{MCG}_{\text{5-15-years}}\) and \(\text{GCG}_{\text{5-15-years}}\), and five of them were screened out to distinguish \(\text{MCG}_{\text{5-15-years}}\) and \(\text{GCG}_{\text{5-15-years}}\), 12 of them to discriminate \(\text{MCG}_{\text{5-10-15-years}}\), and six of them to differentiate \(\text{GCG}_{\text{5-10-15-years}}\). Thus, our data suggest that 15 volatile components might be used as unique chemo-markers for discrimination among MCG and GCG with different growth years. The proposed approach could be applied to directly distinguish MCG and GCG with different growth years and to identify the differentiating chemo-markers, which is an important criterion for evaluating the effectiveness, safety, and quality stability of ginseng. In addition, the research has digitized the traditional identification method of \"nose smell\" by HS-SPME-GC-MS, which is of great significance for the inheritance and innovation of traditional identification methods.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/molecules28052016/s1](https://www.mdpi.com/article/10.3390/molecules28052016/s1)]([https://www.mdpi.com/article/10.3390/molecules28052016/s1](https://www.mdpi.com/article/10.3390/molecules28052016/s1)), Figure S1: 200 permutation tests and VIP plots for the OPLS-DA models. \(\text{MCG}_{\text{5-15 years}}\) VS \(\text{GCG}_{\text{5-15 years}}\) (A,B); \(\text{MCG}_{\text{5-years}}\) VS \(\text{MCG}_{\text{10-years}}\) (C,D); \(\text{MCG}_{\text{5-years}}\) VS \(\text{MCG}_{\text{15-years}}\) (E,F); \(\text{MCG}_{\text{10-years}}\) VS \(\text{MCG}_{\text{15-years}}\) (G,H); \(\text{GCG}_{\text{5-years}}\) VS \(\text{GCG}_{\text{10-years}}\) (I,J); \(\text{GCG}_{\text{5-years}}\) VS \(\text{GCG}_{\text{15-years}}\) (K,L); \(\text{GCG}_{\text{10-years}}\) VS \(\text{GCG}_{\text{15-years}}\) (M,N).  
L.H. and H.X. conceptualization, project administration, funding acquisition, writing--review and editing. L.Z. and S.L. investigation and formal analysis. P.W. Writing--original draft. D.W., Y.Z. and W.L. formal analysis. All authors have read and agreed to the published version of the manuscript.  
This work was supported by grants from Scientific and technological the National Natural Science Foundation of China (No. 81830111), the Scientific and technological innovation project of the China Academy of Chinese Medical Sciences (No. CI2021A04904, CI2021B015, CI2021A04907), the Fundamental Research Funds for the Central public Welfare Research Institutes (ZXKT17058, ZZ13-YQ-095), and the Intergovernmental Cooperation in International Science and Technology Innovation (No. 2022 YFE0119300).  
Not applicable.  
The data presented in this study are available in Supplementary Materials.  
The authors declare that they have no conflicts of interest.  
Samples of the compounds are available from the authors.
# Enhancing PAPR and Throughput for DFT-s-OFDM System Using FTN and IOTA Filtering
## 2 Signal Model and Performance Metrics
### General Waveform Framework  
Multi-carrier waveform supports parallel transmission, where high-rate data stream is decomposed into multiple parallel low-rate sub-data streams. Each sub-data stream is independently modulated and superimposed to form the transmission signal.  
Orthogonal Frequency Division Multiplexing (OFDM) waveform is a special kind of multi-carrier transmission technology. In the OFDM system, the transceiver filters form the special modulation filters, where the transmitting filter can be realized by IDFT and the receiving filter can be realized by DFT. With the usage of cyclic prefix, OFDM technology solves the problem of multi-path fading with low complexity. The following part describes the general framework of OFDM.  
Denote \(s\) as the symbol index, \(l\) as the sub-carrier index, \(L\) as the number of sub-carrier and \(n\) as the time index. Define \(d_{s,l}\in\mathbb{C}\) as the source symbol. Then the general waveform framework based on the concept of OFDM can be expressed as  
\[x(n)=\sum_{s=-\infty}^{\infty}\sum_{l=0}^{L-1}d_{s,l}\cdot p(n-sL)\cdot e^{l2 \pi lF(n-sL)}, \tag{1}\]  
where \(p(n)\) is the prototype filter and \(e^{l2\pi lF(n-sL)}\) is the Fourier transformation kernel corresponding to the value of frequency shift in the \(l\)-th sub-carrier, where \(F\) is the reciprocal of \(L\), i.e., \(F=1/L\). The above-mentioned time and frequency transformation relationship is also illustrated in Figure 1.
# Deep Learning for Audio Event Detection and Tagging on Low-Resource Datasets
## 2 Task Factorisation
### Audio Event Detection (WHEN)  
In our proposed task factorisation, the WHEN network performs a single class audio event detection as the first intermediate task towards full transcription. For a multi-class dataset, one would have to train a separate network for each class in order to perform single class event detection. However, in a low-resource dataset, training an audio event detector for each class can be nearly impossible. The number of classes might be too large, making it a time-consuming task. Furthermore, some of the classes might have very rare occurrences, limited to only a couple of recordings, hence making it infeasible to train a neural network for them. Nevertheless, many low-resource datasets are usually used for discriminating subclasses of a general class e.g., song of different bird species, sound of different car engines, barking of different dog breeds, and notes produced by an instrument. These subclasses usually share some common features and characteristics, hence, in order to achieve a good performance in the audio event detection task, we propose considering all subclasses as one general class and train a single WHEN network to perform single class event detection. This reduces the training time compared to training one network for each subclass and also solves any training issues caused by rare events.
# High Throughput Data Relay in UAV Wireless Networks
## 4 Proposed Solution
### Power Balance  
A power balance mechanism is implemented to ensure the signal power received at the UAV from the DS supported victims are the same during each time slot, as presented by (5d). Upon solving sub-problem (7), the victim bandwidth scheduling variable is determined. As the transmission power for the paired victims are both \(P\), therefore when the UAV locates at a position where its distances to the paired victims are same, the received power can be balanced. In order to obtain such UAV's position, the coefficients \(A_{n}[k]\) and \(B_{n}[k]\) in constraint (10a) should be pre-adjusted to be the same. Power balance is the operation of coefficients pre-adjustments, which is implemented to connect sub-problems (7) and (10).
# Analytical Solution and Experimental Validation of the Electromagnetic Field in an IPT System
## 5 Simulation Verification
### Simulation Verification of the Electric Field Produced by a Single-Turn Current-Carrying Coil  
As shown in Figure 7, the radius of the coil is \(a\) and the coordinates of points A\({}_{1}\), A\({}_{2}\) and A\({}_{3}\) are \((a/2,\,\pi/2,\,0)\), \((a,\,\pi/2,\,0)\) and \((3a/2,\,\pi/2,\,0)\), respectively. The moduli of the electric field, changing with z at \(z_{1}\), \(z_{2}\) and \(z_{3}\), are given in Figure 8a-d, where \(I\) and \(f\) denote the effective current value and the frequency in the coil, respectively. In Figure 8, MAT represents the results obtained by the analytical results of electric field using MATLAB, and COM represents the simulation results using the finite element software COMSOL.  
Figure 6: Numerical integration method for the analytical solution.  
The analysis results coincide with the simulation results and the maximal relative error is below 1%. This is because the analytical solution was deduced under the assumption that the current-carrying coil is treated as a wire with no cross section. Since the distance from the test point to the current-carrying coil is much longer than the coil diameter, the relative error caused by the coil diameter can be ignored. Similarly, the accuracy of the analytical solution of the magnetic field strength can be verified also.
# Nonlinear Seismic Response of Multistory Steel Frames with Self-Centering Tension-Only Braces
## 2 Analytical Model of SC-TOBFs  
### Mechanics of SC-TOB  
Figure 1a illustrates the schematic of the SC-TOB system [27; 28], which employs a set of PT tendons (shown as a whole unit, for illustrative purposes) to provide a restoring force to achieve self-centering, a frictional device to dissipate seismic input energy, and a high strength steel (HSS) cable as the main bracing element. One end of the PT tendon is anchored to the blocking plate, and the other end is passed around the pulley and connected with the frictional device after pre-tensioning it. The pulley within the SC-TOB is adopted to reduce the strain by half on the PT tendons, if the same  
elongation occurs, leading to a double elongation capacity of the bracing system. The mechanics of the SC-TOB system can be explained using an analytical model as presented in Figure 1b, wherein \(k_{t}\), \(k_{t}\), and \(k_{\mathrm{c}}\ =\) the axial stiffnesses of the PT tendons, frictional device, and HSS cable, respectively; \(F=\) the frictional resistance provided by the frictional device; and \(T_{t,0}\ =\) the pre-tension of the PT tendons. R represents the blocking plate to restrict the left movement of the frictional device.  
Figure 1c depicts the flag-shaped hysteresis of the SC-TOB. When the load \(P\) is too small to overcome the sum of \(T_{\mathrm{t,0}}\) and \(F\), only the HSS cable works at _stage o-a_, hence the initial stiffness of the SC-TOB, \(k_{1}\), is equal to \(k_{\mathrm{c}}\). As \(P\) increases to reach the activation load \(P_{a}\), given by \(P_{a}\ =\ T_{\mathrm{t,0}}\ +\ F\), the PT tendons start to stretch, and meanwhile the energy dissipation mechanism is activated. At _stage a-b_, the axial stiffness of the SC-TOB decreases from \(k_{1}\) to the post-activation stiffness \(k_{2}\), given by \(k_{2}=\left(k_{t}^{-1}+k_{t}^{-1}+k_{\mathrm{c}}^{-1}\right)^{-1}\). Upon unloading, the friction device is locked, with the friction value varying from \(F\) at _event b_ to \(-F\) at _event c_. During this stage, as the frictional device remains stationary, the stiffness of the SC-TOB is recovered to \(k_{1}\). As \(P\) further unloads (_stage c-d_), the SC-TOB is pulled back to its initial position under the influence of the tensioning tendons, and the stiffness is once again reduced to \(k_{2}\). With continuous unloading during _stage d-o_, as the movement of the friction device is restricted by the blocking plate, the stiffness of the system once again recovers to \(k_{1}\).