esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Routing Protocols for UAV Communication
### Geographic Routing Protocol (GRP)  
The GRP is an efficient and attractive approach, mostly because no end-to-end route is built before data transmission. GRP also enables each node to maintain local one-hop connectivity, which improves network scalability [30]. GRP forwards data packets in the following manner: If a source node desires to send packets to commence communication, the router sends a request-to-send (RTS) packet to its neighbors, and only neighbors within a restricted assigned sextant (forwarding area) near the destination are compatible to respond with a clear-to-send (CTS) packet, forming a set of potential forwarding nodes. The source node then chooses a single node as a subsequent relay by using a comprehensive reactive technique to transport data packets based on particular selection criteria. Furthermore, the fundamental description of the GRP approach's relay node selection takes into account unexpected scenarios. Compared to traditional ad hoc routing techniques, GRP has several advantages. Changes can be made node by node and packet by packet by taking into account extra Quality-of-Service (QoS) metrics related to the next-hop neighbors, such as latency or available bandwidth. However, one of the key drawbacks of GRP is the complexity and overheads associated with a distributed location database service.
# Schumann Resonances and the Human Body: Questions About Interactions, Problems and Prospects
## 5 Conclusions  
Based on the data from all the studies performed, which were available in the literature, it can be argued that there is a proven interaction between the human body and SRs. This interaction has been studied most significantly between SRs and the human nervous system. The available data provide grounds to tentatively assert the following: (1) the parameters of Schumann resonance signals and the electrical activity of the human body are interconnected; (2) SRs with spectral peaks at approximately 8, 14, 20, 26 and 33 Hz correspond to the electroencephalographic activity of the human brain in frequency and intensity, with an average magnetic field strength of about 1-2 picoteslas and an electric field strength of approximately 0.1-1 mV/m at an average thickness of the cerebral cortex of about 3 mm; (3) the human brain is sensitive to the effects of SRs and reacts to them by changing its functional state in accordance with the frequencies of impact; and (4) the autonomic nervous system of the human body is synchronized with the dynamics of SRs. SRs affect functional indicators of the cardiovascular system: heart rate and blood pressure. The ongoing study of the influence of SRs on the course and pathogenesis of NCDs is a promising direction. Existing evidence shows that low-frequency SRs decrease the risk of developing acute myocardial infarction, and there is a tendency for them to promote cases of chronic kidney disease. SRs are an important external natural factor influencing the human body, coordinating metabolic processes and the functional activity of its cells. It is necessary to take into account the importance of their physiological influence, as well as the possibility of the activation of pathogenetic mechanisms of NCDs.  
Conceptualization, G.N., G.J., M.L., M.P., R.M., I.A.B. and A.V.; Data curation, I.A.B. and A.V.; Formal analysis, M.L., R.M. and A.V.; Investigation, G.N., G.T. and M.P.; Methodology, M.L., M.P. and R.M.; Project administration, A.V.; Resources, I.A.B.; Supervision, I.A.B. and G.J.; Validation, G.J., M.P., R.M. and A.V.; Visualization, G.N.; Writing--original draft, G.N.; Writing--review & editing, G.J., M.P., R.M., I.A.B., M.L. and A.V. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
We wish to thank to Lithuanian University of Health Sciences, Kaunas University of Technology and HeartMath Institute for supporting research on the influence of the Earth's magnetic field on the human body.  
The authors declare no conflicts of interest.
# Use of Total Least Squares Adjustment in Geodetic Applications
## 3 Practical Examples
### The Regression Line
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Weights** & \(k\) & \(r\) & \(\sigma_{k}\) & \(\sigma_{r}\) & \(\phi_{0}\) \\ \hline LS & no & \(-0.53958\) & 5.76119 & 0.04213 & 0.18949 & 0.316 \\ \hline LS\_SVD & no & \(-0.53958\) & 5.76119 & / & / & \\ \hline TLS\_SVD & no & \(-0.54886\) & 5.81004 & / & / & \\ \hline WLS & yes & \(-0.61081\) & 6.10011 & 0.06234 & 0.42406 & 2.072 \\ \hline WTLS & yes & \(-0.48053\) & 5.47991 & 0.07062 & 0.35925 & 1.218 \\ \hline GLS & yes & \(-0.46397\) & 5.39746 & 0.07134 & 0.35427 & 1.154 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Adjustment of the regression line according to different approaches. Figure 2: Solutions for the regression line. The above comparison of \"displacements\" in the \(y\)-direction and perpendicular to it is, of course, only meaningful if we do not consider weights or if they are the same for all \"measurements\" (\(\mathbf{P}=\mathbf{I}\)). When considering weights, we are not dealing with perpendicular distances, but with some oblique ones. If we now consider the above example with a uniform weighting matrix, we see the difference in the results--the sum of the squares of the deviations of the points from the regression line:  
* Perpendicular distance from line: \(\sum_{LS}d_{1}^{2}=\sum_{GLS}d_{1}^{2}=0.78748>0.78649=\sum_{WTLS}d_{1}^{2};\)
* Distance from line in direction \(y\): \(\sum_{LS}d_{y}^{2}=\sum_{GLS}d_{y}^{2}=0.89480<0.89593=\sum_{WTLS}d_{y}^{2}.\)  
In the case of a uniform weighting matrix for the observation (=input point coordinates), the solution for the regression line for LS and GLS is the same. We can also see that the WTLS solution is such that it minimizes the perpendicular distances of the points from the computed line.
## 3 Results and Discussion
### Analysis of the Effects of YLDI on the Volatile Fingerprints of Green tea Using GC-E-Nose  
The tea samples subjected to YLDI underwent analysis via GC-E-Nose, and the volatile fingerprints were obtained. Distinctive variations were observed in the aroma profiles of tea samples under YLDI (Figure S2). Notably, the tea samples exposed to 1000 Lux exhibited fewer volatile peaks on the MXT-1701 and MXT-5 columns compared to the other four treatments, indicating a unique aroma profile for the 1000 Lux samples. The volatile fingerprints of the 500 Lux and 2000 Lux samples were similar, with only minor differences in peak intensities, indicating comparable odor profiles. Additionally, the 6000 Lux samples exhibited lower peak intensities on the MXT-1701 column than other samples. The above results suggest that different light intensities directly influence the volatile fingerprints of green tea.  
To explore the effects of YLDI on the aroma quality of green tea, a supervised PLS-DA was employed for multivariate statistical analysis. This approach aims to construct a correlation model that correlates sample categories with metabolic expression levels [20; 21]. As illustrated in Figure 1A, the tea samples subjected to 500 Lux and 2000 Lux were clustered at the first quadrant of the score plot, whereas those exposed to 4000 Lux and 6000 Lux were grouped at the fourth quadrant. The 1000 Lux samples were clustered separately on the third quadrant, aligning with the findings from sensory assessment. The model parameters were R\({}^{2}\)Y = 0.987 and Q\({}^{2}\) = 0.882, respectively, indicating robust explanatory power and predictive ability. In addition, a permutation test was performed with 200 iterations. The parameters of R\({}^{2}\) = (0.0, 0.717) and Q\({}^{2}\) = (0.0, \(-\)0.465) validated the model's reliability without overfitting (Figure 1B). In summary, GC-E-Nose serves as a complementary and validation tool for traditional sensory evaluation and can rapidly and accurately distinguish tea samples by integrating with multivariate statistical analysis.
## 2 Overload in SAG Mills
### SAG Mill  
Semi-autogenous grinding (SAG) mills are vital in the mining industry, facilitating the extraction of metals like copper by breaking down ore into fine particles for subsequent flotation processes. Their performance significantly impacts the productivity and profitability of mining operations, especially in copper mining, which is crucial for the global economy. The rising demand for copper, driven by technologies such as electric vehicles and renewable energy, underscores the critical role of SAG mills. However, these mills face challenges, particularly overloads, which can cause increased wear, reduced efficiency, unscheduled shutdowns, and financial losses. Overloads also amplify energy consumption and operational costs, highlighting the need to understand and address their causes to develop effective prevention strategies.
# An Energy-Aware Method for Selecting Cluster Heads in Wireless Sensor Networks
### Hierarchical Routing Protocol  
In this protocol, sensor nodes with a higher energy level are used to send and process the data. In contrast, the nodes with lower energy can sense the other sensor node that is in the vicinity of the target. In fact, in this protocol, we can enhance scalability, lifetime, and overall energy efficiency by creating clusters and assigning several tasks to the cluster head. Furthermore, merging and aggregating the data are performed to reduce the number of sent messages to the base station. Therefore, hierarchical routing is an effective protocol to reduce energy in a cluster. The hierarchical routing method is divided into two layers. The first layer is utilized to select an appropriate cluster head, and the second layer is used for the routing procedure. The hierarchical routing protocol is the most efficacious method to reduce energy consumption. Moreover, the Low-Energy Adaptive Clustering Hierarchy (LEACH), the Energy Efficient Clustering Scheme (EECS), and the Base station Controlled Dynamic Clustering Protocol (BCDCP) are the most important protocols, which belong to the hierarchical routing protocol.  
In this paper, we aim to introduce a novel method to select an appropriate cluster head. We first model this problem by using multi-factor decision-making according to four factors. Then, we use the cluster splitting process and analytical hierarchy process algorithms to propose a novel method in order to solve this problem. The main contributions of this paper are as follows:  
* All of the sensor nodes are prioritized according to four essential factors, including energy, mobility, distance to the centre, and the size of the data queue.
* The optimal number of cluster heads is computed, and the Cluster Splitting Process (CSP) algorithm is utilized to distribute the load on the cluster heads and cover all network nodes.
* The minimum spanning tree algorithm is utilized to distribute the energy load among the cluster heads.
* We consider the particular time to send the data from the sensor node to its cluster head.  
The organization of the paper is as follows. In Section 2, the related research works and previous techniques for cluster head selection are reviewed. We propose our method in Section 3. In Section 4, the effectiveness of our proposed method is evaluated. In Section 5, we express challenges and future work in WSNs. The conclusion are provided in Section 6.
# A Real-Time Map Restoration Algorithm Based on ORB-SLAM3
## 4 Conclusions  
This paper proposes a real-time map restoration algorithm based on ORB-SLAM3. This method reversely tracks all frames before successful initialization, and accelerates the loop closure detection. We can generate a complete map in real time regardless of the initialization effect of the system. After comparing the RMSE of ATE, the accuracy was increased by 47.51% on average. Compared with standard deviation, the stability is increased by 52.18% on average. After comparing the time consumption, the time efficiency in the loop closure detection stage increases by 55.96% on average, and the average tracking time of the system decreases by 13% on average. Experiments show that the proposed algorithm can achieve map restoration while ensuring real-time performance. The complete map generated by this algorithm is significant for off-line robot tracking.  
The map restoration method proposed in this paper is mainly for the scenes with difficult initialization, and the effect is not obvious in simple scenes. In addition, the proposed algorithm is based on the assumption of low dynamic environment and does not eliminate dynamic objects, so the accuracy in an high dynamic environment will decrease. In the future, dynamic detection function should be added so that robots can adapt to more complex environments.  
All named authors initially contributed a significant part to the paper. Conceptualization, Q.L.; methodology, Q.L.; software, Q.L.; validation, Q.L.; formal analysis, Q.L. and J.L.; investigation, Q.L. and J.L.; data curation, Q.L.; writing--original draft preparation, Q.L.; writing--review and editing, W.H., Q.L., J.L. and K.Z.; visualization, Q.L.; supervision, W.H., Q.L., J.L. and L.S.; project administration, W.H., Q.L., J.L. and H.Q. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
Figure 10: Motion blur in V203 sequences. The image in the red rectangle is the blurred image.
# On the Accuracy of Turbulence Model Simulations of the Exhaust Manifold
## 3 Exhaust Manifold Analysis
### Material Fluid Properties  
Carbon-oxide-nitride is used in the CFD analysis of an exhaust manifold due to its unique properties, which make it suitable for modeling exhaust gases. With a density of 1 kg/m\({}^{3}\), it accurately represents gases, indicating a low-density fluid that aligns well with the behavior of exhaust gases, which are less dense than liquids or solids. The specific heat capacity (C\({}_{\text{P}}\)) is represented as a piecewise polynomial, enabling precise modeling of the variation in heat capacity with temperature. This is particularly important in exhaust systems, where gas temperature can vary significantly. The relatively low thermal conductivity of 0.0454 W/mK is consistent with the thermal properties of gases, which generally exhibit lower thermal conductivities compared to solids or liquids. The viscosity of 1.72 \(\times\) 10\({}^{-5}\) kg/m.s indicates a low-viscosity fluid, which is typical of gases, thereby facilitating the examination of flow dynamics in the exhaust manifold.  
The specific heat capacity (C\({}_{\text{P}}\)) can be mathematically expressed as a piecewise polynomial function of temperature. The model divides the temperature range into several segments, each represented by a polynomial of a specific degree:  
\[C_{P}(T)=\left\{\begin{array}{cc}a_{1}T^{n}+a_{2}T^{n-1}+\ldots+a_{n+1}&\quad \quad\text{For $T_{0}<T<T_{1}$}\\ b_{1}T^{m}+b_{2}T^{m-1}+\ldots+b_{m+1}&\quad\quad\text{For $T_{1}<T<T_{2}$}\\ \vdots&\\ k_{1}T^{p}+k_{2}T^{p-1}+\ldots+k_{p+1}&\quad\quad\text{For $T_{n-1}<T<T_{n}$} \end{array}\right. \tag{19}\]  
where:  
\(a_{i},\ b_{i},\ldots,\ k_{i}\) are the coefficients of the polynomial segments for each respective temperature range. \(T_{0},\ T_{1},\ldots,\ T_{n}\) are the temperature breakpoints that define the range for each polynomial segment. \(n,\ m,\ \ldots,\ p\) are the highest degrees of the polynomials in their respective segments.  
For the given range from 300 K to 5000 K, the coefficients are defined as a\({}_{1}\) = 560.0615, a\({}_{2}\) = 1.755483, a\({}_{3}\) = -0.001770155, a\({}_{4}\) = 1.16292 \(\times\) 10\({}^{-6}\), and a\({}_{5}\) = 3.775009 \(\times\) 10\({}^{-10}\).  
These properties collectively facilitate a thorough analysis of the thermal and flow characteristics within the exhaust manifold, guaranteeing the precise capture of the gas's compressibility and varying thermal behavior.
# Comparative Analysis of 5G Mobile Communication Network Architectures
## 3 Proposed Architectures
### MEC Architecture  
In a 5G system based on network virtualization, the logical functions to process the user traffic at the base station and core instrument are integrated into a user plane function and is distributed to serviced regions. Furthermore, considering the service-based architecture, which aims to fuse the network and application, the 5G system supports a communication path between the user plane function and the application. The MEC architecture takes advantage of this feature by placing an application function, which was located on the central cloud originally, to a local cloud at individual application servers and connects it with the user plane function. The MEC architecture reduces the delay time significantly by moving the application function closer to the UE. However, due to the distributed allocation of computing resources, the available computing resource per location is limited, making the architecture vulnerable to regional traffic spikes as shown in Figure 2.
## 2 Methodology
### Basic Feature Processing
#### 2.3.1 Temporal Feature Processing  
Temporal information is one of the core dimensions of trajectory data. Time in the raw data is stored as Unix timestamps, which need to be converted to a human-readable format and have time period features extracted.  
Through the timeSegment() function, it was detected that the data contains 10 time periods, each spanning 2 h, all during weekday morning peak hours (7:00 a.m. to 9:00 a.m.). This finding has significant implications for subsequent traffic pattern analysis, as morning peak hours represent the most congested period in urban traffic and are a key focus for signal control optimization.
## 1 Introduction
This enables decision-makers to promptly acquire firsthand information from the disaster scene and make swift decisions, thereby reducing the decision-making time and enhancing efficiency. Moreover, the intelligent emergency management mode also employs big data analysis and artificial intelligence technology to facilitate intelligent scheduling and the optimal allocation of rescue resources. Various aspects including rescue personnel, materials, and equipment are comprehensively considered to ensure their prompt arrival at the disaster site. Simultaneously, through establishing a unified information platform and implementing a data sharing mechanism, different departments can collaborate across boundaries while staying updated on each other's progress and resource requirements. Such coordination forms a formidable force that enhances the overall emergency response efficiency. The rural intelligent emergency management mode not only enables an effective response to natural disasters but also facilitates the sustainable development of rural areas through long-term monitoring and planning. For instance, by analyzing influential factors such as climate change and natural disasters, it becomes possible to formulate rational agricultural production plans and implement ecological protection measures that enhance the quality of the ecological environment and economic development level in rural regions. This paper establishes a solid theoretical foundation for emergency management, expanding its theoretical framework while enhancing the scientific nature of emergency decision-making. The construction of the rural intelligent emergency management mode contributes significantly by providing a comprehensive and scientifically grounded system specifically tailored for rural areas. This mode takes into account the unique characteristics and complexities associated with these regions, integrating information technology with emergency management practices to achieve intelligent, precise, and efficient outcomes. Moreover, it enhances the scientific basis of emergency decision-making processes. Traditional modes of emergency management often rely on experience and subjective judgment, making them susceptible to decision errors or delayed responses. In contrast, the rural intelligent emergency management mode employs modern information technology and data mining techniques to conduct more accurate analysis and prediction regarding potential emergencies. Consequently, this approach improves both accuracy and timeliness in making critical decisions during emergencies while effectively reducing disaster-related losses.
# MIMO LS-SVR-Based Multi-Point Vibration Response Prediction in the Frequency Domain
## 3 Experimental Verification
### Experimental Design and Data Processing
#### 3.2.2 Fourier Transform of the Vibration Response Data  
The data were first transformed from the time domain to the frequency domain using a fast Fourier transform. Thus, frequency data with a frequency from 0 Hz to 6400 Hz were obtained and the frequency resolution \(\Delta f\) was 4 Hz. Then, based on the frequency resolution, the total number of frequencies \(\Omega\) was 1601 groups.
## 2 Materials and Methods
### Methods  
#### 2.4.1 MODIS GPP Algorithm  
In this study, we adopted the MODIS GPP algorithm combined with the Sentinel-3 OLCI FAPAR product to calculate GPP so that the result is comparable with the GPP derived from MODIS FAPAR. The MODIS GPP is a representative LUE model algorithm that calculates GPP by employing the amount of PAR absorbed by vegetation [59]. The algorithm is developed as follows:  
\[\text{GPP}=\varepsilon\times\text{FAPAR}\times\text{SW}_{\text{rad}}\times 0.45 \tag{2}\]  
\[\varepsilon=\varepsilon_{\text{max}}\times\text{f}(\text{T}_{\text{min}}) \times\text{f}(\text{VPD}) \tag{3}\]  
\[f(\text{T}_{\text{min}})=\begin{cases}0&\text{T}_{\text{min}}<\text{TMIN}_{ \text{min}}\\ \frac{\text{T}_{\text{min}}-\text{TMIN}_{\text{min}}}{\text{TMIN}_{\text{max}} -\text{TMIN}_{\text{min}}}&\text{TMIN}_{\text{min}}<\text{T}_{\text{min}}< \text{TMIN}_{\text{max}}\\ 1&\text{T}_{\text{min}}>\text{TMIN}_{\text{max}}\end{cases} \tag{4}\]  
\[f(\text{VPD})=\begin{cases}0&\text{VPD}>\text{VPD}_{\text{max}}\\ \frac{\text{VPD}_{\text{max}}-\text{VPD}_{\text{min}}}{\text{VPD}_{\text{max}} -\text{VPD}_{\text{min}}}&\text{VPD}_{\text{min}}<\text{VPD}<\text{VPD}_{ \text{max}}\\ 1&\text{VPD}<\text{VPD}_{\text{min}}\end{cases} \tag{5}\]  
where \(\varepsilon_{\text{max}}\) represents the maximum LUE, \(\text{TMIN}_{\text{min}}\) is the daily minimum temperature (T\({}_{\text{min}}\)) at which \(\varepsilon\) = 0, \(\text{TMIN}_{\text{max}}\) is the daily T\({}_{\text{min}}\) at which \(\varepsilon\) = \(\varepsilon_{\text{max}}\), \(\text{VPD}_{\text{max}}\) is the daylight average VPD at which \(\varepsilon\) = \(\varepsilon_{\text{max}}\), \(\text{VPD}_{\text{min}}\) is the daylight average VPD at which \(\varepsilon\) = 0, FAPAR is from the OLCI FAPAR product (OGVI), and \(\text{SW}_{\text{rad}}\) is the incident solar shortwave radiation. Here, \(\varepsilon_{\text{max}}\), \(\text{TMIN}_{\text{min}}\), \(\text{TMIN}_{\text{max}}\), \(\text{VPD}_{\text{max}}\), and \(\text{VPD}_{\text{min}}\) were determined according to the biome properties LUT for each biome types (Table 2), while \(\text{T}_{\text{min}}\), VPD, and \(\text{SW}_{\text{rad}}\) were obtained from flux tower sites. Additionally, a 1.5 km \(\times\) 1.5 km area centered on every flux tower site was extracted to represent the flux footprint, FAPAR, and OTCI [5].