esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Setup and Measured Results in the Salt Mine
### Different Mining Structures in Underground Mines  
Figure 5 shows the tunnel and room-pillar mining schemes in the Khewara salt mine. Using the deployment scenario shown in Figure 2, RSSI was measured in a mine tunnel with node separation of up to 110 m. Theoretical calculations indicate that RSSI decreases from \(-41\) to \(-85\) dBm. During the experiments, fluctuations in RSSI were observed, with an irregular trend ranging from \(-60\) to \(-84\) dBm. These variations in the propagation range may be described in terms of human (miner) and metal object movements and multi-path effects. Furthermore, the presence of different gases in the atmosphere can also affect wave propagation and signal strength.  
An average variation of 2.6% was observed between the theoretical and measured values. The comparison of RSSIs measured for single and multi-hop tunnel scenarios with up to 70 m of separation is shown in Figure 6a. In the room-pillar mining scenario, the RSSI values displayed a consistent trend of decrease in all the scenarios, except for  
Figure 4: (**a**) Wizzi mote Tx-Rx and (**b**) node deployment in the salt mine.  
an abrupt increase, which was observed at 40 m. This could have been due to potential reflected waves at the receiver. The behavior described persisted consistently across all node placements, with an average variation of 3.8% recorded across multiple measurements taken on different days. Figure 6b depicts the measured RSSI values for both single and multi-hop room-pillar scenarios, with node separations of up to 70 m.
###### Abstract  
Smartphone industries are seeking to maximize screen size, but a perfect full-screen phone is not yet available due to the unoccupied area required for the mobile antennas. Since a touchscreen is coated with transparent conductive layers, antennas below the touchscreen cannot create outward radiation. Accordingly, a non-metallic area is left for the antennas, which reduces the screen size. In this paper, we propose a design and integration of antennas for future millimeter-wave 5G smartphones to suit a perfect full-screen display. The proposed design consists of three modules, including a 1 \(\times\) 12 patch antenna array at 28 GHz, a 1 \(\times\) 8 patch array at 38 GHz, and a loop antenna at 2.45 GHz. The design environment consists of fully metallic top and bottom enclosures, mimicking the touchscreen and the printed circuit board, respectively. The three antennas were implemented on the flanks. Although the design area was limited and contained in parallel conductive plates, the three antennas still provided broad impedance bandwidths (26.5-32.0 GHz, 35.2-42.0 GHz, and 2.41-2.48 GHz) and high gains (16.7 dBi, 16.4 dBi, and 4.3 dBi). The isolation was larger than 20 dB, and the scanning ranges were \(\pm\)45\({}^{\circ}\). The proposed scheme is the first antenna system designed for perfect full-screen display phones.  
antenna arrays; mobile antennas; multifrequency antennas; system integration; wireless LAN +
Footnote †: journal: _Electronics Letters_  
1
Footnote 1: [[https://www.mdpi.com/journal/electronics](https://www.mdpi.com/journal/electronics)]([https://www.mdpi.com/journal/electronics](https://www.mdpi.com/journal/electronics))
## 4 Results
### Detection Results of the Anchoring Start Phase Based on Pose Estimation  
This section presents the results of detecting the anchoring start time using the proposed pose estimation method. The accuracy of the detected anchoring start time was evaluated using the mean absolute error (MAE) and \(R^{2}\) score, as defined in Equation (2). Across 58 video clips, the method demonstrated an average error of approximately 0.29 s, with an \(R^{2}\) score of 0.91.  
\[\text{MAE}=\frac{1}{N}\sum_{i=1}^{N}(|y_{i}-\hat{y}_{i}|) \tag{2}\]  
Figure 6 visualizes the scatter plot and regression line of each video clip detected by the proposed method and the actual anchoring start time. It shows that the start time detected by the proposed method is very close to the actual start time for most of the video clips.
# Adaptive Decision Support System for On-Line Multi-Class Learning and Object Detection
## 2 Proposed System
### Modeling
#### 2.1.3 Weak Classifier Initialization  
A weak classifier is composed of a feature and a threshold. The threshold of the weak classifier is calculated by the corresponding feature and pattern pool. We took the training of three-class classification as an example to introduce the calculating method. There would be \(2^{3}-1\) combinations of strong classifiers for three-class classification, as shown in Table 2. For all the weak classifiers in each strong classifier, we calculated the average response value of positive patterns (\(P_{man}\)), the average response value of negative patterns (\(N_{mean}\)), and the threshold according to the corresponding pattern pool. Here, we took \(S^{1}(v)\) and \(S^{2}(v)\) as an example, as shown in Figure 9. Since we used Adaboost [9] to build our system, the threshold of the feature was set using the middle value of \(P_{mean}\) and \(N_{mean}\), as in (5).  
\[Threshold=\frac{(P_{mean}+N_{mean})}{2} \tag{5}\]  
Finally, calculate the Polar, as in (6).  
\[Polar=\left\{\begin{array}{ll}1,&\text{if $P_{mean}\geq N_{mean}$}\\ -1,&\text{if $P_{mean}<N_{mean}$}\end{array}\right. \tag{6}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Strong Classifier** & **Positive Pattern** & **Negative Pattern** \\ \hline \(S^{1}(v)\) & Class 1, Class 2, Class 3 & Negative pattern \\ \hline \(S^{2}(v)\) & Class 1, Class 2 & Class 3, Negative pattern \\ \hline \(S^{3}(v)\) & Class 1, Class 3 & Class 2, Negative pattern \\ \hline \(S^{4}(v)\) & Class 2, Class 3 & Class 1, Negative pattern \\ \hline \(S^{5}(v)\) & Class 1 & Class 2, Class 3, Negative pattern \\ \hline \(S^{6}(v)\) & Class 2 & Class 1, Class 3, Negative pattern \\ \hline \(S^{7}(v)\) & Class 3 & Class 1, Class 2, Negative pattern \\ \hline \hline \end{tabular}
\end{table}
Table 2: Training data of each classifier.  
Figure 9: Calculation of response value.  
If the pattern is positive while the response value is larger than the threshold, the polar is 1. The contrary is \(-1\). Since the HOG and LBP have multiple dimensions, the determination adopted a majority decision. If the number of dimensions determined as a positive pattern was larger than the number of dimensions determined as a negative pattern, this pattern was viewed as a positive pattern. Otherwise, it was a negative pattern.
###### Abstract
As there is no limit to the complexity of an ETA model somewhat more exotic methods, such as the artificial bee colony algorithm are also represented in the recent literature [25]. As a continuation of our previous work, we investigated possible architectural solutions to capture the interconnectedness of public transport networks and its effects on the accuracy of the prediction. All urban transport networks are, as the name suggests, networks consisting of directly or indirectly connected routes. Disruptions in a specific part of the network can have an impact on vehicles in different areas of the system [26]. Therefore, it is expected that any prediction that is made based on either the entire network or a more extensive part of the network could improve ETA and other predictions. Some examples that address a similar approach are studies that include vehicles on the same route in their prediction, allowing any algorithm to have a better view of the state of the network and thus improving prediction accuracies [27; 28; 29]. To the best of the author's knowledge, only one study used true network-based information including some short-term historical data from the entire network in their ETA predictions [30]. Examples from freight networks are more common and have demonstrated that a prediction based on multiple network-based models can improve ETA predictions [31; 32]. Another example demonstrates a similar approach for the prediction of taxi ETAs [33]. In sequential data, such as language translation, the so-called attention mechanisms can significantly improve predictive performance. The underlying idea is that the attention head will learn the importance of the order of words in a sentence and will focus more on the important parts of the query. In practice, this is achieved by using an encoder-decoder model with either an attention mechanism in between or a flavour of the attention head that doubles as a decoder. Various versions of attention mechanisms have been described in the recent literature [34; 35; 36]. In this study, we present a working concept of a model architecture allowing to leverage the state of an entire transport network to make ETA and next-step predictions. To this end a combination of an attention mechanism with a dynamically changing recurrent neural network (RNN) based encoder library. This study presents a pilot investigation into the suitability of this novel model architecture but does not claim superiority. The findings in this study should be considered as a possible and promising avenue for further research into this novel architecture.
# Hop-by-Hop Multipath Overlay Routing for Optimizing Network Resource Allocation in WANs
## 5 Conclusions  
In this study, we propose a hop-by-hop multipath routing approach for overlay networks aimed at large-scale all-to-all transmission in WANs. Compared with existing source-based methods, hop-by-hop multipath routing offers improved link utilization and scalability. The method consists of two key components. First, we propose an overlay node placement algorithm, MOP, which enhances the performance of hop-by-hop multipath transmission. To minimize path overlap and optimize underlay link utilization, we define hop-by-hop path diversity for each overlay node and optimize the overall diversity with a heuristic algorithm. Second, we present a next-hop selection algorithm, ONHS, designed to adjust transmission paths at intermediate overlay nodes by sensing the network congestion state of overlay tunnels. The path selection process considers both single-hop and multi-hop scenarios. For single-hop path selection, the algorithm relies on local congestion information to make decisions for the next-hop node. For multi-hop path selection, the algorithm leverages global congestion awareness, temporarily filtering out certain next-hop nodes to reduce congestion in multi-hop paths, which alleviates the overall network congestion.  
We validated the performance of our method through experiments on real-world and randomly generated large-scale topologies. Simulations conducted with ns-3 demonstrated the effectiveness of both MOP and ONHS. The results show that MOP achieves higher underlay link utilization, greater hop-by-hop path diversity, and an increased number of end-to-end paths across various topologies and overlay node configurations. Furthermore, the ONHS algorithm successfully avoids congested tunnels in both single-hop and multi-hop path selections, alleviating congestion and optimizing network performance.  
Conceptualization, Y.Z. and C.W.; methodology, Y.Z. and C.W.; software, Y.Z.; writing--original draft preparation, Y.Z.; writing--review and editing, Y.Z., C.W. and H.D.; supervision, C.W. and H.D.; project administration, H.D.; funding acquisition, H.D. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the National Key R&D Program of China: Application Demonstration of Polymorphic Network Environment for Computing from the Eastern Areas to the Western. (Project No. 2023 YFB2906404).  
Data Availability Statement: Data are contained within the article.  
We would like to express our gratitude to [PERSON], [PERSON] and [PERSON] for their meaningful support for this work.  
Figure 19: Total throughput vs. time in 20-node overlay network.
# Design of a Technique for Accelerating the WSN Convergence Process
## 3 Theoretical Principles Used in Research
### Fast Recovery of the WSN  
The WSN is a network focused on monitoring the physical environment using small and energy-efficient sensor devices. These sensors collect data on various variables such as temperature, humidity, lighting, or movement and transmit them via wireless communication. A WSN is often used in applications such as environmental monitoring, smart homes, industrial control, and so on.  
In WSNs [58], routing takes place by sending data between sensor devices in the network and the final destination. Routing in a WSN has some specific characteristics compared with traditional networks.  
The basic flow of routing in a WSN is described below:  
Topology distribution: At the beginning of routing, sensor devices in the network inform each other of their presence and availability. This can be performed by periodically sending and receiving signals between sensors or by using dedicated routers.  
Choosing a router: In some WSNs, special sensors are designated that perform the function of routers. These routers have the task of receiving, processing, and transmitting data from other sensor devices. The choice of a router can be determined based on various factors, such as energy, distance from the target, availability of neighboring sensors, and so on.  
Creating a routing table: Each sensor device in the network creates a routing table that contains information about neighboring devices and the best path to the destination. Routing tables can be updated based on a change in topology or network status.  
Message routing: When a sensor device wants to send a message to a destination, it uses a routing table to decide the optimal path. The message is then passed from one sensor device to another in the network until it reaches its destination.  
Handling missing messages: Due to the limited energy and resources of sensor devices, messages may be lost, or data transmission errors may occur. Therefore, techniques for handling missing messages, such as retransmission, buffers, patch codes, and so on, are often used in WSNs.  
The choice of the routing protocol is important, since the nondelivery of a data packet to the destination node may occur, for example, due to a packet collision, a channel or node failure, radio interference, or the movement of nodes, when the node has moved out of range of another node.  
The most used reactive routing protocol is AODV. Such a protocol does not create any alternate paths, so there is only one path, which in the event of an outage causes nodes to start dropping data packets until a new path is created. This outage can cause unwanted problems if the system transmits data requiring real-time communication. How fast recovery options can be added to this protocol is described in the subsections below.
# Microbial Growth Dynamics in Minced Meat Enriched with Plant Powders
## 3 Results
### Total Viable Counts in Raw Meat Samples  
Similar growth dynamics of microorganisms were observed in the raw minced pork samples. In the initial raw pork samples containing 13% of fat, total microbial counts were on average in the range of 4.30-4.75 log cfu/g (Figure 1A). In the following days, the number of microorganisms increased in most samples and was above 8 log cfu/g by day 12. Compared to the other samples, the total number of microbes in the Apple+On+BCber, Apple+Ga+Tom, and Apple+CHber+Rber samples were lower until day 9 of storage (4.48, 6.11 and 6.45 log cfu/g, respectively). In these samples, the rapid growth of microorganisms started after the 9 th day of storage, when the numbers of mesophilic microorganisms were 5.06, 7.57, and 8.89 log cfu/g, respectively. However, a statistically significant difference (\(p<0.05\)) compared to the control (raw minced meat) in mean microbial counts during the storage time was only found for raw minced meat with Apple+On+BCber.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Plant Powder** & **Total Viable Count** & _Pseudomonas Count_ & **Yeast and Mould** \\  & **(cfu/g)** & **(cfu/g)** & **Count (cfu/g)** \\ \hline Apple & 150 & \textless{}100 & 30 \\ Blackcurrant berry & 680 & \textless{}100 & 230 \\ Rowanberry & 100 & \textless{}100 & 140 \\ Tomato & 200 & \textless{}100 & 40 \\ Onion & 2100 & \textless{}100 & 350 \\ Garlic & 1200 & \textless{}100 & 1900 \\ Chokeberry & 4500 & \textless{}100 & 20 \\ Rhubarb petioles & 400 & \textless{}100 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The microbial counts in plant powders.  
In the series of experiments with a higher fat content of minced pork samples (27%), the total number of microorganisms increased from an initial range of 3.49 to 4.82 log cfu/g to 7.40 to 9.75 log cfu/g at the end of the storage period on day 12 (Figure 1B). Differences in total microbial counts between control and minced meat samples supplemented with plant powders were not statistically significant. Compared with 13% fat-containing minced pork samples, microbial growth inhibition was lower for 27% fat-minced pork samples enriched with plant powders.
# Historical Eco-Environmental Quality Mapping in China with Multi-Source Data Fusion
## 2 Materials and Methods
### Assessment of China's EEQ  
#### 2.3.1 Principle of Indicator Selection  
Regarding the evaluation of EEG, several principles have been put forward by scholars for the selection of evaluation indicators. [PERSON] emphasized that evaluation indicators should be scientific, dominant, practical, and comprehensive. [PERSON] suggested that the evaluation of EEG should be ecologically sound and meet scientific standards. [PERSON] proposed following principles such as importance, typology and regionality, sustainable use, and recognizing the value of noble ecosystems and priceless natural resources. Furthermore, the China General Environmental Monitoring Station highlighted the importance of indicators being representative, comprehensive, concise, comprehensive, and easily applicable. These principles provide valuable guidance for the development and selection of evaluation indexes in the assessment of EEG [35].  
In this study, we adopted the principles of indicator selection proposed by the China General Environmental Monitoring Station and carefully selected eight indicators that take into account the specific ecological environment of China and factors such as data accessibility.  
To further assess the appropriateness of the selected indicators, we conducted a covariance diagnostic analysis using the linear regression analysis method to determine if there was any redundancy among the eight indicators [36]. The variance inflation factor (VIF) and tolerance (TOL) are commonly used indicators for diagnosing multicollinearity [37]. VIF and TOL are reciprocals, and if TOL \(>\) 0.1 or VIF \(<\) 10, it indicates that the selected evaluation indices are reasonable.  
Figure 2: M-RSEQI calculation flow chart.  
Since the indicators for each year are independent, we conducted the diagnostic analysis using the 2017 data in this study. The results, presented in Table 2, demonstrate that there was no information redundancy among the eight selected indicators. This indicates that the indicators chosen for this study were appropriate and do not exhibit multicollinearity issues.
## 2 Related Work
The authors of [31] investigated the beamwidth selection and power allocation problem in a NOMA mmWave system. They employed the NOMA scheme during the beam alignment phase and solved a convex optimization problem for maximizing the sum rate. The authors of [32] formulated mixed-integer nonlinear programming (MINLP) to maximize the network throughput and energy efficiency in mmWave heterogeneous networks, taking into account the tradeoff between alignment overhead and effective data transmission time. They proposed a novel outer approximation algorithm to solve the MINLP problem. However, in all the above previous work, beam alignment is periodically performed, which results in incurring periodic overhead.
## 5 Discussion
In this study, we address these challenges by evaluating a diverse range of lakes within a broad spatial study area. Using GEE as a cloud computing platform, we developed a methodology for processing data at a large spatial scale. Our approach aimed to overcome the limitations of traditional approaches to training empirical models in specific waterbodies by using a large in situ dataset from a range of heterogeneous lakes. A recent study conducted by [PERSON] et al. [70] focused on estimating Chl-a on a broad scale by considering approximately 3000 of the largest global lakes (>50 km\({}^{2}\)). While their model demonstrated satisfactory performance modeling Chl-a concentration from Sentinel-2 data (R\({}^{2}\) value of 0.74), the emphasis on large-sized lakes in their field data leaves the question of whether this is suitable for addressing the prevalence of high levels of algal blooms in many small- and mid-sized lakes worldwide unanswered. One of the significant findings of their study was the impact of meteorological factors and lake characteristics on Chl-a concentrations, which they incorporated using machine learning algorithms. This current study shows that our approach using an in situ dataset from a wide geographic and limnologic distribution of lakes can be successfully regressed against a Sentinel-2 dataset to achieve phycocyanin concentration estimates that are sufficiently accurate to be useful for practical purposes. Since the linear regression model is limited in terms of understanding interactions between different lake factors, in future studies, we will explore the potential benefits of applying alternative algorithms (e.g., machine learning approaches) that are capable of simultaneously incorporating multiple lake characteristics. This would enhance our understanding of the interactions between various lake factors, with the expectation that this could lead to more accurate model estimations.
## 2 Modeling of Subsystem
### Probability Density Function Fitting
#### 2.3.4 Logarithmic Normal Distribution  
The probability density function of the logarithmic normal distribution has two parameters, \(\mu\) and \(\sigma\). The accumulative distribution function meets the relation as shown in Equation (14) if the function is converted to standard normal distribution.  
\[F(\ln t_{i})=\Phi\biggl{(}\frac{\ln t_{i}-\mu}{\sigma}\biggr{)}=\Phi(z_{i}) \tag{14}\]  
The linear equation can be constructed for the logarithmic normal distribution as shown in Equation (15).  
\[\ln t_{i}=\sigma z_{i}+\mu \tag{15}\]  
If the sample data meet the logarithmic normal distribution, \(z_{i}\) and \(\ln t_{i}\) are linear and the observed value of parameters \(\mu\) and \(\sigma\) can be calculated by Equation (16).  
\[\left\{\begin{array}{c}\hat{\mu}=b\\ \hat{\sigma}=a\end{array}\right. \tag{16}\]