esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In this paper, a power loss analysis and a control strategy of an active balancing circuit based on a bidirectional flyback converter are proposed and evaluated. From the analysis of the experimental data, the maximum error between the loss model and the measured results is 5.06%, the minimum error is 0.02%, and the average absolute error is 1.48%. Moreover, the designed bidirectional flyback converter has the highest efficiency when the balancing current reaches 2 A, and it can also reduce the time required for completing the balancing process. Therefore, we can derive the most important parameter in the balancing process by the proposed power loss model according to the desired conditions, such as balancing speed or system efficiency. Finally, the system applies a bidirectional control strategy, compared with the cell-to-pack and the pack-to-cell control, which has a less balancing duration as 2727 s and a less maximum voltage error 50 mV in the case of the balancing current as 1 A, to improve the efficiency of the balancing process.  
Figure 13: Measured results of each cell’s voltage using implemented bidirectional control strategy (**a**) actual hardware measurements (**b**) processed results.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Control Strategy** & **Balancing Duration** & **Maximum Voltage Error** \\ \hline Cell-to-pack & 2783 s & 100 mV \\ Pack-to-cell & 5175 s & 130 mV \\ Bidirectional & 2727 s & 50 mV \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison among three measured results of balancing processes.  
**Author Contributions:** Conceptualization, C.-H.L. and Y.-L.L.; Methodology, Y.-L.L.; Software, Y.-L.L. and S.-J.-Y.; Validation, Y.-L.L. and S.-J.Y.; Formal analysis, Y.-L.L.; Investigation, C.-H.L.; Resources, C.-H.L.; Data curation, Y.-L.L.; Writing--original draft preparation, Y.-L.L.; Writing--review and editing, C.-H.L. and S.-J.Y.; Project administration, C.-H.L.; Funding acquisition, C.-H.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Ministry of Science and Technology, Taiwan, R.O.C., grant number MOST 106-2221-E-011-094-MY3 and by the National Chung-Shan Institute of Science and Technology, grant number NCSIST-104-V409(107).  
**Acknowledgments:** The authors sincerely appreciate much supports from the Taiwan Building Technology Center from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education in Taiwan.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Results and Discussion
### Quantitative Evaluation of Overpressure in Different Genesis Types
#### 4.3.3 Geothermal Decline  
An increase in formation temperature leads to the expansion of fluid volume, which in turn results in an increase in fluid pressure. Conversely, a decrease in formation temperature causes both fluid volume and rock skeleton volume to contract, leading to a reduction in fluid pressure. Previous studies have demonstrated that, for a given cooling amplitude, the volume change of pore fluid is greater than that of the rock skeleton [54; 55]. Therefore, the cooling effect contributes to a decrease in formation pressure.  
Concerning the decrease in pressure attributed to the geothermal decline, previous studies have primarily derived this relationship through a combination of [PERSON]'s law and fluid state equations, or via simulation experiments examining the temperature-pressure relationship of fluids [56; 57]. Based on experiments evaluating the temperature-pressure relationship in closed-flow systems, [PERSON] et al. [56] found that when pressure exceeds 15 MPa, the temperature-pressure relationship in such systems is nearly parallel, with a slope of 1.076 MPa/\({}^{\circ}\)C. Consequently, this study employs this model to calculate the pressure reduction resulting from geothermal decline.  
Previous studies have indicated that the geothermal gradient in the area extending from central Sichuan to southwestern Sichuan was highest during the Early Triassic, ranging from 55 \({}^{\circ}\)C/km to 60 \({}^{\circ}\)C/km. By the end of the Triassic, this gradient had decreased to between 30 \({}^{\circ}\)C/km and 38 \({}^{\circ}\)C/km, and at the end of the Early Cretaceous, it further declined to between 21 \({}^{\circ}\)C/km and 29 \({}^{\circ}\)C/km. Currently, the geothermal gradient is approximately 22 to 26 \({}^{\circ}\)C/km [58; 59]. In the study area, the Qiongzhusi Formation reached its maximumancient burial depth by the end of the Early Cretaceous. Based on the geothermal gradient at that time and the current geothermal gradient, the reduction pressure due to the decrease in geothermal temperature was calculated. The results indicated that the pressure reduction caused by the decrease in geothermal temperature ranged from 32.38 MPa to 86.8 MPa, with an average of 56.56 MPa, and the impact of the geothermal decrease on Well W207 was the most significant (Figure 7d, Table 2).
# Digital Pre-Distortion of Carrier Frequency Offset for Reliable Wi-Fi Enabled IoTs
## 7 Conclusions  
Wi-Fi enabled IoT devices have been developed to communicate in narrowband in order to take advantage of improved receiver sensitivity, robustness to interference, and energy efficiency. However, in dynamically interfered channels or poor wireless channel conditions, the data rate decreases due to inaccurately estimated CFOs and compensation. If the proposed digital pre-distortion method is applied, such CFO estimation error can be minimized, allowing the above described problem to be  
Figure 4: packet error ratio (PER) over CFOs; digital CFO predistortion scheme vs. conventional design.  
Figure 5: Training of digital predistorted carrier frequency offset; residual offset vs. frame index.  
overcome. Accordingly, data packet can be transmitted at high speed. As a next generation WLAN system that can ensure high efficiency in terms of throughput and coverage, the next version of the IEEE 802.11 WLAN standard, called \"high-efficiency WLAN (HEW)\", is under discussion [21; 22]. IEEE 802.11 ax WLANs will evolve toward dynamic bandwidth operation in narrower and wider bandwidths. The proposed digital predistortion scheme will achieve a substantial enhancement in next generation wireless LANs.  
Combining effects of CFO and DC offset degrades the signal quality near DC subcarriers in the conventional schemes due to DC cancellers at the receiver side. Futhermore, when the wireless channel is very poor due to noise, interference, and channel effect, it is difficult to accurately estimate the CFO. In this paper, the digital CFO predistortion scheme was proposed for a reliable communication link in dense networks. Simulation-driven evaluation results demonstrated that the proposed scheme can improve received signal quality in terms of packet error rate and error vector magnitude. This work focused on single antenna wireless system targetted for IoT applications. As future work, this work will be extended to combine multiple antenna systems that are more susceptible for carrier frequency offsets and implemented in a real-test bed.  
The author declares no conflict of interest.
## 3 The Proposed Method
### Semantic Analysis  
In product reviews, users express their sentiments, feelings, preferences, etc., in words. To explore the implicit content of user-product reviews, many studies have used the attention mechanism to analyze the semantic features in sentences [9; 15; 44]. There is a hierarchical relationship between the words and sentences of a review. Multiple words form a sentence, and multiple sentences can be organized into one review. Based on the hierarchical relationships in the text, this study used a hierarchical attention neural network [15] to analyze the semantic meanings in reviews.  
This study uses GloVe [30], a word embedding method, to convert the texts of user and product reviews into word vectors. The word vectors were then fed into a hierarchical attentional neural network for analysis, as shown in Figure 3. This method calculates the word vectors and word weights, as well as the sentence vectors and sentence weights, based on the hierarchical and contextual relationships in the review text. Finally, the sentence vector and the sentence weight are multiplied to produce a weighted sentence vector. Similar to the user and product aspects, the weighted sentence vectors of the same aspect were summed to generate the aspect semantic vectors of users and products, respectively.  
Figure 3: Attention Neural Network.  
Assume that the user reviews contain \(m\) aspects and that each aspect contains \(n\) sentences. According to the analysis results of the hierarchical attention neural network, we multiplied the weight \(\alpha_{u,x}^{i}\) of sentence \(i\) in aspect \(x\) by vector \(\vec{sl_{u,x}^{\rightarrow}}\) to obtain the weighted sentence vector. Then, all the sentence vectors in aspect \(x\) were summed to obtain the semantic vector \(\vec{V_{u,x}^{\rightarrow}}\) of user aspect \(x\), as shown in the following equation:  
\[\vec{V_{u,x}}=\sum_{x=1}^{m}\sum_{i=1}^{n}\alpha_{u,x}^{i}\cdot\vec{sl_{u,x}^{ \rightarrow}} \tag{11}\]  
Like the user aspect semantic method, assume that the product reviews contain \(m\) aspects and that each aspect contains \(n\) sentences. According to the results of the hierarchical attention neural network, we multiplied sentence weight \(\alpha_{p,l}^{i}\) contained in aspect \(l\) by sentence vector \(\vec{sl_{p,l}^{\rightarrow}}\) to obtain the weighted sentence vector. All sentence vectors in aspect \(l\) were then summed to obtain the semantic vector \(\vec{V_{p,l}^{\rightarrow}}\) of user aspect \(l\), as shown in the following equation.  
\[\vec{V_{p,l}}=\sum_{l=1}^{m}\sum_{i=1}^{n}\alpha_{p,l}^{i}\cdot\vec{sl_{p,l}^{ \rightarrow}} \tag{12}\]
# Real-Time Implementation of Multiband Spectrum Sensing Using SDR Technology
## 3 Implementation of MBSS Technique Using SDR Technology
### Detection of Primary Users  
This section presents the last block of this implementation: the detection of \(PU_{s}\) in the spectrum. This procedure receives the PSD on a linear scale \(R^{\prime}_{i}(k)\) as an input parameter and delivering two outputs: (i) the signal \(R^{\prime}_{i-dBm}(k)\), which is the PSD on the logarithmic scale, and its occupation (i.e., starts and ends of frequency edges of detected bands); (ii) the binary decision, i.e., whether each of these detected bands corresponds to noise or a possible \(PU\) transmission. This structure is shown in Figure 13.  
This block consists of different submodules, as shown in Figure 14: (i) detection of frequency bands via the MRA and ML (in this submodule, the MRA is used to obtain the approximation coefficients, which are classified with the K-means algorithm and obtain the possible spectral windows; (ii) detection of PUs via the HFD (in this submodule, the auto-correlation of each window is calculated through the fractal dimension of Higuchi. At the end of this block, each spectral window is obtained on a logarithmic scale, as well as the occupation of each window. This MBSS technique was completely detailed in the previously published work by the authors in [12]. In this new proposal, the novelty is the developed platform to implement this technique using general-purpose communication devices.  
In Figure 14, we can observe the result of applying this detection block. Here, the occupancy signal, indicating a \(PU\) transmission or noise, is displayed as a binary representation updated every 100 ms.  
Figure 13: Detection of \(PU\) module.
## 4 Discussion and Conclusions
### Can Gait-Related EEG Data be Trained and Classified by an ANN?  
The answer to this question has implications for the development of novel brain-computer interface (BCI) applications and neurofeedback training. So far, BCIs for movement control are largely based on the motor imagery of leg, arm, or finger movements. Such applications are quite helpful, if the aim is to substitute real movements with imagined movements in patients who have lost their limbs, arms, etc. In the present study, data were trained by an artificial neural network to discriminate EEG activity by classifying gait phases. A gait phase change corresponds to a toe-off or initial contact event. Although preliminary, it was possible to classify events from non-events with high accuracy. This is very promising, as the chosen architecture is simple compared to other time series classification approaches. One possible extension could be the application of ConvLSTM networks, i.e., a combination of convolutional neural networks (CNNs) and LSTM networks. Those networks have been shown to outperform simple LSTM networks due to complex features extracted from the convolutional layers. Besides changes in the architecture, there are number of additional ways of improving the network results, which have not been applied in this work, because the network served as a proof of concept of the applicability of the framework.  
Conceptualization, C.H. and M.M.; methodology, C.H. and M.M.; software, M.M.; experimental set-up and paradigm, C.H.; validation, C.H. and M.M.; formal analysis, C.H. and M.M.; investigation, C.H.; resources, C.H.; data curation, C.H.; writing--original draft preparation, C.H. writing--review, editing and revision, C.H. and M.M.; visualization, C.H. and M.M.; supervision, C.H. and M.M.; project administration, C.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Acknowledgments:** The authors like to thank the subjects who voluntarily supported this study during the measurements and our master's student, [PERSON].  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Data Availability Statement:** The data supporting the conclusions of this manuscript can be made available by the corresponding authors, without undue reservation, to any qualified researcher upon request. Due to the informed consent form in which the possibility of raw data being published online was not explicitly stated, the raw data cannot be made accessible in online repositories.
# A Dynamic Urban Mobility Index from Clustering of Vehicle Speeds in a Tourist-Heavy City
## 5 Conclusions  
In conclusion, our study addressed the challenges of implementing smart city initiatives in the context of a developing country where there is a concern with access to high-resolution data. Additionally, we developed a dynamic mobility index that measures the ease of vehicle transport within the city by examining geolocated GPS traces of vehicles. This study particularly focused on the city of Baguio, The Philippines, a highly urbanized tourist hotspot in the country. The research utilized GPS data from probe vehicles, a public and private transport mix, to introduce the _Zone-based Speed Index (ZSI)_. Based on speed clusters, this mobility index dynamically infers monthly thresholds, classifying zones as _fast_ or _slow_. The proposed ZSI effectively captured fluctuations in vehicle mobility, such as the impact of typhoons and holidays, providing a high-resolution characterization of hourly urban vehicle mobility. The insights gained from this dynamic mobility index offer valuable contributions to the development and optimization of transportation systems in monitoring the state of city traffic and assessing the performance of smart city initiatives.  
Conceptualization, M.L., J.F.V. and C.M.; methodology, M.D., D.D., J.F.V. and M.L.; software, M.D., D.D. and M.L.; validation, M.L., M.D., D.D. and J.F.V.; formal analysis, M.L. and J.F.V.; investigation, M.L. and J.F.V.; resources, J.F.V.; data curation, J.F.V. and M.L.; writing--original draft preparation, M.L., J.F.V. and D.D.; writing--review and editing, M.L., J.F.V., M.D. and D.D.; visualization, M.L. and D.D.; supervision, C.M.; project administration, M.L., funding acquisition, C.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Philippine Council for Industry, Energy and Emerging Technology Research and Development (DOST-PCIEERD) under Project MINERVA: Monitoring of Indicators for Efficient Redevelopment and Value Assessment, Grant # 2022-10237. The funding agency had no involvement in the preparation of the manuscript.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy and internal agreements made with the local government of Baguio city.  
The authors are grateful for the assistance and support provided by the Local Government Unit of Baguio city.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# MLW-gcForest: A Multi-Weighted gcForest Model for Cancer Subtype Classification by Methylation Data
## 3 Results
### Results
#### 3.3.1 Classification Performance of Different Machine Learning Methods for Five Cancer Subtypes
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & **BRCA** & **LUAD** & **LIHC** & **GBM** & **STAD** \\ \hline \multirow{4}{*}{SVM} & ACC & 0.752 & 0.762 & 0.714 & 0.694 & 0.674 \\  & Pre & 0.755 & 0.754 & 0.726 & 0.723 & 0.619 \\  & Recall & 0.709 & 0.742 & 0.693 & 0.732 & 0.574 \\  & F\({}_{1}\) & 0.731 & 0.748 & 0.709 & 0.727 & 0.596 \\ \hline \multirow{4}{*}{KNN} & ACC & 0.745 & 0.750 & 0.688 & 0.631 & 0.706 \\  & Pre & 0.774 & 0.746 & 0.708 & 0.683 & 0.697 \\  & Recall & 0.743 & 0.739 & 0.686 & 0.736 & 0.736 \\  & F\({}_{1}\) & 0.758 & 0.742 & 0.697 & 0.709 & 0.716 \\ \hline \multirow{4}{*}{LR} & ACC & 0.730 & 0.746 & 0.718 & 0.669 & 0.658 \\  & Pre & 0.728 & 0.756 & 0.708 & 0.683 & 0.609 \\  & Recall & 0.663 & 0.726 & 0.701 & 0.726 & 0.559 \\  & F\({}_{1}\) & 0.694 & 0.741 & 0.704 & 0.704 & 0.583 \\ \hline \multirow{4}{*}{RF} & ACC & 0.691 & 0.676 & 0.716 & 0.730 & 0.674 \\  & Pre & 0.527 & 0.532 & 0.693 & 0.751 & 0.546 \\  & Recall & 0.475 & 0.508 & 0.699 & 0.753 & 0.563 \\  & F\({}_{1}\) & 0.500 & 0.520 & 0.696 & 0.752 & 0.554 \\ \hline \multirow{4}{*}{gcForest} & ACC & 0.852 & 0.820 & 0.804 & 0.836 & 0.757 \\  & Pre & 0.859 & 0.821 & 0.798 & 0.857 & 0.733 \\  & Recall & 0.826 & 0.819 & 0.778 & 0.850 & 0.788 \\  & F\({}_{1}\) & 0.842 & 0.820 & 0.788 & 0.853 & 0.760 \\ \hline \multirow{4}{*}{MLW-gcForest} & ACC & 0.915 & 0.866 & 0.873 & 0.885 & 0.876 \\  & Pre & 0.923 & 0.863 & 0.845 & 0.863 & 0.872 \\ \cline{1-1}  & Recall & 0.916 & 0.852 & 0.829 & 0.878 & 0.821 \\ \cline{1-1}  & F\({}_{1}\) & 0.919 & 0.857 & 0.837 & 0.870 & 0.846 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of different methods on the independent test datasets. Figure 6: _Cont._
# UAV Swarm Real-Time Rerouting by Edge Computing D* Lie Algorithm
## 2 Methods and Materials
### No-Fly Zone Avoidance  
In 2020, the Civil Aeronautics Administration of the Republic of China (Taiwan) amended the Civil Aviation Act to include a \"Special Section for Remotely Controlled UAVs\", announcing areas where UAV use was restricted or prohibited, including airports. Article 99-13 of the Civil Aviation Act forbids the use of remote controlled drones flying within a certain distance of restricted areas to ensure public welfare and safety [17]. Therefore, following route planning, it is necessary to determine whether the chosen route conflicts with the no-fly zones. This is accomplished using the route planning method of the A* search algorithm, which considers the radius of group formation.  
The A* search algorithm finds the lowest cost path with multiple nodes, combining best-first search and [PERSON]'s algorithm to find an optimal path while carrying out heuristic search to improve the algorithm's efficiency.  
In the A* search algorithm, the cost function \(f(n)\) of the computing core is Equation (3), where \(g(n)\) represents the travel distance function from the starting point to any target point \(n\), and \(h(n)\) represents the estimated distance function from any target point n to the endpoint. When \(h(n)=0\), the target calculation ends. This study used Euclidean distance, which is generally called straight-line distance.  
\[f(n)=g(n)+h(n)\]The A* search algorithm is a simple and practical method in which its core rules are Open, Close, and Dead End. Open represents the current expandable point, and it will be cleared after each expansion. Close represents a point that has been traversed and cannot be expanded again. When the calculation is over, the points stored in Close are connected in sequence to form the final path. Dead End represents points that cannot be expanded and in which the calculation process will not be cleared.
###### Abstract  
With the development of smart cities, the use of unmanned aerial vehicles (UAVs) for interactive information exchange between air and ground can provide effective support for the deployment of emergency work. However, the existing UAV air-to-ground channels often use a single channel model. Considering that the density and distribution of obstructions on information transmission paths at different heights are different, only using a single channel model greatly affects the reliability of communications. Aiming at addressing the different channel characteristics of air-to-ground channels at different heights, a height-based adaptive SUUL-SULA channel model is proposed in this paper. Firstly, in the ultra-low altitude environment, the influence of large-scale fading and small-scale fading on the envelope of the received signal is discussed based on the classic LOO model, and the probability density function and bit error rate model of the received signal are derived. Secondly, a SULA channel model based on [PERSON] model is proposed in the low-altitude environment. The uniform circular array beamforming technology is adopted to realize the design of the Doppler frequency shift compensation algorithm. Finally, the simulation results show that the SUUL-SULA model effectively reduces the bit error rate of the system and improves the reliability of communication. Therefore, this model can provide effective physical support for the application of UAV in smart city emergency management.  
emergency management; channel model; shadow fading; beamforming; Doppler effect 1  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] Modeling and Performance Optimization of Unmanned Aerial Vehicle Channels in Urban Emergency Management  
1
## 2 Materials and Methods
### Crop Health Assessment  
Baby leaf lettuce (_Lactuca sativa_ cv. Maverik) plants grown in greenhouses were used to test the performance of the multispectral image alignment model. Images of individual baby lettuce leaves were acquired using the multispectral camera at a distance of 1 m from the ground.  
Images were collected from two groups of plants: lettuce grown under a conventional irrigation system (healthy) and lettuce not irrigated for 7 days (unhealthy). For each group, 100 images were acquired. To assess the physiological condition of the leaves, chlorophyll concentration (\(\mu\)mol/m\({}^{2}\)) was measured using an Apogee MC-100 Portable Chlorophyll Meter (Apogee Instruments, Inc., 721 West 1800 North, Logan, UT, USA).  
Several vegetation indices (VIs) (listed in Table 1) were calculated for each leaf following the application of the alignment models. The mean value of individual leaves was used to correlate spectral variations with chlorophyll content. Additionally, the NDVI with a threshold of 0.40 was applied as a binary mask to calculate the leaf area (cm\({}^{2}\)).  
Image analysis and vegetation index calculations were performed using MATLAB software (2023b) from MathWorks.